bluuebunny/crossref_metadata_2025_split huggingface dataset:
Remotes sensing capabilities on land subsidence and coastal water hazard and disaster studies
Authors: Andreas Heri , Abidin Hasanuddin Z. , Sarsito Dina A. , Pradipta Dhota 
Published: July 2020

Abstract
Lowering on land surface which is commonly known as land subsidence is quite an issue in Indonesia today especially for coastal low elevation young sediment area, including cities or urban area lies on top of it. Jakarta is one of the city with most famous for land subsidence at the moment. If we go along northern coast of Java, many cities are experiencing the same situation as Jakarta and even more. As the impact from land subsidence, those coastal areas may experience water disaster such as sea water inundation and intrusion, wider expansion of flood and aquifer damage. Investigation of such areas in large and even regional scale with limited research budget is a problem. Nevertheless we found way for at least reducing the problem by using the capabilities of remote sensing product, geological and geostatistic data. We can identify land subsidence, coastal effected by subsidence like mentioned above by only time series of satellite image archive such as Google earth or Landsat free distributed, geological map and published geostatistic data like water consumption and population growth, with sample validated by land subsidence measurement and groundwater monitoring points. This paper will highlight our finding.
Deforestation detection using multitemporal satellite images
Authors: Candra Danang Surya 
Published: July 2020

Abstract
Indonesia a megadiverse country which has the largest area of forest with the richest biodiversity in the world. Unfortunately, the forest-loss in this country is quite big, especially in Kalimantan and Sumatera. Therefore, a robust method for deforestation method has to be developed to monitor deforestation to minimize the forest-loss in this country. In this paper, we proposed a method for deforestation detection called Multitemporal Deforestation Detection (MDD). The basic idea of this method is to utilize the difference of reflectance values on the target image and original image. Band selection was used to select bands in developing the algorithm. To improve the accuracy of the results, NDVI and dNBR were combined to the algorithm. As a result, the MDD can detect deforestation from forest to heterogeneous land cover such as open land, land clearing for plantation, urban, road and small road, and burnt area accurately. The advantage of the MDD is that it does not interfere cloud and cloud shadow on the original image. As it is an automatic algorithm, it can support in detecting deforestation for a big dataset.
A preliminary study on machine learning and google earth engine for mangrove mapping
Authors: Kamal Muhammad , Farda Nur Mohammad , Jamaluddin Ilham , Parela Artha , Wikantika Ketut , Prasetyo Lilik Budi , Irawan Bambang 
Published: July 2020

Abstract
The alarming rate of global mangrove forest degradation corroborates the need for providing fast, up-to-date and accurate mangrove maps. Conventional scene by scene image classification approach is inefficient and time consuming. The development of Google Earth Engine (GEE) provides a cloud platform to access and seamlessly process large amount of freely available satellite imagery. The GEE also provides a set of the state-of-the-art classifiers for pixel-based classification that can be used for mangrove mapping. This study is an initial effort which is aimed to combine machine learning and GEE for mapping mangrove extent. We used two Landsat 8 scenes over Agats and Timika Papua area as pilot images for this study; path 102 row 64 (2014/10/19) and path 103 row 63 (2013/05/16). The first image was used to develop local training areas for the machine learning classification, while the second one was used as a test image for GEE on the cloud. A total of 838 points samples were collected representing mangroves (244), non-mangroves (161), water bodies (311), and cloud (122) class. These training areas were used by support vector machine classifier in GEE to classify the first image. The classification result show mangrove objects could be efficiently delineated by this algorithm as confirmed by visual checking. This algorithm was then applied to the second image in GEE to check the consistency of the result. A simultaneous view of both classified images shows a corresponding pattern of mangrove forest, which mean the mangrove object has been consistently delineated by the algorithm.
Post-liquefaction reconsolidation of coastal area in Talise Beach, Palu, Indonesia
Authors: Tampubolon Togi , Yanti Jeddah 
Published: July 2020

