应用基于对象的卫星图像分析绘制卡拉巴赫组学地区Aghdam地区LC/LU变化

A. Rasouli, M. Asgarova, S. Safarov
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摘要

查明亚美尼亚武装部队30年来占领卡拉巴赫及其邻近领土所造成的环境后果是一项重要而紧迫的研究任务。因此,应用基于物体的图像分析程序来检查从亚美尼亚占领中解放出来的卡拉巴赫地区在阿格达姆地区的土地覆盖和土地利用(LC/LU)情况和变化。首先,利用动态阈值索引(Dynamic threshold Indexing, DTI)算法,通过建立NDWI、NDVI、NBRI和AVBI等多个光谱指数来显示主LC;下一步,为了识别研究区域内精确的LU变化,通过在Trimble识别设置中附带高级监督分类技术,考虑了基于规则的最近邻分类(NNC) (eCognition Developer, 2019)。DTI的结果表明,从2016年到2021年,Aghdam地区的LC变化相当有意义。植被覆盖面积显著减少(10.2%),非植被覆盖面积增加(11.8%),变化最显著的是45.1 km的脆弱土地(26.8%)。随后,基于规则的NNC方法证实了研究区内各种负LU变化主要发生在林牧混合类型(9.8%)。此外,退化土地面积增加了35%,荒地面积增加了4.4%。值得注意的是,水资源和农业人均收入的总体变化最小,分别为3.4%和0.3%。总体精度为0.95,Kappa统计量为0.93,证实了最终LC/LU产量的显著变化。因此,在政府官员和决策者开始重建和恢复项目之前,地理学家、生态系统科学家和遥感专家最紧迫的任务是对阿塞拜疆解放区的现状进行准确的图像处理和测绘。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping of LC/LU changes inside the Aghdam district of the Karabakh omics region applying object-based satellite image analysis
Identification of the environmental consequences of the 30-year occupation of Karabakh and its adjacent territories by the Armenian armed formations is an important and urgent research task. Object-Based Image Analysis (OBIA) procedures were accordingly applied to examine the condition and changes in landcover and landuse (LC/LU) in the territories of Karabakh liberated from Armenian occupation within the Aghdam District. Firstly, Dynamic Thresholds Indexing (DTI) algorithms were operated to display the main LC by developing several spectral NDWI, NDVI, NBRI, and AVBI indices. At the next step, to recognize precise LU changes inside the study area, a rule-based Nearest Neighbour Classify (NNC) was considered by accompanying an advanced supervised classification technique within the Trimble eCognition setting (eCognition Developer, 2019). DTI results indicated that from 2016 to 2021 inside the Aghdam District, LC changes are quite meaningful. A significant decrease in vegetated cover (10.2 %), increases in the non-vegetated area (11.8 %), and the most noticeable changes are observed in vulnerable lands of about 45.1 km (26.8 %). Subsequently, the rule-based NNC method approved various negative LU changes inside the study area that had occurred predominantly to the mixed forest-pasture classes (9.8 %). Besides, the areas of degraded lands have increased by 35 % and barren lands by 4.4 % according to the study. It should be noted that water and agricultural LU demonstrate the least changes overall of 3.4 % and 0.3 %, respectively. The overall accuracy of 0.95 and Kappa statistics of 0.93 confirmed the significant changes in the final LC/LU productions. Consequently, accurate image processing and mapping of the current situation of the liberated regions of Azerbaijan have to be the most urgent tasks of the geographers, ecosystem scientists, and remote sensing specialists prior to the start of reconstruction and rehabilitation projects by government officials and decision-makers.
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