A robust Multi-Band Water Index (MBWI) for

2 downloads 0 Views 3MB Size Report
Received 13 April 2017; Received in revised form 28 January 2018; Accepted 30 January 2018. ⁎ Corresponding author at: ...... simulate shadow areas according to a mathematical model method. In ... editors and three anonymous reviewers for their comments, which have .... areas on IKONOS image by combing ALS data.
Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

Contents lists available at ScienceDirect

Int J Appl Earth Obs Geoinformation journal homepage: www.elsevier.com/locate/jag

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

T



Xiaobiao Wanga,b,c, Shunping Xiea,b,c, , Xueliang Zhanga,b,c, Cheng Chend, Hao Guoe, Jinkang Dua,b,c, Zheng Duanf a

School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu Province, 210023, China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, Jiangsu Province, 210023, China c Collaborative Innovation Center of South China Studies, Nanjing University, Nanjing, Jiangsu Province, 210023, China d Center for Eco-Environmental Research, Nanjing Hydraulic Research Institute, Nanjing, 210029, China e State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang Province, 830011, China f Chair of Hydrology and River Basin Management, Technical University of Munich, Munich, 80333, Germany b

A R T I C L E I N F O

A B S T R A C T

Keywords: Water index Pure pixel Information extraction Low reflectance surface Landsat OLI

Surface water is vital resources for terrestrial life, while the rapid development of urbanization results in diverse changes in sizes, amounts, and quality of surface water. To accurately extract surface water from remote sensing imagery is very important for water environment conservations and water resource management. In this study, a new Multi-Band Water Index (MBWI) for Landsat 8 Operational Land Imager (OLI) images is proposed by maximizing the spectral difference between water and non-water surfaces using pure pixels. Based on the MBWI map, the K-means cluster method is applied to automatically extract surface water. The performance of MBWI is validated and compared with six widely used water indices in 29 sites of China. Results show that our proposed MBWI performs best with the highest accuracy in 26 out of the 29 test sites. Compared with other water indices, the MBWI results in lower mean water total errors by a range of 9.31%–25.99%, and higher mean overall accuracies and kappa coefficients by 0.87%–3.73% and 0.06–0.18, respectively. It is also demonstrated for MBWI in terms of robustly discriminating surface water from confused backgrounds that are usually sources of surface water extraction errors, e.g., mountainous shadows and dark built-up areas. In addition, the new index is validated to be able to mitigate the seasonal and daily influences resulting from the variations of the solar condition. MBWI holds the potential to be a useful surface water extraction technology for water resource studies and applications.

1. Introduction Surface water, as the most crucial terrestrial resources, is undergoing spatial and temporal changes caused by many factors such as land use/cover change (LUCC), climate changes, seasonal changes, and environmental changes throughout of the world (Alamgir et al., 2016; Ayeni et al., 2016; Brown et al., 2012; Halabisky et al., 2016; Hansen and Loveland, 2012; Mlejnková and Sovová, 2010; Soundharajan et al., 2016; Viswanathan et al., 2016; Wilson, 2015). Spatial distribution of surface water resources is of great importance for water-related studies and planning activities, which are associated with water resource management (Araral and Wu, 2016; Din et al., 2007; Gardelle et al., 2009; Pires et al., 2017; Prigent et al., 2012;), water environments (Chang et al., 2015; Novoa et al., 2012; Sallo et al., 2017), water

ecology (Jia et al., 2016; Luo et al., 2010; Sharma et al., 2009; Yang et al., 2016), water disasters such as floods and water shortages (Argyle et al., 2016; Dou, 2016; Hardy et al., 2017; Kummu et al., 2016; Wolski et al., 2017), water quality monitoring and assessment (Guttler et al., 2013; Hamid et al., 2016; Novoa et al., 2012; Schneider et al., 2016; Shi et al., 2015, 2017; Urbanski et al., 2016), and water-related diseases (Dambach et al., 2012; Phung et al., 2015; Shen et al., 2013). Remote sensing is a useful technique for monitoring changes of land cover information, and that provides a significant data sources for surface water extraction. Due to the wide coverages, repeatable observations and multi-band features, remote sensing offers an invaluable complementary data at both local and global scales (Palmer et al., 2015). Landsat imagery is one of the most extensively used data sources for identification, change analysis and detection of surface water



Corresponding author at: School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu Province, 210023, China. E-mail addresses: [email protected] (X. Wang), [email protected] (S. Xie), [email protected] (X. Zhang), [email protected] (C. Chen), [email protected] (H. Guo), [email protected] (J. Du), [email protected] (Z. Duan). https://doi.org/10.1016/j.jag.2018.01.018 Received 13 April 2017; Received in revised form 28 January 2018; Accepted 30 January 2018 0303-2434/ © 2018 Elsevier B.V. All rights reserved.

