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Fractional Vegetation Cover Estimation Algorithm for FY-3B Reflectance Data Based on Random Forest Regression Method
fractional vegetation cover FY-3B reflectance data random forest regression method
2023/12/8
As an important land surface vegetation parameter, fractional vegetation cover (FVC) has been widely used in many Earth system ecological and climate models. In particular, high-quality and reliable F...
Development of Semantic Maps of Vegetation Cover from UAV Images to Support Planning and Management in Fine-Grained Fire-Prone Landscapes
U-Net convolutional neural network (CNN) shrub detection heterogeneous land cover mapping UAV imagery Mediterranean forest
2023/12/1
In Mediterranean landscapes, the encroachment of pyrophytic shrubs is a driver of more frequent and larger wildfires. The high-resolution mapping of vegetation cover is essential for sustainable land ...
Revealing the Structure and Composition of the Restored Vegetation Cover in Semi-Arid Mine Dumps Based on LiDAR and Hyperspectral Images
ecological restoration hierarchical classification vegetation structure LiDAR vegetation species
2023/12/1
Remotely sensed images with low resolution can be effectively used for the large-area monitoring of vegetation restoration, but are unsuitable for accurate small-area monitoring. This limits researche...
Vegetation Cover Estimation Using Convolutional Neural Networks
CNN segmentation UAV vegetation cover estimation
2024/2/4
Vegetation is an important parameter in all bio- and ecosystems, and it should be monitored to conserve and restore the environment. This paper presents a design and implementation of a compact system...
Estimating Vertical Distribution of Vegetation Cover in Temperate Heterogeneous Forests Using Airborne Laser Scanning Data
Forest Structure Remote Sensing Airborne Laser Scanning
2013/3/4
Vertical structure is important for understanding forest environment, yet difficult to characterize, especially in temperate heterogeneous forests where the structure is complex. This study used data ...