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Volume 8
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ISPRS Int. J. Geo-Inf., Volume 8, Issue 2 (February 2019) – 49 articles

Cover Story (view full-size image): In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network and the building volumes. First, we present a methodological and technical framework for evaluating social, psychological, and energy potentials of urban form. Second, we present a method to identify distinct clusters of urban form and, for each, explore the trade-offs between the three potentials. We applied this method to two case studies (Weimar and Zurich), identifying nine types of urban form and their respective potential trade-offs, which are directly applicable for the assessment of strategic decisions regarding urban form during the early planning stages. View this paper.
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16 pages, 9117 KiB  
Article
A Novel Process-Oriented Graph Storage for Dynamic Geographic Phenomena
by Cunjin Xue, Chengbin Wu, Jingyi Liu and Fenzhen Su
ISPRS Int. J. Geo-Inf. 2019, 8(2), 100; https://doi.org/10.3390/ijgi8020100 - 25 Feb 2019
Cited by 15 | Viewed by 3773
Abstract
There exists a sort of dynamic geographic phenomenon in the real world that has a property which is maintained from production through development to death. Using traditional storage units, e.g., point, line, and polygon, researchers face great challenges in exploring the spatial evolution [...] Read more.
There exists a sort of dynamic geographic phenomenon in the real world that has a property which is maintained from production through development to death. Using traditional storage units, e.g., point, line, and polygon, researchers face great challenges in exploring the spatial evolution of dynamic phenomena during their lifespan. Thus, this paper proposes a process-oriented two-tier graph model named PoTGM to store the dynamic geographic phenomena. The core ideas of PoTGM are as follows. 1) A dynamic geographic phenomenon is abstracted into a process with a property that is maintained from production through development to death. A process consists of evolution sequences which include instantaneous states. 2) PoTGM integrates a process graph and a sequence graph using a node–edge structure, in which there are four types of nodes, i.e., a process node, a sequence node, a state node, and a linked node, as well as two types of edges, i.e., an including edge and an evolution edge. 3) A node stores an object, i.e., a process object, a sequence object, or a state object, and an edge stores a relationship, i.e., an including or evolution relationship between two objects. Experiments on simulated datasets are used to demonstrate an at least one order of magnitude advantage of PoTGM in relation to relationship querying and to compare it with the Oracle spatial database. The applications on the sea surface temperature remote sensing products in the Pacific Ocean show that PoTGM can effectively explore marine objects as well as spatial evolution, and these behaviors may provide new references for global change research. Full article
(This article belongs to the Special Issue Spatial Databases: Design, Management, and Knowledge Discovery)
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Figure 1
<p>Comparisons of the (<b>a</b>) instantaneous and (<b>b</b>) continuous changes.</p> Full article ">Figure 2
<p>The concept model of the geographic process.</p> Full article ">Figure 3
<p>The six types of instantaneous states.</p> Full article ">Figure 4
<p>The four types of evolution relationships between instantaneous states.</p> Full article ">Figure 5
<p>A process-oriented two-tier graph model: The solid line represents the including relationships, and the dot line represents the evolution relationships.</p> Full article ">Figure 6
<p>The storage of a process graph A: The process is linked by <span class="html-italic">NodeID</span> and <span class="html-italic">EdgeID</span>.</p> Full article ">Figure 7
<p>The storage of a sequence graph A: The sequence is linked by <span class="html-italic">NodeID</span> and <span class="html-italic">EdgeID</span>.</p> Full article ">Figure 8
<p>A simulated process.</p> Full article ">Figure 9
<p>A schema of PoTGM based on Neo4j.</p> Full article ">Figure 10
<p>A schema of the object relational database: The black line links the process objects, and the orange line links the evolution relationships.</p> Full article ">Figure 11
<p>Comparisons of the database storage capacities.</p> Full article ">Figure 12
<p>Comparisons of querying objects in different database sizes.</p> Full article ">Figure 13
<p>Comparisons of the querying evolution relationships.</p> Full article ">Figure 14
<p>The evolution of an abnormal sea surface temperature variation and its evolutions: The background is made up of monthly anomalies of the sea surface temperature; different colors of the Sequence ID represent different evolution sequences; and the black arrow represents an evolution, developing, merging, or splitting relationship.</p> Full article ">Figure 15
<p>The relationship between the evolution of the mined process and the El Niño event.</p> Full article ">
20 pages, 11243 KiB  
Article
A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran
by Mahmoud Reza Delavar, Amin Gholami, Gholam Reza Shiran, Yousef Rashidi, Gholam Reza Nakhaeizadeh, Kurt Fedra and Smaeil Hatefi Afshar
ISPRS Int. J. Geo-Inf. 2019, 8(2), 99; https://doi.org/10.3390/ijgi8020099 - 23 Feb 2019
Cited by 103 | Viewed by 10831
Abstract
Environmental pollution has mainly been attributed to urbanization and industrial developments across the globe. Air pollution has been marked as one of the major problems of metropolitan areas around the world, especially in Tehran, the capital of Iran, where its administrators and residents [...] Read more.
Environmental pollution has mainly been attributed to urbanization and industrial developments across the globe. Air pollution has been marked as one of the major problems of metropolitan areas around the world, especially in Tehran, the capital of Iran, where its administrators and residents have long been struggling with air pollution damage such as the health issues of its citizens. As far as the study area of this research is concerned, a considerable proportion of Tehran air pollution is attributed to PM10 and PM2.5 pollutants. Therefore, the present study was conducted to determine the prediction models to determine air pollutions based on PM10 and PM2.5 pollution concentrations in Tehran. To predict the air-pollution, the data related to day of week, month of year, topography, meteorology, and pollutant rate of two nearest neighbors as the input parameters and machine learning methods were used. These methods include a regression support vector machine, geographically weighted regression, artificial neural network and auto-regressive nonlinear neural network with an external input as the machine learning method for the air pollution prediction. A prediction model was then proposed to improve the afore-mentioned methods, by which the error percentage has been reduced and improved by 57%, 47%, 47% and 94%, respectively. The most reliable algorithm for the prediction of air pollution was autoregressive nonlinear neural network with external input using the proposed prediction model, where its one-day prediction error reached 1.79 µg/m3. Finally, using genetic algorithm, data for day of week, month of year, topography, wind direction, maximum temperature and pollutant rate of the two nearest neighbors were identified as the most effective parameters in the prediction of air pollution. Full article
(This article belongs to the Special Issue Spatial Analysis of Pollution and Risk in a Changing Climate)
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<p>Monthly changes in PM<sub>2.5</sub> concentration in Tehran in 2016.</p> Full article ">Figure 2
<p>A map of the wind speed and direction of the city of Tehran and its surroundings (<a href="https://www.windfinder.com/#10/35.6841/51.3474/2019-02-12T18:00Z" target="_blank">https://www.windfinder.com/#10/35.6841/51.3474/2019-02-12T18:00Z</a>).</p> Full article ">Figure 3
<p>The data employed in this research.</p> Full article ">Figure 4
<p>The proposed best air pollution prediction model.</p> Full article ">Figure 5
<p>Tehran air pollution and weather stations.</p> Full article ">Figure 6
<p>Chitgar station minimum temperature time series.</p> Full article ">Figure 7
<p>Chitgar station maximum temperature time series.</p> Full article ">Figure 8
<p>Chitgar station wind speed time series.</p> Full article ">Figure 9
<p>Chitgar station wind direction time series.</p> Full article ">Figure 10
<p>Chitgar station humidity time series.</p> Full article ">Figure 11
<p>Chitgar station air pressure time series.</p> Full article ">Figure 12
<p>Time series (<b>a</b>) unrefined (<b>b</b>) refined humidity in Chitgar Station.</p> Full article ">Figure 13
<p>Time series (<b>a</b>) unrefined (<b>b</b>) refined air pressure in Chitgar Station.</p> Full article ">Figure 14
<p>Time series (<b>a</b>) unrefined (<b>b</b>) refined minimum temperature in Chitgar Station.</p> Full article ">Figure 15
<p>Time series (<b>a</b>) unrefined (<b>b</b>) refined maximum temperature in Chitgar Station.</p> Full article ">Figure 16
<p>Time series (<b>a</b>) unrefined (<b>b</b>) refined wind speed in Chitgar Station.</p> Full article ">Figure 17
<p>Time series (<b>a</b>) unrefined (<b>b</b>) refined wind direction in Chitgar Station.</p> Full article ">Figure 18
<p>Compensation for missing data by fitting the spline function.</p> Full article ">Figure 19
<p>Compensation for missing data by fitting the Fourier series function.</p> Full article ">Figure 20
<p>Savitzky–Golay filter related to the PM<sub>10</sub> pollutant (<b>a</b>) unrefined (<b>b</b>) refined time series.</p> Full article ">Figure 21
<p>Savitzky–Golay filter the PM<sub>2.5</sub> pollutant (<b>a</b>) unrefined (<b>b</b>) refined time series.</p> Full article ">Figure 22
<p>Comparison of (<b>a</b>) observation map and prediction maps of (<b>b</b>) NARX, (<b>c</b>) SVR, (<b>d</b>) GWR and (<b>e</b>) ANN methods for PM<sub>2.5</sub> (µg/m<sup>3</sup>).</p> Full article ">Figure 23
<p>Comparison of (<b>a</b>) observation map and prediction maps of (<b>b</b>) NARX, (<b>c</b>) SVR, (<b>d</b>) GWR, and (<b>e</b>) ANN methods PM<sub>10</sub> (µg/m<sup>3</sup>).</p> Full article ">
18 pages, 5073 KiB  
Article
An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons
by Zhanlong Chen, Xiaochuan Ma, Liang Wu and Zhong Xie
ISPRS Int. J. Geo-Inf. 2019, 8(2), 98; https://doi.org/10.3390/ijgi8020098 - 23 Feb 2019
Cited by 8 | Viewed by 3416
Abstract
Accurate and reasonable clustering of spatial data results facilitates the exploration of patterns and spatial association rules. Although a broad range of research has focused on the clustering of spatial data, only a few studies have conducted a deeper exploration into the similarity [...] Read more.
Accurate and reasonable clustering of spatial data results facilitates the exploration of patterns and spatial association rules. Although a broad range of research has focused on the clustering of spatial data, only a few studies have conducted a deeper exploration into the similarity approach mechanism for clustering polygons, thereby limiting the development of spatial clustering. In this study, we propose a novel fuzzy similarity approach for spatial clustering, called Extend Intuitionistic Fuzzy Set-Interpolation Boolean Algebra (EIFS-IBA). When discovering polygon clustering patterns by spatial clustering, this method expresses the similarities between polygons and adjacent graph models. Shape-, orientation-, and size-related properties of a single polygon are first extracted, and are used as indices for measuring similarities between polygons. We then transform the extracted properties into a fuzzy format through normalization and fuzzification. Finally, the similarity graph containing the neighborhood relationship between polygons is acquired, allowing for clustering using the proposed adjacency graph model. In this paper, we clustered polygons in Staten Island, United States. The visual result and two evaluation criteria demonstrated that the EIFS-IBA similarity approach is more expressive compared to the conventional similarity (ConS) approach, generating a clustering result more consistent with human cognition. Full article
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<p>Numbering spatial polygons.</p> Full article ">Figure 2
<p>Explanation of the relative concept. (<b>a</b>) Smallest bounding rectangle and orientation. (<b>b</b>) Skeletons between adjacent polygons.</p> Full article ">Figure 3
<p>Graphical interpretation of the Intuitionistic Fuzzy Set-Interpolation Boolean Algebra (IFS-IBA) operation on polygons. Legend: GP, generalized product.</p> Full article ">Figure 4
<p>Adjacency relationship graph. (<b>a</b>) The polygon; (<b>b</b>) the constructed adjacency relationship graph; and (<b>c</b>) the corresponding extended adjacency relationship graph model (EAGM).</p> Full article ">Figure 5
<p>Case study regions. (<b>a1</b>,<b>a2</b>,<b>b1</b>,<b>b2</b>,<b>c1</b>,<b>c2</b>) are Google maps and GIS vector maps corresponding to regions <span class="html-italic">a</span>, <span class="html-italic">b</span>, and <span class="html-italic">c</span>, respectively.</p> Full article ">Figure 6
<p>The clustering result obtained by human cognition on experimental region <b><span class="html-italic">b</span></b>.</p> Full article ">Figure 7
<p>The whole similarity between polygon pairs in experimental region <b><span class="html-italic">a</span></b>.</p> Full article ">Figure 8
<p>Clustering results in experimental region <b><span class="html-italic">a</span></b>.</p> Full article ">Figure 9
<p>Clustering results of different similarity approaches.</p> Full article ">Figure 10
<p>The ICGF differences and excellence rates, where (<b>a</b>–<b>f</b>) corresponded to the EIFS-IBA, ConS, HauEu, Eu, and Hamm.</p> Full article ">Figure 10 Cont.
<p>The ICGF differences and excellence rates, where (<b>a</b>–<b>f</b>) corresponded to the EIFS-IBA, ConS, HauEu, Eu, and Hamm.</p> Full article ">
15 pages, 3137 KiB  
Article
Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation
by Mustafa Ustuner and Fusun Balik Sanli
ISPRS Int. J. Geo-Inf. 2019, 8(2), 97; https://doi.org/10.3390/ijgi8020097 - 21 Feb 2019
Cited by 55 | Viewed by 9310
Abstract
In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting [...] Read more.
