scholarly journals The Development of Interpretataion Method For Remote Sensing Imagery In Determining The Candidate of Landslide In Leitimur Paninsula, Ambon Island

2017 ◽  
Vol 15 (1) ◽  
pp. 20 ◽  
Author(s):  
Ferad Puturuhu ◽  
Projo Danoedoro ◽  
Junun Sartohadi ◽  
Danang Srihadmoko
Keyword(s):  
Remote Sensing ◽  
Debris Flow ◽  
Spatial Data ◽  
Landsat 8 ◽  
Land Resource ◽  
Digital Imagery ◽  
Visual Interpretation ◽  
Remote Sensing Imagery ◽  
Soil Creep ◽  
Landslide Event

ABSTRAKPenginderaa jauh merupakan salah satu metode yang digunakan untuk menjawab permasalahan penelitian tentang teknologi perolehan data spasial dan sekaligus permasalahan kewilayahan serta manajemen sumber daya laha. Pemanfaatan metode penginderaan jauh untuk penelitian landslide dianataranya metode interpretasi citra secara visual dan digital.  Tujuan penelitian ini adalah membandingkan akurasi metode interpretasi dan menentukan lokasi kejadian landslide. Citra yang digunakan dalam penelitian ini adalah citra Landsat 8, Quickbird dan SRTM. Metode yang digunakan untuk menentukan kandidat landslide adalah interpretasi visual berlapis, Interpretasi citra digital dengan NDVI, OBIA, Toposhape, dan kombinasi NDVI-OBIA, dan NDVI-OBIA-Toposhape. Penggunaan metode interpretasi kejadian landslide yang terbaik adalah interpretasi visual berlapis dengan presentase 90 %. Interpretasi digital dengan NDVI mempunyai ketelitian 47 %, OBIA ketelitiannya  45 %, Toposhape 47 %, kombinasi NDVI-OBIA 47 %, dan Kombinasi NDVI-OBIA-Toposhape 53 %. Dari interpretasi visual berlapis dan pengamatan lapangan diperoleh tipe landslide yang ditemukan yaitu nendatan/slump (soil rotational slide) dalam jumlah yang banyak 7 titik (38.9%), rayapan tanah (soil creep),  aliran bahan rombakan (debris flow), longsor translasi dengan material tanah (earths Slide), dan  nendatan majemuk (multiple rotational slide).Kata kunci: Pengembanga, Metode, Interpretasi Citra, Penginderaan Jauh, Kandidat,    Landslide, Paninsula LeitimurABSTRACTRemote sensing is one of the methods used to address the problem of research on spatial data acquisition technologies and is also acquiring the problems of territorial and land resource management. The utilization of remote sensing method for the landslide research is visual and digital imagery interpretation. The purpose of this study was to compare the accuracy of the method of interpretation and determine the location of the landslide event. The imagery that used in this study was Landsat 8, Quickbird and SRTM. The method that used to determine the candidate of landslide was the layered visual interpretation, digital imagery interpretation with NDVI, OBIA, Toposhape, and combination-OBIA NDVI and NDVI-OBIA-Toposhape. The use of the interpretation method for the landslide event is the best of layered-visual interpretation with a percentage of 90%. Digital interpretation with NDVI has a 47% of its accuracy, thoroughness OBIA 45%, Toposhape 47%, the combination of NDVI-OBIA 47%, and the combination of NDVI-OBIA-Toposhape 53%. From  the layered-visual interpretation and field observations were obtained type of landslide found that soil rotational slide in large quantities 7 points (38.9%), creep soil (soil creep), the flow of material destruction (debris flow), landslides translation with soil materials (earths slide) and multiple rotational slide.Keywords: Development, Method, Imagery Interpretation, Remote Sensing, Candidate of Landslide, Landslide and Leitimur JaizirahCitation: Puturuhu, F., Danoedoro, P., Sartohadi, J. and Srihadmoko, D. (2017). The Development of Interpretataion Method for Remote Sensing Imagery In Determining The Candidate of Landslide In Leitimur Paninsula, Ambon Island. Jurnal Ilmu Lingkungan, 15(1), 20-34, doi:10.14710/jil.15.1.20-34

scholarly journals Remote Sensing Investigation of Geological Hazards in Xingguo County, Jiangxi Province

