Establishment of an early monitoring system is essential to minimize drought damage to agricultural products. Spatiotemporal analysis of both meteorological and agricultural land use conditions can be used for this purpose by collecting time series remotely sensed images over multiple-seasons. The vulnerability of agricultural farmlands to drought varies over agricultural areas according to the different types of cropping systems. In the present study, the cropping patterns of various agricultural areas on Lombok Island, in the eastern part of Indonesia were first classified, and then the monthly precipitation changes over fifteen years were analyzed. Eight types of cropping systems were identified in the agricultural areas based on an image classification using synthetic Landsat8 OLI (Operational Land Imager) and MODIS (MODerate resolution Imaging Spectroradiometer) images. An accuracy assessment was carried out using training samples data collected in the field surveys, together with interpretation of high-resolution satellite images. The total accuracy was 74.53%, with a Kappa coefficient of 0.67. Next, the above-normal, normal, and subnormal rainfall per year were selected based on the fifteen years monthly rainfall data of TRMM (Tropical Rainfall Measuring Mission), and then the Vegetation Health Index (VHI) derived from the MODIS images was then analyzed for the eight classified types of cropping systems in the agricultural areas for each case of years. As a result, a high possibility to drought was identified in the areas where double cropping paddy fields and single cropping dry fields in rainy season. By contrast, areas with triple cropping paddy fields and dry fields throughout a year had a relatively low possibility to drought in this study area.
In recent years, the increased occurrence of sudden heavy rains in Japan has increased the occurrences of surface runoff from sloping farmlands. These runoff events negatively affect the ecosystems owing to the leaching of fertilizer components and/or heavy metals into local watersheds. Parshall flumes have been conventionally used for measuring the extent of surface runoff combined with water-level gauges installed at the bottom edge of sloping farmlands. However, data obtained using this equipment are unreliable because of the direct contact of sensors with various objects, such as pile-ups of crop residues and/or soil particles. To avoid this problem, we propose a novel method herein. Firstly, we devised a time-lapse camera which shot the inside-view images of the Parshall flume and used automatically captured time-lapse images of water flow running through the Parshall flume to estimate the precise runoff extent by detecting the instantaneous water depth using image processing techniques. Second, we devised an automated video-capturing system and positioned it 1.8 m above the ground in an outdoor artificial sloping field in Kagoshima, Japan. Vertically captured video-images of the surface runoff were obtained during a severe runoff event. We analyzed the captured video-images using particle tracking velocimetry to determine the velocity of the moving surface water. The widths of surface water flow were also measured on the video-images. We estimated the runoff amounts using the velocity and the flow width, in addition to the given slope gradient and a literature value of the equivalent roughness coefficient for the soil. The results of two techniques are comparable, especially at severe rainfall and runoff occasions, in which one was measured using the Parshall flume with time-lapse images and the other was measured using the vertically captured video-images. The proposed method may provide estimates of the amount of the surface runoff during intense rainfall events solely using video-images.