In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine (LightGBM). For the classification of different crops (maize, potato, wheat, sunflower, and alfalfa) in the test site, multi-temporal polarimetric C-band RADARSAT-2 images were acquired over an agricultural area near Konya, Turkey. Four different decomposition models (Cloude–Pottier, Freeman–Durden, Van Zyl, and Yamaguchi) were employed to evaluate polarimetric target decomposition for crop classification. Besides the polarimetric target decomposed parameters, the original polarimetric features (linear backscatter coefficients, coherency, and covariance matrices) were also incorporated for crop classification. The experimental results demonstrated that polarimetric target decompositions, with the exception of Cloude–Pottier, were found to be superior to the original features in terms of overall classification accuracy. The highest classification accuracy (92.07%) was achieved by Yamaguchi, whereas the lowest (75.99%) was achieved by the covariance matrix. Model-based decompositions achieved higher performance with respect to eigenvector-based decompositions in terms of class-based accuracies. Furthermore, the results emphasize the added benefits of model-based decompositions for crop classification using PolSAR data. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Test site (R/G/B: HH/VH/VV).</p> Full article ">Figure 2
<p>Images from different dates (<b>a</b>) 13 June, (<b>b</b>) 7 July, (<b>c</b>) 31 July, and (<b>d</b>) 24 August.</p> Full article ">Figure 3
<p>Flowchart of the full polarimetric synthetic aperture radar (PolSAR) data processing.</p> Full article ">Figure 4
<p>Classification accuracies (overall accuracies).</p> Full article ">Figure 5
<p>Class accuracies based on F1-score.</p> Full article ">Figure 6
<p>Temporal backscatter changes on polarizations.</p> Full article ">Figure 7
<p>Classified images ((<b>a</b>): Backscatter Coefficient, (<b>b</b>): Cloude-Pottier Decomposition, (<b>c</b>): T Matrix, (<b>d</b>): C Matrix, (<b>e</b>): Freeman-Durden Decomposition, (<b>f</b>): Yamaguchi Decomposition, and (<b>g</b>): Van Zyl Decomposition).</p> Full article ">
19 pages, 1889 KiB  
Article
Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1
by Michele Melchiorri, Martino Pesaresi, Aneta J. Florczyk, Christina Corbane and Thomas Kemper
ISPRS Int. J. Geo-Inf. 2019, 8(2), 96; https://doi.org/10.3390/ijgi8020096 - 18 Feb 2019
Cited by 107 | Viewed by 13256
Abstract
The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics describing the human presence on the planet that is based mainly on two quantitative factors: (i) the spatial distribution (density) of built-up structures and (ii) the spatial distribution (density) of [...] Read more.
The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics describing the human presence on the planet that is based mainly on two quantitative factors: (i) the spatial distribution (density) of built-up structures and (ii) the spatial distribution (density) of resident people. Both of the factors are observed in the long-term temporal domain and per unit area, in order to support the analysis of the trends and indicators for monitoring the implementation of the 2030 Development Agenda and the related thematic agreements. The GHSL uses various input data, including global, multi-temporal archives of high-resolution satellite imagery, census data, and volunteered geographic information. In this paper, we present a global estimate for the Land Use Efficiency (LUE) indicator—SDG 11.3.1, for circa 10,000 urban centers, calculating the ratio of land consumption rate to population growth rate between 1990 and 2015. In addition, we analyze the characteristics of the GHSL information to demonstrate how the original frameworks of data (gridded GHSL data) and tools (GHSL tools suite), developed from Earth Observation and integrated with census information, could support Sustainable Development Goals monitoring. In particular, we demonstrate the potential of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for Sustainable Development Goal 11. The results of our research demonstrate that there is potential to raise SDG 11.3.1 from a Tier II classification (manifesting unavailability of data) to a Tier I, as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata. Full article
(This article belongs to the Special Issue Geo-Information and the Sustainable Development Goals (SDGs))
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Example of GHSL data, three geospatial layers that map built-up areas (GHS-BUILT) (<b>a</b>), three geospatial layers that map resident population (GHS-POP) (<b>b</b>), three geospatial layers that map settlement typologies (GHS-SMOD), and (<b>c</b>) displayed at 1km spatial resolution in the area of Tokyo (Japan) and compared to the imagery base map.</p> Full article ">Figure 2
<p>Multi-temporal global (<b>a</b>) and regional (<b>b</b>) extent of built-up areas and population in urban centers at corresponding epochs as estimated from GHSL data (GHS-POP and GHS-BUILT).</p> Full article ">Figure 3
<p>Comprehensive visualization of the Land Use Efficiency (LUE) value in circa 10,000 urban centers that were computed in the period 1990–2015.</p> Full article ">Figure 4
<p>Regional comparison of built-up areas per capita.</p> Full article ">
11 pages, 645 KiB  
Article
Investigating Roundabout Properties and Bicycle Accident Occurrence at Swiss Roundabouts: A Logistic Regression Approach
by Daria Hollenstein, Martin Hess, Denis Jordan and Susanne Bleisch
ISPRS Int. J. Geo-Inf. 2019, 8(2), 95; https://doi.org/10.3390/ijgi8020095 - 18 Feb 2019
Cited by 4 | Viewed by 3849
Abstract
The positive effects of active mobility on mental and physical health as well as on air quality are widely acknowledged. Increasing the share of active travel is therefore an aim in many countries. Providing bicycle-safe infrastructure is one way to promote cycling. Roundabouts [...] Read more.
The positive effects of active mobility on mental and physical health as well as on air quality are widely acknowledged. Increasing the share of active travel is therefore an aim in many countries. Providing bicycle-safe infrastructure is one way to promote cycling. Roundabouts are a common traffic infrastructure and are supposed to facilitate safe and smooth traffic flow. However, data on road traffic accidents indicate an over-proportional involvement of cyclists in accidents at roundabouts. In the present study, the influence of roundabout geometry and traffic flow on bicycle accident occurrence was investigated using a logistic regression approach on twelve parameters of N = 294 mostly small- and mini-sized single-lane roundabouts in the Canton of Berne, Switzerland. Average weekday motorized traffic was identified as a major factor in explaining bicycle accident occurrence at roundabouts. Further, the radius of the central island, the location of the roundabout (in town vs. out of town) and the number of legs were significantly related to bicycle accident occurrence. While these results are in general agreement with findings from similar studies, the findings regarding the central island’s radius and the number of legs underpin the need for roundabout type-specific studies: Some parameters may not prove relevant in intermediate- to large-sized roundabouts, but become critical in small or mini roundabouts, which are common in Switzerland and numerous in the present sample. Full article
(This article belongs to the Special Issue Selected Papers from the 10th International Conference on Geographic Information Science)
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<p>Roundabout geometry attributes: radius of the central island [IRAD], width of the circulatory roadway [WIDTH], eccentricity of the leg’s axis [ECC] and sector as defined by the intersection of two adjacent legs’ axes.</p> Full article ">Figure 2
<p>Accident occurrence probability vs. average weekday motorized traffic: Estimated probabilities of having at least one accident recorded at a roundabout with small (2.6, midpoint first quartile) [orange], median (6.15) [black] and large (10.7, midpoint last quartile) [blue] radius of the central island against average weekday motorized traffic for roundabouts located in town with three [dotted line style], four [dashed] and five [continuous line style] legs.</p> Full article ">Figure 3
<p>Accident occurrence probability vs. radius of the central island: Estimated probabilities of having had at least one accident recorded at a roundabout with low (3048, midpoint first quartile) [orange], median (10,903) [black] and high (24,239, midpoint last quartile) [blue] average weekday motorized traffic against the radius of the central island for roundabouts located in town with three [dotted line style], four [dashed] and five [continuous line style] entering roadways.</p> Full article ">
23 pages, 6581 KiB  
Article
A Comparative Study of Statistics-Based Landslide Susceptibility Models: A Case Study of the Region Affected by the Gorkha Earthquake in Nepal
by Sansar Raj Meena, Omid Ghorbanzadeh and Thomas Blaschke
ISPRS Int. J. Geo-Inf. 2019, 8(2), 94; https://doi.org/10.3390/ijgi8020094 - 18 Feb 2019
Cited by 74 | Viewed by 7960
Abstract
As a result of the Gorkha earthquake in 2015, about 9000 people lost their lives and many more were injured. Most of these losses were caused by earthquake-induced landslides. Sustainable planning and decision-making are required to reduce the losses caused by earthquakes and [...] Read more.
As a result of the Gorkha earthquake in 2015, about 9000 people lost their lives and many more were injured. Most of these losses were caused by earthquake-induced landslides. Sustainable planning and decision-making are required to reduce the losses caused by earthquakes and related hazards. The use of remote sensing and geographic information systems (GIS) for landslide susceptibility mapping can help planning authorities to prepare for and mitigate the consequences of future hazards. In this study, we developed landslide susceptibility maps using GIS-based statistical models at the regional level in central Nepal. Our study area included the districts affected by landslides after the Gorkha earthquake and its aftershocks. We used the 23,439 landslide locations obtained from high-resolution satellite imagery to evaluate the differences in landslide susceptibility using analytical hierarchy process (AHP), frequency ratio (FR) and hybrid spatial multi-criteria evaluation (SMCE) models. The nine landslide conditioning factors of lithology, land cover, precipitation, slope, aspect, elevation, distance to roads, distance to drainage and distance to faults were used as the input data for the applied landslide susceptibility mapping (LSM) models. The spatial correlation of landslides and these factors were identified using GIS-based statistical models. We divided the inventory into data used for training the statistical models (70%) and data used for validation (30%). Receiver operating characteristics (ROC) and the relative landslide density index (R-index) were used to validate the results. The area under the curve (AUC) values obtained from the ROC approach for AHP, FR and hybrid SMCE were 0.902, 0.905 and 0.91, respectively. The index of relative landslide density, R-index, values in sample datasets of AHP, FR and hybrid SMCE maps were 53%, 58% and 59% for the very high hazard classes. The final susceptibility results will be beneficial for regional planning and sustainable hazard mitigation. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Map showing the location of the study area and field photographs: (<b>a</b>) Mailung Khola and (<b>b</b>) camp near Mailung Khola hydropower plant (<b>c</b>) road section near Ramche (<b>d</b>) road section near Syaprubesi.</p> Full article ">Figure 2
<p>Landslide inventory map of the study area with the distribution of training and validation datasets.</p> Full article ">Figure 3
<p>Thematic maps used in this study (<b>a</b>) land cover, (<b>b</b>) distance to roads, (<b>c</b>) elevation, (<b>d</b>) lithology, (<b>e</b>) faults, (<b>f</b>) drainage, (<b>g</b>) precipitation, (<b>h</b>) slope angle, and (<b>i</b>) slope aspect.</p> Full article ">Figure 3 Cont.
<p>Thematic maps used in this study (<b>a</b>) land cover, (<b>b</b>) distance to roads, (<b>c</b>) elevation, (<b>d</b>) lithology, (<b>e</b>) faults, (<b>f</b>) drainage, (<b>g</b>) precipitation, (<b>h</b>) slope angle, and (<b>i</b>) slope aspect.</p> Full article ">Figure 4
<p>Output landslide susceptibility maps for each model; (<b>a</b>) analytical hierarchy process (AHP), (<b>b</b>) frequency ratio (FR), and (<b>c</b>) hybrid spatial multi-criteria evaluation (SMCE).</p> Full article ">Figure 5
<p>An example the performance of the three landslide susceptibility models in a sub-area of the case study area. (<b>a</b>) Frequency ratio; (<b>b</b>) analytical hierarchy process; (<b>c</b>) spatial multi-criteria evaluation.</p> Full article ">Figure 6
<p>Receiver operating characteristics (ROC) representing quality method success rate curves for the three methods.</p> Full article ">Figure 7
<p>R-index in susceptibility classes for hybrid SMCE, AHP and FR.</p> Full article ">Figure 8
<p>The comparison of the resulting weights for the classes of each factor based on the FR and AHP model.</p> Full article ">
25 pages, 14999 KiB  
Article
Delimitating Urban Commercial Central Districts by Combining Kernel Density Estimation and Road Intersections: A Case Study in Nanjing City, China
by Jing Yang, Jie Zhu, Yizhong Sun and Jianhua Zhao
ISPRS Int. J. Geo-Inf. 2019, 8(2), 93; https://doi.org/10.3390/ijgi8020093 - 16 Feb 2019
Cited by 33 | Viewed by 6633
Abstract
An urban, commercial central district is often regarded as the heart of a city. Therefore, quantitative research on commercial central districts plays an important role when studying the development and evaluation of urban spatial layouts. However, conventional planar kernel density estimation (KDE) and [...] Read more.
An urban, commercial central district is often regarded as the heart of a city. Therefore, quantitative research on commercial central districts plays an important role when studying the development and evaluation of urban spatial layouts. However, conventional planar kernel density estimation (KDE) and network kernel density estimation (network KDE) do not reflect the fact that the road network density is high in urban, commercial central districts. To solve this problem, this paper proposes a new method (commercial-intersection KDE), which combines road intersections with KDE to identify commercial central districts based on point of interest (POI) data. First, we extracted commercial POIs from Amap (a Chinese commercial, navigation electronic map) based on existing classification standards for urban development land. Second, we calculated the commercial kernel density in the road intersection neighborhoods and used those values as parameters to build a commercial intersection density surface. Finally, we used the three standard deviations method and the commercial center area indicator to differentiate commercial central districts from areas with only commercial intersection density. Testing the method using Nanjing City as a case study, we show that our new method can identify seven municipal, commercial central districts and 26 nonmunicipal, commercial central districts. Furthermore, we compare the results of the traditional planar KDE with those of our commercial-intersection KDE to demonstrate our method’s higher accuracy and practicability for identifying urban commercial central districts and evaluating urban planning. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
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Figure 1
<p>Overview of the procedure for delineating urban, commercial central districts. POI: point of interest; KDE: kernel density estimation.</p> Full article ">Figure 2
<p>Illustration of the study area (Nanjing, China): (<b>a</b>) distribution of the Nanjing road network and 13 districts in China, (<b>b</b>) spatial distribution density of all the POIs, and (<b>c</b>) detailed map of some POI distributions.</p> Full article ">Figure 3
<p>Illustration of commercial-intersection KDE: (<b>a</b>) the delimitation of road intersection neighborhood, and (<b>b</b>) simple example of commercial-intersection kernel function.</p> Full article ">Figure 4
<p>Main steps of the commercial-intersection KDE algorithm.</p> Full article ">Figure 5
<p>Illustration of commercial POI distribution: (<b>a</b>) spatial distribution density of commercial POIs and (<b>b</b>) detailed map of some commercial POI distributions.</p> Full article ">Figure 6
<p>Kernel density surfaces: (<b>a</b>) result of commercial-intersection KDE with 300-m bandwidth and (<b>b</b>) result of planar KDE with 300 bandwidth.</p> Full article ">Figure 7
<p>Density statistics chart of grid cells: (<b>a</b>) result of commercial-intersection KDE and (<b>b</b>) result of planar KDE.</p> Full article ">Figure 7 Cont.
