基于遥感的沙区制图及其变化。以伊拉克南部Al-Muthanna省东北部为例

IF 0.4 Q4 REMOTE SENSING
Awad A. Sahar, M. Rasheed, Dhia A. A.-H. Uaid, Ammar A. Jasim
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引用次数: 4

摘要

沙质地区是世界上干旱和半干旱气候地区的主要问题,威胁着城市生活、建筑、农业甚至人类健康。遥感技术是研究沙区和积沙动态特征的有效工具之一。在这项研究中,开发了两个新的指数来区分沙质地区和非沙质地区。第一个被称为归一化差异沙质地区指数(NDSAI),该指数基于以下假设:沙质地区的含水量(湿度)低于其他土地覆盖类别。另一个被称为沙质地区表面温度指数(SASTI),它是建立在沙质土壤表面温度最高的假设之上的。将提出的指数的结果与其他研究人员先前提出的两个指数进行了比较,即归一化差异沙丘指数NDSI和Eolain映射指数(EMI)。沙质指数的准确性评估表明,NDSAI提供了非常好的性能,总体准确性为89 %. SASTI可以隔离许多沙质和非沙质像素,总体精度约为86 %. NDSI的性能较低,总体精度约为82 %. 它无法将植被区与沙质区进行分类或隔离,在沙漠环境中可能具有更好的性能。用陆地卫星的SWIR1波段计算的NDSAI的性能优于用同一卫星的SWIR2波段计算的NDAI的性能。EMI性能不如其他方法稳健,因为它不适用于提取不同土地覆盖区域的沙质表面。通过比较1987年至2017年期间的沙地面积,使用了变化检测技术。结果显示,40年来,沙质地区的数量有所增加。这一增长的百分比约为20 % 至30 % 与1987年相比,2002年和2017年。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq
Sandy areas are the main problem in regions of arid and semi-arid climate in the world that threaten urban life, buildings, agricultural, and even human health. Remote sensing is one of the technologies that can be used as an effective tool in dynamic features study of sandy areas and sand accumulations. In this study, two new indices were developed to separate the sandy areas from the non-sandy areas. The first one is called the Normalized Differential Sandy Areas Index (NDSAI) that has been based on the assumption that the sandy area has the lowest water content (moisture) than the other land cover classes. The second other is called the Sandy Areas Surface Temperature index (SASTI) which was built on the assumption that the surface temperature of sandy soil is the highest. The results of proposed indices have been compared with two indices that were previously proposed by other researchers, namely the Normalized Differential Sand Dune Index NDSI and the Eolain Mapping Index (EMI). The accuracy assessment of the sandy indices showed that the NDSAI provides very good performance with an overall accuracy of 89 %. The SASTI can isolate many sandy and non-sandy pixels with an overall accuracy about 86 %. The performance of the NDSI is low with an overall accuracy about 82 %. It fails to classify or isolate the vegetation area from the sandy area and might have better performance in desert environments. The performing of NDSAI that is calculated with the SWIR1 band of the Landsat satellite is better than the performing of NDSI that is calculated with the SWIR2 band of the same satellite. EMI performance is less robust than other methods as it is not useful for extracting sandy surfaces in area with different land covers. Change detection techniques were used by comparing the areas of the sandy lands for the periods from 1987 to 2017. The results showed an increase in sandy areas over four decades. The percentage of this increase was about 20 % to 30 % during 2002 and 2017 compared to 1987.
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来源期刊
Revista de Teledeteccion
Revista de Teledeteccion REMOTE SENSING-
CiteScore
1.80
自引率
14.30%
发文量
11
审稿时长
10 weeks
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