Abstract:

Forest canopy plays a significant role on community biodiversity maintenance, which is the key eco-boundary of forest atmosphere interactions. As an indicator of canopy dynamic, gap is one of the most important factors in maintaining the long-term transformation of ecosystems. In recent times, biodiversity monitoring has been the focus of much attention and study. As such, the problem of how to accurately describe gap features such as size and distribution requires urgent resolution. Furthermore, it is necessary to combine these features with monitoring data from permanent plots in forests in order to bring about solutions for issues relevant to community construction. These solutions will also help researchers achieve a better understanding of the maintenance system of community species diversity in forests. In this study, a practical canopy gap monitoring system was formed on the basis of different image extraction methods using lightweight Unmanned Aerial Vehicles (UAV) and a Geographic Information System (GIS). A 20 ha permanent monitoring plot in Dinghushan, classified as subtropical forest stand in South China, was selected as the data source. The results obtained from different gap extraction methods were scientifically analyzed after a strict classification. Results indicated that the red, green, and blue (RGB) band image classification was applied as the method of extracting remotely sensed images in the monitoring system built in the study. The extracted results were significantly similar to precise field measures. We emphasized penetrability of canopy as a whole in order to quantify the concept depths of the forest gap in subtropical forest stands; we confirmed that the gap could be established in only the lowest height of the canopy. The gap accuracy of supervised classification based on the DJI Phantom UAV was 72.3%, a value lower than the 98.7% attributed to the MD4-1000 UAV. The MD4-1000 is able to fly at specific heights according to geographic states and retrieve relevant tree height data; however, the cost of its use need to be considered. The differences in data obtainment between extraction and actual measurement will also increase while the gradient of plot rises. Therefore, the DJI Phantom UAV is considered suitable for large plot gap extraction due to its accuracy and high efficiency and despite its low-resolution ratio and decreased mission function. Nonetheless, a total of 2 706 individuals and 17 species were identified by MD4-1000 UAV. A high-resolution drone is available for partial species identification based on reduced flight altitude. This paper therefore indicates that monitoring forest canopies of permanent plots by using UAV on the basis of near-ground remote sensing is able to provide a database for study in community assembly. It is expected that species diversity maintenance can also be investigated by the inclusion of variants in our study perspective. t is expected that the inclusion of variants in our study perspective could provide further database for study in forest community assembly. Hopefully species diversity maintenance could be investigated by new perspective of near-ground remote sensing.

Key words: Unmanned Aerial Vehicles, subtropical evergreen broad-leaved forest, Dinghushan, forest gap, species identification