<p>Density statistics chart of grid cells: (<b>a</b>) result of commercial-intersection KDE and (<b>b</b>) result of planar KDE.</p> Full article ">Figure 8
<p>Curve plot of the classification number and weight mean: (<b>a</b>) result of commercial-intersection KDE and (<b>b</b>) result of planar KDE.</p> Full article ">Figure 8 Cont.
<p>Curve plot of the classification number and weight mean: (<b>a</b>) result of commercial-intersection KDE and (<b>b</b>) result of planar KDE.</p> Full article ">Figure 9
<p>Commercial kernel density surface and commercial-intersection kernel density surface: (<b>a</b>) the kernel density surface of the commercial POIs at determined by planar KDE with 300-m bandwidth, (<b>b</b>) the commercial-intersection kernel density surface as determined by commercial-intersection KDE with 300-m bandwidth, and (<b>c</b>) the density difference map between the commercial kernel density surface and commercial-intersection kernel density surface.</p> Full article ">Figure 9 Cont.
<p>Commercial kernel density surface and commercial-intersection kernel density surface: (<b>a</b>) the kernel density surface of the commercial POIs at determined by planar KDE with 300-m bandwidth, (<b>b</b>) the commercial-intersection kernel density surface as determined by commercial-intersection KDE with 300-m bandwidth, and (<b>c</b>) the density difference map between the commercial kernel density surface and commercial-intersection kernel density surface.</p> Full article ">Figure 10
<p>Statistical chart of the density difference between the commercial kernel density surface and commercial-intersection kernel density surface.</p> Full article ">Figure 11
<p>Urban commercial central district extraction results: (<b>a</b>) result extracted from commercial-intersection kernel density surface and (<b>b</b>) result extracted from commercial POIs kernel density surface.</p> Full article ">Figure 12
<p>Commercial-intersection kernel density surface: (<b>a</b>) result with 150-m bandwidth, (<b>b</b>) result with 600-m bandwidth, and (<b>c</b>) result with 1200-m bandwidth.</p> Full article ">Figure 12 Cont.
<p>Commercial-intersection kernel density surface: (<b>a</b>) result with 150-m bandwidth, (<b>b</b>) result with 600-m bandwidth, and (<b>c</b>) result with 1200-m bandwidth.</p> Full article ">Figure 13
<p>Prior range of commercial central districts: (<b>a</b>) Xinjiekou and (<b>b</b>) Hunan Road.</p> Full article ">
22 pages, 14939 KiB  
Article
Function-Based Search of Place Using Theoretical, Empirical and Probabilistic Patterns
by Emmanuel Papadakis, George Baryannis, Andreas Petutschnig and Thomas Blaschke
ISPRS Int. J. Geo-Inf. 2019, 8(2), 92; https://doi.org/10.3390/ijgi8020092 - 16 Feb 2019
Cited by 8 | Viewed by 4293
Abstract
Searching for places rather than traditional keyword-based search represents significant challenges. The most prevalent method of addressing place-related queries is based on place names but has limited potential due to the vagueness of natural language and its tendency to lead to ambiguous interpretations. [...] Read more.
Searching for places rather than traditional keyword-based search represents significant challenges. The most prevalent method of addressing place-related queries is based on place names but has limited potential due to the vagueness of natural language and its tendency to lead to ambiguous interpretations. In previous work we proposed a system-oriented logic-based formalization of place that goes beyond place names by introducing composition patterns of place which enable function-based search of space. In this study, we introduce flexibility into these patterns in terms of what is included when describing the spatial composition of a place using two distinct approaches, based on modal and probabilistic logic. Additionally, we propose a novel automated process of extracting these patterns relying on both theoretical and empirical knowledge, using statistical and spatial analysis and statistical relational learning. The proposed methodology is exemplified through the use case of locating all areas within London that support shopping-related functionality. Results show that the newly introduced patterns can identify more relevant areas, additionally offering a more fine-grained representation of the level of support of the required functionality. Full article
(This article belongs to the Special Issue Selected Papers from the 10th International Conference on Geographic Information Science)
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<p>Empirical pattern extraction and probabilistic pattern learning processes.</p> Full article ">Figure 2
<p>Bayesian network for a functional implication in a probabilistic pattern.</p> Full article ">Figure 3
<p>Bayesian network for a place functioning as a shopping area.</p> Full article ">Figure 4
<p>Results using theoretical pattern.</p> Full article ">Figure 5
<p>Results using empirical pattern.</p> Full article ">Figure 6
<p>Results using probabilistic pattern.</p> Full article ">Figure 7
<p>Results using probabilistic pattern with adjusted scores.</p> Full article ">Figure 8
<p>Comparison of populations per score for theoretical and empirical patterns.</p> Full article ">Figure 9
<p>Evaluation of results against stated land use in OpenStreetMap.</p> Full article ">
14 pages, 2775 KiB  
Article
A Field Data Acquisition Method and Tools for Hazard Evaluation of Earthquake-Induced Landslides with Open Source Mobile GIS
by Mauro De Donatis, Giulio F. Pappafico and Roberto W. Romeo
ISPRS Int. J. Geo-Inf. 2019, 8(2), 91; https://doi.org/10.3390/ijgi8020091 - 15 Feb 2019
Cited by 4 | Viewed by 4121
Abstract
The PARSIFAL (Probabilistic Approach to pRovide Scenarios of earthquake Induced slope FAiLures) method was applied to the survey of post-earthquake landslides in central Italy for seismic microzonation purposes. In order to optimize time and resources, while also reducing errors, the paper-based method of [...] Read more.
The PARSIFAL (Probabilistic Approach to pRovide Scenarios of earthquake Induced slope FAiLures) method was applied to the survey of post-earthquake landslides in central Italy for seismic microzonation purposes. In order to optimize time and resources, while also reducing errors, the paper-based method of survey data sheets was translated into digital formats using such instruments as Tablet PCs, GPS and open source software (QGIS). To the base mapping consisting of Technical Regional Map (Carta Tecnica Regionale—CTRs) at the scale of 1:10,000, layers were added with such sensitive information as the Inventory of Landslide Phenomena in Italy (Inventario dei Fenomeni Franosi in Italia—IFFI), for example. A database was designed and implemented in the SQLite/SpatiaLite Relational DataBase Management System (RDBMS) to store data related to such elements as landslides, rock masses, discontinuities and covers (as provided by PARSIFAL). To facilitate capture of the datum on the ground, data entry forms were created with Qt Designer. In addition to this, the employment of some QGIS plug-ins, developed for digital surveying and enabling of quick annotations on the map and the import of images from external cameras, was found to be of considerable use. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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<p>Example of the form (for rock masses) for data collecting used in the field survey (modified from the original form in Italian).</p> Full article ">Figure 2
<p>Example of tables for geotechnical data codes (adapted from ISRM [<a href="#B16-ijgi-08-00091" class="html-bibr">16</a>]).</p> Full article ">Figure 3
<p><span class="html-italic">Parsifal</span> database schema: (<b>a</b>) core tables; (<b>b</b>) reference geographic tables; (<b>c</b>) reference tables for geotechnical data coding (for all tables attributes and codes see the HTML file mentioned in <a href="#app1-ijgi-08-00091" class="html-app">Appendix A</a> and linked here <a href="http://www.geologiapplicata.uniurb.it/download/parsifal.html" target="_blank">http://www.geologiapplicata.uniurb.it/download/parsifal.html</a>).</p> Full article ">Figure 4
<p>Data acquisition forms: (<b>a</b>) Landslide data recording form where the “Landslide Code” and “GPS Coordinates” fields are automatically filled; (<b>b</b>) “Landslide” tab attributes; (<b>c</b>) Attributes of the “Rock Mass” tab; (<b>d</b>) Attributes of the “Cover” tab for recording cover Quaternary deposits data; (<b>e</b>) “Discontinuities” tab showing the two main form pages “Geometry” and “Characteristics”; (<b>f</b>) Fields of “Set Spacing (m)” tab where minimum, maximum and mean spacing values of a single set of discontinuities can be recorded; (<b>g</b>) Fields of “Persistence” tab where minimum, maximum, and mean persistence values of a single set of discontinuities can be recorded; (<b>h</b>) “Characteristics” tab showing the form pages “JCS” and “Surface” where the “Roughness” and “Weathering Grade” fields are included; (<b>i</b>) “JCS” form page showing the fields to be populating with the joint wall compressive strength test results; (<b>l</b>) “Photo” tab including “Photo Code” showing the image preview and file path.</p> Full article ">Figure 5
<p>Input forms with combo boxes containing a drop-down list of the geotechnical data values required by the Parsifal method: (<b>a</b>) example of “Grain Size” attributes; (<b>b</b>) example of “Rock Mass Type” attributes.</p> Full article ">Figure 6
<p>QGIS window with layers of the PARSIFAL project. The BeePen plug-in (at the bottom) allows sketches and annotations on the map, as shown.</p> Full article ">
16 pages, 5405 KiB  
Article
An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification
by Panpan Ge, Jun He, Shuhua Zhang, Liwei Zhang and Jiangfeng She
ISPRS Int. J. Geo-Inf. 2019, 8(2), 90; https://doi.org/10.3390/ijgi8020090 - 15 Feb 2019
Cited by 21 | Viewed by 3442
Abstract
Urban land use information is critical to urban planning, but the increasing complexity of urban systems makes the accurate classification of land use extremely challenging. Human activity features extracted from big data have been used for land use classification, and fusing different features [...] Read more.
Urban land use information is critical to urban planning, but the increasing complexity of urban systems makes the accurate classification of land use extremely challenging. Human activity features extracted from big data have been used for land use classification, and fusing different features can help improve the classification. In this paper, we propose a framework to integrate multiple human activity features for land use classification. Features were fused by constructing a membership matrix reflecting the fuzzy relationship between features and land use types using the fuzzy c-means (FCM) clustering method. The classification results were obtained by the fuzzy comprehensive evaluation (FCE) method, which regards the membership matrix as the fuzzy evaluation matrix. This framework was applied to a case study using taxi trajectory data from Nanjing, and the outflow, inflow, net flow and net flow ratio features were extracted. A series of experiments demonstrated that the proposed framework can effectively fuse different features and increase the accuracy of land use classification. The classification accuracy achieved 0.858 (Kappa = 0.810) when the four features were fused for land use classification. Full article
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<p>Flowchart of the proposed framework.</p> Full article ">Figure 2
<p>Study area. (<b>a</b>) Geographical location of Nanjing; (<b>b</b>) Study area divided into 500 m × 500 m cells.</p> Full article ">Figure 3
<p>Land use in Nanjing.</p> Full article ">Figure 4
<p>Classification results of feature combinations. (<b>a</b>) Outflow and inflow features; (<b>b</b>) outflow, inflow and net flow features; (<b>c</b>) outflow, inflow and net flow ratio features; (<b>d</b>) outflow, inflow, net flow and net flow ratio features.</p> Full article ">Figure 5
<p>Confusion matrices of feature combinations. (<b>a</b>) Outflow and inflow features; (<b>b</b>) outflow, inflow and net flow features; (<b>c</b>) outflow, inflow and net flow ratio features; (<b>d</b>) outflow, inflow, net flow and net flow ratio features.</p> Full article ">Figure 6
<p>Classification results based on different methods. (<b>a</b>) The method using the EM algorithm and the time series that integrates the outflow and inflow features (OI_EM method); (<b>b</b>) framework in this study.</p> Full article ">Figure 7
<p>Confusion matrix of the OI_EM method.</p> Full article ">Figure 8
<p>Centers of land use types and clusters centers. (<b>a</b>)–(<b>d</b>) Centers of land use types; (<b>e</b>)–(<b>h</b>) cluster centers.</p> Full article ">
18 pages, 5070 KiB  
Article
A Quaternion-Based Piecewise 3D Modeling Method for Indoor Path Networks
by Hongdeng Jian, Xiangtao Fan, Jian Liu, Qingwen Jin and Xujie Kang
ISPRS Int. J. Geo-Inf. 2019, 8(2), 89; https://doi.org/10.3390/ijgi8020089 - 15 Feb 2019
Cited by 5 | Viewed by 3156
Abstract
Generating 3D path models (with textures) from indoor paths is a good way to improve the visualization performance of 3D indoor path analysis. In this paper, a quaternion-based piecewise 3D modeling method is proposed to automatically generate highly recognizable 3D models for indoor [...] Read more.