Abstract
Periodic generated, sequence of earthquakes with magnitude 7.5 at a depth of 10 km occurred in Palu Koro fault, Central Sulawesi, and was trigger tsunamis wave in Talise beach reach coastline in few minutes. It causes entire coastline become extremely attacked and moves gravitationally of the ground at a slope as well, known as flow of liquefaction. Reconsolidation of coastline structure in post-liquefaction event in Palu’s Talise beach had been heaving due to devastating aftermath of the earthquake and tsunami that hit Sulawesi. Lack and rough terrain are intensively associated with the difficult to assess post-area, so remote sensing can be efficiently and quickly to identify potential post-area occurrence. Satellite multispectral such as Sentinel, and SRTM DEM applied with a new approach to corporate post-event liquefaction in Talise beach, Central Sulawesi, Indonesia. In this result, we depict the 3D surface topography of pre and post event to analyse the liquefaction impact. We found the differentiation approach of pre and post event imply the liquefaction in variation of dimensional ratio. Therefore, our hypothesis may be useful to the preliminary research for mitigation and revitalization in post-area.
The effects of DEM specification on watershed model in Mataram City, West Nusa Tenggara Province
Authors: Susetyo Danang Budi , Purwono Nugroho , Rahadiati Ati 
Published: July 2020

Abstract
One of data resources for hydrologic modelling is Digital Elevation Model (DEM). In hydrologic modelling, watershed delineation is an important step to create boundary of inundation area, so that DEM plays a significant role in watershed model. Nowadays, there are many sources of DEM data available in Indonesia that was provided by Geospatial Information Agency (BIG), including LiDAR and DEMNAS (National DEM). Based on its resolution, DEMNAS is classified as data for medium scale mapping, while LiDAR is used for large scale mapping. For hydrological modelling, medium scale data has been widely applied, while the large-scale hydrological modelling is still limited. The availability of large-scale data is quite large, including the City of Mataram, is a good source to examine its effect for hydrological modelling, so this research is conducted to see the effects of DEM sources, namely LiDAR and DEMNAS, for watershed model generation. Analysis is conducted by comparison to existing official watershed in that area. From seven watersheds, LiDAR produce better geometry in four areas, while DEMNAS is better in the rest. This research is expected to support a policy relating hydrological modelling at large scale.
Correlation analysis of satellite-based mangrove index and mangrove forest health at Segara Anakan Cilacap, Central Java, Indonesia
Authors: Winarso Gathot , Kamal Muhammad , Rosid Syamsu , Asriningrum Wikanti , Supriyatna Jatna 
Published: July 2020

Abstract
This study aims to introduce satellite-based Mangrove Index to substitute mangrove health indicator based on Normalized Difference Vegetation Index (NDVI) when it was failed to represent mangrove health at Segara Anakan mangrove area, Cilacap, Central Java, Indonesia. The Mangrove Index has potential to be implemented as indicator of mangrove health because it is deduced from two main characteristics of mangrove forest, which are vegetation condition and hydro-period parameters. Previous mangrove health identification was mainly conducted using visual analysis during the field check and used previous reports that could not be statistically analyzed. The correlation between Mangrove Index and mangrove health condition was analyzed to measure the degree and form of relationship between them as a basis for mangrove health modelling. We conducted paper review to synthesize the mangrove health condition and enriched our analysis by incorporating as many field data as possible related to canopy cover, stand density, seedling density, stake density, biodiversity index, soil moisture, pH, soil temperature, air temperature and air humidity. Our finding shows that the degree of correlation for each parameter were low. The maximum coefficient of determination (R2
) of 0.5 was found for combination between stand and sapling density divided by soil moisture. The combination these parameters was selected because Mangrove Index was produced from image bands that has high spectral reflectance response to vegetation (NIR) and soil moisture (SWIR). This result is not a final because limited parameters were incorporated in the analysis. Further deeper analysis to simulate all field parameters need to be done in the future.
Identifying geothermal steam spots based on binary logistic method to a high resolution imagery and field measurement
Authors: Gandes F. Z. , Saepuloh A. , Wikantika K. 
Published: July 2020

