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.

Abstract:Recently, droughts have become widespread in the Northern Hemisphere, including in Mongolia. The ground surface condition, particularly vegetation coverage, affects the occurrence of dust storms. The main sources of dust storms in the Asian region are the Taklimakan and Mongolian Gobi desert regions. In these regions, precipitation is one of the most important factors for growth of plants especially in arid and semi-arid land. The purpose of this study is to clarify the relationship between precipitation and vegetation cover dynamics over 29 years in the Gobi region. We compared the patterns between precipitation and Normalized Difference Vegetation Index (NDVI) for a period of 29 years. The precipitation and vegetation datasets were examined to investigate the trends during . Cross correlation analysis between the precipitation and the NDVI anomalies was performed. Data analysis showed that the variations of NDVI anomalies in the east region correspond well with the precipitation anomalies during this period. However, in the southwest region of the Gobi region, the NDVI had decreased regardless of the precipitation amount, especially since 2010. This result showed that vegetation in this region was more degraded than in the other areas.

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Abstract:In the Mongolian Plateau, the desert steppe, mountains, and dry lake bed surfaces may affect the process of dust storm emissions. Among these three surface types, dry lake beds are considered to contribute a substantial amount of global dust emissions and to be responsible for "hot spots" of dust outbreaks. The land cover types in the study area were broadly divided into three types, namely desert steppe, mountains, and dry lake beds, by a classification based on Normalized Difference Water Index (NDWI) calculated from MODIS Terra satellite images, and Digital Elevation Model (DEM). This dry lake beds extracting method using remote sensing offers a new technique for identifying dust hot spots and potential untapped groundwater in the dry lands of the Gobi region. In the study area, frequencies of dry lake bed formation were calculated during the period of 2001 to 2014. The potential dry lake area corresponded well with the length of the river network based on hydrogeological characterization (R 2 = 0.59, p < 0.001). We suggest that the threshold between dry lake bed areas and the formation of ephemeral lakes in semi-arid regions is eight days of total precipitation.