干旱区生态环境知识资源中心(Arid): Mapping Agricultural Landuse Patterns from Time Series of Landsat 8 Using Random Forest Based Hierarchial Approach

Arid

DOI	10.3390/rs11050601
	Mapping Agricultural Landuse Patterns from Time Series of Landsat 8 Using Random Forest Based Hierarchial Approach
	Pareeth, Sajid 1; Karimi, Poolad 1; Shafiei, Mojtaba 2; De Fraiture, Charlotte 1,3
通讯作者	Pareeth, Sajid
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:5
英文摘要	Increase in irrigated area, driven by demand for more food production, in the semi-arid regions of Asia and Africa is putting pressure on the already strained available water resources. To cope and manage this situation, monitoring spatial and temporal dynamics of the irrigated area land use at basin level is needed to ensure proper allocation of water. Publicly available satellite data at high spatial resolution and advances in remote sensing techniques offer a viable opportunity. In this study, we developed a new approach using time series of Landsat 8 (L8) data and Random Forest (RF) machine learning algorithm by introducing a hierarchical post-processing scheme to extract key Land Use Land Cover (LULC) types. We implemented this approach for Mashhad basin in Iran to develop a LULC map at 15 m spatial resolution with nine classes for the crop year 2015/2016. In addition, five irrigated land use types were extracted for three crop years2013/2014, 2014/2015, and 2015/2016using the RF models. The total irrigated area was estimated at 1796.16 km(2), 1581.7 km(2) and 1578.26 km(2) for the cropping years 2013/2014, 2014/2015 and 2015/2016, respectively. The overall accuracy of the final LULC map was 87.2% with a kappa coefficient of 0.85. The methodology was implemented using open data and open source libraries. The ability of the RF models to extract key LULC types at basin level shows the usability of such approaches for operational near real time monitoring.
英文关键词	irrigated area Mashhad agriculture landuse remote sensing Random Forest Landsat 8
类型	Article
语种	英语
国家	Netherlands ; Iran
开放获取类型	gold, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000462544500129
WOS关键词	IRRIGATED AGRICULTURE ; COVER CLASSIFICATION ; WATER
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/2XILL650/218342
作者单位	1.IHE Delft Inst Water Educ, Water Sci & Engn Dept, NL-2611 AX Delft, Netherlands; 2.East Water & Environm Res Inst, Hydroinformat Dept, Mashhad 9188737176, Razavi Khorasan, Iran; 3.Wageningen Univ & Res Ctr, Dept Environm Sci, NL-6700 HB Wageningen, Netherlands
推荐引用方式 GB/T 7714	Pareeth, Sajid,Karimi, Poolad,Shafiei, Mojtaba,et al. Mapping Agricultural Landuse Patterns from Time Series of Landsat 8 Using Random Forest Based Hierarchial Approach[J],2019,11(5).
APA	Pareeth, Sajid,Karimi, Poolad,Shafiei, Mojtaba,&De Fraiture, Charlotte.(2019).Mapping Agricultural Landuse Patterns from Time Series of Landsat 8 Using Random Forest Based Hierarchial Approach.REMOTE SENSING,11(5).
MLA	Pareeth, Sajid,et al."Mapping Agricultural Landuse Patterns from Time Series of Landsat 8 Using Random Forest Based Hierarchial Approach".REMOTE SENSING 11.5(2019).