Remote Sensing

148 KiB

Open AccessCorrection

Correction: Van Beek, J. et al. Stem Water Potential Monitoring in Pear Orchards through WorldView-2 Multispectral Imagery. Remote Sens. 2013, 5, 6647–6666

by Jonathan Van Beek, Laurent Tits, Ben Somers, Pieter Janssens, Wendy Odeurs, Hilde Vandendriessche, Tom Deckers and Pol Coppin

Remote Sens. 2014, 6(2), 1760-1761; https://doi.org/10.3390/rs6021760 - 24 Feb 2014

Viewed by 5968

Abstract

The suitability of high resolution satellite imagery to provide the water status in orchard crops, i.e. stem water potential (Ψ_stem) was evaluated in [1]. However, the contribution of a number of collaborators was not properly acknowledged. Pieter Janssens, Wendy Odeurs, Hilde [...] Read more.

The suitability of high resolution satellite imagery to provide the water status in orchard crops, i.e. stem water potential (Ψ_stem) was evaluated in [1]. However, the contribution of a number of collaborators was not properly acknowledged. Pieter Janssens, Wendy Odeurs, Hilde Vandendriessche and Tom Deckers all provided a substantial contribution to the conception and the design of the work. They furthermore had a leading role in the acquisition, processing, analysis, and interpretation of the reference evapotranspiration (ETo) and Ψ_stem data. The article [1] would not have been possible without their valuable input, and the authors would like to correct the authors list as follows. [...] Full article

4239 KiB

Open AccessArticle

GIS-Based Roughness Derivation for Flood Simulations: A Comparison of Orthophotos, LiDAR and Crowdsourced Geodata

by Helen Dorn, Michael Vetter and Bernhard Höfle

Remote Sens. 2014, 6(2), 1739-1759; https://doi.org/10.3390/rs6021739 - 24 Feb 2014

Cited by 43 | Viewed by 12518

Abstract

Natural disasters like floods are a worldwide phenomenon and a serious threat to mankind. Flood simulations are applications of disaster control, which are used for the development of appropriate flood protection. Adequate simulations require not only the geometry but also the roughness of [...] Read more.

Natural disasters like floods are a worldwide phenomenon and a serious threat to mankind. Flood simulations are applications of disaster control, which are used for the development of appropriate flood protection. Adequate simulations require not only the geometry but also the roughness of the Earth’s surface, as well as the roughness of the objects hereon. Usually, the floodplain roughness is based on land use/land cover maps derived from orthophotos. This study analyses the applicability of roughness map derivation approaches for flood simulations based on different datasets: orthophotos, LiDAR data, official land use data, OpenStreetMap data and CORINE Land Cover data. Object-based image analysis is applied to orthophotos and LiDAR raster data in order to generate land cover maps, which enable a roughness parameterization. The vertical vegetation structure within the LiDAR point cloud is used to derive an additional floodplain roughness map. Further roughness maps are derived from official land use data, OpenStreetMap and CORINE Land Cover datasets. Six different flood simulations are applied based on one elevation data but with the different roughness maps. The results of the hydrodynamic–numerical models include information on flow velocity and water depth from which the additional attribute flood intensity is calculated of. The results based on roughness maps derived from LiDAR data and OpenStreetMap data are comparable, whereas the results of the other datasets differ significantly. Full article

► Show Figures

215 KiB

Open AccessEditorial

Acknowledgement to Reviewers of Remote Sensing in 2013

by Remote Sensing Editorial Office

Remote Sens. 2014, 6(2), 1725-1738; https://doi.org/10.3390/rs6021725 - 24 Feb 2014

Viewed by 6728

Abstract

The publisher and editors of the Remote Sensing would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2013 for Remote Sensing. [...] Full article

986 KiB

Open AccessArticle

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

by Kaifang Shi, Bailang Yu, Yixiu Huang, Yingjie Hu, Bing Yin, Zuoqi Chen, Liujia Chen and Jianping Wu

Remote Sens. 2014, 6(2), 1705-1724; https://doi.org/10.3390/rs6021705 - 20 Feb 2014

Cited by 513 | Viewed by 34951

Abstract

The nighttime light data records artificial light on the Earth’s surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were [...] Read more.

The nighttime light data records artificial light on the Earth’s surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration’s National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the GDP and EPC (which is from the country’s statistical data) at provincial- and prefectural-level divisions of mainland China. The result of the linear regression shows that R² values of TNL from NPP-VIIRS with GDP and EPC at multiple scales are all higher than those from DMSP-OLS data. This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC. Full article

(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)

► Show Figures

1135 KiB

Open AccessArticle

A Comparative Analysis of EO-1 Hyperion, Quickbird and Landsat TM Imagery for Fuel Type Mapping of a Typical Mediterranean Landscape

by Giorgos Mallinis, Georgia Galidaki and Ioannis Gitas

Remote Sens. 2014, 6(2), 1684-1704; https://doi.org/10.3390/rs6021684 - 20 Feb 2014

Cited by 37 | Viewed by 10049

Abstract

Forest fires constitute a natural disturbance factor and an agent of environmental change with local to global impacts on Earth’s processes and functions. Accurate knowledge of forest fuel extent and properties can be an effective component for assessing the impacts of possible future [...] Read more.

Forest fires constitute a natural disturbance factor and an agent of environmental change with local to global impacts on Earth’s processes and functions. Accurate knowledge of forest fuel extent and properties can be an effective component for assessing the impacts of possible future wildfires on ecosystem services. Our study aims to evaluate and compare the spectral and spatial information inherent in the EO-1 Hyperion, Quickbird and Landsat TM imagery. The analysis was based on a support vector machine classification approach in order to discriminate and map Mediterranean fuel types. The fuel classification scheme followed a site-specific fuel model within the study area, which is suitable for fire behavior prediction and spatial simulation. The overall accuracy of the Quickbird-based fuel type mapping was higher than 74% with a quantity disagreement of 9% and an allocation disagreement of 17%. Both classifications from the Hyperion and Landsat TM fuel type maps presented approximately 70% overall accuracy and 16% allocation disagreement. The McNemar’s test indicated that the overall accuracy differences between the three produced fuel type maps were not significant (p < 0.05). Based on both overall and individual higher accuracies obtained with the use of the Quickbird image, this study suggests that the high spatial resolution might be more decisive than the high spectral resolution in Mediterranean fuel type mapping. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

► Show Figures

Graphical abstract

448 KiB

Open AccessReview

A Review of Swidden Agriculture in Southeast Asia

by Peng Li, Zhiming Feng, Luguang Jiang, Chenhua Liao and Jinghua Zhang

Remote Sens. 2014, 6(2), 1654-1683; https://doi.org/10.3390/rs6021654 - 20 Feb 2014

Cited by 99 | Viewed by 21352

Abstract

Swidden agriculture is by far the dominant land use system in the mountainous regions of Southeast Asia (SEA). It provides various valuable subsistence products to local farmers, mostly the poor ethnic minority groups. Controversially, it is also closely connected with a number of [...] Read more.

Swidden agriculture is by far the dominant land use system in the mountainous regions of Southeast Asia (SEA). It provides various valuable subsistence products to local farmers, mostly the poor ethnic minority groups. Controversially, it is also closely connected with a number of environmental issues. With the strengthening regional economic cooperation in SEA, swidden agriculture has experienced drastic transformations into other diverse market-oriented land use types since the 1990s. However, there is very limited information on the basic geographical and demographic data of swidden agriculture and the socio-economic and biophysical effects of the transformations. International programs, such as the Reducing Emissions from Deforestation and forest Degradation (REDD), underscore the importance of monitoring and evaluating swidden agriculture and its transition to reduce carbon emission due to deforestation and forest degradation. In this context, along with the accessibility of Landsat historical imagery, remote sensing based techniques will offer an effective way to detect and monitor the locations and extent of swidden agriculture. Many approaches for investigating fire occurrence and burned area can be introduced for swidden agriculture mapping due to the common feature of fire relatedness. In this review paper, four broad approaches involving spectral signatures, phenological characteristics, statistical theory and landscape ecology were summarized for swidden agriculture delineation. Five research priorities about swidden agriculture involving remote sensing techniques, spatial pattern, change, drivers and impacts were proposed accordingly. To our knowledge, a synthesis review on the remote sensing and outlook on swidden agriculture has not been reported yet. This review paper aims to give a comprehensive overview of swidden agriculture studies in the domains of debated definition, trends, remote sensing methods and outlook research in SEA undertaken in the past two decades. Full article

► Show Figures

1104 KiB

Open AccessArticle

Performance Analysis of MODIS 500-m Spatial Resolution Products for Estimating Chlorophyll-a Concentrations in Oligo- to Meso-Trophic Waters Case Study: Itumbiara Reservoir, Brazil

by Igor Ogashawara, Enner H. Alcântara, Marcelo P. Curtarelli, Marcos Adami, Renata F. F. Nascimento, Arley F. Souza, José L. Stech and Milton Kampel

Remote Sens. 2014, 6(2), 1634-1653; https://doi.org/10.3390/rs6021634 - 20 Feb 2014

Cited by 20 | Viewed by 7993

Abstract

Monitoring chlorophyll-a (chl-a) concentrations is important for the management of water quality, because it is a good indicator of the eutrophication level in an aquatic system. Thus, our main purpose was to develop an alternative technique to monitor chl-a [...] Read more.

