Monitoring and Classification of Karst Rocky Desertification with Landsat 8 OLI Images Using Spectral Indices, Multi-Endmember Spectral Mixture Analysis and Support Vector Machine

IF 3.1 Q2 ENGINEERING, GEOLOGICAL International Journal of Engineering and Geosciences Pub Date : 2023-03-16 DOI:10.26833/ijeg.1149738
Çağan Alevkayali, Onur Yayla, Yıldırım Atayeter
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引用次数: 2

Abstract

Karst Rocky Desertification (KRD) is the reduction of vegetative productivity of this land with the release of bedrock as a result of the full or partial transportation of the fertile soil through natural processes and human activities in karst landscapes. The purpose of this study is to reveal the effectiveness of Remote Sensing methods in monitoring, mapping, and evaluating KRD. Landsat 8 OLI images were used to carry out these procedures. In monitoring this process, Karst Bare Rock Index (KBRI), Normalized Difference Rock Index (NDRI), Carbonate Rock Index 2 (CRI2), Normalized Difference Build-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Dimidiate Pixel Model (DPM), Multi Endmember Spectral Mixture Analysis (MESMA) and Support Vector Machine (SVM) were used from the spectral indices. In order to evaluate the results obtained, KRD was divided into 4 basic classes such as none, mild, moderate, and severe. According to these classification levels, it was determined that SVM method had the highest accuracy. For this reason, it was concluded that the SVM method can be used effectively in determining KRD. In the study, it was concluded that the KRD strengthens as one goes from south to north and from west to east in the research area. This study points out KRD is one of the effective land problems in the Mediterranean region, Turkey.
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Landsat 8 OLI遥感影像对喀斯特石漠化的监测与分类
喀斯特石漠化(KRD)是指由于喀斯特景观中的自然过程和人类活动使肥沃的土壤全部或部分迁移,导致该土地的植被生产力下降,基岩释放。本研究的目的是揭示遥感方法在监测、绘制和评估KRD方面的有效性。Landsat 8 OLI图像用于执行这些程序。在监测这一过程中,从光谱指数中使用了岩溶裸露岩石指数(KBRI)、归一化差异岩石指数(NDRI)、碳酸盐岩指数2(CRI2)、归一化差分累积指数(NDBI)、归一化差值植被指数(NDVI)、二分像素模型(DPM)、多端元光谱混合分析(MESMA)和支持向量机(SVM)。为了评估所获得的结果,KRD被分为4个基本类别,如无、轻度、中度和重度。根据这些分类水平,确定SVM方法具有最高的准确度。因此,可以得出结论,SVM方法可以有效地用于确定KRD。研究表明,研究区KRD呈自南向北、自西向东的增强趋势。该研究指出,KRD是土耳其地中海地区的一个有效的土地问题。
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CiteScore
4.00
自引率
0.00%
发文量
12
审稿时长
30 weeks
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