Generating 3D path models (with textures) from indoor paths is a good way to improve the visualization performance of 3D indoor path analysis. In this paper, a quaternion-based piecewise 3D modeling method is proposed to automatically generate highly recognizable 3D models for indoor path networks. To create such models, indoor paths are classified into four types of basic elements: corridor, stairs, elevator and node, which contain six kinds of edges and seven kinds of nodes. A quaternion-based method is devised to calculate the coordinates of the designed elements, and a piecewise 3D modeling method is implemented to create the entire 3D indoor path models in a 3D GIS scene. The numerical comparison of 3D scene primitives in different visualization modes indicates that the proposed method can generate detailed and irredundant models for indoor path networks. The result of 3D path analysis shows that indoor path models can improve the visualization performance of a 3D indoor path network by displaying paths with different shapes, textures and colors and that the models can maintain a high rendering efficiency (above 50 frames per second) in a 3D GIS scene containing more than 50,000 polygons and triangles. Full article
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<p>Indoor path network in a 3D virtual scene. (<b>a</b>) Overall view. (<b>b</b>) Edges and nodes.</p> Full article ">Figure 2
<p>Design of a corridor element.</p> Full article ">Figure 3
<p>Design of a stair element.</p> Full article ">Figure 4
<p>Design of an elevator element.</p> Full article ">Figure 5
<p>Design of a node element. (<b>a</b>) Circular node. (<b>b</b>) Arc node.</p> Full article ">Figure 6
<p>Example of calculation of coordinates by trigonometry.</p> Full article ">Figure 7
<p>Example of calculating coordinates with quaternions.</p> Full article ">Figure 8
<p>The case of the middle point of AB not being at the origin.</p> Full article ">Figure 9
<p>Calculation of coordinates of a circular node.</p> Full article ">Figure 10
<p>Calculation of coordinates of an arc node.</p> Full article ">Figure 11
<p>Quaternion rotations at an arc node.</p> Full article ">Figure 12
<p>Calculation of coordinates by quaternion interpolation.</p> Full article ">Figure 13
<p>Calculation of coordinates of stair elements. (<b>a</b>) Side view. (<b>b</b>) Stereoscopic view. (<b>c</b>) Top view.</p> Full article ">Figure 14
<p>Piecewise 3D modeling method of an indoor path network.</p> Full article ">Figure 15
<p>Piecewise 3D modeling of the basic elements in a 3D scene.</p> Full article ">Figure 16
<p>Piecewise 3D modeling of an indoor path network in a 3D virtual scene.</p> Full article ">Figure 17
<p>Visualization of a 3D path analysis with indoor path models.</p> Full article ">Figure 18
<p>Frame rates of 3D path analysis with indoor path models. Stage 1: no lines, 1–10s; Stage 2: paths visualized as lines, 11–21s; Stage 3: paths visualized as models, 22–37s; Stage 4: path analysis with 3D models: 38–53s.</p> Full article ">
17 pages, 5273 KiB  
Article
Integrating Island Spatial Information and Integer Optimization for Locating Maritime Search and Rescue Bases: A Case Study in the South China Sea
by Xiao Zhou, Liang Cheng, Fangli Zhang, Zhaojin Yan, Xiaoguang Ruan, Kaifu Min and Manchun Li
ISPRS Int. J. Geo-Inf. 2019, 8(2), 88; https://doi.org/10.3390/ijgi8020088 - 15 Feb 2019
Cited by 24 | Viewed by 4739
Abstract
Maritime search and rescue (SAR) operations are critical for ensuring safety at sea. Islands have been considered as feasible solutions for the construction of new maritime SAR bases to improve the capacity of SAR operations in remote sea areas. This paper proposes a [...] Read more.
Maritime search and rescue (SAR) operations are critical for ensuring safety at sea. Islands have been considered as feasible solutions for the construction of new maritime SAR bases to improve the capacity of SAR operations in remote sea areas. This paper proposes a new framework, based on island spatial information, for determining the optimal locations for maritime SAR bases. The framework comprises four steps. First, candidate islands for the construction of maritime SAR bases are selected. Second, the potential rescue demand is estimated by employing ship location data and marine incident data. In the third step, the response time from candidate islands to any site at sea is calculated, with explicit consideration of the impact of sea conditions on the ship’s speed. Fourth, the final island locations are proposed by solving the maximal covering location problem (MCLP). The proposed framework was applied to the South China Sea. The results showed that there would be a decrease of 1.09 h in terms of the mean access time for the South China Sea if the six selected island bases were constructed, whilst the primary coverage increased from 62.63% to 80.02% when using a 6-hour threshold. This new framework is expected to contribute to improvements in safety at sea and should be applicable to any sea area where the construction of island rescue bases is being considered. Full article
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<p>Framework for selecting the location for a maritime search and rescue base.</p> Full article ">Figure 2
<p>The ship-wave relative direction.</p> Full article ">Figure 3
<p>The response time for maritime SAR. It is calculated for each cell by summing up the costs of moving from a cell center to another through least-cost route.</p> Full article ">Figure 4
<p>Location of the South China Sea (SCS), and distribution of seaports around the SCS.</p> Full article ">Figure 5
<p>Flowchart diagram of location selection.</p> Full article ">Figure 6
<p>The locations of candidate islands in the South China Sea.</p> Full article ">Figure 7
<p>The potential demand density in the South China Sea.</p> Full article ">Figure 8
<p>Response times with the maximum coverage solution arrangements. The purple triangles represent the inshore SAR bases, and the green pentagons represent potential island bases.</p> Full article ">
21 pages, 8144 KiB  
Article
Multi-Sensor UAV Application for Thermal Analysis on a Dry-Stone Terraced Vineyard in Rural Tuscany Landscape
by Grazia Tucci, Erica Isabella Parisi, Giulio Castelli, Alessandro Errico, Manuela Corongiu, Giovanna Sona, Enea Viviani, Elena Bresci and Federico Preti
ISPRS Int. J. Geo-Inf. 2019, 8(2), 87; https://doi.org/10.3390/ijgi8020087 - 15 Feb 2019
Cited by 54 | Viewed by 5861
Abstract
Italian dry-stone wall terracing represents one of the most iconic features of agricultural landscapes across Europe, with sites listed among UNESCO World Heritage Sites and FAO Globally Important Agricultural Heritage Systems (GIAHS). The analysis of microclimate modifications induced by alterations of hillslope and [...] Read more.
Italian dry-stone wall terracing represents one of the most iconic features of agricultural landscapes across Europe, with sites listed among UNESCO World Heritage Sites and FAO Globally Important Agricultural Heritage Systems (GIAHS). The analysis of microclimate modifications induced by alterations of hillslope and by dry-stone walls is of particular interest for the valuation of benefits and drawbacks of terraces cultivation, a global land management technique. The aim of this paper is to perform a thermal characterization of a dry-stone wall terraced vineyard in the Chianti area (Tuscany, Italy), to detect possible microclimate dynamics induced by dry-stone terracing. The aerial surveys were carried out by using two sensors, in the Visible (VIS) and Thermal InfraRed (TIR) spectral range, mounted on Unmanned Aerial Vehicles (UAVs), with two different flights. Our results reveal that, in the morning, vineyard rows close to dry-stone walls have statistically lower temperatures with respect to the external ones. In the afternoon, due to solar insulation, temperatures raised to the same value for each row. The results of this early study, jointly with the latest developments in UAV and sensor technologies, justify and encourage further analyses on local climatic modifications in terraced landscapes. Full article
(This article belongs to the Special Issue Data Acquisition and Processing in Cultural Heritage)
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<p>Vigna Grospoli vineyard in Lamole. Photo by G. Castelli.</p> Full article ">Figure 2
<p>Location of the study area. Red border—North area; blue border—South area of the Grospoli vineyard in Tuscany region (Italy).</p> Full article ">Figure 3
<p>Each vineyard row has been labelled as: internal (blue) for the closest rows to the dry-stone wall (dotted line), external rows (red) for the ones at the edge of the terrace (black lines). All the rows comprised between internal and external have been identified as intermediate (green).</p> Full article ">Figure 4
<p>The orthophoto of the study area produced by the photogrammetric processing of the images acquired from the RGB flight over the Grospoli vineyard.</p> Full article ">Figure 5
<p>Thermal maps of the Grospoli vineyard in (<b>a</b>) morning (08.50 CET) and (<b>b</b>) afternoon (15:00 CET). The enlarged windows highlight internal and external rows, showing their respective thermal behaviours.</p> Full article ">Figure 6
<p>Difference between the afternoon thermal map and the morning orthomosaics highlighting, in the enlarged windows, the behaviour of the internal and external rows.</p> Full article ">Figure 7
<p>Scatter plot of morning and afternoon temperatures for external (circles) and internal (black squares) rows.</p> Full article ">Figure 8
<p>Temperature average values along the hillside terraced Grospoli vineyard. (<b>a</b>) North area; (<b>b</b>) South area. White and grey timeseries represent morning and afternoon temperatures respectively. Black dots represent internal rows temperature mean values.</p> Full article ">Figure 9
<p>Sun position over the Grospoli vineyard on 8 September 2017, at (<b>a</b>) 08:00 CET (solar rays perpendicular to the rows) and (<b>b</b>) 15:00 CET (solar rays irradiating all the rows).</p> Full article ">
19 pages, 112922 KiB  
Article
Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM
by Ping Liu and Xi Chen
ISPRS Int. J. Geo-Inf. 2019, 8(2), 86; https://doi.org/10.3390/ijgi8020086 - 14 Feb 2019
Cited by 12 | Viewed by 4134
Abstract
Remote sensing has been widely used in vegetation cover research but is rarely used for intercropping area monitoring. To investigate the efficiency of Chinese Gaofen satellite imagery, in this study the GF-1 and GF-2 of Moyu County south of the Tarim Basin were [...] Read more.
Remote sensing has been widely used in vegetation cover research but is rarely used for intercropping area monitoring. To investigate the efficiency of Chinese Gaofen satellite imagery, in this study the GF-1 and GF-2 of Moyu County south of the Tarim Basin were studied. Based on Chinese GF-1 and GF-2 satellite imagery features, this study has developed a comprehensive feature extraction and intercropping classification scheme. Textural features derived from a Gray level co-occurrence matrix (GLCM) and vegetation features derived from multi-temporal GF-1 and GF-2 satellites were introduced and combined into three different groups. The rotation forest method was then adopted based on a Support Vector Machine (RoF-SVM), which offers the advantage of using an SVM algorithm and that boosts the diversity of individual base classifiers by a rotation forest. The combined spectral-textural-multitemporal features achieved the best classification result. The results were compared with those of the maximum likelihood classifier, support vector machine and random forest method. It is shown that the RoF-SVM algorithm for the combined spectral-textural-multitemporal features can effectively classify an intercropping area (overall accuracy of 86.87% and kappa coefficient of 0.78), and the classification result effectively eliminated salt and pepper noise. Furthermore, the GF-1 and GF-2 satellite images combined with spectral, textural, and multi-temporal features can provide sufficient information on vegetation cover located in an extremely complex and diverse intercropping area. Full article
(This article belongs to the Special Issue Geographic Information Science in Forestry)
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Full article ">Figure 1
<p>Map showing the study area: (<b>a</b>) study area in the Tarim Basin; (<b>b</b>) study area illustrated with false colour combinations (near-infrared, red, and green bands) for September 18, 2016. The green points denote training samples and the blue points denote validation samples.</p> Full article ">Figure 2
<p>Growth period and sample images of study area; (<b>a</b>) Sample GF-1 images acquired on May 10; (<b>b</b>) Sample GF-1 image acquired on 2 June 24, 2016; (<b>c</b>) Sample GF-2 image acquired on September 18, 2016.</p> Full article ">Figure 3
<p>Workflow for feature selection.</p> Full article ">Figure 4
<p>RoF-SVM classification flowchart.</p> Full article ">Figure 5
<p>Overall accuracy (%) with respect to the number of subsets <span class="html-italic">K</span>.</p> Full article ">Figure 6
<p>Overall accuracy (%) versus ensemble size (n).</p> Full article ">Figure 7
<p>Crop classification maps based on the RoF-SVM: (<b>A</b>) True colour map of GF-2; (<b>a</b>) Sample area of (<b>A</b>); (<b>B</b>) RoF-SVM classification results of F1; (<b>b</b>) Sample area of (<b>B</b>); (<b>C</b>) RoF-SVM classification results of F-2; (<b>c</b>) Sample area of (<b>C</b>); (<b>D</b>) RoF-SVM classification results of F3; (<b>d</b>) Sample area of (<b>D</b>).</p> Full article ">Figure 8
<p>Overall accuracy of three combinations of different methods.</p> Full article ">Figure 9
<p>Crop classification maps of F3 combinations: (<b>A</b>) MLC classification results; (<b>a</b>) Sample area of (<b>A</b>); (<b>B</b>) SVM classification results; (<b>b</b>) Sample area of (<b>B</b>); (<b>C</b>) RF classification results; (<b>c</b>) Sample area of (<b>C</b>); (<b>D</b>) RoF-SVM classification results; (<b>d</b>) Sample area of (<b>D</b>).</p> Full article ">
16 pages, 6819 KiB  
Article
Scenario-Based Risk Assessment of Earthquake Disaster Using Slope Displacement, PGA, and Population Density in the Guyuan Region, China
by Jianqi Zhuang, Jianbing Peng, Xinghua Zhu and Weiliang Huang
ISPRS Int. J. Geo-Inf. 2019, 8(2), 85; https://doi.org/10.3390/ijgi8020085 - 14 Feb 2019
Cited by 15 | Viewed by 4812
Abstract
Mega-earthquakes that occur in mountainous areas of densely populated cities are particularly catastrophic, triggering large landslides, destroying more buildings, and usually resulting in significant death tolls. In this paper, earthquake scenarios in the Guyuan Region of China are used as an example to [...] Read more.