Abstract
The increasing population of a country has an impact on the number of energy needs that must be provided. Therefore, alternative sources of energy need to be developed to meet those needs. Indonesia is known as The Ring of Fire has very high geothermal resource potential. A study of geothermal mapping technology needs to be done to optimize the utilization of this energy. This study aims to recognize the relation between temperature and surface roughness on an up-flow zone of geothermal system and identify the steam spots at surface. We used a high resolution ALOS-2 PALSAR-2 images to obtain surface roughness and ASTER images to obtain a corrected Land Surface Temperature (LST). The study was conducted in an open area Papandayan Crater, Garut, West Java (Indonesia). Data pre-processing including atmospheric and geometric corrections were applied following an image processing to obtain the surface roughness and the corrected LST by estimating non-unique surface emissivity, pixel fitting between measured surface temperature and roughness at field, and identifying the steam spots of the geothermal system using binary logistic regression method. Equally good models of co-polarization and cross-polarization obtained because of the high correlation between co-polarization and cross-polarization surface roughness. There was a positive relationship between land surface temperature and surface roughness in the manifestation area of geothermal resources and surface roughness known more dominant in determining the manifestations of geothermal resources than surface temperature.
Spatial modelling of anthropogenic based peat land fire case study of the Kepulauan Meranti District
Authors: Turmudi  , Sutrisno Dewayany , Nahib Irmadi , Suryanta Jaka , Kardono Priyadi , Suwarno Yatin , Rahadiati Ati , Dewi Ratna Sari 
Published: July 2020

Abstract
Forest fires in Indonesia are 99% due to human intentional factors. The practice of burning forests is done because it is easy, fast, and inexpensive, and the assumption that the ashes produced will fertilize the soil. The practice of burning forests is done primarily for the purpose of clearing agricultural land and plantations. Land and forest fires will be even worse if they occur on peat lands. Fires on peat lands consist of crown fire, surface fire, and ground fire which have a more severe impact on environmental, social and economic impacts. In terms of management, it is also more difficult and longer than fires on mineral land. On the other hand, pea tlands in Indonesia, 30%, are peat lands that have the potential for agricultural and plantation cultivation. The main objective of this paper is to use spatial modeling to provide an early warning system for forest fires on peat lands based on anthropogenic aspects in the Meranti Islands Regency, Riau Province. Data compiled based on anthropogenic aspects include: accessibility, population distribution, land clearing patterns, land use, and land status. Spatial analysis was carried out on anthropogenic aspects and the distribution of hotspots that had a level of confident (c> 80%). The results of the paper indicate that anthropogenic aspects that are dominant in the occurrence of forest fires are accessibility of road than river.
The effect of building density on land surface temperature, (Case Study: Turikale District, Maros Regency)
Authors: Asyraf Muhammad Syahrul , Damayanti Astrid , Dimyati Muhammad 
Published: July 2020

Abstract
The amount of land built has increased over the years as the population increases. This results in a higher building density, such as in Turikale District, Maros Regency. Each object on the earth’s surface has its distinctive temperature, such as the buildings. The heat of the earth’s surface on certain location points will be captured by the image as an area that has a different surface temperature. The purpose of this study is to determine the temporal-spatial pattern of building density and surface temperature in Turikale Subdistrict, Maros Regency in 2013 and 2018 and is expected to be useful for Turikale District spatial planning and urban environment. Building density data and surface temperature are obtained from Landsat 8 Path 114 row 063 in 2018 and 2013. The data is processed using the Normalized Difference Built-up Index (NDBI) algorithm and Land Surface Temperature (LST) using ArcGIS. Furthermore, an analysis of differences in building density patterns with land surface temperature is analyzed spatially and temporally. The results of the study show that the spatial pattern of building density grows to be higher in density each year, especially in densely populated residential areas and near Jalan Daya-Maros, Turikale District. Temporal patterns of land surface temperatures show significant increases between 2013 and 2018.
Determining location of tsunami disaster temporary evacuation shelter (TES) utilizes network analysis in City of Makassar, South Sulawesi Province
Authors: Ismiati Feni , Damayanti Astrid , Dimyati Muhammad 
Published: July 2020

