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Journal ArticleDOI

The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features

01 May 1996-International Journal of Remote Sensing (Taylor & Francis Group)-Vol. 17, Iss: 7, pp 1425-1432
TL;DR: The Normalized Difference Water Index (NDWI) as mentioned in this paper is a new method that has been developed to delineate open water features and enhance their presence in remotely-sensed digital imagery.
Abstract: The Normalized Difference Water Index (NDWI) is a new method that has been developed to delineate open water features and enhance their presence in remotely-sensed digital imagery. The NDWI makes use of reflected near-infrared radiation and visible green light to enhance the presence of such features while eliminating the presence of soil and terrestrial vegetation features. It is suggested that the NDWI may also provide researchers with turbidity estimations of water bodies using remotely-sensed digital data.
Citations
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Journal ArticleDOI
Hanqiu Xu1
TL;DR: In this paper, the normalized difference water index (NDWI) was modified by substitution of a middle infrared band such as Landsat TM band 5 for the near infrared band used in the NDWI.
Abstract: The normalized difference water index (NDWI) of McFeeters (1996) was modified by substitution of a middle infrared band such as Landsat TM band 5 for the near infrared band used in the NDWI. The modified NDWI (MNDWI) can enhance open water features while efficiently suppressing and even removing built‐up land noise as well as vegetation and soil noise. The enhanced water information using the NDWI is often mixed with built‐up land noise and the area of extracted water is thus overestimated. Accordingly, the MNDWI is more suitable for enhancing and extracting water information for a water region with a background dominated by built‐up land areas because of its advantage in reducing and even removing built‐up land noise over the NDWI.

3,234 citations


Cites background or methods from "The use of the Normalized Differenc..."

  • ...…Ministry of Education; Fuzhou, Fujian 350002, China (Received 24 July 2005; in final form 22 January 2006 ) The normalized difference water index (NDWI) of McFeeters (1996) was modified by substitution of a middle infrared band such as Landsat TM band 5 for the near infrared band used in the NDWI....

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  • ...As a result, water features have positive values and thus are enhanced, while vegetation and soil usually have zero or negative values and therefore are suppressed (McFeeters 1996)....

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  • ...However, the method can suppress non-water features but not remove them, and therefore the normalized difference water index (NDWI) was proposed by McFeeters (1996) to achieve this goal....

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  • ...The NDWI is expressed as follows (McFeeters 1996): NDWI~ Green{NIR GreenzNIR ð1Þ where Green is a green band such as TM band 2, and NIR is a near infrared band such as TM band 4....

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  • ...McFeeters (1996) pointed out that the NDWI could be used for detecting water turbidity....

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Journal ArticleDOI
TL;DR: The devised NDBI method was applied to map urban land in the city of Nanjing, eastern China and results at an accuracy of 92.6% indicate that it can be used to fulfil the mapping objective reliably.
Abstract: Remotely sensed imagery is ideally used to monitor and detect land cover changes that occur frequently in urban and peri-urban areas as a consequence of incessant urbanization. It is a lengthy process to convert satellite imagery into land cover map using the existing methods of manual interpretation and parametric image classification digitally. In this paper we propose a new method based on Normalized Difference Built-up Index (NDBI) to automate the process of mapping built-up areas. It takes advantage of the unique spectral response of built-up areas and other land covers. Built-up areas are effectively mapped through arithmetic manipulation of re-coded Normalized Difference Vegetation Index (NDVI) and NDBI images derived from TM imagery. The devised NDBI method was applied to map urban land in the city of Nanjing, eastern China. The mapped results at an accuracy of 92.6% indicate that it can be used to fulfil the mapping objective reliably. Compared with the maximum likelihood classification method, t...

1,690 citations

Journal ArticleDOI
TL;DR: The spectral characteristics of vegetation are introduced and the development of VIs are summarized, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision.
Abstract: Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV) Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas

1,190 citations

Journal ArticleDOI
TL;DR: A new Automated Water Extraction Index (AWEI) is introduced improving classification accuracy in areas that include shadow and dark surfaces that other classification methods often fail to classify correctly, using Landsat 5 TM images of several water bodies.

1,158 citations


Cites methods from "The use of the Normalized Differenc..."

  • ...Xu (2006) therefore proposed another index, called Modified Normalized Difference Water Index (MNDWI), where McFeeters (1996) NDWI was modified by replacing band 4 by band 5 of Landsat 5 TM....

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  • ...…(a) thematic classification (Lira, 2006), (b) linear unmixing (Sethre, Rundquist, & Todhunter, 2005), (c) single-band thresholding (Jain, Singh, Jain, & Lohani, 2005) and (d) two-band spectral water indices (Jain, Saraf, Goswami, & Ahmad, 2006; McFeeters, 1996; Rogers & Kearney, 2004; Xu, 2006)....

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  • ...…(band 4) of Landsat TM. Rogers and Kearney (2004) used another NDWI for water extraction where they applied bands 3 and 5 of Landsat TM. McFeeters (1996) proposed a threshold of 0 for extracting surface water using the raw digital number of Landsat, where all positive NDWI values would…...

