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 Application of geo-informatics engineering to estimate above-ground carbon sequestration

2018 ◽  
Vol 38 (3) ◽  
pp. 303-308
Author(s):  
Teerawong Laosuwan ◽  
Yannawut Uttaruk ◽  
Tanutdech Rotjanakusol ◽  
Kusuma Arsasana
Keyword(s):  
Carbon Sequestration ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Regression Equations ◽  
Landsat 8 Oli ◽  
Green Vegetation ◽  
Simple Ratio ◽  
The Relationship

This research aims to estimate above-ground carbon sequestration of orchards by using the data collected from Landsat 8 OLI. Regression equations are applied to study the relationship between the amount of above-ground carbon sequestration and vegetation indices from Landsat 8 OLI, in which the data was collected in 2015 in 3 methods: 1) Difference Vegetation Index (DVI), 2) Green Vegetation Index (GVI), and 3) Simple Ratio (SR). The results are as follows: 1) By DVI method, it results in the equation y = 0.3184e0.0482x and the coefficient of determination R² = 0.8457. The amount of the above-ground sequestration calcula-tion's result is 213.176 tons per rai. 2) Using the GVI method, it results in the equation y = 0.2619e0.0489x and the coefficient of determination R²=0.8763. The amount of the above-ground sequestration calculation's result is 220.510 tons per rai. 3) Using the SR method, it results in the equation y = 0.8900e0.0469x and the coefficient of determination R² = 0.7748. The amount of the above-ground sequestration calculation's result is 234.229 tons per rai.

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 Geometric Accuracy Assessment for Shoreline Derived from NDWI, MNDWI, and AWEI Transformation on Various Coastal Physical Typology in Jepara Regency using Landsat 8 OLI Imagery in 2018

2019 ◽  
Vol 6 (1) ◽  
pp. 55
Author(s):  
Arief Wicaksono ◽  
Pramaditya Wicaksono
Keyword(s):  
Accuracy Assessment ◽  
Landsat 8 ◽  
Geometric Accuracy ◽  
Landsat 8 Oli ◽  
Visual Interpretation ◽  
Geospatial Information ◽  
Index Method ◽  
Water Index ◽  
Subjective Threshold ◽  
Map Accuracy

Landsat 8 OLI imagery and water index utilization is expected to be able to complete the shoreline data that is difficult to obtain by using terrestrial and hydrographic surveys. In fact, coastal areas in Indonesia have a variety of coastal physical typology so that each water index characteristic in obtaining shoreline data needs to be understood in order to use water index method effectively. The objectives of this study are to map the shoreline using NDWI, MNDWI, and AWEI transformations and assess the shoreline geometric accuracy on various coastal physical typology. The shoreline derived from water index is obtained from Landsat 8 OLI imagery, while the reference shoreline for accuracy assessment is obtained from visual interpretation on Planet Scope imagery. Threshold 0 and subjective threshold based on per coastal physical typology sample experiments are used to separate land-sea. The horizontal accuracy standard of the shoreline derived from water index uses the regulation from Geospatial Information Agency of Indonesia No.15 in 2014 on technical guidelines for basic map accuracy. The results consisted of 1:100,000 scale shoreline map and shoreline geometric accuracy per coastal physical typology. Based on the shoreline geometry accuracy assessment, NDWI has the lowest shoreline geometry accuracy on artificial coast (RMSE=24.13 m). MNDWI has the lowest shoreline geometry accuracy on land deposition coast (RMSE=15.84 m), marine deposition coast (RMSE=29.53 m), and volcanic coast (RMSE=10 m). AWEIsh has the lowest shoreline geometry accuracy on the organic coast (RMSE=13.47 m), while AWEI does not superior to any coastal physical typology.

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.

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