scholarly journals Subpixel Surface Water Extraction (SSWE) Using Landsat 8 OLI Data

Water ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 653 ◽  
Author(s):  
Longhai Xiong ◽  
Ruru Deng ◽  
Jun Li ◽  
Xulong Liu ◽  
Yan Qin ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Extraction ◽  
Landsat 8 ◽  
Landsat 8 Oli

Surface Water Extraction From Landsat 8 OLI Imagery Using the LBV Transformation

2017 ◽  
Vol 10 (10) ◽  
pp. 4417-4429 ◽  
Author(s):  
Tianyuan Zhang ◽  
Huazhong Ren ◽  
Qiming Qin ◽  
Chengye Zhang ◽  
Yuanheng Sun
Keyword(s):  
Surface Water ◽  
Water Extraction ◽  
Landsat 8 ◽  
Landsat 8 Oli

scholarly journals Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

2018 ◽  
Vol 10 (5) ◽  
pp. 755 ◽  
Author(s):  
Wei Jiang ◽  
Guojin He ◽  
Tengfei Long ◽  
Yuan Ni ◽  
Huichan Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Water ◽  
Multilayer Perceptron ◽  
Satellite Images ◽  
Water Extraction ◽  
Landsat 8 ◽  
Landsat 8 Oli

Analysis of Landsat-8 OLI imagery for land surface water mapping

2014 ◽  
Vol 5 (7) ◽  
pp. 672-681 ◽  
Author(s):  
Zhiqiang Du ◽  
Wenbo Li ◽  
Dongbo Zhou ◽  
Liqiao Tian ◽  
Feng Ling ◽  
...  
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Land Surface Water ◽  
Water Mapping

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 Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2580 ◽  
Author(s):  
Tri Acharya ◽  
Anoj Subedi ◽  
Dong Lee
Keyword(s):  
Surface Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pure Water ◽  
Confusion Matrix ◽  
Threshold Value ◽  
Environmental Noise ◽  
Water Extraction ◽  
Landsat 8 ◽  
Water Indices

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

scholarly journals Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery

2016 ◽  
Vol 8 (7) ◽  
pp. 584 ◽  
Author(s):  
Huan Xie ◽  
Xin Luo ◽  
Xiong Xu ◽  
Haiyan Pan ◽  
Xiaohua Tong
Keyword(s):  
Surface Water ◽  
Urban Environments ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Mapping

scholarly journals Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2769 ◽  
Author(s):  
Tri Dev Acharya ◽  
Anoj Subedi ◽  
Dong Ha Lee
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Recursive Partitioning ◽  
Learning Algorithms ◽  
High Elevation ◽  
Machine Learning Algorithms ◽  
Water Extraction ◽  
Support Vector ◽  
Landsat 8 ◽  
Water Index

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance.

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