scholarly journals Automated Built-Up Extraction Index: A New Technique for Mapping Surface Built-Up Areas Using LANDSAT 8 OLI Imagery

2019 ◽  
Vol 11 (17) ◽  
pp. 1966 ◽  
Author(s):  
Firozjaei ◽  
Sedighi ◽  
Kiavarz ◽  
Qureshi ◽  
Haase ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Threshold Value ◽  
P Value ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
European Cities ◽  
Relative Standard ◽  
Optimum Threshold

Accurate built-up area extraction is one of the most critical issues in land-cover classification. In previous studies, various techniques have been developed for built-up area extraction using Landsat images. However, the efficiency of these techniques under different technical and geographical conditions, especially for bare and sandy areas, is not optimal. One of the main challenges of built-up area extraction techniques is to determine an optimum and stable threshold with the highest possible accuracy. In many of these techniques, the optimum threshold value fluctuates substantially in different parts of the image scene. The purpose of this study is to provide a new index to improve built-up area extraction with a stable optimum threshold for different environments. In this study, the developed Automated Built-up Extraction Index (ABEI) is presented to improve the classification accuracy in areas containing bare and sandy surfaces. To develop and evaluate the accuracy of the new method for built-up area extraction with Landsat 8 OLI reflective bands, five test sites located in the Iranian cities (Babol, Naqadeh, Kashmar, Bam and Masjed Soleyman), eleven European cities (Athens, Brussels, Bucharest, Budapest, Ciechanow, Hamburg, Lyon, Madrid, Riga, Rome and Porto) and high resolution layer imperviousness (HRLI) data were used. Each site has varying environmental and complex surface coverage conditions. To determine the optimal weights for each of the Landsat 8 OLI reflective bands, the pure pixel sets for different classes and the improved gravitational search algorithm (IGSA) optimization were used. The Kappa coefficient and overall error were calculated to evaluate the accuracy of the built-up extraction map. Additionally, the ABEI performance was compared with the urban index (UI) and normalized difference built-up index (NDBI) performances. In each of the five test sites and eleven cities, the extraction accuracy of the built-up areas using the ABEI was higher than that using the UI, and NDBI (P-value of 0.01). The relative standard deviations of the optimal threshold values for the ABEI and UI were 27 and 155% (at five test sites) and were 16 and 37% (at eleven European cities), respectively, which indicates the stability of the ABEI threshold value when the location and environmental conditions change. The results of this study demonstrated that the ABEI can be used to extract built-up areas from other land covers. This index is effective even in bare soil and sandy areas, where other indices experience major challenges.

scholarly journals MAPPING OF THE SEAGRASS COVER ALONG THE MEDITERRANEAN COAST OF TURKEY USING LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 1103-1105 ◽  
Author(s):  
T. Bakirman ◽  
M. U. Gumusay ◽  
I. Tuney
Keyword(s):  
Accuracy Assessment ◽  
Mediterranean Coast ◽  
Environmental Data ◽  
Benthic Habitat ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Carbon Dioxide Absorption ◽  
Landsat Images ◽  
Marine Animals ◽  
Seagrass Cover

Benthic habitat is defined as ecological environment where marine animals, plants and other organisms live in. Benthic habitat mapping is defined as plotting the distribution and extent of habitats to create a map with complete coverage of the seabed showing distinct boundaries separating adjacent habitats or the use of spatially continuous environmental data sets to represent and predict biological patterns on the seafloor. Seagrass is an essential endemic marine species that prevents coast erosion and regulates carbon dioxide absorption in both undersea and atmosphere. Fishing, mining, pollution and other human activities cause serious damage to seabed ecosystems and reduce benthic biodiversity. According to the latest studies, only 5–10% of the seafloor is mapped, therefore it is not possible to manage resources effectively, protect ecologically important areas. In this study, it is aimed to map seagrass cover using Landsat 8 OLI images in the northern part of Mediterranean coast of Turkey. After pre-processing (e.g. radiometric, atmospheric, water depth correction) of Landsat images, coverage maps are produced with supervised classification using in-situ data which are underwater photos and videos. Result maps and accuracy assessment are presented and discussed.

