National Digital Elevation Model Research Articles

Regional Lineament Pattern and Morphotectonic Analysis: The Investigation of Geological Structures and Present-Time Relative Tectonic Activity in the Tin Granite Area of Belitung Island, Indonesia

Belitung Island is located on the East Coast of Sumatra and is the southernmost extension of the Southeast Asian granite belt. Despite the flat terrain of the island, numerous granite outcrops provide insight into the past tectonic activities that caused the uplift in the region. This study analyzes the current state of Belitung's tectonic activity by examining its morphotectonic index and lineament pattern. A National Digital Elevation Model (DEMNAS) dataset with a resolution of up to 8.1 m will be used to assess the geological patterns and relative tectonic activity from the surface. The relationship between the regional lineament system and morphotectonic quantification throughout the landforms of Belitung Island will also be considered. The modified Segmented Tracing Algorithm (m-STA) technique extracted the lineament features. The Index of Relative Active Tectonic (IATR) was calculated by averaging several morphotectonic indices, such as asymmetry factors (AF), stream-length index (SL), mountain-front sinuosity (Smf), and valley floor width-height ratio (VF) factors, to quantify the relative tectonic activity of the area. The combination of the two methods shows that Belitung is currently experiencing relatively weak tectonic activity compared to the past. This is supported by the surface appearance, which is mainly composed of lowlands. Several granite outcrops and highlands are aligned along the NW-SE and NE-SW directions, corresponding to the main geological structures in the area.

ANALYSIS OF THE EXISTENCE OF GEOTHERMAL MANIFESTATIONS USING FAULT FRACTURE DENSITY (FFD) IN TEMPURAN DISTRICT, MAGELANG REGENCY

Being in an active fire lane means that Indonesia has geothermal potential. The geothermal research location is in the Tempuran area, Magelang Regency. The method used is by using alignment in the National Digital Elevation Model (DEMNas) data and calculating it using the Fault Fracture Density (FFD) method, the result of which is a contour map. This research aims to identify the existence of geothermal manifestations with FFD values. The research results show that the FFD value in the Tempuran geothermal manifestation area varies between 0.09 - 1.80 km/km2. The FFD value shows that the Tempuran geothermal manifestation is in a low density zone. There is no direct correlation between the presence of geothermal manifestations and the FFD value. The Tempuran geothermal manifestation is thought to be related to geological formations composed of volcanic rock and related to an aquifer system, as well as faults that trend northwest-southeast.

Predicting the Distribution of Mangrove Forests through Random Forest Algorithm by Combining Spectral Features and DEM Data

Using satellite data and machine learning-based classification methods for mangrove forest identification has gained popularity due to their effectiveness in producing high accuracy. Therefore, this research developed a random forest classification model using various dataset combinations representing spectral and topographical aspects to identify mangrove forests in Segara Anakan, Cilacap. We used the Sentinel-2 imagery acquired in 2022. Two types of digital elevation model (DEM) data were utilized, namely the National Digital Elevation Model (DEMNAS) and Multi-Error-Removed Improved-Terrain DEM (MERIT DEM). Another critical dataset used is the Normalized Difference Moisture Index (NDMI), derived from the ratio between near-infrared (NIR) and shortwave-infrared (SWIR) bands. The classification method used is the random forest algorithm on six different dataset combinations, including IMAGE, IMAGE+NDMI, IMAGE+DEMNAS, IMAGE+MERIT DEM, IMAGE+NDMI+DEMNAS, and IMAGE+NDMI+ MERIT DEM. The results showed that the combination of Image+DEMNAS and Image+NDMI+DEMNAS datasets was able to identify the mangrove forests more optimally. Incorporating DEM data alongside IMAGE and NDMI datasets resulted in a remarkable level of accuracy in mangrove forest mapping, exceeding 90%. DEM data has a very important role in increasing the classification accuracy of mangrove forests using the random forest algorithm.