Abstract
The city of Makassar is a vulnerable area of the tsunami because it is located on the southwest coast of Sulawesi Island. The population is the highest number on Sulawesi Island. People will cause a lot of losses in the event of a tsunami in Makassar City. Therefore, preventive measures are needed as disaster mitigation. One of them is to determine the location of the Temporary Evacuation Place (TES) in the tsunami-affected area. The purpose of this study is to determine the location of TES on the coast of Makassar City. The variables used are DEM, land use, and travel time. TES locations are determined using GIS, the network analysis method with the tool service area. The analysis used was to sort outbuildings in tsunami-affected regions based on the size and height of buildings according to the standards of the Federation Emergency Management Agency (FEMA). Based on the results of the research, there are 14 TES that are distributed in Makassar City. TES is in densely populated residential areas affected by the tsunami with an average height of 0-5 m above sea level. The TES in Makassar City is in the form of mosques, auditoriums, schools, and factories.
Bathymetry extraction of empirical models using SPOT 7 satellite imagery
Authors: Setiawan Kuncoro Teguh , Ginting Devica Natalia BR. , Manessa Masita D.M. , Surahman  , Winarso Gathot , Anggraini Nanin , Basith Abdul , Asriningrum Wikanti , Rosid Syamsu , Supardjo A. Harsono 
Published: July 2020

Abstract
Bathymetry is information on the depth of the sea that can describe the hydrographic conditions of the seabed. Satellite derived bathymetry has several methods that can be applied to the batimetric extraction process using optical satellite images. One of the satellite images that can be used is SPOT 7. The image is the latest generation of SPOT which has 4 multispectral channels with a spatial resolution of 6 meters so that it can be extracted to obtain information on shallow water bathymetry. The bathymetry extraction method uses the Van Hengel and Spitzer methods, 1991. The VS method is the principal component analysis method of three bands with respect to the rotation transformation factor. The purpose of this study is to extract SPOT 7 satellite image data to produce relative bathymetry information in shallow seas using the VS algorithm. The location of the study was carried out on Karimunjawa Island, Jepara Regency, Central Java. The data used is SPOT 7 imagery that was acquired May 18, 2017. The extraction algorithm is used the VS 1991 method. The results show that from SPOT 7 images produce depth index values ranging from 0.0985 to 0.6378.
Lombok earthquakes using DInSAR techniques based on Sentinel 1A data (case study: Lombok, West Nusa Tenggara)
Authors: Nurtyawan Rian , Yulanda Muhammad Fachri 
Published: July 2020

Abstract
On July 29th, 2018 there was an earthquake of 6.4 magnitude occurred in Lombok Island, West Nusa Tenggara. The repercussions of the earthquake cause the area to deform. Deformation refers to any changes in the shape and size of an object. Hence the purpose of this research is to measure the deformation cause by the aforementioned earthquake in Lombok. The deformation calculation is conduct by using the SAR data from Sentinel-1A Satellite Imagery (Interferometric Wide Swath (IW)) using DInSAR method. The effect of deformation cause by an earthquake in Lombok based on DInSAR method is -0.001 m up to -0.134 m with the average -0.026 m and the image coherence is 0.32, with the range for coherence is 0 to 1. The small value of coherence cause by the vegetation density within the research area. The vegetation density will affect the imagery coherence value due to its movement and development thus effecting the backscatter. Hence, the deformation data situate within the vegetation density area will result less accurate data.
The relationship between night time light and population in 2000 and 2015 in The Java Island
Authors: Putri Anika , Mukhtar Mutia Kamalia , Widyastuti Ratri , Manessa Masita Dwi Mandini , Supriatna  , Yoanna  
Published: July 2020

Abstract
Night time light may indicate that a region is well developed. The phenomena can DNB satellite imagery. The amount of reflected light can be attributed to many population activities and indicates the number of inhabitants in a region. Based on the purpose of this research is to analyze the dynamics of population growth in Java in 2000 and 2015 based on the brightness level of light at night. The method be seen from the sheer reflection of light at night that is received by the VIIRS-used is by a classification method of K-Means and a descriptive analysis. The results showed that as intensity level higher, the number of population is also higher. High-intensity categories generally occur in the provincial capital, and there are new cities that are developing and entering the high-intensity category as well.
Identification of Oil Palm Plantation on Multiscatter and Resolution of SAR Data Using Variety of Classifications Algorithm (Case Study: Asahan District, North Sumatera Province)
Authors: Darmawan S. , Carolita I. , Ananta E. 
Published: July 2020

