No abstract

No abstract

Advancing land degradation in the irrigated areas of Central Asia hinders sustainable development of this predominantly agricultural region. To support decisions on mitigating cropland degradation, this study combines linear trend analysis and spatial logistic regression modeling to expose a land degradation trend in the Khorezm region, Uzbekistan, and to analyze the causes. Time series of the 250-m MODIS NDVI, summed over the growing seasons of 2000–2010, were used to derive areas with an apparent negative vegetation trend; this was interpreted as an indicator of land degradation. About one third (161,000 ha) of the region’s area experienced negative trends of different magnitude. The vegetation decline was particularly evident on the low-fertility lands bordering on the natural sandy desert, suggesting that these areas should be prioritized in mitigation planning. The results of logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table (odds = 330 %), land-use intensity (odds = 103 %), low soil quality (odds = 49 %), slope (odds = 29 %), and salinity of the groundwater (odds = 26 %). Areas, threatened by land degradation, were mapped by fitting the estimated model parameters to available data. The elaborated approach, combining remote-sensing and GIS, can form the basis for developing a common tool for monitoring land degradation trends in irrigated croplands of Central Asia.

Accurate classification and mapping of crops is essential for supporting sustainable land management. Such maps can be created based on satellite remote sensing; however, the selection of input data and optimal classifier algorithm still needs to be addressed especially for areas where field data is scarce. We exploited the intra-annual variation of temporal signatures of remotely sensed observations and used prior knowledge of crop calendars for the development of a two-step processing chain for crop classification. First, Landsat-based time-series metrics capturing within-season phenological variation were preprocessed and analyzed using Google Earth Engine cloud computing platform. The developmental stage of each crop was modeled by fitting harmonic function. The model's output was further used for the automatic generation of training samples. Second, several classification methods (support vector machines, random forest, decision fusion) were tested. As input data for crop classification, composites based on Sentinel-1 and Landsat images were used. Overall classification accuracies exceeded 80% when the seasonal composites were used. Winter cereals were the most accurately classified, while we observed misclassifications among summer crops. The proposed approach offers a potential to accurately map crops in the areas where in situ field data are scarce or unavailable.

Abstract:Cropland abandonment is globally widespread and has strong repercussions for regional food security and the environment. Statistics suggest that one of the hotspots of abandoned cropland is located in the drylands of the Aral Sea Basin (ASB), which covers parts of post-Soviet Central Asia, Afghanistan and Iran. To date, the exact spatial and temporal extents of abandoned cropland remain unclear, which hampers land-use planning. Abandoned land is a potentially valuable resource for alternative land uses. Here, we mapped the abandoned cropland in the drylands of the ASB with a time series of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from [2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016]. To overcome the restricted ability of a single classifier to accurately map land-use classes across large areas and agro-environmental gradients, "stratum-specific" classifiers were calibrated and classification results were fused based on a locally weighted decision fusion approach. Next, the agro-ecological suitability of abandoned cropland areas was evaluated. The stratum-specific classification approach yielded an overall accuracy of 0.879, which was significantly more accurate (p < 0.05) than a "global" classification without stratification, which had an accuracy of 0.811. In 2016, the classification results showed that 13% (1.15 Mha) of the observed irrigated cropland in the ASB was idle (abandoned). Cropland abandonment occurred mostly in the Amudarya and Syrdarya downstream regions and was associated with degraded land and areas prone to water stress. Despite the almost twofold population growth and increasing food demand in the ASB area from 1990 to 2016, abandoned cropland was also located in areas with high suitability for farming. The map of abandoned cropland areas provides a novel basis for assessing the causes leading to abandoned cropland in the ASB. This contributes to assessing the suitability of abandoned cropland for food or bioenergy production, carbon storage, or assessing the environmental trade-offs and social constraints of recultivation.

Land degradation (LD) is one of the biggest global challenges for the people's livelihoods and environment. Remote Sensing plays an unprecedented role in LD mapping, assessment and monitoring at multiple spatial and temporal scales. Regardless of a big potential of Remote Sensing to support LD studies, there are still quite a few challenges that impede its successful application. This paper provides a logical synthesis of the role of Remote Sensing for LD assessments. First, background information on definition of LD and existing assessment frameworks are provided. This follows with the synthesis of the areas of application of Remote Sensing for LD analysis and a brief review of the major Remote Sensing variables used in LD studies. The paper further argues for multi-scale and cross-scale LD assessments calling for application-oriented solutions and highlighting the need of in situ data for validation of Remote Sensing-based LD maps. This claim is illustrated by an example of a case study in Uzbekistan.ARTICLE HISTORY

Irrigated croplands in Central Asia are highly prone to land degradation due to their environmentally fragile physical settings and intensive agricultural practices. This study: (i) assesses the state of croplands in irrigated areas in northern Uzbekistan, based on the time series of MODIS-NDVI imagery; (ii) analyzes relationships between the identified trend of cropland degradation and soil quality, terrain characteristics, population density, and land use; and (iii) synthesizes the results which form the basis for recommendations on spatial targeting of land rehabilitation measures. The NDVI-based cropland degradation assessment revealed a significant decline of cropland productivity across 23% (94,835 ha) of the arable area in the study region between 2000 and 2010. We conclude that the degraded cropland identified within areas of high population density and with better quality soils, can be prioritized for rehabilitation measures. For degraded croplands located in sparsely populated areas with poorer quality soils, other alternatives (such as leaving cropland fallow) may be more effective depending on the severity of degradation and economic viability of rehabilitation options. Zusammenfassung: �ew�sserte Anbau� �chen in �entralasien zeigen eine starke Anf�lligkeit f�r �odendegradation. Ur-�ew�sserte Anbau��chen in �entralasien zeigen eine starke Anf�lligkeit f�r �odendegradation. Ursachen hierf�r sind physische Umweltein��sse sowie intensive Landwirtschaft. Diese Studie: (i) beurteilt den �ustand der bew�sserten, landwirtschaftlich genutzten Fl�che im nördlichen Usbekistan, aufgrund einer �eitreihe von MODIS-NDVI �ildern; (ii) sie setzt die ermittelte �odendegradationsentwicklung in �eziehung zu �odenqualit�t, Gel�ndeeigenschaften, �evölkerungsdichte, Landnutzung und (iii) stellt die Ergebnisse so dar, dass Empfehlungen zu �odenrehabilitierungsmaßnahmen f�r verschiedene Gebiete abgeleitet werden können. Eine NDVI-basierte �ewertung der �odendegradation zeigte einen signifikanten R�ckgang der Leistungsf�higkeit von Acker��chen von 23% (94.835 ha) auf den Anbau��chen des Untersuchungsgebiets zwischen 2000-2010. Degradierte Acker��chen, in Gebieten mit hoher �evölkerungsdichte und �öden besserer Qualit�t, sollen f�r Rehabilitationsmaßnahmen vorgeschlagen werden. F�r die degradierten Fl�chen, die in sp�rlich besiedelten Gebieten mit weniger guter �odenqualit�t liegen, muss entschieden werden, ob die landwirtschaftliche Nutzung eingestellt wird. Dieses h�ngt von der Schwere der Degradation sowie der Wirtschaftlichkeit der Rehabilitationsmaßnahmen ab.

No abstract