Satellite monitoring of global land cover changes and their impact on climate

1995 ◽  
Vol 31 (2-4) ◽  
pp. 395-413 ◽  
Author(s):  
Ramakrishna R. Nemani ◽  
Steven W. Running
Keyword(s):  
Land Cover ◽  
Satellite Monitoring ◽  
Land Cover Changes ◽  
Global Land Cover ◽  
Global Land

scholarly journals Integrating Land-Cover Products Based on Ontologies and Local Accuracy

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 236
Author(s):  
Ling Zhu ◽  
Guangshuai Jin ◽  
Dejun Gao
Keyword(s):  
Land Cover ◽  
Semantic Similarity ◽  
Mixed Forest ◽  
Global Scale ◽  
Semantic Mapping ◽  
Global Land Cover ◽  
Resolution Observation ◽  
Local Accuracy ◽  
Global Land ◽  
Broad Leaf

Freely available satellite imagery improves the research and production of land-cover products at the global scale or over large areas. The integration of land-cover products is a process of combining the advantages or characteristics of several products to generate new products and meet the demand for special needs. This study presents an ontology-based semantic mapping approach for integration land-cover products using hybrid ontology with EAGLE (EIONET Action Group on Land monitoring in Europe) matrix elements as the shared vocabulary, linking and comparing concepts from multiple local ontologies. Ontology mapping based on term, attribute and instance is combined to obtain the semantic similarity between heterogeneous land-cover products and realise the integration on a schema level. Moreover, through the collection and interpretation of ground verification points, the local accuracy of the source product is evaluated using the index Kriging method. Two integration models are developed that combine semantic similarity and local accuracy. Taking NLCD (National Land Cover Database) and FROM-GLC-Seg (Finer Resolution Observation and Monitoring-Global Land Cover-Segmentation) as source products and the second-level class refinement of GlobeLand30 land-cover product as an example, the forest class is subdivided into broad-leaf, coniferous and mixed forest. Results show that the highest accuracies of the second class are 82.6%, 72.0% and 60.0%, respectively, for broad-leaf, coniferous and mixed forest.

scholarly journals Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020 - iMap World 1.0

2021 ◽  
Vol 258 ◽  
pp. 112364
Author(s):  
Han Liu ◽  
Peng Gong ◽  
Jie Wang ◽  
Xi Wang ◽  
Grant Ning ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Data Cubes ◽  
Global Land Cover ◽  
Global Land

A remote sensing based vegetation classification logic for global land cover analysis

1995 ◽  
Vol 51 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Steven W. Running ◽  
Thomas R. Loveland ◽  
Lars L. Pierce ◽  
R.R. Nemani ◽  
E.R. Hunt
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Classification ◽  
Global Land Cover ◽  
Global Land
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