Remote Sensing

888 KiB

Open AccessArticle

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

by Michele Meroni, Felix Rembold, Michel M. Verstraete, Rene Gommes, Anne Schucknecht and Gora Beye

Remote Sens. 2014, 6(6), 5868-5884; https://doi.org/10.3390/rs6065868 - 20 Jun 2014

Cited by 35 | Viewed by 9384

Abstract

In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation [...] Read more.

In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean r = 0.78). The negative correlation between delays in SOS and CFAPAR is stronger (mean r = −0.71) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean r = 0.75). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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Graphical abstract

Graphical abstract
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(a) The study area encompassing the main Sahel eco-regions. The herbaceous and cropland land covers are the union of GLC2000 (Global Land Cover 2000) herbaceous cover dominated classes (Classes 13 to 15) and cropland classes (Classes 16 to 18), respectively. Blue points in Senegal refer to the location of field measurements sites (zoom in (b)). Full article ">
Average start (SOS) (a), end (EOS) (b) and length (c) of the growing season and the cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR) value (d) during the growing season. Average values are computed over the set of phenological variables extracted from the FAPAR time series (n = 15, years 1998–2012). Phenological variables and CFAPAR are shown for the herbaceous and cropland land covers (other classes in white) and the five main eco-regions of the Sahel (other regions in grey). Full article ">
Visualization of the correlation between CFAPAR vs. GSL, Peak value and ΔSOS in the RGB color space. The composite is based upon the coefficients of determination (R2) of the linear regression CFAPAR vs. GSL (Red), Peak (Green) and ΔSOS (Blue); no stretching is applied. Main land cover types are represented by the thick black lines (two simplified polygons, herbaceous cover in the north and crop cover in the south). Full article ">
Correlation coefficient between CFAPAR and GSL (a); Peak (b); ΔSOS (c). Land cover polygons (thick black lines) are as in <a href="#f3-remotesensing-06-05868" class="html-fig">Figure 3</a>. Pixels where the linear regression is not significant (n.s.) at p = 0.05 are masked in grey. Full article ">
Percentage of variation of the mean seasonal biomass production proxy (CFAPAR) for a one-day anomaly in the timing of SOS. The metric is computed as the slope of the linear regression CFAPAR vs. ΔSOS expressed as a percentage of the pixel-level mean CFAPAR. Pixels where the linear regression is not significant (n.s.) at p = 0.05 are masked in grey. Full article ">
(a) Linear regression statistics and scatterplot between measured biomass (years 2005–2011) and CFAPAR; (b) average of the site-level correlation coefficients and statistical significance: the green bar refers to the correlation between measured biomass and CFAPAR; the blue and yellow bars refer to the correlations of GSL, ΔSOS and Peak vs. measured biomass (blue) or CFAPAR (yellow), respectively. Error bars refer to ±1 standard deviation. Full article ">

1242 KiB

Open AccessArticle

Assessment of Surface Urban Heat Islands over Three Megacities in East Asia Using Land Surface Temperature Data Retrieved from COMS

by Youn-Young Choi, Myoung-Seok Suh and Ki-Hong Park

Remote Sens. 2014, 6(6), 5852-5867; https://doi.org/10.3390/rs6065852 - 20 Jun 2014

Cited by 49 | Viewed by 8166

Abstract

Surface urban heat island (SUHI) impacts control the exchange of sensible heat and latent heat between land and atmosphere and can worsen extreme climate events, such as heat waves. This study assessed SUHIs over three megacities (Seoul, Tokyo, Beijing) in East Asia using [...] Read more.

Surface urban heat island (SUHI) impacts control the exchange of sensible heat and latent heat between land and atmosphere and can worsen extreme climate events, such as heat waves. This study assessed SUHIs over three megacities (Seoul, Tokyo, Beijing) in East Asia using one-year (April 2011–March 2012) land surface temperature (LST) data retrieved from the Communication, Ocean and Meteorological Satellite (COMS). The spatio-temporal variations of SUHI and the relationship between SUHI and vegetation activity were analyzed using hourly cloud-free LST data. In general, the LST was higher in low latitudes, low altitudes, urban areas and dry regions compared to high latitudes, high altitudes, rural areas and vegetated areas. In particular, the LST over the three megacities was always higher than that in the surrounding rural areas. The SUHI showed a maximum intensity (10–13 °C) at noon during the summer, irrespective of the geographic location of the city, but weak intensities (4–7 °C) were observed during other times and seasons. In general, the SUHI intensity over the three megacities showed strong seasonal (diurnal) variations during the daytime (summer) and weak seasonal (diurnal) variations during the nighttime (other seasons). As a result, the temporal variation pattern of SUHIs was quite different from that of urban heat islands, and the SUHIs showed a distinct maximum at noon of the summer months and weak intensities during the nighttime of all seasons. The patterns of seasonal and diurnal variations of the SUHIs were clearly dependent on the geographic environment of cities. In addition, the intensity of SUHIs showed a strong negative relationship with vegetation activity during the daytime, but no such relationship was observed during the nighttime. This suggests that the SUHI intensity is mainly controlled by differences in evapotranspiration (or the Bowen ratio) between urban and rural areas during the daytime. Full article

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1956 KiB

Open AccessArticle

Monitoring of Irrigation Schemes by Remote Sensing: Phenology versus Retrieval of Biophysical Variables

by Nadia Akdim, Silvia Maria Alfieri, Adnane Habib, Abdeloihab Choukri, Elijah Cheruiyot, Kamal Labbassi and Massimo Menenti

Remote Sens. 2014, 6(6), 5815-5851; https://doi.org/10.3390/rs6065815 - 20 Jun 2014

Cited by 24 | Viewed by 9639

Abstract

The appraisal of crop water requirements (CWR) is crucial for the management of water resources, especially in arid and semi-arid regions where irrigation represents the largest consumer of water, such as the Doukkala area, western Morocco. Simple and (semi) empirical approaches have been [...] Read more.

The appraisal of crop water requirements (CWR) is crucial for the management of water resources, especially in arid and semi-arid regions where irrigation represents the largest consumer of water, such as the Doukkala area, western Morocco. Simple and (semi) empirical approaches have been applied to estimate CWR: the first one is called K_c-NDVI method, based on the correlation between the Normalized Difference Vegetation Index (NDVI) and the crop coefficient (K_c); the second one is the analytical approach based on the direct application of the Penman-Monteith equation with reflectance-based estimates of canopy biophysical variables, such as surface albedo (r), leaf area index (LAI) and crop height (h_c). A time series of high spatial resolution RapidEye (REIS), SPOT4 (HRVIR1) and Landsat 8 (OLI) images acquired during the 2012/2013 agricultural season has been used to assess the spatial and temporal variability of crop evapotranspiration ET_c and biophysical variables. The validation using the dual crop coefficient approach (K_cb) showed that the satellite-based estimates of daily ET_c were in good agreement with ground-based ET_c, i.e., R² = 0.75 and RMSE = 0.79 versus R² = 0.73 and RMSE = 0.89 for the K_c-NDVI, respective of the analytical approach. The assessment of irrigation performance in terms of adequacy between water requirements and allocations showed that CWR were much larger than allocated surface water for the entire area, with this difference being small at the beginning of the growing season. Even smaller differences were observed between surface water allocations and Irrigation Water Requirements (IWR) throughout the irrigation season. Finally, surface water allocations were rather close to Net Irrigation Water Requirements (NIWR). Full article

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

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Graphical abstract

1833 KiB

Open AccessArticle

Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine

by Chen Chen, Wei Li, Hongjun Su and Kui Liu

Remote Sens. 2014, 6(6), 5795-5814; https://doi.org/10.3390/rs6065795 - 19 Jun 2014

Cited by 227 | Viewed by 12804

Abstract

Extreme learning machine (ELM) is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization. In this paper, we propose to integrate spectral-spatial information for hyperspectral [...] Read more.

Extreme learning machine (ELM) is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization. In this paper, we propose to integrate spectral-spatial information for hyperspectral image classification and exploit the benefits of using spatial features for the kernel based ELM (KELM) classifier. Specifically, Gabor filtering and multihypothesis (MH) prediction preprocessing are two approaches employed for spatial feature extraction. Gabor features have currently been successfully applied for hyperspectral image analysis due to the ability to represent useful spatial information. MH prediction preprocessing makes use of the spatial piecewise-continuous nature of hyperspectral imagery to integrate spectral and spatial information. The proposed Gabor-filtering-based KELM classifier and MH-prediction-based KELM classifier have been validated on two real hyperspectral datasets. Classification results demonstrate that the proposed methods outperform the conventional pixel-wise classifiers as well as Gabor-filtering-based support vector machine (SVM) and MH-prediction-based SVM in challenging small training sample size conditions. Full article

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5230 KiB

Open AccessArticle

Crop Condition Assessment with Adjusted NDVI Using the Uncropped Arable Land Ratio

by Miao Zhang, Bingfang Wu, Mingzhao Yu, Wentao Zou and Yang Zheng

Remote Sens. 2014, 6(6), 5774-5794; https://doi.org/10.3390/rs6065774 - 19 Jun 2014

Cited by 32 | Viewed by 13781

Abstract

Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics [...] Read more.

Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics (based on crop condition profiles). Since this type of method will generate false information if there are changes in crop rotation, cropping area or crop phenology, information on cropped/uncropped arable land is integrated to improve the accuracy of crop condition monitoring. The study proposes a new method to retrieve adjusted NDVI for cropped arable land during the growing season of winter crops by integrating 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data at 250-m resolution with a cropped and uncropped arable land map derived from the multi-temporal China Environmental Satellite (Huan Jing Satellite) charge-coupled device (HJ-1 CCD) images at 30-m resolution. Using the land map’s data on cropped and uncropped arable land, a pixel-based uncropped arable land ratio (UALR) at 250-m resolution was generated. Next, the UALR-adjusted NDVI was produced by assuming that the MODIS reflectance value for each pixel is a linear mixed signal composed of the proportional reflectance of cropped and uncropped arable land. When UALR-adjusted NDVI data are used for crop condition assessment, results are expected to be more accurate, because: (i) pixels with only uncropped arable land are not included in the assessment; and (ii) the adjusted NDVI corrects for interannual variation in cropping area. On the provincial level, crop growing profiles based on the two kinds of NDVI data illustrate the difference between the regular and the adjusted NDVI, with the difference depending on the total area of uncropped arable land in the region. The results suggested that the proposed method can be used to improve the assessment of early crop condition, but additional evaluation in other major crop producing regions is needed to better assess the method’s application in other regions and agricultural systems. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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1457 KiB

Open AccessArticle

3D Ground Penetrating Radar to Detect Tree Roots and Estimate Root Biomass in the Field

by Shiping Zhu, Chunlin Huang, Yi Su and Motoyuki Sato

Remote Sens. 2014, 6(6), 5754-5773; https://doi.org/10.3390/rs6065754 - 18 Jun 2014

Cited by 68 | Viewed by 12761

Abstract

The objectives of this study were to detect coarse tree root and to estimate root biomass in the field by using an advanced 3D Ground Penetrating Radar (3D GPR) system. This study obtained full-resolution 3D imaging results of tree root system using 500 [...] Read more.

The objectives of this study were to detect coarse tree root and to estimate root biomass in the field by using an advanced 3D Ground Penetrating Radar (3D GPR) system. This study obtained full-resolution 3D imaging results of tree root system using 500 MHz and 800 MHz bow-tie antennas, respectively. The measurement site included two larch trees, and one of them was excavated after GPR measurements. In this paper, a searching algorithm, based on the continuity of pixel intensity along the root in 3D space, is proposed, and two coarse roots whose diameters are more than 5 cm were detected and delineated correctly. Based on the detection results and the measured root biomass, a linear regression model is proposed to estimate the total root biomass in different depth ranges, and the total error was less than 10%. Additionally, based on the detected root samples, a new index named “magnitude width” is proposed to estimate the root diameter that has good correlation with root diameter compared with other common GPR indexes. This index also provides direct measurement of the root diameter with 13%–16% error, providing reasonable and practical root diameter estimation especially in the field. Full article

(This article belongs to the Special Issue Close-Range Remote Sensing by Ground Penetrating Radar)

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(a) Surveying scene of larch trees using 3D GPR, the red dotted lines show the survey area and the blue circles shows the locations of the larch tree stumps. (b) Excavating scene of one larch tree root, the red dotted lines show the excavation area. Full article ">
Moving trajectories of antennas recorded by the 3D GPR system, and blue circles show the locations of the tree stumps. (a) 800 MHz shielded antenna; (b) 500 MHz shielded antenna. Full article ">
Schematic illustration of the extracted GPR indexes: (a) A migrated profile along a scanning line perpendicular with the root orientation. Point P locates at the peak position of the waveform right above the root. One vertical curve (indicated by solid line, at y = 1.13 m) and one horizontal curve (indicated by dashed line, at depth = 0.138 m) passing through peak P are extracted for subsequent analysis; (b) Definitions of high amplitude area and different time intervals between zero crossings are indicated based on the extracted vertical waveform; (c) Magnitude width Δw of root is defined as the width (cm) between the −3 dB positions below the peak P in the extracted horizontal magnitude curve after Hilbert transform. Full article ">
Different horizontal slices extracted from the 3D migrated cube at the different depths. (a,b) Migrated slices at 10 cm depth using the 500 MHz and 800 MHz antennas, respectively; (c,d) Migrated slices at 20 cm depth using the 500 MHz and 800 MHz antennas, respectively; (e,f) Migrated slices at 30 cm depth using the 500 MHz and 800 MHz antennas, respectively. White arrows indicate the reflections from the roots in the migrated data set, and black dotted circles show the locations of the tree stumps. Full article ">
(a–c) True distribution scenes of one larch roots in 2.0 m × 2.0 m area, and each yellow grid represents the 20 cm × 20 cm area. After excavation at 10 cm depth each time, the exposed roots are cut off and their diameters and weights were measured separately. (d–f) Migrated slices extracted from the data cube corresponding to the depths of (a–c). Full article ">
(a–d) Visualization of 3D detection results of the tree root using Matlab in different viewpoints, red arrows indicate north. Full article ">
Spliced GPR profiles of root 1 and 2 samples extracted along Y direction from the migrated data cube. The serial number of abscissa represents each root sample which has a same width of 7 pixels (16.8 cm). (a) Amplitude profile of 20 samples all extracted from root 1; (b) Magnitude profile of the samples from root 1 after Hilbert transform; (c) Amplitude profile of 17 samples all extracted from root 2; (d) Magnitude profile of the samples from root 2 after Hilbert transform. Full article ">

1845 KiB

Open AccessArticle

A Novel Clustering-Based Feature Representation for the Classification of Hyperspectral Imagery

by Qikai Lu, Xin Huang and Liangpei Zhang

Remote Sens. 2014, 6(6), 5732-5753; https://doi.org/10.3390/rs6065732 - 18 Jun 2014

Cited by 18 | Viewed by 8042

Abstract

In this study, a new clustering-based feature extraction algorithm is proposed for the spectral-spatial classification of hyperspectral imagery. The clustering approach is able to group the high-dimensional data into a subspace by mining the salient information and suppressing the redundant information. In this [...] Read more.

In this study, a new clustering-based feature extraction algorithm is proposed for the spectral-spatial classification of hyperspectral imagery. The clustering approach is able to group the high-dimensional data into a subspace by mining the salient information and suppressing the redundant information. In this way, the relationship between neighboring pixels, which was hidden in the original data, can be extracted more effectively. Specifically, in the proposed algorithm, a two-step process is adopted to make use of the clustering-based information. A clustering approach is first used to produce the initial clustering map, and, subsequently, a multiscale cluster histogram (MCH) is proposed to represent the spatial information around each pixel. In order to evaluate the robustness of the proposed MCH, four clustering techniques are employed to analyze the influence of the clustering methods. Meanwhile, the performance of the MCH is compared to three other widely used spatial features: the gray-level co-occurrence matrix (GLCM), the 3D wavelet texture, and differential morphological profiles (DMPs). The experiments conducted on four well-known hyperspectral datasets verify that the proposed MCH can significantly improve the classification accuracy, and it outperforms other commonly used spatial features. Full article

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2731 KiB

Open AccessArticle

Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

by Thomas Mueller, Gunnar Dressler, Compton J. Tucker, Jorge E. Pinzon, Peter Leimgruber, Ralph O. Dubayah, George C. Hurtt, Katrin Böhning-Gaese and William F. Fagan

Remote Sens. 2014, 6(6), 5717-5731; https://doi.org/10.3390/rs6065717 - 18 Jun 2014

Cited by 67 | Viewed by 15198

Abstract

Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of [...] Read more.

Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth’s human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects. Full article

