scholarly journals Sidik Cepat Potensi Karst Rocky Desertification (KRD) Menggunakan Citra Landsat 8 OLI: Studi di Kawasan Karst Gunungsewu Bagian Barat

2020 ◽  
Vol 34 (2) ◽  
pp. 150
Author(s):  
Pendi Tri Sutrisno ◽  
Sigit Heru Murti ◽  
Eko Haryono
Keyword(s):  
Digital Elevation Model ◽  
Karst Area ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Digital Elevation ◽  
Dem Analysis ◽  
Elevation Model ◽  
Total Study Area

 Abstrak. Proses identifikasi kondisi lingkungan dapat dilakukan melalui adanya sidik cepat pemetaan Karst Rocky Desertification (KRD), termasuk di kawasan karst Gunungsewu bagian barat. Tujuan dari kajian ini adalah untuk mengetahui secara cepat potensi intensitas proses KRD yang terjadi di wilayah kajian, menggunakan metode analisis data citra penginderaan jauh multispektral. Metode yang digunakan adalah pengolahan citra secara digital menjadi citra indeks NDVI dan BI ditunjang dengan menggunakan analisis Digital Elevation Model (DEM) untuk menghasilkan data kemiringan lereng. Kriteria kelas potensi terjadinya KRD yang dihasilkan yaitu non KRD, potensi KRD rendah, potensi KRD sedang dan potensi KRD tinggi dengan luas total wilayah kajian 56.686,17 Ha. Wilayah kajian masih didominasi kelas non KRD dengan luas 32.140,56 Ha, sedangkan potensi KRD rendah seluas 24.447,72 Ha, kelas potensi KRD sedang seluas 96,53 Ha dan potensi KRD tinggi seluas 1,36 Ha. Abstract. Identification of environmental conditions can be done through the rapid mapping of karst rocky desertification (KRD) process. The purpose of this study is to know rapidly the potential of KRD processes, using Landsat 8 OLI multispectral image that covering the western part of Gunungsewu karst area. The method used is digital image processing of NDVI and BI index supported by using Digital Elevation Model (DEM) analysis to produce slope data. Criteria of KRD potential in this study are non KRD, low KRD potential, medium KRD potential and high KRD potential for total study area of 56.686,17 Ha. The study area dominated by non-KRD class with an area of 32.140,56 Ha, while the low KRD potential is 24.447,72 Ha, the medium KRD potential is 96,53 Ha and high KRD potential is 1,36 Ha.

scholarly journals Numerical Modelling Based on Digital Elevation Model (DEM) Analysis of Debris Flow at Rinjani Volcano, West Nusa Tenggara, Indonesia

2021 ◽  
Vol 7 (3) ◽  
pp. 279
Author(s):  
Muhammad Fatih Qodri ◽  
Noviardi Noviardi ◽  
Al Hussein Flowers Rizqi ◽  
Lindung Zalbuin Mase
Keyword(s):  
Debris Flow ◽  
Digital Elevation Model ◽  
High Speed ◽  
Human Life ◽  
Mass Movement ◽  
High Viscosity ◽  
Particle Flows ◽  
Digital Elevation ◽  
Dem Analysis ◽  
Elevation Model

Debris flow is a disaster occurring in cases where a sediment particle flows at high speed, down to the slope, and usually with high viscosity and speed. This disaster is very destructive and human life-threatening, especially in mountainous areas. As one of the world’s active volcanoes in the world, Rinjani had the capacity to produce over 3 million m3 volume material in the 2015 eruption alone. Therefore, this study proposes a numerical model analysis to predict the debris flow release area (erosion) and deposition, as well as the discharge, flow height, and velocity. The Digital Elevation Model (DEM) was analyzed in ArcGIS, to acquire the Cartesian coordinates and “hillshade” form. This was also used as a method to produce vulnerable areas in the Jangkok watershed. Meanwhile, the Rapid Mass Movement Simulation (RAMSS) numerical modeling was simulated using certain parameters including volume, friction, and density, derived from the DEM analysis results and assumptions from similar historical events considered as the best-fit rheology. In this study, the release volume was varied at 1,000,000 m3, 2,000,000 m3, and 3,000,000 m3, while the simulation results show movement, erosion, and debris flow deposition in Jangkok watershed. This study is bound to be very useful in mitigating debris flow as disaster anticipation and is also expected to increase community awareness, as well as provide a reference for structural requirements, as a debris flow prevention.

