ORCID Profile
0000-0001-9110-8401
Current Organisation
Badan Riset dan Inovasi Nasional Republik Indonesia
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Publisher: Elsevier BV
Date: 11-2023
Publisher: MDPI AG
Date: 08-04-2021
DOI: 10.3390/RS13081446
Abstract: This study assessed the accuracy of land cover change (2000–2018) maps compiled from Landsat images with either automated digital processing or with visual interpretation for a tropical forest area in Indonesia. The accuracy assessment used a two-stage stratified random s ling involving a confusion matrix for assessing map accuracy and by estimating areas of land cover change classes and associated uncertainty. The reference data were high-resolution images from SPOT 6/7 and high-resolution images finer than 5 m obtained from Open Foris Collect Earth. Results showed that the map derived from automated digital processing had lower accuracy (overall accuracy 73–77%) compared to the map based on visual interpretation (overall accuracy 80–84%). The automated digital processing map error was in differentiating between native forest and plantation areas. While the visual interpretation map had a higher accuracy, it did not consistently differentiate between native forest and shrub areas. Future improvement of the digital map requires more accurate differentiation between forest and plantation to better support national forest monitoring systems for sustainable forest management.
Publisher: EDP Sciences
Date: 2019
DOI: 10.1051/E3SCONF/20199404003
Abstract: High resolution images data from Terrasar-X are used to extract digital elevation model (DEM) using stereo radargrammetry in the attempt to achieve better resolution of terrain surface in Indonesia. As s le in this study, stereo pairs images from TerraSAR-X StripMap mode (~3m resolution) on Madiun city is used with difference of incidence angle around ~18.88 degree to extract the elevation of the area. Furthermore, field observation on the selected area will be used on elevation accuracy assessment. The digital surface elevation (DSM) generated by stereo radargrammetry in this study shows us high resolution with spatial pixel spacing 5.57 meter and elevation accuracy around ~4 meter.
Publisher: Insight Society
Date: 27-12-2017
Publisher: IOP Publishing
Date: 06-2019
DOI: 10.1088/1755-1315/280/1/012013
Abstract: Landsat-8 has various channels that function to identify an object. The vegetation index algorithm which is based on remote sensing involves several bands and can describe the percentage of canopy and density of vegetation. More than 100 vegetation index algorithms and each can be used in accordance with the research objectives. In this paper we will discuss the utilization of Landsat-8 metric data with the parameters of Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) and several parameters in metric data with various features to produce indications of rapid land change, especially to detect changes in tree cover area to lose tree cover and vice versa. For this purpose, the annual Landsat-8 metrics data is located in Riau Province. To compare both NDVI and NBR parameters, the trial and error method is used and the results are compared visually to the two different images of the year. The result is that the NBR parameters with a maximum-70 feature and the threshold for tree cover loss and tree cover gain respectively more than -0.1 provide tangible results in looking at the tree cover changes in Riau Province. In the analysis, other information is needed, for ex le, a map of the Forest Area to see further whether the changes that occur are in the forest area or not, which will certainly provide different treatment.
Publisher: Indonesian National Institute of Aeronautics and Space (LAPAN)
Date: 12-04-2017
DOI: 10.30536/J.IJRESES.2014.V11.A2607
Abstract: This research analyzed the radiometric correction method using SPOT-4 imageries to produce the same reflectance for the same land cover. Top of Atmosphere (TOA) method was applied in previous radiometric correction approach, this TOA approach was upgraded with the reflectance effect from difference satellite viewing angle. The 250 scene of Central Kalimantan SPOT-4 imageries from 2006 until 2012 with varies viewing angle was used. This research applied two-step approaches, the first step is TOA correction, and the second step is normalization using a linear function of reflectance and satellite viewing angle. Gain and offset coefficient of this linear function was calculated using an iterative approach to producing the same reflectance in the forest area. The target of iterative processed is to minimize the standard deviation of a digital number from a forest area in the selected region. The result shows that the standard deviation of a digital number from a forest area in the two steps approach are 8.6, 16.5, and 16.8 for band 1, band 3 and band 4. These values are smaller compared with the standard deviation of digital number result from TOA approach are 15.0, 28,3 and 34.7 for band 1, band 3 and band 4. Decreasing the standard deviation shows the homogeneity of forest reflectance that could be seen in the seamless result. This algorithm can be applied for making seamless SPOT-4 mosaic whole of Indonesia.
