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

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Woody biomass production is a critical indicator to evaluate land use management and the dynamics of the global carbon cycle (sequestration/emission) in terrestrial ecosystems. The objective of the present study was to develop through a case study in Sudan an operational multiscale remote sensing-based methodology for large-scale estimation of woody biomass in tropical savannahs. Woody biomass estimation models obtained by different authors from destructive field measurements in different tropical savannah ecosystems were expressed as functions of tree canopy cover (CC). The field-measured CC data were used for developing regression equations with atmospherically corrected and reflectance-based vegetation indices derived from Landsat ETM+ (Enhanced Thematic Mapper) imagery. Among a set of vegetation indices, the Normalized Difference Vegetation Index (NDVI) provided the best correlation with CC (R 2 = 0.91) and was hence selected for woodland woody biomass estimation. After validation of the CC-NDVI model and its applicability to MODIS (Moderate Resolution Imaging Spectroradiometer) data, time series MODIS NDVI data (MOD13Q1) were used to partition the woody component from the herbaceous component for sparse woodlands, woodlands and forests defined by FAO Land Cover Map. Following the weighting of the estimation models based on the dominant woody species in each vegetation community, NDVI-based woody biomass models were applied according to their weighted ratios to the decomposed summer and autumn woody NDVI images in all vegetation communities in the whole of Sudan taking the year 2007 for example. The results were found to be in good agreement with the results from other authors obtained by field measurements or by other remote sensing methods using MODIS and Lidar data. It is concluded that the proposed approach is operational and can be applied for a reliable large-scale assessment of woody biomass at a ground resolution of 250 m in tropical savannah woodlands of any month or season.

Soil salinization affects crop production and food security. Mapping spatial distribution and severity of salinity is essential for agricultural management and development. This study was aimed to test the effectiveness of machine learning algorithms for soil salinity mapping taking the Mussaib area in Central Mesopotamia as an example.A combined dataset consisting of Landsat 5 Thematic Mapper (TM) and ALOS L-band radar data acquired at the same time was used for fulfilling the task. Relevant biophysical indicators were derived from the TM images, and the soil component was retrieved by removing the vegetation contribution from the L-band radar backscattering coefficients. Field-measured salinity at the three corner plots of triangles were averaged to represent the salinity of these triangular areas. These averaged plots were converted into raster by either direct rasterization or buffering-based rasterization into different cell size to create the training set (TS). One of the three triangle corners was randomly selected to constitute a validation set (VS). Using this TS, the support vector regression (SVR) and random forest regression (RFR) algorithms were then applied to the combined dataset for salinity prediction. Results revealed that RFR performed better than SVR with higher accuracy (93.4-94.2% vs. 85.2-89.4%) and less normalized root mean square error (NRMSE; 6.10-7.69% vs. 10.29-10.52%) when calibrated with both TS and VS.In comparison, prediction by multivariate linear regression (MLR) achieved in our previous study using the same datasets also showed less NRMSE than SVR. Hence, both RFR and MLR are recommended for soil salinity mapping. KEYWORDScombined optical-radar dataset, field sample rasterization, random forest regression, soil salinity prediction, support vector regression

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Soil salinity has become one of the major problems 4 affecting crop production and food security in Mesopotamia, Iraq. 5 There is a pressing need to quantify and map the spatial extent and 6 distribution of salinity in the country in order to provide relevant 7 references for the central and local governments to plan sustain-8 able land use and agricultural development. The aim of this study 9 was to conduct such quantification and mapping in Mesopotamia 10 using an integrated, multiscale modeling approach that relies 11 on remote sensing. A multiyear, multiresolution, and multisen-12 sor dataset composed of mainly Landsat ETM+ and MODIS data 13 of the period 2009-2012 was used. Results show that the local-14 scale salinity models developed from pilot sites with vegetated and 15 nonvegetated areas can reliably predict salinity. Salinity maps pro-16 duced by these models have a high accuracy of about 82.5-83.3% 17 against the ground measurements. Regional salinity models devel-18 oped using integrated samples from all pilot sites could predict 19 soil salinity with an accuracy of 80% based on comparison to 20 regional measurements along two transects. It is hence concluded 21 that the multiscale models are reasonably reliable for assessment 22 of soil salinity at local and regional scales. The methodology 23 proposed in this paper can minimize problems induced by crop 24 rotation, fallowing, and soil moisture content, and has clear advan-25 tages over other mapping approaches. Further testing is needed 26 while extending the mapping approaches and models to other 27 salinity-affected environments.

Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.

Implementation of land management policies influences land use and hence causes environmental change.Taking the Ordos rangelands in China as a case study, this paper explores the potential of remote sensing to assess in dryland areas the impacts of policies on the environment. Thirteen Landsat images of the period 1978−2010 were acquired and those corresponding to the starting dates of implementation of different policies were selected for land cover change analysis; others were used to check the detected change and track the NDVI (Normalized Difference Vegetation Index) trajectory matched with timeseries of meteorological data for calibration of natural response of rangelands to rainfall. The results indicate that policy impacts are complex and include both positive and negative aspects depending on the locality in space. On one hand, policies have aroused the enthusiasm of people in agricultural production and sand-control leading to recovery of about 2618 km 2 of desertified rangeland and sandy land, and economic growth; on the other hand, provoked vegetation degradation with an accumulated area of 2439 km 2 when policies cannot reconcile the conflict between environmental protection and the interest of rural people. However, degradation is not absolute and can be mitigated by implementation of rational policies.