A weighted normalized difference water index for water extraction using Landsat imagery

An Giang Province is one of the key economic regions of Mekong Delta and of Vietnam. With the development of urbanization and industrialization, An Giang has been suffering a burden from natural disasters, including salinity intrusion, drought, and riverbank erosion, due to natural and anthropological drivers. Amongst them, riverbank erosion is a key problem of the An Giang province, caused by changes in hydrological and sediment characteristics because of hydropower development and sand exploitation in the upstream part. In this study, we investigated the riverbank changes by using the water extraction index based on the Landsat imagery data. Amongst three extraction indices, such as Normalized Difference Water Index (NDWI), Modified Normalized Water Index (MNDWI), and Automated Water Extraction Index (AWEI), AWEI was identified the suitable index for the study area replied on the assessment of the index performance in extracting the riverbank in the four test sites in An Giang province (An Phu District, Vinh Hoa District, Cho Moi District, and Vam Nao River). Based on that, AWEI was then used for riverbank extraction for the study area in the period 1989-2015. After using the AWEI riverbank extracting method, Linear Regression Rate (LRR) had been applied to estimate the rate of the riverbank changes in the study area. The results stated that the rate of riverbank erosion was high in meandering river segments and upper part of islets, such as Tan Chau (-33m/year), Cho Moi (-36m/year) and Vam Nao (-3.07m/year). Besides analyzing the rate of erosion, this research also discusses some potential reasons as well as protection method to mitigate this problem. This study reveals that it is crucial to take sustainable measures to mitigate erosion in An Giang province.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w4-197-2018 ◽

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

Abstract. 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.

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Automatic Extraction of Water Bodies from Landsat Imagery Using Perceptron Model

Journal of Computational Environmental Sciences ◽

10.1155/2015/903465 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 16

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.

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

10.21203/rs.3.rs-1040118/v1 ◽

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.

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A NEW MULTI-SPECTRAL THRESHOLD NORMALIZED DIFFERENCE WATER INDEX (MST-NDWI) WATER EXTRACTION METHOD – A CASE STUDY IN YANHE WATERSHED

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-2557-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 2557-2564

Author(s):

Y. Zhou ◽

H. Zhao ◽

H. Hao ◽

C. Wang

Keyword(s):

Extraction Method ◽

Accuracy Assessment ◽

Spectral Characteristics ◽

Water System ◽

Landsat Tm ◽

Water Extraction ◽

Water Volume ◽

Threshold Segmentation ◽

Water Index ◽

Normalized Difference Water Index

Accurate remote sensing water extraction is one of the primary tasks of watershed ecological environment study. Since the Yanhe water system has typical characteristics of a small water volume and narrow river channel, which leads to the difficulty for conventional water extraction methods such as Normalized Difference Water Index (NDWI). A new Multi-Spectral Threshold segmentation of the NDWI (MST-NDWI) water extraction method is proposed to achieve the accurate water extraction in Yanhe watershed. In the MST-NDWI method, the spectral characteristics of water bodies and typical backgrounds on the Landsat/TM images have been evaluated in Yanhe watershed. The multi-spectral thresholds (TM1, TM4, TM5) based on maximum-likelihood have been utilized before NDWI water extraction to realize segmentation for a division of built-up lands and small linear rivers. With the proposed method, a water map is extracted from the Landsat/TM images in 2010 in China. An accuracy assessment is conducted to compare the proposed method with the conventional water indexes such as NDWI, Modified NDWI (MNDWI), Enhanced Water Index (EWI), and Automated Water Extraction Index (AWEI). The result shows that the MST-NDWI method generates better water extraction accuracy in Yanhe watershed and can effectively diminish the confusing background objects compared to the conventional water indexes. The MST-NDWI method integrates NDWI and Multi-Spectral Threshold segmentation algorithms, with richer valuable information and remarkable results in accurate water extraction in Yanhe watershed.

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Fusion of Sentinel-1 radar and Sentinel-2 MSI imagery for water extraction in Tibetan plateau

10.5194/egusphere-egu2020-4512 ◽

2020 ◽

Author(s):

