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Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.

COVID-19 is currently the hot topic of discussion by scientists because of its ability to quickly spread, in line with everyday human activities. One of the environmental factors related to climatic parameters, such as the air temperature, contributed to the spreading of COVID-19 in the last four months. Its distribution ability is no longer local as it successfully halts the important activities in many countries globally. This study aims to explain the opportunity of geospatial analysis in handling the COVID-19 distribution locally based on the characteristics of demographic data. Various data, including the confirmed positive for COVID-19, age-based population, and Landsat 8 satellite imagery data were used to determine the spatial characteristics of the COVID-19 distribution per September 2020 in Bandung, Indonesia. An inverse distance weighted (IDW), Moran's I index and local indicator spatial association (LISA), and a proposed ratio of the elderly population against the population with confirmed positive for COVID-19 (CoVE) were used as the approach to determine its distribution characteristics. The information derived from Landsat 8 satellite imagery, such as the residential area, surface temperature, and humidity based on the supervised classification, land surface temperature (LST), and the normalized difference water index (NDWI) was used to perform the analysis.  The results showed that the positive population of COVID-19 was concentrated in Bandung city. However, with a Moran's I value of 0.316, not all are grouped into the same category. There are only 8, 2, 5, and 3 districts categorized as HH, HL, LL, and LH. However, the areas with a large or small number of elderlies do not always correlate with the high number of confirmed positives for COVID-19. There are only 3, 1, and 3 districts classified as HH, HL, and LL. They were represented by the values of Moran's I, for about 0.057. The positive relationship between confirmed positive for COVID-19 and the built-up area, surface temperature, humidity, and the elderly population based on the coefficient of determination (R2) were 0.03, 0.28, 0.25, and 0.019, respectively. The study also shows that the vulnerability of those areas is relatively low. The study shows that the vulnerabilities in these areas are relatively low and the recommendation for COVID-19 widespread mitigation has to consider the demographic characteristics precisely in the large scale social restrictions (LSSR).

Assessing land quality has important use in understanding the capability of soil in producing food. The area of paddy fields in Majalaya Subdistrict is located around the industrial zone and this situation is urgent to understand the land quality of paddy field due to the influence effect of industrial waste to its growth. A combination of regression model and Landsat 8 image to estimate soil pH distribution is used to predict the land quality. The result of this study is shown that the regression model of red and near infrared (NIR) band combination is used to predict soil pH has been successfully given the smallest error (RMSe) as the soil pH accuracy is 1.18 and related to the land quality assessment based on predicted soil pH is shown that in the whole area of paddy field has the acid situation of soil pH.Keywords: Spectral, Soil pH; Regression, Land Quality; Land  Suitability

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Drought monitoring is important for the paddy planting planning. Remote sensing is one tool can be used for it. Paddy field monitoring based on the soil moisture gives much knowledge related to the water content in the soil. Soil moisture analysis in this study is using Normalized Different Water Index (NDWI), Linear Soil Moisture (LSM), and Tasseled Cap. Soil moisture change could explain based on calculation results of NDWI, Linear Soil Moisture (LSM), and Tasseled Cap Transformation (TCT). Based on the results has explained that the driest year occurs in 2015 and June 2016 has a higher soil moisture. Comparison with the radar shows that the results of soil moisture analysis with Landsat was effective can be used with results relatively close to the radar results.