A comparative study of remote sensing classification methods for monitoring and assessing desert vegetation using a UAV-based multispectral sensor
A comparative study of remote sensing classification methods for monitoring and assessing desert vegetation using a UAV-based multispectral sensor
复制标题
DOI：
10.1007/s10661-020-08330-1
复制
发表时间：
2020-05-23
影响因子：
3
通讯作者：
Gholoum, M.
中科院分区：
文献类型：
--
作者：
Al-Ali, Z. M.;Abdullah, M. M.;Gholoum, M.
恢复计划需要对植被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的最强大的技术之一。然而，由于沙漠植物的特殊结构，在使用卫星图像方面，特别是在干旱地区，观察到了一些限制。因此，这项研究是在科威特的Al Abdali保护区进行的，该保护区主要由Rhanterium epapposum群落组成。这项工作的目的是确定无人驾驶航空器（UAV）多光谱图像是否可以通过检查植被指数和使用无人驾驶航空器的极高多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，转换后的植被指数（TDVI）对干旱灌木和草地的识别效果优于归一化植被指数（NDVI）。结果发现，NDVI低估了植被覆盖度，特别是在高植被覆盖度的位置。研究还发现，支持向量机（SVM）和最大似然（ML）分类器表现出更高的准确性，具有93%的显着整体准确性和0.89的Kappa系数。因此，我们得出结论，SVM和ML是评估沙漠植被的最佳分类器，使用带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，特别是在处理原生沙漠植被等微小植物时。我们还认为，这些方法是合适的目的，评估植被覆盖率，以支持植被重建和恢复计划。
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.