{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T18:17:15Z","timestamp":1770574635909,"version":"3.49.0"},"reference-count":89,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T00:00:00Z","timestamp":1629763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["01LL1701"],"award-info":[{"award-number":["01LL1701"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Increasing woody cover and overgrazing in semi-arid ecosystems are known to be the major factors driving land degradation. This study focuses on mapping the distribution of the slangbos shrub (Seriphium plumosum) in a test region in the Free State Province of South Africa. The goal of this study is to monitor the slangbos encroachment on cultivated land by synergistically combining Synthetic Aperture Radar (SAR) (Sentinel-1) and optical (Sentinel-2) Earth observation information. Both optical and radar satellite data are sensitive to different vegetation properties and surface scattering or reflection mechanisms caused by the specific sensor characteristics. We used a supervised random forest classification to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were derived based on expert knowledge and in situ information from the Department of Agriculture, Land Reform and Rural Development (DALRRD). We found that the Sentinel-1 VH (cross-polarization) and Sentinel-2 SAVI (Soil Adjusted Vegetation Index) time series information have the highest importance for the random forest classifier among all input parameters. The modelling results confirm the in situ observations that pastures are most affected by slangbos encroachment. The estimation of the model accuracy was accomplished via spatial cross-validation (SpCV) and resulted in a classification precision of around 80% for the slangbos class within each time step.<\/jats:p>","DOI":"10.3390\/rs13173342","type":"journal-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T04:43:43Z","timestamp":1629780223000},"page":"3342","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0127-2804","authenticated-orcid":false,"given":"Marcel","family":"Urban","sequence":"first","affiliation":[{"name":"Department for Earth Observation, Friedrich-Schiller-University, 07743 Jena, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2563-9017","authenticated-orcid":false,"given":"Konstantin","family":"Schellenberg","sequence":"additional","affiliation":[{"name":"Department for Earth Observation, Friedrich-Schiller-University, 07743 Jena, Germany"}]},{"given":"Theunis","family":"Morgenthal","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Land Reform and Rural Development (DALRRD), Pretoria 0001, South Africa"}]},{"given":"Cl\u00e9mence","family":"Dubois","sequence":"additional","affiliation":[{"name":"Department for Earth Observation, Friedrich-Schiller-University, 07743 Jena, Germany"}]},{"given":"Andreas","family":"Hirner","sequence":"additional","affiliation":[{"name":"German Aerospace Center, German Remote Sensing Data Center, 51147 Oberpfaffenhofen, Germany"}]},{"given":"Ursula","family":"Gessner","sequence":"additional","affiliation":[{"name":"German Aerospace Center, German Remote Sensing Data Center, 51147 Oberpfaffenhofen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2156-6794","authenticated-orcid":false,"given":"Buster","family":"Mogonong","sequence":"additional","affiliation":[{"name":"South African Environmental Observation Network (SAEON), Arid Lands Node, Kimberley 0001, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9155-7378","authenticated-orcid":false,"given":"Zhenyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, University of Augsburg, 86159 Augsburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9878-7232","authenticated-orcid":false,"given":"Jussi","family":"Baade","sequence":"additional","affiliation":[{"name":"Department for Physical Geography, Friedrich-Schiller-University, 07743 Jena, Germany"}]},{"given":"Anneliza","family":"Collett","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Land Reform and Rural Development (DALRRD), Pretoria 0001, South Africa"}]},{"given":"Christiane","family":"Schmullius","sequence":"additional","affiliation":[{"name":"Department for Earth Observation, Friedrich-Schiller-University, 07743 Jena, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1080\/22797254.2017.1378926","article-title":"The role of Remote Sensing in land degradation assessments: Opportunities and challenges","volume":"50","author":"Dubovyk","year":"2017","journal-title":"Eur. 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