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Table 1 Seven commonly used water indices specifically applied to Landsat imagery from the previous literature. The input is surface reflectance (ρ) for each band from b1 to b7 derived from the Landsat TM/ETM+ multi-spectral bands, except that WI2006 applied the top-of-atmosphere reflectance, which is scaled linearly to an 8-bit digital number (DN). Index

Equation

TCW (Crist, 1985) NDWI (McFeeters, 1996)

0.0315ρb1 + 0.2021ρb2 + 0.3102ρb3 + 0.1594ρb4 − 0.6806ρb5 − 0.6109ρb7

MNDWI (Xu, 2006)

ρb2 − ρb4 ρb2 + ρb4 ρb2 − ρb5 ρb2 + ρb5

WI2006 (Danaher and Collett, 2006) AWEInsh (Feyisa et al., 2014) AWEIsh (Feyisa et al., 2014) WI2015 (Fisher et al., 2016)

50 − 19 ln(DNb2) − 52.18 ln(DNb3) + 83.32 ln(DNb4) − 14.78 ln(DNb5) + 11.875(DNb7) + 14.135 ln(DNb2) ln(DNb3) − 13.95ln(DNb2) ln (DNb4) + 3.935ln(DNb2)ln(DNb5) − 0.77ln(DNb2)ln(DNb7) − 3.785ln(DNb3) ln(DNb4) + 7.37ln(DNb3)ln(DNb5) − 4.675 ln(DNb3)ln (DNb7) − 5.41ln(DNb4)ln(DNb5) + 1.08ln(DNb4)ln(DNb7) + 1.265ln(DNb5) ln(DNb7) 4(ρb2 − ρb5) − (0.25ρb4 + 2.75ρb7 )

ρb1 + 2.5ρb2 − 1.5(ρb4 + ρb5) − 0.25ρb7 1.7204 + 171ρb2 + 3ρb3 − 70ρb4 − 45ρb5 − 71ρb7

with surface water when they designed water indices. Coefficients of AWEI are determined by an iterative empirical procedure to maximize the difference between water and non-water surfaces, which could make the index overwhelmingly dependent on the selected pure pixels samples. Though a number of techniques for extracting surface water are introduced in the previous studies, it remains a difficult problem to accurately extract surface water in areas where the background surfaces have low reflectance, such as mountainous shadows, high building shadows and dark built-up areas like asphalt roads and dark building materials in downtown. The existence of low reflectance surfaces is easy to lead to misclassification because of the similar low reflectance with surface water. We attempt to address the mentioned problems by proposing a new MBWI in this study to improve the accuracy of surface water extraction under the confused backgrounds. The robustness of the new index MBWI is comprehensively evaluated and compared with other six widely used water indices under various climatic zones and seasons. The objective of this study is to formulate a new water index (MBWI) that can consistently extract surface water with high accuracy in presence of various environmental noises.

(Kiselev et al., 2015; Li et al., 2016; Li et al., 2011; Mueller et al., 2016; Puertas et al., 2013; Rokni et al., 2015; Senay et al., 2016; Sheng et al., 2016; Tewkesbury et al., 2015; Tourian et al., 2015; Tulbure et al., 2016; Yang et al., 2015a, 2015b). In comparison with its predecessors, Landsat 8 has several advantages: (1) it significantly improves imaging capacity by acquiring approximate 725 images per day; (2) three additional spectral bands (central wavelengths: 0.443 μm, 1.375 μm, 11.45 ± 0.555 μm) are added to provide more spectral information; (3) the time delay (less to 8 h after acquisition) make near-real time application being possible; (4) higher geometrical and radiometric correction accuracies are achieved (Loveland and Irons, 2016). With the development of satellite technology and the increasing number of the available remote sensing datasets, numerous methods of surface water extraction have been developed, within which the water index, as a simple and useful method, has been widely used in extracting surface water. Several water indices have already been applied to identify surface water from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM+) imagery. Seven commonly used water indices are presented in Table 1, and they could be broadly categorized into two types: two-band and multi-band indices. Two-band indices are ease of use because of the simple form. The normalized difference water index (NDWI) formulated by McFeeters (1996) found out maximum reflectance of surface water in green band of Multispectral Scanner (MSS) images, while non-water surfaces in near-infrared (NIR) band. Xu (2006) modified the NDWI (called as MNDWI) by replacing NIR in NDWI with short-wave infrared (SWIR) band of TM images and found that the classification results of MNDWI were better than NDWI in urban areas. The reflectance of the confused surfaces is similar with surface water, which is easy to mistake those into surface water using two-band method. Therefore, the two indices perform well in natural surfaces without mountainous shadows and in urban areas without dark built-up areas. Multi-band indices considering more spectral signals may have advantages over the two-band method in identifying surface water. The tasseled cap wetness (TCW) index (Crist, 1985) empirically identified surface water by using decide coefficients derived from maximum variability in a new dimension based on all six spectral bands of TM images. The water index (WI2006) used the natural algorithm of each bands top-of-atmosphere (TOA) reflectance and interaction terms. It was found that the logarithm transform results were better than the untransformed (Danaher and Collett, 2006). Similar to WI2006, the water index (WI2015) utilized the same area-of-interest polygons to sample training data and statistical analysis to best separate the training classes (Fisher et al., 2016). The dissimilarity of two indices is that the input of WI2015 is surface reflectance rather than TOA reflectance. Based on pure pixels samples, the two automated water extraction indices (AWEI) were developed by Feyisa et al. (2014): one is for scenes without shadow (AWEInsh) and the other is for scenes with shadows and/or dark surfaces (AWEIsh). The aforementioned indices except for AWEI do not focus on the confused surfaces having similar reflectance

