PDF) Assessment of map similarity of categorical maps using Kappa statistics | Marco Painho and Sandra Caeiro - Academia.edu
Remote Sensing | Free Full-Text | Vegetation Fuel Mapping at Regional Scale Using Sentinel-1, Sentinel-2, and DEM Derivatives—The Case of the Region of East Macedonia and Thrace, Greece
Implications of the 2019–2020 megafires for the biogeography and conservation of Australian vegetation | Nature Communications
Comparison of land cover maps: (A) Land cover map derived from Landsat... | Download Scientific Diagram
Full article: Comparison of a Sentinel-2 land cover map obtained through multi-temporal analysis with the official forest cartography. the case of Galicia (Spain)
Equal e unequal map and corresponding Kappa statistics, using the... | Download Scientific Diagram
Map of global vegetation dynamics for 1982-2012. Data were obtained... | Download Scientific Diagram
Remote Sensing | Free Full-Text | Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery
Remote Sensing | Free Full-Text | ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine
ESSD - An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach
Global humid tropics forest structural condition and forest structural integrity maps | Scientific Data
GMD - Predicting global terrestrial biomes with the LeNet convolutional neural network
![Potential vegetation of China (from the Vegetation Map of China [37]). | Download Scientific Diagram](https://www.researchgate.net/profile/Ensheng-Weng/publication/225521221/figure/fig1/AS:302647916679168@1449168294596/Potential-vegetation-of-China-from-the-Vegetation-Map-of-China-37_Q320.jpg "Potential vegetation of China (from the Vegetation Map of China [37]). | Download Scientific Diagram")
Potential vegetation of China (from the Vegetation Map of China [37]). | Download Scientific Diagram
![Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential [PeerJ]](https://dfzljdn9uc3pi.cloudfront.net/2018/5457/1/fig-6-full.png "Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential [PeerJ]")
Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential [PeerJ]
Multi-decadal land cover change analysis over Sundarbans Mangrove Forest of Bangladesh: A GIS and remote sensing based approach - ScienceDirect
The Potential of Fuzzy Logic for Quantitative Land Cover Change Analysis Basing on Historical Topographic Maps – topic of research paper in Earth and related environmental sciences. Download scholarly article PDF and
A high‐resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring - Metzger - 2013 - Global Ecology and Biogeography - Wiley Online Library
An updated Vegetation Map of China (1:1000000) - ScienceDirect
Remote Sensing | Free Full-Text | Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France
Remote Sensing | Free Full-Text | Advancing High-Resolution Land Cover Mapping in Colombia: The Importance of a Locally Appropriate Legend
Mapcurves: a quantitative method for comparing categorical maps | SpringerLink
Sensors | Free Full-Text | Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels