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

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Trends of NDVI for different groups of anthropogenic biomes (after [<a href="#b13-remotesensing-06-05717" class="html-bibr">13</a>]): Wildlands (A,B), Rangelands (C,D), Forested (E,F), Croplands (G,H), Villages (I,J) and Dense settlements (K,L). (Left): Annual mean NDVI and trends based on generalized least square models with an AR1 autocorrelation structure. β: coefficient of year, d: coefficient of determination, significance codes: * p < 0.05, ** p < 0.001, ns: not significant. (Right): Distributions of per-pixel Theil-Sen estimators, red line: median of distribution; black line indicates zero; als: area land surface of the globe. Full article ">
Trends of NDVI for different groups of anthropogenic biomes (after [<a href="#b13-remotesensing-06-05717" class="html-bibr">13</a>]): Wildlands (A,B), Rangelands (C,D), Forested (E,F), Croplands (G,H), Villages (I,J) and Dense settlements (K,L). (Left): Annual mean NDVI and trends based on generalized least square models with an AR1 autocorrelation structure. β: coefficient of year, d: coefficient of determination, significance codes: * p < 0.05, ** p < 0.001, ns: not significant. (Right): Distributions of per-pixel Theil-Sen estimators, red line: median of distribution; black line indicates zero; als: area land surface of the globe. Full article ">
Annual change of NDVI of ecoregions, based on ecoregional means of per-pixel Theil-Sen estimators) vs. human population density. Circle diameters are proportional to the size (area) of each ecoregion. Red line indicates trend based on generalized least squares model with exponential spatial autocorrelation structure and accounting for differences in sizes of ecoregions in the variance function (β: 0.00032, p < 0.0001, Δ AIC: 105, coef. determination: 0.13). For corresponding analyses by continent, see <a href="#f3-remotesensing-06-05717" class="html-fig">Figure 3</a>. Full article ">
Annual change of NDVI of ecoregions (Africa (A), Asia (B), Australia (C), Europe (D), North America (E), South America (F), based on ecoregional means of per-pixel Theil-Sen estimators) vs. log10 of human population density. Circle diameters are proportional to the size (area) of each ecoregion. Red line indicates trend based on generalized least squares model with exponential spatial autocorrelation structure and accounting for differences in sizes of ecoregions in the variance function. β: coefficient of log10 of human population density, d: coefficient of determination, significance codes: *** p < 0.0001, ns: not significant. Full article ">
(A) Trends in NDVI across the globe, 1981–2010. Ecoregional [<a href="#b19-remotesensing-06-05717" class="html-bibr">19</a>] extremes for NDVI increase (defined as the 5% of land surface with the fastest increases in NDVI, n = 73 ecoregions) are in red, whereas ecoregional extremes for NDVI decrease (defined as the 5% of land surface with the fastest decreases in NDVI, n = 38 ecoregions) are in blue; (B) Boxplots contrast the ecoregional extremes in A for increases (n = 73) and decreases (n = 38) in terms of NDVI trends, population density, percent converted lands [<a href="#b13-remotesensing-06-05717" class="html-bibr">13</a>], percent irrigated lands, and nitrogen deposition. Full article ">

2234 KiB

Open AccessArticle

Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data

by Lu Liang, Yanlei Chen, Todd J. Hawbaker, Zhiliang Zhu and Peng Gong

Remote Sens. 2014, 6(6), 5696-5716; https://doi.org/10.3390/rs6065696 - 18 Jun 2014

Cited by 45 | Viewed by 10342

Abstract

Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data [...] Read more.

Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data gaps of long-term infestation monitoring, but the elimination of observational noise and attributing changes quantitatively are two main challenges in its effective application. Here, we present a forest growth trend analysis method that integrates Landsat temporal trajectories and decision tree techniques to derive annual forest disturbance maps over an 11-year period. The temporal trajectory component successfully captures the disturbance events as represented by spectral segments, whereas decision tree modeling efficiently recognizes and attributes events based upon the characteristics of the segments. Validated against a point set sampled across a gradient of MPB mortality, 86.74% to 94.00% overall accuracy was achieved with small variability in accuracy among years. In contrast, the overall accuracies of single-date classifications ranged from 37.20% to 75.20% and only become comparable with our approach when the training sample size was increased at least four-fold. This demonstrates that the advantages of this time series work flow exist in its small training sample size requirement. The easily understandable, interpretable and modifiable characteristics of our approach suggest that it could be applicable to other ecoregions. Full article

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The study area used for our analyses. The Southern Rocky Mountain ecoregion is shown in blue, and Grand County, Colorado, USA, is shown in red. The imagery is a false-color composite of one Landsat image acquired on 21 August 2009. Full article ">
Forest type map for Grand County, from (a) the LANDFIRE existing vegetation type data and (b) the number of clear Landsat observations over the period of 2000 to 2011. Full article ">
The decision tree used to attribute temporal segments into the different disturbance classes. Full article ">
Tukey boxplot for (a) the averaged annual normalized burn ratio (NBR, multiplied by 100) changes of stable, regrowth, MPB mortality and clearcut plots; (b) current vertex values of post-MPB mortality, post-clearcut events and healthy plots. The bottom and top of the box are the first and third quartiles, and the band inside the box is the median. The whiskers represent the 1.5 interquartile range of the lower and upper quartile. Full article ">
The proportion of validation samples that were erroneously labeled in other classes.Notes: H, healthy; M, MPB mortality; C, clearcut; H2M is explained as “healthy samples classified to MPB mortality”. Full article ">
(a) Classification results of the forest growth trend analysis in the years 2000, 2005 and 2011; (b) maps of the onset year, duration and magnitude of mountain pine beetle (MPB) mortality. Full article ">
Relationships between accuracy and sample size for the maximum likelihood classifier (MLC) and random forests (RF) using multiple training sets. Graphs arranged from left to right display the trends of overall accuracy, the producer’s accuracy and the user’s accuracy for the mountain pine beetle (MPB) mortality class as the training sample proportion increases from 0.1 to one. Blue and solid lines represent RF results, whereas green and dashed lines represent MLC results. Red arrows indicate the mean accuracies produced by the forest growth trend analysis in the years 2005, 2009 and 2011. Full article ">
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88 KiB

Open AccessEditorial

Calibration and Verification of Remote Sensing Instruments and Observations

by Richard Müller

Remote Sens. 2014, 6(6), 5692-5695; https://doi.org/10.3390/rs6065692 - 17 Jun 2014

Cited by 19 | Viewed by 6253

Abstract

Satellite instruments are nowadays a very important source of information. The physical quantities (essential variables) derived from satellites are utilized in a wide field of applications, in particular in atmospheric physics and geoscience. In contrast to ground measurements the physical quantities are not [...] Read more.

Satellite instruments are nowadays a very important source of information. The physical quantities (essential variables) derived from satellites are utilized in a wide field of applications, in particular in atmospheric physics and geoscience. In contrast to ground measurements the physical quantities are not directly measured, but have to be retrieved from satellite observations. Satellites observe hereby the reflection or emission of radiation by the Earth's surface or atmosphere, which enables the retrieval of respective physical quantities (essential variables). The physical basis for the retrieval is the interaction of the radiation with the Earth’s atmosphere and surface. This interaction is defined by radiative transfer, which favors the use of radiances and their respective units within retrieval methods. [...] Full article

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

1372 KiB

Open AccessArticle

A Photogrammetric and Computer Vision-Based Approach for Automated 3D Architectural Modeling and Its Typological Analysis

by Jesús García-Gago, Diego González-Aguilera, Javier Gómez-Lahoz and Jesús Ignacio San José-Alonso

Remote Sens. 2014, 6(6), 5671-5691; https://doi.org/10.3390/rs6065671 - 17 Jun 2014

Cited by 24 | Viewed by 10590

Abstract

Thanks to the advances in integrating photogrammetry and computer vision, as well as in some numeric algorithms and methods, it is possible to aspire to turn 2D (images) into 3D (point clouds) in an automatic, flexible and good-quality way. This article presents a [...] Read more.

Thanks to the advances in integrating photogrammetry and computer vision, as well as in some numeric algorithms and methods, it is possible to aspire to turn 2D (images) into 3D (point clouds) in an automatic, flexible and good-quality way. This article presents a new method through the development of PW (Photogrammetry Workbench) (and how this could be useful for architectural modeling). This tool enables the user to turn images into scale 3D point cloud models, which have a better quality than those of laser systems. Moreover, the point clouds may include the respective orthophotos with photographic texture. The method allows the study of the typology of architecture and has been successfully tested on a sample of ten religious buildings located in the region of Aliste, Zamora (Spain). Full article

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987 KiB

Open AccessArticle

A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data

by Mingliang Che, Baozhang Chen, Huifang Zhang, Shifeng Fang, Guang Xu, Xiaofeng Lin and Yuchen Wang

Remote Sens. 2014, 6(6), 5650-5670; https://doi.org/10.3390/rs6065650 - 17 Jun 2014

Cited by 21 | Viewed by 6908

Abstract

Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a [...] Read more.

Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a logistic function to fit the leaf area index (LAI) curve. We applied the resulting asymptotic lines and the curvature extrema to derive the vegetation phenophases of germination, green-up, maturity, senescence, defoliation and dormancy. The new proposed S-curve function has been tested in a specific area (Shangdong Province, China), characterized by a specific pattern in leaf area index (LAI) time course due to the dominant presence of crops. The function has not yet received any global testing. The identified phenophases were validated against measurement stations in Shandong Province. (i) From the site-scale comparison, we find that the detected phenophases using the S-curve (SC) algorithm are more consistent with the observations than using the logistic (LC) algorithm and the asymmetric Gaussian (AG) algorithm, especially for the germination and dormancy. The phenological recognition rates (PRRs) of the SC algorithm are obviously higher than those of two other algorithms. The S-curve function fits the LAI curve much better than the logistic function and asymmetric Gaussian function; (ii) The retrieval results of the SC algorithm are reliable and in close proximity to the green-up observed data whether using the AVHRR LAI or the improved MODIS LAI. Three inversion algorithms shows the retrieval results based on AVHRR LAI are all later than based on improved MODIS LAI. The bias statistics reveal that the retrieval results based on the AVHRR LAI datasets are more reasonable than based on the improved MODIS LAI datasets. Overall, the S-curve algorithm has the advantage of deriving vegetation phenophases across time and space as compared to the LC algorithm and the AG algorithm. With the SC algorithm, the vegetation phenophases can be extracted more effectively. Full article

