Identification of standing dead trees in typical Robinia pseudoacacia plantation in the Loess Plateau of Shaanxi Province based on YOLOv8
During ecological restoration on the Loess Plateau,unsuitable afforestation efforts combined with frequent occurrences of extreme drought have precipitated the degradation and occasionally the demise of Robinia pseudoacacia plantation,resulting in the presence of standing dead trees.This study aimed at precisely identifying and analyzing the spatial distribution patterns of standing dead trees in the R.pseudoacacia plantation of Shaanxi Province.To achieve this,six sample plots were established across a precipitation gradient on the Loess Plateau,and high-resolution RGB imagery of these R.pseudoacacia plantation was captured using drones.This research utilized a variety of deep lear-ning object detection models to identify standing dead trees effectively.Among these models,YOLOv8 stood out for its detection efficiency and accuracy.This study involved training and testing the R.pseudoacacia plantation's dataset and integrating RGB images to examine the distribution patterns of standing dead trees.The distribution pattern of standing dead trees in R.pseudoacacia plantation was analyzed,and the degradation degree of R.pseudoacacia plantation was reflected by standing dead trees'index(SDTI),which also facilitated the analysis of the impact of slope orientation.After training,the YOLOv8 model achieved an average frames per second(FPS)of 68.7 frames/s,with a validation set average precision mean(mAP)of 94.6％and an F1 score of 0.92,which were all superior to other models exa-mined.A thorough assessment of key performance metrics and the generalizability of various object detection models guided the choice of YOLOv8 for an in-depth examination of standing dead trees distribution patterns in R.pseudoaca-cia plantation.The analysis indicated a negative correlation between the SDTI and the precipitation gradient in the R.pseudoacacia plantation of the Shaanxi Loess Plateau.Additionally,SDTI differences were observed in relation to slope aspect,displaying a sequence from sunny to semi-sunny,semi-shady,and shady slopes.This study furnished a scientific foundation for evaluating the degradation levels of R.pseudoacacia plantation on the Loess Plateau,enabling their informed management and sustainable operation.By employing advanced object detection models like YOLOv8,it provides essential insights into forest management practices,thereby supporting the development of more effective and sustainable ecological restoration and conservation strategies for this vital region.
Loess PlateauRobinia pseudoacacia plantationdeep learningUAV remote sensingstanding dead trees index(SDTI)YOLOv8
国家科技期刊平台
NSTL
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