scholarly journals ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 821-828
Author(s):  
N. Aslan ◽  
D. Koc-San
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Irrigated Agriculture ◽  
Time Interval ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Study Results ◽  
Landsat 7

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88&thinsp;% for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6&thinsp;&deg;C for 2001 and 6.8&thinsp;&deg;C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r<sup>2</sup>&thinsp;=&thinsp;0.7 and r<sup>2</sup>&thinsp;=&thinsp;0.9 for 2001 and 2014, respectively).

scholarly journals DETECTING THE AFFECTED AREAS OF MOUNT SINABUNG ERUPTION USING LANDSAT 8 IMAGERIES BASED ON REFLECTANCE CHANGE

2017 ◽  
Vol 12 (1) ◽  
pp. 49
Author(s):  
. Suwarsono ◽  
. Hidayat ◽  
Jalu Tejo Nugroho ◽  
. Wiweka ◽  
. Parwati ◽  
...  
Keyword(s):  
Volcanic Eruption ◽  
Threshold Value ◽  
The State ◽  
Single Band ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Volcano Eruption ◽  
Optical Images ◽  
Before And After ◽  
Reflectance Change

The position of Indonesia as part of a "ring of fire" bringing the consequence that the life of the nation and the state will also be influenced by volcanism. Therefore, it is necessary to map rapidly the affected areas of a volcano eruption. Objective of the research is to detect the affected areas of Mount Sinabung eruption recently in North Sumatera by using optical images Landsat 8 Operational Land Imager (OLI). A pair of Landsat 8 images in 2013 and 2014, period before and after eruption, was used to analysis the reflectance change from that period. Affected areas of eruption was separated based on threshold value of reflectance change. The research showed that the affected areas of Mount Sinabung eruption can be detected and separated by using Landsat 8 OLI images based on the change of reflectance value band 4, 5 and NDVI. Band 5 showed  the highest values of decreasing and band 4 showed the highest values of increasing. Compared with another uses of single band, the combination of both bands (NDVI) give the best result for detecting the affected areas of  volcanic eruption.

scholarly journals Estimation of Surface Runoff to Support Flood Hazards Early Warning Using Landsat 8 OLI Imagery

2020 ◽  
Vol 2 ◽  
pp. 14-18
Author(s):  
Ipung ◽  
Oita Mulazahwa Erlangga ◽  
Nastasya Andam Dewi ◽  
Evan Ardi Kristya Pandhadha ◽  
Wirastuti Widyatmanti
Keyword(s):  
Surface Runoff ◽  
Flow Density ◽  
Landsat 8 ◽  
Runoff Coefficient ◽  
Flood Hazards ◽  
Vegetation Density ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Watershed Characteristics ◽  
Density Flow

This study aims to determine the estimated surface runoff in the Diro sub-watershed in Kulon Progo Regency using OLI Landsat 8 imagery. Landsat images are used to determine the type of land cover which is one of the watershed characteristics. The method used to determine the surface runoff coefficient value in the Diro sub-watershed was using the cook’s method. The parameters used to determine the value of surface runoff include vegetation density, flow density, soil type and slope. The results showed that the sub-watershed Diro has a surface coefficient value of 0.7999 and is in the high category.

Surface Material Classification Using Landsat-8 OLI Image Acquired in Ulsan by the Different Machine Learning Techniques

2017 ◽  
Vol 865 ◽  
pp. 650-656
Author(s):  
Yun Jae Choung ◽  
Myung Hee Jo
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Machine Learning Techniques ◽  
Landsat 8 ◽  
Surface Material ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Material Classification ◽  
Learning Techniques ◽  
The Given

Surface material classification is an important task for the preservation of land properties and the management of land development plans. The use of remotely sensed images is efficient for the surface material classification task without human access. This research aims to select the most appropriate machine learning technique for the surface material classification task using the remotely sensed images. In this research, the three different machine learning techniques (MD (Minimum Distance), MLC (Maximum Likelihood Classification), and SVM (Support Vector Machine)) were applied for surface material classification using the Landsat-8 OLI (Operational Land Imager) image acquired in Ulsan, South Korea, in the following steps. First, the training samples for each land cover in the given Landsat images were selected by manual labor. Next, the different machine learning techniques (MD, MLC, and SVM) were applied on the given Landsat images, respectively, for carrying out the surface material classification tasks. The accuracies of the three land cover classification maps generated by the different techniques were assessed using the ground truths. Finally, accuracy comparison was conducted for selecting the most suitable approach for classifying the various surface materials in Ulsan. The statistical results show that the SVM classifier is superior to the MD and MLC classifiers for carrying out surface material classification using the given Landsat-8 OLI image.