Publisher: Informa UK Limited
Date: 21-11-2022
Publisher: Indonesian National Institute of Aeronautics and Space (LAPAN)
Date: 21-06-2017
DOI: 10.30536/J.IJRESES.2015.V12.A2692
Abstract: Almost every dry season, there are large forest/land fires in several regions in Indonesia, especially in Kalimantan and Sumatra in the dry season of August to September 2015 a forest fire in 6 provinces namely West Kalimantan, Central Kalimantan, South Kalimantan, Riau, Jambi, and South Sumatra. Even some parties proposed that the Government of Indonesia declares them as a national disaster. The low-resolution remote sensing data have been widely used for monitoring the occurrence of forest/land fires (hotspots), and mapping of burnt scars. The hotspot detection was done by utilizing the data of NOAA-AVHRR and MODIS data which have a lower spatial resolution (1 km). In order to increase the level of detail and accuracy of product information, this research is done by using Landsat 8 TIRS (Thermal Infrared Sensor) band which has a greater spatial resolution of 100 m. The purpose of this research is to find and to determine the threshold value of the brightness temperature of the TIRS data to identify the source of fire smoke. The data used is the Landsat 8 of several parts of Borneo during the period of 24 August to 18 September 2015 recorded by the LAPAN's receiving station. Landsat - 8 TIRS band was converted into brightness temperature in degrees Celsius, then dots in a region that is considered the source of the smoke if the temperature of each pixel in the region 43oC, and given the attributes with the highest temperatures of the pixels in the region. The source of the smoke was obtained through visual interpretation of the objects in the multispectral Natural Color Composite (NCC) and True Color Composite (TCC) images. Analysis of errors (commission error) is obtained by comparing the temperature detected by TIRS band with a visual appearance of the source of the smoke. The result of the experiment showed that there were detected 9 scenes with high temperatures over 43oC from the 27 scenes Kalimantan Landsat 8 data, which include 153 sites. The accuracy (commission error) of identification results using temperature ≥ 51°C is 0%, temperature ≥ 47°C is 10%, and temperature ≥ 43°C is 30.5%.
Publisher: MDPI AG
Date: 21-12-2021
DOI: 10.3390/RS14010003
Abstract: Over the last 18 years, Indonesia has experienced significant deforestation due to the expansion of oil palm and rubber plantations. Accurate land cover maps are essential for policymakers to track and manage land change to support sustainable forest management and investment decisions. An automatic digital processing (ADP) method is currently used to develop land cover change maps for Indonesia, based on optical imaging (Landsat). Such maps produce only forest and non-forest classes, and often oil palm and rubber plantations are misclassified as native forests. To improve accuracy of these land cover maps, this study developed oil palm and rubber plantation discrimination indices using the integration of Landsat-8 and synthetic aperture radar Sentinel-1 images. Sentinel-1 VH and VV difference ( .5 dB) and VH backscatter intensity were used to discriminate oil palm plantations. A combination of Landsat-8 NDVI, NDMI with Sentinel-1 VV and VH were used to discriminate rubber plantations. The improved map produced four land cover classes: native forest, oil palm plantation, rubber plantation, and non-forest. High-resolution SPOT 6/7 imagery and ground truth data were used for validation of the new classified maps. The map had an overall accuracy of 92% producer’s accuracy for all classes was higher than 90%, except for rubber (65%), and user’s accuracy was over 80% for all classes. These results demonstrate that indices developed from a combination of optical and radar images can improve our ability to discriminate between native forest and oil palm and rubber plantations in the tropics. The new mapping method will help to support Indonesia’s national forest monitoring system and inform monitoring of plantation expansion.
Location: Indonesia
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