| Guidong county in chenzhou city,hunan province is one of four major forest regions in Hunan province and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.In the remote sensing estimation of regional forest biomass,the combination of active and passive remote sensing data can improve the estimation accuracy.In this study,the 2014 Landsat8 OLI image and the 2014 Sentine-1A image of Guidong County,Chenzhou City,Hunan Province and the data of 43 forest resources continuous inventory fixed sample plots in 2014 are taken as the main information sources.With the help of ENVI,SNAP and R software,using the following three methods of active remote sensing(Sentinel-1A data),passive remote sensing(Landsat 8 OLI data)and active-passive combination(Sentinel-1A data combined with Landsat 8 OLI data)and four models of multiple linear regression,random forest,artificial neural network and bagging algorithm to select characteristic variables of regional forest biomass,modeling parameter,model accuracy evaluation and making forest biomass spatial map.Finally,based on the spatial distribution of forest biomass in the study area,analyzing the spatial distribution characteristics of biomass in the study area.The results show that:(1)In the selection of characteristic variables,reflectance and texture features in red band(B4),infrared band(B5),normalized vegetation index(NDVI),backscattering coefficient and texture characteristics of cross polarization(VH)play an important role in forest biomass retrieval.(2)The comparison and analysis of the accuracy of the four remote sensing estimation models show that regardless of the single data source or the combination of the two,the random forest algorithm has the highest prediction accuracy,followed by the artificial neural network and the bagging algorithm,and the multivariate linear regression has the lowest prediction accuracy.(3)The comprehensive accuracy of remote sensing estimation from three different data sources is arranged in order from high to low as follows active-passive combination > passive remote sensing> active remote sensing.(4)The average forest biomass of Guidong County is 53.68 t/hm2,and the proportion of forest area with high biomass(>90 t/hm2)is only 16.03%.It mainly distributes in the southeast and southwest of Guidong County with high altitude and steep slope.This study provides a new approach for the modeling and analysis of subtropical forest biomass based on different remote sensing data sources and modeling algorithms.(5)The analysis of the characteristics of biomass spatial distribution in guidong county shows that the distribution of biomass in guidong county is not uniform,presenting a strip distribution pattern with a gradual decrease from east to west. |
