A Novel Water Index for Extraction of Water Features using Landsat-8 Images in Prayagraj District, India

A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs), such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices.

Download Full-text

MODIFIED OPTIMIZATION WATER INDEX (MOWI) FOR LANDSAT-8 OLI/TIRS

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

10.5194/isprs-archives-xlii-4-w4-185-2017 ◽

2017 ◽

Vol XLII-4/W4 ◽

pp. 185-190 ◽

Cited By ~ 2

Author(s):

M. Moradi ◽

M. Sahebi ◽

M. Shokri

Keyword(s):

Water Resources ◽

Spatial Scales ◽

Landsat Imagery ◽

Human Life ◽

Spectral Band ◽

Remote Sensing Data ◽

Particle Swarm Algorithm ◽

Landsat 8 ◽

Water Index ◽

Water Detection

Water is one of the most important resources that essential need for human life. Due to population growth and increasing need of human to water, proper management of water resources will be one of the serious challenges of next decades. Remote sensing data is the best way to the management of water resources due time and cost effectiveness over a greater range of temporal and spatial scales. Between many kinds of satellite data, from SAR to optic or from high resolution to low resolution, Landsat imagery is more interesting data for water detection and management of earth surface water. Landsat8 OLI/TIRS is the newest version of Landsat satellite series. In this paper, we investigated the full spectral potential of Landsat8 for water detection. It is developed many kinds of methods for this purpose that index based methods have some advantages than other methods. Pervious indices just use a limited number of spectral band. In this paper, Modified Optimization Water Index (MOWI) defined by consideration of a linear combination of bands that each coefficient of bands calculated by particle swarm algorithm. The result shows that modified optimization water index (MOWI) has a proper performance on different condition like cloud, cloud shadow and mountain shadow.

Download Full-text

Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province

Indonesian Journal of Geography ◽

10.22146/ijg.36113 ◽

2018 ◽

Vol 50 (2) ◽

pp. 154

Author(s):

Ardiansyah Ardiansyah ◽

Revi Hernina ◽

Weling Suseno ◽

Faris Zulkarnain ◽

Ramadhani Yanidar ◽

...

Keyword(s):

High Resolution ◽

Urban Environment ◽

Vegetation Index ◽

Training Sample ◽

Predictor Variables ◽

Landsat 8 ◽

Building Density ◽

Multi Index ◽

Water Index ◽

Index Approach

This study developed a model to identify the percent of building density (PBD) of DKI Jakarta Province in each pixel of Landsat 8 imageries through a multi-index approach. DKI Jakarta province was selected as the location of the study because of its urban environment characteristics. The model was constructed using several predictor variables i.e. Normalized Difference Built-up Index (NDBI), Soil-adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and surface temperature from thermal infrared sensor (TIRS). The calculation of training sample data was generated from high-resolution imagery and was correlated to the predictor variables using multiple linear regression (MLR) analysis. The R values of predictor variables are significantly correlated. The result of MLR analysis shows that the predictor variables simultaneously have correlation and similar pattern to the PBD based on high-resolution imageries. The Adjusted R Square value is 0,734, indicates that all four variables influences predicting the PBD by 73%.

Download Full-text

A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2018.01.018 ◽

2018 ◽

Vol 68 ◽

pp. 73-91 ◽

Cited By ~ 24

Author(s):

Xiaobiao Wang ◽

Shunping Xie ◽

Xueliang Zhang ◽

Cheng Chen ◽

Hao Guo ◽

...

Keyword(s):

Surface Water ◽

Landsat 8 ◽

Automated Extraction ◽

Landsat 8 Oli ◽

Water Index ◽

Multi Band

Download Full-text

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Water ◽

10.3390/w9040256 ◽

2017 ◽

Vol 9 (4) ◽

pp. 256 ◽

Cited By ~ 50

Author(s):

Yan Zhou ◽

Jinwei Dong ◽

Xiangming Xiao ◽

Tong Xiao ◽

Zhiqi Yang ◽

...