Geoinformation mapping of landscapes on the example of the Primorskiy Ridge (Baikal region)

2020 ◽  
Vol 954 (12) ◽  
pp. 20-30
Author(s):  
Yu.V. Vanteeva ◽  
Е.А. Rasputina ◽  
S.V. Solodyankina
Keyword(s):  
Digital Elevation Model ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Topographic Wetness Index ◽  
Space Images ◽  
Topographic Position Index ◽  
Digital Elevation ◽  
Different Seasons ◽  
Position Index ◽  
Elevation Model

The authors present the results of geoinformation mapping the Primorskiy Ridge landscapes using Landsat 8 satellite images, the digital elevation model SRTM and the factor-dynamic classification of geosystems. At the first stage, the remote sensing data for different seasons were classified using the ISODATA method. Then, using the digital elevation model, the landforms were classified basing upon the topographic position index. According to combining the classification parameters of one of the space images and digital elevation model, each polygon is automatically assigned to a certain preliminary type of landscapes using boolean expressions. Legend adjustments were made basing upon the fieldwork materials. As a result, a digital landscape map of the southern part of the Primorsky Ridge was created; it reflects the landscape structure at the level of facies groups and contains attributive information about the landform, altitude, slope and aspect, topographic wetness index. The analysis of the landscape pattern showed a high fragmentation of landscape polygons, formed due to overlay operations, which indicates the need for generalization of landscape contours.

scholarly journals STUDI PENDAHULUAN PENGINDERAAN JAUH DAN SISTEM INFORMASI GEOGRAFIS UNTUK ANALISIS MEDAN SKALA TINJAU DI SEBAGIAN PROVINSI SUMATERA SELATAN

GEOMATIKA ◽  
2018 ◽  
Vol 24 (2) ◽  
pp. 107
Author(s):  
Heratania Aprilia Setyowati ◽  
Ratna Nurani ◽  
Sigit Heru Murti Budi Santosa
Keyword(s):  
Digital Elevation Model ◽  
Shuttle Radar Topography Mission ◽  
Landsat 8 ◽  
Digital Elevation ◽  
Elevation Model

<p class="Papertext">Beragam cara dapat digunakan untuk mengetahui karakteristik suatu wilayah, salah satunya adalah analisis medan yang merupakan studi sistematik yang memanfaatkan data penginderaan jauh untuk menggali asal muasal, riwayat geomorfologi, dan komponen suatu bentang lahan. Tujuan dari studi pendahuluan ini untuk mengetahui karakteristik medan yang ada di sebagian daerah Sumatera Selatan melalui analisis medan dengan pembuatan sekuen medan yang berbasis citra penginderaan jauh. Citra Landsat 8 digunakan untuk mendapatkan informasi tutupan lahan dan bentuk lahan. Citra SRTM (<em>Shuttle Radar Topography Mission</em>) digunakan untuk menghasilkan data DEM (<em>Digital Elevation Model</em>), <em>h</em><em>illshade</em>, dan <em>s</em><em>lope</em> yang selanjutnya diturunkan menjadi peta topografi. Peta Geologi digunakan untuk menurunkan informasi mengenai jenis tanah. Peta arah aliran dan akumulasi air digunakan untuk menurunkan informasi kondisi drainase. Selanjutnya semua peta di<em>overlay</em> dan digunakan untuk menarik garis sekuen medan sebagai dasar identifikasi karakteristik medan. Berdasarkan hasil studi pendahuluan ini, dapat dikenali bahwa karakteristik medan sebagian Sumatera Selatan berbentuk lahan vulkanik, struktural dan fluvial dengan proses geomorfologi berupa erosi vertikal, transportasi, deposisi, dan sedimentasi. Aplikasi Penginderaan Jauh dan SIG dengan metode sekuen medan dapat digunakan untuk mengetahui karakteristik medan suatu wilayah.</p><p><em><br /></em></p>

scholarly journals ANALISIS CITRA SATELIT LANDSAT 8 DAN DEMNAS UNTUK IDENTIFIKASI PROSPEK PANAS BUMI DI KABUPATEN ACEH TENGAH, PROVINSI ACEH

