The use of remote sensing data for urban studies has increased along with the availability of Very High-Resolution (VHR) satellite data such as IKONOS, Quickbird, Worldview, and the Pleiades. This study aimed to evaluate the use of Pleiades-1A imagery and object based image analysis (OBIA) method to extract the information of urban green spaces in some areas of Jakarta, Indonesia. Multiresolution segmentation and spectral difference segmentation were then applied to the imagery respectively. Support Vector Machine (SVM) was performed for the classification phase, followed by an expert-knowledge refinement. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified Soil Adjusted Vegetation Index (MSAVI) were derived from the imagery to help the classification process. The results showed two classes of landcover, that consists of ''urban green'' and ''non-urban green''. The accuracy assessment was then performed using the visual interpretation followed by field measurements as reference data. By using the area-based similarity measurement framework, this study scored 86 % for overall accuracy. The similarity measurement showed values above 87 % for all 20 samples. This study found that the proposed methods gave a more into ''similar'' results to the reference data, than the ''dissimilar''. The segmentation and classification rule set built in this study still need further study to see how effective the proposed method when applied to different cities with a different landuse/landcover characteristic.
Drought is a natural hazard indicated by the decreasing of rainfall and water storage and impacting agricultural sector. Agricultural drought assessment has been used to monitor agricultural sustainability, particularly in East Java as national agricultural production center. Identification of drought characteristics -correlated with El Niño-Southern Oscillation, and agricultural impact on paddy fields and rice production using VHI (Vegetation Health Index) were conducted. VHI is produced by TCI (Temperature Condition Index) and VCI (Vegetation Condition Index) derived from MODIS satellite data, LST (Land Surface Temperature) and EVI (Enhanced Vegetation Index), respectively. The results showed agricultural drought usually started in June, maximum in October and ended in November. Onset and end time drought tends to follow monsoonal rainfall pattern. El Niño 2015 showed long duration of agricultural drought (i.e. ± 5 months), high severity (i.e. mild-extreme drought; VHI 0-40) and areal extent of drought approx. 197,343 km 2 , while during La Niña 2010 the areal extent was approx. 28,685 km 2 with mild-severe drought (VHI 10-40). Impact of agricultural drought on paddy fields showed wider (smaller) areal extent in sub-round 3 (sub-round 1) from September-December (January-April). Areal extent of drought was negatively correlated with rice production (r=-0.79) which significant in 99 % confidence level.
Flood is the most frequent hydro-meteorological disaster in Indonesia. Flood disasters in the Bandung basin result from increasing population density, especially in the Citarum riverbank area, accompanied by land use changes in upstream of the Citarum catchment area which has disrupted the river’s function. One of the basic issues that need to be investigated is which areas of the Bandung basin are prone to flooding. This study offers an effective and efficient method of mapping flood-prone areas based on flood events that have occurred in the past through the use of historical remote sensing image data. In this research, Landsat-8 imagery was used to observe the inundated area in the Bandung basin in the past (2014–2018) using an improved algorithm, the modified normalized water index (MNDWI). The results of the study show that MNDWI is the appropriate parameter to be used to detect flooded areas in the Bandung basin area that have heterogeneous land surface conditions. The flood-prone area was determined based on flood events for 2014 to 2018, identified as inundated areas in the images. The estimation of the flood-prone area in the Bandung basin is 11,886.87 ha. Most of the flood-prone areas are in the subdistricts of Rancaekek, Bojongsoang, Solokan Jeruk, Ciparay, Cileunyi, Bale Endah and Cikancung. This area geographically or naturally is a water habitat area. Therefore, if the area will be used for residential, this will have consequences that flood will always be a threat to the area.
The observation of smoke because of land and forest fires in some regions in Indonesia mostly use the composite image visually. This study aims to develop the detection model of forest and land fire smoke using a digital analysis, which will be faster in supporting spatial information on emergency response in monitoring forest and land fire smoke. The method used is multithreshold method and compare it with the existing model that is by modification of method Li et al. (2015). The data used is Suomi NPP-VIIRS satellite imagery. The results concluded that the VIIRS image can be used to detect the smoke and smoke distribution of forest fire and digital smoke. The multi-threshold model uses reflectance data obtained from the M4 visible channel, and the brightness temperature data obtained from the LWIR VIIRS M14 channel, with an average accuracy of 82.2% with a Commision error of 9.8% and an Ommision error of 10%. While the model of modification Li is based only on reflectance of visible-channel data i.e. channel M1, M2, M3, and SWIR VIIRS M11 channel, which has an average accuracy of 72.3% with a Commision error of 0.3% and an Ommision error of 27.4%. The multithreshold model is a model that has the potential to be applied to detect forest and land fire smoke.
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