Pemetaan Daerah Potensi Longsor Menggunakan Analisis Fault Fracture Density Pada Data DEMNAS Di Wilayah Cisarua, Kabupaten Bandung Barat, Jawa Barat

The Cisarua area is located in the northern part of Bandung City and is one of the sub-districts in West Bandung Regency and has steep topography in several locations, giving rise to the potential for landslides. The eastern region is part of the Lembang Fault which is still active in the North Bandung area. The potential for landslides related to the geological structure in this area can be identified by looking at the lineament patterns of the valleys, the presence of lithological offsets and others. The geological structure that developed in the Cisarua area was identified based on the results of analysis of satellite imagery data and the National Digital Elevation Model (DEMNAS) to identify the presence of geological structures and determine the direction of the dominant force. This study aims to determine the distribution of potential landslides in the Cisarua area using the Fault Fracture Density (FFD) method. Linearity analysis is estimated using the Fault Fracture Density (FFD) method. This method produces an analysis based on the straightness of the ridges and valleys around Cisarua which is then processed using a rosette diagram and interpreted as the dominant lineament trending west-east and northeast- southwest, this dominant lineament corresponds to the direction of the Lembang Fault. The lineament density level value describes a weak zone in the rock which creates the potential for landslide hazard. The weak zone in red indicates that the study area has a high landslide hazard potential, which is red, and the light green color indicates that the study area has a low landslide hazard potential. The potential for landslides in the study area is spread more in areas close to regional geological structures such as faults when compared to other areas. This pattern indicates that the presence of geological structures has an influence on the occurrence of landslides in potential areas.

Developing landslide susceptibility map using Artificial Neural Network (ANN) method for mitigation of land degradation

<p>Landslides are one of the crucial problems that have an impact on land degradation and human life. This study aimed to develop vulnerability maps using ANN to mitigate land degradation in the Bromo Tengger Semeru with the extending area of Universal Transverse Mercator (UTM) Coordinate System Top 91277639, Bottom 911569, Left 692860, and Right 706860. The method applied the Artificial Neural Network (ANN) model using RStudio machine learning. Landslides were mapped using Sentinel Image and Orthomozaic photo interpretation from data acquisition using Unmanned Aerial Vehicle (UAV). The landslide control factor data was obtained through DEMNAS (National Digital Elevation Model) with a spatial resolution of 8 meters. Data normalisation was conducted using the Mix-Max method before it was processed using RStudio. The landslide existing for ANN workflow was processed using the Bioclim model. The results showed landslide susceptibility was categorised into four classes i.e., low susceptibility (29.83%), which was spatially spread on most in the lower slopes, moderate susceptibility (3.11%), high susceptibility (2.99%), and very high susceptibility (15.94) which is scattered on the upper slope to the middle slope of the watershed. The most significant factor influencing the landslide is the topography factor, with a Relative Importance (RI) value of 0.86; the hydrological factor, with an RI of 0.833 and the surface feature, with an RI of 0.355. The results of the landslide susceptibility model are very proper for land degradation mitigation strategies. It has high accuracy through an Area Under Curve (AUC) of 0.965 and a Precision Recall Curve (PRC) of 0.976.</p>

K-span: Open and reproducible spatial analytics using scientific workflows

This paper describes the design, development, and testing of a general-purpose scientific-workflows tool for spatial analytics. Spatial analytics processes are frequently complex, both conceptually and computationally. Adaptation, documention, and reproduction of bespoke spatial analytics procedures represents a growing challenge today, particularly in this era of big spatial data. Scientific workflow systems hold the promise of increased openness and transparency with improved automation of spatial analytics processes. In this work, we built and implemented a KNIME spatial analytics (“K-span”) software tool, an extension to the general-purpose open-source KNIME scientific workflow platform. The tool augments KNIME with new spatial analytics nodes by linking to and integrating a range of existing open-source spatial software and libraries. The implementation of the K-span system is demonstrated and evaluated with a case study associated with the original process of construction of the Australian national DEM (Digital Elevation Model) in the Greater Brisbane area of Queensland, Australia by Geoscience Australia (GA). The outcomes of translating example spatial analytics process into a an open, transparent, documented, automated, and reproducible scientific workflow highlights the benefits of using our system and our general approach. These benefits may help in increasing users’ assurance and confidence in spatial data products and in understanding of the provenance of foundational spatial data sets across diverse uses and user groups.

