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International Journal of Remote Sensing and Earth Sciences (IJReSES)
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Back Pages IJReSES Vol. 15, No. 1(2018)

Editor, Journal

International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Back Pages IJReSES Vol. 15, No. 1(2018)

DETERMINATION OF THE BEST METHODOLOGY FOR BATHYMETRY MAPPING USING SPOT 6 IMAGERY: A STUDY OF 12 EMPIRICAL ALGORITHMS

Manessa, Masita Dwi Mandini, Haidar, Muhammad, Hartuti, Maryani, Kresnawati, Diah Kirana

International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Abstract

For the past four decades, many researchers have published a novel empirical methodology for bathymetry extraction using remote sensing data. However, a comparative analysis of each method has not yet been done. Which is important to determine the best method that gives a good accuracy prediction. This study focuses on empirical bathymetry extraction methodology for multispectral data with three visible band, specifically SPOT 6 Image. Twelve algorithms have been chosen intentionally, namely, 1) Ratio transform (RT); 2) Multiple linear regression (MLR); 3) Multiple nonlinear regression (RF); 4) Second-order polynomial of ratio transform (SPR); 5) Principle component (PC); 6) Multiple linear regression using relaxing uniformity assumption on water and atmosphere (KNW); 7) Semiparametric regression using depth-independent variables (SMP); 8) Semiparametric regression using spatial coordinates (STR); 9) Semiparametric regression using depth-independent variables and spatial coordinates (TNP), 10) bagging fitting ensemble (BAG); 11) least squares boosting fitting ensemble (LSB); and 12) support vector regression (SVR). This study assesses the performance of 12 empirical models for bathymetry calculations in two different areas: Gili Mantra Islands, West Nusa Tenggara and Menjangan Island, Bali. The estimated depth from each method was compared with echosounder data; RF, STR, and TNP results demonstrate higher accuracy ranges from 0.02 to 0.63 m more than other nine methods. The TNP algorithm, producing the most accurate results (Gili Mantra Island RMSE = 1.01 m and R2=0.82, Menjangan Island RMSE = 1.09 m and R2=0.45), proved to be the preferred algorithm for bathymetry mapping.

Front Pages IJReSES Vol. 14, No. 2(2017)

Editor, Journal

International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Front Pages IJReSES Vol. 14, No. 2(2017)

CARBON STOCK ESTIMATION OF MANGROVE VEGETATION USING REMOTE SENSING IN PERANCAK ESTUARY, JEMBRANA DISTRICT, BALI

Hastuti, Amandangi Wahyuning, Suniada, Komang Iwan, Islamy, Fikrul

International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Abstract

Mangrove vegetation is one of the forest ecosystems that offers a potential of substantial greenhouse gases (GHG) emission mitigation, due to its ability to sink the amount of CO2 in the atmosphere through the photosynthesis process. Mangroves have been providing multiple benefits either as the source of food, the habitat of wildlife, the coastline protectors as well as the CO2 absorber, higher than other forest types. To explore the role of mangrove vegetation in sequestering the carbon stock, the study on the use of remotely sensed data in estimating carbon stock was applied. This paper describes an examination of the use of remote sensing data particularly Landsat-data with the main objective to estimate carbon stock of mangrove vegetation in Perancak Estuary, Jembrana, Bali. The carbon stock was estimated by analyzing the relationship between NDVI, Above Ground Biomass (AGB) and Below Ground Biomass (BGB). The total carbon stock was obtained by multiplying the total biomass with the carbon organic value of 0.47. The study results show that the total accumulated biomass obtained from remote sensing data in Perancak Estuary in 2015 is about 47.20±25.03 ton ha-1 with total carbon stock of about 22.18±11.76 tonC ha-1and CO2 sequestration 81.41±43.18 tonC ha-1.

Back Pages IJReSES Vol. 14, No. 2(2017)

Editor, Journal

International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Back Pages IJReSES Vol. 14, No. 2(2017)

CAN THE PEAT THICKNESS CLASSES BE ESTIMATED FROM LAND COVER TYPE APPROACH?

