scholarly journals Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4333 ◽  
Author(s):  
Poliyapram Vinayaraj ◽  
Nevrez Imamoglu ◽  
Ryosuke Nakamura ◽  
Atsushi Oda
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Landsat 8 ◽  
Multispectral Images ◽  
Water Index ◽  
Tuning Parameters ◽  
Water Classification ◽  
Water Region ◽  
Normalized Difference Water Index ◽  
Perceptron Learning

Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches—Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network—using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.

scholarly journals Analysis of riverbank changes in Ho Chi Minh city in the period 1989 – 2015

2019 ◽  
Vol 2 (2) ◽  
pp. 80-88
Author(s):  
Thu Trang Hoang ◽  
Khoi Nguyen Dao ◽  
Loi Thi Pham ◽  
Hong Van Nguyen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Ho Chi Minh City ◽  
Landsat 8 ◽  
River Bank ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Multi Temporal ◽  
Erosion And Accretion ◽  
Landsat Satellite

The objective of this study was to analyze the changes of riverbanks in Ho Chi Minh City for the period 1989-2015 using remote sensing and GIS. Combination of Modified Normalized Difference Water Index (MNDWI) and thresholding method was used to extract the river bank based on the multi-temporal Landsat satellite images, including 12 Landsat 4-5 (TM) images and 2 Landsat 8 images in the period 1989-2015. Then, DSAS tool was used to calculate the change rates of river bank. The results showed that, the processes of erosion and accretion intertwined but most of the main riverbanks had erosion trend in the period 1989-2015. Specifically, the Long Tau River, Sai Gon River, Soai Rap River had erosion trends with a rate of about 10.44 m/year. The accretion process mainly occurred in Can Gio area, such as Dong Tranh river and Soai Rap river with a rate of 8.34 m/year. Evaluating the riverbank changes using multi-temporal remote sensing data may contribute an important reference to managing and protecting the riverbanks.

scholarly journals Contributions of remote sensing in the diachronic study of the spatial and temporal evolution of the Ahmed El Hansali dam water reservoir from 2002-03 to 2018-19

2020 ◽  
Vol 183 ◽  
pp. 02004
Author(s):  
Tarik El Orfi ◽  
Mohamed El Ghachi ◽  
Sébastien Lebaut
Keyword(s):  
Remote Sensing ◽  
Negative Impact ◽  
Water Reservoir ◽  
Water Levels ◽  
Landsat 8 ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7 ◽  
Diachronic Study ◽  
Flooded Area

The OumErRbiabasin is one of the watersheds with the largest number of hydraulic infrastructures in Morocco. These hydraulic structuressupply water for drinking, industrial and agricultural uses. The Ahmed El Hansali dam is a 740 Mm³ reservoir located near Zaouyat Cheikh andhave an active storage of473 Mm³. The succession of dry years in the OumErRbiabasin has had a negative impact on the water resource and has caused a remarkable decrease in the reservoir of the Ahmed el Hansali dam. In this paper, the MNDWI (Modified Normalized Difference Water Index) from Landsat 5-TM, Landsat 7-ETM, and Landsat 8-OLI satellite images was used to estimate the spatial and temporal fluctuations of the volumes of water stored in the reservoir between hydrological years 2002-03 and 2018-19. Results show that the volumes estimated by remote sensing reasonably match the volumes estimated by the OumErRbia Hydraulic Basin Agency (OERHBA)using recorded water levels and reservoir storage curve for years 2002-03 and 2013-14; the determination coefficient R² exceeds 0.90. The mapping of the extent of the dam’s impoundment has shown a very significant decreasein the flooded area level during dry years.

scholarly journals Study on Monitoring Water Area in Irrigation Area by Local Space Self-correlation Index

2022 ◽  
Author(s):  
tao su ◽  
Jian Wang ◽  
Xingyuan Cui ◽  
Lei Wang
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Hot Spot ◽  
Water Extraction ◽  
Vegetation Coverage ◽  
Hot Spot Analysis ◽  
Water Index ◽  
Water Classification ◽  
Normalized Difference Water Index ◽  
Spot Analysis

