scholarly journals Zonasi Distribusi Tanaman Hutan di Taman Nasional Gunung Semeru Berdasarkan Integrasi Nilai Indeks Vegetasi dan Digital Elevation Model

2020 ◽  
Vol 1 (2) ◽  
pp. 64-70
Author(s):  
Aqilla Fitdhea Anesta ◽  
Angga Febry Fatman ◽  
Mamad Sugandi
Keyword(s):  
Digital Elevation Model ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Digital Elevation ◽  
Elevation Model

Penelitian ini menggunakan ilmu penginderaan jauh, yang mana memungkinkan kita dapat melakukan penelitian tanpa harus datang langsung ke lokasi dan diharapkan mendapatkan data yang memiliki cakupan yang luas dari citra satelit. Tujuan penelitian ini adalah untuk mengetahui persebaran vegetasi berdasarkan ketinggian tempat dan nilai indeks vegetasi pada TN-BTS. Data dalam penelitian ini adalah citra Landsat 8 bulan November 2019 dan data ketinggian dari DEMNAS. Data tersebut diolah dengan menggunakan Normalized Difference Vegetation Index (NDVI) dan reklasifikasi ketinggian kedalam tiga zona ketinggian, antara lain sub-montana, montana dan sub-alpin. Integrasi kedua data tersebut akan menghasilkan peta distribusi tanaman hutan terhadap ketiga zona ketinggian. Studi menunjukkan bahwa semakin bertambahnya ketinggian tempat akan diikuti dengan pengurangan nilai NDVI. Ini menunjukkan bahwa berkurangnya vegetasi baik kerapatan, jenis dan kualitas tumbuhnya. Demikian halnya dengan semakin rendah suatu ketinggian tempat akan diikuti dengan tingginya nilai NDVI dan vegetasi yang nampak akan lebih rapat dan lebih beragam jenisnya. Tetapi, nilai NDVI hanya mempunyai sedikit pengaruh terhadap zonasi ketinggian. Hasil yang diharapkan dari studi ini adalah menunjukkan karakteristik utuh dari hubungan NDVI dengan ketinggian pada ketiga zona yang dikaji.

scholarly journals Spatial Downscaling of GPM Annual and Monthly Precipitation Using Regression-Based Algorithms in a Mountainous Area

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Chesheng Zhan ◽  
Jian Han ◽  
Shi Hu ◽  
Liangmeizi Liu ◽  
Yuxuan Dong
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mountainous Area ◽  
Monthly Precipitation ◽  
Practical Applications ◽  
Precipitation Measurement ◽  
Digital Elevation ◽  
Elevation Model ◽  
Global Precipitation ◽  
Spatial Downscaling

As a fundamental component in material and energy circulation, precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. In this study, we investigated two downscaling algorithms based on the Multiple Linear Regression (MLR) and the Geographically Weighted Regression (GWR), respectively. They were employed to downscale annual and monthly precipitation obtained from the Global Precipitation Measurement (GPM) Mission in Hengduan Mountains, Southwestern China, from 10 km × 10 km to 1 km × 1 km. Ground observations were then used to validate the accuracy of downscaled precipitation. The results showed that (1) GWR performed much better than MLR to regress precipitation on Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM); (2) coefficients of GWR models showed strong spatial nonstationarity, but the spatial mean standardized coefficients were very similar to standardized coefficients of MLR in terms of intra-annual patterns: generally NDVI was positively related to precipitation when monthly precipitation was under 166 mm; DEM was negatively related to precipitation, especially in wet months like July and August; contribution of DEM to precipitation was greater than that of NDVI; (3) residuals’ correction was indispensable for the MLR-based algorithm but should be removed from the GWR-based algorithm; (4) the GWR-based algorithm rather than the MLR-based algorithm produced more accurate precipitation than original GPM precipitation. These results indicated that GWR is a promising method in satellite precipitation downscaling researches and needed to be further studied.

scholarly journals Soil use and occupation in Caracol Settlement: a multitemporal assessment using remote sensing and geoprocessing techniques

2020 ◽  
Vol 9 (12) ◽  
pp. e30891211029
Author(s):  
Odemir Coelho da Costa ◽  
José Francisco dos Reis Neto ◽  
Ana Paula Garcia Oliveira
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Cover ◽  
The United States ◽  
United States Geological Survey ◽  
Vegetation Landscape ◽  
Digital Elevation ◽  
Elevation Model ◽  
Gis Software

This study focused on the application of remote sensing and geoprocessing techniques to quantify the agroecological use of Caracol settlement area in order to quantify the vegetated areas, as well as the use and occupation of the soil in the years 2000, 2010 and 2020, in the months of May of each year. To achieve the objectives, computational tools (Quantum GIS software) were used, as well as data from Landsat 5 and 8 satellites, bands 3 and 4, 4 and 5 respectively. Vector data from the database of the Brazilian Institute of Geography and Statistics (IBGE), a Digital Elevation Model (DEM), from the United States Geological Survey (USGS/NASA) for evaluation of the watersheds were also used. For vegetation analysis, as well as temporal evolution, the Normalized Difference Vegetation Index (NDVI) was used, with this it was possible to evaluate by means of thematic maps and tables containing the quantification and classification of vegetation and soil cover. It was evident in the present study that there were significant changes in the vegetation landscape over two decades, through anthropic activity by settled families, that were responsible for such changes in the use and soil cover of Caracol settlement.