Monitoring chlorophyll-a (chl-a) concentrations is important for the management of water quality, because it is a good indicator of the eutrophication level in an aquatic system. Thus, our main purpose was to develop an alternative technique to monitor chl-a in time and space through remote sensing techniques. However, one of the limitations of remote sensing is the resolution. To achieve a high temporal resolution and medium space resolution, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m reflectance product, MOD09GA, and limnological parameters from the Itumbiara Reservoir. With these data, an empirical (O14a) and semi-empirical (O14b) algorithm were developed. Algorithms were cross-calibrated and validated using three datasets: one for each campaign and a third consisting of a combination of the two individual campaigns. Algorithm O14a produced the best validation with a root mean square error (RMSE) of 30.4%, whereas O14b produced an RMSE of 32.41% using the mixed dataset calibration. O14a was applied to MOD09GA to build a time series for the reservoir for the year of 2009. The time-series analysis revealed that there were occurrences of algal blooms in the summer that were likely related to the additional input of nutrients caused by rainfall runoff. During the winter, however, the few observed algal blooms events were related to periods of atmospheric meteorological variations that represented an enhanced external influence on the processes of mixing and stratification of the water column. Finally, the use of remote sensing techniques can be an important tool for policy makers, environmental managers and the scientific community with which to monitor water quality. Full article

► Show Figures

2585 KiB

Open AccessArticle

Monitoring Wetlands Ecosystems Using ALOS PALSAR (L-Band, HV) Supplemented by Optical Data: A Case Study of Biebrza Wetlands in Northeast Poland

by Katarzyna Dabrowska-Zielinska, Maria Budzynska, Monika Tomaszewska, Maciej Bartold, Martyna Gatkowska, Iwona Malek, Konrad Turlej and Milena Napiorkowska

Remote Sens. 2014, 6(2), 1605-1633; https://doi.org/10.3390/rs6021605 - 20 Feb 2014

Cited by 40 | Viewed by 9622

Abstract

The aim of the study was to elaborate the remote sensing methods for monitoring wetlands ecosystems. The investigation was carried out during the years 2002–2010 in the Biebrza Wetlands. The meteorological conditions at the test site varied from extremely dry to very wet. [...] Read more.

The aim of the study was to elaborate the remote sensing methods for monitoring wetlands ecosystems. The investigation was carried out during the years 2002–2010 in the Biebrza Wetlands. The meteorological conditions at the test site varied from extremely dry to very wet. The authors propose applying satellite remote sensing data acquired in the optical and microwave spectrums to classify wetlands vegetation habitats for the assessment of vegetation changes and estimation of wetlands’ biophysical properties to improve monitoring of these unique, very often physically impenetrable, areas. The backscattering coefficients (σ°) calculated from ALOS PALSAR FBD (Advanced Land Observing Satellite, Phased Array type L-band Synthetic Aperture Radar, Fine Beam Dual Mode) images registered at cross polarization HV on 12 May 2008 were used to classify the main wetland communities using ground truth observations and the visual interpretation method. As a result, the σ° values were distributed among the six wetlands’ vegetation classes: scrubs, sedges-scrubs, sedges, reeds, sedges-reeds, rushes, and the areas of each community and changes were assessed. Also, the change in the biophysical variable as Leaf Area Index (LAI) is described using the information from PALSAR data. Strong linear relationships have been found between LAI and σ° derived for particular wetland classes, which then were applied to elaborate the maps of LAI distribution. The other variables used to characterize the changing environmental conditions are: surface temperature (T_s) calculated from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) and Normalized Difference Vegetation Index (NDVI) from ENVISAT MERIS (ENVIronmental SATellite MEdium Resolution Imaging Spectrometer). Differences of almost double T_s between “dry” and “wet” years were noticed that reflect observed weather conditions. The highest values of NDVI occurred in years with a sufficient amount of precipitation with the lowest in “dry” years. NDVI values variances within the same wetlands class resulted mainly from the differences in soil moisture. The results of this study show that the satellite data from microwave and optical spectrum gave the repetitive spatial information about vegetation growth conditions and could be used for monitoring wetland ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing of Peatlands I)

► Show Figures

1145 KiB

Open AccessArticle

Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution

by Junliang He, Yong Zha, Jiahua Zhang and Jay Gao

Remote Sens. 2014, 6(2), 1587-1604; https://doi.org/10.3390/rs6021587 - 20 Feb 2014

Cited by 21 | Viewed by 7823

Abstract

Aerosol optical depth (AOD) is a critical variable in estimating aerosol concentration in the atmosphere, evaluating severity of atmospheric pollution, and studying their impact on climate. With the assistance of the 6S radiative transfer model, we simulated apparent reflectancein relation to AOD in [...] Read more.

Aerosol optical depth (AOD) is a critical variable in estimating aerosol concentration in the atmosphere, evaluating severity of atmospheric pollution, and studying their impact on climate. With the assistance of the 6S radiative transfer model, we simulated apparent reflectancein relation to AOD in each Moderate Resolution Imaging Spectroradiometer (MODIS) waveband in this study. The closeness of the relationship was used to identify the most and least sensitive MODIS wavebands. These two bands were then used to construct three aerosol indices (difference, ratio, and normalized difference) for estimating AOD quickly and effectively. The three indices were correlated, respectively, with in situ measured AOD at the Aerosol Robotic NETwork (AERONET) Lake Taihu, Beijing, and Xianghe stations. It is found that apparent reflectance of the blue waveband (band 3) is the most sensitive to AOD while the mid-infrared wavelength (band 7) is the least sensitive. The difference aerosol index is the most accurate in indicating aerosol-induced atmospheric pollution with a correlation coefficient of 0.585, 0.860, 0.685, and 0.333 at the Lake Taihu station, 0.721, 0.839, 0.795, and 0.629 at the Beijing station, and 0.778, 0.782, 0.837, and 0.643 at the Xianghe station in spring, summer, autumn and winter, respectively. It is concluded that the newly proposed difference aerosol index can be used effectively to study the level of aerosol-induced air pollution from MODIS satellite imagery with relative ease. Full article

► Show Figures

Frequency distribution of AOD recorded over September 2011–Augst 2012 at the Lake Taihu station. Full article ">
Variation of surface reflectance as recorded in the seven MODIS wavebands in four seasons. Full article ">
Variation of apparent reflectance with seven MODIS wavebands in four seasons. Full article ">
Relationship between apparent reflectance simulated using the 6S model with AOD by MODIS wavebands. (a) spring; (b) summer; (c) autumn; (d) winter. Full article ">
Regression results of the three proposed aerosol indices (DAI, RAI, NDAI) against AOD (550 nm) as observed at the Lake Taihu station. (a–c) spring; (d–f) summer; (g–i) autumn; (j–l) winter. Full article ">
Regression results of the three proposed aerosol indices (DAI, RAI, NDAI) against AOD (550 nm) as observed at the Lake Taihu station. (a–c) spring; (d–f) summer; (g–i) autumn; (j–l) winter. Full article ">
Regression results of the three proposed aerosol indices (DAI, RAI, NDAI) against AOD (550 nm) as observed at the Beijing station. (a–c) spring; (d–f) summer; (g–i) autumn; (j–l) winter. Full article ">
Regression results of the three proposed aerosol indices (DAI, RAI, NDAI) against AOD (550 nm) as observed at the Beijing station. (a–c) spring; (d–f) summer; (g–i) autumn; (j–l) winter. Full article ">
Regression results of the three proposed aerosol indices (DAI, RAI, NDAI) against AOD (550 nm) as observed at the Xianghe station. (a–c) spring; (d–f) summer; (g–i) autumn; (j–l) winter. Full article ">
Regression results of the three proposed aerosol indices (DAI, RAI, NDAI) against AOD (550 nm) as observed at the Xianghe station. (a–c) spring; (d–f) summer; (g–i) autumn; (j–l) winter. Full article ">

2189 KiB

Open AccessArticle

Slope Superficial Displacement Monitoring by Small Baseline SAR Interferometry Using Data from L-band ALOS PALSAR and X-band TerraSAR: A Case Study of Hong Kong, China

by Fulong Chen, Hui Lin and Xianzhi Hu

Remote Sens. 2014, 6(2), 1564-1586; https://doi.org/10.3390/rs6021564 - 20 Feb 2014

Cited by 22 | Viewed by 8612

Abstract

Owing to the development of spaceborne synthetic aperture radar (SAR) platforms, and in particular the increase in the availability of multi-source (multi-band and multi-resolution) data, it is now feasible to design a surface displacement monitoring application using multi-temporal SAR interferometry (MT-InSAR). Landslides have [...] Read more.

