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Lookup NU author(s): Dr Louise RayneORCiD, Dr Filippo Brandolini, Jen MakovicsORCiD, Hope Irvine
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Existing landcover products depicting degraded land do not accurately show the extent of desertification of traditional cultivation systems. These products rely on recent NDVI (Normalised Difference Vegetation Index) time series and statistical data. Historical desertification is missed, although these abandoned fields are distinctive in satellite imagery as reflectant and smooth surfaces. We present our Google Earth Engine workflow for detecting desertification using satellite data (full details are in our recent paper). We used the random forest algorithm to classify five landcover categories including desertified fields, applied to a data stack comprising a 13-band Sentinel-2 composite and derived tasselled cap components, and a Sentinel-1 VV-polarisation composite. We test our approach for case studies of the Skoura and Draa oases in southern Morocco with a resulting accuracy of 74-76% for the desertification class.
Author(s): Rayne L, Brandolini F, Makovics J, Hayes-Rich E, Levy J, Irvine H, Assi L, Bokbot Y
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS 2024)
Year of Conference: 2024
Online publication date: 27/05/2024
Acceptance date: 04/03/2024
Publisher: IEEE
URL: https://doi.org/10.1109/M2GARSS57310.2024.10537387
DOI: 10.1109/M2GARSS57310.2024.10537387
Library holdings: Search Newcastle University Library for this item
ISBN: 9798350358582
