Exploring the potential of Sentinel-2A satellite data for aboveground biomass estimation in fragmented Himalayan subtropical pine forest

The Sentinel-2 A satellite having embedded advantage of red edge spectral bands offers multispectral imageries with improved spatial,spectral and temporal resolutions as compared to the other contemporary satellites providing medium resolution data.Our study was aimed at exploring the potential of Sentinel-2 A imagery to estimate Above Ground Biomass(AGB) of Subtropical Pine Forest in Pakistan administered Kashmir.We developed an AGB predictive model using field inventory and Sentinel 2 A based spectral and textural parameters along with topographic features derived from ALOS Digital Elevation Model(DEM).Field inventory data was collected from 108 randomly distributed plots(0.1 ha each) across the study area.The stepwise linear regression method was employed to investigate the potential relationship between field data and corresponding satellite data.Biomass and carbon mapping of the study area was carried out through established AGB estimation model with R(o.86),R2(0.74),adjusted R2(0.72) and RMSE value of 33 t/ha.Our results showed that first order textures(mean,standard deviation and variance) significantly contributed in AGB predictive modeling while only one spectral band ratio made contribution from spectral domain.Our study leads to the conclusion that Sentinel-2 A optical data is a potential source for AGB estimation in subtropical pine forest of the area of interest with added benefit of its free of cost availability,higher quality data and long-term continuity that can be utilized for biomass carbon distribution mapping in the resource constraint study area for sustainable forest management.

Time-series surface water reconstruction method(TSWR)based on spatial distance relationship of multi-stage water boundaries

Spatiotemporal continuity of surface water datasets widely known for its significance in the surface water dynamic monitoring and assessments,are faced with drawbacks like cloud influence,which hinders the direct extraction of data from time-series remote sensing images.This study proposes a Time-series Surface Water Reconstruction method(TSWR).The initial stage of this method involves the effective use of remote sensing images to automatically construct multi-stage surface water boundaries based on Google Earth Engine(GEE).Then,we reconstructed regions the reconstruction of regions with missing water pixels using the distance relationship between the multi-stage water boundaries in previous and later periods.When applied to 10 large rivers around the world,this method yielded an overall accuracy of 98%for water extraction,an RMSE of 0.41 km2.Furthermore,time-series reconstruction tests conducted in 2020 on the Lancang and Danube rivers revealed a significant improvement in the image availability.These findings demonstrated that this method could not only be used to accurately reconstruct the surface water distribution missing water images,but also to depict a more pronounced time variation characteristic.The successful application of this method on GEE demonstrates its importance for use on large scales or in global studies.