Mega-earthquakes that occur in mountainous areas of densely populated cities are particularly catastrophic, triggering large landslides, destroying more buildings, and usually resulting in significant death tolls. In this paper, earthquake scenarios in the Guyuan Region of China are used as an example to study earthquake disaster risk assessment and a method of assessment is proposed that uses the peak ground acceleration (PGA), landslides triggered by the earthquake, and the effects on the population. The method is used to develop scenarios for earthquake disaster risk assessment along the Haiyuan and Liupanshan Faults for earthquake magnitudes of Ms 7.0, 7.5, 8.0, and 8.5 triggered by one of the two faults. The quantitative earthquake disaster risk maps in the study area were developed by integrating the values of the at-risk elements for the earthquake factor, population, and landslide hazard. According to the model results, the high-hazard zone was mainly located in the severely affected areas along the faults and on the western side of the faults. These results can be useful for emergency preparation planning, response plans, and resource assessment. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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<p>The engineering geology and faults around the study region: (<b>A</b>) Hard rock; (<b>B</b>) loess; (<b>C</b>) moderate rock; (<b>D</b>) very soft rock; and (<b>E</b>) soft rock. (The earthquake data is from the China Earthquake Administration).</p> Full article ">Figure 2
<p>Flow chart showing the steps involved in producing a seismic risk map (<span class="html-italic">c</span> is the cohesive strength, <span class="html-italic">φ</span> is the internal friction angle, <span class="html-italic">r</span> is the material unit weigh, <span class="html-italic">a</span> is the slope angle, <span class="html-italic">F<sub>s</sub></span> is the static factor of safety, and <span class="html-italic">D<sub>N</sub></span> is the landslide movement distance in cm. DEM is Digital Elevation Model).</p> Full article ">Figure 3
<p>The slope, population density, and distance to faults; (<b>a</b>) the slope of the study area, (<b>b</b>) the population density of the study area, (<b>c</b>) the distance to the Haiyuan Fault, and (<b>d</b>) the distance to the Liupanshan Fault.</p> Full article ">Figure 4
<p>The distribution of the landslide movement distance (D<sub>N</sub>) along the Haiyuan and Liupanshan Faults: The seismic magnitude of <span class="html-italic">Ms</span> 7.5 along the Haiyuan Fault (<b>a</b>), <span class="html-italic">Ms</span> 8.5 along the Haiyuan Fault (<b>b</b>), <span class="html-italic">Ms</span> 7.5 along the Liupanshan Fault (<b>c</b>), and <span class="html-italic">Ms</span> 8.5 along the Liupanshan Fault (<b>d</b>).</p> Full article ">Figure 5
<p>The area percentage of the displacement triggered by the Haiyuan/Liupanshan Fault at different magnitudes; the grey gradual change bar is the area percentage of the displacement associated with the y-axis, triggered by the Haiyuan Fault at the seismic scenario, and the blue gradual change bar is the area percentage of the displacement associated with the y-axis, triggered by the Liupanshan Fault at the seismic scenario).</p> Full article ">Figure 6
<p>The slope failure distribution with a displacement above 18 cm; (<b>a</b>) seismic magnitude of <span class="html-italic">Ms</span> 7.5 along the Haiyuan Fault, (<b>b</b>) seismic magnitude of <span class="html-italic">Ms</span> 8.5 along the Haiyuan Fault, (<b>c</b>) seismic magnitude of <span class="html-italic">Ms</span> 7.5 along the Liupanshan Fault, and (<b>d</b>) seismic magnitude of <span class="html-italic">Ms</span> 8.5 along the Liupanshan fault.</p> Full article ">Figure 7
<p>The distribution of the peak ground acceleration (<span class="html-italic">a</span><sub>max</sub>) along the Haiyuan Fault and the Liupanshan Fault: (<b>a</b>–<b>d</b>): The seismic magnitude is <span class="html-italic">Ms</span> 7.0–8.5 along the Haiyuan Fault, and (<b>e</b>–<b>h</b>): The seismic magnitude is <span class="html-italic">Ms</span> 7.0–8.5 along the Liupanshan Fault.</p> Full article ">Figure 7 Cont.
<p>The distribution of the peak ground acceleration (<span class="html-italic">a</span><sub>max</sub>) along the Haiyuan Fault and the Liupanshan Fault: (<b>a</b>–<b>d</b>): The seismic magnitude is <span class="html-italic">Ms</span> 7.0–8.5 along the Haiyuan Fault, and (<b>e</b>–<b>h</b>): The seismic magnitude is <span class="html-italic">Ms</span> 7.0–8.5 along the Liupanshan Fault.</p> Full article ">Figure 8
<p>Risk assessment zoning maps in the Guyuan Region, at the township level, for a seismic scenario of <span class="html-italic">Ms</span> 7.0–8.5 along the Haiyuan Fault and the Liupanshan Fault: (<b>a</b>–<b>d</b>): A seismic scenario of <span class="html-italic">Ms</span> 7.0–8.5 along the Haiyuan Fault; and (<b>e</b>–<b>h</b>): A seismic scenario of <span class="html-italic">Ms</span> 7.0–8.5 along the Liupanshan Fault).</p> Full article ">
23 pages, 3050 KiB  
Article
Studying Social Uses of 3D Geovisualizations: Lessons Learned from Action-Research Projects in the Field of Flood Mitigation Planning
by Florence Jacquinod and Julia Bonaccorsi
ISPRS Int. J. Geo-Inf. 2019, 8(2), 84; https://doi.org/10.3390/ijgi8020084 - 14 Feb 2019
Cited by 18 | Viewed by 3834
Abstract
Risk management seeks more and more the mobilization of all citizens, including elected representatives and inhabitants. Three-dimensional (3D) geovisualizations have been used between 2009 and 2017 in order to associate citizens to flood mitigation policies along the river Rhône. We focused our studies [...] Read more.
Risk management seeks more and more the mobilization of all citizens, including elected representatives and inhabitants. Three-dimensional (3D) geovisualizations have been used between 2009 and 2017 in order to associate citizens to flood mitigation policies along the river Rhône. We focused our studies on the effects 3D geovisualizations can have on the communication and understanding of information and their ability to foster exchanges between heterogeneous actors as well as participation of the grand public to planning processes. Facing both discrepancies in scientific studies of the uses of 3D geovisualizations and a lack of validated theoretical elements, we resorted to an exploratory method based on grounded theory and ethnographic observation in order to produce empirical knowledge on the uses of 3D geovisualizations in collective settings, including heterogeneous actors (risk managers, elected representatives, citizens). Observation showed that 3D geovisualizations can be useful for the dissemination of information about flood risk. Many observed effects were not anticipated during the production of 3D geovisualizations. Qualitative analysis of empirical data through actor–network theory and from a communication studies perspective shed light on some factors influencing the roles of 3D geovisualizations and help put into perspective existing and sometimes contradictory scientific works on 3D geovisualizations’ uses. Full article
(This article belongs to the Special Issue Human-Centered Geovisual Analytics and Visuospatial Display Design)
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<p>Aerial view from a 3D geovisualization of a district impacted by a simulated flood. Image prepared by F. Jacquinod from data from IGN and CNR.</p> Full article ">Figure 2
<p>Aerial view from a 3D geovisualization of a district impacted by a simulated flood. (<b>a</b>) and (<b>b</b>) show how water heights were represented, through a 3D transparent volume. Images prepared by F. Jacquinod from data from IGN and CNR.</p> Full article ">Figure 3
<p>Two images from two different moments in the simulated flood, from a high vantage point: (<b>a</b>) shows water height at day three (three days after the beginning of the simulated flood); (<b>b</b>) shows water heights at day five.</p> Full article ">Figure 4
<p>Interface proposed to the public on tablets for the visualization of the simulated flood from a chosen point of view. At the top is a temporal navigation panel to choose a point in the timeline of the simulated flood. On the right is a panel that can be hidden or displayed and that provide a link between water flow (m<sup>3</sup> per second) and historical floods that have occurred on the territory. It also enables the user to access historical pictures for some historical floods. At the bottom is a spatial navigation panel to go to the left or right, look up or down and zoom in or out. The information tool gives more details on how the flood was simulated.</p> Full article ">Figure 5
<p>Example of the 360° images produced for immersive visualization (with a smartphone inserted into a virtual reality (VR) headset). Purple and green panels can be used to navigate between different moments in time (during the simulated flood). Grey panel gives information about water flows (m<sup>3</sup> per second), point in time (number of days since the beginning of the flood), and the corresponding level of vigilance (here red).</p> Full article ">Figure 6
<p>Pictures taken during the events: (<b>a</b>) shows inhabitants consulting the 3D geovisualization with a tablet from a high vantage point (same viewpoint as the one selected for the images of the simulated flood they are visualizing); (<b>b</b>) shows inhabitants using the VR headset (background), while another is being shown the tablet by a mediator (foreground).</p> Full article ">
26 pages, 8805 KiB  
Article
Accuracy Analysis of a 3D Model of Excavation, Created from Images Acquired with an Action Camera from Low Altitudes
by Damian Wierzbicki and Marcin Nienaltowski
ISPRS Int. J. Geo-Inf. 2019, 8(2), 83; https://doi.org/10.3390/ijgi8020083 - 13 Feb 2019
Cited by 15 | Viewed by 4668
Abstract
In the last few years, Unmanned Aerial Vehicles (UAVs) equipped with compact digital cameras, have become a cheap and efficient alternative to classic aerial photogrammetry and close-range photogrammetry. Low-altitude photogrammetry has great potential not only in the development of orthophoto maps but is [...] Read more.
In the last few years, Unmanned Aerial Vehicles (UAVs) equipped with compact digital cameras, have become a cheap and efficient alternative to classic aerial photogrammetry and close-range photogrammetry. Low-altitude photogrammetry has great potential not only in the development of orthophoto maps but is also increasingly used in surveying and rapid mapping. This paper presents a practical aspect of the application of the custom homemade low-cost UAV, equipped with an action camera, to obtain images from low altitudes and develop a digital elevation model of the excavation. The conducted analyses examine the possibilities of using low-cost UAVs to deliver useful photogrammetric products. The experiments were carried out on a closed excavation in the town of Mince (north-eastern Poland). The flight over the examined area was carried out autonomously. A photogrammetric network was designed, and the reference areas in the mine were measured using the Global Navigation Satellite System-Real Time Kinematic (GNSS-RTK) method to perform accuracy analyses of the excavation 3D model. Representation of the created numerical terrain model was a dense point cloud. The average height difference between the generated dense point cloud and the reference model was within the range of 0.01–0.13 m. The difference between the volume of the excavation measured by the GNSS kinematic method and the volume measured on the basis of a dense point cloud was less than 1%. The obtained results show that the application of the low-cost UAV equipped with an action camera with a wide-angle lens, allows for obtaining high-accuracy images comparable to classic, compact digital cameras. Full article
(This article belongs to the Special Issue Applications and Potential of UAV Photogrammetric Survey)
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<p>Ultralight frame of own design.</p> Full article ">Figure 2
<p>Wiring diagram of quadcopter basic components (based on Reference [<a href="#B45-ijgi-08-00083" class="html-bibr">45</a>]).</p> Full article ">Figure 3
<p>The method of attaching the three-axis gimbal together with the GoPro Hero 5 camera to the discussed multirotor–Unmanned Aerial Vehicle (UAV).</p> Full article ">Figure 4
<p>The designed UAV system (from the left: FPV receiver with cardboard, quadcopter with FPV system, mounted gimbal and GoPro camera, control equipment and LiPo battery).</p> Full article ">Figure 5
<p>The study site.</p> Full article ">Figure 6
<p>Photograph showing part of the tested excavation.</p> Full article ">Figure 7
<p>(<b>a</b>) Ground Control Point marked in the field with a white and black chessboard; (<b>b</b>) Project of the ground control point.</p> Full article ">Figure 8
<p>The arrangement of photogrammetric network points on the study site.</p> Full article ">Figure 9
<p>Distribution of measured cross-sections for further accuracy analyses.</p> Full article ">Figure 10
<p>Overall methodology of data processing.</p> Full article ">Figure 11
<p>Errors on the point cloud created in areas flooded with water (marked in red ellipses).</p> Full article ">Figure 12
<p>Dense point cloud of the study site.</p> Full article ">Figure 13
<p>Mapping the discontinuity lines through a point cloud. (<b>a</b>) Photo of the area under analysis (<b>b</b>) Cloud of points in the analysed area.</p> Full article ">Figure 14
<p>Volume calculation of reference plane method (based on Reference [<a href="#B55-ijgi-08-00083" class="html-bibr">55</a>]).</p> Full article ">Figure 15
<p>The generated Triangular Irregular Network-Digital Terrain Model (TIN-DTM) used to measure the volume of the excavation.</p> Full article ">Figure 16
<p>Distribution of differences in the height of the point cloud in relation to the reference model (Test Area No. 1).</p> Full article ">Figure 17
<p>A graph showing a distribution of the cloud deviations of Test Area No. 1.</p> Full article ">Figure 18
<p>Distribution of differences in the height of the point cloud in relation to the reference model (Test Area No. 2).</p> Full article ">Figure 19
<p>A graph showing a distribution of the cloud deviations of Test Area No. 2.</p> Full article ">Figure 20
<p>Distribution of differences in the height of the point cloud in relation to the reference model (Test Area No. 3).</p> Full article ">Figure 21
<p>A graph showing a distribution of the cloud deviations of Test Area No. 3.</p> Full article ">Figure 22
<p>Distribution of differences in the height of the point cloud in relation to the reference model (Test Area No. 4).</p> Full article ">Figure 23
<p>A graph showing a distribution of the cloud deviations of Test Area No. 4.</p> Full article ">Figure 24
<p>Density distribution of the entire generated point cloud.</p> Full article ">
18 pages, 10135 KiB  
Article
A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data
by Zeinab Ghaemi and Mahdi Farnaghi
ISPRS Int. J. Geo-Inf. 2019, 8(2), 82; https://doi.org/10.3390/ijgi8020082 - 13 Feb 2019
Cited by 29 | Viewed by 5216
Abstract
Extracting the latent knowledge from Twitter by applying spatial clustering on geotagged tweets provides the ability to discover events and their locations. DBSCAN (density-based spatial clustering of applications with noise), which has been widely used to retrieve events from geotagged tweets, cannot efficiently [...] Read more.