Abstract
The purpose of this study was to find the best classification algorithm for determining the classification of oil palm plantation area on SAR data. This research begins with collecting primary data and secondary data, where primary data in the form of radar images include Sentinel-1, ALOS PALSAR, and TerraSAR-X images, while secondary data of palm oil age block data are used as a references derived from plantation agency of Indonesia and high resolution imagery, after that radiometric and geometric corrections, classification and accuracy assessment. The classification methods to be used are Parallelepiped, Maximum Likelihood, and Support Vector Machine. The results of this study is the Support Vector Machine algorithm has the highest overall accuracy value among the three methods used with 77.57% for TerraSAR-X, 86.18% for Sentinel-1 and 91.17% for ALOS PALSAR, The other results on this study are map of land cover for oil palm plantations in the district of Asahan, North Sumatra Province.
Acquisition programming integration of image satellites in LAPAN
Authors: Gumelar O , Hutapea D Y , Wahyuningsih S D , Dyatmika H S 
Published: July 2020

Abstract
Acquisition of satellites images programming become an important issue in LAPAN, due to inaccuracy in acquiring image location. Clouds and smokes cause the low quality of the image and also different time acquisition could make a different synoptic view between different sensor. Some integration of three kinds of sensor has to be created for achieving good location with a high quality image. The other reason why the integration needed that because there is still no research about it. This research aim is to get the best acquisition plan by using three different sensors mechanism like SPOT 6/7, Pleiades and Terra SAR-X. This integration also helps Indonesia government in achieving Government National Program.
Modelling of surface roughness on agriculture area using Radarsat-2 satellite
Authors: Nurtyawan Rian , Ferdiyanti Geta Ayu , Budiharto Agung , Wikantika Ketut 
Published: July 2020

Abstract
This research aims to model surface roughness for agricultural land using RADARSAT-2 satellite imagery. To obtain the model of surface roughness, the digital number of radar image are converted into backscattering coefficient and used as basis of surface roughness modeling. Furthermore, using the relationship between the backscattering coefficient, local incidence angle, and the wavelength was made initial models for surface roughness. The new equation that can model surface roughness on agricultural land is obtained by performing calibration between the value of the surface roughness from initial models and combinations of initials models with the value of surface roughness from field measurement that the equation resulted y=-591987x5+(9.106)x4-(6.107)x3+(2.108)x2-(3.108)x+(2.108) with RMSE value = 0.31 cm, where y is the value of surface roughness modeled (cm) and x is the surface roughness value of the combination of initial models used during the calibration process (cm). The combination of HV, VH polaristation and local incident angle (h0HV+h0VH)x cos ϴ produces a correlation value (R2) = 0.804.
Evaluation of atmospheric correction algorithms for Sentinel-2 over paddy field area
Authors: Ulfa Kurnia , Hendayani  , Oktavia Masnita Indriani , Pradono Kuncoro Adi , Fibriawati Liana , Muchsin Fadila , Candra Danang Surya , Damanik Kiki Winda Veronica 
Published: July 2020

Abstract
Atmospheric correction is essential in satellite data processing to reduce atmospheric and lighting effects by studying the physical parameter of the earth’s surface. In this study, ATCOR and 6S algorithms were evaluated for Sentinel-2 over paddy field area. In this evaluation, level-1C Top of Atmosphere (TOA) Sentinel-2 image was used as an input data. The spectral pattern analysis of the results was used to assess the reliability of the methods. As a result, the both methods produced the correct spectral patterns. Moreover, the results showed that the longer the wavelength the less the improvement values. It starts from the blue band, 43,2% for the fallow phase and 60% for the vegetative phase of BOA corrected image. In the other hand, the NDVI values of fallow and vegetative phases that derived from the two methods are not greatly different.