Forest Disturbance Detection in Mountainous Areas Based on SAR and Optical Data Fusion
Efficient forest disturbance detection methods can prevent and mitigate forest disasters in time and protect the ecosystem.To address the issue of forest interference in mountainous areas,which is often affected by terrain when integrating multi-source data,this study develops a slope direction classification algorithm to delineate the detection area.This mitigates the effect of terrain relief on the radar rate of change(RCR).A novel forest disturbance detection method was proposed on the basis of the fusion of synthetic aperture radar(SAR)and optical satellite data,utilizing an enhanced RCR approach with NDVI time series.The results were as follows:1)The enhanced RCR methodology markedly expands the detection area 19.48％through the slope classification method,encompassing a greater scope of interference areas and enhancing the detection accuracy.2)The overall detection accuracy based on the fusion data of SAR and optical satellite is 89.24％,which is 11.11％and 13.32％higher than that of SAR and optical satellite with only a single sensor.Compared with the single-sensor method,this research method can obtain rich,continuous detection information under different time and weather conditions,and it has greater potential and advantages in improving the detection capability of forest disturbance,which can provide more comprehensive and accurate information support for forest resource management and ecological protection in the future.
forest disturbanceSARradar change ratioNDVI time seriesslope direction classification
NETL
NSTL
万方数据
