Chiharu Hongo

Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia

Existing methods for rice field classification have some limitations due to the large variety of land covers attributed to rice fields. This study used temporal variance analysis of daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images to discriminate rice fields from other land uses. The classification result was then compared with the reference data. Regression analysis showed that regency and district comparisons produced coefficients of determination (R 2) of 0.97490 and 0.92298, whereas the root mean square errors (RMSEs) were 1570.70 and 551.36 ha, respectively. The overall accuracy of the method in this study was 87.91%, with commission and omission errors of 35.45% and 17.68%, respectively. Kappa analysis showed strong agreement between the results of the analysis of the MODIS data using the method developed in this study and the reference data, with a kappa coefficient value of 0.8371. The results of this study indicated that the algorithm for variance analysis of multitemporal MODIS images could potentially be applied for rice field mapping.

Development of an Environmentally Advanced Basin Model in Asia

The second issue is environmental problems resulting from the spread of modern agricultural methods. Since the Green Revolution, Asian nations have greatly increased land productivity through the widespread use of modern agricultural methods such as the adoption of highyielding varieties and chemical fertilizers, in response to population growth. However, modern agricultural methods that promote uniform cultivation simplify ecosystems and are harmful to regional biodiversity. This in turn erodes regional characteristics and weakens the ability of regions to adapt to external change, which carries the risk that a major environmental change could result in catastrophic damages.

Effect of soil type on the time-course of changes in sugar beet (Beta vulgaris L.) productivity in Tokachi District, Hokkaido, Japan

Abstract To clarify the effect of soil type on changes in sugar beet (Beta vulgaris L.) productivity since 1980 in Tokachi District (Hokkaido, Japan), we analyzed yield data from 121 settlements from 1980 to 2002 using maps of parent materials and surface organic matter contents in a geographical information system. The soil types were Brown Lowland soils, Andosols with an alluvial subsoil, Wet Andosols and Andosols. The sugar beet yields were highest in the Andosols and moderate in Andosols with an alluvial subsoil. Yields in Brown Lowland soils in the 1980s were similar to those in Andosols, but decreased below the yields in the Andosols by the 1990s. The yields in Wet Andosols were the lowest in the 1980s, but have been similar to those in Andosols with an alluvial subsoil since 1990. Thus, productivity appears to have varied over time in Brown Lowland soils and Wet Andosols. The correlation coefficients between yields and cumulative daily mean temperature from late April to mid-July since 1990 were highest in the Andosols (r = 0.67), lowest in the Brown Lowland soils (r = 0.50) and intermediate in the other soil types (r = 0.54–0.60). However, the magnitude of the correlation between the yield and the cumulative precipitation since 1990 was lowest in the Andosols (r = –0.22), highest in the Brown Lowland soils (r = –0.58) and intermediate in the other soil types (r = –0.44 to –0.45). These results suggest that the present soil water environment in the Andosols is superior to that in the other soil types.

Estimating soil carbon stocks in an upland area of Tokachi District, Hokkaido, Japan, by satellite remote sensing

Soil carbon stocks (SCSs) in an upland area of Tokachi District, Hokkaido, Japan, were estimated by satellite remote sensing and a soil survey. The soil parent materials in the studied area were alluvial deposits, volcanic ash plus alluvial deposits, and volcanic ash. Surface soil carbon concentrations (SSCCs) were negatively correlated with satellite image data (green, red, and near-infrared reflectance) for each parent material. The highest correlations between reflectance and SSCCs were obtained from red wavelength reflectance for alluvial deposits (r = –0.82, p < 0.01) and volcanic ash plus alluvial deposits (r = –0.91, p < 0.01), and from near-infrared reflectance for volcanic ash (r = –0.90, p < 0.01). We generated an SSCC map of the study area using the regression equations and satellite reflectance data. The soil survey results showed that SSCCs were positively correlated with SCSs in the 0–30 cm depth interval for each parent material (best-fit regressions: alluvial deposits, r = 0.97; volcanic ash plus alluvial deposits, r = 0.97; volcanic ash, r = 0.97), and they were also positively correlated with SCSs in the 30–95 cm depth interval for volcanic ash (r = 0.94). We were therefore able to generate a map of estimated SCSs from the SSCC map and the regression equations developed between SSCCs and SCSs. The estimated and measured SCSs in both depth intervals showed an almost 1:1 relationship, with root-mean-square errors of 19.5 (0–30 cm) and 28.6 Mg carbon (C) ha–1 (30–95 cm). According to the SCS maps, SCSs at 0–30 cm depth in areas of alluvial deposits, volcanic ash plus alluvial deposits, and volcanic ash were mostly 50–150, 100–200, and 50–250 Mg C ha–1, respectively, whereas at 30–95 cm depth in the volcanic ash area they were mostly less than 250 Mg C ha–1.

Estimation of water requirement for rice cultivation using satellite data

The impact of climate change on food production in Indonesia is anticipated to be a level where food production in 2050 will decline ranging from 38% to more than ten-folds of the current production, and it is necessary to improve a cultivation management system aiming at increasing rice yield and also the stability. For this improvement, effective use of irrigation water and water distribution management plan is critical and has to be carefully reviewed and designed. In this study, the amount of irrigation water necessary for rice cultivation at each stage of its growth was calculated and reviewed using satellite data acquired in dry cultivation season for the paddy field of West Java, Indonesia. The results of the study make it clear that the new method developed in the study can calculate and estimate the water amount to be required at each growth stage of rice; the requirement amount can be visualized by each pixel of satellite data; also, the change of the water requirement amount can be checked on almost real-time basis.

Detection of rice-planted area using multi-temporal ALOS/PALSAR data

A rice-planted area map is a basic information resource for rice production management. In this study, a method is proposed to detect the annual rice-planted area by using multi-temporal phased array type L-band synthetic aperture radar. This method addresses two problems: (i) the σ0 value of planted paddies is not consistent; and (ii) annual data fails to reveal time series change of σ0 value in rice-planted area. Data were acquired from both planted and non-planted paddies in the dry season. Accuracy assessment was performed based on field survey data. As a result, the proposed method was effective at revealing the annual rice-planted area. It was also found that data acquired in the rice growth stage contributes to determining the rice-planted area with high accuracy. Further investigation is required that sets more detailed classes to reduce the rate of misclassification and eliminates small regions in detected images.