What is he best known for?
The fields of study he is best known for:
- Ecology
- Statistics
- Artificial intelligence
Qinghua Guo mainly investigates Lidar, Remote sensing, Physical geography, Precipitation and Tree.
His work carried out in the field of Lidar brings together such families of science as Triangulated irregular network, Terrain, Digital elevation model, Elevation and Forest ecology.
His work deals with themes such as Algorithm and Canopy, which intersect with Remote sensing.
His Physical geography research also works with subjects such as
- Growing season that intertwine with fields like Primary production, Plant growth, Crop yield and Vegetation type,
- Spatial heterogeneity and related Northern Hemisphere.
His Precipitation research incorporates elements of Driving factors, Irrigation, Climate change, Groundwater and Normalized Difference Vegetation Index.
Qinghua Guo focuses mostly in the field of Irrigation, narrowing it down to matters related to Climatology and, in some cases, Urbanization.
His most cited work include:
- Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999 (311 citations)
- Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999 (311 citations)
- A New Method for Segmenting Individual Trees from the Lidar Point Cloud (296 citations)
What are the main themes of his work throughout his whole career to date?
Qinghua Guo mainly focuses on Remote sensing, Lidar, Vegetation, Ecology and Canopy.
The Remote sensing study combines topics in areas such as Tree, Point cloud, Leaf area index and Crown.
His Lidar research integrates issues from Segmentation, Elevation, Forest inventory, Forest ecology and Scale.
Qinghua Guo interconnects Biomass, Spatial heterogeneity, Atmospheric sciences and Carbon cycle in the investigation of issues within Vegetation.
His Spatial heterogeneity study which covers Growing season that intersects with Physical geography.
His Normalized Difference Vegetation Index study integrates concerns from other disciplines, such as Hydrology, Urbanization, Climatology and Precipitation.
He most often published in these fields:
- Remote sensing (59.20%)
- Lidar (50.57%)
- Vegetation (26.44%)
What were the highlights of his more recent work (between 2017-2021)?
- Remote sensing (59.20%)
- Lidar (50.57%)
- Canopy (17.82%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Remote sensing, Lidar, Canopy, Vegetation and Tree.
His Remote sensing study combines topics in areas such as Pixel, Forest inventory and Crown.
His Lidar research is multidisciplinary, relying on both Shuttle Radar Topography Mission, Digital elevation model, Segmentation and Hyperspectral imaging.
He has researched Canopy in several fields, including Snow, Forest restoration, Leaf area index and Basal area.
His Vegetation research includes themes of Snowpack, Forest ecology, Atmospheric sciences and Drought tolerance.
His Tree research includes elements of Land cover and Random forest.
Between 2017 and 2021, his most popular works were:
- Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms. (49 citations)
- Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species (43 citations)
- Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping (31 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Statistics
- Artificial intelligence
Qinghua Guo focuses on Remote sensing, Lidar, Canopy, Tree and Artificial intelligence.
Qinghua Guo combines subjects such as Artificial neural network and Classifier with his study of Remote sensing.
His research in Lidar intersects with topics in Agronomy and Vegetation.
He has included themes like Multi-source, Random forest and Ecotone in his Canopy study.
His Tree research is multidisciplinary, incorporating perspectives in Competition, Terrain, Physical geography and Crown.
His Artificial intelligence research focuses on subjects like Algorithm, which are linked to Woody plant, Decision tree, Object, Species distribution and Afforestation.
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