UAVs for improving seasonal vegetation assessment in arid environments

Abstract

In the last few decades, revegetation strategies for ecosystem restoration have received great attention in dryland studies, especially those related to the restoration and revegetation of native desert plants to combat land degradation. Long-term monitoring and assessment are critical for the restoration programs to track the progress of the restoration program goals. The effectiveness and success of monitoring depend on the selected methods with respect to spatial and temporal scales. Traditional methods for vegetation monitoring are time-consuming, expensive, and require considerable labor efforts (manpower) in terms of field measurements, collecting samples, lab analysis, and the difficulty of accessing some study areas. Thus, satellite remote sensing images have been widely used to monitor land degradation and restoration programs using multispectral and hyperspectral sensors and indices such as NDVI, which is the most popular index for vegetation monitoring. However, such techniques showed many limitations when used in arid ecosystems, especially for seasonal vegetation assessments, which could mislead the monitoring and assessment of the restoration projects. This paper discusses lessons learned from previous research work, including the limitations of using satellite remote sensing in arid ecosystems and the use of UAV methods to overcome these issues and challenges to provide more accurate outcomes for seasonal assessment of vegetation in arid landscapes.