Extracting the latent knowledge from Twitter by applying spatial clustering on geotagged tweets provides the ability to discover events and their locations. DBSCAN (density-based spatial clustering of applications with noise), which has been widely used to retrieve events from geotagged tweets, cannot efficiently detect clusters when there is significant spatial heterogeneity in the dataset, as it is the case for Twitter data where the distribution of users, as well as the intensity of publishing tweets, varies over the study areas. This study proposes VDCT (Varied Density-based spatial Clustering for Twitter data) algorithm that extracts clusters from geotagged tweets by considering spatial heterogeneity. The algorithm employs exponential spline interpolation to determine different search radiuses for cluster detection. Moreover, in addition to spatial proximity, textual similarities among tweets are also taken into account by the algorithm. In order to examine the efficiency of the algorithm, geotagged tweets collected during a hurricane in the United States were used for event detection. The output clusters of VDCT have been compared to those of DBSCAN. Visual and quantitative comparison of the results proved the feasibility of the proposed method. Full article
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<p>Processing workflow.</p> Full article ">Figure 2
<p>Distribution of geotagged tweets.</p> Full article ">Figure 3
<p>Tweets with various contents.</p> Full article ">Figure 4
<p>The pseudo code of VDCT algorithm.</p> Full article ">Figure 5
<p>Variation of the silhouette coefficient in response to different <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mi>t</mi> </msub> </mrow> </semantics></math> values.</p> Full article ">Figure 6
<p>Extracted clusters using VDCT clustering algorithm.</p> Full article ">Figure 7
<p>Extracted clusters using DBSCAN clustering algorithm.</p> Full article ">Figure 8
<p>(<b>a</b>) A part of the study area with more variation in density; (<b>b</b>) Extracted clusters by VDCT and (<b>c</b>) Extracted clusters by DBSCAN.</p> Full article ">Figure 9
<p>Word Cloud generated for Cluster number 2 (C2) of DBSCAN.</p> Full article ">Figure 10
<p>Word Cloud generated for Clusters (<b>a</b>) 2, (<b>b</b>) 3 and (<b>c</b>) 4 extracted by VDCT and (<b>d</b>) their positions on map.</p> Full article ">Figure 11
<p>Word Cloud generated for Cluster 5 of DBSCAN.</p> Full article ">Figure 12
<p>Word Cloud generated for Clusters (<b>a</b>) 7 and (<b>b</b>) 8 extracted by VDCT and (<b>c</b>) their positions on map.</p> Full article ">Figure 13
<p>Clusters (<b>a</b>) number 8 of DBSCAN, (<b>b</b>) number 7 of DBSCAN, (<b>c</b>) number 11 of VDCT and (<b>d</b>) number 10 of VDCT.</p> Full article ">Figure 14
<p>Generated Word Cloud of (<b>a</b>) C8 DBSCAN and (<b>b</b>) C11 VDCT.</p> Full article ">Figure 15
<p>Generated Word Cloud of (<b>a</b>) C7 DBSCAN and (<b>b</b>) C10 VDCT.</p> Full article ">Figure 16
<p>Word Cloud generated for clusters: (<b>a</b>) C1, (<b>c</b>) C3, (<b>e</b>) C4 and (<b>g</b>) C6 of DBSCAN and (<b>b</b>) C1, (<b>d</b>) C5, (<b>f</b>) C6 and (<b>h</b>) C9 of VDCT.</p> Full article ">
19 pages, 33327 KiB  
Article
A New, Score-Based Multi-Stage Matching Approach for Road Network Conflation in Different Road Patterns
by Müslüm Hacar and Türkay Gökgöz
ISPRS Int. J. Geo-Inf. 2019, 8(2), 81; https://doi.org/10.3390/ijgi8020081 - 13 Feb 2019
Cited by 10 | Viewed by 3837
Abstract
Road-matching processes establish links between multi-sourced road lines representing the same entities in the real world. Several road-matching methods have been developed in the last three decades. The main issue related to this process is selecting the most appropriate method. This selection depends [...] Read more.
Road-matching processes establish links between multi-sourced road lines representing the same entities in the real world. Several road-matching methods have been developed in the last three decades. The main issue related to this process is selecting the most appropriate method. This selection depends on the data and requires a pre-process (i.e., accuracy assessment). This paper presents a new matching method for roads composed of different patterns. The proposed method matches road lines incrementally (i.e., from the most similar matching to the least similar). In the experimental testing, three road networks in Istanbul, Turkey, which are composed of tree, cellular, and hybrid patterns, provided by the municipality (authority), OpenStreetMap (volunteered), TomTom (private), and Basarsoft (private) were used. The similarity scores were determined using Hausdorff distance, orientation, sinuosity, mean perpendicular distance, mean length of triangle edges, and modified degree of connectivity. While the first four stages determined certain matches with regards to the scores, the last stage determined them with a criterion for overlapping areas among the buffers of the candidates. The results were evaluated with manual matching. According to the precision, recall, and F-value, the proposed method gives satisfactory results on different types of road patterns. Full article
(This article belongs to the Special Issue Multi-Source Geoinformation Fusion)
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<p>Study area and road networks composed of tree (<b>a</b>), cellular (<b>b</b>), and hybrid (<b>c</b>) patterns.</p> Full article ">Figure 2
<p>Two candidates for matching (<b>a</b>), minimum distances from Line r to Line g (<b>b</b>), from Line g to Line r (<b>c</b>), and maximum of minimum distances (i.e., Hausdorff distance) (<b>d</b>).</p> Full article ">Figure 3
<p>Orientation angles, intervals, and classes.</p> Full article ">Figure 4
<p>The sinuous length (S) and the straight-line distance (d) of a road line.</p> Full article ">Figure 5
<p>A road line (continuous) and its perpendicular distances (dashed).</p> Full article ">Figure 6
<p>Centroids of road lines and triangulated irregular network (TIN) (dashed lines).</p> Full article ">Figure 7
<p>Degree (<b>a</b>) and modified degree (<b>b</b>) of connectivity of road lines.</p> Full article ">Figure 8
<p>Scoring based on the similarity of the candidate matches.</p> Full article ">Figure 9
<p>Workflow of the proposed method.</p> Full article ">Figure 9 Cont.
<p>Workflow of the proposed method.</p> Full article ">Figure 10
<p>Accuracy distribution of similarity indicators.</p> Full article ">Figure 11
<p>Alignment by rubber-sheet transformation.</p> Full article ">Figure 12
<p>A sample for the mismatches: Istanbul Metropolitan Municipality (IMM)(green) and Basarsoft (red).</p> Full article ">Figure 13
<p>A sample for the incorrect matches: OSM (blue) and TomTom (orange).</p> Full article ">Figure 14
<p>OpenStreetMap (OSM) (blue) and TomTom (orange) road lines and the sample extends (red rectangles).</p> Full article ">
19 pages, 28543 KiB  
Article
Shore Construction Detection by Automotive Radar for the Needs of Autonomous Surface Vehicle Navigation
by Andrzej Stateczny, Witold Kazimierski, Paweł Burdziakowski, Weronika Motyl and Marta Wisniewska
ISPRS Int. J. Geo-Inf. 2019, 8(2), 80; https://doi.org/10.3390/ijgi8020080 - 13 Feb 2019
Cited by 43 | Viewed by 4886
Abstract
Autonomous surface vehicles (ASVs) are becoming more and more popular for performing hydrographic and navigational tasks. One of the key aspects of autonomous navigation is the need to avoid collisions with other objects, including shore structures. During a mission, an ASV should be [...] Read more.
Autonomous surface vehicles (ASVs) are becoming more and more popular for performing hydrographic and navigational tasks. One of the key aspects of autonomous navigation is the need to avoid collisions with other objects, including shore structures. During a mission, an ASV should be able to automatically detect obstacles and perform suitable maneuvers. This situation also arises in near-coastal areas, where shore structures like berths or moored vessels can be encountered. On the other hand, detection of coastal structures may also be helpful for berthing operations. An ASV can be launched and moored automatically only if it can detect obstacles in its vicinity. One commonly used method for target detection by ASVs involves the use of laser rangefinders. The main disadvantage of this approach is that such systems perform poorly in conditions with bad visibility, such as in fog or heavy rain. Therefore, alternative methods need to be sought. An innovative approach to this task is presented in this paper, which describes the use of automotive three-dimensional radar on a floating platform. The goal of the study was to assess target detection possibilities based on a comparison with photogrammetric images obtained by an unmanned aerial vehicle (UAV). The scenarios considered focused on analyzing the possibility of detecting shore structures like berths, wooden jetties, and small houses, as well as natural objects like trees or other kinds of vegetation. The recording from the radar was integrated into a single complex radar image of shore targets. It was then compared with an orthophotomap prepared from AUV camera pictures, as well as with a map based on traditional land surveys. The possibility and accuracy of detection for various types of shore structure were statistically assessed. The results show good potential for the proposed approach—in general, objects can be detected using the radar—although there is a need for development of further signal processing algorithms. Full article
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<p>Research equipment: (<b>a</b>) ASV (Marine Technology HydroDron-1); (<b>b</b>) radar (Smartmicro UMRR Automotive Type 42) mounted on HydroDrone; (<b>c</b>) UAV (DJI Mavic Pro).</p> Full article ">Figure 2
<p>The unmanned aerial vehicle (UAV) photogrammetry process.</p> Full article ">Figure 3
<p>Flight plans: (<b>a</b>) single grid; (<b>b</b>) free flight.</p> Full article ">Figure 4
<p>Scheme for radar measurements.</p> Full article ">Figure 5
<p>(<b>a</b>) Camera position uncertainties (XY plane), where blue ellipses indicate the position uncertainty, scaled for readability; (b) Number of photographs that potentially see the scene.</p> Full article ">Figure 6
<p>(<b>a</b>) Tie-point position uncertainties. (<b>b</b>) Resolution of individual tie-point positions.</p> Full article ">Figure 7
<p>Reconstructed orthophotomap.</p> Full article ">Figure 8
<p>Objects selected for the study: (<b>a</b>) wooden jetty; (<b>b</b>) metal structure on wooden jetty; (<b>c</b>) paddle boat; (<b>d</b>) metal component of trailer; (<b>e</b>) trees; (<b>f</b>) building and trees; (<b>g</b>) sandy shore.</p> Full article ">Figure 9
<p>Radar image of analyzed scene for dynamic (<b>a</b>) and static (<b>b</b>) scenario. Analyzed objects are additionally marked and labeled.</p> Full article ">Figure 10
<p>Photogrammetric and radar images of paddle boat.</p> Full article ">Figure 11
<p>Photogrammetric and radar picture of a metal structure on a wooden jetty used for accuracy determination.</p> Full article ">
29 pages, 7908 KiB  
Article
The Application of the Hybrid GIS Spatial Multi-Criteria Decision Analysis Best–Worst Methodology for Landslide Susceptibility Mapping
by Ljubomir Gigović, Siniša Drobnjak and Dragan Pamučar
ISPRS Int. J. Geo-Inf. 2019, 8(2), 79; https://doi.org/10.3390/ijgi8020079 - 12 Feb 2019
Cited by 43 | Viewed by 4996
Abstract
The main goal of this article is to produce a landslide susceptibility map by using the hybrid Geographical Information System (GIS) spatial multi-criteria decision analysis best–worst methodology (MCDA-BWM) in the western part of the Republic of Serbia. Initially, a landslide inventory map was [...] Read more.
The main goal of this article is to produce a landslide susceptibility map by using the hybrid Geographical Information System (GIS) spatial multi-criteria decision analysis best–worst methodology (MCDA-BWM) in the western part of the Republic of Serbia. Initially, a landslide inventory map was prepared using the National Landslide Database, aerial photographs, and also by carrying out field surveys. A total of 1082 landslide locations were detected. This methodology considers the fifteen conditioning factors that are relevant to landslide susceptibility mapping: the elevation, slope, aspect, distance to the road network, distance to the river, distance to faults, lithology, the Normalized Difference Vegetation Index (NDVI), the Topographic Wetness Index (TWI), the Stream Power Index (SPI), the Sediment Transport Index (STI), annual rainfall, the distance to urban areas, and the land use/cover. The expert evaluation takes into account the nature and severity of the observed criteria, and it was tested by using two scenarios: the different aggregation methods of the BWM. The prediction performances of the generated maps were checked by the receiver operating characteristics (ROCs). The validation results confirmed that the areas under the ROC curve for the weighted linear combination (WLC) and the ordered weighted averaging (OWA) aggregation methods of the MCDA-BWM have a very high accuracy. The results of the landslide susceptibility assessment obtained by applying the proposed best–worst method were the first step in the development of landslide risk management and they are expected to be used by local governments for effective management planning purposes. Full article
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<p>The location of the study area (Mačva and Kolubara Districts, and the Tara National Park).</p> Full article ">Figure 2
<p>The flowchart of the applied methodology.</p> Full article ">Figure 3
<p>The topographical factors related to landslides: (<b>a</b>) the elevation; (<b>b</b>) the aspect; (<b>c</b>) the slope; (<b>d</b>) the topographic wetness index (TWI); (<b>e</b>) the stream power index (SPI); (<b>f</b>) the sediment transport index (STI).</p> Full article ">Figure 4
<p>The environmental factors related to landslides: (<b>a</b>) the soil type, (<b>b</b>) the distance to the river, (<b>c</b>) lithology, (<b>d</b>) the distance to faults, (<b>e</b>) the NDVI, (<b>f</b>) rainfall.</p> Full article ">Figure 4 Cont.
<p>The environmental factors related to landslides: (<b>a</b>) the soil type, (<b>b</b>) the distance to the river, (<b>c</b>) lithology, (<b>d</b>) the distance to faults, (<b>e</b>) the NDVI, (<b>f</b>) rainfall.</p> Full article ">Figure 5
<p>The social factors related to landslides: (<b>a</b>) the distance to roads, (<b>b</b>) the distance to urban areas, (<b>c</b>) the land use/cover.</p> Full article ">Figure 6
<p>The final landslide susceptibility map created by applying the WLC aggregation method.</p> Full article ">Figure 7
<p>The final landslide susceptibility map created by using the OWA aggregation method.</p> Full article ">Figure 8
<p>The receiver operating characteristic (ROC) curves for: (<b>a</b>) the WLC aggregation, (<b>b</b>) the OWA aggregation methods.</p> Full article ">
13 pages, 4747 KiB  
Article
V-Factor Indicator in the Assessment of the Change in the Attractiveness of View as a Result of the Implementation of a Specific Planning Scenario
by Magda Pluta and Bartosz Mitka
ISPRS Int. J. Geo-Inf. 2019, 8(2), 78; https://doi.org/10.3390/ijgi8020078 - 11 Feb 2019
Cited by 2 | Viewed by 2626
Abstract
This paper describes the algorithm of the view factor (V-factor). It is based on an analysis of visibility, taking into account the attractiveness of the observed elements in a three-dimensional space. The results of the V-factor analysis provide input for the decision-making process [...] Read more.
