Wetlands are considered as dynamic ecosystems that have been changed by various factors and to protect the environment, it is necessary to identify these changes, but since terrain investigation of wetlands requires a lot of time and money, remote sensing technology can be used in this regard. The purpose of this research is to investigate the capability of TVDI, MTVDI, VTCI and TVX along with MODIS satellite products including NDVI, EVI and LST in wetland classification. Due to the multiplicity of images used, coding in MATLAB software was used. In some of the mentioned indicators, triangular and trapezoidal method was used, which uses the parameters of vegetation and surface temperature to detect surface moisture. For this purpose, six Iranian wetlands during the period 2000–2016 were studied. The images were classified using SVM method. This classification was done in March and August in all years. The study results showed that using the mentioned indicators will make it possible to classify all studied wetlands with kappa coefficient above 0.9 and overall accuracy above 0.94. On the one hand, investigation of area changes in wetlands has shown that no decreasing or increasing trend was found in the study period.

湿地被认为是被各种因素改变的动态生态系统，为了保护环境，有必要识别这些变化，但由于湿地地形调查需要大量的时间和金钱，在这方面可以使用遥感技术. 本研究的目的是研究 TVDI、MTVDI、VTCI 和 TVX 以及 MODIS 卫星产品（包括 NDVI、EVI 和 LST）在湿地分类中的能力。由于使用的图像的多样性，使用了 MATLAB 软件中的编码。其中部分指标采用三角梯形法，利用植被和地表温度等参数来检测地表水分。为此，对 2000 年至 2016 年期间的六个伊朗湿地进行了研究。使用 SVM 方法对图像进行分类。这种分类是在所有年份的 3 月和 8 月进行的。研究结果表明，使用上述指标将有可能对所有研究的湿地进行分类，kappa系数在0.9以上，整体精度在0.94以上。一方面，对湿地面积变化的调查表明，研究期间没有发现减少或增加的趋势。