Mapping vegetation biophysical and biochemical properties using unmanned aerial vehicles-acquired imagery

ABSTRACT

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.

Acknowledgements

This project was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-386183 to Dr Yuhong He and the Graduate Expansion Funds from the Department of Geography, University of Toronto Mississauga to Bing Lu. We thank to the UAV flight crew from Arrowonics Technology Ltd. and the managers of the Koffler Scientific Reserve. We acknowledge the field assistance from Dr He’s group at the University of Toronto Mississauga. We also thank Transport Canada for providing a Special Flight Operating Certificate: [Numbers 5812-15-14-2016-1 and 5812-15-14-2014-3].

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Discovery Grant [Grant Number RGPIN-386183] and Department of Geography, University of Toronto Mississauga under Graduate Expansion Funds.