Dan Li ◽

Baosheng Wu ◽

Bowei Chen ◽

Yanjun Wang ◽

Yi Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Radar Data ◽

Google Earth ◽

Water Extraction ◽

Tibet Plateau ◽

Water Index ◽

Normalized Difference Water Index ◽

Google Earth Engine ◽

Optical Imagery ◽

Sentinel 2

Abstract: Water plays a vital role in plants, animals and human survival, as well as water resources planning and protection. The spatial and temporal changes of rivers have a profound impact on climate change and the scientific protection of the regional ecological environment in Qingzang-Tibet plateau. Due to the influence of snow and cloud cover, optical remote sensing images in this region have less effective coverage. Many researches in the past mainly faced the challenge of misclassification caused by shadows from cloud and mountain. In this study, we proposed a method to improve the extraction of rivers by reducing the effect of shadows by fusing Sentinel-1 radar data and Sentinel-2 optical imagery. For the optical imagery, water indices including MNDWI (Modified Normalized Difference Water Index) and RNDWI (Revised Normalized Difference Water Index) and morphological operations were used to extract the river coverage. In addition, radar data is used to extract water in areas where there is no optical image coverage or where optical images are misclassified by using a combination of both the histogram and Otsu threshold methods. The GEE (Google Earth Engine) platform is used to implement the analysis using two classification datasets at a regional level. Relevant results from Sentinel-1 and Sentinel-2 data showed that the RNDWI has a more accurate water extraction results in this region. We further compared the final river width results with the manually measured samples from Google Earth and situ data of hydrological stations for accuracy assessment. The R2 value is 0.90, and the standard deviation is 18.663m. The river width can be estimated well by this method, which can provide basic data for the study of water in depopulated zone.Keywords: Remote sensing, shadow removal, water extraction, water index, Otsu threshold, Google Earth Engine

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Developing land-state maps of Tram Chim National Park in Vietnam based on monitoring results

Geodesy and Cartography ◽

10.22389/0016-7126-2020-963-9-53-64 ◽

2020 ◽

Vol 963 (9) ◽

pp. 53-64

Author(s):

V.F. Kovyazin ◽

Thi Lan Anh Dang ◽

Viet Hung Dang

Keyword(s):

National Park ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Likelihood Method ◽

Wetland Area ◽

Water Index ◽

Effi Ciency ◽

Normalized Difference Water Index ◽

Specially Protected Natural Areas ◽

Protected Natural Areas

Tram Chim National Park in Southern Vietnam is a wetland area included in the system of specially protected natural areas (SPNA). For the purposes of land monitoring, we studied Landsat-5 and Sentinel-2B images obtained in 1991, 2006 and 2019. The methods of normalized difference vegetation index (NDVI) and water objects – normalized difference water index (NDWI) were used to estimate the vegetation in National Park. The allocated land is classifi ed by the maximum likelihood method in ENVI 5.3 into categories. For each image, a statistical analysis of the land after classifi cation was performed. Between 1991 and 2019, land changes occurred in about 57 % of the Tram Chim National Park total area. As a result, the wetland area has signifi cantly reduced there due to climate change. However, the area of Melaleuca forests in Tram Chim National Park has increased due to the effi ciency of reforestation in protected areas. Melaleuca forests are also being restored.

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Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy11081486 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1486

Author(s):

Chris Cavalaris ◽

Sofia Megoudi ◽

Maria Maxouri ◽

Konstantinos Anatolitis ◽

Marios Sifakis ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Model Development ◽

Wheat Yield ◽

Growth Period ◽

Yield Data ◽

Water Index ◽

Normalized Difference Water Index ◽

Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

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Changes in surface water drive the movements of Shoebills

Scientific Reports ◽

10.1038/s41598-021-95093-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Marta Acácio ◽

Ralf H. E. Mullers ◽

Aldina M. A. Franco ◽

Frank J. Willems ◽

Arjun Amar

Keyword(s):

Surface Water ◽

Optimal Foraging ◽

Animal Movement ◽

Bird Species ◽

Individual Variability ◽

Home Ranges ◽

Age Classes ◽

Water Index ◽

Normalized Difference Water Index ◽

Conservation Actions

AbstractAnimal movement is mainly determined by spatial and temporal changes in resource availability. For wetland specialists, the seasonal availability of surface water may be a major determinant of their movement patterns. This study is the first to examine the movements of Shoebills (Balaeniceps rex), an iconic and vulnerable bird species. Using GPS transmitters deployed on six immature and one adult Shoebills over a 5-year period, during which four immatures matured into adults, we analyse their home ranges and distances moved in the Bangweulu Wetlands, Zambia. We relate their movements at the start of the rainy season (October to December) to changes in Normalized Difference Water Index (NDWI), a proxy for surface water. We show that Shoebills stay in the Bangweulu Wetlands all year round, moving less than 3 km per day on 81% of days. However, average annual home ranges were large, with high individual variability, but were similar between age classes. Immature and adult Shoebills responded differently to changes in surface water; sites that adults abandoned became drier, while sites abandoned by immatures became wetter. However, there were no differences in NDWI of areas used by Shoebills before abandonment and newly selected sites, suggesting that Shoebills select areas with similar surface water. We hypothesise that the different responses to changes in surface water by immature and adult Shoebills are related to age-specific optimal foraging conditions and fishing techniques. Our study highlights the need to understand the movements of Shoebills throughout their life cycle to design successful conservation actions for this emblematic, yet poorly known, species.

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