International Journal of Remote Sensing and Earth Sciences (IJReSES)
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APPLICATION OF LAPAN A3 SATELLITE DATA FOR THE IDENTIFICATION OF PADDY FIELDS USING OBJECT BASED IMAGE ANALYSIS (OBIA)
Full text linkThe role of agriculture is directly related to SDG No.2, which is running a programme until 2030 to reduce national poverty, eradicate hunger by increasing food security and improving nutrition and support sustainable agriculture. Problems faced include the reduction in agricultural land, which results in lower rice production, and the limited information on the monitoring of paddy fields using spatial data. The purpose of this study is to identify paddy fields using LAPAN A3 satellite imagery based on OBIA classification. The data used were from LAPAN A3 multispectral imagery dated 19 June 2017, Landsat 8 imagery dated 17 June 2017, DEM SRTM (BIG), and the Administrative Boundary Map (BIG). The analysis method was segmentation by grouping image pixels, and supervised classification by taking several sample areas based on Random Stratified Sampling. The results will be carried using a confusion matrix. The classification results produced four classes; watery paddy fields, vegetation paddy fields, fallow paddy fields, and non-paddy fields, using of the green, red, and NIR bands for the LAPAN A3 data. From the results of the segmentation process, there remain some oversegmented features in the appearance of the same object. Oversegmentation is due to an inaccurate value assignment to each algorithm parameter when the segmentation process is performed. For example, watery paddy fields appear almost the same as open land (fallow paddy fields), the water object is darker purple. The visual classification results (Landsat 8 data) are considered as the reference for the digital classification results (LAPAN A3). Forty-eight samples were taken and divided into four classes, with each class consisting of 12 samples. The results of the accuracy test show that the total accuracy of the object-based digital classification for visual classification is 62.5% with a Kappa accuracy value of 0.5. The conclusion is that LAPAN A3 data can be used to identify paddy fields based on spectral resolution and to complement Landsat 8 data. To improve the accuracy of the classification results, more samples and the correct RGB composition are needed
MAPPING BURNT AREAS USING THE SEMI-AUTOMATIC OBJECT-BASED IMAGE ANALYSIS METHOD
Full text linkForest and land fires in Indonesia take place almost every year, particularly in the dry season and in Sumatra and Kalimantan. Such fires damage the ecosystem, and lower the quality of life of the community, especially in health, social and economic terms. To establish the location of forest and land fires, it is necessary to identify and analyse burnt areas. Information on these is necessary to determine the environmental damage caused, the impact on the environment, the carbon emissions produced, and the rehabilitation process needed. Identification methods of burnt land was made both visually and digitally by utilising satellite remote sensing data technology. Such data were chosen because they can identify objects quickly and precisely. Landsat 8 image data have many advantages: they can be easily obtained, the archives are long and they are visible to thermal wavelengths. By using a combination of visible, infrared and thermal channels through the semi-automatic object-based image analysis (OBIA) approach, the study aims to identify burnt areas in the geographical area of Indonesia. The research concludes that the semi-automatic OBIA approach based on the red, infrared and thermal spectral bands is a reliable and fast method for identifying burnt areas in regions of Sumatra and Kalimantan
DETECTION AND ANALYSIS OF SURFACE URBAN COOL ISLAND USING THERMAL INFRARED IMAGERY OF SALATIGA CITY, INDONESIA
Full text linkThe detection and monitoring of the dynamics of urban micro-climatesneeds to be performedeffectively, efficiently, consistently and sustainably inan effort to improve urban resilience to suchphenomena. Thermal remote sensing posesses surface thermal energy detection capabilities which can be converted into surface temperatures and utilised to analyse the urban micro-climate phenomenon overlarge areas, short periods of time, and at low cost. This paper studies the surface urban cool island (SUCI) effect, the reverse phenomenon of the surface urban heat island (SUHI) effect, in an effort to provide cities with resistance to the urban microclimate phenomenon.The study also aims to detect urban micro-climate phenomena, and to calculate the intensity and spatial distribution of SUCI. The methods used include quantitative-descriptive analysis of remote sensing data, including LST extraction, spectral transformation, multispectral classification for land cover mapping, and statistical analysis. The results show that the urban micro-climate phenomenon in the form of SUHI in the middle of the city of Salatiga is due to the high level of building density in the area experiencing the effect, which mostly has a normal surface temperature based on the calculation of the threshold, while the relative SUCI occurs at the edge of the city. SUCI intensity in Salatiga ranges between -6.71°C and0°C and is associated with vegetation