Mapping Mangrove Above-Ground Carbon Using Multi-Source Remote Sensing Data and Machine Learning Approach in Loh Buaya, Komodo National Park, Indonesia

Mangrove forests provide numerous valuable ecosystem services and can sequester a large volume of carbon that can help mitigate climate change impacts. Modeling mangrove carbon with robust and valid approaches is crucial to better understanding existing conditions. The study aims to estimate mangrove Above-Ground Carbon (AGC) at Loh Buaya located in the Komodo National Park (Indonesia) using novel Extreme Gradient Boosting (XGB) and Genetic Algorithm (GA) analyses integrating multiple sources of remote sensing (optical, Synthetic Aperture Radar (SAR), and Digital Elevation Model (DEM)) data. Several steps were conducted to assess the model’s accuracy, starting with a field survey of 50 sampling plots, processing the images, selecting the variables, and examining the appropriate machine learning (ML) models. The effectiveness of the proposed XGB-GA was assessed via comparison with other well-known ML techniques, i.e., the Random Forest (RF) and the Support Vector Machine (SVM) models. Our results show that the hybrid XGB-GA model yielded the best results (R2 = 0.857 in the training and R2 = 0.758 in the testing phase). The proposed hybrid model optimized by the GA consisted of six spectral bands and five vegetation indices generated from Sentinel 2B together with a national DEM that had an RMSE = 15.40 Mg C ha−1 and outperformed other ML models for quantifying mangrove AGC. The XGB-GA model estimated mangrove AGC ranging from 2.52 to 123.89 Mg C ha−1 (with an average of 57.16 Mg C ha−1). Our findings contribute an innovative method, which is fast and reliable using open-source data and software. Multisource remotely sensed data combined with advanced machine learning techniques can potentially be used to estimate AGC in tropical mangrove ecosystems worldwide.

Lineament Extraction Analysis Using Digital Elevation Model (DEM) in Lahendong Geothermal Area, North Sulawesi

Sulawesi is located in the triple junction which makes it has a high complex tectonic setting implicated for the emergence of geothermal potential. Existing geological structure near the research area is only in the top-right of the research area which far from any of the hot spring points. This research aims to analyze the lineament extraction to find out the alleged structure and the density area to determine the permeability of the Lahendong geothermal area using National Digital Elevation Model (DEMNAS) data imagery. Digital Elevation Model (DEM) data is processed using Remote Sensing Photogrammetry application to produce lineament extraction. Lineament density map is carried out using a Geographic Information System (SIG) application and then processed to generate rosette diagram. Based on the lineament extraction and lineament density analysis result, we can interpret that the Lahendong geothermal area is dominated with high density, which is interpreted as good permeability. Lineaments in the Lahendong geothermal area is northwest-southeast and almost north-south. The extracted lineament is also providing the alleged structure in the research area.

Placement Location Mapping for Nature-based Solutions in Flood Vulnerable Areas as a Disaster Management Alternative in Bantul Regency

The purpose of this study is to determine potential locations around flood-vulnerable areas for developing the concept of Nature-based Solutions (NbS) in Bantul Regency-Yogyakarta, Indonesia. The data utilized includes rainfall, slope, elevation, land use, soil type, landform, distance from the river, actual flood data, and historical flood data. This data was obtained from the Climate Hazards Group InfraRed Precipitation with Station, National Digital Elevation Model, Bantul Regional Development Planning Agency, and Bantul Regional Disaster Management Agency. The data were overlaid between the flood vulnerability maps processed using the Spatial Multi-Criteria Analysis method and Hydromorphological Landscape Unit Floodplain maps. Results of data processing and analysis for floodplain-based NbS indicate several locations for the implementation of NbS in the pilot project area, particularly at the confluence of the Opak and Oyo rivers. In the intersection of the Opak and Oyo River, various NbS types can be identified and applied. The suggested NbS includes porous pavements, riparian zones, detention basins, and inland wetlands. The placement of the four NbS is based on environmental conditions and the characteristics of each NbS object to prevent flooding.

