What is he best known for?
The fields of study he is best known for:
- Remote sensing
- Ecosystem
- Climate change
Samuel N. Goward mostly deals with Remote sensing, Vegetation, Remote sensing, Normalized Difference Vegetation Index and Plant cover. His Remote sensing study combines topics in areas such as Spatial ecology, Humidity and Otter. His Vegetation research includes themes of Land cover, Logging, Advanced very-high-resolution radiometer and Biome.
His Advanced very-high-resolution radiometer study integrates concerns from other disciplines, such as Climatology and Spatial heterogeneity. His research in Normalized Difference Vegetation Index intersects with topics in Canopy and Leaf area index. His Plant cover research integrates issues from Earth observation, Data processing and Satellite imagery.
His most cited work include:
- Global Primary Production: A Remote Sensing Approach (774 citations)
- Landsat's Role in Ecological Applications of Remote Sensing (621 citations)
- North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer (593 citations)
What are the main themes of his work throughout his whole career to date?
Remote sensing, Vegetation, Remote sensing, Advanced very-high-resolution radiometer and Disturbance are his primary areas of study. His Remote sensing research incorporates themes from Land cover and Meteorology. His work on Plant cover is typically connected to Photosynthetically active radiation as part of general Vegetation study, connecting several disciplines of science.
His Remote sensing research includes elements of Government and Environmental resource management. Many of his studies on Advanced very-high-resolution radiometer apply to Normalized Difference Vegetation Index as well. Samuel N. Goward has researched Thematic Mapper in several fields, including Image resolution and Spectral bands.
He most often published in these fields:
- Remote sensing (64.29%)
- Vegetation (24.29%)
- Remote sensing (17.86%)
What were the highlights of his more recent work (between 2010-2020)?
- Forest dynamics (10.00%)
- Remote sensing (64.29%)
- Forestry (7.86%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Forest dynamics, Remote sensing, Forestry, Disturbance and Disturbance. His work deals with themes such as Vegetation and Physical geography, which intersect with Forest dynamics. His Vegetation study combines topics from a wide range of disciplines, such as Lidar, Ancillary data, Terrain and Cross-validation.
His Remote sensing study frequently draws connections between related disciplines such as Forest inventory. His research in Forestry tackles topics such as Deforestation which are related to areas like Afforestation and Biogeochemical cycle. His Disturbance research includes themes of Land use, Biomass, Canopy, Carbon cycle and Earth system science.
Between 2010 and 2020, his most popular works were:
- Recent rates of forest harvest and conversion in North America (159 citations)
- United States Forest Disturbance Trends Observed Using Landsat Time Series (110 citations)
- Carbon consequences of forest disturbance and recovery across the conterminous United States (109 citations)
In his most recent research, the most cited papers focused on:
- Remote sensing
- Ecosystem
- Artificial intelligence
Samuel N. Goward mostly deals with Remote sensing, Forest inventory, Forest dynamics, Ecosystem and Vegetation. His Remote sensing research focuses on Thematic Mapper in particular. Samuel N. Goward focuses mostly in the field of Forest inventory, narrowing it down to topics relating to Carbon sequestration and, in certain cases, Carbon sink, Atmospheric sciences, Climatology and Old-growth forest.
His Forest dynamics research incorporates elements of Vegetation dynamics and National forest. His studies in Ecosystem integrate themes in fields like Biomass, Productivity and Physical geography. His Vegetation research is multidisciplinary, incorporating elements of Terrain, Cross-validation, Elevation, Altimeter and Lidar.
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.