scholarly journals ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 821-828 ◽  
Author(s):  
N. Aslan ◽  
D. Koc-San
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Irrigated Agriculture ◽  
Time Interval ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Study Results ◽  
Landsat 7

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88&thinsp;% for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6&thinsp;&deg;C for 2001 and 6.8&thinsp;&deg;C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r&lt;sup&gt;2&lt;/sup&gt;&thinsp;=&thinsp;0.7 and r&lt;sup&gt;2&lt;/sup&gt;&thinsp;=&thinsp;0.9 for 2001 and 2014, respectively).

scholarly journals PlanetScope and Landsat 8 Imageries for Bathymetry Mapping

2020 ◽  
Vol 8 (2) ◽  
pp. 143 ◽  
Author(s):  
Bassam Gabr ◽  
Mostafa Ahmed ◽  
Yehia Marmoush
Keyword(s):  
Linear Model ◽  
Coastal Zone Management ◽  
Image Resolution ◽  
Landsat Image ◽  
Landsat 8 ◽  
Northern Coast ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Spatiotemporal Resolution ◽  
Single Beam

Bathymetry has a great importance in coastal projects. Obtaining proper bathymetric information is necessary for navigation, numerical modeling, and coastal zone management studies. Over the past three decades, a number of measuring protocols have been validated for bathymetry mapping, either by means of echosounding or LIght Detection and Ranging (LIDAR). Although these traditional methods hold a high vertical accuracy, they may have limitations in accessibility for some areas. Remote sensing (RS) techniques can be alternatively utilized for bathymetry extraction and update for such cases. The satellite derived bathymetry (SDB) can be analytically or empirically obtained based on various RS datasets with different spatiotemporal resolution. The current study proposes a methodology to spatially enhance the Landsat-derived bathymetry. Two different satellite images, i.e., Landsat and PlantScope with a spatial resolution of 30 and 3 m respectively have been assessed in bathymetry mapping. The Landsat image resolution has been spatially enhanced to match the Planetscope resolution. The panchromatic band of the Landsat image has been downscaled and used for pan-sharpening the multispectral bands. The bathymetry was empirically estimated from the blue and green spectral bands using the linear model by Lyzenga. The SDB model was calibrated using field measurements of water depths observed by a single beam echosounder. The Bathymetry detection methodology has been applied in an area of the Northern coast of Egypt. The SDB from the PlanetScope, Landsat 8 OLI, and Enhanced Landsat 8 OLI were assessed using error analysis. It was found that the Enhanced Landsat has a comparable result with the PlanetScope. The root mean square error is 0.38 and 0.43 m for PlanetScope and Enhanced Landsat, respectively. The current methodology was also tested by the ratio transform model for SDB and the results revealed the same conclusion as the linear model. Thus, the developed algorithm provides SDB using free Landsat images that is of comparable accuracy to the relatively expensive PlanetScope SDB.

scholarly journals Comparison of Various Spectral Indices for Optimum Extraction of Tropical Wetlands Using Landsat 8 OLI

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Syamani D. Ali ◽  
Hartono Hartono ◽  
Projo Danoedoro
Keyword(s):  
Threshold Value ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Tropical Wetlands ◽  
Landsat 8 Oli ◽  
Threshold Method ◽  
Geospatial Information ◽  
Otsu Method ◽  
Green Vegetation ◽  
Tropical Areas