scholarly journals Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis

2019 ◽  
Vol 16 (12) ◽  
pp. 2206 ◽  
Author(s):  
Niu ◽  
Li ◽  
Qiu ◽  
Xu ◽  
Huang ◽  
...  
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Distribution Patterns ◽  
Oncomelania Hupensis ◽  
Spatial And Temporal Patterns ◽  
Jianghan Plain ◽  
Coverage Area ◽  
Digital Elevation ◽  
Elevation Model

Schistosomiasis is a snail-borne parasitic disease endemic to the tropics and subtropics, whose distribution depends on snail prevalence as determined by climatic and environmental factors. Here, dynamic spatial and temporal patterns of Oncomelania hupensis distributions were quantified using general statistics, global Moran’s I, and standard deviation ellipses, with Maxent modeling used to predict the distribution of habitat areas suitable for this snail in Gong’an County, a severely affected region of Jianghan Plain, China, based on annual average temperature, humidity of the climate, soil type, normalized difference vegetation index, land use, ditch density, land surface temperature, and digital elevation model variables; each variable’s contribution was tested using the jackknife method. Several key results emerged. First, coverage area of O. hupensis had changed little from 2007 to 2012, with some cities, counties, and districts alternately increasing and decreasing, with ditch and bottomland being the main habitat types. Second, although it showed a weak spatial autocorrelation, changing negligibly, there was a significant east–west gradient in the O. hupensis habitat area. Third, 21.9% of Gong’an County’s area was at high risk of snail presence; and ditch density, temperature, elevation, and wetting index contributed most to their occurrence. Our findings and methods provide valuable and timely insight for the control, monitoring, and management of schistosomiasis in China.

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 Mapping of Landslide Susceptible Zones by Using Frequency Ratios at Bluncong Subwatershed, Bondowoso Regency

UKaRsT ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 126
Author(s):  
Didik Efendi ◽  
Entin Hidayah ◽  
Akhmad Hasanuddin
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Local Governments ◽  
Vegetation Index ◽  
Data Interpretation ◽  
Geographical Information ◽  
Landsat 8 ◽  
Digital Elevation ◽  
Elevation Model ◽  
Frequency Ratios

Landslides are the disasters that frequently happen in Bluncong sub-watershed. These incidents have caused damage and malfunction of road infrastructure, bridges, and irrigation buildings. Therefore, it is important to anticipate landslides through mapping of landslide-susceptibility areas The objective of this study is to map landslide susceptibility at Bluncong sub watershed, Bondowoso, by using Geographical Information System and remote sensing. The landslide susceptibility analysis and mapping are conducted based on landslide occurrences with the Frequency Ratio approach. The landslide sites are identified from field survey data interpretation. Digital Elevation Model maps, geological data, land uses and rivers data, and Landsat 8 images are collected, processed, and then built into the GIS platform's spatial database. The selected factors that cause landslide occurrences are land use, distance to river, aspect, slope, elevation, curvature, and the vegetation index (NDVI). The results show that the accuracy of the map is acceptable. The frequency ratio model gained the area under curve (AUC) value of 0.79. It is found that 9.08% of the area has very high landslide susceptibility. Local governments can use this study's mapping results to minimize the risk at landslidesusceptible zones

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 Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 879
Author(s):  
Ducthien Tran ◽  
Dawei Xu ◽  
Vanha Dang ◽  
Abdulfattah.A.Q. Alwah
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Data Sources ◽  
Road Density ◽  
Rapid Urbanization ◽  
Spatial Factors ◽  
Digital Elevation ◽  
Important Means ◽  
Elevation Model

In the context of climate change and rapid urbanization, urban waterlogging risks due to rainstorms are becoming more frequent and serious in developing countries. One of the most important means of solving this problem lies in elucidating the roles played by the spatial factors of urban surfaces that cause urban waterlogging, as well as in predicting urban waterlogging risks. We applied a regression model in ArcGIS with internet open-data sources to predict the probabilities of urban waterlogging risks in Hanoi, Vietnam, during the period 2012–2018 by considering six spatial factors of urban surfaces: population density (POP-Dens), road density (Road-Dens), distances from water bodies (DW-Dist), impervious surface percentage (ISP), normalized difference vegetation index (NDVI), and digital elevation model (DEM). The results show that the frequency of urban waterlogging occurrences is positively related to the first four factors but negatively related to NDVI, and DEM is not an important explanatory factor in the study area. The model achieved a good modeling effect and was able to explain the urban waterlogging risk with a confidence level of 67.6%. These results represent an important analytic step for urban development strategic planners in optimizing the spatial factors of urban surfaces to prevent and control urban waterlogging.

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