Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa
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 (<i>Seriphium plumosum</i>) in a test region in the Free State Province of South Africa. The goa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/17/3342 |
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| author | Marcel Urban Konstantin Schellenberg Theunis Morgenthal Clémence Dubois Andreas Hirner Ursula Gessner Buster Mogonong Zhenyu Zhang Jussi Baade Anneliza Collett Christiane Schmullius |
| author_facet | Marcel Urban Konstantin Schellenberg Theunis Morgenthal Clémence Dubois Andreas Hirner Ursula Gessner Buster Mogonong Zhenyu Zhang Jussi Baade Anneliza Collett Christiane Schmullius |
| author_sort | Marcel Urban |
| collection | DOAJ |
| description | 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 (<i>Seriphium plumosum</i>) 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. |
| first_indexed | 2024-03-10T08:04:48Z |
| format | Article |
| id | doaj.art-4a5efabe3c6e44acabab0ae428113d7e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:04:48Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4a5efabe3c6e44acabab0ae428113d7e2023-11-22T11:07:25ZengMDPI AGRemote Sensing2072-42922021-08-011317334210.3390/rs13173342Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South AfricaMarcel Urban0Konstantin Schellenberg1Theunis Morgenthal2Clémence Dubois3Andreas Hirner4Ursula Gessner5Buster Mogonong6Zhenyu Zhang7Jussi Baade8Anneliza Collett9Christiane Schmullius10Department for Earth Observation, Friedrich-Schiller-University, 07743 Jena, GermanyDepartment for Earth Observation, Friedrich-Schiller-University, 07743 Jena, GermanyDepartment of Agriculture, Land Reform and Rural Development (DALRRD), Pretoria 0001, South AfricaDepartment for Earth Observation, Friedrich-Schiller-University, 07743 Jena, GermanyGerman Aerospace Center, German Remote Sensing Data Center, 51147 Oberpfaffenhofen, GermanyGerman Aerospace Center, German Remote Sensing Data Center, 51147 Oberpfaffenhofen, GermanySouth African Environmental Observation Network (SAEON), Arid Lands Node, Kimberley 0001, South AfricaKarlsruhe Institute of Technology, University of Augsburg, 86159 Augsburg, GermanyDepartment for Physical Geography, Friedrich-Schiller-University, 07743 Jena, GermanyDepartment of Agriculture, Land Reform and Rural Development (DALRRD), Pretoria 0001, South AfricaDepartment for Earth Observation, Friedrich-Schiller-University, 07743 Jena, GermanyIncreasing 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 (<i>Seriphium plumosum</i>) 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.https://www.mdpi.com/2072-4292/13/17/3342shrub encroachmentslangbosland degradationEarth observationtime seriesSentinel-1 |
| spellingShingle | Marcel Urban Konstantin Schellenberg Theunis Morgenthal Clémence Dubois Andreas Hirner Ursula Gessner Buster Mogonong Zhenyu Zhang Jussi Baade Anneliza Collett Christiane Schmullius Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa Remote Sensing shrub encroachment slangbos land degradation Earth observation time series Sentinel-1 |
| title | Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa |
| title_full | Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa |
| title_fullStr | Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa |
| title_full_unstemmed | Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa |
| title_short | Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa |
| title_sort | using sentinel 1 and sentinel 2 time series for slangbos mapping in the free state province south africa |
| topic | shrub encroachment slangbos land degradation Earth observation time series Sentinel-1 |
| url | https://www.mdpi.com/2072-4292/13/17/3342 |
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