Abstract Landsat remote sensing image is a widely used data source in water remote sensing. Normalized difference water index (NDWI), modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) are commonly used water extraction classifiers. In the process of their application, because the threshold varies with the location and time of the research object, how to select the threshold with the highest classification accuracy is a time-consuming and challenging task. The purpose of this study was to explore a method that can not only improve the accuracy of water extraction, but also provide a fixed threshold, and can meet the requirements of automatic water extraction. We introduced the local spatial auto correlation statistics and calculate the Getis-Ord Gi* index to have hot spot analysis. Comparative analysis showed that the accuracy of water classification had been greatly improved through hot spot analysis. AWEIsh classifier had the best classification accuracy under the condition of INVERSE_DISTANCE neighborhood rule and Z>1.96, and the accuracy changes least in different time, different location and different vegetation coverage images. Therefore, in the process of regional water extraction, hot spot analysis method was effective, which was helpful to improve the accuracy of water extraction.

scholarly journals Operational Monitoring and Damage Assessment of Riverine Flood-2014 in the Lower Chenab Plain, Punjab, Pakistan, Using Remote Sensing and GIS Techniques

2020 ◽  
Vol 12 (4) ◽  
pp. 714 ◽  
Author(s):  
Asif Sajjad ◽  
Jianzhong Lu ◽  
Xiaoling Chen ◽  
Chikondi Chisenga ◽  
Nayyer Saleem ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Damage Assessment ◽  
Response Assessment ◽  
Geographical Information ◽  
Landsat 8 ◽  
Water Index ◽  
Flood Duration ◽  
Gis Techniques ◽  
Normalized Difference Water Index ◽  
Future Events

In flood-prone areas, the delineation of the spatial pattern of historical flood extents, damage assessment, and flood durations allow planners to anticipate potential threats from floods and to formulate strategies to mitigate or abate these events. The Chenab plain in the Punjab region of Pakistan is particularly prone to flooding but is understudied. It experienced its worst riverine flood in recorded history in September 2014. The present study applies Remote Sensing (RS) and Geographical Information System (GIS) techniques to estimate the riverine flood extent and duration and assess the resulting damage using Landsat-8 data. The Landsat-8 images were acquired for the pre-flooding, co-flooding, and post-flooding periods for the comprehensive analysis and delineation of flood extent, damage assessment, and duration. We used supervised classification to determine land use/cover changes, and the satellite-derived modified normalized difference water index (MNDWI) to detect flooded areas and duration. The analysis permitted us to calculate flood inundation, damages to built-up areas, and agriculture, as well as the flood duration and recession. The results also reveal that the floodwaters remained in the study area for almost two months, which further affected cultivation and increased the financial cost. Our study provides an empirical basis for flood response assessment and rehabilitation efforts in future events. Thus, the integrated RS and GIS techniques with supporting datasets make substantial contributions to flood monitoring and damage assessment in Pakistan.

scholarly journals Improving flood monitoring in rural areas using remote sensing

2018 ◽  
Vol 14 (1) ◽  
pp. 160-171
Author(s):  
Zahra Ghofrani ◽  
Victor Sposito ◽  
Robert Faggian
Keyword(s):  
Rural Areas ◽  
Flood Control ◽  
Control Measures ◽  
Landsat 8 ◽  
Protective Measures ◽  
Rainfall Events ◽  
Water Index ◽  
Flood Monitoring ◽  
Normalized Difference Water Index ◽  
Heavy Rainfall Events

Abstract Precise information on the extent of inundated land is required for flood monitoring, relief, and protective measures. In this paper, two spectral indices, Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), were used to identify inundated areas during heavy rainfall events in the Tarwin catchment, Victoria, Australia, using Landsat-8 OLI imagery. By integrating the assessed condition of levees, this research also explains the inefficiency of the flood control measures of this region of Australia. NDWI and MNDWI indices performed well, but water features were enhanced better in the NDWI-derived image, with an accuracy of 96.04% and Kappa coefficient of 0.83.

Mariculture Zones Extraction Using NDWI and NDVI

2013 ◽  
Vol 659 ◽  
pp. 153-155 ◽  
Author(s):  
Hong Jun Pan ◽  
Xue Xian Li ◽  
Guang Wei Wang ◽  
Chong Song Qi
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Distribution Planning ◽  
Surface Features ◽  
Water Index ◽  
Normalized Difference Water Index

On the analysis of spectral characteristics of Aoshan remote sensing images, we find the spectral differences between mariculture zones and other surface features. This paper combines normalized difference water index with mariculture zones distribution planning to complete the extraction and the statistics of the mariculture zones, in order to effectively achieve the regulation of mariculture zones.

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