This paper describes the algorithm of the view factor (V-factor). It is based on an analysis of visibility, taking into account the attractiveness of the observed elements in a three-dimensional space. The results of the V-factor analysis provide input for the decision-making process when selecting the most advantageous planning scenario so that the harmony of landscape and ecological balance can be maintained. The V-factor indicator can be successfully used in the process of spatial planning, in particular, at the stage of determining the parameters of new buildings and lines of sight between planned buildings. The purpose of the indicator is to determine the numerical values for observation points, thus facilitating a comparative assessment of the attractiveness of view available from the special points in space. The analysis uses a 3D space model that includes an integrated existing and planning state designed on the basis of planning scenarios. The V-factor analysis takes into account the distance of the observation point from the observed object, vertical and horizontal angles of observation, and the aesthetic value of the observed object. As a result, an average value of the V-factor indicator was obtained for each planning scenario, which facilitated the determination of the more beneficial one in terms of the attractiveness of view. Full article
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<p>3D model of the city hall, located within the studied area, modelled based on the data from terrestrial laser scanning. Source: own work.</p> Full article ">Figure 2
<p>Two different planning concepts for the studied area resulting from the integration of the existing state in the field with the planned state based on the local development plan: (<b>a</b>) scenario 1, (<b>b</b>) scenario 2. Source: own work.</p> Full article ">Figure 3
<p>Example of (<b>a</b>) an existing building; (<b>b</b>) a planned 3D object. Source: own work.</p> Full article ">Figure 4
<p>Designation of zones for the town hall with assigned attractiveness weight. Source: own work.</p> Full article ">Figure 5
<p>Method for determining a single point of view. Source: own work.</p> Full article ">Figure 6
<p>Placement of viewpoints on walls of zones. Source: own work.</p> Full article ">Figure 7
<p>Visible value sightlines. Source: own work.</p> Full article ">Figure 8
<p>Optimal distance for any zone. Source: own work.</p> Full article ">Figure 9
<p>V-factor algorithm. Source: own work.</p> Full article ">Figure 10
<p>The change of the attractiveness of the available view for observation points as a result of the implementation of scenario 2 instead of scenario 1. Source: own work.</p> Full article ">Figure 11
<p>View for observation point number 47: (<b>a</b>) available view in scenario 1 (<b>b</b>) available view in scenario 2. Source: own work.</p> Full article ">
25 pages, 727 KiB  
Review
Progress and Challenges on Entity Alignment of Geographic Knowledge Bases
by Kai Sun, Yunqiang Zhu and Jia Song
ISPRS Int. J. Geo-Inf. 2019, 8(2), 77; https://doi.org/10.3390/ijgi8020077 - 6 Feb 2019
Cited by 26 | Viewed by 5033
Abstract
Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming [...] Read more.
Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming the semantic gap. Thus, many efforts have been made in this field. This paper initially proposes basic definitions and a general framework for the entity alignment of GKBs. Specifically, the state-of-the-art of algorithms of entity alignment of GKBs is reviewed from the three aspects of similarity metrics, similarity combination, and alignment judgement; the evaluation procedure of alignment results is also summarized. On this basis, eight challenges for future studies are identified. There is a lack of methods to assess the qualities of GKBs. The alignment process should be improved by determining the best composition of heterogeneous features, optimizing alignment algorithms, and incorporating background knowledge. Furthermore, a unified infrastructure, techniques for aligning large-scale GKBs, and deep learning-based alignment techniques should be developed. Meanwhile, the generation of benchmark datasets for the entity alignment of GKBs and the applications of this field need to be investigated. The progress of this field will be accelerated by addressing these challenges. Full article
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
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<p>Schematic diagram for entity alignment of geographic knowledge bases (GKBs). (Note: GKB<sub>1</sub> and GKB<sub>2</sub> represent two geographic knowledge bases to be integrated, and e<sub>1</sub> and e<sub>2</sub> are two entities to be aligned from GKB<sub>1</sub> and GKB<sub>2</sub>, respectively.).</p> Full article ">Figure 2
<p>Standard workflow for entity alignment of GKBs.</p> Full article ">
23 pages, 23595 KiB  
Article
Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
by Ahmad Saleem Nouman, Ata Chokhachian, Daniele Santucci and Thomas Auer
ISPRS Int. J. Geo-Inf. 2019, 8(2), 76; https://doi.org/10.3390/ijgi8020076 - 5 Feb 2019
Cited by 27 | Viewed by 5591
Abstract
Environmental data acquisition tools are broadly used for climate monitoring and urban comfort assessment followed by data mining and sensing techniques for putting into evidence the relationship between environmental qualities of urban spaces and human well-being. Within this context, an environmental toolkit is [...] Read more.
Environmental data acquisition tools are broadly used for climate monitoring and urban comfort assessment followed by data mining and sensing techniques for putting into evidence the relationship between environmental qualities of urban spaces and human well-being. Within this context, an environmental toolkit is a fundamental tool to evaluate transient outdoor comfort. This study explains the prototyping and validation of a mobile environmental sensor kit. The results show the prototype has reasonable accuracy despite its affordability with respect to industrial sensors. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The schematic hardware connection of the model.</p> Full article ">Figure 2
<p>The process of testing environmental sensors.</p> Full article ">Figure 3
<p>Wiring and schematic diagram of the conclusive model (Fritzing).</p> Full article ">Figure 4
<p>The model’s exterior frame and setting of the components.</p> Full article ">Figure 5
<p>An outline of data acquisition system.</p> Full article ">Figure 6
<p>Globe temperature, air temperature, and relative humidity curves.</p> Full article ">Figure 7
<p>Globe temperature, wind speed, and mean radiant temperature curves.</p> Full article ">Figure 8
<p>Comparison of red, green, blue (RGB) colors and illuminance.</p> Full article ">Figure 9
<p>Approximation of solar radiation with RGB and lux sensor.</p> Full article ">Figure 10
<p>Validation setup with prototype and the test reference.</p> Full article ">Figure 11
<p>Validation graph for globe temperature.</p> Full article ">Figure 12
<p>The linear regression graph for globe temperature.</p> Full article ">Figure 13
<p>Validation graph for air temperature.</p> Full article ">Figure 14
<p>The linear regression graph for air temperature.</p> Full article ">Figure 15
<p>Validation graph for relative humidity.</p> Full article ">Figure 16
<p>Validation graph for wind speed.</p> Full article ">Figure 17
<p>The linear regression graph for wind speed.</p> Full article ">Figure 18
<p>Validation graph for solar radiation.</p> Full article ">Figure 19
<p>Georeferencing accuracy visualized over campus map; GPS location mapped with Grasshopper3D.</p> Full article ">Figure 20
<p>Validation graph altitude.</p> Full article ">Figure 21
<p>Validation graph for number of pseudoranges.</p> Full article ">Figure 22
<p>Validation graph for data logger.</p> Full article ">Figure 23
<p>(<b>a</b>) Region of assessment in city of Munich; (<b>b</b>) the route.</p> Full article ">Figure 24
<p>(<b>a</b>) Figure ground map; (<b>b</b>) GPS signals over the path.</p> Full article ">Figure 25
<p>(<b>a</b>) Elevation of the path (m); (<b>b</b>) walking ground speed (km/h).</p> Full article ">Figure 26
<p>Transient temperature maps: (<b>a</b>) globe temperature (°C); (<b>b</b>) air temperature (°C).</p> Full article ">Figure 27
<p>(<b>a</b>) Relative humidity (%); (<b>b</b>) wind speed (m/s).</p> Full article ">Figure 28
<p>(<b>a</b>) Solar radiation (w/m<sup>2</sup>); (<b>b</b>) noise level maps (dB).</p> Full article ">Figure 29
<p>(<b>a</b>) Mean radiant temperature (°C); (<b>b</b>) universal thermal climate index maps (°C).</p> Full article ">Figure 30
<p>Particulate matter maps: (<b>a</b>) PM 2.5; (<b>b</b>) PM 10.</p> Full article ">Figure 31
<p>Map for detected particles &gt;0.3 micrometer/0.1 L.</p> Full article ">
21 pages, 950 KiB  
Review
Shoreline Detection using Optical Remote Sensing: A Review
by Seynabou Toure, Oumar Diop, Kidiyo Kpalma and Amadou Seidou Maiga
ISPRS Int. J. Geo-Inf. 2019, 8(2), 75; https://doi.org/10.3390/ijgi8020075 - 5 Feb 2019
Cited by 141 | Viewed by 12886
Abstract
With coastal erosion and the increased interest in beach monitoring, there is a greater need for evaluation of the shoreline detection methods. Some studies have been conducted to produce state of the art reviews on shoreline definition and detection. It should be noted [...] Read more.
With coastal erosion and the increased interest in beach monitoring, there is a greater need for evaluation of the shoreline detection methods. Some studies have been conducted to produce state of the art reviews on shoreline definition and detection. It should be noted that with the development of remote sensing, shoreline detection is mainly achieved by image processing. Thus, it is important to evaluate the different image processing approaches used for shoreline detection. This paper presents a state of the art review on image processing methods used for shoreline detection in remote sensing. It starts with a review of different key concepts that can be used for shoreline detection. Then, the applied fundamental image processing methods are shown before a comparative analysis of these methods. A significant outcome of this study will provide practical insights into shoreline detection. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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<p>Schematic typical beach profile, terminology and zonation [<a href="#B10-ijgi-08-00075" class="html-bibr">10</a>].</p> Full article ">Figure 2
<p>Sketch of the spatial relationship between many of the commonly used shoreline indicator [<a href="#B8-ijgi-08-00075" class="html-bibr">8</a>].</p> Full article ">
23 pages, 2957 KiB  
Article
Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan
by Xinning Zhu, Tianyue Sun, Hao Yuan, Zheng Hu and Jiansong Miao
ISPRS Int. J. Geo-Inf. 2019, 8(2), 74; https://doi.org/10.3390/ijgi8020074 - 4 Feb 2019
Cited by 20 | Viewed by 4748
Abstract
Identifying group movement patterns of crowds and understanding group behaviors are valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile [...] Read more.
Identifying group movement patterns of crowds and understanding group behaviors are valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management. Full article
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<p>Distribution of the distance and time interval between adjacent records. (<b>a</b>) Distribution of the distance between adjacent records; (<b>b</b>) Distribution of the time interval between adjacent records.</p> Full article ">Figure 2
<p>Group movement patterns. The results of different patterns are exhibited in the table.</p> Full article ">Figure 3
<p>Modeling framework diagram.</p> Full article ">Figure 4
<p>Two trajectories moving together with different sampling rate.</p> Full article ">Figure 5
<p>Time to visit the scenic spot for group and individual tourists.</p> Full article ">Figure 6
<p>Average time spent in different scenic areas.</p> Full article ">Figure 7
<p>Trip distance of tourists for group and individual tourists.</p> Full article ">Figure 8
<p>Geographical distribution of group tourists, <span class="html-italic">X</span>-axis is provinces, <span class="html-italic">Y</span>-axis is the number of detected group from each province.</p> Full article ">Figure 9
<p>OD matrix of group tourists. <span class="html-italic">X</span>-axis is the scenic ID of scenic areas and <span class="html-italic">Y</span>-axis is top 10 provinces with the most group tourists. (Top 1 is Sichuan, Top 10 is Hubei).</p> Full article ">Figure 10
<p>Distribution of tourists in Sanya.</p> Full article ">Figure 11
<p>Top 10 popular routes with three scenic areas.</p> Full article ">
13 pages, 13808 KiB  
Article
Grassland Dynamics and the Driving Factors Based on Net Primary Productivity in Qinghai Province, China
by Xiaoxu Wei, Changzhen Yan and Wei Wei
ISPRS Int. J. Geo-Inf. 2019, 8(2), 73; https://doi.org/10.3390/ijgi8020073 - 2 Feb 2019
Cited by 15 | Viewed by 3352
Abstract
Qinghai province is an important part of the Tibetan Plateau, and is characterized by extremely fragile ecosystems. In the last few decades, grasslands in this province have been influenced profoundly by climate change, as well as human activities. Here, we use the Carnegie-Ames-Stanford [...] Read more.
Qinghai province is an important part of the Tibetan Plateau, and is characterized by extremely fragile ecosystems. In the last few decades, grasslands in this province have been influenced profoundly by climate change, as well as human activities. Here, we use the Carnegie-Ames-Stanford Approach (CASA) model to assess the dynamics of temperate steppe, alpine steppe, temperate meadow, alpine meadow, sparse grassland and herbaceous wetland via actual net primary productivity (NPPa). Our findings showed that: (1) From 2001 to 2016, the average NPPa in Qinghai province showed a fluctuation presented a generally increasing trend. The mean value of NPPa was 114.27 g C m−2 year−1, and the increase rate was 0.47 g C cm−2 year−1. (2) There were NPPa increase rate discrepancies among the six typical grassland biomes. Herbaceous wetland had the highest change rate, closely followed by alpine steppe, temperate steppe, alpine meadow, temperate meadow and sparse grassland. (3) The largest area of restoration mainly impacted by climate change reached 47.08% of the total grassland area, with human activities accounting for 21.74%. By contrast, the deteriorated area induced by human activities accounted for 9.78% of the total grassland. (4) Temperature may have been a greater factor than precipitation in driving grassland change during the study period. Decreasing grazing intensity and implementing effective protection measures were favorable to grassland restoration. Full article
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<p>Location of the study area and spatial distribution of six grassland types in Qinghai province.</p> Full article ">Figure 2
<p>Comparisons of simulated net primary productivity (NPP) from the Carnegie-Ames-Stanford Approach (CASA) model and observed NPP.</p> Full article ">Figure 3
<p>Spatial pattern of the mean annual (<b>a</b>) and NPP<sub>a</sub> for the different grassland types (<b>b</b>) from 2001 to 2016 in Qinghai province.</p> Full article ">Figure 4
<p>Spatial patterns of the changing trends for (<b>a</b>) NPP<sub>a</sub>, (<b>b</b>) NPP<sub>p</sub>, and (<b>c</b>) NPP<sub>h</sub> for the different grassland types (<b>d</b>) from 2001 to 2016 in Qinghai province. 3.3. The reasons for NPP<sub>a</sub> change.</p> Full article ">Figure 5
<p>Spatial patterns of driving forces for NPP<sub>a</sub> changes from 2001 to 2016.</p> Full article ">Figure 6
<p>Spatial distribution of the trends for (<b>a</b>) the annual mean temperature and (<b>b</b>) the annual total precipitation from 2001 to 2016 in Qinghai province.</p> Full article ">Figure 7
<p>Spatial patterns of the correlation coefficients between NPP<sub>a</sub> and temperature, NPP<sub>a</sub> and precipitation from 2001 to 2016.</p> Full article ">Figure 8
<p>The dynamics of NPP<sub>a</sub> and livestock numbers from 2001 to 2016 in Qinghai province.</p> Full article ">
16 pages, 9907 KiB  
Article
Re-Arranging Space, Time and Scales in GIS: Alternative Models for Multi-Scale Spatio-Temporal Modeling and Analyses
by Yi Qiang and Nico Van de Weghe
ISPRS Int. J. Geo-Inf. 2019, 8(2), 72; https://doi.org/10.3390/ijgi8020072 - 1 Feb 2019
Cited by 10 | Viewed by 4793
Abstract
The representations of space and time are fundamental issues in GIScience. In prevalent GIS and analytical systems, time is modeled as a linear stream of real numbers and space is represented as flat layers with timestamps. Despite their dominance in GIS and information [...] Read more.
