Extraction of Forest Disturbance Parameters and Estimation of Forest Height Based on Long Time-Series Landsat

Abstract

ObjectiveForest disturbance is the main factor that influences forest height. This study was aimed to extract forest disturbance parameter based on remote sensing image and to know its effect on forest height estimation. MethodLiangshui National Nature Reserve in Dailing District, Heilongjiang Province, China, was selected as study area, thirty-three periods of Landsat thematic mapper and enhanced thematic mapper plus(Landsat TM/ETM+) multispectral data from 1984 to 2006 in Liangshui National Nature Reserve were acquired as data sources. The tasseled cap transform was conducted to extracttwo disturbance monitoring indices of the tasseled cap angle(TCA)and tasseled cap distance(TCD). Time trajectory analysis method Landsat-based detection of trends in disturbance and recovery(LandTrendr) was applied to conduct time series reconstruction of the Landsat TCA(TCD) images and extract timeseries disturbance parameters of forest:previous year prior to disturbance onset(DBYEA), spectral value prior to disturbance onset(DBVAL), disturbance duration(DDUR), disturbance magnitude(DMAG), time to start recovery after disturbance(RBYEAR), spectral value for recovery start after disturbance(RBVAL), recovery magnitude(RMAG), recovery duration(RDUR).Two sets of spectral information variables of single-temporal Landsat image with or without time-series disturbance parameters were applied to estimate forest height by using random forest algorithm. ResultCompared withthe tree height inversion model based on the spectral information of single-temporal Landsat image, the prediction accuracy of the tree height inversion model according tocombination of the spectral information of single-temporal Landsat image with 16 time-series perturbation parameters based on TCA and TCD increased by 6.34%, and the RMSE decreased by 0.5 m. In the tree height inversion model, the time-series perturbation parameters based on TCA extraction were more important than those based on TCD extraction. ConclusionTime-series disturbance information of forest extracted based on LandTrendr method could enhance the correlation between reflectance and tree height, and could also improve the prediction accuracy of the tree height model based on remote sensing. Time-series perturbation parameters based on TCA extraction is better than those based on TCD extraction to interpret forest height estimation. The method can be used as a reference for remote sensing inversion of forest parameters(e.g., tree height and biomass).