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Remote Sens. 2019, 11(2), 207; https://doi.org/10.3390/rs11020207

Cropland Mapping Using Fusion of Multi-Sensor Data in a Complex Urban/Peri-Urban Area

1
Department of Earth Sciences, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba 263-8522, Japan
2
Department of Informatics, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan
3
Center for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Japan
*
Author to whom correspondence should be addressed.
Received: 19 November 2018 / Revised: 13 January 2019 / Accepted: 13 January 2019 / Published: 21 January 2019
(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)
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Abstract

Urban and Peri-urban Agriculture (UPA) has recently come into sharp focus as a valuable source of food for urban populations. High population density and competing land use demands lend a spatiotemporally dynamic and heterogeneous nature to urban and peri-urban croplands. For the provision of information to stakeholders in agriculture and urban planning and management, it is necessary to characterize UPA by means of regular mapping. In this study, partially cloudy, intermittent moderate resolution Landsat images were acquired for an area adjacent to the Tokyo Metropolis, and their Normalized Difference Vegetation Index (NDVI) was computed. Daily MODIS 250 m NDVI and intermittent Landsat NDVI images were then fused, to generate a high temporal frequency synthetic NDVI data set. The identification and distinction of upland croplands from other classes (including paddy rice fields), within the year, was evaluated on the temporally dense synthetic NDVI image time-series, using Random Forest classification. An overall classification accuracy of 91.7% was achieved, with user’s and producer’s accuracies of 86.4% and 79.8%, respectively, for the cropland class. Cropping patterns were also estimated, and classification of peanut cultivation based on post-harvest practices was assessed. Image spatiotemporal fusion provides a means for frequent mapping and continuous monitoring of complex UPA in a dynamic landscape. View Full-Text
Keywords: Urban and Peri-urban Agriculture (UPA); heterogeneous; spatio-temporal fusion; synthetic NDVI Urban and Peri-urban Agriculture (UPA); heterogeneous; spatio-temporal fusion; synthetic NDVI
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Nduati, E.; Sofue, Y.; Matniyaz, A.; Park, J.G.; Yang, W.; Kondoh, A. Cropland Mapping Using Fusion of Multi-Sensor Data in a Complex Urban/Peri-Urban Area. Remote Sens. 2019, 11, 207.

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