Owing to the development of spaceborne synthetic aperture radar (SAR) platforms, and in particular the increase in the availability of multi-source (multi-band and multi-resolution) data, it is now feasible to design a surface displacement monitoring application using multi-temporal SAR interferometry (MT-InSAR). Landslides have high socio-economic impacts in many countries because of potential geo-hazards and heavy casualties. In this study, taking into account the merits of ALOS PALSAR (L-band, good coherence preservation) and TerraSAR (X-band, high resolution and short revisit times) data, we applied an improved small baseline InSAR (SB-InSAR) with 3-D phase unwrapping approach, to monitor slope superficial displacement in Hong Kong, China, a mountainous subtropical zone city influenced by over-urbanization and heavy monsoonal rains. Results revealed that the synergistic use of PALSAR and TerraSAR data produces different outcomes in relation to data reliability and spatial-temporal resolution, and hence could be of significant value for a comprehensive understanding and monitoring of unstable slopes. Full article

► Show Figures

Graphical abstract

783 KiB

Open AccessArticle

Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data

by Feng Zhao, Yanbo Huang, Yiqing Guo, Krishna N. Reddy, Matthew A. Lee, Reginald S. Fletcher and Steven J. Thomson

Remote Sens. 2014, 6(2), 1538-1563; https://doi.org/10.3390/rs6021538 - 20 Feb 2014

Cited by 21 | Viewed by 8256

Abstract

In this paper, we aim to detect crop injury from glyphosate, a herbicide, by both traditionally used spectral indices and newly extracted features with leaf hyperspectral reflectance data for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton. The new features were extracted by canonical analysis [...] Read more.

In this paper, we aim to detect crop injury from glyphosate, a herbicide, by both traditionally used spectral indices and newly extracted features with leaf hyperspectral reflectance data for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton. The new features were extracted by canonical analysis technique, which could provide the largest separability to distinguish the injured leaves from the healthy ones. Spectral bands used for constructing these new features were selected based on the sensitivity analysis results of a physically-based leaf radiation transfer model (leaf optical PROperty SPECTra model, PROSPECT), which could help extend the effectiveness of these features to a wide range of leaf structures and growing conditions. This approach has been validated with greenhouse measured data acquired in glyphosate treatment experiments. Results indicated that glyphosate injury could be detected by NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and DVI (Difference Vegetation Index) in 48 h After the Treatment (HAT) for soybean and in 72 HAT for cotton, but the other spectral indices either showed little use for separation, or did not show consistent separation for healthy and injured soybean and cotton. Compared with the traditional spectral indices, the new features were more feasible for the early detection of glyphosate injury, with leaves sprayed with a higher rate of glyphosate solution having larger feature values. This trend became more and more pronounced with time. Leaves sprayed with different glyphosate rates showed some separability 24 HAT using the new features and could be totally distinguished at and beyond 48 HAT for both soybean and cotton. These findings demonstrated the feasibility of applying leaf hyperspectral reflectance measurements for the early detection of glyphosate injury using these newly proposed features. Full article

► Show Figures

1245 KiB

Open AccessArticle

Modeling Accumulated Volume of Landslides Using Remote Sensing and DTM Data

by Zhengchao Chen, Bing Zhang, Yongshun Han, Zhengli Zuo and Xiaoyong Zhang

Remote Sens. 2014, 6(2), 1514-1537; https://doi.org/10.3390/rs6021514 - 19 Feb 2014

Cited by 33 | Viewed by 10592

Abstract

Landslides, like other natural hazards, such as avalanches, floods, and debris flows, may result in a lot of property damage and human casualties. The volume of landslide deposits is a key parameter for landslide studies and disaster relief. Using remote sensing and digital [...] Read more.

Landslides, like other natural hazards, such as avalanches, floods, and debris flows, may result in a lot of property damage and human casualties. The volume of landslide deposits is a key parameter for landslide studies and disaster relief. Using remote sensing and digital terrain model (DTM) data, this paper analyzes errors that can occur in calculating landslide volumes using conventional models. To improve existing models, the mechanisms and laws governing the material deposited by landslides are studied and then the mass balance principle and mass balance line are defined. Based on these ideas, a novel and improved model (Mass Balance Model, MBM) is proposed. By using a parameter called the “height adaptor”, MBM translates the volume calculation into an automatic search for the mass balance line within the scope of the landslide. Due to the use of mass balance constraints and the height adaptor, MBM is much more effective and reliable. A test of MBM was carried out for the case of a typical landslide, triggered by the Wenchuan Earthquake of 12 May 2008. Full article

► Show Figures

802 KiB

Open AccessArticle

Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China

by Yunxiang Jin, Xiuchun Yang, Jianjun Qiu, Jinya Li, Tian Gao, Qiong Wu, Fen Zhao, Hailong Ma, Haida Yu and Bin Xu

Remote Sens. 2014, 6(2), 1496-1513; https://doi.org/10.3390/rs6021496 - 19 Feb 2014

Cited by 142 | Viewed by 11723

Abstract

Grassland biomass is essential for maintaining grassland ecosystems. Moreover, biomass is an important characteristic of grassland. In this study, we combined field sampling with remote sensing data and calculated five vegetation indices (VIs). Using this combined information, we quantified a remote sensing estimation [...] Read more.

Grassland biomass is essential for maintaining grassland ecosystems. Moreover, biomass is an important characteristic of grassland. In this study, we combined field sampling with remote sensing data and calculated five vegetation indices (VIs). Using this combined information, we quantified a remote sensing estimation model and estimated biomass in a temperate grassland of northern China. We also explored the dynamic spatio-temporal variation of biomass from 2006 to 2012. Our results indicated that all VIs investigated in the study were strongly correlated with biomass (α < 0.01). The precision of the model for estimating biomass based on ground data and remote sensing was greater than 73%. Additionally, the results of our analysis indicated that the annual average biomass was 11.86 million tons and that the average yield was 604.5 kg/ha. The distribution of biomass exhibited substantial spatial heterogeneity, and the biomass decreased from the eastern portion of the study area to the western portion. The interannual biomass exhibited strong fluctuations during 2006–2012, with a coefficient of variation of 26.95%. The coefficient of variation of biomass differed among the grassland types. The highest coefficient of variation was found for the desert steppe, followed by the typical steppe and the meadow steppe. Full article

► Show Figures

Spatial distribution of grassland types and sampling sites for three regions of Xilingol in Inner Mongolia, China. Region I is the meadow steppe region, Region II is the typical steppe region and Region III is the desert steppe region. The numbers of model points are 921 samples, and the numbers of verification points are 213 samples. Full article ">
Relationship between VI and biomass: Region I, Region II and Region III. Region I and Region II selected the power function model based on NDVI; Region III selected the linear function model based on SAVI. Full article ">
Relationship between the estimated and actual biomass values: Region I, Region II and Region III. The model precision for the relationship between the estimated biomass and the actual biomass was above 73%. Full article ">
The 7-year-average grassland biomass between 2006 and 2012 in the Xilingol grassland. Full article ">
Biomass variation in Xilingol grassland from 2006 to 2012. The green line is the Harvest Year threshold, and the red line is the Lean Year threshold. The black line is the interannual variation of biomass estimation. Full article ">
The interannual variation of biomass in different grassland types. Full article ">
Biomass-CV plot for different grassland types. Biomass was calculated as the 7-year-average grassland biomass in different grassland types. CV was calculated from the annual biomass of grassland types from 2006 to 2012. Full article ">

2104 KiB

Open AccessArticle

Evaluation of InSAR and TomoSAR for Monitoring Deformations Caused by Mining in a Mountainous Area with High Resolution Satellite-Based SAR

by Donglie Liu, Yunfeng Shao, Zhenguo Liu, Björn Riedel, Andrew Sowter, Wolfgang Niemeier and Zhengfu Bian

Remote Sens. 2014, 6(2), 1476-1495; https://doi.org/10.3390/rs6021476 - 19 Feb 2014

Cited by 43 | Viewed by 12106

Abstract

Interferometric Synthetic Aperture Radar (InSAR) and Differential Interferometric Synthetic Aperture Radar (DInSAR) have shown numerous applications for subsidence monitoring. In the past 10 years, the Persistent Scatterer InSAR (PSI) and Small BAseline Subset (SBAS) approaches were developed to overcome the problem of decorrelation [...] Read more.

Interferometric Synthetic Aperture Radar (InSAR) and Differential Interferometric Synthetic Aperture Radar (DInSAR) have shown numerous applications for subsidence monitoring. In the past 10 years, the Persistent Scatterer InSAR (PSI) and Small BAseline Subset (SBAS) approaches were developed to overcome the problem of decorrelation and atmospheric effects, which are common in interferograms. However, DInSAR or PSI applications in rural areas, especially in mountainous regions, can be extremely challenging. In this study we have employed a combined technique, i.e., SBAS-DInSAR, to a mountainous area that is severely affected by mining activities. In addition, L-band (ALOS) and C-band (ENVISAT) data sets, 21 TerraSAR-X images provided by German Aerospace Center (DLR) with a high resolution have been used. In order to evaluate the ability of TerraSAR-X for mining monitoring, we present a case study of TerraSAR-X SAR images for Subsidence Hazard Boundary (SHB) extraction. The resulting data analysis gives an initial evaluation of InSAR applications within a mountainous region where fast movements and big phase gradients are common. Moreover, the experiment of four-dimension (4-D) Tomography SAR (TomoSAR) for structure monitoring inside the mining area indicates a potential near all-wave monitoring, which is an extension of conventional InSAR. Full article

(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)

► Show Figures

5226 KiB

Open AccessArticle

A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E

by David Bröhan and Lars Kaleschke

Remote Sens. 2014, 6(2), 1451-1475; https://doi.org/10.3390/rs6021451 - 18 Feb 2014

Cited by 34 | Viewed by 9226

Abstract

We infer the fractional coverage of sea ice leads (as concentration) in the Arctic from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) brightness temperatures. The lead concentration resolves leads of at least 3 km in width. We introduce a new [...] Read more.

