scholarly journals Automatic Extraction of Water Bodies from Landsat Imagery Using Perceptron Model

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Kshitij Mishra ◽  
P. Rama Chandra Prasad
Keyword(s):  
Satellite Imagery ◽  
Water Body ◽  
Landsat Imagery ◽  
Threshold Value ◽  
Water Bodies ◽  
Limit Function ◽  
Linear Predictor ◽  
Water Index ◽  
Feature Vectors ◽  
Normalized Difference Water Index

Extraction of water bodies from satellite imagery has been widely explored in the recent past. Several approaches have been developed to delineate water bodies from different satellite imagery varying in spatial, spectral, and temporal characteristics. The current study puts forward an automatic approach to extract the water body from a Landsat satellite imagery using a perceptron model. Perceptron involves classification based on a linear predictor function that merges few characteristic properties of the object commonly known as feature vectors. The feature vectors, combined with the weights, sum up to provide an input to the output function which is a binary hard limit function. The feature vector in this study is a set of characteristic properties shown by a pixel of the water body. Low reflectance of water in SWIR band, comparison of reflectance in different bands, and a modified normalized difference water index are used as descriptors. The normalized difference water index is modified to enhance its reach over shallow regions. For this study a threshold value of 2 has been proved as best among the three possible threshold values. The proposed method accurately and quickly discriminated water from other land cover features.

scholarly journals Extraction of Water-body Area from High-resolution Landsat Imagery

2018 ◽  
Vol 8 (6) ◽  
pp. 4111
Author(s):  
B. Chandrababu Naik ◽  
B. Anuradha
Keyword(s):  
Surface Water ◽  
Satellite Data ◽  
Water Body ◽  
Water Area ◽  
Water Bodies ◽  
Landsat 8 ◽  
Indexing Methods ◽  
Water Index ◽  
Surface Water Body ◽  
Normalized Difference Water Index

Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.

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.

scholarly journals MONITORING EROSION AND ACCRETION SITUATION IN THE COASTAL ZONE AT KIEN GIANG PROVINCE

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 197-203
Author(s):  
N. T. H. Diep ◽  
N. T. Loi ◽  
N. T. Can
Keyword(s):  
Land Use ◽  
Coastal Area ◽  
Coastal Erosion ◽  
Landsat Imagery ◽  
Mekong Delta ◽  
The West ◽  
Shoreline Changes ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Erosion And Accretion

<p><strong>Abstract.</strong> Kien Giang is one of the coastal provinces in the Mekong Delta which is facing the problem of coastal erosion to affect people’s life in the coastal area. This project aims to monitor shoreline and to assess landslide and accretion situation in the period from 1975 to 2015 in the coastal area of Kien Giang province. The study applied Normalized Difference Water Index (MNWI) method and water level extraction using LANDSAT imagery from 1975 to 2015 for highlight the shoreline. Thus, analysis was identified erosion and accretion areas based on shoreline changes and land use influenced by landslides and deposition. The results show to create shoreline changes from 1997 to 2015 in the coastal area of Kien Giang province. A landslide occurred in the west from Nguyen Viet Khai commune to Thuan Hoa commune and Nam Yen commune to Vinh Hoa Hiep commune, Rach Gia city, Kien Giang province. An accretion situation was determined in the areas from Thuan Hoa commune, An Minh district to Nam Thai commune, An Bien district, Kien Giang province, Rach Gia sea encroachment at Rach Gia town and Ha Tien encroachment area at Ha Tien town, Kien Giang province. In general, the coastal area of Kien Giang province has a predominant tendency of accretion, however, the occurrence of erosion and accretion are happened interlacing in the coastal area at Kien Giang province.</p>

scholarly journals The Dynamic Change of Bosten Lake Area in Response to Climate in the Past 30 Years

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 4 ◽  
Author(s):  
Xiaoai Dai ◽  
Xingping Yang ◽  
Meilian Wang ◽  
Yu Gao ◽  
Senhao Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Area ◽  
Water Body ◽  
Meteorological Factors ◽  
Lake Basin ◽  
Bosten Lake ◽  
Water Index ◽  
The Past ◽  
Normalized Difference Water Index

The widely distributed lakes, as one of the major components of the inland water system, are the primary available freshwater resources on the earth and are sensitive to accelerated climate change and extensive human activities. Lakes play an important role in the terrestrial water cycle and biogeochemical cycle and substantially influence the health of humans living in the surrounding areas. Given the importance of lakes in the ecosystem, long-term monitoring of dynamic changes has important theoretical and practical significance. Here, we extracted water body information and monitored the long-term dynamics of Bosten Lake, which is the largest inland lake in China. We quantified the meteorological factors of the study area from the observation data of meteorological stations between 1988 and 2018. The characteristics of climate change and its correlation with the change of area in the Bosten Lake Basin in the past 30 years were analyzed. The major contributions of this study are as follows: (1) The initial water body was segmented based on the water index model Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) with a pre-assigned threshold value. The results were evaluated with the area extracted through artificial visual interpretation. Then we conducted mathematical morphology operators, opening and closing operations, and median filter to eliminate noise to ensure the accuracy of water body information extraction from the Bosten Lake. A long-term water surface area database of the Bosten Lake was established from high-resolution remote sensing images during 1988–2018. (2) Due to the seasonal difference of snow, ice content, and other objects on images, the areadynamics of Bosten Lake in the recent 30 years were analyzed separately in dry season and rainy season. The water surface area of Bosten Lake showed large inter-annual variations between 1988–2018. (3) Based on the assumption that climatic change has more direct effects on lake than human activities, six meteorological factors were selected to analyze the impacts of climate change on the annual mean lake surface area. The result indicated that in the past 30 years, climate conditions in the Bosten Lake Basin fluctuated greatly. We conducted correlations analysis between the areal dynamics of the Bosten Lake and the meteorological factors. Here, the annual average evaporation had the highest correlation with the areal dynamics of Bosten Lake followed by air temperature, precipitation, sunshine hours, and relative humidity, while the annual average wind speed had the weakest correlation.

