Capability assessment of two water indexes in remote sensing data in order to water bodies classification (case study: Gorganroud River - North east of Iran)

2020 ◽  
Author(s):  
edith eishoeei ◽  
Mirhassan Miryaghoubzadeh
Keyword(s):  
River Basin ◽  
Near Infrared ◽  
Landsat Tm ◽  
Water Bodies ◽  
Long Distance ◽  
Mid Infrared ◽  
Water Index ◽  
North East ◽  
Normalized Difference Water Index ◽  
East Of Iran

<p>Normalized Difference Water Index (NDWI) has been widely used to detect water bodies and enhance them in the satellite imagery. In order to determine water bodies in Landsat TM, Mid-Infrared and Green bands are used but this combination is often encountered with vegetation, soil and build-up land noises and the water bodies area was not calculated accurately and most of the time the results are higher than the actual area and was overestimated, NDWI does not remove soil and vegetation noises completely because of using the NIR band reflection, therefore, to eliminate these noises, Modified Normalized Difference Water Index (MNDWI) with different bands in Landsat TM such as Shortwave and Near-Infrared bands has been used and best image that shows water bodies more accurate has been provided. We need to test different band combination and also different NDWI and MNDWI indexes in the range of Red, Near-Infrared, Shortwave Infrared and Mid-Infrared to determine the best performing index. For this purpose, Gorganroud river basin was selected as study area, which is located in north-east of Iran and is one of the largest rivers in Iran and because of 2 dams located in the river basin and long distance of river, studying water bodies could be easier in comparing with other river basins of Iran. we compared NDWI and MNDWI indices and results shown that MNDWI index using Landsat TM bands Green and Mid-infrared has higher accuracy than NDWI and other calculated indices with different bands of Landsat TM. It can remove the vegetation, soil and build-up noises better than NDWI and water bodies can be shown clearly. The MNDWI is more suitable to extract water bodies and study the information of water regions with dominating the soil, vegetation and build-up land noises because of its advantage in reducing or even removing those noises over NDWI.</p><p><strong>Key words:</strong> Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Landsat 5, water bodies, Gorganroud river basin</p>

scholarly journals Daphnia diversity in water bodies of the Po River Basin

2016 ◽  
Author(s):  
Silvia Marková ◽  
Catia Maurone ◽  
Erica Racchetti ◽  
Marco Bartoli ◽  
Valeria Rossi
Keyword(s):  
River Basin ◽  
North American ◽  
Water Bodies ◽  
12S Rrna ◽  
Long Distance ◽  
Po River ◽  
The North ◽  
North Eastern ◽  
Northern European Russia ◽  
Po River Basin

<p>Shallow water bodies dominate the areal extent of continental waters and host a proportion of biodiversity higher than the percentage of Earth’s surface they cover. <em>Daphnia</em> is a key component of small aquatic ecosystems food webs. Here we present the result of a survey in 24 ponds located in the core of Po river Basin, to assess the actual spreading of <em>Daphnia</em> species in one of the most productive areas of the Northern hemisphere. By using diagnostic genetic markers (<em>12S rRNA </em>and <em>ND5 </em>genes) we identified five <em>Daphnia</em> species: <em>D. ambigua</em>, <em>D. curvirostris</em>, <em>D. longispina</em>, <em>D. obtusa</em> and <em>D. pulex </em>in fourteen ponds. Additional analyses of two nuclear genes (<em>LdhA</em> and <em>Rab4</em>) revealed that <em>D. pulex</em> in the study area is native European strain. In opposite, <em>D. ambigua</em> shared haplotype with the North-Eastern American lineage that was introduced to Europe by long-distance dispersal. In the Po river Basin we identified a highly divergent lineage of <em>D. longispina </em>group that formed a clade with individuals from northern European Russia and might represent a new <em>Daphnia </em>species. <em>Daphnia</em> species in the Cremona province have European origin, except for <em>D. ambigua</em> which is a North American species spreading across Europe. Future attention will require monitoring of invasive species, particularly <em>D. ambigua</em> and the North American invasive clone of <em>D. pulex </em>that is already present in Northern Italy. </p>

scholarly journals IDENTIFICATION OF INUNDATED AREA USING NORMALIZED DIFFERENCE WATER INDEX (NDWI) ON LOWLAND REGION OF JAVA ISLAND

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Suwarsono ◽  
Jalu Tejo Nugroho ◽  
Wiweka
Keyword(s):  
Emergency Response ◽  
Flood Disaster ◽  
Landsat Tm ◽  
Water Index ◽  
Normalized Difference Water Index

Flood disaster is a major issues due to its frequently events on several areas in Indonesia. Delineation of inundated area caused by flood is needed to support disaster emergency response. The objective of this research was to identify inundated areas using NDWI methos from Landsat TM/ETM+ data on lowland regions of Java island. A pair of the data (before and during the flood) were in each observation areas. Observation areas were selected in several location of lowland regions of Java island where great event of flood occurred during the last decades. The thresholds values of NDWI change were used to separate the flood and non flood areas. The results showed that the extent of inundated area caused by flood on lowland regions can be identifyed and separated based on NDWI variables extracted from Landsat TM/ETM+.