AHP GIS-Based Gully Erosion Susceptibility Modelling in Tropical Volcano Environment

Gully erosion is the most destructive form of erosion and contributes significantly to the accumulation of water sediment in an area. This study aims to map the gully erosion susceptibility in the Kodil watershed, Central Java, Indonesia. The base data used in this study are from Sentinel-2 with a resolution of 10 m and DEMNAS (National Digital Elevation Model of Indonesia) with a resolution of 8 m. The Analytical Hierarchy Process (AHP) was used to assess the weight of each parameter that influences the gully erosion susceptibility. Overall, there were ten parameters used in this study, namely a) elevation (m); b) slope gradient (0); c) slope aspect; d) slope curvature; e) topographic wetness index; f) distance from the river (m); g) distance from the road (m); h) land use; i) soil texture; and j) soil aggregate. An accuracy test used the AUC (Area under Curve). The results of spatial data analysis proved that 855 ha (6.34%) of land in the Kodil DAS has a high susceptibility to gully erosion. Testing with AUC shows that the AUC training value was 0.742, the AUC test was 0.737, and the accumulative AUC value was 0.73, so the model results fall into the good category. The novel aspect of this study is the use of texture and soil aggregate parameters that have never been studied before. The decision to employ soil parameters is inextricably linked to the characteristics of the research site, which has extremely thick soil layers with high clay content. The gully erosion susceptibility model helps plan the development of land resources in its sustainability aspect. Keywords: gully erosion, analytical hierarchy process, susceptibility modeling, tropical volcano environment. https://doi.org/10.55463/issn.1674-2974.50.2.20

Investigation of paleochannel identification using radar and optical images on placer deposits in Bangka Barat Regency, Indonesia

This article is about a visual investigation of paleochannel identification using ALOS 2 PALSAR 2 (Advanced Land Observing Satellite 2 Phased Array type L-band Synthetic Aperture Radar 2) radar satellite image. The radar image has a higher layer-penetration ability than the optical image so that the characteristics of near-surface materials are possible to be detected, including the morphology of the abandoned river stream. The purpose of ancient channels investigation is to localize potential areas of tin placer deposits, including rare earth elements as associated minerals. This investigation was aided by LANDSAT 8 optical image and National Digital Elevation Model (DEMNAS) data. Visual identification has been performed based on the shape, color, pattern, texture, and position against other morphologies in the image. The criteria for paleochannels are the channels detected using ALOS 2 PALSAR 2 image but not in LANDSAT 8 optical image and DEMNAS data. Based on these criteria, eleven traces of paleochannels have been well identified. Their occurrences are generally associated with meanders, open areas, and near the coast. The detected paleochannels are generally purple to dark in the color composite of the ALOS 2 PALSAR 2 image. These detected rivers are in pink to purplish-green zones and have a random appearance.

Relative tectonic activity assessment of the Northern Sumatran Fault using geomorphic indices

Geomorphic indices have widely been applied to assess relative tectonic activity. The relative tectonic activity is mainly associated with relative uplift and long-term tectonic deformation. By considering this, the strike-slip basins along the Northern Sumatran Fault become suitable locations for the assessment. This study aims to evaluate the relative tectonic activity of the Northern Sumatran Fault with the focus on its strike-slip basins using geomorphic indices. This study utilized the Indonesian national digital elevation model (DEMNAS) with spatial resolution at 8.25-m. Here are the six geomorphic indices applied in this study along with the average values of the results: mountain front sinuosity (1.14–1.94), valley width-to-height ratio (0.58–1.93), stream length-gradient index (93.81–599.70), index of basin shape (1.66–3.16), asymmetric factor (7.45–19.93), and hypsometric integral (0.41–0.59). These results were used to generate the index of relative tectonic activity. The Northern Sumatran Fault exhibits, generally, alternating moderate and high tectonic activities and there is a northward decrease from very high to moderate tectonic activity in its northern part. The variation of relative tectonic activity of the fault coincides with the distribution of seismicity. This may suggest that the long-term tectonic deformation of the Northern Sumatran Fault has persisted to recent time.

Development of a land stability index for land damage assessment: the case of a nickel mine, North Konawe, Indonesia

Assessment of land damage has been included in several Indonesian government policies, but it tends to have zero dimensions or only one point in the year. This study tried to propose an inter-time assessment of land damage with a land stability index by including the development of knowledge and technology at nickel mining sites in the easternmost part of North Konawe Regency. Orthophoto and Digital Elevation Model (DEM) images from drones in 2020 were taken in a field survey and used as primary data. While the SPOT 5 Imagery in 2014 and National DEM were collected as secondary data. The developed method combining slope, soil, TRI (Terrain Ruggedness Index), and land cover factor has been considered moderately accurate. Applying the method between different periods has produced a temporal land stability index where a positive value means more unstable, zero means unchanged, and a negative value means more stable. The results showed that after six years, the largest area due to nickel mining in the area has not changed much or had zero value. This is because the area tends to remain a natural forest. The more stable area is located in the southern part of the study site. However, the increasingly unstable area is located in the northern part of the study site. If no reclamation action is taken, the potential for further damage will occur.

