A5105297681- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Land Use and Ecosystem Services
- Remote Sensing and Land Use
- Soil Geostatistics and Mapping
- 3D Surveying and Cultural Heritage
- Leaf Properties and Growth Measurement
Southwest Forestry University
2024-2025
The spectrophotometer method is costly, time-consuming, laborious, and destructive to the plant. Samples will be lost during transportation process, can only obtain sample point data. This poses a challenge estimation of chlorophyll content at regional level. In this study, in order improve accuracy, new collaborative inversion using Landsat 8 Global Ecosystem Dynamics Investigation (GEDI) proposed. Specifically, data set combined with preprocessed two remote-sensing (RS) factors construct...
Bamboo forests are paramount in forest ecosystems due to their rapid growth and huge carbon sequestration potential. Consequently, the estimation of bamboo biomass emerges as a critical research endeavor remote sensing. The study used GEDI data source estimate regional-scale above-ground (AGB) Dendrocalamus giganteus. outcomes reveal that: (1) results showed that power function emerged most efficacious model, with coefficient determination (R²) = 0.87 root mean square error (RMSE) 0.00051...
ICESat-2 and GEDI offer unique capabilities for terrain canopy height retrievals; however, their performance measurement precision are significantly affected by conditions. Furthermore, differences in data scales complicate direct comparisons of capabilities. This study evaluates the accuracy retrievals from LiDAR complex environments. Jinghong City Pu’er Southwest China were selected as areas, with high-precision airborne serving a reference. Ground elevation retrieval accuracies compared...
Bamboo forests, as some of the integral components forest ecosystems, have emerged focal points in forestry research due to their rapid growth and substantial carbon sequestration capacities. In this paper, satellite-borne lidar data from GEDI ICESat-2/ATLAS are utilized main information sources, with Landsat 9 DEM covariates, combined 51 pieces ground-measured data. Using random regression (RFR), boosted tree (BRT), k-nearest neighbor (KNN), Cubist, extreme gradient boosting (XGBoost),...
The Leaf Area Index (LAI) plays a crucial role in assessing the health of forest ecosystems. This study utilized ICESat-2/ATLAS as primary information source, integrating 51 measured sample datasets, and employed Sequential Gaussian Conditional Simulation (SGCS) method to derive surface grid for area. backscattering coefficient texture feature factor from Sentinel-1, well spectral band vegetation index factors Sentinel-2, were integrated. random (RF), gradient-boosted regression tree (GBRT)...
Chlorophyll content is a vital indicator for evaluating vegetation health and estimating productivity. This study addresses the issue of Global Ecosystem Dynamics Investigation (GEDI) data discreteness explores its potential in chlorophyll content. used empirical Bayesian Kriging regression prediction (EBKRP) method to obtain continuous distribution GEDI spot parameters an unknown space. Initially, 52 measured sample were employed screen modeling with Pearson RF methods. Next, optimization...