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Graphical abstract

Graphical abstract
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The terrain in Shandong Province (a), the spatial distribution of the phenology stations (a) and the spatial distribution of the vegetation types (b). In Figure 1b, the abbreviation NET is temperate needle-leaf evergreen trees; BDT is temperate broad-leaf deciduous trees; BDS is temperate broad-leaf deciduous shrubs. Full article ">
Calculation schematic of vegetation phenophases (site position: 36.7°N, 119.1°E; year: 2005). The LAI data are processed with the cubic spline interpolation. yl (yr) is the fitted value of the left (right) S-curve function, and kl (kr) is the corresponding curvature. Full article ">
Comparison of retrieval results using the three inversion algorithms for different phenophases: (a) germination, (b) green-up, (c) maturation, (d) senescence, (e) defoliation, and (f) dormancy. The subscript numbers 1, 2 and 3 represent the Liaocheng, Taian and Weifang sites, respectively. The abbreviations Obs, LC, AG and SC represent the phenology observation data, logistic, asymmetric Gaussian and S-curve, respectively. Full article ">
Comparison of fitted LAI using the S-curve function, logistic function and asymmetric Gaussian function for the year 1986 at the (a) Liaocheng, (b) Taian, and (c) Weifang site, respectively. The black dash line represents the transition time. Full article ">
Comparison of green-up dates derived using the three inversion algorithms based on the AVHRR LAI data and improved MODIS LAI data at the (a) Heze, (b) Huimin, (c) Liaocheng, (d) Jining, (e) Taian, (f) Zibo, (g) Linyi, (h) Weifang, (i) Wendeng, and (j) Laiyang site, respectively. The superscript numbers 1 and 2 refer to the results based on the AVHRR LAI and the improved MODIS LAI, respectively. The superscript number 3 refers the dates were between early green-up and late green-up. Full article ">

1548 KiB

Open AccessArticle

Remote Sensing Assessment of Forest Disturbance across Complex Mountainous Terrain: The Pattern and Severity of Impacts of Tropical Cyclone Yasi on Australian Rainforests

by Robinson I. Negrón-Juárez, Jeffrey Q. Chambers, George C. Hurtt, Bachir Annane, Stephen Cocke, Mark Powell, Michael Stott, Stephen Goosem, Daniel J. Metcalfe and Sassan S. Saatchi

Remote Sens. 2014, 6(6), 5633-5649; https://doi.org/10.3390/rs6065633 - 17 Jun 2014

Cited by 23 | Viewed by 7790

Abstract

Topography affects the patterns of forest disturbance produced by tropical cyclones. It determines the degree of exposure of a surface and can alter wind characteristics. Whether multispectral remote sensing data can sense the effect of topography on disturbance is a question that deserves [...] Read more.

Topography affects the patterns of forest disturbance produced by tropical cyclones. It determines the degree of exposure of a surface and can alter wind characteristics. Whether multispectral remote sensing data can sense the effect of topography on disturbance is a question that deserves attention given the multi-scale spatial coverage of these data and the projected increase in intensity of the strongest cyclones. Here, multispectral satellite data, topographic maps and cyclone surface wind data were used to study the patterns of disturbance in an Australian rainforest with complex mountainous terrain produced by tropical cyclone Yasi (2011). The cyclone surface wind data (H*wind) was produced by the Hurricane Research Division of the National Oceanic and Atmospheric Administration (HRD/NOAA), and this was the first time that this data was produced for a cyclone outside of United States territory. A disturbance map was obtained by applying spectral mixture analyses on satellite data and presented a significant correlation with field-measured tree mortality. Our results showed that, consistent with cyclones in the southern hemisphere, multispectral data revealed that forest disturbance was higher on the left side of the cyclone track. The highest level of forest disturbance occurred in forests along the path of the cyclone track (±30°). Levels of forest disturbance decreased with decreasing slope and with an aspect facing off the track of the cyclone or away from the dominant surface winds. An increase in disturbance with surface elevation was also observed. However, areas affected by the same wind intensity presented increased levels of disturbance with increasing elevation suggesting that complex terrain interactions act to speed up wind at higher elevations. Yasi produced an important offset to Australia’s forest carbon sink in 2010. We concluded that multispectral data was sensitive to the main effects of complex topography on disturbance patterns. High resolution cyclone wind surface data are needed in order to quantify the effects of topographic accelerations on cyclone related forest disturbances. Full article

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Hurricane Yasi track (dashed red line) and wind field (yellow isotachs). Wind classification follows the Saffir–Simpson Hurricane Scale: tropical storm (TS: 63–118 km/h (18–32 m/s)), hurricane category one (H1: 119–153 km/h (33–42 m/s)), hurricane category two (H2: 154–177 km/h (43–49 m/s)), hurricane category three (H3: 178–208 km/h (50–58 m/s)). The map also shows the tropical forested areas (Evergreen Broadleaf), in dark green, obtained using yearly land cover type data (L3 Global 500 m SIM Grid V051, MCD12Q1, Section 2.2). Ocean and water bodies are shown in black and land cover types other than tropical forests are shown in gray. The inlet shows the location of the study area. Full article ">
(a) Relationship between field-measured mortality and ΔNPV. The linear regression (blue line), and the 95% confidence (solid red line) and prediction (dashed red line) bands are shown; (b) Forest disturbance severity (ΔNPV) for the Vexcel, Landsat and MODIS data used. The location of the 400 m × 10 m transect which covered the full range of disturbance values is shown in white in the inset in Vexcel image. Landsat ΔNPV and MODIS ΔNPV show the impact across the whole forested area affected by Yasi. ΔNPV was not calculated over forested areas affected by cloud cover (dark grey in Landsat and dark green in MODIS) in either pre or post Yasi images. In the MODIS scene, ocean and water bodies are shown in black and land cover types other than tropical forests are shown in intensities of gray obtained using MCD12Q1. Yasi wind intensities (<a href="#f1-remotesensing-06-05633" class="html-fig">Figure 1</a>) are shown in dashed lines. For a comparison of the regional impact of Yasi on tropical forested areas, see <a href="#f1-remotesensing-06-05633" class="html-fig">Figure 1</a>. Data in (b) partially after [<a href="#b18-remotesensing-06-05633" class="html-bibr">18</a>]. Full article ">
MODIS-ΔNPV was used to analyze the effect of wind direction and topography across the whole forested area impacted by Yasi’s winds ≥18 m/s. (a) Winds from the NE to SW impacted forests with maximum forest disturbances (maximum ΔNPV) produced by NE winds; (b) Both wind speed and direction influence the severity of disturbance (ΔNPV); (c) The effect of surface orientation (aspect) and slope angle on disturbance. ΔNPV and slope angle were binned into 30°-aspect intervals between 0 and 360 (12 bins). For each interval the average was taken and multiplied by a weight value (number of pixel in the bin divided by the number of total pixels) and centered at each aspect bin interval. ΔNPV was finally normalized. (d) Association between ΔNPV, surface height and cyclones wind speeds (H*Wind, m/s). Data in (a), (c) and partially (d) are from [<a href="#b18-remotesensing-06-05633" class="html-bibr">18</a>]. Full article ">
Scaling up from local (a) to regional (b) derived disturbance. The scaling up was performed by aggregating pixels to the respective pixel size of comparison. The linear regression (blue line), and the 95% confidence (solid red line) and prediction (dashed red line) bands are shown. Data in (b) are from [<a href="#b18-remotesensing-06-05633" class="html-bibr">18</a>]. Full article ">

936 KiB

Open AccessArticle

Estimation of Mass Balance of the Grosser Aletschgletscher, Swiss Alps, from ICESat Laser Altimetry Data and Digital Elevation Models

by Jan Kropáček, Niklas Neckel and Andreas Bauder

Remote Sens. 2014, 6(6), 5614-5632; https://doi.org/10.3390/rs6065614 - 17 Jun 2014

Cited by 27 | Viewed by 7893

Abstract

Traditional glaciological mass balance measurements of mountain glaciers are a demanding and cost intensive task. In this study, we combine data from the Ice Cloud and Elevation Satellite (ICESat) acquired between 2003 and 2009 with air and space borne Digital Elevation Models (DEMs) [...] Read more.

Traditional glaciological mass balance measurements of mountain glaciers are a demanding and cost intensive task. In this study, we combine data from the Ice Cloud and Elevation Satellite (ICESat) acquired between 2003 and 2009 with air and space borne Digital Elevation Models (DEMs) in order to derive surface elevation changes of the Grosser Aletschgletscher in the Swiss Alps. Three different areas of the glacier are covered by one nominal ICESat track, allowing us to investigate the performance of the approach under different conditions in terms of ICESat data coverage, and surface characteristics. In order to test the sensitivity of the derived trend in surface lowering, several variables were tested. Employing correction for perennial snow accumulation, footprint selection and adequate reference DEM, we estimated a mean mass balance of −0.92 ± 0.18 m w.e. a⁻¹. for the whole glacier in the studied time period. The resulting mass balance was validated by a comparison with another geodetic approach based on the subtraction of two DEMs for the years 1999 and 2009. It appears that the processing parameters need to be selected depending on the amount of available ICESat measurements, quality of the elevation reference and character of the glacier surface. Full article

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

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2003 KiB

Open AccessArticle

A Bayesian Based Method to Generate a Synergetic Land-Cover Map from Existing Land-Cover Products

by Guang Xu, Hairong Zhang, Baozhang Chen, Huifang Zhang, Jianwu Yan, Jing Chen, Mingliang Che, Xiaofeng Lin and Xianming Dou

Remote Sens. 2014, 6(6), 5589-5613; https://doi.org/10.3390/rs6065589 - 16 Jun 2014

Cited by 18 | Viewed by 8884

Abstract

Global land cover is an important parameter of the land surface and has been derived by various researchers based on remote sensing images. Each land cover product has its own disadvantages and limitations. Data fusion technology is becoming a notable method to fully [...] Read more.

