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Forest aboveground biomass (AGB) assessments are essential for accurate understanding of carbon accounting under forest disturbance effects and climate change. We mapped AGB data (from 1986 to 2016) by combining the forest inventories and multisource remotely sensed data, including the Ice, Cloud, and Land Elevation Satellite data and Landsat dense time series imagery, and L-band Synthetic Aperture Radar (PALSAR) mosaic data in Guangdong, China. We used random forest (RF) and stochastic gradient boosting (SGB) algorithms to determine the optimal variables of statistical models for mapping and validation of the AGB purpose. Our results showed that the Geoscience Laser Altimeter System (GLAS)-based AGB correlated well (R 2 adj  = 0.89, n = 277, p < 0.001, RMSE = 21.24t/ha) with those obtained using the field-based method that used an RF-based approach, although inevitably, there is a saturation problem. The combined remotely sensed optical and radar imagery and ancillary data sets for mapping AGB using the RF algorithm yielded a stronger (R 2 adj  = 0.86, n = 558, p < 0.001, RMSE = 11.35t/ha) linear correlation with those produced using the GLAS waveform data than that produced using the SGB algorithm. The overall accuracy and Kappa coefficient of mapping forests based on the PALSAR-forest/non-forest Landsat-based phenology for AGB masking were approximately 92.1% and 0.83, respectively. Additionally, the total amount of AGB had increased from 1986 to 2016 by 55.9%. The same increasing trend was observed for total AGB in both mid-subtropical (from 42% to 62%) and south-subtropical (from 38% to 57%) evergreen broadleaved forests, whereas a decreasing trend was witnessed in the tropical forest, particularly after 2010. There was an upward trend of total AGB among the four economic zones of Guangdong; the mountainous area had the highest AGB value distribution, accounting for 58%–70%, followed by the Pearl River Delta region (20%–30%), the western coast of Guangdong (3%–9%), and the eastern coast of Guangdong (2%–7%). The resulting provincial continuous forest AGB maps will provide a better evaluation of carbon dynamic in southern China. [ABSTRACT FROM AUTHOR]

Forest cover change is critical in the regulation of global and regional climate change through the alteration of biophysical features across the Earth's surface. The accurate assessment of forest cover change can improve our understanding of its roles in the regulation processes of surface temperature. In spite of this, few researchers have attempted to discern the varying effects of multiple satellite-derived forest changes on local surface temperatures. In this study, we quantified the actual contributions of forest loss and gain associated with evapotranspiration (ET) and albedo to local surface temperature in Guangdong Province, China using an improved spatiotemporal change pattern analysis method, and explored the interrelationships between surface temperature and air temperature change. We specifically developed three forest change products for Guangdong, combining satellite observations from Landsat, PALSAR, and MODIS for comparison. Our results revealed that the adjusted simple change detection (SCD)-based Landsat/PALSAR forest cover data performed relatively well. We found that forest loss and gain between 2000 and 2010 had opposite effects on land surface temperature (LST), ET, and albedo. Forest gain led to a cooling of −0.12 ± 0.01 °C, while forest loss led to a warming of 0.07 ± 0.01 °C, which were opposite to the anomalous change of air temperature. A reduced warming to a considerable cooling was estimated due to the forest gain and loss across latitudes. Specifically, mid-subtropical forest gains increased LST by 0.25 ± 0.01 °C, while tropical forest loss decreased LST by −0.16 ± 0.05 °C, which can demonstrate the local differences in an overall cooling. ET induced cooling and warming effects were appropriate for most forest gain and loss. Meanwhile, the nearby temperature changes caused by no-change land cover types more or less canceled out some of the warming and cooling. Albedo exhibited negligible and complex impacts. The other two products (i.e., the GlobeLand30 and MCD12Q1) affect the magnitude of temperature response due to the discrepancies in forest definition, methodology, and data resolution. This study highlights the non-negligible contributions of high-resolution maps and a robust temperature response model in the quantification of the extent to which forest gain reverses the climate effects of forest loss under global warming. [ABSTRACT FROM AUTHOR]