2. Study areas and dataset 2.1. Study areas The performance and robustness of the new multi-band water index (MBWI) are comprehensively evaluated in China covering a range of different surface water conditions and surroundings with low reflectance surfaces. The low reflectance surroundings have a negative impact on the accuracy of surface water extraction such as mountainous shadows, high building shadows, dark built-up areas, and black soils. There are five different climatic zones in China (Lam et al., 2005), including temperate monsoon climate, tropical monsoon climate, subtropical monsoon climate, temperate continental climate, and plateau climate. The monsoon climate covers the East and South of China and it brings abundant rainfall to these parts. The continental climate is located in the Northwest of China, where the humid airflow is hindered by terrain factors. In this study, a number of 29 test sites were selected from 17 Landsat 8 OLI images ranging from humid tropic through subtropic to dry temperate regions (see Fig. 1). As can be seen from Fig. 1, the rectangles represent the size of remote sensing images and that colors indicate various climatic zones, and the red points located in the images stand for test sites. The test sites are selected by visual interpretation based on the available remote sensing dataset to sufficiently consider the influence factors of surface water extraction. The selection principles as follows: (1) sites in the same climate are supposed to have different water shapes and sizes including natural or artificial lakes, reservoirs, ponds and rivers. (2) one or more influence factors including shadows and dark surfaces are sufficiently reflected in the selected sites. The number of test sites is 74

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 1. Locations of the selected 29 test sites in China that are distributed in different climatic zones.

Table 2 Description of the selected 29 test sites in this study. Site number

Region

Water type

Major noise

Landform

Climate

Humid/Arid

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Xizang Taiwan Hainan

Lakes River Rivers/Lakes Rivers/Lakes River Small river Lakes Lakes Rivers/Likes Small river Rivers/Lakes Lakes Rivers Rivers Small river Rivers Small rivers Rivers Lakes Reservoir/Lakes Rivers/Lakes Rivers Rivers Rivers/Lakes Small rivers Lakes Small river Small rivers Rivers

Shadows/Soils Built-up Shadows Built-up Soils/shadows Shadows Shadows Built-up/Soils Built-up Built-up/Soils Built-up/Soils Built-up/Shadows Shadows Built-up/Soils Built-up Built-up/Soils Soils Built-up/Shadows Shadows Built-up Built-up/Soils Built-up/Shadows Built-up/Shadows Shadows Built-up Soils Built-up/Shadows Built-up/Shadows Shadows

Mountainous Hilly Hilly Hilly Flat Mountainous Hilly Flat Hilly Flat Hilly Hilly Mountainous Flat Flat Flat Hilly Hilly Hilly Hilly Mountainous Mountainous Mountainous Mountainous Mountainous Mountainous Mountainous Mountainous Mountainous

Plateau Tropical Tropical

Semiarid Humid Humid

Temperate Temperate Temperate Temperate

Semiarid Semiarid Arid Arid

Temperate Temperate

Semi-humid Semi-humid

Temperate

Semi-humid

Temperate Temperate Subtropical

Humid Humid Humid

Subtropical

Humid

Subtropical

Humid

Subtropical

Humid

Subtropical

Humid

Ningxia Qinghai Xinjiang Gansu Hebei Shandong Henan

Heilongjiang Jilin Fujian

Sichuan Hunan

Yunnan Guizhou

75

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

summarized in Table 4.

Table 3 Description of the information about the Landsat 8 Operational Land Imager. OLI Band #

Band Name

Center Wavelength (nm)

Spatial resolution (m)

SNR

1 2 3 4 5 6 7 8 9

Coastal Aerosol Blue Green Red NIR SWIR1 SWIR2 Panchromatic Cirrus

443 482 561 655 865 1609 2201 590 1373

30 30 30 30 30 30 30 15 30

237 367 304 227 201 267 327 148 160

2.3. Reference data In total, 1704 water and 18950 non-water samples are randomly selected from the 29 test sites to assess the accuracy of surface water extraction. The water/non-water label of each sample is determined by comparing with high-resolution datasets, including the true-color sharpening Landsat OLI images (15 m) and the available high-resolution imagery from Google Earth (Yang et al., 2015a, 2015b). 3. Method The method consists of five steps (see Fig. 2): (1) applying radiometric calibration and atmospheric correction to an archive of Landsat OLI imagery; (2) selecting pure pixels; (3) formulating a new multiband water index according to band difference between water and nonwater surfaces; (4) using K-means cluster method to automatically obtain the results of surface water mapping; (5) assessing the accuracy of surface water extraction. Each step is described in more detail below.

planned as follows: Five test sites are chosen in the northwest with temperate continental climate, and three in arid and two in semiarid regions. Eight test sites in north and northeast are temperate monsoon climate, and six in semi-humid and two in humid regions. Three test sites in the south are tropical monsoon climate, and twelve test sites in the southwest are subtropical monsoon climate. Only one test site is selected in Tibet Plateau because the water types and land cover types in this zone are relatively simple compared with those of other zones. The water bodies selected from the test sites have great variabilities, consisting of small freshwaters, artificial reservoirs, natural and artificial lakes, and rivers with diverse chemical compositions and turbidities. A brief description of the test sites is shown in Table 2.

