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

Eunice Nduati,Wei Yang,Jong Park,Akihiko Kondoh,Akbar Matniyaz,Yuki Sofue

doi:10.3390/rs11020207

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.

Highlights

Uncertain climatic conditions, high population growth, commodity price fluctuation, urbanization, and allocation of agricultural produce to non-food consumption uses all threaten global and regional food security [1,2,3,4,5,6]
The cropland extent in the context of this study is all land used for crop cultivation, excluding paddy fields
We demonstrated how, using the intermittent moderate resolution Landsat and daily Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance imagery, information that can be used to distinguish croplands from other land cover types can be retrieved

Summary

Introduction

High population growth, commodity price fluctuation, urbanization, and allocation of agricultural produce to non-food consumption uses all threaten global and regional food security [1,2,3,4,5,6]. Common to UPA-related studies is the need for spatially explicit cropland data [7,8,9]. Due to competing land use demands and the high value attached to land in urban and peri-urban areas, UPA agricultural production units tend to be small, spatially dispersed, and fragmented. This finding is supported by Thebo et al [7] and Martellozzo et al [8], who observed that the scale and methods used to generate cropland information are ill-suited to capturing urban croplands and that, given the local nature of UPA, global scale analysis leads to generalizations which can be misleading

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Discussion

Conclusion