An accurate estimation of forest biomass is important for global carbon balance and climate change studies.The integration of hyperspectral and high-resolution data can provide abundant spectral and spatial detail information,and accurate biomass estimates.However,acquiring hyperspectral and high-resolution data at the same time is difficult,and related applications of integrated data are limited in the range of subtropical forests.This study used hyperspectral and high-resolution data acquired simultaneously in a subtropical natural secondary forest with field-measured data to estimate forest biomass.It provided a new approach in estimating the biomass of subtropical forests accurately.In the study,LiDAR-CCD-hyperspectral-integrated sensor was used to obtain hyperspectral and high-resolution data at the same time in a subtropical natural secondary forest.A total of 30 square plots（30×0） were established across the study site.First,tree crown features were extracted from high-resolution images by multi-scale segmentation based on edge detection.Second,five sets of spectral metrics were extracted from hyperspectral images.Third,the metrics extracted from hyperspectral and high-resolution images were integrated to build forest biomass models by using stepwise regression.Finally,cross-validations were used to assess the estimation accuracy of the models.The accuracy of integrated models（R²=0.54-0.62） is higher than that of hyperspectral models（R² =0.48-0.57）;the accuracy of aboveground biomass in hyperspectral model（R² =0.57） is higher than that of belowground biomass（R² =0.48）;and the accuracy of aboveground biomass in the integrated model is（R²=0.62） higher than that of belowground biomass（R²=0.54）.Cross-validation results indicate that the integrated model is better than the hyperspectral one,and that hyperspectral and high-resolution data obtained at the same time through integrated sensor can be used in estimating biomass in subtropical forests effectively.This study provided the effective approaches in estimating the biomass of subtropical forests by using remotely sensed data,and it proved that the integration of hyperspectral and high-resolution data can also help in estimating forest biomass accurately.This approach may provide an important basis for research on ecosystem and carbon cycle in subtropical forests.

subtropical forest;biomass estimation;remote sensing reversion;high resolution data;hyperspectral data