Woody plants in arid ecosystems are important components of vegetation carbon storage ,also affect the ecological processes and land grazing capacity. Satellite remote sensing provides an effective technical method for long-term dynamic detection of vegetation. However, even if the spatial resolution that reach the high-resolution satellite image with the accuracy of the meter level is difficult to accurately distinguish the herbs and woody in sparse forest steppe and evaluate the annual and inter-annual dynamic. It is a new method of UAV-based vegetation monitoring platform to accurately monitor the vegetation dynamics of sparse forest steppe on the landscape scale.The purpose of the study was to propose an integrative tool for quickly, accurately distinguish the vegetation types and estimating Fractional vegetation coverage (FVC) by coupling a UAS monitoring platform with decision tree algorithms. We applied this tool to observe the vegetation dynamics in elm (Ulmus pumila) sparse forest grassland ecosystem (ESFOGE) plot during a growing season in 2016-2018. Using the vegetation phenological model, the maximum and minimum FVC in 2017 and 2018 were calculated relative to the rate of change in 2016, and the sensitivity of woody and herbaceous plants in the elm to precipitation and grazing intensity was evaluated.
Results/Conclusions
The results show:1) The UAV-based vegetation remote sensing platform has flexible flight time that obtain landscape-scale centimeter-level image data ; 2) Image classification method by machine learning can efficiently and accurately calculate elm herbs and the woody plant coverage, the kappa coefficients of classification accuracy in the all growing season are 0.72, 0.74 and 0.63, respectively; 3) the precipitation is below (297 mm), close to (392 mm) and above in 2017, 2018 and 2016, the annual precipitation (427 mm) of woody plants in severe grazing was 9.4%, 8.3% and 13.5%, respectively, and the maximum herb cover was 66.8%, 62.5% and 64.7%; the maximum coverage woody plants of mild grazing was 21.4%, 20.5% and 20.6% respectively, and the herbs plants was 63.4%, 61.6% and 68.0% respectively. Overall, this research proved that UAV vegetation remote sensing platform is an efficient and flexible vegetation monitoring tool for arid regions. The annual precipitation that between 14% to 21% of the annual precipitation does not generate significant change in the elm. The grazing intensity is the main factor leading to the difference of different types of vegetation in the elm, and the coverage of woody plants is the most sensitive factor response to grazing intensity.