Theme 1-2: Rangeland/Grassland Ecology--Poster Sessions
Description
Dry savannahs are water-limited and under increasing anthropogenic pressure. Thus, considering climate change and the unprecedented pace and scale of rangeland deterioration, we need methods for assessing the status of such rangelands that are easy to apply, yield reliable and repeatable results that can be applied over large spatial scales. Global and local scale monitoring of rangelands through satellite data and labour-intensive field measurements respectively, are limited in accurately assessing the spatiotemporal heterogeneity of vegetation dynamics to provide crucial information that detects degradation in its early stages. Fortunately, newly emerging techniques such as unmanned aerial vehicles (UAVs), associated miniaturized sensors and improving digital photogrammetric software allow us to transcend these limitations. Yet, they have not been extensively calibrated with rangeland functional attributes. In our study, we fill this gap by testing the relationship between UAV-acquired multispectral imagery and field data collected in discrete sample plots in a Namibian dryland savannah along a degradation gradient. The first results are based on a supervised classifier performed on the very high resolution multispectral imagery to distinguish between rangeland functional attributes, with a relatively good match to the field observations. Integrating UAV-based observations to improve rangeland monitoring could greatly assist in climate-adapted rangeland management.
Citation
Amputu, V.; Tielbörger, K.; and Knox, N., "Assessment of Rangeland Condition in a Dryland System Using UAV-Based Multispectral Imagery" (2022). IGC Proceedings (1993-2023). 3.
https://uknowledge.uky.edu/igc/24/1-2/3
Included in
Assessment of Rangeland Condition in a Dryland System Using UAV-Based Multispectral Imagery
Dry savannahs are water-limited and under increasing anthropogenic pressure. Thus, considering climate change and the unprecedented pace and scale of rangeland deterioration, we need methods for assessing the status of such rangelands that are easy to apply, yield reliable and repeatable results that can be applied over large spatial scales. Global and local scale monitoring of rangelands through satellite data and labour-intensive field measurements respectively, are limited in accurately assessing the spatiotemporal heterogeneity of vegetation dynamics to provide crucial information that detects degradation in its early stages. Fortunately, newly emerging techniques such as unmanned aerial vehicles (UAVs), associated miniaturized sensors and improving digital photogrammetric software allow us to transcend these limitations. Yet, they have not been extensively calibrated with rangeland functional attributes. In our study, we fill this gap by testing the relationship between UAV-acquired multispectral imagery and field data collected in discrete sample plots in a Namibian dryland savannah along a degradation gradient. The first results are based on a supervised classifier performed on the very high resolution multispectral imagery to distinguish between rangeland functional attributes, with a relatively good match to the field observations. Integrating UAV-based observations to improve rangeland monitoring could greatly assist in climate-adapted rangeland management.