We infer the fractional coverage of sea ice leads (as concentration) in the Arctic from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) brightness temperatures. The lead concentration resolves leads of at least 3 km in width. We introduce a new algorithm based on the progressive probabilistic Hough transform to automatically infer lead positions and orientations from daily AMSR-E satellite observations. Because the progressive probabilistic Hough transform often detects an identical lead several times the algorithm clusters neighboring leads that belong to one lead position. A first comparison of automatically detected lead positions and orientations with manually detected lead positions and orientations reveals that 57% of the reference leads are correctly determined. Around 11% of automatically detected leads are located where no reference lead occurs. The automatically detected lead orientations are distributed slightly differently from the reference lead orientations. A second comparison of automatically detected leads in the Fram Strait to leads in a wide swath mode Advanced Synthetic Aperture Radar scene shows a good agreement. We provide an Arctic-wide time series of lead orientations for winters from 2002 to 2011. For example, while a lead orientation of 110° with respect to the Greenwich meridian prevails in the Fram Strait, lead orientations in the Beaufort Sea are more isotropically distributed. We find significant preferred lead orientations almost everywhere in the Arctic Ocean when averaged over the entire AMSR-E time series. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing)

► Show Figures

Graphical abstract

Graphical abstract
Full article ">
Principle of the Hough transform: An arbitrary point (x′,y′) in a coordinate system is intersected by lines with arbitrary slopes m and y-offsets b (a). All lines are described by one parameter line (b). The parameter lines (c) belong to a chain of corresponding points (d). The points are connected via a line with the slope m′ and the y-intersect b′ indicated by a red arrow. Full article ">
Comparison between unclustered leads (red lines) and clustered leads (yellow lines) in the Fram Strait and a regional zoom north of Greenland on 14 March 2011. The lead concentration is depicted in blueish colors. The cluster distance d equals 4 pixels or 25 km. Full article ">
Principle of the cluster algorithm: Leads (black dotted lines) are reduced to lead centers (black cross, abbreviated to L) in a test data set (a). The numbers in the proximity matrix (b) represent the distance between the lead centers. All distances greater than the maximal distance d = 2 are whitened. Intersecting rows and columns with an entry identify lead clusters belonging to one cluster. The clustered lead centers are depicted as gray squares and the clustered leads as gray lines (a). Full article ">
Sketch of the algorithm outline. Full article ">
Comparison between reference leads (red) and clustered leads (yellow) detected by the Hough transform in the Fram Strait on 14 March 2011. Full article ">
Comparison between reference leads (red) and clustered leads (yellow) detected by the Hough transform in the Beaufort Sea on 14 November 2004. Full article ">
Comparison between lead orientations detected by the Hough transform with reference lead orientations in a histogram (a) and as probability density functions (b) in the Fram Strait on 14 March 2011. The Gauss curve in (b) is shown as reference. Full article ">
Comparison between lead orientations detected by the Hough transform with reference lead orientations in a histogram (a) and as probability density functions (b) in the Beaufort Sea on 14 November 2004. The Gauss curve in (b) is shown as reference. Full article ">
Validation of clustered leads detected by the Hough transform with an ASAR scene shows a high accordance for larger leads. Leads (red lines) inferred from AMSR-E lead concentration (blueish pixels) are compared to ASAR observations north of Greenland (box in <a href="#f2-remotesensing-06-01451" class="html-fig">Figure 2</a>) on 14 March 2011. Full article ">

1090 KiB

Open AccessArticle

Vicarious Calibration of Beijing-1 Multispectral Imagers

by Zhengchao Chen, Bing Zhang, Hao Zhang and Wenjuan Zhang

Remote Sens. 2014, 6(2), 1432-1450; https://doi.org/10.3390/rs6021432 - 18 Feb 2014

Cited by 18 | Viewed by 8392

Abstract

For on-orbit calibration of the Beijing-1 multispectral imagers (Beijing-1/MS), a field calibration campaign was performed at the Dunhuang calibration site during September and October of 2008. Based on the in situ data and images from Beijing-1 and Terra/Moderate Resolution Imaging Spectroradiometer (MODIS), three [...] Read more.

For on-orbit calibration of the Beijing-1 multispectral imagers (Beijing-1/MS), a field calibration campaign was performed at the Dunhuang calibration site during September and October of 2008. Based on the in situ data and images from Beijing-1 and Terra/Moderate Resolution Imaging Spectroradiometer (MODIS), three vicarious calibration methods (i.e., reflectance-based, irradiance-based, and cross-calibration) were used to calculate the top-of-atmosphere (TOA) radiance of Beijing-1. An analysis was then performed to determine or identify systematic and accidental errors, and the overall uncertainty was assessed for each individual method. The findings show that the reflectance-based method has an uncertainty of more than 10% if the aerosol optical depth (AOD) exceeds 0.2. The cross-calibration method is able to reach an error level within 7% if the images are selected carefully. The final calibration coefficients were derived from the irradiance-based data for 6 September 2008, with an uncertainty estimated to be less than 5%. Full article

(This article belongs to the Special Issue Calibration and Verification of Remote Sensing Instruments and Observations)

► Show Figures

Channel layout of the Beijing-1/MS (Left) and the relationship between the cameras and images of the Beijing-1/MS (Right). Full article ">
Image of Dunhuang site acquired by Beijing-1 on 13 September 2008. The image is a mosaic of images from bank0 (Right) and bank1 (Left). The area within the blue lines is the overlap. The track of the surface reflectance measurements in Area A is shown in the bottom right of the image. Full article ">
(a) Average reflectance of Area A acquired on 3, 6, and 13 September, and average reflectance of Area C acquired on 18 October. (b) Ratios of band-weight reflectance of Beijing-1/MS at 0 zenith angle compared to that of other zenith angles at the same azimuth angle. Numbers in the legend refer to the azimuth angle (in degrees). Full article ">
Aerosol optical depth (AOD) at 550 nm (Left) and diffuse-to-global irradiance ratios (Right) during Dunhuang experiments. Full article ">
Workflow for vicarious calibration of Beijing-1/MS. Full article ">
Plots of the DNs and corresponding top-of-atmosphere (TOA) radiance (LTOA) calibrated via the three vicarious methods. Full article ">
Results derived by reflectance-based method and irradiance-based method under different aerosol types. Full article ">

25409 KiB

Open AccessArticle

Temperature and Snow-Mediated Moisture Controls of Summer Photosynthetic Activity in Northern Terrestrial Ecosystems between 1982 and 2011

by Jonathan Barichivich, Keith R. Briffa, Ranga Myneni, Gerard Van der Schrier, Wouter Dorigo, Compton J. Tucker, Timothy J. Osborn and Thomas M. Melvin

Remote Sens. 2014, 6(2), 1390-1431; https://doi.org/10.3390/rs6021390 - 14 Feb 2014

Cited by 78 | Viewed by 13967

Abstract

Recent warming has stimulated the productivity of boreal and Arctic vegetation by reducing temperature limitations. However, several studies have hypothesized that warming may have also increased moisture limitations because of intensified summer drought severity. Establishing the connections between warming and drought stress has [...] Read more.