Keyword(s):

Surface Water ◽

Water Body ◽

Landsat 8 ◽

Open Surface ◽

Body Mapping ◽

Water Index ◽

Landsat 7 ◽

Surface Water Bodies ◽

Water Indices ◽

Sentinel 2

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

Download Full-text

Improving flood monitoring in rural areas using remote sensing

Water Practice & Technology ◽

10.2166/wpt.2018.118 ◽

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.

Download Full-text

A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

Remote Sensing ◽

10.3390/rs12101611 ◽

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.

Download Full-text

A Comparative Study of Water Indexes and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

10.20944/preprints201911.0218.v1 ◽

2019 ◽

Author(s):

Feifei Pan ◽

Xiaohuan Xi ◽

Cheng Wang

Keyword(s):

Image Classification ◽

Landsat Imagery ◽

Red Light ◽

Computational Cost ◽

Google Earth ◽

Classification Algorithms ◽

Landsat 8 ◽

Water Index ◽

Otsu Method ◽

Better Than

To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEIns, and AWEIs) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or ±1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEIns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEIns and AWEIs are better than NDWI, MNDWI and MNDWI2. AWEIns works better than AWEIs under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEIns.

Download Full-text

Comparison between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Assessment of Vegetation Indices

Nigerian Journal of Environmental Sciences and Technology - March 2018 ◽

10.36263/nijest.2017.02.0046 ◽

2017 ◽

Vol 1 (2) ◽

pp. 355-366

Author(s):

E. O. Makinde ◽

A. D. Obigha

Keyword(s):

Statistical Analysis ◽

Land Cover ◽

Vegetation Index ◽

Vegetation Indices ◽

Thematic Mapper ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Water Index ◽

Landsat 7 ◽

Index 2

The Landsat system has contributed significantly to the understanding of the Earth observation for over forty years. Since May 2013, data from Landsat 8 has been available online for download, with substantial differences from its predecessors, having an extended number of spectral bands and narrower bandwidths. The objectives of this research were majorly to carry out a cross comparison analysis between vegetation indices derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and also performed statistical analysis on the results derived from the vegetation indices. Also, this research carried out a change detection on four land cover classes present within the study area, as well as projected the land cover for year 2030. The methods applied in this research include, carrying out image classification on the Landsat imageries acquired between 1984 – 2016 to ascertain the changes in the land cover types, calculated the mean values of differenced vegetation indices derived from the four land covers between Landsat 7 ETM+ and Landsat 8 OLI. Statistical analysis involving regression and correlation analysis were also carried out on the vegetation indices derived between the two sensors, as well as scatter plot diagrams with linear regression equation and coefficients of determination (R2). The results showed no noticeable differences between Landsat 7 and Landsat 8 sensors, which demonstrates high similarities. This was observed because Global Environmental Monitoring Index (GEMI), Improved Modified Triangular Vegetation Index 2 (MTVI2), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) and Land Surface Water Index (LSWI) had smaller standard deviations. However, Renormalized Difference Vegetation Index (RDVI), Anthocyanin Reflectance Index 1 (ARI1) and Anthocyanin Reflectance Index 2 (ARI2) performed relatively poorly because their standard deviations were high. the correlation analysis of the vegetation indices that both sensors had a very high linear correlation coefficient with R2 greater than 0.99. It was concluded from this research that Landsat 7 ETM+ and Landsat 8 OLI can be used as complimentary data.

Download Full-text

Dynamic Threshold Selection for the Classification of Large Water Bodies within Landsat-8 OLI Water Index Images

10.20944/preprints201612.0141.v1 ◽

2016 ◽

Cited By ~ 1

Author(s):

Fangfang Zhang ◽

Junsheng Li ◽

Qian Shen ◽

Bing Zhang ◽

Huping Ye ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Water Bodies ◽

Landsat 8 ◽

Threshold Selection ◽

Water Index ◽

Sensing Data ◽

Twin Peaks ◽

Large Water ◽

Threshold Range

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

Download Full-text