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- Zandler, H.; Brenning, A.; Samimi, C.
- Remote sensing of environment 2015 v.158 pp. 140-155
- vegetation cover, etc ; Landsat; artificial intelligence; biomass; carbon; dry environmental conditions; least squares; models; monitoring; prediction; remote sensing; shrinkage; shrubs; uncertainty; vegetation index; Tajikistan; Show all 16 Subjects
- ... Remote sensing based biomass estimation in arid environments is essential for monitoring degradation and carbon dynamics. However, due to the low vegetation cover in these regions, satellite-based research is challenging. Numerous potentially useful remotely-sensed predictor variables have been proposed, and several statistical and machine-learning techniques are available for empirical spatial mo ...
- Brooks, Colin; Grimm, Amanda; Shuchman, Robert; Sayers, Michael; Jessee, Nathaniel
- Remote sensing of environment 2015 v.157 pp. 58-71
- vegetation cover, etc ; Cladophora; Landsat; aesthetics; biomass; fisheries; habitats; humans; lakes; monitoring; reflectance; remote sensing; submerged aquatic plants; variance; water quality; wildlife; Lake Huron; Lake Ontario; Show all 18 Subjects
- ... In the Laurentian Great Lakes, the prolific growth of submerged aquatic vegetation (SAV, dominated by the filamentous green alga Cladophora) is negatively impacting human and wildlife health, fisheries, and aesthetic conditions. The distribution of Cladophora and similar SAV in the Great Lakes and how that has changed over recent decades has been unknown, and the magnitude of the current problem r ...