Leaf disease in rubber leaves causes a significant effect on latex production, especially Pestalotiopsis sp. This disease has caused a massive leaf fall in many plantations in Indonesia. Hyperspectral-based analysis can identify the difference in spectral leaf due to disease. The site location is Sembawa Rubber Research Institute in Sembawa, Palembang, Indonesia. Further, this study also compared other fungi leaf diseases, namely Oidium sp., Collectotrichum sp., and Corrynesspora sp. The leaf spectral reflectance was measured using Ocean Optics USB 2000+ UV-VIS Spectrometer, which measured the spectral response from 350-850 nm. This study aims at (a) defining the spectral signature of rubber leaf infected by leaf fall disease, b) analyzing whether the spectral response of infected leaf can be distinguished, and (c) defining the most useful wavelengths for discriminating spectral responses. The methodology used in this study is spectral response curve, principal components, and partial least square discriminant analyses. As a result, the average spectra indicated differences in color-infected leaves in the chlorophyll-associated wavelengths 490-589, 681–685, 718-752, and 839-875 nm. At the same time, the identification of disease type based on the spectral response found that it is not separable due to high similarity in the spectral curve.
Drought explains a stage of water scarcity at a particular time in a certain location. In land, drought has impacts in many ways. Crop failure or “puso” will occur from a short-term drought, while economic disruption might happen in a long-term drought period. Several parts of Indonesia were periodically impacted by drought during the dry season, including Indramayu Regency. As one of the largest rice barns in West Java, 63% of Indramayu is covered by the rice field. Therefore, monitoring agricultural drought in such locations is challenging due to the extensive damage caused by the event. This study aims to analyze the spatial and temporal distribution of drought in Indramayu’s rice fields. This study utilizes the Normalized Difference Drought Index (NDDI) generated from Landsat 8 OLI-TIRS to identify drought locations. Observation is carried out during the dry months of July, August, September, and October. Most severe drought areas were found in October, especially in three sub-districts, including Gantar, Kroya, and Haurgeulis. Statistical-based correlation analysis shows a significant relationship between NDDI and distance from the irrigation network, although a relatively stronger correlation was found between NDDI and crop productivity. Conclusively, NDDI was successfully applied to identify drought areas in Indramayu Regency.
The 2019 Indian Ocean Dipole (IOD) event has impacted Indonesia in many ways. The extreme weather events due to the changes in rainfall patterns and increased average temperature were causing severe agricultural drought in some areas, including Bekasi Regency. Monitoring agricultural drought is challenging due to the nature and extent of the damage caused by the event. This study aims to identify some of the agricultural drought events in Bekasi based on soil moisture features (SMI). The approximate agricultural drought model was generated from Normalized Difference Drought Index (NDDI), while soil moisture information was derived from the Soil Moisture Index (SMI). Landsat 8 OLI-TIRS was utilized for generating both indexes. The analysis was carried out in the dry months of 2019, including July, August, September, and October, where the lowest rainfalls were found. The study founds that more than 50% of the area was damaged by severe drought every month. Most of the severe drought occurred in September, damaging 50,919.32 hectares (91%) of rice fields. Statistical-based Pearson’s correlation shows a significant relationship between NDDI and SMI, with R coefficient ranges from -0.37 to -0.74, especially from July to October. Conclusively, both indexes were successfully applied to picture agricultural drought phenomena in Bekasi Regency.
The national law in Indonesia stated that cities are required to have 30% of green open space from the total area. Tangerang City is one of the capital's buffer cities that has been continuously grown since the 1990s. The development of the city is quite rapid, and the emergence of various activities such as household activities, transportation, and industry are encouraging changes in green open space areas. Data from the regional statistic shows that open green space in Tangerang City is only 12.56% (2,319.21 hectares) of the total area. The less open green space area might degrade its main function as an absorber of emissions or pollutants, especially for carbon dioxide gas (CO2). This study aims to estimate the biomass, total CO2 stored by vegetation. This study uses direct measurements and vegetation index to formulate the ideal biomass formula. The pre-field activities begin with the extraction of NDVI (Normalized Differential Vegetation Index) and EVI (Enhanced Vegetation Index) from SPOT 7 imageries. The biomass allometric formulas used in this study are the Brown and Lugo equation, to find biomass values from tree stand parameters such as diameter breast height (DBH) and tree height. Quantitative and spatial analysis used in this study is a regression analysis of biomass and vegetation index value. The results show EVI has a better regression value and total biomass of around 26 million kilograms.
Paddy is a strategic commodity in Indonesia. Paddy crop divided into hundreds of varieties with diverse characteristics. Therefore, information about the characteristics of each rice variety is needed. Also, several studies on the spectral characteristics of rice varieties have been carried out. These studies applied the vegetation indices approach to plant canopies. The aim of this study is detecting the spectral characteristics of rice varieties based on vegetation indices. Several vegetation indices, derived from Red, Green, Blue (RGB) bands, namely Excess Green Vegetation Index (ExG), Normalized Green Red Difference Index (NGRDI), and Visible Atmospherically Resistant Index (VARI). Paddy field image derived from Unmanned Aerial Vehicle (UAV) was carried out to analyzed three rice varieties namely Ciherang, IR 64, and IR 42. The result showed that three rice varieties in Bekasi Regency have diverse spectral characteristics. It evident from the spectral minimum-maximum value of each variety, especially using the NGRDI. Ciherang has the highest spectral value (at the beginning of growth) and IR 42 has the highest spectral value (at the middle and end of growth).
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