2021 ◽  
Vol 16 (3) ◽  
pp. 166-184
Author(s):  
Lano Adhitya Permana ◽  
Husin Setia Nugraha ◽  
Sukaesih
Keyword(s):  
Principal Component Analysis ◽  
Surface Temperature ◽  
Digital Elevation Model ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Principal Component ◽  
Landsat 8 ◽  
Fracture Density ◽  
Digital Elevation ◽  
Elevation Model

Gabungan beberapa analisis pada citra satelit Landsat dan Digital Elevation Model Nasional (DEMNAS) dapat dipergunakan untuk mengidentifikasi indikasi area prospek panas bumi. Analisis dilakukan di Kabupaten Aceh Tengah yang diawali dari informasi keberadaan mata air panas pada peta geologi regional lembar Takengon. Metoda penginderaan jauh seperti metoda Fault and Fracture Density (FFD) dan interpretasi circular feature diterapkan pada citra DEMNAS. Sedangkan metoda Land Surface Temperature (LST) dan Direct Principal Component Analysis (DPCA) diterapkan pada citra Landsat 8. Kenampakan circular feature, anomali LST dan indikator adanya mineral ubahan bersuhu tinggi, dapat digunakan untuk memperkirakan keberadaan sumber panas. Sedangkan penerapan FFD digunakan untuk memperoleh indikator adanya zona dengan permeabilitas tinggi yang diperlukan dalam sistem panas bumi.   Hasil penelitian menunjukkan bahwa indikasi sumber panas diperkirakan berada pada komplek vulkanik Gunung Telege yang berada di daerah Kecamatan Atu Lintang. Hal ini diperlihatkan dengan adanya circular feature dan anomali LST yang terdapat di daerah tersebut. Penerapan metoda FFD mengindikasikan adanya zona outflow yang berada di sekitar manifestasi mata air panas yang terletak di sebelah barat laut Gunung Telege. Sedangkan dari hasil penerapan metoda DPCA sulit untuk diinterpretasi dikarenakan belum adanya pemisahan yang tegas antara indikator zona argilik lanjut dan zona propilitik dari hasil DPCA tersebut. Hal ini kemungkinan disebabkan adanya nilai pencampuran antar beberapa indikasi mineral dalam satu piksel yang sama. Secara umum, penggunaan metoda penginderaan jauh di Kabupaten Aceh Tengah dapat membantu untuk memberikan petunjuk awal adanya kemungkinan sistem panas bumi di daerah tersebut

scholarly journals Fire on the Water Towers: Mapping Burn Scars on Mount Kenya Using Satellite Data to Reconstruct Recent Fire History

2019 ◽  
Vol 11 (2) ◽  
pp. 104
Author(s):  
Mary C. Henry ◽  
John K. Maingi ◽  
Jessica McCarty
Keyword(s):  
Digital Elevation Model ◽  
Fire History ◽  
Slope Aspect ◽  
Burned Area ◽  
Semiarid Region ◽  
Landsat 8 ◽  
Hydroelectric Power ◽  
Active Fire ◽  
Digital Elevation ◽  
Elevation Model

Mount Kenya is one of Kenya’s ‘water towers’, the headwaters for the country’s major rivers including the Tana River and Ewaso Nyiro River, which provide water and hydroelectric power to the semiarid region. Fires affect water downstream, but are difficult to monitor given limited resources of local land management agencies. Satellite-based remote sensing has the potential to provide long term coverage of large remote areas on Mount Kenya, especially using the free Landsat data archive and moderate resolution imaging spectroradiometer (MODIS) fire products. In this study, we mapped burn scars on Mount Kenya using 30 m Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) derived dNBR (change in normalized burn ratio) and MODIS active fire detection and burned area data for fires occurring from 2004 to 2015. We also analyzed topographic position (elevation, slope, aspect) of these fires using an ASTER global digital elevation model (GDEM v2) satellite-derived 30 m digital elevation model (DEM). Results indicate that dNBR images calculated from data acquired about one year apart were able to identify large fires on Mount Kenya that match locations (and timing) of MODIS active fire points and burned areas from the same time period, but we were unable to detect smaller and/or older fires.

