2022
DOI: 10.3390/rs14205207
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Predicting Habitat Properties Using Remote Sensing Data: Soil pH and Moisture, and Ground Vegetation Cover

Abstract: Remote sensing data comprise a valuable information source for many ecological landscape studies that may be under-utilized because of an overwhelming amount of processing methods and derived variables. These complexities, combined with a scarcity of quality control studies, make the selection of appropriate remote sensed variables challenging. Quality control studies are necessary to evaluate the predictive power of remote sensing data and also to develop parsimonious models underpinned by functional variable… Show more

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Cited by 3 publications
(1 citation statement)
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“…Moisture conditions, soil pH, and field vegetation cover were modeled using LiDAR data and natural resource maps, as described in Haugen et al. ( 2022 ), but with some adjustments (described in Text S1 ). Moisture conditions were modeled using vegetation types as bio‐indicators for wet to very dry moisture conditions (5 levels) as response, and solar radiation load, sediment type, site index, and topographic wetness as predictors.…”
Section: Methodsmentioning
confidence: 99%
“…Moisture conditions, soil pH, and field vegetation cover were modeled using LiDAR data and natural resource maps, as described in Haugen et al. ( 2022 ), but with some adjustments (described in Text S1 ). Moisture conditions were modeled using vegetation types as bio‐indicators for wet to very dry moisture conditions (5 levels) as response, and solar radiation load, sediment type, site index, and topographic wetness as predictors.…”
Section: Methodsmentioning
confidence: 99%