Recent warming has stimulated the productivity of boreal and Arctic vegetation by reducing temperature limitations. However, several studies have hypothesized that warming may have also increased moisture limitations because of intensified summer drought severity. Establishing the connections between warming and drought stress has been difficult because soil moisture observations are scarce. Here we use recently developed gridded datasets of moisture variability to investigate the links between warming and changes in available soil moisture and summer vegetation photosynthetic activity at northern latitudes (>45°N) based on the Normalized Difference Vegetation Index (NDVI) since 1982. Moisture and temperature exert a significant influence on the interannual variability of summer NDVI over about 29% (mean r² = 0.29 ± 0.16) and 43% (mean r² = 0.25 ± 0.12) of the northern vegetated land, respectively. Rapid summer warming since the late 1980s (~0.7 °C) has increased evapotranspiration demand and consequently summer drought severity, but contrary to earlier suggestions it has not changed the dominant climate controls of NDVI over time. Furthermore, changes in snow dynamics (accumulation and melting) appear to be more important than increased evaporative demand in controlling changes in summer soil moisture availability and NDVI in moisture-sensitive regions of the boreal forest. In boreal North America, forest NDVI declines are more consistent with reduced snowpack rather than with temperature-induced increases in evaporative demand as suggested in earlier studies. Moreover, summer NDVI variability over about 28% of the northern vegetated land is not significantly associated with moisture or temperature variability, yet most of this land shows increasing NDVI trends. These results suggest that changes in snow accumulation and melt, together with other possibly non-climatic factors are likely to play a significant role in modulating regional ecosystem responses to the projected warming and increase in evapotranspiration demand during the coming decades. Full article

(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))

► Show Figures

Graphical abstract

1637 KiB

Open AccessArticle

A Comparative Study on Satellite- and Model-Based Crop Phenology in West Africa

by Elodie Vintrou, Agnès Bégué, Christian Baron, Alexandre Saad, Danny Lo Seen and Seydou B. Traoré

Remote Sens. 2014, 6(2), 1367-1389; https://doi.org/10.3390/rs6021367 - 13 Feb 2014

Cited by 30 | Viewed by 8694

Abstract

Crop phenology is essential for evaluating crop production in the food insecure regions of West Africa. The aim of the paper is to study whether satellite observation of plant phenology are consistent with ground knowledge of crop cycles as expressed in agro-simulations. We [...] Read more.

Crop phenology is essential for evaluating crop production in the food insecure regions of West Africa. The aim of the paper is to study whether satellite observation of plant phenology are consistent with ground knowledge of crop cycles as expressed in agro-simulations. We used phenological variables from a MODIS Land Cover Dynamics (MCD12Q2) product and examined whether they reproduced the spatio-temporal variability of crop phenological stages in Southern Mali. Furthermore, a validated cereal crop growth model for this region, SARRA-H (System for Regional Analysis of Agro-Climatic Risks), provided precise agronomic information. Remotely-sensed green-up, maturity, senescence and dormancy MODIS dates were extracted for areas previously identified as crops and were compared with simulated leaf area indices (LAI) temporal profiles generated using the SARRA-H crop model, which considered the main cropping practices. We studied both spatial (eight sites throughout South Mali during 2007) and temporal (two sites from 2002 to 2008) differences between simulated crop cycles and determined how the differences were indicated in satellite-derived phenometrics. The spatial comparison of the phenological indicator observations and simulations showed mainly that (i) the satellite-derived start-of-season (SOS) was detected approximately 30 days before the model-derived SOS; and (ii) the satellite-derived end-of-season (EOS) was typically detected 40 days after the model-derived EOS. Studying the inter-annual difference, we verified that the mean bias was globally consistent for different climatic conditions. Therefore, the land cover dynamics derived from the MODIS time series can reproduce the spatial and temporal variability of different start-of-season and end-of-season crop species. In particular, we recommend simultaneously using start-of-season phenometrics with crop models for yield forecasting to complement commonly used climate data and provide a better estimate of vegetation phenological changes that integrate rainfall variability, land cover diversity, and the main farmer practices. Full article

► Show Figures

The synoptic station locations and a map of the crop production systems in South Mali [<a href="#b27-remotesensing-06-01367" class="html-bibr">27</a>]. Full article ">
Four transition dates based on the EVI curvature-change rate from the MODIS data time series. The solid line is an ideal time series for the vegetation index data, and the dashed line is the rate of change in the VI data curvature. The circles indicate transition dates: 1: start-of-season (SOS); 2: start-of-maximum (SMAX); 3: end-of-maximum (EMAX); and 4: end-of-season (EOS), adapted from <a href="#f2-remotesensing-06-01367" class="html-fig">Figure 2</a> in [<a href="#b23-remotesensing-06-01367" class="html-bibr">23</a>]. Full article ">
An example of start-of-season extraction on a national scale (averaged on a 20 × 20 km grid) and Bougouni- and Segou-station scale (defined by a 10 × 10 km polygon) and masked using a 2007 crop map [<a href="#b35-remotesensing-06-01367" class="html-bibr">35</a>]. Full article ">
Examples of model-derived phenometrics (dotted lines) calculated with Zhang’s non-linear functions for two sets of LAI simulations using the SARRA-H crop model for the Kita synoptic station in 2007. The green curve represents the maize LAI simulation (fertilized in the dark, non-fertilized in light); the orange curve represents the Guinea Sorghum LAI simulation (fertilized in the dark, non-fertilized in light). For each fertilized curve, the dotted lines correspond to the following from left to right: (i) start-of-season (SOS); (ii) start-of-maximum (SMAX) of season; (iii) end-of-maximum (EMAX) of season; and (iv) end-of-season (EOS). Full article ">
Satellite- (pink) and model-derived (violet) phenometrics boxplots were calculated for eight synoptic stations, ranged from north (Top) to south (Bottom), in 2007. The mean signed difference (MSD) is the difference between the model- and satellite-derived phenometric values in days. Full article ">
Linear regression for the median satellite- and model-derived phenometrics values for the eight stations during 2007. The diagonal dotted lines represent the 1:1 line. Full article ">
Satellite- (pink) and model-derived phenometrics boxplots (violet) calculated for Segou and Sikasso from 2002 to 2008. The mean signed difference (MSD) corresponds to the difference in days between the model- and satellite-derived phenometrics. Full article ">
Linear regression of the satellite- and model-derived phenometric median values for Segou and Sikasso from 2002 to 2008. The diagonal dotted lines represent the 1:1 lines. The SOS points represented by the orange circles were inconsistent for the rainfall distribution, which is discussed below (Section 4.4). Full article ">
Rainfall (mm), LAI simulation (blue curve) and model- (dotted blue lines) and satellite-derived (dotted red lines) phenometric barplots for the two years, which indicate inconsistencies (Segou in 2002 and Sikasso in 2006), including for the LAI millet choho simulation in Segou and sorghum guinea in Sikasso (blue curve). From left to right, the dotted lines correspond to the start-of-season, start-of-maximum of season, end-of-maximum of season, and end-of-season, respectively. The red circle indicates a failed sowing date, and the green circle indicates a successful sowing date. Full article ">

841 KiB

Open AccessArticle

Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data

by Mariana Belgiu, Ivan Tomljenovic, Thomas J. Lampoltshammer, Thomas Blaschke and Bernhard Höfle

Remote Sens. 2014, 6(2), 1347-1366; https://doi.org/10.3390/rs6021347 - 12 Feb 2014

Cited by 97 | Viewed by 13621

Abstract

Accurate information on urban building types plays a crucial role for urban development, planning, and management. In this paper, we apply Object-Based Image Analysis (OBIA) methods to extract buildings from Airborne Laser Scanner (ALS) data and investigate the possibility of classifying detected buildings [...] Read more.

Accurate information on urban building types plays a crucial role for urban development, planning, and management. In this paper, we apply Object-Based Image Analysis (OBIA) methods to extract buildings from Airborne Laser Scanner (ALS) data and investigate the possibility of classifying detected buildings into “Residential/Small Buildings”, “Apartment Buildings”, and “Industrial and Factory Building” classes by means of domain ontology and machine learning techniques. The buildings objects are classified using exclusively the information computed from the ALS data. To select the relevant features for predicting the classes of interest, the Random Forest classifier has been applied. The ontology-based classification yielded convincing results for the “Residential/Small Buildings” class (F-Measure 97.7%), whereas the “Apartment Buildings” and “Industrial and Factory Buildings” classes achieved less accurate results (F-Measure 60% and 51%, respectively). Full article

► Show Figures

Graphical abstract

1049 KiB

Open AccessArticle

Absolute Calibration of Optical Satellite Sensors Using Libya 4 Pseudo Invariant Calibration Site

by Nischal Mishra, Dennis Helder, Amit Angal, Jason Choi and Xiaoxiong Xiong

Remote Sens. 2014, 6(2), 1327-1346; https://doi.org/10.3390/rs6021327 - 12 Feb 2014

Cited by 79 | Viewed by 11044

Abstract

The objective of this paper is to report the improvements in an empirical absolute calibration model developed at South Dakota State University using Libya 4 (+28.55°, +23.39°) pseudo invariant calibration site (PICS). The approach was based on use of the Terra MODIS as [...] Read more.