Evaluation of land resilience against natural disasters using ecosystem services approach in Kendari City, Southeast Sulawesi, Indonesia

Kendari City has multiple disasters hazard, especially floods and landslides. Consequently, the city requires systematic preventive efforts to minimize the potential disaster risk. This study aims to create a spatial model to determine the resilience of the land against floods and landslides with an ecosystem services approach. We used Sentinel-2 and DEMNAS (National Digital Elevation Model of Indonesia) as our primary data collection tools. Sentinel-2 was used to compile land use maps, and DEMNAS was the basis for compiling landform maps. The integration of the two was carried out by the analytic hierarchy process (AHP). The results showed that 8,259.98 acres (30.01 percent) of land in Kendari had low resilience to disasters. Those areas are located in a residential area on the TWH (Rocky hill over mixed sedimentary rocks) and KHY (Coalescent estuarine/riverine plain) landforms. The dominant disaster hazard in the area is inundation flooding that occurs almost every year in Kendari. Poor soil infiltration capacity is one factor affecting the area become vulnerable to flooding.

Development of Low-Cost 3D Building Model Using National Digital Elevation Model in Urban Area of Bandung City, Indonesia

Sustainable city planning is one of many countries' 2030 sustainable development goals. In carrying out sustainable urban planning, it is necessary to look at the 3D aspect of the building. However, 3D mapping for large areas is very expensive. So we need an alternative to make three-dimensional buildings in urban areas that are cheaper. This study will review how well the three-dimensional reconstruction of urban areas uses open-source data from the Indonesian National Digital Elevation Model (DEMNAS) with a spatial resolution of 8 meters. This study found that the RMSE value of the DEMNAS 3D model reached 2.320 meters with the optimum 3-dimensional reconstruction at a medium building height around 5-7 meters. Three-dimensional modelling with DEMNAS will be good in relatively flat areas. This research hope can help better and lower cost sustainable city planning.

Ownership Protection on Digital Elevation Model (DEM) Using Transform-Based Watermarking

This research aims to protect Digital Elevation Model (DEM) data from piracy or counterfeiting. An invisible watermark inserted into the data, which will not considerably change the data value, is necessary. The proposed method involves the use of the two-dimensional discrete cosine transform (2D DCT), a combination of 2D DCT and discrete wavelet transform (DWT), and two-dimensional discrete Fourier transform (2D DFT) in the frequency domain. The data used include a National DEM file downloaded from the geoportal of the Geospatial Information Agency (Badan Informasi Geospasial—BIG). Three files represent mountainous, lowland/urban, and coastal areas. An “attack” is also conducted on the watermarked DEM by cropping. The results indicate that the watermarked DEM is well recognized. The watermark can be read 100% for 2D DCT, while that for 2D DFT can be read 90.50%. The distortion value of the elevation data under the DCT technique demonstrates the smallest maximum value of 0.1 m compared with 4.5 and 1.1 m for 2D DFT and 2D DCT–DWT. Meanwhile, the height difference (Max Delta), the peak signal-to-noise ratio, and the root mean squared error (RMSE) are highest in mountainous, lowland, and coastal areas, respectively. Overall, the 2D DCT is also superior to the 2D DFT and the2D DCT–DWT. Although only one can recognize the nine watermarks inserted on each sheet, DEMs attacked by the cropping process can still be identified. However, this finding can sufficiently confirm that DEMs belong to BIG.

