Accurate forest above-ground biomass (AGB) is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. Recently launched Sentinel imagery offers a new opportunity for forest AGB mapping and monitoring. In this study, texture characteristics and backscatter coefficients of Sentinel-1, in addition to multispectral bands, vegetation indices, and biophysical variables of Sentinal-2, based on 56 measured AGB samples in the center of the Changbai Mountains, China, were used to develop biomass prediction models through geographically weighted regression (GWR) and machine learning (ML) algorithms, such as the artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that texture characteristics and vegetation biophysical variables were the most important predictors. SVR was the best method for predicting and mapping the patterns of AGB in the study site with limited samples, whose mean error, mean absolute error, root mean square error, and correlation coefficient were 4 × 10 −3 , 0.07, 0.08 Mg·ha −1 , and 1, respectively. Predicted values of AGB from four models ranged from 11.80 to 324.12 Mg·ha −1 , and those for broadleaved deciduous forests were the most accurate, while those for AGB above 160 Mg·ha −1 were the least accurate. The study demonstrated encouraging results in forest AGB mapping of the normal vegetated area using the freely accessible and high-resolution Sentinel imagery, based on ML techniques.Traditional field-based measurements provide the most accurate AGB values, but they are destructive and spatially limited [10,11]. Uncertainty and bias in field measurements obviously exist, particularly those with large trees and tropical issues [4,5]. Combining remote sensing and sample plot data has become a popular method to generate spatially explicit estimations of forest AGB [12,13]. Various types of remote-sensing data are used for forest biomass estimation such as optical sensor data, radio detection and ranging (radar) data, and light detection and ranging (LiDAR) data, with each one having certain advantages over the others [14,15]. Optical sensors were first applied to retrieve the horizontal forest structure and AGB assessments through field sampling, due to their aggregate spectral signatures (reflectance or vegetation indices) with global coverage, repetitiveness, and cost-effectiveness [16,17]. Optical remote sensing data from a number of platforms, such as IKONOS, Quickbird, Worldview, ZY-3, systeme probatoire d'observation de la terre (SPOT), Sentinel, Landsat, and moderate-resolution imaging spectroradiometer (MODIS), with spatial resolutions varying from less than one meter to hundreds of meters, have been used by numerous researchers for biomass estimation [18][19][20]. However, the widespread usage of optical data is limited...

In an attempt to characterize the subsurface structure that is related to fossil mantle plume activity, a comprehensive geophysical investigation was conducted in the Emeishan Large Igneous Province (ELIP). The nature and geometry of the crust were examined within the scheme of the domal structure of ELIP, which comprises the Inner, Intermediate and Outer zones, which are defined on the basis of the biostratigraphy of pre-volcanic sediments. The bulk crustal properties within the Inner Zone are characterized by high density, high P-wave velocity, high Vp/Vs ratios and large crustal thickness. A visible continuous seismic converter is present in the upper part of the crust in the whole Intermediate Zone and the eastern part of the Inner Zone, but it is absent in the Inner Zone, where another seismic converter is observed in the lower part of the crust. The geometric configuration of these converters is attributable to the addition of mantle-derived melts to the pre-existing crust and subsequent interaction between them. The crustal geometry, which is delineated by the migrated image of receiver functions from the passive seismic experiment, and the crustal 3 properties collectively suggest that a mafic layer of 15-20 km thickness and 150-180 km width exists at the base of the crust in the Inner Zone. Such a mafic layer reflects a vertical crustal growth through magmatic underplating at the base of the crust and intraplating within the upper crust. The salient spatial correlation between the deep crustal structure and the dome strongly supports a genetic link between crustal thickening and plume activity, if the pre-volcanic domal uplift is generated by the Permian Emeishan mantle plume. This arrangement is further supported by the consistency of the extent of crustal uplift estimated by isostatic equilibrium modeling and sedimentary data. This study therefore characterizes and provides evidence for a plume-modified crust in a large igneous province.