Thermal and multispectral images from Unmanned Aerial Vehicles (UAVs) for water presence detection in temporary streams: first results

2021 ◽  
Author(s):  
Massimo Micieli ◽  
Gianluca Botter ◽  
Giuseppe Mendicino ◽  
Alfonso Senatore
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Near Infrared ◽  
Infrared Band ◽  
Temporary Streams ◽  
Water Index ◽  
Long Wavelength ◽  
Research Activities ◽  
Aerial Vehicles ◽  
Normalized Difference Water Index ◽  
First Results

&lt;p&gt;UAVs (Unmanned Aerial Vehicles) are increasingly used for monitoring river networks with a broad range of purposes. In this contribution, we focus on the use of multispectral sensors, either in the thermal infrared band LWIR (Long-wavelength infrared, 8-15 &amp;#181;m) or in the infrared band NIR (Near-infrared, 0.75-1.4 &amp;#181;m) to map network dynamics in temporary streams. Specifically, we discuss the first results of a set of surveys carried out in 2020 within a small river catchment located in northern Calabria (southern Italy), as part of the research activities of the ERC-funded DyNET project. Preliminary, a rigorous methodology was identified to perform on-site surveys and to process and analyse the acquired images. Experimental results show that the combined use of LWIR and NIR sensors is a suitable solution for detecting water presence in channels characterized by different hydraulic and morphologic conditions. LWIR sensors alone allow one to discriminate water presence only when the thermal contrast with the surrounding environment is high. On the other hand, NIR sensors permit to detect the presence of water in most of the analyzed settings through the estimate of the Normalized Difference Water Index (NDWI). However, NIR sensors can be misled in case of shallow water depth, due to the NIR radiation emitted by the riverbed merging with that of the water. Overall, the study demonstrates that a combined LWIR/NIR approach allows addressing a broader range of conditions. Moreover, the information provided can be further enhanced by combining it with geomorphologic information and basic hydraulic concepts.&lt;/p&gt;

scholarly journals SUAS Based Multispectral Imagery for Monitoring Wetland Inundation and Vegetation

2019 ◽  
Author(s):  
Dustin Dehm ◽  
Richard Becker ◽  
Alexandra Godre
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Correction Method ◽  
Water Levels ◽  
Water Volume ◽  
Unmanned Aerial Systems ◽  
Short Term ◽  
Water Index ◽  
Normalized Difference Water Index

Mapping short-term wetland vegetation and water storage changes is valuable for monitoring the biogeochemical processes of wetland systems. Old Woman Creek National Estuarine Research Reserve is a dynamic freshwater estuary which experiences intermittent changes in water level over the course of a year. Small unmanned aerial systems (sUAS) are useful tools in monitoring changes as they are rapidly deployed, repeatable, and high-resolution. In this study, commercial quadcopters were paired with a red/green/near-infrared MAPIR Survey 3W camera to produce normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) maps to observe short-term changes at OWC. Orthomosaics were produced for flights on 8 days throughout 2018 and early 2019. The orthomosaics were calibrated to bottom-of-atmosphere reflectance using the Empirical Line Correction method, after which NDVI and NDWI maps were created. The NDVI maps allowed vegetation extent and density changes over time and for National Estuarine Reserve System (NERRS) Classification Codes to be applied to zones of interest. NDWI provided water extent at different water levels and when paired with LiDAR and bathymetric data yielded water volume and residence time estimates.

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 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 A Method for Reflectance Index Wavelength Selection from Moisture-Controlled Soil and Crop Residue Samples

2017 ◽  
Vol 60 (5) ◽  
pp. 1479-1487 ◽  
Author(s):  
Ali Hamidisepehr ◽  
Michael P. Sama ◽  
Aaron P. Turner ◽  
Ole O. Wendroth
Keyword(s):  
Remote Sensing ◽  
Moisture Content ◽  
Near Infrared ◽  
Statistical Significance ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Agricultural Materials ◽  
Reflectance Measurements

Abstract. Reflectance indices are a method for reducing the dimensionality of spectral measurements used to quantify material properties. Choosing the optimal wavelengths for developing an index based on a given material and property of interest is made difficult by the large number of wavelengths typically available to choose from and the lack of homogeneity when remotely sensing agricultural materials. This study aimed to determine the feasibility of using a low-cost method for sensing the moisture content of background materials in traditional crop remote sensing. Moisture-controlled soil and wheat stalk residue samples were measured at varying heights using a reflectance probe connected to visible and near-infrared spectrometers. A program was written that used reflectance data to determine the optimal pair of narrowband wavelengths to calculate a normalized difference water index (NDWI). Wavelengths were selected to maximize the slope of the linear index function (i.e., sensitivity to moisture) and either maximize the coefficient of determination (R2) or minimize the root mean squared error (RMSE) of the index. Results showed that wavelengths centered near 1300 nm and 1500 nm, within the range of 400 to 1700 nm, produced the best index for individual samples. Probe height above samples and moisture content were examined for statistical significance using the selected wavelengths. The effect of moisture was significant for both bare soil and wheat stalks, but probe height was only significant for wheat stalk samples. The index, when applied to all samples, performed well for soil samples but poorly for wheat stalk samples. Index calculations from soil reflectance measurements were highly linear (R2 &gt; 0.95) and exhibited small variability between samples at a given moisture content, regardless of probe height. Index calculations from wheat stalk reflectance measurements were highly variable, which limited the usefulness of the index for this material. Based on these results, it is expected that crop residues, such as wheat stalks, will reduce the accuracy of remotely sensed soil surface moisture measurements. Keywords: Near-infrared reflectance, Normalized difference water index, Remote sensing, Soil moisture, Spectroscopy.

Sign in / Sign up

Export Citation Format

Share Document