scholarly journals Analysis of Landsat-8 OLI Imagery for Estimating Exposed Bedrock Fractions in Typical Karst Regions of Southwest China Using a Karst Bare-Rock Index

2018 ◽  
Vol 10 (9) ◽  
pp. 1321 ◽  
Author(s):  
Jie Pei ◽  
Li Wang ◽  
Ni Huang ◽  
Jing Geng ◽  
Jianhua Cao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Southwest China ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Independent Experiment ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Bare Rock

Karst rocky desertification (KRD) has become the primary ecoenvironmental problem in the karst regions of southwest China. The rapid and efficient acquisition of exposed bedrock fractions (EBF) is crucial for the monitoring and assessment of KRD degree and distribution within the highly heterogeneous landscapes. Remote-sensing indices provide a useful method for the quick mapping of the EBF at large scales. The currently available rock indices, however, are faced with insensitivity to bedrock change characteristics, which greatly limits their performances and suitability. To address this problem, we proposed a novel karst bare-rock index (KBRI) that applies shortwave-infrared (SWIR) and near-infrared (NIR) bands from Landsat-8 OLI imagery to maximally distinguish between exposed bedrock and other land cover types in southwest China. A linear regression model was thus established between KBRI and the EBF derived from in situ measurements. The model developed here was then validated with an independent experiment and applied over a large geographic area to produce regional maps of EBF in southwest China. Experimental results showed good performance on root mean square error (5.59%), mean absolute error (4.63%), root mean absolute percentage error (13.59%), and coefficient of determination (0.72), respectively. The advantages of the proposed method are reflected in its simplicity and minimal requirements for auxiliary data while still achieving comparatively better accuracy than existing related indices. Thus, the KBRI has the great potential for the application in other regions around the world with the similar geological backgrounds, thereby helping to address the similar or other related environmental issues. Results of this study provide baseline data for the KRD assessment and karst-ecosystem management in southwest China.

scholarly journals Application of an Index-Based Approach in Geospatial Techniques for the Mapping of Flood Hazard Areas: A Case of Cape Coast Metropolis in Ghana

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3483
Author(s):  
Samuel Yaw Danso ◽  
Yi Ma ◽  
Yvonne Dodzi Ami Adjakloe ◽  
Isaac Yeboah Addo
Keyword(s):  
Digital Elevation Model ◽  
Flood Hazard ◽  
Landsat 8 ◽  
Soil Drainage ◽  
Cape Coast ◽  
Geospatial Techniques ◽  
Digital Elevation ◽  
African Soil ◽  
High Flood ◽  
Elevation Model

Floods remain one of the disasters that destroy properties, livelihoods, and in extreme situations, take lives. As a way of prevention, geospatial applications have been employed in many cities to map flood zones and predict floods. For a country such as Ghana, floods have been ranked as the second fatal disaster after epidemics leading to several kinds of research to resolve them. To date, the Cape Coast Metropolis (CCM) has received little attention in terms of research, though flood cases in the area continue to escalate. This study, therefore, examines the use of geospatial techniques as tools in addressing flood problems in the CCM of Ghana. From a Digital Elevation Model, hydrologic variables were generated using the ArcGIS software (Esri, Redlands, CA, USA). The soil drainage classification for the study was generated from a downloaded African Soil Grid Drainage map, while other important factors that influenced flooding in the CCM were obtained from Landsat 8 imagery. Over 21% of the CCM was classified as high flood hazard zones with areas around the river Kakum estuary being flood hotspots. It is, therefore, recommended that the CCM Assembly fund dredging of streams/rivers and promote afforestation along river banks to reduce the risk of flooding within the metropolis.

