Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 549581
Title Mapping agricultural landuse patterns from time series of Landsat 8 using random forest based hierarchial approach
Author(s) Pareeth, Sajid; Karimi, Poolad; Shafiei, Mojtaba; Fraiture, Charlotte De
Source Remote Sensing 11 (2019)5. - ISSN 2072-4292
Department(s) Water Resources Management
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Agriculture - Irrigated area - Landsat 8 - Landuse - Mashhad - Random Forest - Remote sensing

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 years-2013/2014, 2014/2015, and 2015/2016-using the RF models. The total irrigated area was estimated at 1796.16 kmc, 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.

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