3.1. Image preprocessing Radiometric calibration and atmospheric correction of the satellite imagery are pre-requisite for producing uniform and high quality data materials (Chander et al., 2009). In terms of radiometric calibration, TOA Radiance Rλ [W/(m2·sr·μm)]is calculated by applying a linear transformation equation (Eq. (1)) to the digital numbers (DN) of Landsat 8 OLI imagery to correct influence of the angle of the solar zenith among various data acquisitions. Relevant calibration coefficients (i.e., the off set factor Oλand the gain Gλ) are furnished in the metadata file.

2.2. Dataset The Operational Land Imager (OLI) was launched aboard the Landsat 8 satellite in February 2013. Spectral bands of the Operational Land Imager (bands 1–9) and their single-to-nose ratios (SNR) are presented in Table 3 (Morfitt et al., 2015). Observation are quantized over a 12-bits dynamic range and provided to user as 16-bit number (Loveland and Irons, 2016). The selected Landsat 8 OLI images acquired from 2013 to 2016 are obtained from the official website of the United States Geology Survey (USGS). Seventeen images with little cloud cover are selected in this study, and these images are orthorectified and terrain corrected by Level 1T Product Generation System (USGS, 2012). Surface water is among the most dynamic features of surfaces on the earth, and that chemical and physical properties are easy to vary with climatic and seasonal changes. The variations of water properties could directly make surface water reflectance change. Hence, image selection takes climatic and seasonal conditions, surface water features, and surroundings into account. Detailed attributes of the selected images are

Rλ = Gλ × DN + Oλ

(1)

The quality of satellite scenes is impacted by gaseous scattering and aerosological absorption, which results in the unexpected bias and errors of spectra and signal. Therefore, an atmospheric correction is implemented on TOA radiance using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module in ENVI v.5.1 (Exelis Visual Information Solutions, 2010) to obtain surface reflectance. The atmospheric parameters are set according to the seasonal-latitude surface temperature module based on a look-up table (ExelisHelp, 2010). Parameters were specified depending on imaging time and location: the mid-latitude summer or winter atmospheric model, the rural or urban aerosol model, and the 2-Band aerosol retrieval method (Ke et al., 2015; Qin et al., 2015). Several previous

Table 4 Summary of Landsat 8 OLI images used in this study. Number

Region

Path/row

Acquisition date

Site center

Cloud cover

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Xizang Taiwan Hainan Ningxia Qinghai Xinjiang Gansu Hebei/8 Shandong Henan Heilongjiang Jilin Fujian/13 Sichuan Hunan Yunnan Guizhou

145/36 118/44 124/47 129/33 133/35 144/27 135/32 123/33 121/36 124/37 114/28 117/30 119/42 129/39 124/40 129/43 126/41

2016/10/05 2013/12/03 2013/10/26 2016/05/30 2016/07/29 2014/05/02 2016/09/29 2014/09/04 2015/10/27 2015/12/03 2016/10/12 2016/04/24 2013/10/23 2014/08/13 2015/10/16 2015/03/09 2016/08/29

81.0164°E 34.6109°N 119.7338°E 23.1126°N 109.4811°E 18.7876°N 106.9993°E 38.9044°N 99.9721°E 36.0430°N 86.7168°E 47.4492°N 98.1897°E 40.3329°N 116.2708°E 38.9046°N 118.0907°E 34.6105°N 113.0521°E 33.1771°N 132.5727°E 46.0299°N 126.8975°E 43.1847°N 118.9035°E 25.9928°N 104.5584°E 30.3062°N 111.9261°E 28.8691°N 103.0980°E 24.5530°N 108.4764°E 27.4313°N

2.84% 4.25% 0.11% 0.05% 0.46% 0.93% 0.14% 0.19% 3.17% 0.48% 0.05% 0.35% 0.18% 3.74% 0.11% 0.01% 0.04%

76

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Landsat 8 OLI Scenes

Image preprocessing

Surface reflectance

MNFT, PPI

MBWI value

High resolution images

K-means cluster

Pure pixel samples

Water/non-water discrimination

Pure pixels reflectance

Surface water mapping result

Multi-band comparison analysis

Reference data

Coefficients determined

Confusion Matrix

Multi-Band Water Index

Accuracy assessment Fig. 2. Methodology flowchart.

Reflection Radiometer (ASTER) Digital Elevation Model (DEM), mountainous shadows are determined. Pure pixels of bright built-up areas are sampled from heterogeneous features such as airport runways and buildings with bright materials in the downtown area, and the samples of dark built-up areas are also extracted from high building groups in the urban area. Homogenous artificial fields in the midst of basin are the source of the pure farmland pixel samples. Finally, some artificial fields exposed black and the barren land outside of city are chosen as pure soil pixel samples. A total of 395 pure pixel samples are selected from the Landsat 8 OLI image. The pure pixel of surface types is different in six reflectance bands (see Fig. 3(a)–(g)), which is a basic data for finding out the spectral difference between water and non-water surfaces, and designing a new water index. Separability of spectral signatures of the samples from the seven primary surfaces is verified by Jeffries-Mantusita’s pairwise separability measure (Richards, 1993). The pairs of land cover types are found to be separable with values about 2.00 except for a pair of vegetation and mountainous shadow, whose value merely reaches 1.72.

