Maozhen Zhang

TL;DR: Li et al. as mentioned in this paper provided a brief overview of current forest biomass estimation methods using both Landsat Thematic mapper (TM) image and LiDAR data, and demonstrated that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems.

TL;DR: In this article, a methodology for mapping and analyzing spatial uncertainty of forest carbon estimates is developed to address the challenges of producing reliable maps of carbon using the data from inconsistent sizes of plots and image pixels.

TL;DR: In this paper, the authors investigated uncertainties of mapping aboveground forest carbon due to location errors of sample plots for Lin-An County of China, and found that randomly perturbing plot locations within 10 distance intervals statistically did not result in biased population mean predictions of above ground forest carbon at a significant level of 0.05, but increased root mean square errors of the maps.

TL;DR: This study investigated the uncertainties of mapping and scaling up aboveground forest carbon (AGFC) due to plot location errors in Wu-Yuan of East China and found that scaling up the spatial data mitigated the impacts of Plot location errors on the map accuracy compared to those without the up-scaling.

TL;DR: A comparative study on the performance of eight linear regression parameter estimation methods in the subtropical forest biomass remote sensing model development shows that BIC performs best in comprehensive evaluation, while NNG, Cp and AIC perform poorly as a whole.