{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:47:22Z","timestamp":1764175642110,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T00:00:00Z","timestamp":1712102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Erosion is a powerful force that has moulded the Earth ever since water has been present on its rocky surface. In its seemingly harmless pursuit, erosion threatens ecosystems, reduces agricultural production, and impacts water quality. When trying to investigate erosion, there is no easy way to identify hotspots, only leaving the possibility of predicting where erosion should be occurring. This study aimed to develop a method to identify erosion using Synthetic Aperture Radar (SAR) images in a process called Coherent Change Detection (CCD). In doing so, it was found that CCD can be used to identify erosion due to rain events; however, false positives were also found due to soil moisture changes. This study used a new method for removing soil moisture effects that utilised the drying out of the soil to map where changes had occurred. This helped limit false positives, but more work is required to ensure soil moisture does not interfere with the results. Field data comprising aerial imagery and soil sampling were collected to improve the SAR processing as well as validate the results. The results of this study indicate the feasibility of developing an erosion analysis system capable of providing near real-time data specifically for arid regions.<\/jats:p>","DOI":"10.3390\/rs16071263","type":"journal-article","created":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T03:37:21Z","timestamp":1712115441000},"page":"1263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Mapping Erosion Hotspots: Coherent Change Detection in the Quilpie Region, Queensland, Australia"],"prefix":"10.3390","volume":"16","author":[{"given":"Kyran","family":"Cook","sequence":"first","affiliation":[{"name":"School of Surveying and Built Environment, University of Southern Queensland, Toowoomba 4350, Australia"}]},{"given":"Armin","family":"Agha Karimi","sequence":"additional","affiliation":[{"name":"School of Surveying and Built Environment, University of Southern Queensland, Toowoomba 4350, Australia"}]},{"given":"Alistair","family":"Grinham","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, The University of Queensland, Brisbane 4072, Australia"}]},{"given":"Kevin","family":"McDougall","sequence":"additional","affiliation":[{"name":"School of Surveying and Built Environment, University of Southern Queensland, Toowoomba 4350, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.catena.2005.03.007","article-title":"Modeling response of soil erosion and runoff to changes in precipitation and cover","volume":"61","author":"Nearing","year":"2005","journal-title":"CATENA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.envsoft.2015.11.024","article-title":"Assimilating satellite imagery and visible\u2013near infrared spectroscopy to model and map soil loss by water erosion in Australia","volume":"77","author":"Teng","year":"2016","journal-title":"Environ. 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