In Indonesia, agriculture is one of the main economic sectors and source of national income for the country. Therefore, precise agricultural mapping and information are very important for both the national and regional administration. Satellite remote sensing is viewed as the most effective tool for identifying the agriculture croplands for wide area. However, cloud coverage in tropical region limits the use of conventional optical remote sensing. For this reason, the Synthetic Aperture Radar (SAR) application has advantages for monitoring agriculture in tropical region. This study applied ALOS PALSAR imagery with full polarization mode for classifying the agricultural croplands. A study area of about 50km² located in Central Java was chosen as it consists of the mixed and complex agriculture croplands common to Java Island. The backscatter intensity of the full polarization image was processed and combined to extract the most suitable parameter. Two kinds of polarimetric decomposition were also applied and analyzed for a better understanding of the polarimetric scattering mechanisms of agricultural croplands. Overall, the integration of backscatter intensities (HH,HV,VH,VV, and HH+HV) and Freeman and Durden polarimetric decomposition showed the best accuracy for classifying the agriculture croplands with an overall accuracy 74.11% and a kappa coefficient of 0.62. This fact implies that the integration of both backscatter intensity and polarimetric decomposition methods was able to compensate for the weakness of each component in discriminating the complex agricultural croplands.

We examined the effects of the bromide dioxin BDE209, a toxic bromide flame retardant, and toxic cadmium and mercury, which interact with BDE209 in soil, on four different varieties of water spinach (Ipomoea aquatica). The experiment was based on an L27(3¹³) orthogonal array, which examined three concentrations each of BDE209, cadmium, and mercury, and four varieties of water spinach. The 3³ X 4 = 108 treatment combinations generated were divided into three blocks in a greenhouse of the Jinan University campus. Within each block, each treatment combination was executed in a random pot in which one of four water spinach varieties was planted. To detect interactions among several toxic materials, multifactor experiments, in which each factor has a few levels, are important. In this paper, we outlined the complex design of the experiment and how to analyze the resulting data. Experiments examining organisms exposed to hazardous materials often result in missing data, since the organisms often do not grow normally. In our experiment, we determined a multiple regression equation using non-missing data, and the missing values were filled in based on the equation.

The oil palm cultivation on peatland area requires particular peat management techniques, such as compaction and drainage, to maintain water table conditions and minimize the leaning oil palms. However, these managements are hardly ever applied by smallholders, which cause frequent leaning oil palms or even toppling trees. This study attempted to explore the ALOS-2 PALSAR-2 full polarimetry data to classify the normal, replanted, and leaning oil palm conditions in three representative areas of smallholders’ plantations on peatland in Riau, Indonesia. Texture analysis was applied to an individual and combination of backscatter, radar vegetation index, and polarimetric parameters derived from decomposition. The results indicated that mean feature produced the optimum classification among the other texture features, while the polarimetric parameters are particularly useful for characterizing tree stand conditions of leaning oil palms. The most effective combination is the mean texture analysis of 15-bands Synthetic Aperture Radar (SAR) parameter in a 7 × 7 window size with an overall accuracy of 68.69% (Kappa statistic: 0.60), 70.18% (0.55), and 72.75% (0.60) in areas 1, 2, and 3, respectively. This study demonstrated a step forward for the methodology of classifying oil palm conditions using single data of PALSAR-2.

Sugarcane mono-cropping is distributed in northern Negros Island. Due to the high permeability of the underlying limestone, it has high possibility that nitrates derived from nitrogen in fertilizers, human and animal waste can easily leach to underground and cause high nitrogen load. Nitrogen load to ground surface from each nitrogen sources and that to groundwater was estimated using statistical and survey data by unit load in this study. About 80% of this area was for sugarcane cultivation, and more than 80% of nitrogen load was from nitrogen fertilizer for sugarcane cultivation. According to nitrogen balance in this area, 40-50% of nitrogen load to ground surface consist of that to underground. It will be necessary to consider the fertilizer management to reduce nitrogen load in this area, such as reduction for amount of nitrogen fertilizer application or application timing of nitrogen fertilizer in sugarcane cultivation.