干旱区生态环境知识资源中心(Arid): Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa

Arid

DOI	10.3390/rs13173342
	Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa
	Urban, Marcel; Schellenberg, Konstantin; Morgenthal, Theunis; Dubois, Clemence; Hirner, Andreas; Gessner, Ursula; Mogonong, Buster; Zhang, Zhenyu; Baade, Jussi; Collett, Anneliza; Schmullius, Christiane
通讯作者	Urban, M (corresponding author), Friedrich Schiller Univ, Dept Earth Observat, D-07743 Jena, Germany.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:17
英文摘要	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.
英文关键词	shrub encroachment slangbos land degradation Earth observation time series Sentinel-1 Sentinel-2 Synthetic Aperture Radar (SAR) Soil Adjusted Vegetation Index (SAVI) machine learning
类型	Article
语种	英语
开放获取类型	gold, Green Published, Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000694481000001
WOS关键词	FRACTIONAL WOODY COVER ; SHRUB ENCROACHMENT ; BUSH ENCROACHMENT ; RANDOM FOREST ; CLOUD SHADOW ; VEGETATION ; EXPANSION ; RAINFALL
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368474
作者单位	[Urban, Marcel; Schellenberg, Konstantin; Dubois, Clemence; Schmullius, Christiane] Friedrich Schiller Univ, Dept Earth Observat, D-07743 Jena, Germany; [Morgenthal, Theunis; Collett, Anneliza] Dept Agr Land Reform & Rural Dev DALRRD, ZA-0001 Pretoria, South Africa; [Hirner, Andreas; Gessner, Ursula] German Aerosp Ctr, German Remote Sensing Data Ctr, D-51147 Oberpfaffenhofen, Germany; [Mogonong, Buster] South African Environm Observat Network SAEON, ZA-0001 Arid Lands Node, Kimberley, South Africa; [Zhang, Zhenyu] Univ Augsburg, Karlsruhe Inst Technol, D-86159 Augsburg, Germany; [Baade, Jussi] Friedrich Schiller Univ, Dept Phys Geog, D-07743 Jena, Germany
推荐引用方式 GB/T 7714	Urban, Marcel,Schellenberg, Konstantin,Morgenthal, Theunis,et al. Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa[J],2021,13(17).
APA	Urban, Marcel.,Schellenberg, Konstantin.,Morgenthal, Theunis.,Dubois, Clemence.,Hirner, Andreas.,...&Schmullius, Christiane.(2021).Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa.REMOTE SENSING,13(17).
MLA	Urban, Marcel,et al."Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa".REMOTE SENSING 13.17(2021).