Exploring the relative importance of satellite-derived descriptors of production, topography and land cover for predicting breeding bird species richness over Ontario, Canada

摘要: In this paper we investigate the relative predictive power of a number remote sensing-derived environmental descriptors land cover and productivity to predict species richness breeding birds in Ontario, Canada. Specifically, first developed suite (productivity, cover, elevation). These were based on readily available data, including MODerate-resolution Imaging Spectroradiometer (MODIS) onboard Terra Aqua satellites terrain data from Shuttle Radar Topography Mission (SRTM). We then assessed capacity descriptors, using decision tree approach, estimate all birds, groups bird habitat nesting groupings, summarized Ontario Breeding Bird Atlas. Results indicated that variance distributions total richness, as well groups, predicted by (with explained ranging between 47 75%) with predictions clearly related both (as modeled diversity) vegetation productivity. Modeling demonstrates initial partitioning is most often class, indicating it may be driving variable richness; however, information energy critical defining how many occur each type. The results indicate remotely sensed can provide an effective tool for predicting at regional scales.