Global land cover is an important parameter of the land surface and has been derived by various researchers based on remote sensing images. Each land cover product has its own disadvantages and limitations. Data fusion technology is becoming a notable method to fully integrate existing land cover information. In this paper, we developed a method to generate a synergetic global land cover map (synGLC) based on Bayes theorem. A state probability vector was defined to precisely and quantitatively describe the land cover classification of every pixel and reduce the errors caused by legends harmonization and spatial resampling. Simple axiomatic approaches were used to generate the prior land cover map, in which pixels with high consistency were regarded to be correct and then used as benchmark to obtain posterior land cover map. Validation results show that our hybrid land cover map (synGLC, the dataset is available on request) has the best overall performance compared with the existing global land cover products. Closed shrub-lands and permanent wetlands have the highest uncertainty in our fused land cover map. This novel method can be extensively applied to fusion of land cover maps with different legends, spatial resolutions or geographic ranges. Full article

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2050 KiB

Open AccessArticle

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

by Oliver Cartus, Josef Kellndorfer, Wayne Walker, Carol Franco, Jesse Bishop, Lucio Santos and José María Michel Fuentes

Remote Sens. 2014, 6(6), 5559-5588; https://doi.org/10.3390/rs6065559 - 16 Jun 2014

Cited by 113 | Viewed by 19086

Abstract

A spatially explicit map of aboveground carbon stored in Mexico’s forests was generated from empirical modeling on forest inventory and spaceborne optical and radar data. Between 2004 and 2007, the Mexican National Forestry Commission (CONAFOR) established a network of ~26,000 permanent inventory plots [...] Read more.

A spatially explicit map of aboveground carbon stored in Mexico’s forests was generated from empirical modeling on forest inventory and spaceborne optical and radar data. Between 2004 and 2007, the Mexican National Forestry Commission (CONAFOR) established a network of ~26,000 permanent inventory plots in the frame of their national inventory program, the Inventario Nacional Forestal y de Suelos (INFyS). INFyS data served as model response for spatially extending the field-based estimates of carbon stored in the aboveground live dry biomass to a wall-to-wall map, with 30 × 30 m² pixel posting using canopy density estimates derived from Landsat, L-Band radar data from ALOS PALSAR, as well as elevation information derived from the Shuttle Radar Topography Mission (SRTM) data set. Validation against an independent set of INFyS plots resulted in a coefficient of determination (R²) of 0.5 with a root mean square error (RMSE) of 14 t∙C/ha in the case of flat terrain. The validation for different forest types showed a consistently low estimation bias (<3 t∙C/ha) and R²s in the range of 0.5 except for mangroves (R² = 0.2). Lower accuracies were achieved for forests located on steep slopes (>15°) with an R² of 0.34. A comparison of the average carbon stocks computed from: (a) the map; and (b) statistical estimates from INFyS, at the scale of ~650 km² large hexagons (R² of 0.78, RMSE of 5 t∙C/ha) and Mexican states (R² of 0.98, RMSE of 1.4 t∙C/ha), showed strong agreement. Full article

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5333 KiB

Open AccessArticle

Monitoring Trends in Light Pollution in China Based on Nighttime Satellite Imagery

by Pengpeng Han, Jinliang Huang, Rendong Li, Lihui Wang, Yanxia Hu, Jiuling Wang and Wei Huang

Remote Sens. 2014, 6(6), 5541-5558; https://doi.org/10.3390/rs6065541 - 16 Jun 2014

Cited by 62 | Viewed by 12700

Abstract

China is the largest developing country worldwide, with rapid economic growth and the highest population. Light pollution is an environmental factor that significantly influences the quality and health of wildlife, as well as the people of any country. The objective of this study [...] Read more.

China is the largest developing country worldwide, with rapid economic growth and the highest population. Light pollution is an environmental factor that significantly influences the quality and health of wildlife, as well as the people of any country. The objective of this study is to model the light pollution spatial pattern, and monitor changes in trends of spatial distribution from 1992 to 2012 in China using nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System. Based on the intercalibration of nighttime light imageries of the study area from 1992 to 2012, this study obtained the change trends map. This result shows an increase in light pollution of the study area; light pollution in the spatial scale increased from 2.08% in the period from 1992–1996 to 2000–2004, to 5.64% in the period from 2000–2004 to 2008–2012. However, light pollution change trends presented varying styles in different regions and times. In the 1990s, the increasing trend in light pollution regions mostly occurred in larger urban cities, which are mainly located in eastern and coastal areas, whereas the decreasing trend areas were chiefly industrial and mining cities rich in mineral resources, in addition to the central parts of large cities. Similarly, the increasing trend regions dominated urban cities of the study area, and the expanded direction changed from larger cities to small and middle-sized cities and towns in the 2000s. The percentages of regions where light pollution transformed to severe and slight were 5.64% and 0.39%, respectively. The results can inform and help identify how local economic and environmental decisions influence our global nighttime environment, and assist government agencies in creating environmental protection measures. Full article

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

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1369 KiB

Open AccessArticle

Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012

by Jihye Ahn, Sungwook Hong, Jaeil Cho, Yang-Won Lee and Hosang Lee

Remote Sens. 2014, 6(6), 5520-5540; https://doi.org/10.3390/rs6065520 - 16 Jun 2014

Cited by 18 | Viewed by 6796

Abstract

Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain [...] Read more.

Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain the impacts of climate changes on the sea ice extent. However, the statistical models require improvements to achieve better predictions by incorporating techniques that can deal with temporal variation of the relationships between sea ice concentration and climate factors. In this paper, we describe the statistical approaches by ordinary least squares (OLS) regression and a time-series method for modeling sea ice concentration using satellite imagery and climate reanalysis data for the Barents and Kara Seas during 1979–2012. The OLS regression model could summarize the overall climatological characteristics in the relationships between sea ice concentration and climate variables. We also introduced autoregressive integrated moving average (ARIMA) models because the sea ice concentration is such a long-range dataset that the relationships may not be explained by a single equation of the OLS regression. Temporally varying relationships between sea ice concentration and the climate factors such as skin temperature, sea surface temperature, total column liquid water, total column water vapor, instantaneous moisture flux, and low cloud cover were modeled by the ARIMA method, which considerably improved the prediction accuracies. Our method may also be worth consideration when forecasting future sea ice concentration by using the climate data provided by general circulation models (GCM). Full article

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1294 KiB

Open AccessArticle

Synthetic Aperture Radar Image Clustering with Curvelet Subband Gauss Distribution Parameters

by Erkan Uslu and Songul Albayrak

Remote Sens. 2014, 6(6), 5497-5519; https://doi.org/10.3390/rs6065497 - 16 Jun 2014

Cited by 2 | Viewed by 5748

Abstract

Curvelet transform is a multidirectional multiscale transform that enables sparse representations for signals. Curvelet-based feature extraction for Synthetic Aperture Radar (SAR) naturally enables utilizing spatial locality; the use of curvelet-based feature extraction is a novel method for SAR clustering. The implemented method is [...] Read more.

Curvelet transform is a multidirectional multiscale transform that enables sparse representations for signals. Curvelet-based feature extraction for Synthetic Aperture Radar (SAR) naturally enables utilizing spatial locality; the use of curvelet-based feature extraction is a novel method for SAR clustering. The implemented method is based on curvelet subband Gaussian distribution parameter estimation and cascading these estimated values. The implemented method is compared against original data, polarimetric decomposition features and speckle noise reduced data with use of k-means, fuzzy c-means, spatial fuzzy c-means and self-organizing maps clustering methods. Experimental results show that the curvelet subband Gaussian distribution parameter estimation method with use of self-organizing maps has the best results among other feature extraction-clustering performances, with up to 94.94% overall clustering accuracies. The results also suggest that the implemented method is robust against speckle noise. Full article

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978 KiB

Open AccessArticle

An Object-Based Approach for Fire History Reconstruction by Using Three Generations of Landsat Sensors

by Thomas Katagis, Ioannis Z. Gitas and George H. Mitri

Remote Sens. 2014, 6(6), 5480-5496; https://doi.org/10.3390/rs6065480 - 12 Jun 2014

Cited by 8 | Viewed by 7007

Abstract

In this study, the capability of geographic object-based image analysis (GEOBIA) in the reconstruction of the recent fire history of a typical Mediterranean area was investigated. More specifically, a semi-automated GEOBIA procedure was developed and tested on archived and newly acquired Landsat Multispectral [...] Read more.