The representations of space and time are fundamental issues in GIScience. In prevalent GIS and analytical systems, time is modeled as a linear stream of real numbers and space is represented as flat layers with timestamps. Despite their dominance in GIS and information visualization, these representations are inefficient for visualizing data with complex temporal and spatial extents and the variation of data at multiple temporal and spatial scales. This article presents alternative representations that incorporate the scale dimension into time and space. The article first reviews a series of work about the triangular model (TM), which is a multi-scale temporal model. Then, it introduces the pyramid model (PM), which is the extension of the TM for spatial data, and demonstrates the utility of the PM in visualizing multi-scale spatial patterns of land cover data. Finally, it discusses the potential of integrating the TM and the PM into a unified framework for multi-scale spatio-temporal modeling. This article systematically documents the models with alternative arrangements of space and time and their applications in analyzing different types of data. Additionally, this article aims to inspire the re-thinking of organizations of space, time, and scales in the future development of GIS and analytical tools to handle the increasing quantity and complexity of spatio-temporal data. Full article
(This article belongs to the Special Issue Selected Papers from the 10th International Conference on Geographic Information Science)
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<p>The transformation from the linear model to the triangular model (TM). (<b>a</b>) Time intervals in the linear model. (<b>b</b>) Projecting a time interval into a point in the TM. (<b>c</b>) Time intervals in (<b>a</b>) represented in the TM (adapted from [<a href="#B13-ijgi-08-00072" class="html-bibr">13</a>]).</p> Full article ">Figure 2
<p>Representing temporal relations in the TM. (<b>a</b>) Thirteen topological relations between time intervals. (<b>b</b>) The representation of the temporal relations in the TM. (adapted from [<a href="#B15-ijgi-08-00072" class="html-bibr">15</a>]).</p> Full article ">Figure 3
<p>Representing temporal relations as spatial relations in the TM. (<b>a</b>) Time intervals (<span class="html-italic">I</span><sub>1–4</sub>) in the linear model. (<b>b</b>) Time intervals (<span class="html-italic">I</span><sub>1–4</sub>) in the TM. (<b>c</b>) the <span class="html-italic">before</span> zone of <span class="html-italic">I</span><sub>1</sub>.</p> Full article ">Figure 4
<p>Temporal queries in the TM by creating 2D zones. (<b>a</b>) Selecting intervals <span class="html-italic">during I</span><sub>1</sub>. (<b>b</b>) Selecting intervals <span class="html-italic">contained</span> by <span class="html-italic">I</span><sub>1</sub>. (<b>c</b>) Selecting intervals <span class="html-italic">containing I</span><sub>1</sub> and <span class="html-italic">in-between</span> (<span class="html-italic">I</span><sub>2</sub>, <span class="html-italic">I</span><sub>3</sub>) (adapted from [<a href="#B14-ijgi-08-00072" class="html-bibr">14</a>]).</p> Full article ">Figure 5
<p>The interface of the GeoTM with a map view in the left and TM view in the right, which are dynamically linked. Temporal queries can be performed by creating geometric zones in the TM (adapted from [<a href="#B14-ijgi-08-00072" class="html-bibr">14</a>]).</p> Full article ">Figure 6
<p>Rough time interval of a military feature’s lifetime in a time series of aerial photos.</p> Full article ">Figure 7
<p>Representation of rough time intervals in the TM. (<b>a</b>) The linear representation. (<b>b</b>) Constructing a rough time interval in the TM. (<b>c</b>) Intervals in (a) in the TM. (<b>d</b>). Overlap between a query zone <b><span class="html-italic">A</span></b> and a rough time interval <b><span class="html-italic">R</span>(<span class="html-italic">I</span>)</b> (adpated from [<a href="#B17-ijgi-08-00072" class="html-bibr">17</a>]).</p> Full article ">Figure 8
<p>(<b>a</b>) Visualizing rough time intervals of the military features in the WWI aerial photos. The dark areas are clusters of the intervals and <span class="html-italic">I</span><sub>1–4</sub> indicate difference phases (time intervals) of the war. (<b>b</b>) Selecting features of Cluster 2 (artillery attack from the Allies army) in the GeoTM (modified from [<a href="#B17-ijgi-08-00072" class="html-bibr">17</a>]).</p> Full article ">Figure 9
<p>Representing the <span class="html-italic">during</span> relation to a fuzzy time interval <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>I</mi> <mo>˜</mo> </mover> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> in TM (adapted from [<a href="#B15-ijgi-08-00072" class="html-bibr">15</a>]).</p> Full article ">Figure 10
<p>Representing temporal relations of a fuzzy time interval <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>I</mi> <mo>˜</mo> </mover> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> in TM (adapted from [<a href="#B15-ijgi-08-00072" class="html-bibr">15</a>]).</p> Full article ">Figure 11
<p>Representing time series in the TM. (<b>a</b>) Time series represented in a line chart and color-coded linear raster. (<b>b</b>) The TM representation of the base intervals in a time series. (<b>c</b>) The TM representation of all intervals in a time series. (<b>d</b>) Rasterized TM with grey-coded attributes.</p> Full article ">Figure 12
<p>A comparison of the line chart (<b>a</b>) and TM representation (<b>b</b>) of the moving speed of a soccer player in a game (adapted from [<a href="#B18-ijgi-08-00072" class="html-bibr">18</a>]).</p> Full article ">Figure 13
<p>Using map algebra to compare air quality in Beijing in 2007 and 2008. (<b>a</b>) Time series of PM10 AQI in Beijing in 2007 and 2008. (<b>b</b>) The TM of the 2007 AQI. (<b>c</b>) The TM of 2008 AQI. (<b>d</b>) The binary result of subtracting the 2007 TM from the 2008 TM (TM<sub>2008</sub>–TM<sub>2007</sub>).</p> Full article ">Figure 14
<p>Illustration of an image pyramid (<b>left</b>) and the configuration of the pyramid model for a raster (<b>right</b>).</p> Full article ">Figure 15
<p>Representing square areas in different sizes as points in the 3D pyramid space.</p> Full article ">Figure 16
<p>Calculating local fractal dimensions of a land cover raster. (<b>a</b>) A binary land cover raster in the wetland in the Mississippi Delta. (<b>b</b>) Local fractal dimension of the land cover data in a 11 × 11 cells moving window. (<b>c</b>) Local fractal dimension in a 21 × 21 cells moving window. (<b>d</b>) Local fractal dimension in a 31 × 31 cells moving window.</p> Full article ">Figure 17
<p>Constructing a pyramid model (PM) from the land cover data at the base layer.</p> Full article ">Figure 18
<p>The isosurface of voxels with a 0.99 fractal dimension in the PM. (<b>a</b>) An oblique view. (<b>b</b>) A top-down view. (<b>c</b>) A horizontal view along the <span class="html-italic">x</span> axis (east). (<b>d</b>) A horizontal view along the <span class="html-italic">y</span> axis (north).</p> Full article ">Figure 19
<p>The representation of multi-scale spatio-temporal data in the continuous spatio-temporal model (CSTM). (<b>a</b>): Time series of spatial data. (<b>b</b>): Time series of PMs. (<b>c</b>): PMs of different time intervals in a TM.</p> Full article ">
28 pages, 13942 KiB  
Review
Evaluation of the Influence of Disturbances on Forest Vegetation Using the Time Series of Landsat Data: A Comparison Study of the Low Tatras and Sumava National Parks
by Premysl Stych, Josef Lastovicka, Radovan Hladky and Daniel Paluba
ISPRS Int. J. Geo-Inf. 2019, 8(2), 71; https://doi.org/10.3390/ijgi8020071 - 31 Jan 2019
Cited by 13 | Viewed by 4244
Abstract
This study focused on the evaluation of forest vegetation changes from 1992 to 2015 in the Low Tatras National Park (NAPANT) in Slovakia and the Sumava National Park in Czechia using a time series (TS) of Landsat images. The study area was damaged [...] Read more.
This study focused on the evaluation of forest vegetation changes from 1992 to 2015 in the Low Tatras National Park (NAPANT) in Slovakia and the Sumava National Park in Czechia using a time series (TS) of Landsat images. The study area was damaged by wind and bark beetle calamities, which strongly influenced the health state of the forest vegetation at the end of the 20th and beginning of the 21st century. The analysis of the time series was based on the ten selected vegetation indices in different types of localities selected according to the type of forest disturbances. The Landsat data CDR (Climate Data Record/Level 2) was normalized using the PIF (Pseudo-Invariant Features) method and the results of the Time Series were validated by in-situ data. The results confirmed the high relevance of the vegetation indices based on the SWIR bands (e.g., NDMI) for the purpose of evaluating the individual stages of the disturbance (especially the bark beetle calamity). Usage of the normalized Landsat data Climate Data Record (CDR/Level 2) in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability of the corrected data. Full article
(This article belongs to the Special Issue GIS for Safety & Security Management)
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Graphical abstract
Full article ">Figure 1
<p>The first phase of a bark beetle calamity in the Low Tatras (Photo by J. Lastovicka).</p> Full article ">Figure 2
<p>Map of the used localities in the Low Tatras (Source: Own work/ESRI ArcMap Basemap). The number of the points represents the ID numbers.</p> Full article ">Figure 3
<p>A forest affected by a bark beetle and wind disturbance in the Sumava NP (source: R. Hladky).</p> Full article ">Figure 4
<p>Map of the used localities in the Sumava NP (Source: Own work/ESRI ArcMap Basemap). The number of the points represents the ID numbers.</p> Full article ">Figure 5
<p>Time series of the vegetation indices for the areas affected by the wind calamity (Locality 1); (<b>a</b>) and the statistical information (<b>b</b>) (Source: Own work).</p> Full article ">Figure 6
<p>Time series of the vegetation indices for the area affected by the bark beetle (Locality 2) (<b>a</b>); and the statistical information (<b>b</b>) (Source: Own work).</p> Full article ">Figure 6 Cont.
<p>Time series of the vegetation indices for the area affected by the bark beetle (Locality 2) (<b>a</b>); and the statistical information (<b>b</b>) (Source: Own work).</p> Full article ">Figure 7
<p>Time series of the vegetation indices for localities with minimal disturbance (<b>a</b>); and the statistical information (<b>b</b>) (Locality 3) (Source: Own work).</p> Full article ">Figure 8
<p>Time series of the vegetation indices for the area affected by the bark beetle and wind (<b>a</b>); and the statistical information (<b>b</b>) (Locality 4) (Source: Own work).</p> Full article ">Figure 9
<p>Time series of the locality with minimal disturbance (<b>a</b>); and the statistical information (<b>b</b>) (Locality 5) (Source: Own work).</p> Full article ">Figure 10
<p>Time series of the vegetation indices for the area affected by the bark beetle and wind (<b>a</b>); and the statistical information (<b>b</b>) (Locality 6) (Source: Own work).</p> Full article ">Figure 11
<p>Time series of the vegetation indices for the area affected by the bark beetle (<b>a</b>); and the statistical information (<b>b</b>) (Locality 7) (Source: Own work).</p> Full article ">Figure 12
<p>Time series of the locality with minimal disturbance (<b>a</b>); and the statistical information (<b>b</b>) (Locality 8) (Source: Own work).</p> Full article ">Figure 13
<p>Time series of the vegetation indices for the area affected by the bark beetle (<b>a</b>); and the statistical information (<b>b</b>) (Locality 9) (Source: Own work).</p> Full article ">Figure 14
<p>Time series of locality with minimal disturbance (<b>a</b>); and the statistical information (<b>b</b>) (Locality 10) (Source: Own work).</p> Full article ">Figure 15
<p>Time series of the vegetation indices for the area affected by the bark beetle and the wind calamity (<b>a</b>); and the statistical information (<b>b</b>) (the average from the Low Tatras National Park) (Source: Own work).</p> Full article ">Figure 16
<p>Time series of the vegetation indices of the areas with minimal disturbance (<b>a</b>); and the statistical information (<b>b</b>) (the average from the Low Tatras National Park) (Source: Own work).</p> Full article ">Figure 17
<p>Time series of the vegetation indices for the area affected by the bark beetle and the wind calamity (<b>a</b>); and the statistical information (<b>b</b>) (the average from the Sumava National Park) (Source: Own work).</p> Full article ">Figure 17 Cont.
<p>Time series of the vegetation indices for the area affected by the bark beetle and the wind calamity (<b>a</b>); and the statistical information (<b>b</b>) (the average from the Sumava National Park) (Source: Own work).</p> Full article ">Figure 18
<p>Time series of the vegetation indices of the areas with minimal disturbance (<b>a</b>); and the statistical information (<b>b</b>) (the average from the Sumava National Park) (Source: Own work).</p> Full article ">Figure 18 Cont.
<p>Time series of the vegetation indices of the areas with minimal disturbance (<b>a</b>); and the statistical information (<b>b</b>) (the average from the Sumava National Park) (Source: Own work).</p> Full article ">Figure A1
<p>Statistical information about the vegetation indices values in the Low Tatras NP (Source: Own work).</p> Full article ">Figure A2
<p>Statistical information about the vegetation indices values in the Sumava NP (Source: Own work).</p> Full article ">
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