2019 ◽  
Vol 131 ◽  
pp. 01056
Author(s):  
Min Yu ◽  
Jiangqin Chao
Keyword(s):  
Remote Sensing ◽  
Debris Flow ◽  
Jiangxi Province ◽  
Landsat 8 ◽  
Geological Environment ◽  
Geological Disasters ◽  
Work Area ◽  
Ground Collapse ◽  
Geological Disaster ◽  
Remote Sensing Interpretation

Xingguo County is located in the middle and low hilly mountainous areas. The area of the landslide, collapse and debris flow geological disasters is large. The sudden geological disasters such as landslides and mudslides caused by heavy rainfall are increasing year by year. This study mainly used high-altitude aerial imagery (0.5m) and Landsat 8 OLI satellite imagery covering Xingguo County as the data source, carried out remote sensing interpretation of geological environment background conditions and geological disasters in the whole area, and carried out on-site verification. At the same time, the correlation between the stratigraphic structure, topography and other factors in the study area and the spatial distribution characteristics of geological disaster points are discussed. The results show that: (1) based on remote sensing image interpretation of 377 geological disaster points; 83 landslide points, 229 hidden danger points, 17 collapse points, 26 hidden danger points, 1 hidden danger point, ground collapse point 1 At 20 places in the geological environment. (2) From the results of remote sensing interpretation, the types of geological disasters in the work area are mainly landslides and landslide hazards (including collapse type), and there are fewer collapses, collapses and debris flow hazards, and most landslide hazard points are unstable. (3) From the distribution of geological disasters, it is mainly within the scope of artificial influence. The construction of excavation slopes on the roads leads to instability of the slopes and induces disasters under the influence of rainfall. In addition, there are a large number of artificial mining mines in the work area. These places are also prone to geological disasters due to unreasonable mining and subsequent prevention and control work. (4) Areas with strong human activities, areas near the fault structure and water system roads are the main influencing factors for geological disasters in the work area.

scholarly journals Discriminative Feature Learning Constrained Unsupervised Network for Cloud Detection in Remote Sensing Imagery

2020 ◽  
Vol 12 (3) ◽  
pp. 456
Author(s):  
Weiying Xie ◽  
Jian Yang ◽  
Yunsong Li ◽  
Jie Lei ◽  
Jiaping Zhong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Learning ◽  
Residual Error ◽  
Observation System ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Adversarial Learning ◽  
Remote Sensing Imagery ◽  
Discriminative Feature ◽  
Latent Space

Cloud detection is a significant preprocessing step for increasing the exploitability of remote sensing imagery that faces various levels of difficulty due to the complexity of underlying surfaces, insufficient training data, and redundant information in high-dimensional data. To solve these problems, we propose an unsupervised network for cloud detection (UNCD) on multispectral (MS) and hyperspectral (HS) remote sensing images. The UNCD method enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. The UNCD enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. First, a compact representation of the original imagery is obtained by a latent adversarial learning constrained encoder. Meanwhile, the majority class with sufficient samples (i.e., background pixels) is more accurately reconstructed than the clouds with limited samples by the decoder. An image discriminator is used to prevent the generalization of out-of-class features caused by latent adversarial learning. To further highlight the background information in the deep latent space, a multivariate Gaussian distribution is introduced. In particular, the residual error with clouds highlighted and background samples suppressed is applied in the cloud detection in deep latent space. To evaluate the performance of the proposed UNCD method, experiments were conducted on both MS and HS datasets that were captured by various sensors over various scenes, and the results demonstrate its state-of-the-art performance. The sensors that captured the datasets include Landsat 8, GaoFen-1 (GF-1), and GaoFen-5 (GF-5). Landsat 8 was launched at Vandenberg Air Force Base in California on 11 February 2013, in a mission that was initially known as the Landsat Data Continuity Mission (LDCM). China launched the GF-1 satellite. The GF-5 satellite captures hyperspectral observations in the Chinese Key Projects of High-Resolution Earth Observation System. The overall accuracy (OA) values for Images I and II from the Landsat 8 dataset were 0.9526 and 0.9536, respectively, and the OA values for Images III and IV from the GF-1 wide field of view (WFV) dataset were 0.9957 and 0.9934, respectively. Hence, the proposed method outperformed the other considered methods.