This research specifically aims to investigate the most accurate spectral indices in extracting wetlands geospatial information taking South Kalimantan, Indonesia, as an example of wetlands in tropical areas. Ten spectral indices were selected for testing their ability to extract wetlands, those are NDVI, NDWI, MNDWI, MNDWIs2, NDMI, WRI, NDPI, TCWT, AWEInsh, andAWEIsh. Tests were performed on Landsat 8 OLI path/row 117/062 and 117/063. The threshold method which was used to separate the wetland features from the spectral indices imagery is Otsu method. The results of this research showed that generally MNDWIs2 was the most optimal spectral indices in wetlands extraction. Especially tropical wetlands that rich with green vegetation cover. However, MNDWIs2 is very sensitive to dense vegetation, this feature has the potential to be detected as wetlands. Furthermore, to improve the accuracy and prevent detection of the dryland vegetation as wetlands, the threshold value should be determined carefully.

scholarly journals PHÂN TÍCH CÁC CHỈ SỐ NƯỚC TỪ ẢNH VỆ TINH LANDSAT 8 OLI ĐỂ CHIẾT TÁCH ĐƯỜNG BỜ BIỂN KHU VỰC TÂY NAM VIỆT NAM

2019 ◽  
Vol 18 (4) ◽  
pp. 339-349
Author(s):  
Tran Anh Tuan ◽  
Le Dinh Nam ◽  
Nguyen Thi Anh Nguyet ◽  
Pham Viet Hong ◽  
Nguyen Thi Ai Ngan ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Threshold Value ◽  
Absolute Error ◽  
Acquisition Time ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Coast Of Vietnam ◽  
Water Indices

The paper presents results of analysis of water indices using remote sensing data to extract an instantaneous shoreline at the time of image acquisition on the southwest coast of Vietnam. The water indices as NDWI (Normalized Difference Water Index), MNDWI (Modified Normalized Difference Water Index), and AWEI (Automated Water Extraction Index) were calculated from Landsat 8 OLI imagery. Then, an extracted distribution histogram of water indices’ values in the study area was used to separate the land from the sea. The position having abnormal frequency of pixels on the histogram is the threshold value to determine the boundary of land and water, and it is considered the shoreline. The study showed the threshold values of NDWI, MNDWI and AWEI which were defined at 0.12, 0.17 and 0.18 respectively. The precision of shoreline extraction from each respective water index was verified by field survey data using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) methods. The verified results showed that MAE and MSE of the shorelines extracted from all three water indices were lower than an allowed limit of 30 m (equivalent to spatial resolution of the Landsat 8 image). However, the shoreline extracted from AWEI had the highest accuracy and it was considered the most appropriate shoreline at the acquisition time of image.

scholarly journals COASTLINE ZONE EXTRACTION USING LANDSAT-8 OLI IMAGERY, CASE STUDY: BODRUM PENINSULA, TURKEY

2019 ◽  
Vol XLII-4/W12 ◽  
pp. 101-104 ◽  
Author(s):  
T. Isiacik Colak ◽  
G. Senel ◽  
C. Goksel
Keyword(s):  
Satellite Imagery ◽  
Satellite Image ◽  
Threshold Value ◽  
Unsupervised Classification ◽  
Boundary Line ◽  
Free Access ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Coast Line ◽  
Line Extraction

<p><strong>Abstract.</strong> Coastline extraction is a fundamental work for coastal resource management and coastal environmental protection. Today, by using digital image processing techniques, coastline extraction can be done with remote sensing imagery systems. In this study, Landsat 8 Operational Land Imagery (OLI) data have been the main data source due to free access and sufficient spatial resolution for coast line extraction. This research is focused on determining the coastline length and measuring land area by using Landsat 8 OLI satellite image for Bodrum Peninsula, Turkey. Three commonly used methods have been applied in order to determine sea-land boundary line and its length, and area of the study area. The Automatic Water Extraction Index (AWEI), Iterative Self-Organizing Data Analysis Technique (ISODATA) unsupervised classification technique and on screen digitizing method was chosen for identification of coastal boundaries. Results of coastline length and land areas of Bodrum by using AWEI, ISODATA and on-screen digitizing are compared with each other. This study shows that with using optimal threshold value, AWEI can be used for coast line extraction method with coherently for Landsat 8 OLI satellite imagery. The overall results show that coastline extraction from satellite imagery can be done with sufficient accuracy using spectral water indices instead of time consuming on-screen digitizing.</p>

Sign in / Sign up

Export Citation Format

Share Document