OPTIMIZATION OF RICE FIELD CLASSIFICATION MODEL BASED ON THRESHOLD INDEX OF MULTITEMPORAL LANDSAT IMAGES
Full text linkThe development of rice land classification models in 2018 has shown that the phenology-based threshold of rice crops from the multi-temporal Landsat image index can be used to classify rice fields relatively well. The weakness of the models was the limitations of the research area, which was confined to the Subang region, West Java, so it is was deemed necessary to conduct further research in other areas. The objective of this study is to obtain optimal parameters of classification model of rice and land based on multi-temporal Landsat image indexes. The study was conducted in several districts of rice production centers in South Sulawesi and West Java (besides Subang). The threshold method was employed for the Landsat Image Enhanced Vegetation Index (EVI). Classification accuracy was calculated in two stages, the first using detailed scale reference information on rice field base, and the second using field data (from a survey). Based on the results of the analysis conducted on several models, the highest accuracy is generated by the three index parameter models (EVI_min, EVI_max, and EVI_range) and adjustable threshold with 94.8% overall accuracy. Therefore this model was acceptable for used for nationally rice fields mapping
ANALYSIS OF WATER PRODUCTIVITY IN THE BANDA SEA BASED ON REMOTE SENSING SATELLITE DATA
Full text linkAbstract. This study examines the density of potential fishing zone (PFZ) points and chlorophyll-a concentration in the Banda Sea. The data used are those on chlorophyll-a from the Aqua MODIS satellite, PFZ points from ZAP and the monthly southern oscillation index. The methods used are single image edge detection, polygon center of mass, density function and a Hovmoller diagram. The result of the analysis show that productivity of chlorophyll-a in the Banda Sea is influenced by seasonal factors (dry season and wet season) and ENSO phenomena (El Niño and La Niña). High productivity of chlorophyll-a  occurs during in the dry season with the peak in August, while low productivity occurs in the wet season and the transition period, with the lowest levels in April and December. The variability in chlorophyll-a production is influenced by the global El Niño and La Niña phenomena; production increases during El Niño and decreases during La Niña. Tuna conservation areas have as lower productivity of chlorophyll-a and PFZ point density compared to the northern and southern parts of the Banda Sea. High density PFZ point regions are associated with regions that have higher productivity of chlorophyll-a, namely the southern part of the Banda Sea, while low density PFZ point areas are associated with regions that have a low productivity of chlorophyll-a, namely tuna conservation areas. The effect of the El Niño phenomenon in increasing chlorophyll-a concentration is stronger in the southern part of study area than in the tuna conservation area. On the other hand, the effect of La Niña phenomenon in decreasing chlorophyll-a concentration is stronger in the tuna conservation area than in the southern and northern parts of the study area.Â
ASSESSMENT OF THE ACCURACY OF DEM FROM PANCHROMATIC PLEIADES IMAGERY (CASE STUDY: BANDUNG CITY. WEST JAVA)
Full text linkPleiades satellite imagery is very high resolution. with 0.5 m spatial resolution in the panchromatic band and 2.5 m in the multispectral band. Digital elevation models (DEM) are digital models that represent the shape of the Earth's surface in three-dimensional (3D) form. The purpose of this study was to assess DEM accuracy from panchromatic Pleaides imagery. The process conducted was orthorectification using ground control points (GCPs) and the rational function model with rational polynomial coefficient (RFC) parameters. The DEM extraction process employed photogrammetric methods with different parallax concepts. Accuracy assessment was made using 35 independent check points (ICPs) with an RMSE accuracy of ± 0.802 m. The results of the Pleaides DEM image extraction were more accurate than the National DEM (DEMNAS) and SRTM DEM. Accuracy testing of DEMNAS results showed an RMSE of ± 0.955 m. while SRTM DEM accuracy was ± 17.740 m. Such DEM extraction from stereo Pleiades panchromatic images can be used as an element on base maps with a scale of 1: 5.000