scholarly journals Damage assessment of chilli thrips using high resolution multispectral satellite data

2021 ◽  
Vol 21 (4) ◽  
pp. 480-487
Author(s):  
Mathyam Prabhakar ◽  
Merugu Thirupathi, ◽  
G. Srasvan Kumar ◽  
U. Sai Sravan ◽  
M. Kalpana ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Data ◽  
Land Surface ◽  
Vegetation Indices ◽  
Superior Performance ◽  
Water Index ◽  
Sensing Technology ◽  
Significant Difference ◽  
Normalized Difference Water Index ◽  
Sentinel 2A

Remote sensing technology offers an effective, rapid and reliable tool for assessing pest severity in vegetation. Ground based hyperspectral radiometry studies revealed significant difference in the reflectance spectra between healthy and thrip damaged vegetation. Space borne multispectral reflectance from Sentinel 2A satellite data of chilli thrip infested canopy has significant differences in red region (Band 4 – 664.6 nm), NIR region (Bands 5, 6, 7, 8 & 8A having central wavelengths at 704.1, 740.5, 782.8 & 832.8 nm, respectively) and SWIR region (Bands 11 & 12 having central wavelengths at 1613.7 and 2202.4 nm). In this study, an attempt was made to discriminate healthy and pest affected chilli crop in the multispectral satellite imagery using several multispectral vegetation indices. Of these, land surface water index, LSWI (p=0.018) and normalized difference water index, NDWI (p=0.001) were found significant. These indices were used to classify chilli fields in the satellite imagery into severe, moderate and healthy classes. Superior performance of LSWI over NDWI with overall accuracy of 93.80 and Kappa Coefficient of 0.89 was observed. Moran's Index was used to study the spatial distribution of chilli thrips and observed strong clustering (I= 0.9073, p=0.0001).

scholarly journals Rich CNN Features for Water-Body Segmentation from Very High Resolution Aerial and Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1912
Author(s):  
Zhili Zhang ◽  
Meng Lu ◽  
Shunping Ji ◽  
Huafen Yu ◽  
Chenhui Nie
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Satellite Imagery ◽  
Water Body ◽  
Feature Fusion ◽  
Feature Representation ◽  
Water Bodies ◽  
Feature Maps ◽  
Very High

Extracting water-bodies accurately is a great challenge from very high resolution (VHR) remote sensing imagery. The boundaries of a water body are commonly hard to identify due to the complex spectral mixtures caused by aquatic vegetation, distinct lake/river colors, silts near the bank, shadows from the surrounding tall plants, and so on. The diversity and semantic information of features need to be increased for a better extraction of water-bodies from VHR remote sensing images. In this paper, we address these problems by designing a novel multi-feature extraction and combination module. This module consists of three feature extraction sub-modules based on spatial and channel correlations in feature maps at each scale, which extract the complete target information from the local space, larger space, and between-channel relationship to achieve a rich feature representation. Simultaneously, to better predict the fine contours of water-bodies, we adopt a multi-scale prediction fusion module. Besides, to solve the semantic inconsistency of feature fusion between the encoding stage and the decoding stage, we apply an encoder-decoder semantic feature fusion module to promote fusion effects. We carry out extensive experiments in VHR aerial and satellite imagery respectively. The result shows that our method achieves state-of-the-art segmentation performance, surpassing the classic and recent methods. Moreover, our proposed method is robust in challenging water-body extraction scenarios.

scholarly journals A New Method for Continuous Monitoring of Black and Odorous Water Body Using Evaluation Parameters: A Case Study in Baoding

2022 ◽  
Vol 14 (2) ◽  
pp. 374
Author(s):  
Xueying Zhou ◽  
Zhaoqiang Huang ◽  
Youchuan Wan ◽  
Bin Ni ◽  
Yalong Zhang ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Body ◽  
Quantitative Research ◽  
Spectral Characteristics ◽  
Quality Parameters ◽  
Living Environment ◽  
Water Bodies ◽  
Labor Costs ◽  
Water Index ◽  
Water Classification

Water is an important factor in human survival and development. With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly prominent. It not only affects the living environment of residents in the city, but also threatens their diet and water quality. Therefore, the accurate monitoring and management of urban black and odorous water bodies is particularly important. At present, when researching water quality issues, the methods of fixed-point sampling and laboratory analysis are relatively mature, but the time and labor costs are relatively high. However, empirical models using spectral characteristics and different water quality parameters often lack universal applicability. In addition, a large number of studies on black and odorous water bodies are qualitative studies of water body types, and there are few spatially continuous quantitative analyses. Quantitative research on black and odorous waters is needed to identify the risk of health and environmental problems, as well as providing more accurate guidance on mitigation and treatment methods. In order to achieve this, a universal continuous black and odorous water index (CBOWI) is proposed that can classify waters based on evaluated parameters as well as quantitatively determine the degree of pollution and trends. The model of CBOWI is obtained by partial least squares machine learning through the parameters of the national black and odorous water classification standard. The fitting accuracy and monitoring accuracy of the model are 0.971 and 0.738, respectively. This method provides a new means to monitor black and odorous waters that can also help to improve decision-making and management.

Sign in / Sign up

Export Citation Format

Share Document