The objective of this paper is to report the improvements in an empirical absolute calibration model developed at South Dakota State University using Libya 4 (+28.55°, +23.39°) pseudo invariant calibration site (PICS). The approach was based on use of the Terra MODIS as the radiometer to develop an absolute calibration model for the spectral channels covered by this instrument from visible to shortwave infrared. Earth Observing One (EO-1) Hyperion, with a spectral resolution of 10 nm, was used to extend the model to cover visible and near-infrared regions. A simple Bidirectional Reflectance Distribution function (BRDF) model was generated using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) observations over Libya 4 and the resulting model was validated with nadir data acquired from satellite sensors such as Aqua MODIS and Landsat 7 (L7) Enhanced Thematic Mapper (ETM+). The improvements in the absolute calibration model to account for the BRDF due to off-nadir measurements and annual variations in the atmosphere are summarized. BRDF models due to off-nadir viewing angles have been derived using the measurements from EO-1 Hyperion. In addition to L7 ETM+, measurements from other sensors such as Aqua MODIS, UK-2 Disaster Monitoring Constellation (DMC), ENVISAT Medium Resolution Imaging Spectrometer (MERIS) and Operational Land Imager (OLI) onboard Landsat 8 (L8), which was launched in February 2013, were employed to validate the model. These satellite sensors differ in terms of the width of their spectral bandpasses, overpass time, off-nadir-viewing capabilities, spatial resolution and temporal revisit time, etc. The results demonstrate that the proposed empirical calibration model has accuracy of the order of 3% with an uncertainty of about 2% for the sensors used in the study. Full article

(This article belongs to the Special Issue Calibration and Verification of Remote Sensing Instruments and Observations)

► Show Figures

3973 KiB

Open AccessArticle

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

by Xiangguo Lin and Jixian Zhang

Remote Sens. 2014, 6(2), 1294-1326; https://doi.org/10.3390/rs6021294 - 7 Feb 2014

Cited by 110 | Viewed by 11152

Abstract

Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third [...] Read more.

Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third step is our main contribution. Particularly, the iterative judgment is based on the framework of the classic progressive TIN (triangular irregular network) densification (PTD) method, but with basic processing unit being a segment rather than a single point. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. Experimental results suggest that, compared with the PTD method, the SBF approach is capable of preserving discontinuities of landscapes and removing the lower parts of large objects attached on the ground surface. As a result, the SBF approach is able to reduce omission errors and total errors by 18.26% and 11.47% respectively, which would significantly decrease the cost of manual operation required in post-processing. Full article

(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))

► Show Figures

1092 KiB

Open AccessArticle

Validation of the Two Standard MODIS Satellite Burned-Area Products and an Empirically-Derived Merged Product in South Africa

by Philemon Tsela, Konrad Wessels, Joel Botai, Sally Archibald, Derick Swanepoel, Karen Steenkamp and Philip Frost

Remote Sens. 2014, 6(2), 1275-1293; https://doi.org/10.3390/rs6021275 - 4 Feb 2014

Cited by 54 | Viewed by 10068

Abstract

The 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) burned area products, MCD45A1, MCD64A1, and a merged product were validated across six study sites in South Africa using independently-derived Landsat burned-area reference data during the fire season of 2007. The objectives of this study were [...] Read more.

The 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) burned area products, MCD45A1, MCD64A1, and a merged product were validated across six study sites in South Africa using independently-derived Landsat burned-area reference data during the fire season of 2007. The objectives of this study were to: (i) investigate the likelihood of the improved detection of small burns through an empirically-derived merged product; (ii) quantify the probability of detection by each product using sub-pixel burned area measures; and, (iii) compare the mean percent concurrence of burned pixels between the standard products over a ten-year time series in each site. Results show that MCD45A1 presented higher detection probabilities (i.e., 3.0%–37.9%) for small fractions ≤50%, whereas MCD64A1 appeared more reliable (i.e., 12.0%–89.2%) in detecting large fractions >50% of a burned MODIS pixel, respectively. Overall, the merged product demonstrated improved detection of the burned area in all fractions. This paper also demonstrates that, on average, >50% of MODIS burned pixels temporally concur between the MCD45A1 and MCD64A1 products in each site. These findings have significant implications for fire monitoring in southern Africa and contribute toward the understanding of the range and of the sources of errors present in the MODIS burned area products. Full article

(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)

► Show Figures

The biome map showing the location of the validation sites and multi-temporal Landsat 5 Thematic Mapper (TM) acquisitions distributed across the four fire-prone biomes in South Africa. Full article ">
Spatial analysis of the burned areas interpreted and produced from multi-temporal Landsat TM data over the six validation sites. The burned area patterns show a diversity of burned shapes, from narrow and/or fragmented (0.36, 12.5, 50 ha) to large and compact (>1,000 ha). Full article ">
Comparison of Landsat (interpreted) burned-area reference data and the MCD45A1 product to derive the Ce and Oe by use of a confusion matrix. Full article ">
The 500-m MODIS reference grids used to compute the fractional sub-pixel burned areas corresponding to the Landsat TM interpreted burns. Full article ">
Histograms of MODIS sub-pixel burned area analysis used to depict the relationship between burned area proportions according to Landsat TM and the probability of detection by the three MODIS products over the six validation sites. Full article ">
Mean percent concurrence of burned pixels between the MCD45A1 and MCD64A1 products based on a ten-year time series, January 2002 to December 2011, across the six study sites. Full article ">
Histograms of the brightness levels for Landsat reference pixels interpreted as burned from the TM temporal-difference between vegetation index (VI)date2 and VIdate1 across the selected sites. Full article ">

2902 KiB

Open AccessArticle

The Stalled Recovery of the Iraqi Marshes

by Richard H. Becker

Remote Sens. 2014, 6(2), 1260-1274; https://doi.org/10.3390/rs6021260 - 30 Jan 2014

Cited by 11 | Viewed by 8153

Abstract

The Iraqi (Mesopotamian) Marshes, an extensive wetlands system in Iraq, has been heavily impacted by both human and climate forces over the past decades. In the period leading up to the Second Gulf War in 2002, the marshlands were shrinking due to both [...] Read more.

The Iraqi (Mesopotamian) Marshes, an extensive wetlands system in Iraq, has been heavily impacted by both human and climate forces over the past decades. In the period leading up to the Second Gulf War in 2002, the marshlands were shrinking due to both a policy of draining and water diversion in Iraq and construction of dams upstream on the Tigris and Euphrates rivers. Following the war through 2006, this trend was reversed as the diversions were removed and active draining stopped. A combination of MODIS and GRACE datasets were used to determine the change in surface water area (SWA) in the marshes, marshland extent and change in mass both upriver in the Tigris and Euphrates watersheds and in the marshlands. Results suggest that the post war dam removal and decreased pumping in 2003 provided only temporary respite for the marshlands (2003–2006 SWA: 1,477 km² increase (600%), water equivalent depth (WED): +2.0 cm/yr.; 2006–2009: −860 km² (−41%) WED: −3.9 cm/yr.). Unlike in the period 2003–2006, from 2006 forward the mass variations in the marshes are highly correlated with those in the upper and middle watershed (R = 0.86 and 0.92 respectively), suggesting that any recovery due to that removal is complete, and that all future changes are tied more strongly to any climate changes that will affect recharge in the upper Tigris-Euphrates system. Precipitation changes in the watershed show a reduction of an average of 15% below the 15 yr mean in 2007–2011 This corresponds with published ensemble predictions for the 2071–2099 time period, that suggested similar marshland shrinkage should be expected in that time period. Full article

(This article belongs to the Special Issue Hydrological Remote Sensing)

► Show Figures

Extent of the Tigris-Euphrates watershed overlain on MODIS NDVI image showing areas of high vegetation (green). Red Boxes show area of upper watershed (5 × 5 degree box), middle watershed (3 × 3 degree box) and marshes (1 × 1 and 3 × 3 degree box), shown from NW to SE, used in GRACE analysis. Inset shows Tropical Rainfall Measurement Mission (TRMM) precipitation over the watershed is concentrated in the North, through Eastern Turkey, Northern Iraq and Western Iran. Full article ">
Combined MODIS NDVI-MNDWI images for 2001–2012, over Iraqi Marshes. Open water is shown in blue (MNDWI > 0), high vegetation is shown in green, medium in yellow, and low in brown. Full article ">
MODIS derived area for open water (blue), high NDVI (green, surrogate for healthy marshland vegetation) in the area shown in <a href="#f2-remotesensing-06-01260" class="html-fig">Figure 2</a>. Water equivalent depth changes derived from GRACE data with annual average signal removed for 1 × 1 degree area centered on the Marshlands is overlain in red showing agreement between GRACE solution and extent of combined open water and high NDVI regions. MODIS bars are centered over the GRACE acquisition for the same month. Full article ">
Water equivalent depth changes derived from GRACE data for 1 × 1 degree area centered on the Marshlands, a 3 × 3 area centered on the marshlands, a 3 × 3 area in the middle of the T-E watershed, and a 5 × 5 degree area in the upper TE watershed. Standard errors for each measurement are shown in gray. Overlain on these are statistically significant (α = 0.05) best fit model of annual and trend component combined (blue) and linear trend alone (black). Dashed line indicates no trend significantly different from zero slope. Lines show trends in water storage for the time period January 2003–March 2013. Slope and error for each segment are listed in <a href="#t1-remotesensing-06-01260" class="html-table">Table 1</a>. Full article ">
Average annual rainfall for the Tigris Euphrates watershed from TRMM 3B43 data product. Rainfall is calculated by local water year (August through July of the following year). Mean precipitation line shows average for TRMM (January 1998–October 2013) for the entire basin. Full article ">

12032 KiB

Open AccessArticle

Detection of Coal Fire Dynamics and Propagation Direction from Multi-Temporal Nighttime Landsat SWIR and TIR Data: A Case Study on the Rujigou Coalfield, Northwest (NW) China

by Hongyuan Huo, Xiaoguang Jiang, Xianfeng Song, Zhao-Liang Li, Zhuoya Ni and Caixia Gao

Remote Sens. 2014, 6(2), 1234-1259; https://doi.org/10.3390/rs6021234 - 29 Jan 2014

Cited by 49 | Viewed by 11218

Abstract

Coal fires are common and serious phenomena in most coal-producing countries in the world. Coal fires not only burn valuable non-renewable coal reserves but also severely affect the local and global environment. The Rujigou coalfield in Shizuishan City, Ningxia, NW China, is well [...] Read more.

