Existing drought-impacted data are generally rooted from individual reports, which under-represent spatial information. To improve the report, some meteorological satellites have been employed. Nonetheless, due to lacking of spatial resolution, the scale of the data is often excessively coarse. With the availability of long-term Landsat data, estimated extent of drought has been studied. One of the latest methods for this purpose is Vegetation Supply Water Index (VSWI). VSWI is defined as a ratio between vegetation index (in this case NDVI) and land surface temperature (LST, presented in Kelvin). Both data can be derived from remote sensing data containing multispectral reflectance and thermal data, which are available in Landsat data after calibration procedure. In this research, Landsat 7 sensor was applied considering its temporal span. Landsat data were atmospherically corrected to avoid misinterpretation of the results. We found that VSWI can accommodate various state of drought in agricultural fields. Severely affected fields are represented in dark tone, illustrating the absence of vegetation cover when surface temperature rises. Nonetheless, shortcomings of the technique are visually observable. Based on two kinds of rice field (irrigated and rainfed) coupled with two states of field condition (wet and dry), we conclude that dried and waterlogged irrigated rice fields are inseparable due similar value of NDVI. In contrast, vegetated rice field has fairly high VSWI value. The results indicate that further analysis incorporating water index can improve discrimination process.
According to The State of Indonesia’s Forest 2020 report, 23.96 M ha of peat ecosystems in Indonesia are currently damaged. Peat ecosystems have a high level of vulnerability to landscape changes. Some of the main functions of peatland are ecological conservation, energy, and agriculture. One of major agricultural activities on peatland is oil palm plantation. Extra strict land management is needed to reach sustainability and minimize disaster (e.g., land fires) following The Guidelines for the Utilization of Peatlands for Oil Palm Cultivation, regulated by the Minister of Agriculture No. 14/2009. Still, many other policies also control aspects of protection and cultivation on peatlands. The research focused on analyzing dynamics of spatial policy on peatlands for oil palm cultivation. Spatial policies used included spatial pattern of Jambi Province, land permits, and maps of the status of forest areas. This study analyzed inconsistency of policies on spatial patterns, permits, and forest areas status in oil palm plantations on peatland. This research was conducted in Jambi Province, where 13.86% (121,290 ha) was on peatland. The analysis used a logical alignment matrix and GIS. The results showed that oil palm plantations on peatland with HGU/other permits generally aligned to spatial planning, permit, and forest area status. Its area reached 50,598 ha. Peatland utilization should go through suitable technical planning stages and considering actual land use/tenure and water system functioning.
Coronavirus disease 2019 (COVID-19) has been spread globally and brought health and socioeconomic issues. Jakarta tried to accommodate health and economic interests through the Large-Scale Social Restriction (LSSR) policy that should be assessed. This study aims to (1) visualize the spatial patterns of confirmed Covid-19 cases and the locations of potential risk of transmission, and (2) determine the spatial processes underlying the spatial patterns of Covid-19 cases. The emerging hot spot analysis and space-time scan statistic were employed to analyze the dynamic of infected cases and transmission risk. A Geographical Weighted Regression (GWR) model was developed to define factors that influence the spatial transmission. The result shows that spatial transmission keeps continuing, despite a decline in the aggregate pandemic curve during LSSR implementation. This was likely affected by settlements types and population density distribution, and transportation networks. Spatial analysis supports the aggregate pandemic curve to increase the pandemic surveillance effectiveness.
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