Optimum Data Scale for Remote Sensing Vegetation Cover Mapping Using the Multi-criteria Decision Analysis Method

Abstract

Choosing an image with a suitable spatial scale is essential for achieving accurate and cost-effective vegetation maps. While previous studies have mainly focused on traditional vegetation indices (VIs), this study aims to evaluate the sensitivity of various VIs to the spatial resolution of satellite images. Six different satellite images with spatial resolutions ranging from 0.5 to 30 m were utilized in three distinct study regions. Out of the available vegetation indices, 14 VIs were selected and computed, resulting in a total of 252 vegetation maps. The resulting vegetation maps from each VI were compared with a ground reference map, and multiple quality measures were computed. Subsequently, the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method was employed to rank the VIs for each satellite image based on these measures. The results from the TOPSIS method and correlation analysis on the generated vegetation maps demonstrated that higher resolution imagery leads to improved overall accuracy across all VIs, except that fused data do not have any considerable effect on the accuracy of the vegetation maps. In conclusion, it is reasonable to assert that in some scenarios, medium-resolution images can be utilized instead of high-resolution images to achieve satisfactory accuracy. © Indian Society of Remote Sensing 2025.