Coal fires are common and serious phenomena in most coal-producing countries in the world. Coal fires not only burn valuable non-renewable coal reserves but also severely affect the local and global environment. The Rujigou coalfield in Shizuishan City, Ningxia, NW China, is well known for being a storehouse of anthracite coal. This coalfield is also known for having more coal fires than most other coalfields in China. In this study, an attempt was made to study the dynamics of coal fires in the Rujigou coalfield, from 2001 to 2007, using multi-temporal nighttime Landsat data. The multi-temporal nighttime short wave infrared (SWIR) data sets based on a fixed thresholding technique were used to detect and monitor the surface coal fires and the nighttime enhanced thematic mapper (ETM+) thermal infrared (TIR) data sets, based on a dynamic thresholding technique, were used to identify the thermal anomalies related to subsurface coal fires. By validating the coal fires identified in the nighttime satellite data and the coal fires extracted from daytime satellite data with the coal fire map (CFM) manufactured by field survey, we found that the results from the daytime satellite data had higher omission and commission errors than the results from the nighttime satellite data. Then, two aspects of coal fire dynamics were analyzed: first, a quantitative analysis of the spatial changes in the extent of coal fires was conducted and the results showed that, from 2001 to 2007, the spatial extent of coal fires increased greatly to an annual average area of 0.167 km²; second, the spreading direction and propagation of coal fires was analyzed and predicted from 2001 to 2007, and these results showed that the coal fires generally spread towards the north or northeast, but also spread in some places toward the east. Full article

► Show Figures

2912 KiB

Open AccessArticle

The Generalized Difference Vegetation Index (GDVI) for Dryland Characterization

by Weicheng Wu

Remote Sens. 2014, 6(2), 1211-1233; https://doi.org/10.3390/rs6021211 - 29 Jan 2014

Cited by 155 | Viewed by 25392

Abstract

A large number of vegetation indices have been developed and widely applied in terrestrial ecosystem research in the recent decades. However, a certain limitation was observed while applying these indices in research in dry areas due to their low sensitivity to low vegetation [...] Read more.

A large number of vegetation indices have been developed and widely applied in terrestrial ecosystem research in the recent decades. However, a certain limitation was observed while applying these indices in research in dry areas due to their low sensitivity to low vegetation cover. In this context, the objectives of this study are to develop a new vegetation index, namely, the Generalized Difference Vegetation Index (GDVI), and to examine its applicability to the assessment of dryland environment. Based on the field investigation and crop Leaf Area Index (LAI) measurement, five spring and summer Landsat TM and ETM+ images in the frame with Path/Row number of 174/35, and MODIS (Moderate Resolution Imaging Spectroradiometer) LAI and vegetation indices (VIs) data (MOD15A2 and MOD13Q1), of the same acquisition dates as the Landsat images, were acquired and employed in this study. The results reveal that, despite the same level of correlation with the fractional vegetation cover (FVC) as other VIs, GDVI shows a better correlation with LAI and has higher sensitivity and dynamic range in the low vegetal land cover than other vegetation indices, e.g., the range of GDVI is higher than Normalized Difference Vegetation Index (NDVI),Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Wide Dynamic Range Vegetation Index (WDRVI), and Soil-Adjusted and Atmospherically Resistant Vegetation Index (SARVI), by 164%–326% in woodland, 185%–720% in olive plantation, and 190%–867% in rangeland. It is, hence, concluded that GDVI is relevant for, and has great potential in, land characterization, as well as land degradation/desertification assessment in dryland environment. Full article

► Show Figures

Characteristics of GDVI vs. NDVI ((a) GDVI^2 vs. NDVI, (b) GDVI^3 vs. NDVI and (c) GDVI^4 vs. NDVI). Full article ">
Location of the test area. Full article ">
Distribution of the sampling areas taking the Landsat-MODIS image pair of 1 May 2007 as an example. Full article ">
Logarithmic relationships between vegetation indices and LAI (image pair dated 12 July 2010). Full article ">
Logarithmic relationships between vegetation indices and LAI (image pair dated 1 May 2007. (a) GDVI vs. LAI, (b) VIs with positive range vs. LAI, and (c) VIs with negative range vs. LAI). Full article ">
Logarithmic relationships between vegetation indices and LAI (image pair dated 1 May 2007. (a) GDVI vs. LAI, (b) VIs with positive range vs. LAI, and (c) VIs with negative range vs. LAI). Full article ">
Sensitivity of GDVI vs. other vegetation indices for different biomes (image dated 1 May 2007. (a) GDVI^2 vs. VIs, (b) GDVI^3 vs. VIs, and (c) GDVI^4 vs. VIs). Full article ">
Sensitivity of GDVI vs. other vegetation indices for different biomes (image dated 1 May 2007. (a) GDVI^2 vs. VIs, (b) GDVI^3 vs. VIs, and (c) GDVI^4 vs. VIs). Full article ">
Relationships between MODIS Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and GDVI and MODIS LAI (1 May 2007). Full article ">

12276 KiB

Open AccessArticle

Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

by Dirk Eilander, Frank O. Annor, Lorenzo Iannini and Nick Van de Giesen

Remote Sens. 2014, 6(2), 1191-1210; https://doi.org/10.3390/rs6021191 - 29 Jan 2014

Cited by 37 | Viewed by 9338

Abstract

Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach [...] Read more.

Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR) backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir. Full article

(This article belongs to the Special Issue Earth Observation for Water Resource Management in Africa)

► Show Figures

The study area in the Upper East Region of Ghana, overlaid with a base map of small reservoirs in the region. Full article ">
Flow diagram for the growing Bayesian classifier: first, the seeds are initialized (top right) for which a SAR reservoir image is required (top left); then, the iterative Bayesian classification is performed (right middle); finally, a growing filter is applied (right bottom); the algorithm can readily be extended with additional information (left middle). Full article ">
Backscatter intensity distributions and scatter plots for the land and water classes from the samples of four distinct small reservoir backscatter scenarios, i. e., smooth open water, water with vegetation, Bragg scattering and backscatter during a rain event. Full article ">
Jeffries-Matusita (JM) distances for the samples of three distinct backscatter scenarios from small reservoirs, where the error bars show the mean, minimum and maximum JM distances from the different samples. Full article ">
Ground truth (yellow line) and delineation (red line) based on the ‘HH, HV’ polarization combination overlaid on Pauli RGB-images, with red colors for double bounce, green for volume scatter and blue for single bounce; note that the different color scales are used for the different Pauli components to enhance the image contrast. Full article ">
Comparison between classified and ground truth areas of all 29 small reservoir from November 2012, based on the Differential Area Index (DAI) and the Jeffries Matusita (JM) distance. Full article ">
Rainfall time senes (top), the quality of an acquisition based on the Jeffries Matusita (JM) distance (middle) and the mean backscatter intensity of the minimum delineated small reservoir area (bottom), where the boxplots show the median, first and second quartile boundaries and the red crosses are outliers; the error bars show the mean and one standard deviation boundaries. Full article ">
Time series of small reservoir delineation based on the ‘HH, HV’ polarization combination and the basic gBC (red line), the gBC updated with temporal prior τt−1 (blue line) and the gBC updated with both priors τt−1 and τt+1 (green line) overlaid on HH backscatter intensity images; the bottom graphs show the areal variation in time for the same reservoirs, where the crosses show the filtered time series without the rain-affected March 28 acquisition. Full article ">
Time series of the cumulative classified area for 26 small reservoirs, based on the growing Bayesian classifier (gBC) with and without temporal priors (top) and the gBC without temporal priors for different polarization combinations (bottom). Full article ">

3065 KiB

Open AccessArticle

An Extended Fourier Approach to Improve the Retrieved Leaf Area Index (LAI) in a Time Series from an Alpine Wetland

by Xingwen Quan, Binbin He, Yong Wang, Zhi Tang and Xing Li

Remote Sens. 2014, 6(2), 1171-1190; https://doi.org/10.3390/rs6021171 - 29 Jan 2014

Cited by 15 | Viewed by 7734

Abstract

An extended Fourier approach was presented to improve the retrieved leaf area index (LAI_r) of herbaceous vegetation in a time series from an alpine wetland. The retrieval was performed from the Aqua MODIS 8-day composite surface reflectance product (MYD09Q1) from day [...] Read more.

