Vegetation Properties in Human-Impacted Riparian Zones Based on Unmanned Aerial Vehicle (UAV) Imagery: An Analysis of River Reaches in the Yongding River Basin

Abstract

Riparian zones, transitional areas between aquatic and terrestrial ecosystems, have high plant species diversities. However, they are extremely vulnerable to natural factors, such as changes in river hydrological conditions (floods, droughts) and disturbances from human activities (dams, farmland encroachment, etc.). The distribution of plant life forms and variations in the degree of vegetation coverage in a riparian zone can reflect changes in the environmental conditions. In this study, we analyzed eight reaches from the four main tributaries (Dongyang River, Yang River, Sanggan River, and Yongding River) of the Yongding River Basin, which were selected based on their climate, terrain, and degree of human disturbance. One reach was located on the Dongyang River (DYR), two reaches on the Yang River (YR1 and YR2), three on the Sanggan River (SGR1, 2, and 3), and two on the Yongding River (YDR1 and YDR2). Unmanned aerial vehicle (UAV) technology was used to obtain high-resolution, true-color, multispectral images. The distributions of the plant life forms and the differences in the vegetation coverage were analyzed in the eight selected riparian zones. The results showed that grasses dominated the riparian zone and shrubs and trees were sparsely distributed along both banks of all streams, excluding SGR2 and YDR1. The areas with an extremely high vegetation coverage classification accounted for the highest proportion in the DYR (29.3%), YR2 (48.1%), SGR1 (32.9%), SGR2 (39.9%), SGR3 (85.1%), YDR1 (36.7%), and YDR2 (51.1%) reaches. Extremely low vegetation coverage accounted for the highest proportion in the YR2 reach, reaching 37.4%. This study indicated that natural factors and human activities have a serious impact on the distribution of different plants life forms and vegetation coverage classifications in the riparian zones of the Yongding River Basin. We hope that this research can provide practical assistance in the efforts of ecological restoration and the management of riparian vegetation in the Yongding River Basin.

Development and Testing of a UAV Laser Scanner and Multispectral Camera System for Eco-Geomorphic Applications

“…Resolving this level of detail will enable identification of vegetation-flow interactions providing eco-geomorphic insights in addition to those offered from traditional satellite or visible wavelength UAV imagery. While the use of UAV-based multispectral sensing in fluvial research has focussed predominantly on vegetation quality or hydraulic properties such as suspended sediment [61,62], the opportunities to apply methods developed for satellite data at a finer resolution makes combining multispectral rather than traditional RGB imagery with UAV-LS data an exciting prospect for future research.…”

A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data

“…For instance, Palace et al [81] used artificial neural networks (ANN) to classify peatland vegetative cover types in Sweden, while Corti Menses et al [83] used a novel RGB vegetation index (i.e., excess green and excess red) and the point geometry from DSM to classify the density, vitality, and shape of aquatic reed beds in a lake in southern Germany. One paper [85] incorporated thermal bands with RGB for mapping riparian vegetation in the Yongding River Basin in China.…”

“…There is also a growing trend towards multi-sensor workflows, with 22 studies in our pool using a combination of different instruments or camera types (e.g., RGB + LIDAR, RGB + Thermal, RGB + Multispectral) (Figure 3). Such opportunities seem to be most applicable to fixed-wing studies because of their payload capabilities; yet, broadly-focused monitoring efforts also mix fixed-wing and other platforms for complementary observations [4,7,83,85]. Equally promising are emerging efforts to integrate UAVs into broader monitoring pro-grams involving other extensive in situ tools.…”

Automatic Features Detection in a Fluvial Environment through Machine Learning Techniques Based on UAVs Multispectral Data

“…Usually, the features classification in fluvial scenes is based on the exploitation of the satellite images [51] or of orthophotos obtained from photogrammetric image processing [52,53]. This technique can be more advantageous when, due to certain conditions (for example, dense vegetation and the presence of wind during image acquisition), the three-dimensional point cloud, generated during the photogrammetric process, is highly noisy; on the contrary, the direct analysis of the point cloud preserves spatial and threedimensional information of the scene, allowing one to delimit more accurately the different features and to obtain additional structural information [13].…”