scholarly journals A Novel Remote Sensing Index for Extracting Impervious Surface Distribution from Landsat 8 OLI Imagery

2019 ◽  
Vol 9 (13) ◽  
pp. 2631 ◽  
Author(s):  
Hong Fang ◽  
Yuchun Wei ◽  
Qiuping Dai
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Impervious Surface ◽  
Landsat 8 ◽  
The Novel ◽  
Impervious Surfaces ◽  
Surface Distribution ◽  
Remote Sensing Imagery ◽  
Normalised Difference Vegetation Index ◽  
Multispectral Remote Sensing

The area of urban impervious surfaces is one of the most important indicators for determining the level of urbanisation and the quality of the environment and is rapidly increasing with the acceleration of urbanisation in developing countries. This paper proposes a novel remote sensing index based on the coastal band and normalised difference vegetation index for extracting impervious surface distribution from Landsat 8 multispectral remote sensing imagery. The index was validated using three images covering urban areas of China and was compared with five other typical index methods for the extraction of impervious surface distribution, namely, the normalised difference built-up index, index-based built-up index, normalised difference impervious surface index, normalised difference impervious index, and combinational built-up index. The results showed that the novel index provided higher accuracy and effectively distinguished impervious surfaces from bare soil, and the average values of the recall, precision, and F1 score for the three images were 95%, 91%, and 93%, respectively. The novel index provides better applicability in the extraction of urban impervious surface distribution from Landsat 8 multispectral remote sensing imagery.

scholarly journals Landuse Changes Refer to Spatial Planning Regulations at Kelara Watershed Area: An Analysis Using Geospatial Information Technology

2015 ◽  
Vol 29 (1) ◽  
Author(s):  
Andi Ramlan ◽  
Risma Neswati ◽  
Sumbangan Baja ◽  
Muhammad Nathan
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Data ◽  
Spatial Planning ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Geospatial Information ◽  
Survey Techniques ◽  
Information System Technology

The purpose of this study is to analyze land use changes in the Kelara watershed and to assess the suitability of current land use changes with the spatial planning regulation of Jeneponto within Kelara basin. This study integrates various survey techniques, remote sensing, and geographic information system technology analysis. Geospatial information used in this study consists of Landsat ETM 7+ satellite imagery (2009) and Landsat 8 (2014) as well as a number of spatial data based on vector data which is compiled by the Jeneponto Government. Remote sensing data using two time series (2009 and 2014) are analyzed by means of supervised classification and visual classification.  The analysis indicated that land use type for the paddy fields and forests (including mangroves) converted become a current land use which is inconsistent with the spatial planning regulation of Jeneponto.The use of land for settlement tends to increase through conversion of wetlands (rice fields). These conditions provide an insight that this condition will occur in the future, so that providing the direction of land use change can be better prepared and anticipated earlier.

scholarly journals Analisis Kerapatan Vegetasi di Kecamatan Pangandaran melalui Citra Landsat 8

2020 ◽  
Vol 4 (1) ◽  
pp. 32-38
Author(s):  
Dwi Yanti ◽  
Indri Megantara ◽  
Muhamad Akbar ◽  
Sabila Meiwanda ◽  
Syauqi Izzul ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Unsupervised Classification ◽  
Landsat 8 ◽  
Vegetation Density ◽  
Sparse Vegetation ◽  
Intended Object ◽  
Density Map ◽  
Validation Tests

