A simple automated dynamic threshold extraction method for the classification of large water bodies from landsat-8 OLI water index images

2018 ◽  
Vol 39 (11) ◽  
pp. 3429-3451 ◽  
Author(s):  
Fangfang Zhang ◽  
Junsheng Li ◽  
Bing Zhang ◽  
Qian Shen ◽  
Huping Ye ◽  
...  
Keyword(s):  
Extraction Method ◽  
Water Bodies ◽  
Dynamic Threshold ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Large Water

scholarly journals Dynamic Threshold Selection for the Classification of Large Water Bodies within Landsat-8 OLI Water Index Images

2016 ◽  
Author(s):  
Fangfang Zhang ◽  
Junsheng Li ◽  
Qian Shen ◽  
Bing Zhang ◽  
Huping Ye ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Landsat 8 ◽  
Threshold Selection ◽  
Water Index ◽  
Sensing Data ◽  
Twin Peaks ◽  
Large Water ◽  
Threshold Range

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

scholarly journals A NOVEL METHOD FOR WATER AND WATER CANAL EXTRACTION FROM LANDSAT-8 OLI IMAGERY

2018 ◽  
Vol XLII-5 ◽  
pp. 323-328 ◽  
Author(s):  
S. L. K. Reddy ◽  
C. V. Rao ◽  
P. R. Kumar ◽  
R. V. G. Anjaneyulu ◽  
B. G. Krishna
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Additional Information ◽  
Water Canal ◽  
Water Canals

<p><strong>Abstract.</strong> Constituents of hydrologic network, River and water canals play a key role in Agriculture for cultivation, Industrial activities and urban planning. Remote sensing images can be effectively used for water canal extraction, which significantly improves the accuracy and reduces the cost involved in mapping using conventional means. Using remote sensing data, the water Index (WI), Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) are used in extracting the water bodies. These techniques are aimed at water body detection and need to be complemented with additional information for the extraction of complete water canal networks. The proposed index MNDWI-2 is able to find the water bodies and water canals as well from the Landsat-8 OLI imagery and is based on the SWIR2 band. In this paper, we use Level-1 precision terrain corrected OLI imagery at 30 meter spatial resolution. The proposed MNDWI-2 index is derived using SWIR2 (B7) band and Green (B3) band. The usage of SWIR2 band over SWIR1 results in very low reflectance values for water features, detection of shallow water and delineation of water features with rest of the features in the image. The computed MNDWI-2 index values are threshold by making the values greater than zero as 1 and less than zero as zero. The binarised values of 1 represent the water bodies and 0 represent the non-water body. This normalized index detects the water bodies and canals as well as vegetation which appears in the form of noise. The vegetation from the MNDWI-2 image is removed by using the NDVI index, which is calculated using the Top of Atmosphere (TOA) corrected images. The paper presents the results of water canal extraction in comparison with the major available indexes. The proposed index can be used for water and water canal extraction from L8 OLI imagery, and can be extended for other high resolution sensors.</p>

A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery

2018 ◽  
Vol 68 ◽  
pp. 73-91 ◽  
Author(s):  
Xiaobiao Wang ◽  
Shunping Xie ◽  
Xueliang Zhang ◽  
Cheng Chen ◽  
Hao Guo ◽  
...  
Keyword(s):  
Surface Water ◽  
Landsat 8 ◽  
Automated Extraction ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Multi Band

scholarly journals A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1611
Author(s):  
Feifei Pan ◽  
Xiaohuan Xi ◽  
Cheng Wang
Keyword(s):  
Image Classification ◽  
Landsat Imagery ◽  
Google Earth ◽  
Water Bodies ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Blue Band ◽  
Ground Truth Data ◽  
Water Index ◽  
Water Indices

A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods.

Combining Water Indices to Detect Water Bodies using Landsat 8 OLI

2019 ◽  
Vol 25 (5) ◽  
pp. 470-475
Author(s):  
Michelle V. Japitana ◽  
Chul-Soo Ye ◽  
Marlowe Edgar C. Burce
Keyword(s):  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Indices

scholarly journals Delimitation and classification of coastal wetlands: Implications for the environmental management of the Colombian Continental Caribbean

2021 ◽  
Vol 50 (1) ◽  
pp. 121-140
Author(s):  
Santiago Millán ◽  
Jenny Rodríguez ◽  
Paula Sierra
Keyword(s):  
Coastal Wetlands ◽  
Flood Frequency ◽  
Water Bodies ◽  
Landsat 8 ◽  
Adaptation To Climate Change ◽  
Alos Palsar ◽  
Ndvi Index ◽  
Consultation Model ◽  
Uncertainty Models

This article describes the cartographic layer construction process of Colombian Caribbean coastal wetlands at a scale of 1:100,000 and the results obtained in terms of their quantification and typing. Two cartographic layers were constructed and subsequently joined, one of the permanent water bodies and another of temporary water bodies and associated coverages. The layers were generated by multitemporal analysis of 45 Landsat 8-OLI satellite images, based on the NDVI index, uncertainty models by superposition of cartographic attributes, and a flood frequency consultation model on ALOS PALSAR 1 images. As a result, 576,279 ha of coastal wetlands were delimited (1.9 % of total wetlands in Colombia), of which 20.4 % are within protected areas. The cartographic legend makes it possible to typify wetlands based on the coverage and temporality of water bodies; discriminates permanent wetlands (42.7 %) with five categories and temporary wetlands (57.3 %) with 15 categories, mostly distributed in seven large complexes. This study is the first description of the colombian Caribbean coastal wetlands based on a cartographic construction, is methodologically replicable, and will support decision-making in the planning of colombian Caribbean coastal areas, especially for risk management and ecosystem-based adaptation to climate change.

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