In this study, the capability of geographic object-based image analysis (GEOBIA) in the reconstruction of the recent fire history of a typical Mediterranean area was investigated. More specifically, a semi-automated GEOBIA procedure was developed and tested on archived and newly acquired Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images in order to accurately map burned areas in the Mediterranean island of Thasos. The developed GEOBIA ruleset was built with the use of the TM image and then applied to the other two images. This process of transferring the ruleset did not require substantial adjustments or any replacement of the initially selected features used for the classification, thus, displaying reduced complexity in processing the images. As a result, burned area maps of very high accuracy (over 94% overall) were produced. In addition to the standard error matrix, the employment of additional measures of agreement between the produced maps and the reference data revealed that “spatial misplacement” was the main source of classification error. It can be concluded that the proposed approach can be potentially used for reconstructing the recent (40-year) fire history in the Mediterranean, based on extended time series of Landsat or similar data. Full article

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

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1400 KiB

Open AccessArticle

Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data in Savannakhet, Lao PDR

by Phutchard Vicharnakorn, Rajendra P. Shrestha, Masahiko Nagai, Abdul P. Salam and Somboon Kiratiprayoon

Remote Sens. 2014, 6(6), 5452-5479; https://doi.org/10.3390/rs6065452 - 12 Jun 2014

Cited by 70 | Viewed by 14591

Abstract

Savannakhet Province, Lao People’s Democratic Republic (PDR), is a small area that is connected to Thailand, other areas of Lao PDR, and Vietnam via road No. 9. This province has been increasingly affected by carbon dioxide (CO₂) emitted from the transport [...] Read more.

Savannakhet Province, Lao People’s Democratic Republic (PDR), is a small area that is connected to Thailand, other areas of Lao PDR, and Vietnam via road No. 9. This province has been increasingly affected by carbon dioxide (CO₂) emitted from the transport corridors that have been developed across the region. To determine the effect of the CO₂ increases caused by deforestation and emissions, the total above-ground biomass (AGB) and carbon stocks for different land-cover types were assessed. This study estimated the AGB and carbon stocks (t/ha) of vegetation and soil using standard sampling techniques and allometric equations. Overall, 81 plots, each measuring 1600 m², were established to represent samples from dry evergreen forest (DEF), mixed deciduous forest (MDF), dry dipterocarp forest (DDF), disturbed forest (DF), and paddy fields (PFi). In each plot, the diameter at breast height (DBH) and height (H) of the overstory trees were measured. Soil samples (composite n = 2) were collected at depths of 0–30 cm. Soil carbon was assessed using the soil depth, soil bulk density, and carbon content. Remote sensing (RS; Landsat Thematic Mapper (TM) image) was used for land-cover classification and development of the AGB estimation model. The relationships between the AGB and RS data (e.g., single TM band, various vegetation indices (VIs), and elevation) were investigated using a multiple linear regression analysis. The results of the total carbon stock assessments from the ground data showed that the MDF site had the highest value, followed by the DEF, DDF, DF, and PFi sites. The RS data showed that the MDF site had the highest area coverage, followed by the DDF, PFi, DF, and DEF sites. The results indicated significant relationships between the AGB and RS data. The strongest correlation was found for the PFi site, followed by the MDF, DDF, DEF, and DF sites. Full article

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3105 KiB

Open AccessArticle

Assessment of MODIS, MERIS, GEOV1 FPAR Products over Northern China with Ground Measured Data and by Analyzing Residential Effect in Mixed Pixel

by Fei Yang, Hongyan Ren, Xiaoyu Li, Maogui Hu and Yaping Yang

Remote Sens. 2014, 6(6), 5428-5451; https://doi.org/10.3390/rs6065428 - 12 Jun 2014

Cited by 9 | Viewed by 6974

Abstract

Fraction of Photosynthetically Active Radiation (FPAR) is a critical parameter in land surface energy balance and climate modeling. Several global FPAR products are available, but these still require considerable assessment and validation due to low spatial resolution. Three major FPAR products that have [...] Read more.

Fraction of Photosynthetically Active Radiation (FPAR) is a critical parameter in land surface energy balance and climate modeling. Several global FPAR products are available, but these still require considerable assessment and validation due to low spatial resolution. Three major FPAR products that have covered China and provided continuous time series data—MODIS, MERIS and GEOV1—were assessed from 2006–2010. Based on the ground measurement data, the accuracies of these three FPAR products were directly validated for maize and winter wheat over northern China. This investigation also assessed the consistencies among the three FPAR products, and analyzed the residential area in mixed pixels effect on the FPAR products accuracy, at each of the main growth stages of maize and winter wheat. The GEOV1 FPAR product was found to be the most accurate with regression R² values of 0.818 and 0.655 for ground measured maize and winter wheat FPAR. The maize FPAR data were generally more accurate than the winter wheat FPAR data. The MODIS, MERIS and GEOV1 products all indicated that FPAR variations among the growth stages differed from year to year. The scattered residential areas in mixed pixels were found to significantly affect the FPAR data uncertainties, and these were also analyzed in detail. The effect of residential area percentage in mixed pixels on FPAR values differed for different crops, and this was not necessarily in accordance with the FPAR product accuracy. For the mixed pixels, a quadratic polynomial was able to fit the residential area and FPAR data reasonably well with R² values higher than 0.9 for most relationships. Quadratic polynomial fitting may provide a simple and convenient method to assess and reduce the residential area effect on FPAR in the mixed pixels. Full article

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1958 KiB

Open AccessArticle

Comparing Two Photo-Reconstruction Methods to Produce High Density Point Clouds and DEMs in the Corral del Veleta Rock Glacier (Sierra Nevada, Spain)

by Álvaro Gómez-Gutiérrez, José Juan De Sanjosé-Blasco, Javier De Matías-Bejarano and Fernando Berenguer-Sempere

Remote Sens. 2014, 6(6), 5407-5427; https://doi.org/10.3390/rs6065407 - 11 Jun 2014

Cited by 34 | Viewed by 9176

Abstract

In this paper, two methods based on computer vision are presented in order to produce dense point clouds and high resolution DEMs (digital elevation models) of the Corral del Veleta rock glacier in Sierra Nevada (Spain). The first one is a semi-automatic 3D [...] Read more.

In this paper, two methods based on computer vision are presented in order to produce dense point clouds and high resolution DEMs (digital elevation models) of the Corral del Veleta rock glacier in Sierra Nevada (Spain). The first one is a semi-automatic 3D photo-reconstruction method (SA-3D-PR) based on the Scale-Invariant Feature Transform algorithm and the epipolar geometry theory that uses oblique photographs and camera calibration parameters as input. The second method is fully automatic (FA-3D-PR) and is based on the recently released software 123D-Catch that uses the Structure from Motion and MultiView Stereo algorithms and needs as input oblique photographs and some measurements in order to scale and geo-reference the resulting model. The accuracy of the models was tested using as benchmark a 3D model registered by means of a Terrestrial Laser Scanner (TLS). The results indicate that both methods can be applied to micro-scale study of rock glacier morphologies and processes with average distances to the TLS point cloud of 0.28 m and 0.21 m, for the SA-3D-PR and the FA-3D-PR methods, respectively. The performance of the models was also tested by means of the dimensionless relative precision ratio parameter resulting in figures of 1:1071 and 1:1429 for the SA-3D-PR and the FA-3D-PR methods, respectively. Finally, Digital Elevation Models (DEMs) of the study area were produced and compared with the TLS-derived DEM. The results showed average absolute differences with the TLS-derived DEM of 0.52 m and 0.51 m for the SA-3D-PR and the FA-3D-PR methods, respectively. Full article

(This article belongs to the Special Issue Remote Sensing in Geomorphology)

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(a) location of Sierra Nevada mountain range in the Iberian Peninsula, (b) location of the Corral del Veleta rock glacier in Sierra Nevada, (c) 3D view (from the S) of the study area, including camera locations and Terrestrial Laser Scanner (TLS) stations and (d) Panoramic almost vertical view of the Corral del Veleta cirque from the Veleta peak (3398 masl) and the current location of the Corral del Veleta rock glacier. Full article ">
The images used as input in the photo-reconstruction procedures. Full article ">
Horizontal point density for (a) the Semi-Automatic (SA) and (b) the Fully Automatic (FA) 3D photo-reconstruction methods. Full article ">
(a) 3D view of the Corral del Veleta rock glacier hillshade elaborated with the Terrestrial Laser Scanner (TLS) point cloud; below, point clouds obtained by (b) the semi-automatic 3D photo-reconstruction and (c) the fully automatic 3D photo-reconstruction methods and their distances to the TLS point cloud calculated using the cloud-to-cloud distances method proposed by Girardeau-Montaut et al. [<a href="#b51-remotesensing-06-05407" class="html-bibr">51</a>]. Full article ">
DEMs elaborated with the point clouds obtained by means of the (a) Semi-Automatic (SA), (b) Fully Automatic (FA) and (c) Terrestrial Laser Scanner (TLS) methods, respectively, and the absolute differences between the SA, the FA DEMs and the TLS DEM, (d,e) respectively. A transparency of 10% was applied to the DEMs in (a–c) and a hillshade digital model generated by every DEM that was used as a base in order to improve visualization of the glacier morphology. Full article ">
Correspondence between (a) an orthorectified photograph of the Corral del Veleta rock glacier captured for this research and (b) the hillshade model calculated for the same date and hour. Full article ">
(a) Location of the sun in the sky for different times on 28 August 2014 (date planned for the next field survey) and the average hillshade value of pixels and (b) shaded relief model for the estimated optimal time (* 14:00 with an average hillshade value of 202.9). Full article ">

1644 KiB

Open AccessArticle

FAO-56 Dual Model Combined with Multi-Sensor Remote Sensing for Regional Evapotranspiration Estimations

by Rim Amri, Mehrez Zribi, Zohra Lili-Chabaane, Camille Szczypta, Jean Christophe Calvet and Gilles Boulet

Remote Sens. 2014, 6(6), 5387-5406; https://doi.org/10.3390/rs6065387 - 11 Jun 2014

Cited by 12 | Viewed by 7398

Abstract

The main goal of this study is to evaluate the potential of the FAO-56 dual technique for the estimation of regional evapotranspiration (ET) and its constituent components (crop transpiration and soil evaporation), for two classes of vegetation (olives trees and cereals) in the [...] Read more.