Trisakti, Bambang, Julzarika, Atriyon, Nugroho, Udhi C., Yudhatama, Dipo, Lasmana, Yudi

International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Abstract

Indonesia has been known as a home of the tropical peatlands. The peatlands are mainly in Sumatera, Kalimantan and Papua Islands. Spatial information on peatland depth is needed for the planning of agricultural land extensification. The research objective was to develop a preliminary estimation model of peat thickness classes based on land cover approach and analyse its applicability using Landsat 8 image. Ground data, including land cover, location and thickness of peat, were obtained from various surveys and peatlands potential map (Geology Map and Wetlands Peat Map). The land cover types were derived from Landsat 8 image. All data were used to build an initial model for estimating peat thickness classes in Merauke Regency. A table of relationships among land cover types, peat potential areas and peat thickness classes were made using ground survey data and peatlands potential maps of that were best suited to ground survey data. Furthermore, the table was used to determine peat thickness classes using land cover information produced from Landsat 8 image. The results showed that the estimated peat thickness classes in Merauke Regency consist of two classes, i.e., very shallow peatlands and shallow peatlands. Shallow peatlands were distributed at the upper part of Merauke Regency with mainly covered by forest. In comparison with Indonesia Peatlands Map, the number of classes was the two classes. The spatial distribution of shallow peatlands was relatively similar for its precision and accuracy, but the estimated area of shallow peatlands was greater than the area of shallow peatlands from Indonesia Peatlands Map. This research answered the question that peat thickness classes could be estimated by the land cover approach qualitatively. The precise estimation of peat thickness could not be done due to the limitation of insitu data.  

DETECTING THE AREA DAMAGE DUE TO COAL MINING ACTIVITIES USING LANDSAT MULTITEMPORAL (Case Study: Kutai Kartanegara, East Kalimantan)

suwarsono, nFn, Haryani, Nanik Suryo, Prasasti, Indah, Fitriana, Hana Listi, Priyatna, M., Khomarudin, M. Rokhis

International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Abstract

Coal is one of the most mining commodities to date, especially to supply both national and international energy needs. Coal mining activities that are not well managed will have an impact on the occurrence of environmental damage. This research tried to utilize the multitemporal Landsat data to analyze the land damage caused by coal mining activities. The research took place at several coal mine sites in East Kalimantan Province. The method developed in this research is the method of change detection. The study tried to know the land damage caused by mining activities using NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), NDWI (Normalized Difference Water Index) and GEMI (Global Environment Monitoring Index) parameter based change detection method. The results showed that coal mine area along with the damage that occurred in it can be detected from multitemporal Landsat data using NDSI value-based change detection method. The area damage due to coal mining activities  can be classified into high, moderate, and low classes based on the mean and standard deviation of NDSI changes (ΔNDSI). The results of this study are expected to be used to support government efforts and mining managers in post-mining land reclamation activities.

IJReSES Vol. 14 No. 2 December 2017

Editor, Journal

International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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IJReSES Vol. 14 No. 2 December 2017

SPATIAL PROJECTION OF LAND USE AND ITS CONNECTION WITH URBAN ECOLOGY SPATIAL PLANNING IN THE COASTAL CITY, CASE STUDY IN MAKASSAR CITY, INDONESIA

Amri, Syahrial Nur, Adrianto, Luky, Bengen, Dietriech Geoffrey

International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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The arrangement of coastal ecological space in the coastal city area aims to ensure the sustainability of the system, the availability of local natural resources, environmental health and the presence of the coastal ecosystems. The lack of discipline in the supervision and implementation of spatial regulations resulted in inconsistencies between urban spatial planning and land use facts. This study aims to see the inconsistency between spatial planning of the city with the real conditions in the field so it can be used as an evaluation material to optimize the planning of the urban space in the future. This study used satellite image interpretation, spatial analysis, and projection analysis using markov cellular automata, as well as consistency evaluation for spatial planning policy. The results show that there has been a significant increase of open spaces during 2001-2015 and physical development was relatively spreading irregularly and indicated the urban sprawl phenomenon. There has been an open area deficits for the green open space in 2015-2031, such as integrated maritime, ports, and warehousing zones. Several islands in Makassar City are predicted to have their built-up areas decreased, especially in Lanjukang Island, Langkai Island, Kodingareng Lompo Island, Bone Tambung Island, Kodingareng Keke Island and Samalona Island. Meanwhile, the increase of the built up area is predicted to occur in Lumu Island, Barrang Caddi Island, Barrang Lompo Island, Lae-lae Island, and Kayangan Island. The land cover is caused by the human activities. Many land conversions do not comply with the provision of percentage of green open space allocation in the integrated strategic areas, established in the spatial plan. Thus, have the potential of conflict in the spatial plan of marine and small islands in Makassar City.

MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND

Ilham, Aulia, Marzuki, Marza Ihsan

International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Abstract

Machine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for  wide data observation. In remote sensing, machine learning method could be  used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%.