Spatial pattern analysis of Haloxylon ammodendron using UAV imagery - A case study in the Gurbantunggut Desert

Abstract

Spatial patterns are not only the foundation for the understanding of plant interactions, but also reflect the spatial processes among plant populations. The primary requirement of spatial pattern analysis is the collections of location information of individual plants. In this study, we used low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology to obtain regional high-precision remote sensing images for Haloxylon ammodendron (H. ammodendron) forest in the southwestern Gurbantunggut Desert, and extracted spatial position information to analyze the spatial patterns using Ripley's L(r) function. Applying and comparing seven spatial position extraction methods, this study showed that the index RGRI (Red-Green Ratio Index) made 83.46% accuracy in the spatial position extraction, and an accuracy of 79.48% was obtained using NGBDI (Normalized Green-Blue Difference Index), while other five location information extraction methods resulted relatively lower accuracy. Results from spatial pattern analysis indicated that the extraction by UAV remote sensing were consistent with those obtained by field measurements. The H. ammodendron population showed a random distribution within the scale of 0–15 m, which suggested that the dependence of mutual asylum between individuals was low and not important. This distribution may be caused by the intense competition of individual vegetation for soil moisture, nutrients and other resources in desert areas. This study with low-altitude UAV imagery index analysis provided an efficient approach to rapid monitoring of plant population distribution characteristics in desert areas.