Remote-sensing imagery with different spatial resolutions has been widely applied to retrieve vegetation properties in various ecosystems. However, the spatial resolutions of most space-borne or air-borne images (e.g. 2 m) are not fine enough to investigate canopy-level (e.g. 0.5 m)
properties, especially in heterogeneous ecosystems (e.g. grasslands, wetlands). Unmanned aerial vehicles (UAVs) as an emerging remote-sensing platform offer a unique ability to acquire imagery at very high spatial resolution (centimetres). This study utilizes an UAV to investigate vegetation
properties in a heterogeneous grassland, focusing on estimating LAI and chlorophyll content and building digital elevation model (DEM) to support analysing spatial variations of these vegetation properties. The acquired images were mosaicked and geometrically and radiometrically corrected.
LAI and chlorophyll content were then estimated using vegetation indices and textural variables. Results show LAI and chlorophyll content can be quantitatively retrieved (e.g. LAI from 0 to 8, chlorophyll content from 0 to 180 µg cm−2), with higher accuracy
for imagery acquired in the middle growing season. Complex terrain in the study area highly influences the sprout and growth of vegetation. Methods discussed in this article can also be applied to study other ecosystems, such as agricultural fields and wetlands.
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Document Type: Research Article
Department of Geography, University of Toronto Mississauga, Mississauga, ON, Canada
Institute for Aerospace Studies, University of Toronto, North York, ON, Canada
Publication date: August 18, 2018
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