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Open AccessArticle

Spatiotemporal Analysis of Vegetation Cover Change in a Large Ephemeral River: Multi-Sensor Fusion of Unmanned Aerial Vehicle (UAV) and Landsat Imagery

1
Department of Geography, Dartmouth College, 6017 Fairchild, Hanover, NH 03755, USA
2
Environmental Studies Program, Dartmouth College, 6182 Steele, Hanover, NH 03755, USA
3
William H. Neukom Institute for Computational Studies, Dartmouth College, Hanover, NH 03755, USA
*
Author to whom correspondence should be addressed.
Current address: Department of Geography, University of California, Santa Barbara, 1832 Ellison, Santa Barbara, CA 93106, USA.
Current address: Department of Geography, University of Northern Iowa, 205 ITTC, Cedar Falls, IA 50613, USA.
Remote Sens. 2021, 13(1), 51; https://doi.org/10.3390/rs13010051
Received: 20 October 2020 / Revised: 17 December 2020 / Accepted: 20 December 2020 / Published: 25 December 2020
(This article belongs to the Special Issue Drone-Based Ecological Conservation)
Ephemeral rivers in arid regions act as linear oases, where corridors of vegetation supported by accessible groundwater and intermittent surface flows provide biological refugia in water-limited landscapes. The ecological and hydrological dynamics of these systems are poorly understood compared to perennial systems and subject to wide variation over space and time. This study used imagery obtained from an unmanned aerial vehicle (UAV) to enhance satellite data, which were then used to quantify change in woody vegetation cover along the ephemeral Kuiseb River in the Namib Desert over a 35-year period. Ultra-high resolution UAV imagery collected in 2016 was used to derive a model of fractional vegetation cover from five spectral vegetation indices, calculated from a contemporaneous Landsat 8 Operational Land Imager (OLI) image. The Normalized Difference Vegetation Index (NDVI) provided the linear best-fit relationship for calculating fractional cover; the model derived from the two 2016 datasets was subsequently applied to 24 intercalibrated Landsat images to calculate fractional vegetation cover for the Kuiseb extending back to 1984. Overall vegetation cover increased by 33% between 1984 and 2019, with the most highly vegetated reach of the river exhibiting the greatest positive change. This reach corresponds with the terminal alluvial zone, where most flood deposition occurs. The spatial and temporal trends discovered highlight the need for long-term monitoring of ephemeral ecosystems and demonstrate the efficacy of a multi-sensor approach to time series analysis using a UAV platform. View Full-Text
Keywords: desert hydrology; riparian forest; unmanned aerial vehicles; multi-sensor fusion; fractional vegetation cover; Kuiseb; spatiotemporal patterns desert hydrology; riparian forest; unmanned aerial vehicles; multi-sensor fusion; fractional vegetation cover; Kuiseb; spatiotemporal patterns
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    Doi: 10.5281/zenodo.4328408
    Link: https://github.com/brynemorgan/kuiseb-fvc
    Description: The data presented in this study are openly available on GitHub at https://github.com/brynemorgan/kuiseb-fvc. A comprehensive description of the Agisoft PhotoScan Professional 1.3.0 workflow and parameters is available online.
MDPI and ACS Style

Morgan, B.E.; Chipman, J.W.; Bolger, D.T.; Dietrich, J.T. Spatiotemporal Analysis of Vegetation Cover Change in a Large Ephemeral River: Multi-Sensor Fusion of Unmanned Aerial Vehicle (UAV) and Landsat Imagery. Remote Sens. 2021, 13, 51. https://doi.org/10.3390/rs13010051

AMA Style

Morgan BE, Chipman JW, Bolger DT, Dietrich JT. Spatiotemporal Analysis of Vegetation Cover Change in a Large Ephemeral River: Multi-Sensor Fusion of Unmanned Aerial Vehicle (UAV) and Landsat Imagery. Remote Sensing. 2021; 13(1):51. https://doi.org/10.3390/rs13010051

Chicago/Turabian Style

Morgan, Bryn E.; Chipman, Jonathan W.; Bolger, Douglas T.; Dietrich, James T. 2021. "Spatiotemporal Analysis of Vegetation Cover Change in a Large Ephemeral River: Multi-Sensor Fusion of Unmanned Aerial Vehicle (UAV) and Landsat Imagery" Remote Sens. 13, no. 1: 51. https://doi.org/10.3390/rs13010051

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