studies have successfully utilized FLAASH model to retrieve surface reflectance (Dube and Mutanga, 2015; Fan et al., 2015; Xie et al., 2016; Yang et al., 2015a, 2015b). 3.2. Selection of pure pixels Pure pixel is that a pixel only includes a kind of land cover information, and that reflectance is the theoretical basis for identifying different surfaces. Pure pixels are selected to find cues of spectral difference between water and non-water surfaces, providing a reference for the proposed water index. “Pure” pixels are sampled from a Landsat 8 OLI image of Qinhuai River Basin in the East of China, acquired on Mar. 28, 2016. The reason for selecting this place for sampling pure pixels is that it contains all the challenging impacts for identifying surface water: mountainous shadows, dark built-up areas, and other low reflectance features. Land cover types of this place include water, vegetation (forest and non-forest), farmland, mountainous shadow, bright built-up area, dark built-up area and soil (barren and drought land). The methods of Minimum Noise Fraction Transform (MNFT) and Pixel Purity Index (PPI) (Feyisa et al., 2014; Zhang et al., 2015) are used to help extract pure pixels. MNFT is adopted to reduce noises and enhance image quality. A large PPI value indicates a great possibility of a pure pixel. However, not all the pixels with high PPI value are viewed as pure pixel. We selected each pure pixel from the PPI result by further observing the supplemented high-resolution data. Pure pixel samples of water are selected from the center of rivers, lakes, and reservoirs to avert mixed edge pixels. Similarly, vegetation samples are selected within high forest covers located in the surrounding areas of basin boundary. With a help of Advanced Spaceborne Thermal Emission and

3.3. Formulation of a new multi-band water index (MBWI) Spectral difference is an important foundation to successfully discern water from non-water surfaces. With the wavelength increasing from visible to infrared bands, the reflectance of surface water similarly shows a decreasing variation trend, but non-water surfaces don’t have apparent variation trends. In addition, maximum reflectance value of surface water presents in visible bands, while non-water in infrared bands. On the basis of the sufficiently analyzing distinction between water and other low reflectance surfaces, five multispectral bands of 77

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 3. Surface reflectance distributions of pure pixels of the major land cover types. (a)–(g) show that pure pixel samples of the seven typical surfaces are different in six reflective bands of the Landsat 8 OLI image, each box plots explains the location of the 10th, 25th, 50th, 75th and 90th percentiles using horizontal lines (boxes and whiskers) and the circles are 5th and 95th percentiles. (h) presents the result of the new index values derived from the selected pure pixel samples, and Wa, Ve, Fa, Sh, Db, Bb and So refers to water, vegetation, farmland, shadow, dark built-up area, bright built-up area, and soil, respectively.

with increased accuracy (see Eq. (2)). Coefficient used and bands chosen are confirmed according to a rigorously examination of the reflectance characters of the various surface types.

Landsat 8 OLI image are taken to formulate a new Multi-Band Water Index (MBWI) with one coefficient aiming to maximize the contrast of each other. Therefore, the new MBWI is designed to availably restrain non-water features and simultaneously enhance surface water information so that it could preferably acquire surface water mapping

MBWI = 2ρb3 − ρb4 − ρb5 − ρb6 − ρb7 78

(2)

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 4. The process of the new water index formulation. (a) depicts that the average reflectance values resulted from the seven typical surface types are different in six reflectance bands, (b) is a process of determining the optimal coefficients of the new index. Gb and T signs mean Green band and sum of the continuous four reflectance bands ranging from Red to SWIR, respectively. Wa, Ve, Fa, Sh, Db, Bb and So refer to water, vegetation, farmland, shadow, dark built-up area, bright built-up area, and soil, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5. Results of surface water mapping from our proposed index MBWI in different conditions with three test sites (Site 12, 19 and 22, see details in Table 2) being presented as illustrative examples.

where ρ is surface reflectance of multispectral bands of Landsat 8 OLI image: b3 (Green), b4 (Red), b5 (NIR), b6 (SWIR1), b7 (SWIR2). Blue band is not taken to devise the new index since it is sensitive to aerosol variations and cannot discriminate water from non-water

surfaces clearly. Fig. 3(a)–(g) show that surface reflectance values of water, vegetation, farmland, shadow, dark built-up area, and soil are close to 0 in Blue band, which is hard to identify surface water from the aforementioned surfaces. For one thing, Green band value of surface 79

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 6. Distribution of four accuracy measures (a-d) calculated by the seven water indices in all 29 test sites, each box plots explains the locations of the 10th, 25th, 50th, 75th and 90th percentiles using horizontal lines (boxes and whiskers) and the circles are 5th and 95th percentiles.

minimum-distance technique to iteratively classify the pixels to the nearest class. Each of iteration recalculates class means and reclassifies pixels in accordance with the new means. All of the pixels are clustered into the nearest class unless a distance threshold or standard deviation is specified, in which case some pixels cannot meet the selected criteria so that they may be unclassified. This process goes on until the selected pixel change threshold is greater than the number of pixels in each class variation or the maximum number of iterations is reached. K-means cluster method is introduced to automatically obtain classification results of water and non-water surfaces to avoid the artificial errors in looking for the optimum threshold. In general, the common threshold method is a popular way to obtain the results of surface water extraction from remote sensing imagery, while finding out the optimal threshold is an iterative and complicated process as well as a work full of challenge. K-means cluster method is applied to the result calculated by a water index to accomplish automatic identification of the different surface types. The parameter of class number is set as 10, which is slightly larger than the number of surface types in pure pixel selection. K-means clustering results are combined into two classes: water and non-water surfaces.

