Restoration programs require long-term monitoring and assessment of vegetation growth and productivity. Remote sensing technology is considered to be one of the most powerful technologies for assessing vegetation. However, several limitations have been observed with regard to the use of satellite imagery, especially in drylands, due to the special structure of desert plants. Therefore, this study was conducted in Kuwait’s Al Abdali protected area, which is dominated by a Rhanterium epapposum community. This work aimed to determine whether Unmanned Aerial Vehicle (UAV) multispectral imagery could eliminate the challenges associated with satellite imagery by examining the vegetation indices and classification methods for very high multispectral resolution imagery using UAVs. The results showed that the transformed difference vegetation index (TDVI) performed better with arid shrubs and grasses than did the normalized difference vegetation index (NDVI). It was found that the NDVI underestimated the vegetation coverage, especially in locations with high vegetation coverage. It was also found that Support Vector Machine (SVM) and Maximum Likelihood (ML) classifiers demonstrated a higher accuracy, with a significant overall accuracy of 93% and a kappa coefficient of 0.89. Therefore, we concluded that SVM and ML are the best classifiers for assessing desert vegetation and the use of UAVs with multispectral sensors can eliminate some of the major limitations associated with satellite imagery, particularly when dealing with tiny plants such as native desert vegetation. We also believe that these methods are suitable for the purpose of assessing vegetation coverage to support revegetation and restoration programs.

中文翻译：

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使用基于无人机的多光谱传感器监测和评估荒漠植被的遥感分类方法的比较研究。

恢复计划要求对植被的生长和生产力进行长期监测和评估。遥感技术被认为是评估植被最强大的技术之一。但是，由于沙漠植物的特殊结构，在卫星图像的使用方面存在一些局限性，特别是在干旱地区。因此，这项研究是在科威特的阿卜杜勒保护区（Rhanterium epapposum）进行的社区。这项工作旨在确定无人飞行器（UAV）多光谱图像是否可以通过检查植被指数和使用UAV的超高光谱分辨率图像的分类方法来消除与卫星图像相关的挑战。结果表明，干旱灌木和草的转化差异植被指数（TDVI）优于归一化差异植被指数（NDVI）。发现NDVI低估了植被覆盖率，特别是在植被覆盖率较高的地区。还发现，支持向量机（SVM）和最大似然（ML）分类器显示出更高的准确性，整体准确度高达93％，卡伯系数为0.89。因此，我们得出的结论是，SVM和ML是评估沙漠植被的最佳分类器，使用具有多光谱传感器的无人机可以消除与卫星图像相关的一些主要限制，尤其是在处理微小植物（例如原生沙漠植被）时。我们还认为，这些方法适用于评估植被覆盖度以支持植被恢复计划的目的。