What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Remote sensing
Le Wang mainly focuses on Remote sensing, Mangrove, Pixel, Multispectral image and Pattern recognition.
Le Wang has researched Remote sensing in several fields, including Crown, Feature and Land use.
His research integrates issues of Satellite imagery and Normalized Difference Vegetation Index in his study of Mangrove.
His Pixel study integrates concerns from other disciplines, such as Cartography, Population estimation, Dasymetric map and Statistical model.
The Cartography study combines topics in areas such as Areal interpolation, Remote sensing, Markov chain, Multispectral Scanner and Cellular automaton.
His research investigates the connection between Pattern recognition and topics such as Artificial intelligence that intersect with problems in Spectral signature.
His most cited work include:
- Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama (336 citations)
- Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery (324 citations)
- Individual Tree-Crown Delineation and Treetop Detection in High-Spatial-Resolution Aerial Imagery (236 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Remote sensing, Pixel, Cartography, Mangrove and Artificial intelligence.
His Remote sensing research is multidisciplinary, incorporating elements of Land cover, Ecosystem and Scale.
His Pixel course of study focuses on Support vector machine and Satellite imagery.
Le Wang has included themes like Urbanization, Statistics, Thematic Mapper, Census and Fragmentation in his Cartography study.
His Mangrove research is multidisciplinary, incorporating perspectives in Canopy, Forestry and Vegetation.
His Artificial intelligence study also includes
- Pattern recognition that connect with fields like Multispectral image and Feature,
- Spatial analysis which is related to area like Abundance estimation.
He most often published in these fields:
- Remote sensing (58.51%)
- Pixel (29.79%)
- Cartography (23.40%)
What were the highlights of his more recent work (between 2016-2021)?
- Remote sensing (58.51%)
- Mangrove (15.96%)
- Ecosystem (14.89%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Remote sensing, Mangrove, Ecosystem, Vegetation and Lidar.
His study in Remote sensing is interdisciplinary in nature, drawing from both Ranging, Linear regression and Subpixel rendering.
His research integrates issues of Photochemical Reflectance Index and Tree in his study of Mangrove.
His Ecosystem research is multidisciplinary, incorporating elements of Subtropics, Earth observation and Riparian zone, Habitat.
In his work, Normalized Difference Vegetation Index is strongly intertwined with Leaf area index, which is a subfield of Vegetation.
The various areas that Le Wang examines in his Lidar study include Impervious surface and Canopy.
Between 2016 and 2021, his most popular works were:
- Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest (76 citations)
- A review of remote sensing for mangrove forests: 1956–2018 (47 citations)
- Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges (45 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Ecosystem
His primary areas of study are Remote sensing, Mangrove, Vegetation, Leaf area index and Lidar.
His study on Remote sensing is mostly dedicated to connecting different topics, such as Image resolution.
Le Wang interconnects Subtropics, Habitat, Earth observation and Ecosystem process, Ecosystem in the investigation of issues within Mangrove.
His work on Normalized Difference Vegetation Index as part of his general Vegetation study is frequently connected to Precision agriculture, High spatial resolution and Spectral response function, thereby bridging the divide between different branches of science.
His Leaf area index research integrates issues from Mean squared error, Linear regression, Populus euphratica and Shrub.
His Lidar research incorporates themes from Watershed, Canopy, Tree, Ranging and Field.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