ABSTRAKPenginderaan jauh merupakan alat dan teknik untuk mengambil data spasial tanpa menyentuh secara langsung objek yang dituju. Salah satu kegunaan penginderaan jauh adalah mengetahui tingkat kerapatan vegetasi menggunakan metode unsupervised classification K- Means dan perhitungan NDVI. Penelitian ini dilakukan di Kecamatan Pangandaran dan menghasilkan peta kerapatan vegetasi. Hasil klasifikasi kerapatan vegetasi di Kecamatan Pangandaran menghasilkan sebanyak 5 klasifikasi yaitu badan air, vegetasi jarang, cukup rapat, rapat, sangat rapat. Peta kerapatan vegetasi tersebut telah dilakukan uji akurasi dan validasi lapangan dengan akurasi sebesar 25% tingkat akurasi dari hasil interpretasi yang diperoleh menunjukan bahwa peta yang dihasilkan belum memenuhi standar USGS untuk dapat digunakan yaitu sebesar 85%.Kata Kunci: Pengindraan Jauh, NDVI, K-Means- Kerapatan Vegetasi, PangandaranABSTRACTRemote sensing is a tool and technique for retrieving spatial data without touching the intended object. One of the uses of remote sensing is to knowing the level of vegetation density using the unsupervised classification K-Means method and NDVI calculations.This research was conducted in Pandandaran sub-district and produced a map of vegetation density. The results of the classification of vegetation density in the Pangandaran sub-district resulted in as many as 5 classifications namely water bodies, sparse vegetation, fairly dense, dense, very dense. Vegetation density map has been carried out field accuracy and validation tests with an accuracy of 25% The accuracy of the interpretation results obtained shows that the map produced does not meet USGS standards to be used that is equal to 85%

scholarly journals ANALISIS LAHAN KRITIS DAN ARAHAN LAHAN DALAM PENGEMBANGAN WILAYAH PADA SUBDAS DI KABUPATEN KAYONG UTARA MENGGUNAKAN TEKNIK PENGINDERAAN JAUH DAN SISTEM INFORMASI GEOGRAFIS

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Murti Anom Suntoro ◽  
Dwi Astiani ◽  
Wiwik Ekyastuti
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Data ◽  
Geographic Information ◽  
Director General ◽  
Landsat 8 ◽  
Degraded Land ◽  
Technical Guidance

Critical land is a damaged land, thus losing or decreasing its function to the specified or expected limits. The identification and critical lands mapping is essential for the planning and determination of priority watersheds in order to the utilization and development of natural resources and land rehabilitation and soil conservation. Remote sensing is a technique that enable people to collect data without direct field measurement. The using of Landsat 8 image then analyzed by using Geographic Information System (GIS) is being expected to improve the ability to classify land cover, the map was then overlad with parameter map based on Regulation of Director General of Management of Watershed and Social Forestry Number P. 4 / V-Set / 2013 about technical guidance on the preparation of spatial data of other critical lands to identify critical lands in Kayong Utara Regency.Keywords: Degraded land, Geographic Information System (GIS), Remote sensing, overlay

scholarly journals Land Use Land Cover Analysis using Geospatial Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2561-2566
Author(s):  
Babita Singh
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Anthropogenic Activities ◽  
Google Earth ◽  
Landscape Configuration ◽  
Landsat 8 ◽  
Visual Interpretation ◽  
Open Forest

Abstract: Remote sensing and Geographic information system (GIS) techniques can be used for the changing pattern of landscape. The study was conducted in Dehradun, Haridwar and Pauri Garhwal Districts of Uttarakhand State, India. In order to understand dynamics of landscape and to examine changes in the land use/cover due to anthropogenic activities, two satellite images (Landsat 5 and Landsat 8) for 1998 and 2020 were used. Google Earth Engine was used to perform supervised classification. Spectral indices (NDVI, MNDWI, SAVI, NDBI) were calculated in order to identify land cover classes. Both 1998 and 2020 satellite images were classified broadly into six classes namely agriculture, built-up, dense forest, open forest, scrub and waterbody. Using high resolution google earth satellite images and visual interpretation, overall accuracy assessment was performed. For land cover/use change analysis, these images were imported to GIS platform. Landscape configuration was observed by calculating various landscape metrices Images. It was observed that scrub land area had increased from 11 % to 14 % but a decrease in agriculture by 4.65 %. The increased value of NP, PD, PLAND, LPI and decrease in AI landscape indices shows that land fragmentation had increased since 1998. The most fragmented classes were scrub (PD - 3.32 to 5.18) and open forest (PD - 3.57 to 5.07). Decrease in AI for open forest, agriculture, built-up indicated that more fragmented patches of these classes were present. The result confirmed increase in the fragmentation of landscape from 1998 onwards. Keywords: GIS, LULC, landscape metrics, Remote Sensing

scholarly journals A color balancing method for wide range Remote Sensing imagery based on Regionalization