scholarly journals A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine

2021 ◽  
Vol 13 (5) ◽  
pp. 2437-2456
Author(s):  
Bowen Cao ◽  
Le Yu ◽  
Victoria Naipal ◽  
Philippe Ciais ◽  
Wei Li ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Google Earth ◽  
Landsat 8 ◽  
Spectral Features ◽  
Error Matrix ◽  
Cropland Area ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Elevation Model

Abstract. The construction of terraces is a key soil conservation practice on agricultural land in China providing multiple valuable ecosystem services. Accurate spatial information on terraces is needed for both management and research. In this study, the first 30 m resolution terracing map of the entire territory of China is produced by a supervised pixel-based classification using multisource and multi-temporal data based on the Google Earth Engine (GEE) platform. We extracted time-series spectral features and topographic features from Landsat 8 images and the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) data, classifying cropland area (cultivated land of Globeland30) into terraced and non-terraced types through a random forest classifier. The overall accuracy and kappa coefficient were evaluated by 10 875 test samples and achieved values of 94 % and 0.72, respectively. For terrace class, the producer's accuracy (PA) was 79.945 %, and the user's accuracy (UA) was 71.149 %. The classification performed best in the Loess Plateau and southwestern China, where terraces are most numerous. Some northeastern, eastern-central, and southern areas had relatively high uncertainty. Typical errors in the mapping results are from the sloping cropland (non-terrace cropland with a slope of ≥ 5∘), low-slope terraces, and non-crop vegetation. Terraces are widely distributed in China, and the total terraced area was estimated to be 53.55 Mha (i.e., 26.43 % of China's cropland area) by pixel counting (PC) method and 58.46 ± 2.99 Mha (i.e., 28.85 % ± 1.48 % of China's cropland area) by error-matrix-based model-assisted estimation (EM) method. Elevation and slope were identified as the main features in the terrace/non-terrace classification, and multi-temporal spectral features (such as percentiles of NDVI, TIRS2, and BSI) were also essential. Terraces are more challenging to identify than other land use types because of the intra-class feature heterogeneity, interclass feature similarity, and fragmented patches, which should be the focus of future research. Our terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and our terrace map will serve as a landmark for studies on multiple ecosystem service assessments including erosion control, carbon sequestration, and biodiversity conservation. The China terrace map is available to the public at https://doi.org/10.5281/zenodo.3895585 (Cao et al., 2020).

scholarly journals Flood modelling using satellite-based precipitation estimates and digital elevation model in eastern Iraq

2018 ◽  
Vol 6 (1) ◽  
pp. 72 ◽  
Author(s):  
Zaidoon Abdulrazzaq ◽  
Nadia Aziz ◽  
Abdulkareem Mohammed
Keyword(s):  
Digital Elevation Model ◽  
Rainfall Intensity ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Landsat 8 ◽  
Flood Prediction ◽  
Tropical Rainfall ◽  
Digital Elevation ◽  
Elevation Model ◽  
The Relationship

Increasingly available and a virtually uninterrupted supply of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. The study conducted in Wasit province/Eastern Iraq when a flood occurs due to heavy rainfall in May 2013. In this study the capability of Tropical Rainfall Measuring Mission (TRMM) rainfall daily data have been used to estimate the relationship between measured precipitation and the Digital Elevation Model (DEM), also to study the relationship between rainfall intensity and flood waters areas. Rainfall estimation by remote sensing using satellite-derived data from the Tropical Rainfall Measuring Mission (TRMM) is a possible means of supplementing rain gauge data, having the better spatial cover of rainfall fields. The approach used throughout this paper has integrated recently compiled data derived from satellite imagery (rainfall, and digital elevation model) into a GIS geodatabase to study the relationship between rainfall intensity and floodwater's areas then the results' comparison with the Normalized Difference Water Index (NDWI) after the flood. ArcGIS software has been used to process, analyze the archived Tropical Rainfall Measuring Mission (TRMM) precipitation data, and calculate NDWI from Landsat 8 images. In conclusions, the study explains the flood-area clearly captured by the TRMM measurements; and the region’s water increased. Also, good correlation between measured precipitation and the Digital Elevation Model (DEM) has been detected.

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