UTILISATION OF NASA - GFWED AND FIRMS SATELLITE DATA IN DETERMINING THE PROBABILITY OF HOTSPOTS USING THE FIRE WEATHER INDEX (FWI) IN OGAN KOMERING ILIR REGENCY, SOUTH SUMATRA
Full text linkPrevention and mitigation of forest and land fires have important roles considering its various negative impacts. Throughout 2018, in Ogan Komering Ilir District, 864 hectares of land burned. This data increased significantly compared to the burned area in the previous year. Lack of field meteorological observation is still a problem in solving the problem of forest fire in the region. Consequently, we utilize NASA - GFWED and FIRMS satellite data to analyze the hotspots probabilities in Ogan Komering Ilir District, South Sumatra. Conditional probability analysis will be used to find out the likelihood of hotspots based on FWI and FFMC from 2001 to 2016. More than 50 percent of hotspots appear during extreme FFMC class and high to extreme FWI class. The probability of hotspots for extreme FFMC class and extreme FWI class varied between 0.3 to 10.4 % and 0.1 to 3.8 % respectively. Meanwhile, fire-prone areas with the highest density of fires are in the sub-district of Tulung Selapan, and the safest region is the Cengal sub-district
DETECTING THE SURFACE WATER AREA IN CIRATA DAM UPSTREAM CITARUM USING A WATER INDEX FROM SENTINEL-2
Full text linkThis paper describes the detection of the surface water area in Cirata dam, Â upstream Citarum, using a water index derived from Sentinel-2. MSI Level 1C (MSIL1C) data from 16 November 2018 were extracted into a water index such as the NDWI (Normalized Difference Water Index) model of Gao (1996), McFeeters (1996), Roger and Kearney (2004), and Xu (2006). Water index were analyzed based on the presence of several objects (water, vegetation, soil, and built-up). The research resulted in the ability of each water index to separate water and non-water objects. The results conclude that the NDWI of McFeeters (1996) derived from Sentinel-2 MSI showed the best results in detecting the surface water area of the reservoir
A COMPARISON OF RAINFALL ESTIMATION USING HIMAWARI-8 SATELLITE DATA IN DIFFERENT INDONESIAN TOPOGRAPHIES
Full text linkThe Himawari-8 satellite can be used to derive precipitation data for rainfall estimation. This study aims to test several methods for suchestimation employing the Himawari-8 satellite. The methods are compared in three regions with different topographies, namely Bukittinggi, Pontianak and Ambon. The rainfall estimation methods that are tested are auto estimator, IMSRA, non-linear relation and non-linear inversion approaches. Based on the determination of the statistical verification(RMSE, standard deviation and correlation coefficient) of the amount of rainfall, the best method in Bukittinggi and Pontianak was shown to be IMSRA, while for the Ambon region was the non-linear relations. The best methods from each research area were mapped using the Google Maps Application Programming Interface (API)
BATHYMETRIC EXTRACTION USING PLANETSCOPE IMAGERY (CASE STUDY: KEMUJAN ISLAND, CENTRAL JAVA)
Full text linkBathymetry refers to the depth of the seabed relative to the lowest water level. Depth information is essential for various studies of marine resource activities, for managing port facilities and facilities, supporting dredging operations, and predicting the flow of sediment from rivers into the sea. Bathymetric mapping using remote sensing offers a more flexible, efficient,and cost-effective method and covers a largearea. This study aims to determine the ability of Planet Scope imagery to estimate and map bathymetry and to as certain its accuracy using the Stumpf algorithm on the in-situ depth data. PlanetScope level 3B satellite imagery and tide-corrected survey dataare employed; satellite images are useful in high-precision bathymetry extraction.The bathymetric extraction method used the Stumpf algorithm. The research location was Kemujan Island, Karimunjawa Islands, Central Java. The selection of this region wasbased on its water characteristics, which have a reasonably high variation in depth. Based on the results of the data processing, it was found that the PlanetScope image data were able to estimate depths of up to 20 m. In the bathymetric results, the R2 accuracy value was 0.6952, the average RMSE value was 2.85 m,and the overall accuracy rate was 71.68%