water is bigger than the other bands, while Green band values of nonwater surfaces are almost minimum values in their all bands. However, values of non-water surfaces in band 5 (NIR) or band 6 (SWIR) are bigger than their other bands. Thus, some water indices have focused on the relevant three bands, such as NDWI and MNDWI. For another, it is a radical discrimination between water and non-water surfaces that surface water shows similarly decreasing variation trend from Green to SWIR bands. Index values of the selected pure pixel samples indicate that the new index can manage to automatically distinguish water from non-water surfaces (see Fig. 3(h)). The process of the new index formulation conducts in accordance with the average reflectance values of the seven typical surfaces. Fig. 4(a) depicts that distinctions of the seven typical surfaces are various in six reflectance bands. To be convenient for the subsequent description, Green band and sum of the continuous four bands including Red, NIR, and double SWIR are defined as Gb and Total (T), respectively. Based on the results of the spectral difference analysis of water and non-water surfaces, Eq. (1) is accordingly transformed to MBWI=ω*Gb-T, where the coefficient ω needs to be carefully determined to maximize the difference between water and non-water surfaces. The MBWI results with different ω values are presented in Fig. 4(b). A too small or too large value could lower the desired difference. For example, if ω equals to 1, the index values for both water and non-water surfaces are less than 0. As the ω value increases, if it is larger than 4, it is possible to describe built-up areas as surface water because of the similar positive index values. It seems that three or four is also a suitable parameter to formulate the new index, but it fails to accomplish the purpose of maximizing the difference between water and non-water surfaces. By assigning the coefficient ω as 2, the positive index values represent surface water, while the negative means nonwater surfaces. It is a hint that zero is a default threshold for separating water and non-water surfaces.

3.5. Accuracy assessment Four accuracy measures are applied to evaluate the performance of water indices including overall accuracy, kappa coefficient, water total error, and non-water total error. The water total error is defined as the sum of water commission error and water omission error. Water commission error refers to the non-water pixels that are labeled as water. On the contrary, water omission error refers to the water pixels that are labeled as non-water. The definition of non-water total error is similar to that of water total error. Six water indices (AWEInsh, AWEIsh, MNDWI, NDWI, TCW, and WI2006) are selected to compare with the proposed multi-band water index (MBWI) in the 29 test sites under different climatic zones and seasons.

3.4. Surface water extraction K-means cluster (Capó et al., 2017; ExelisHelp, 2010) computes initial class means evenly distributing in the data space, then uses a 80

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 7. Spatial distributions of the outputs of surface water mapping (a–c) drown by the seven water indices in three test sites (Site 9, 11 and 18).

4. Results

other water indices in terms of reasonably higher accuracies and lower errors. Compared with AWEInsh, the new index could result in a clear improvement in terms of mean water total error of 9.31% in the test sites. Surface water maps derived by the seven water indices in three test sites (site 9, 11 and 18) are shown in Fig. 7. It can be seen from Fig. 7(a) that MBWI and WI2006 manage to enhance the contrast between water and non-water surfaces, and produce better surface water maps than other water indices. This demonstrates that MBWI and WI2006 can relieve dark built-up and soil noises under arid conditions represented by the Site 9. In Fig. 7(b), difficulty in surface water extraction also comes from dark built-up areas at this test site. Indices of AWEIsh, MNDWI, NDWI, TCW, and WI2006 produce poor results as shown in Fig. 7(b), while MBWI and AWEInsh show remarkably better results than other water indices. In Fig. 7(c), it can be seen that MBWI, MNDWI, and WI2006 improve the accuracy of surface water extraction compared with other water indices in this test site with mountainous shadows and dark built-up surfaces. The proposed MBWI obtains higher accuracy than MNDWI and WI2006 because of fewer mistakes of labeling dark built-up surface as surface water. These results indicate that MBWI has superiority of improving water extraction accuracy under different surface water environmental conditions.

4.1. Accuracy assessment of the Multi-Band Water Index (MBWI) In this section, the performance of water indices is assessed in the 29 different sites. Fig. 5 shows surface water extraction results of the proposed MBWI in the three test sites (Site 12, 19 and 22) as examples. A certain amount of mountainous shadows, some dark built-up areas, and soils are influence factors in Site 12. Mountainous shadows are major influence factor in Site 19. Some disperse mountainous shadows and small dark built-up surfaces are presented in Site 22 (see Fig. 5(a)). A simple visual examination indicates that the new index makes a success in enhancing the contrast between water and non-water surfaces in the three different environmental conditions (see Fig. 5(b)). Outputs of surface water mapping shown in Fig. 5(c) demonstrate that our proposed index MBWI performs well under the confused surface water surroundings. This result indicates that the MBWI manages to enhance surface water information and simultaneously suppress low reflectance surfaces in the three test sites. Fig. 6 presents distributions of accuracies for the seven water indices in all 29 test sites. The MBWI is more robust than other six water indices under different surface water environmental conditions as its relatively small accuracy change range. Overall, assessment measures of the new index, consisting of mean overall accuracies (99.32%), kappa coefficients (0.95), water total errors (8.09%) and non-water total errors (0.74%) in all 29 test sites, are all better than other water indices. It could be determined that MBWI and AWEInsh are fairly better than all