2015 ◽  
Vol XL-7/W4 ◽  
pp. 103-108 ◽  
Author(s):  
J. Liu ◽  
H. T. Li ◽  
H. Y. Gu
Keyword(s):  
Remote Sensing ◽  
Difficult Problem ◽  
Geographic Information ◽  
Dynamic Monitoring ◽  
Land Resource ◽  
Climatic Data ◽  
Large Area ◽  
Remote Sensing Imagery ◽  
Wide Range ◽  
Algorithmic Framework

Quick mosaicking of wide range remote sensing imagery is an important foundation for land resource survey and dynamic monitoring of environment and nature disasters. It is also technically important for basis imagery of geographic information acquiring and geographic information product updating. This paper mainly focuses on one key technique of mosaicking, color balancing for wide range Remote Sensing imagery. Due to huge amount of data, large covering rage, great variety of climate and geographical condition, color balancing for wide range remote sensing imagery is a difficult problem. In this paper we use Ecogeographic regionalization to divide the large area into several regions based on terrains and climatic data, construct the algorithmic framework of a color balancing method according to the regionalization result, which conduct from region edge to center to fit wide range imagery mosaicking. The experimental results with wide range HJ-1 dataset show that our method can significantly improve the wide range of remote sensing imagery color balancing effects: making images well-proportioned mosaicking and better in keeping images' original information. In summary, this color balancing method based on regionalization could be a good solution for nationwide remote sensing image color balancing and mosaicking.

scholarly journals Tracking Forest Change and Development Using Low-Cost Remote Sensing Imagery and GIS Integration

2009 ◽  
Vol 26 (4) ◽  
pp. 148-155
Author(s):  
Nathan A. Briggs ◽  
Steven A. Sader
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Forest Cover ◽  
Low Cost ◽  
Human Populations ◽  
Forest Cover Change ◽  
Visual Interpretation ◽  
Forest Change ◽  
Remote Sensing Imagery ◽  
High Resolution Images

Abstract Conversion of forestland to other uses is occurring in Maine as growing human populations and desire for second homes are exerting development pressures on privately owned forestland. This study was performed to assess forest cover change and conversion to developed uses in a 636,000-hastudy area in Maine. A three-date time series (2000, 2002, and 2006) of Landsat Thematic Mapper data was analyzed to detect forest cover losses, and overall mapping accuracy was determined to be 91%. Forest cover losses (percentage per year) were aggregated for 81 townships and reported foreach time sequence. Rates of forest cover loss differ among townships and for the same township in different time periods. Visual interpretation of forestland conversion using high-resolution images for a subsample of 24 townships showed that 305 of 4,716 harvested forest hectares (6.47%)was converted to developed uses. The study demonstrates the practical use of low-cost remote-sensing imagery and routine interpretation methods for accurate tracking of forest change and quantification of land use conversion. The methods are adaptable to other states to assist decisionmakersin assessing regional and local land use and planning forest conservation measures.

scholarly journals A topographic framework for estimating spatial variation in hill country grasslands

2016 ◽  
Vol 16 ◽  
pp. 119-125
Author(s):  
R.L. Phillips ◽  
M.R. Eken ◽  
B.C. Rundquist
Keyword(s):  
Remote Sensing ◽  
Spatial Variation ◽  
Plant Height ◽  
Spatial Data ◽  
Field Data ◽  
Data Sources ◽  
Spatiotemporal Variability ◽  
Landsat 8 ◽  
Hill Country ◽  
Data Framework

Livestock graze hill country regions worldwide where grassland biomass or structure is important both economically as forage and enviromentally as habitat for wildlife. Manual measurements of biomass in remote and expansive hill country landscapes are time consuming, expensive, and difficult to estimate due to spatiotemporal variability. Pasture areas where livestock utilisation or grassland biomass is exceptionally high or low could be mapped within a topographic framework. A model was developed that integrates several data sources (elevation, spectra and field data) to estimate hill-country biomass. Topographic data were modelled and used to classify biomass, which ranged from low at summits (1493 kg/ha) to high at toe-slopes (2876 kg/ha). These estimates were compared with the current plant height-based model, which ranged from low (2014 kg/ ha) to high (3032 kg/ha). This paper demonstrates how expansive, heterogeneous grassland landscapes can be assessed seasonally using topographic markers within an integrated spatial data framework. Keywords: Remote sensing, DEM, structure, Landsat 8, forage utilisation, graziers

Sign in / Sign up

Export Citation Format

Share Document