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Volker C. Radeloff

Researcher at University of Wisconsin-Madison

Publications -  347
Citations -  22574

Volker C. Radeloff is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Species richness & Land use, land-use change and forestry. The author has an hindex of 76, co-authored 317 publications receiving 18345 citations. Previous affiliations of Volker C. Radeloff include Amherst College.

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Assessing wildfire exposure in the Wildland-Urban Interface area of the mountains of central Argentina

TL;DR: The findings suggest that central Argentina has a WUI fire problem, and can help focus fire management activities in areas of higher risk, and ultimately provide support for landscape management and planning aimed at reducing wildfire risk in WUI communities.
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Future land-use scenarios and the loss of wildlife habitats in the southeastern United States

TL;DR: Overall, urban and crop expansion were the main drivers of habitat loss, and Reptiles and wildlife species associated with open vegetation were the species groups most vulnerable to future land-use change.
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Light detection and ranging-based measures of mixed hardwood forest structure.

TL;DR: The results suggest that low-density LiDAR intended for terrain mapping is valuable for broad-scale hardwood forest inventories, and first-return univariate models explained more variability than all-return models; however, the differences were small for multivariate models.
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Increasing development in the surroundings of U.S. National Park Service holdings jeopardizes park effectiveness.

TL;DR: The results showed that park establishment was effective in reducing and stopping the fragmenting impact of development within park boundaries but increased amenity levels following park establishment led to enhanced development in the surroundings of both parks, highlighting the need for conservation planning at broader scales, incorporating areas beyond the boundaries of protected areas.

Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series

TL;DR: In this article, a new approach was developed to detect the extent and the exact timing of agricultural land abandonment using spatial and temporal segments derived from Landsat time series for one Landsat footprint in the Caucasus, covering parts of Russia and Georgia.