4.2. Robustness of water indices under different climatic zones The surface water extraction results of the 29 test sites are divided into seven groups according to the climatic zones and the humid 81

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 7. (continued)

seasons for MBWI is smaller than other indices; (2) the MBWI achieves relatively lower errors and higher accuracies than other indices in each season. Among the four seasons, the MBWI performs better in the first three seasons and identifies surface water with relatively higher water total errors of 14.26% in winter. To further show the influence of seasons, water commission and omission errors of surface water extraction in each test site are presented in Fig. 10 in addition to mean accuracy values in Fig. 9. Water commission error and omission error resulted from the seven water indices are different with seasonal and daily changes (see Fig. 10). We can clearly see that error values of MBWI are apparently lower than other indices. Furthermore, error values of MBWI tend to be stable centering to the zero point, showing a relatively greater robustness than all other six indices in terms of the daily influence.

conditions. The mean accuracies of each group are calculated and presented in Fig. 8. In six out of seven climate conditions, surface water extraction accuracy of MBWI is higher than that of other six water indices. Mean kappa coefficients of the MBWI are greater than 0.94 and water total errors are lower than 11.11%, among which minimum accuracies are presented under temperate continental climate with arid condition. It may be ascribed to water pixels always being the presence of mixed pixels under the arid condition and their confused spectral features which is difficult to be distinguished even by a visual interpretation. In semiarid region under temperate continental climate, the MNDWI obtains the highest surface water extraction accuracy, but mean kappa coefficients are only 0.02 higher than the proposed index MBWI. It can be concluded that this demonstrates that overall the MBWI is capable of extracting surface water with superior accuracy under different climate conditions.

4.4. Influence of surroundings on surface water extraction 4.3. Influence of seasonal conditions on surface water extraction According to Table 2, influence factors of surface water extraction are summarized into three categories (built-up areas, shadows, and soils) to adequately assess the effectiveness of water indices. The surface water extraction results of the 29 test sites are divided into three groups based on the category of influence factors. The mean accuracies of each group are calculated and shown in Fig. 11. The proposed index MBWI can successfully identify surface water from the three influence factors with marked performance, and kappa coefficient is all above 0.94. Other six widely used indices are found to have desired results in the confused background with soils being influence factor, and those

Seasonal conditions are also the primary influence aspects which have a negative impact on surface water extraction. The surface water extraction results of the 29 test sites are divided into four groups by season conditions. The mean accuracies of each group for the seven water indices are calculated and presented in Fig. 9. The season is defined according to the standard in North hemisphere as spring covers Mar-May, summer covers Jun-Aug, autumn covers Sep-Nov, and winter covers Dec-Feb. The proposed MBWI is able to mitigate seasonal influence in two aspects: (1) change range of accuracy values at different 82

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 7. (continued)

surfaces to effectively reduce the impacts of low reflectance surfaces for surface water extraction. K-means cluster is applied to index values to automatically generate the classification results without dependence on setting an appropriate threshold. The proposed MBWI is shown to perform well with high accuracy in extracting surface water, even in mountainous and hilly areas with shadows, and in urban areas that cast dark built-up surfaces. The MBWI is proved as a useful and robust method for various types of surface water under different climatic zones and seasons. The optimal coefficients of AWEInsh, AWEIsh, TCW, WI2006, and WI2015 are determined by applying the complicated iterative process or statistical analysis methods, which could make these indices overwhelmingly dependent on the samples and consequently influence the performance of these methods (Bharathi et al., 2017; Hawkins, 2004). Compared with the previous studies, the proposed index MBWI only has one coefficient. Based on the surface reflectance difference of pure pixels of water and non-water surfaces, the coefficient of the new index is determined by comparison analysis to maximize the discrimination between water and non-water surfaces. The determined coefficient

kappa coefficients range from 0.89 to 0.94. The AWEIsh, MNDWI, NDWI, TCW, and WI2006 are observed to be sensitive to built-up surfaces. In addition, the AWEIsh, AWEInsh, NDWI, TCW, and WI2006 are negatively affected by mountainous shadows. The composition of surface water is various in the real world, and turbidity is an important characteristic of water bodies. We select two test sites (Site 12 and Site 22) to validate the robustness of the seven water indices in terms of different turbidities. It is slightly turbid in Site 12 but highly turbid in Site 22. The surface water mapping results by the seven water indices in the two test sites are presented in Fig. 12. The commission and omission errors are marked as red and yellow circles in Fig. 12, respectively. We can see that the surface water can be correctly extracted by MBWI for both sites, showing that the new index has the potential to identify surface water under different turbidities. 5. Discussion The new multi-band water index MBWI proposed in this paper aims at enhancing the spectral difference between water and non-water 83

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 8. Mean values of two accuracy measures for the seven water indices in seven climate conditions. (a)–(e) represent seven different humid conditions under five climatic zones.