The main goal of this study is to evaluate the potential of the FAO-56 dual technique for the estimation of regional evapotranspiration (ET) and its constituent components (crop transpiration and soil evaporation), for two classes of vegetation (olives trees and cereals) in the semi-arid region of the Kairouan plain in central Tunisia. The proposed approach combines the FAO-56 technique with remote sensing (optical and microwave), not only for vegetation characterization, as proposed in other studies but also for the estimation of soil evaporation, through the use of satellite moisture products. Since it is difficult to use ground flux measurements to validate remotely sensed data at regional scales, comparisons were made with the land surface model ISBA-A-gs which is a physical SVAT (Soil–Vegetation–Atmosphere Transfer) model, an operational tool developed by Météo-France. It is thus shown that good results can be obtained with this relatively simple approach, based on the FAO-56 technique combined with remote sensing, to retrieve temporal variations of ET. The approach proposed for the daily mapping of evapotranspiration at 1 km resolution is approved in two steps, for the period between 1991 and 2007. In an initial step, the ISBA-A-gs soil moisture outputs are compared with ERS/WSC products. Then, the output of the FAO-56 technique is compared with the output generated by the SVAT ISBA-A-gs model. Full article

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

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Satellite imagery of the studied area, indicating the locations of the rainfall and climate network stations present on the Kairouan plain. Full article ">
Land use map for the 2008–2009 agricultural season at 1 km spatial resolution, showing (on a scale ranging from 0–1) the proportion of coverage represented by each of three different classes of vegetation present in this area: (a) annual agriculture; (b) pastures; (c) olive trees. Full article ">
Inter-comparison between ISBA-A-gs soil moisture outputs and ERS/WSC products during the period from 1991–2007 (no data for 2001–2003): (a) surface soil moisture (SSM); (b) Soil Water Index (SWI) corresponding to the root zone moisture. Full article ">
Evapotranspiration (ET) simulated by the ISBA-A-gs model as a function of the ET levels simulated by the FAO-56 model over a single ISBA pixel, during the period from 1991–2007, and on dates when remotely sensed ERS/WSC observations were recorded. Full article ">
Inter-comparison between ET outputs from the FAO-56 and ISBA-A-gs models, on dates when remotely sensed ERS/WSC observations were recorded: (a) 1998–1999 agricultural season; (b) 1999–2000 agricultural season. Full article ">
Total annual evapotranspiration maps: (a) 1998–1999 agricultural season; (b) 1999–2000 agricultural season; (c) 2004–2005 agricultural season. Full article ">

1077 KiB

Open AccessArticle

Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China

by Fen Zhao, Bin Xu, Xiuchun Yang, Yunxiang Jin, Jinya Li, Lang Xia, Shi Chen and Hailong Ma

Remote Sens. 2014, 6(6), 5368-5386; https://doi.org/10.3390/rs6065368 - 10 Jun 2014

Cited by 103 | Viewed by 10992

Abstract

The precise and rapid estimation of grassland biomass is an important scientific issue in grassland ecosystem research. In this study, based on a field survey of 1205 sites together with biomass data of the Xilingol grassland for the years 2005–2012 and the “accumulated” [...] Read more.

The precise and rapid estimation of grassland biomass is an important scientific issue in grassland ecosystem research. In this study, based on a field survey of 1205 sites together with biomass data of the Xilingol grassland for the years 2005–2012 and the “accumulated” MODIS productivity starting from the beginning of growing season, we built regression models to estimate the aboveground biomass of the Xilingol grassland during the growing season, then further analyzed the overall condition of the grassland and the spatial and temporal distribution of the aboveground biomass. The results are summarized as follows: (1) The unitary linear model based on the field survey data and “accumulated” MODIS productivity data is the optimum model for estimating the aboveground biomass of the Xilingol grassland during the growing period, with the model accuracy reaching 69%; (2) The average aboveground biomass in the Xilingol grassland for the years 2005–2012 was estimated to be 14.35 Tg, and the average aboveground biomass density was estimated to be 71.32 g∙m⁻²; (3) The overall variation in the aboveground biomass showed a decreasing trend from the eastern meadow grassland to the western desert grassland; (4) There were obvious fluctuations in the aboveground biomass of the Xilingol grassland for the years 2005–2012, ranging from 10.56–17.54 Tg. Additionally, several differences in the interannual changes in aboveground biomass were observed among the various types of grassland. Large variations occurred in the temperate meadow-steppe and the typical grassland; whereas there was little change in the temperate desert-steppe and temperate steppe-desert. Full article

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Grassland types and the distribution of sampling sites in the study area. The total number of sampling sites was 1205. Full article ">
Flowchart of the PSNnet estimation process. Full article ">
Relationship between the estimated aboveground biomass and actual aboveground biomass. Full article ">
Spatial distribution of the aboveground biomass of the Xilingol grassland (g·m−2) from 2005–2012. Full article ">
Variation in the aboveground biomass of the Xilingol grassland from 2005–2012. Full article ">
Interannual variations in the aboveground biomass in different grassland types. Full article ">
Relationship between the accumulated PSNnet data and NDVI data.Note: x is NDVI, y is the accumulated PSNnet data (C_g·m−2). Full article ">
R2 between remotely sensed data and ground survey data. Full article ">

944 KiB

Open AccessArticle

Land Surface Temperature Retrieval from MODIS Data by Integrating Regression Models and the Genetic Algorithm in an Arid Region

by Ji Zhou, Xu Zhang, Wenfeng Zhan and Huailan Zhang

Remote Sens. 2014, 6(6), 5344-5367; https://doi.org/10.3390/rs6065344 - 10 Jun 2014

Cited by 20 | Viewed by 6732

Abstract

The land surface temperature (LST) is one of the most important parameters of surface-atmosphere interactions. Methods for retrieving LSTs from satellite remote sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on Earth’s surface. Many split-window (SW) algorithms, which can [...] Read more.

The land surface temperature (LST) is one of the most important parameters of surface-atmosphere interactions. Methods for retrieving LSTs from satellite remote sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on Earth’s surface. Many split-window (SW) algorithms, which can be applied to satellite sensors with two adjacent thermal channels located in the atmospheric window between 10 μm and 12 μm, require auxiliary atmospheric parameters (e.g., water vapor content). In this research, the Heihe River basin, which is one of the most arid regions in China, is selected as the study area. The Moderate-resolution Imaging Spectroradiometer (MODIS) is selected as a test case. The Global Data Assimilation System (GDAS) atmospheric profiles of the study area are used to generate the training dataset through radiative transfer simulation. Significant correlations between the atmospheric upwelling radiance in MODIS channel 31 and the other three atmospheric parameters, including the transmittance in channel 31 and the transmittance and upwelling radiance in channel 32, are trained based on the simulation dataset and formulated with three regression models. Next, the genetic algorithm is used to estimate the LST. Validations of the RM-GA method are based on the simulation dataset generated from in situ measured radiosonde profiles and GDAS atmospheric profiles, the in situ measured LSTs, and a pair of daytime and nighttime MOD11A1 products in the study area. The results demonstrate that RM-GA has a good ability to estimate the LSTs directly from the MODIS data without any auxiliary atmospheric parameters. Although this research is for local application in the Heihe River basin, the findings and proposed method can easily be extended to other satellite sensors and regions with arid climates and high elevations. Full article

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Graphical abstract

Graphical abstract
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Location of the study area in mainland China. Full article ">
Scatter plots between the atmospheric upwelling radiance of MODIS channel 31 and the other atmospheric parameters. (a) transmittances of channels 31 and 32; (b) upwelling radiance of channel 32. Full article ">
Scatter plot between the simulated atmospheric upwelling radiance in MODIS channel 31 and differences in the at-sensor brightness temperatures of channel 31 and 32 (Tb31–Tb32). Full article ">
The minimum (Min), mean (Mean), and maximum (Max) objective values of all the individuals in each generation during the iteration process. Full article ">
Scatter plot between the estimated LST and the LST input to the MODTRAN4 code for the eighteen radiosonde profiles. The solid line corresponds to a 1:1 relation. Full article ">
Spatial patterns of the land surface temperature in the study area retrieved by the RM-GA method and those provided by MODIS LST/emissivity products. A is vegetation, B is snow, C is gobi, and D is desert. The MODIS images were acquired on 6 July 2007. (a) LST at 04:05UTC retrieved with the RM-GA method. (b) LST at 04:05UTC provided by the MODIS LST/emissivity product. (c) LST at 15:10UTC retrieved with the RM-GA method. (d) LST at 15:10UTC provided by the MODIS LST/emissivity product. Full article ">

2156 KiB

Open AccessArticle

A Circa 2010 Thirty Meter Resolution Forest Map for China

by Congcong Li, Jie Wang, Luanyun Hu, Le Yu, Nicholas Clinton, Huabing Huang, Jun Yang and Peng Gong

Remote Sens. 2014, 6(6), 5325-5343; https://doi.org/10.3390/rs6065325 - 10 Jun 2014

Cited by 49 | Viewed by 12772

Abstract

This study examines the suitability of 30 m Landsat Thematic Mapper (TM), 250 m time-series Moderate Resolution Imaging Spectrometer (MODIS) Enhanced Vegetation Index (EVI) and other auxiliary datasets for mapping forest extent in China at 30 m resolution circa 2010. We calculated numerous [...] Read more.