Land Subsidence of Java North Coast Observed by SAR Interferometry

Land Subsidence became recent issue in environmental management in Indonesia. Large cities in Indonesia, especially in Java Island, are well known to suffer from fast rate land subsidence such as Jakarta, Bandung, and Semarang. However, the phenomena is presumed to also happen in other cities which have large industries and located on the deposit or clay soil layer. The effect of land subsidence may be different in some location, but the coastal zone will have more impact since land subsidence will also induce tidal flood and may cause land area reduction. In this study, we use more than 70 Sentinel-1 data, range from year 2016 to 2020 to map the land subsidence in the Java North Coast. The interferograms are selected based on Small Baseline Subset (SBAS) algorithm. National Digital Elevation Model (DEMNAS) is used in differential InSAR process. Our result shows that many cities along the Java North Coast suffer land subsidence. Jakarta, experience has maximum subsidence more than 5 cm/year. In Central Java, Pekalongan experience up to 7 cm/year of land subsidence, while Semarang and Demak has subsidence rate up to 8 cm/year.

Suitability assessment of global, continental and national digital elevation models for geomorphological analyses in Italy

AbstractDigital elevation models (DEMs) represent a fundamental resource in geomorphological analysis. The increasing availability of open‐access DEMs over wide areas is advantageous, but requires an evaluation of DEM quality and errors. This work applies a hierarchical assessment of global, continental and national DEMs in Italy in order to explore the differences and analyze the vertical accuracy, spatial error distribution and agreement of morphometric measurements. The selected DEMs are compared with local reference data as a ground points dataset, extracted from the national geodetic network, and regional DEMs at high spatial resolution, through both a qualitative and a quantitative approach. The results identify limits and potentialities of the selected DEMs, showing accuracy and errors in height representation, also affected by the topographic characteristics of the surface, such as steep slope in mountain zones and some defects in hydromorphological derivatives that could condition the geomorphological applications.

Coastline Modeling Using Stacked Curve Spline Tension Interpolation

The coastal area is a dynamic environment influenced by atmosphere, land, and ocean interactions. Similarly, the position of coastlines is also changing due to natural and human-induced components, for instance, erosion, wave, daily tide, storm, and coastal development. In literature, coastline position can be identified based on proxies such as coastal features identified from an aerial photo or very high-resolution image and tidal datum-based indicators extracting from a ground survey. This research proposed a method in deriving datum-based coastline by integrating various bathymetric data, including single beam and multibeam echo sounding data, the National Digital Elevation Model, the national bathymetry data, as well as satellite-derived bathymetry data. The stacked curve spline tension method was applied to assimilate those various bathymetric data, and finally, the coastline was generated. Based on the accuracy assessment conducted, coastline similarity accuracy varies; namely, the LAT coastline had an accuracy of 29.28%, the MSL coastline was 65.45%, and the HAT coastline was 47.48%. These variations are due to several reasons, including the lack of input data, the density of depth data that varies greatly, the difference in data acquisition time between the data used for the LPI map and the data used in this study. Although the accuracy values obtained were not sufficiently high, the proposed method was quite promising to adopt. The method can be used as an alternative for the coastline model and overcome data, time, and cost limitations in providing national coastlines

MAPPING FAULTS DISTRIBUTION BASED ON DEM DATA FOR REGIONAL SPATIAL PLAN ASSESSMENT OF SABANG MUNICIPALITY, INDONESIA

Sabang Municipality is located on Weh Island. As a maritime gateway to Indonesia, the island has been inaugurated as an Integrated Economic Development Zone by the Government. A complete infrastructure has been planned and built to support the manufacturing and tourism industries in the region. On the other hand, the island is potentially affected by geological hazards. Tectonically, the island is crossed by the Great Sumatran Fault (GSF), but few studies have shown the fault trace on the island. To ensure sustainable development, such potential geological hazards have to be incorporated into the Regional Spatial Plan (RSP) of the city. In this study, fault distribution on the island were mapped using shaded relief, slope, lineament extraction, orientation, and Fault Fracture Density (FFD) distribution extracted from the National Digital Elevation Model (DEMNAS) data. The map shows that the island is crossed by several fault lines, but the one located along the eastern with northwest-southeast direction is considered tectonically active. However, according to the RSP, the eastern Weh Island is the most developed area. The city center, airport, seaports, dam, sewerage treatment plan, landfills, and diesel power electricity generator for the city are situated along this fault line. It seems that the RSP of Sabang Municipality was issued without taking into account potential geological hazard maps in it. Some recommendations have been proposed to reduce the impact of losses in the future.