Bridging Domains and Resolutions: Deep Learning-Based Land Cover Mapping without Matched Labels.

Monitoring of key Camellia Oleifera phenology features using field cameras and deep learning.

A Cross-Domain Change Detection Network Based on Instance Normalization.

Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China.

Investigating the Identification and Spatial Distribution Characteristics of Camellia oleifera Plantations Using High-Resolution Imagery.

Dynamic Detection of Forest Change in Hunan Province Based on Sentinel-2 Images and Deep Learning.

LightCDNet: Lightweight Change Detection Network Based on VHR Images.

DSNUNet: An Improved Forest Change Detection Network by Combining Sentinel-1 and Sentinel-2 Images.

Forest-CD: Forest Change Detection Network Based on VHR Images.

Estimating the Growing Stem Volume of the Planted Forest Using the General Linear Model and Time Series Quad-Polarimetric SAR Images.

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images.

Mapping Growing Stem Volume of Chinese Fir Plantation Using a Saturation-based Multivariate Method and Quad-polarimetric SAR Images.

Improving Selection of Spectral Variables for Vegetation Classification of East Dongting Lake, China, Using a Gaofen-1 Image.

Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon Density by Combining Image and Plot Data from a Nested and Clustering Sampling Design.

Improvement of Forest Carbon Estimation by Integration of Regression Modeling and Spectral Unmixing of Landsat Data.