首页> 外文会议>International workshop on the analysis of multi-temporal remote sensing images >ASSESSING LAND COVER AND DEGRADATION IN CENTRAL ASIA DESERTS USING SATELLITE IMAGE PROCESSING AND GEOSTATISTICAL METHODS
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ASSESSING LAND COVER AND DEGRADATION IN CENTRAL ASIA DESERTS USING SATELLITE IMAGE PROCESSING AND GEOSTATISTICAL METHODS

机译:利用卫星图像处理和地统计方法评估中亚沙漠中的土地覆盖和降解

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Desertification around watering points has been well observed by satellite images in many drylands around the world. It can be recognized as radial brightness belts fading as a function of the distance from the wells. The primary goal of the study was to characterize the spatial and temporal land degradation/rehabilitation in the Central Asia drylands, in term of vegetation and soil patterns, in different time periods, with respect to the socio-economic changes before and after the collapse of the Soviet Union. More specific objectives of the study were: (1) to develop a geo-statistical model, based on the kriging technique and using high-resolution satellite image processing in order to assess spatial and temporal land cover patterns in three key different time periods (mid-late 1970's, late 1980's, and 2000); (2) to conduct a change detection analysis based on the geo-statistical products in order to assess the direction and intensity of changes between the study periods; and (3) to link the previous findings to the socio-economic situations before and after the collapse of the Soviet Union that influenced the grazing gradients and hence the landuse/ land-cover state of the study site. The Tassel-Cup's Brightness Index was found as the best spectral transformation for enhancing the contrast between the bright degraded areas close to the water wells and the darker surrounding areas far and in-between these wells. Empirical variograms were computed for each of the images and the exponential models were fitted. The Kriging geo-statistical technique utilized the variograms for creating brightness maps. The maps demonstrate the grazing gradient as levels of degrading belts around the wells. Change detection analysis, based on the Kriging maps, reveals some land rehabilitation between the 1975 and the 1987 images. However, mixed results, degradation and rehabilitation, were observed between the 1987 and the 2000 images. Degradation of the area occurs due to recent exploration and exploitation of the gas and oil reserves in the region. Consequently, large areas went through intensive 'technological desertification' that means utilizing large amount of heavy-duty equipments, large-scale plants, and vehicles that damage the soil surface. The rehabilitation of the rangelands can be explained by the historical events of the last decades. Following independence of the former Soviet states in 1991 and the imposition of difficult economic conditions with transition reforms, several major socio-economic changes occurred that caused drastic declines in livestock populations, with the major drop in the number of sheep and goats, and hence vegetation recovery and land rehabilitation.
机译:在世界各地的许多旱地中,卫星图像周围的荒漠化已经很好地观察到。它可以被识别为径向亮度带,作为距离井的距离的函数。该研究的主要目标是在不同的时间段在崩溃之前和之后的社会经济变化方面的植被和土壤模式中的植被和土壤模式中的植被和土壤模式中的空间和时间土地退化/康复。苏联。该研究的更具体目标是:(1)基于Kriging技术和使用高分辨率卫星图像处理来开发地理统计模型,以便在三个关键不同的时间段中评估空间和时间覆盖模式(中-Late 1970年代,20世纪80年代末,2000年); (2)基于地理统计产品进行变化检测分析,以评估研究时期之间变化的方向和强度; (3)在苏联崩溃之前和之后将先前的调查结果联系起来,影响放牧梯度,从而在研究现场的土地使用/陆地覆盖状态。发现流苏杯的亮度指数是最好的光谱转化,用于增强靠近水井的亮化区域与较深的周围区域之间的亮度和围绕这些孔之间的亮度。针对每个图像计算经验变量函数,并且拟合指数模型。 Kriging地理统计技术利用变形函数来创建亮度图。地图将放牧梯度展示为井周围的降级带的水平。根据Kriging地图改变检测分析,揭示了1975年和1987年图像之间的一些土地康复。然而,在1987年和2000个图像之间观察到混合结果,降解和康复。由于近期勘探和开采该地区的天然气和石油储备的勘探和利用,地区的降解发生。因此,大面积经历了密集的“技术荒漠化”,这意味着利用大量的重型设备,大规模植物和损坏土壤表面的车辆。牧场的康复可以通过过去几十年的历史事件来解释。继1991年的前苏联国家独立之后,对转型改革征收困难的经济条件,发生了几项重大的社会经济变革,导致畜牧人口中的急剧下降,绵羊和山羊数量的重大下降,因此植被恢复和土地康复。

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