This study examines the suitability of 30 m Landsat Thematic Mapper (TM), 250 m time-series Moderate Resolution Imaging Spectrometer (MODIS) Enhanced Vegetation Index (EVI) and other auxiliary datasets for mapping forest extent in China at 30 m resolution circa 2010. We calculated numerous spectral features, EVI time series, and topographical features that are helpful for forest/non-forest distinction. In this research, extensive efforts have been made in developing training samples over difficult to map or complex regions. Scene by scene quality checking was done on the initial forest extent results and low quality results were refined until satisfactory. Based on the forest extent mask, we classified the forested area into 6 types (evergreen/deciduous broadleaf, evergreen/deciduous needleleaf, mixed forests, and bamboos). Accuracy assessment of our forest/non-forest classification using 2195 test sample units independent of the training sample indicates that the producer’s accuracy (PA) and user’s accuracy (UA) are 92.0% and 95.7%, respectively. According to this map, the total forested area in China was 164.90 million ha (Mha) circa 2010. It is close to the forest area of 7th National Forest Resource Inventory with the same definition of forest. The overall accuracy for the more detailed forest type classification is 72.7%. Full article

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Graphical abstract

Graphical abstract
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(A) Acquisition years of Landsat TM data. (B) Acquisition months of Landsat TM data. (C) Annual distribution of Landsat TM data. (D) Seasonal and monthly distribution of Landsat TM data (the labels are acquisition months). Full article ">
The workflow for forest/non-forest classification. Full article ">
The forest map of China. Full article ">
The distribution of the test samples and errors. Full article ">
Selected case comparison of the resolution influences (Green color represents forest and black represents non-forest.) From left to right, the image segments were selected from Liaoning Province, Liaoning Province, Hubei Province, and Sichuan Province. Full article ">
Selected case comparison of forest extent products with this product (Green color represents forest and black represents non-forest.) From left to right, the image segments were selected from Inner Mongolia, Shanxi Province, Guizhou Province, and Hubei Province. Full article ">
Selected case comparison of forest extent products with this product in mountainous areas (Green color represents forest and black represents non-forest.) From left to right, the image segments were selected from Taiwan, Sichuan Province, Sichuan Province, and Tibet. Full article ">

11149 KiB

Open AccessArticle

Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving NumericalWeather Forecasting in Niger, Africa

by Mohamed Rasmy, Toshio Koike and Xin Li

Remote Sens. 2014, 6(6), 5306-5324; https://doi.org/10.3390/rs6065306 - 6 Jun 2014

Cited by 4 | Viewed by 6499

Abstract

The development of satellite-based forecasting systems is one of the few affordable solutions for developing regions (e.g., West Africa) that cannot afford ground-based observation networks. Although low-frequency passive microwave data have been used extensively for land surface monitoring, the use of high-frequency passive [...] Read more.

The development of satellite-based forecasting systems is one of the few affordable solutions for developing regions (e.g., West Africa) that cannot afford ground-based observation networks. Although low-frequency passive microwave data have been used extensively for land surface monitoring, the use of high-frequency passive microwave data that contain cloud information is very limited over land because of strong heterogeneous land surface emissions. The Coupled Atmosphere and Land Data Assimilation System (CALDAS) was developed by merging soil moisture information estimated from low-frequency data with corresponding high-frequency data to estimate cloud information and, thus, improve weather forecasting over Niger, West Africa. The results showed that the assimilated soil moisture and cloud distributions were reasonably comparable to satellite retrievals of soil moisture and cloud observations. However, assimilating soil moisture alone within a mesoscale model produced only marginal improvements in the forecast, whereas the assimilation of both soil moisture and cloud distributions improved the simulation of temperature and humidity profiles. Rainfall forecasts from CALDAS also correlated well with satellite retrievals. This indicates the potential use of CALDAS as a reliable forecasting tool for developing regions. Further developments of CALDAS and the inclusion of data from several other sensors will be researched in future studies. Full article

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

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3018 KiB

Open AccessArticle

Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

by Jan Stefanski, Tobias Kuemmerle, Oleh Chaskovskyy, Patrick Griffiths, Vassiliy Havryluk, Jan Knorn, Nikolas Korol, Anika Sieber and Björn Waske

Remote Sens. 2014, 6(6), 5279-5305; https://doi.org/10.3390/rs6065279 - 6 Jun 2014

Cited by 34 | Viewed by 11321

Abstract

The global demand for agricultural products is surging due to population growth, more meat-based diets, and the increasing role of bioenergy. Three strategies can increase agricultural production: (1) expanding agriculture into natural ecosystems; (2) intensifying existing farmland; or (3) recultivating abandoned farmland. Because [...] Read more.

The global demand for agricultural products is surging due to population growth, more meat-based diets, and the increasing role of bioenergy. Three strategies can increase agricultural production: (1) expanding agriculture into natural ecosystems; (2) intensifying existing farmland; or (3) recultivating abandoned farmland. Because agricultural expansion entails substantial environmental trade-offs, intensification and recultivation are currently gaining increasing attention. Assessing where these strategies may be pursued, however, requires improved spatial information on land use intensity, including where farmland is active and fallow. We developed a framework to integrate optical and radar data in order to advance the mapping of three farmland management regimes: (1) large-scale, mechanized agriculture; (2) small-scale, subsistence agriculture; and (3) fallow or abandoned farmland. We applied this framework to our study area in western Ukraine, a region characterized by marked spatial heterogeneity in management intensity due to the legacies from Soviet land management, the breakdown of the Soviet Union in 1991, and the recent integration of this region into world markets. We mapped land management regimes using a hierarchical, object-based framework. Image segmentation for delineating objects was performed by using the Superpixel Contour algorithm. We then applied Random Forest classification to map land management regimes and validated our map using randomly sampled in-situ data, obtained during an extensive field campaign. Our results showed that farmland management regimes were mapped reliably, resulting in a final map with an overall accuracy of 83.4%. Comparing our land management regimes map with a soil map revealed that most fallow land occurred on soils marginally suited for agriculture, but some areas within our study region contained considerable potential for recultivation. Overall, our study highlights the potential for an improved, more nuanced mapping of agricultural land use by combining imagery of different sensors. Full article

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1425 KiB

Open AccessArticle

An Airborne Multispectral Imaging System Based on Two Consumer-Grade Cameras for Agricultural Remote Sensing

by Chenghai Yang, John K. Westbrook, Charles P.-C. Suh, Daniel E. Martin, W. Clint Hoffmann, Yubin Lan, Bradley K. Fritz and John A. Goolsby

Remote Sens. 2014, 6(6), 5257-5278; https://doi.org/10.3390/rs6065257 - 6 Jun 2014

Cited by 45 | Viewed by 11398

Abstract

This paper describes the design and evaluation of an airborne multispectral imaging system based on two identical consumer-grade cameras for agricultural remote sensing. The cameras are equipped with a full-frame complementary metal oxide semiconductor (CMOS) sensor with 5616 × 3744 pixels. One camera [...] Read more.

This paper describes the design and evaluation of an airborne multispectral imaging system based on two identical consumer-grade cameras for agricultural remote sensing. The cameras are equipped with a full-frame complementary metal oxide semiconductor (CMOS) sensor with 5616 × 3744 pixels. One camera captures normal color images, while the other is modified to obtain near-infrared (NIR) images. The color camera is also equipped with a GPS receiver to allow geotagged images. A remote control is used to trigger both cameras simultaneously. Images are stored in 14-bit RAW and 8-bit JPEG files in CompactFlash cards. The second-order transformation was used to align the color and NIR images to achieve subpixel alignment in four-band images. The imaging system was tested under various flight and land cover conditions and optimal camera settings were determined for airborne image acquisition. Images were captured at altitudes of 305–3050 m (1000–10,000 ft) and pixel sizes of 0.1–1.0 m were achieved. Four practical application examples are presented to illustrate how the imaging system was used to estimate cotton canopy cover, detect cotton root rot, and map henbit and giant reed infestations. Preliminary analysis of example images has shown that this system has potential for crop condition assessment, pest detection, and other agricultural applications. Full article

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Remote Sens., Volume 6, Issue 6 (June 2014) – 55 articles , Pages 4647-5884

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