Landsat ETM plus and SRTM Data Provide Near Real-Time Monitoring of Chimpanzee (Pan troglodytes) Habitats in Africa

被引:22
作者
Jantz, Samuel M. [1 ]
Pintea, Lilian [2 ]
Nackoney, Janet [1 ]
Hansen, Matthew C. [1 ]
机构
[1] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
[2] Jane Goodall Inst, Dept Conservat Sci, Vienna, VA 22182 USA
关键词
chimpanzee; conservation; habitat suitability monitoring; MaxEnt; random forests; species distribution model; RANDOM FOREST; DECLINE; MODELS; MODIS;
D O I
10.3390/rs8050427
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
All four chimpanzee sub-species populations are declining due to multiple factors including human-caused habitat loss. Effective conservation efforts are therefore needed to ensure their long-term survival. Habitat suitability models serve as useful tools for conservation planning by depicting relative environmental suitability in geographic space over time. Previous studies mapping chimpanzee habitat suitability have been limited to small regions or coarse spatial and temporal resolutions. Here, we used Random Forests regression to downscale a coarse resolution habitat suitability calibration dataset to estimate habitat suitability over the entire chimpanzee range at 30-m resolution. Our model predicted habitat suitability well with an r(2) of 0.82 (+/-0.002) based on 50-fold cross validation where 75% of the data was used for model calibration and 25% for model testing; however, there was considerable variation in the predictive capability among the four sub-species modeled individually. We tested the influence of several variables derived from Landsat Enhanced Thematic Mapper Plus (ETM+) that included metrics of forest canopy and structure for four three-year time periods between 2000 and 2012. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. Because the models were sensitive to such temporally based predictors, our results are the first to highlight the value of integrating continuously updated variables derived from satellite remote sensing into temporally dynamic habitat suitability models to support near real-time monitoring of habitat status and decision support systems.
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