An extended Fourier approach was presented to improve the retrieved leaf area index (LAI_r) of herbaceous vegetation in a time series from an alpine wetland. The retrieval was performed from the Aqua MODIS 8-day composite surface reflectance product (MYD09Q1) from day of year (DOY) 97 to 297 using a look-up table (LUT) based inversion of a two-layer canopy reflectance model (ACRM). To reduce the uncertainty (the ACRM inversion is ill-posed), we used NDVI and NIR images to reduce the influence of the soil background and the priori information to constrain the range of sensitive ACRM parameters determined using the Sobol’s method. Even so the uncertainty caused the LAI_r versus time curve to oscillate. To further reduce the uncertainty, a Fourier model was fitted using the periodically LAI_r results, obtaining LAI_F. We note that the level of precision of the LAI_F potentially may increase through removing singular points or decrease if the LAI_r data were too noisy. To further improve the precision level of the LAI_r, the Fourier model was extended by considering the LAI_r uncertainty. The LAI_r, the LAI simulated using the Fourier model, and the LAI simulated using the extended Fourier approach (LAI_eF) were validated through comparisons with the field measured LAI. The R² values were 0.68, 0.67 and 0.72, the residual sums of squares (RSS) were 3.47, 3.42 and 3.15, and the root-mean-square errors (RMSE) were 0.31, 0.30 and 0.29, respectively, on DOY 177 (early July 2011). In late August (DOY 233), the R² values were 0.73, 0.77 and 0.79, the RSS values were 38.96, 29.25 and 27.48, and the RMSE values were 0.94, 0.81 and 0.78, respectively. The results demonstrate that the extended Fourier approach has the potential to increase the level of precision of estimates of the time varying LAI. Full article

► Show Figures

698 KiB

Open AccessArticle

In-Field Absolute Calibration of Ground and Airborne VIS-NIR-SWIR Hyperspectral Point Spectrometers

by Offer Rozenstein, Adam Devir and Arnon Karnieli

Remote Sens. 2014, 6(2), 1158-1170; https://doi.org/10.3390/rs6021158 - 29 Jan 2014

Cited by 2 | Viewed by 7434

Abstract

Spectrometer calibration and measurements of spectral radiance are often required when performing ground, aerial, and space measurements. While calibrating a spectrometer in the field using an integrating sphere is practically unachievable, calibration against a quartz halogen (QH) lamp is a quite easy and [...] Read more.

Spectrometer calibration and measurements of spectral radiance are often required when performing ground, aerial, and space measurements. While calibrating a spectrometer in the field using an integrating sphere is practically unachievable, calibration against a quartz halogen (QH) lamp is a quite easy and feasible option. We describe a calibration protocol whereby a professional QH lamp, operating with a stabilized current source, is first calibrated in the laboratory against a US National Institute of Standards and Technology (NIST) traceable integrating sphere and, then, used for the field calibration of a spectrometer before a ground or airborne campaign. Another advantage of the lamp over the integrating sphere is its ability to create a continuous calibration curve at the spectrometer resolution, while the integrating sphere is calibrated only for a few discrete wavelengths. A calibrated lamp could also be used for a secondary continuous calibration of an un-calibrated integrating sphere. Full article

► Show Figures

3188 KiB

Open AccessArticle

Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques

by Amal Allbed, Lalit Kumar and Priyakant Sinha

Remote Sens. 2014, 6(2), 1137-1157; https://doi.org/10.3390/rs6021137 - 29 Jan 2014

Cited by 124 | Viewed by 11717

Abstract

Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semi-arid regions. An integrated approach using remote sensing in addition to various statistical methods has shown success for developing soil salinity prediction models. The aim of this study [...] Read more.

Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semi-arid regions. An integrated approach using remote sensing in addition to various statistical methods has shown success for developing soil salinity prediction models. The aim of this study was to develop statistical regression models based on remotely sensed indicators to predict and map spatial variation in soil salinity in the Al Hassa oasis. Different spectral indices were calculated from original bands of IKONOS images. Statistical correlation between field measurements of Electrical Conductivity (EC), spectral indices and IKONOS original bands showed that the Salinity Index (SI) and red band (band 3) had the highest correlation with EC. Combining these two remotely sensed variables into one model yielded the best fit with R² = 0.65. The results revealed that the high performance of this combined model is attributed to: (i) the spatial resolution of the images; (ii) the great potential of the enhanced images, derived from SI, by enhancing and delineating the spatial variation of soil salinity; and (iii) the superiority of band 3 in retrieving soil salinity features and patterns, which was explained by the high reflectance of the smooth and bright surface crust and the low reflectance of the coarse dark puffy crust. Soil salinity maps generated using the selected model showed that strongly saline soils (>16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors. The results demonstrate that modelling and mapping spatial variation in soil salinity based on regression analysis and remote sensing data is a promising approach, as it facilitates timely detection with a low-cost procedure and allows decision makers to decide what necessary action should be taken in the early stages to prevent soil salinity from becoming prevalent, sustaining agricultural lands and natural ecosystems. Full article

► Show Figures

816 KiB

Open AccessArticle

Impact of Tree Species on Magnitude of PALSAR Interferometric Coherence over Siberian Forest at Frozen and Unfrozen Conditions

by Christian Thiel and Christiane Schmullius

Remote Sens. 2014, 6(2), 1124-1136; https://doi.org/10.3390/rs6021124 - 28 Jan 2014

Cited by 10 | Viewed by 5774

Abstract

Numerous studies demonstrated the potential of the magnitude of interferometric coherence |γ| for forest growing stock volume (GSV) estimation in boreal forests. Coherence derived from images acquired under frozen conditions proved to be of specific interest. This also applies [...] Read more.

Numerous studies demonstrated the potential of the magnitude of interferometric coherence |γ| for forest growing stock volume (GSV) estimation in boreal forests. Coherence derived from images acquired under frozen conditions proved to be of specific interest. This also applies to PALSAR coherence, although affected by a comparatively large temporal baseline of at least 46 days. However, when working with spaceborne L-band data, acquired under unfrozen conditions, a large spread of |γ| was observed at all GSV levels. This scatter negatively affects the correlation of GSV and |γ|. So far, the impact of tree species on |γ| has rarely been studied in this context, although the different tree geometries are likely to have an impact on volumetric decorrelation. This paper presents the results of a study investigating the impact of tree species on PALSAR coherence employing 36 interferograms. The observations show only a small impact of the tree species on |γ| during frozen conditions. At unfrozen conditions, the impact is about three times larger. Deciduous species (aspen, birch, larch) exhibit the lowest |γ|, while coniferous species (fir, pine) feature the highest |γ|. For example, at unfrozen conditions, the |γ| of fir is 0.15 greater than the |γ| of larch, while the mean |γ| of dense forest is 0.38. Accordingly, the impact of tree species on |γ| under unfrozen conditions causes a portion of the observed spread of the GSV-|γ| relationship. Consequently, when aiming at |γ| based GSV assessment using L-band SAR data acquired during unfrozen conditions, the impact of the species on |γ| needs to be considered. For studies aiming at |γ| based GSV estimation across species, PALSAR data acquired at frozen conditions is preferable. Full article

► Show Figures

(a) Map of the study area. Each of the test sites comprises more than 300 stands. Excluding clear-cuts the average growing stock volume (GSV) is 180 m3·ha−1 which corresponds to a tree height of 18 m; (b) Stand-wise canopy proportion of dominating species vs. cumulative proportion of all available stands (in total: 12,243). A stand-wise percentage of the dominating species of 100 means pure stands, a percentage of 80 means that the dominating species covers 80% of the forest stand area. At approximately 90% of the stands the canopy proportion of the dominating tree species is smaller than 80%. Full article ">
Mean (a) and standard deviation (b) of |γ| for dense forest (200–300 m3·ha−1)—all sites. Symbols: average (•), standard error (⊥ ⊤), minimum/maximum (–). Mean and standard deviation are higher for non-frozen conditions. The displayed values are based on all 36 interferograms. Each of the 36 |γ| images is one sample in this population (unfrozen: 20 images, frozen: 16 images). Full article ">
|γ| vs. GSV by dominant tree species for Primorsky South using two single interferograms. (a) and (b): Frozen conditions (18 January 2007–5 March 2007); (c) and (d): Non-frozen conditions (21 July 2007–5 September 2007). Full article ">
|γ| vs. GSV by dominant tree species for Primorsky South using two single interferograms. (a) and (b): Frozen conditions (18 January 2007–5 March 2007); (c) and (d): Non-frozen conditions (21 July 2007–5 September 2007). Full article ">
Mean (a) and standard deviation (b) of |γ| over dense forest (200–300 m3·ha−1) by dominant tree species and environmental conditions—all sites. Symbols: average (•), standard deviation (⊥ ⊤), minimum/maximum (–). The figures at the bottom lines of the diagrams represent the numbers of samples. Full article ">
Deviation Δ of tree species specific |γ| from average |γ| of dense forest for frozen and non-frozen conditions—all sites. The average |γ| of dense forest is shown in <a href="#f2-remotesensing-06-01124" class="html-fig">Figure 2a</a>. Full article ">

Journal Menu

Journal Browser

Remote Sens., Volume 6, Issue 2 (February 2014) – 40 articles , Pages 907-1761

Further Information

Guidelines

MDPI Initiatives

Follow MDPI