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  • ...…the proposed water extraction technique with other methods, wemade preliminary tests of various water indices including the Water Index (WI) of Ouma and Tateishi (2006), the Normalized Difference Water Index (NDWI) of McFeeters (1996) and other indices that Ji et al. (2009) used in their studies....

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  • ..., 2009): (a) thematic classification (Lira, 2006), (b) linear unmixing (Sethre, Rundquist, & Todhunter, 2005), (c) single-band thresholding (Jain, Singh, Jain, & Lohani, 2005) and (d) two-band spectral water indices (Jain, Saraf, Goswami, & Ahmad, 2006; McFeeters, 1996; Rogers & Kearney, 2004; Xu, 2006)....

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Journal ArticleDOI
TL;DR: In this article, the normalized difference water index (NDWI) has been successfully used to delineate surface water features, but two major problems have often encountered: (a) NDWIs calculated from different band combinations [visible, nearinfrared, or shortwave-infrared (SWIR)] can generate different results, and (b) NDWI thresholds vary depending on the proportions of subpixel water/non-water components.
Abstract: The normalized difference water index (NDWI) has been successfully used to delineate surface water features. However, two major problems have been often encountered: (a) NDWIs calculated from different band combinations [visible, nearinfrared, or shortwave-infrared (SWIR)] can generate different results, and (b) NDWI thresholds vary depending on the proportions of subpixel water/non-water components. We need to evaluate all the NDWIs for determining the best performing index and to establish appropriate thresholds for clearly identifying water features. We used the spectral data obtained from a spectral library to simulate the satellite sensors Landsat ETM, SPOT-5, ASTER, and MODIS, and calculated the simulated NDWI in different forms. We found that the NDWI calculated from (green ‐ SWIR)/(green SWIR), where SWIR is the shorter wavelength region (1.2 to 1.8 mm), has the most stable threshold. We recommend this NDWI be employed for mapping water, but adjustment of the threshold based on actual situations is necessary.

603 citations


Cites methods from "The use of the Normalized Differenc..."

  • ...The NDWI data derived from Landsat MSS, TM, and ETM (Jain et al., 2005; McFeeters, 1996; Rogers and Kearney, 2004; Ouma and Tateishi, 2006; Sethre et al., 2005, Xu, 2006), SPOT (Lacaux et al., 2007), MODIS (Chipman and Lillesand, 2007), AVHRR (Chipman and Lillesand, 2007; Jain et al., 2006), and…...

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  • ...Adopting the format of the normalized difference vegetation index (NDVI), McFeeters (1996) developed the normalized difference water index (NDWI), defined as NDWI (rgreen rNIR)/(rgreen rNIR), (1) where rgreen and rNIR are the reflectance of green and NIR bands, respectively....

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  • ...McFeeters (1996) set zero as the threshold....

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References
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Book
01 Dec 1995
TL;DR: Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications.
Abstract: For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Pearson Series Geographic Information Science. Now in full color, the Fourth Edition provides up-to-date information on analytical methods used to analyze digital remote sensing data. Each chapter contains a substantive reference list that can be used by students and scientists as a starting place for their digital image processing project or research. A new appendix provides sources of imagery and other geospatial information.

5,478 citations

Journal ArticleDOI
TL;DR: In this article, the effects of off-nadir viewing and atmospheric constituents, coupled with the need to measure changing surface conditions, emphasize the need for multitemporal measurements of reflected radiation if primary production is to be estimated.
Abstract: Leaf structure and function are shown to result in distinctive variations in the absorption and reflection of solar radiation from plant canopies. The leaf properties that determine the radiation-interception characteristics of plant canopies are directly linked to photosynthesis, stomatal resistance and evapotranspiration and can be inferred from measurements of reflected solar energy. The effects of off-nadir viewing and atmospheric constituents, coupled with the need to measure changing surface conditions, emphasize the need for multitemporal measurements of reflected radiation if primary production is to be estimated.

1,307 citations

Journal ArticleDOI
TL;DR: In this paper, the normalized difference vegetation index (NDVI) is calculated against time, and different cover types have characteristic profiles corresponding closely with their phenology, and the resultant pattern of NDVI values displayed on the images is analyzed in terms of the cover types present and local variations in rainfall.
Abstract: Images at a resolution of 8 km are currently being generated for the whole of Africa, displaying the normalized difference vegetation index (NDVI). These images have undergone a process of temporal compositing to reduce the effects of cloud cover and atmospheric variation. When the NDVI is plotted against time, different cover types are shown to have characteristic profiles corresponding closely with their phenology. The resultant pattern of NDVI values displayed on the images is analyzed in terms of the cover types present and local variations in rainfall. Comparison between images for 1983 and 1984 overall showed considerable similarities, but significant differences were observed in the northward extent of the greening wave in the Sahel, the greening up of the Kalahari Desert and East African communities. It is concluded that vegetation monitoring using NDVI images needs to be associated with scene stratification according to cover type.

501 citations

Journal ArticleDOI
TL;DR: In this article, a comparison of six concurrent Landsat MSS and TM scenes was made to determine the relationship of Landsat digital data with suspended sediments, chlorophyll, and temperature in the surface water of an agricultural lake.

207 citations