would not be optimal for all cases. However, the proposed MBWI has validated and compared with the six widely used water indices in the selected 29 test sites with different surface water surroundings. Results

show that coefficient of the proposed MBWI is effective to improve the contrast of water and non-water surfaces, and produce better surface water mapping results than other six water indices. This is because the 84

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 9. Mean values of four accuracy measures (a–d) for the seven water indices in different seasons.

reflectance of non-water surfaces is similar with surface water, this will also lead to misclassification. When the surface reflectance difference between water and non-water surfaces is not satisfied with the prerequisite of MBWI formulation, MBWI cannot successfully identify surface water from Landsat OLI images. However, the accuracy of surface water extraction of the six widely used water indices may depend on the humid conditions. In humid regions, all the six widely-used water indices perform with high extraction accuracies. The kappa coefficients of these indices are all higher than 0.83 in three humid conditions under tropical, subtropical and temperate monsoon climates, except that the water total errors of NDWI and TCW are relatively higher than other indices under subtropical monsoon climate. In semihumid regions under temperate monsoon climate, the AWEIsh, MNDWI, and NDWI perform with both higher omission and commission errors than other three indices. The water total errors of the former are higher than 38%, while those of the latter are smaller than 27%. In semiarid regions, the AWEIsh, NDWI, and TCW are poor in identifying surface water from the confused environmental conditions with dark built-up surfaces or mountainous shadows. Water total errors of the three indices are larger than 31%, while those of the rest are smaller than 19%. In arid regions under temperate continental climate, the AWEIsh and MNDWI are easy to identify non-water surface as water. The kappa coefficients of the two indices are smaller than 0.75, while those of the rest are higher than 0.86. The properties of surface water are various with seasonal and even daily changes, due to the angle of the sun, radiation hours and atmospheric composition impacts (Feyisa et al., 2014; Yang et al., 2015a, 2015b). The MBWI could mitigate seasonal influence because of the lower errors and higher accuracies than other indices in each season. All the six widely used water indices have relatively higher water total errors in winter compared with those in other seasons. Particularly, water total errors of MNDWI, NDWI and WI2006 in winter can be higher than 30%. The AWEIsh, NDWI, and TCW perform worse than others in all the seasons. Especially, the input of the WI2006 is TOA reflectance without atmospheric corrections, while the input of other indices is surface reflectance. TOA reflectance was the sum of surface and atmosphere, which leads to that the accuracy of surface water

MBWI formulation is dependent on the essential discrimination of surface reflectance between water and non-water surfaces in each spectral band of Landsat OLI image. Namely, the reflectance of surface water similarly shows a decreasing variation trend with the wavelength increasing from visible to infrared bands, but non-water surfaces don’t have apparent variation trends. In addition, maximum reflectance value of surface water presents in visible bands, while non-water in infrared bands. The two important formulation principles make the MBWI perform well in surface water extraction. It could be determined that coefficient is feasible and acceptable. Though the effectiveness of coefficient has been validated with different water types and surroundings, many region spectral differences of water and non-water surfaces and other factors should further be taken into consideration to determine the optimum coefficient. The performance of surface water extraction is usually impacted by various surface water conditions and surroundings (Fisher et al., 2016; Li et al., 2011; Li et al., 2016). Climate conditions should be taken into account to evaluate the practicability of the proposed MBWI and other six commonly used water indices. The accuracies of MBWI are higher than that of other six widely used water indices in six out of seven climate conditions. Mean kappa coefficients of the MBWI are larger than 0.94, among which minimum accuracies are presented under temperate continental climate with arid condition. On one hand, MBWI does not perform well in these areas. This is because the arid region is located in the Northwest of China, where the humid airflow is hindered by terrain factors (Lam et al., 2005). Surface water storage capacities are very finite and the surface water is usually presented as mixed pixels in medium- and low- resolution images. Moreover, the surface reflectance of water edge pixels is a combination of water and other land cover types, which is difficult to find a simple spectral difference to discriminate the edge pixels as water or non-water. Hence, a sub-pixel method may help to improve the accuracy of surface water extraction in such areas. On the other hand, the reflectance character of surface water is usually influenced by water properties, such as turbidity, water color, plankton, and chlorophyll, and so on. The river sediment concentration is possible to make the reflectance of surface water similar as soils, which will mistake surface water into soils. On the contrary, if the 85

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 10. Commission and omission errors (a-g) derive from the seven water indices.

extraction by WI2006 is usually influenced by the atmosphere. Compared with MBWI, the WI2006 could result in higher mean water total errors of 9.63% in the test sites.

It is difficult to discern surface water from other low reflectance surfaces such as mountainous shadows, high building shadows and dark built-up surfaces, which are major sources of misclassification and 86

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 11. Mean values of four accuracy measures (a–d) for the seven water indices facing with different influence factors.

with different climatic zones and water types, several variables are not taken into accounts in our test sites that are likely to impact the robustness of the new index. Shadows of low cloud cover are not considered in designing the new index, the influence of which needs to be further evaluated using additional test sites. The new index is only tested on Landsat OLI data and it deserves to be expanded for surface water extraction from other sensors.

confusion for extracting water surface from remote sensing imagery. In general, a shadow correction algorithm is applied to reduce the impact of shadow, consisting of two steps: detecting the location of shadow and de-shadowing (Shahtahmassebi et al., 2013). The commonly used shadow correction algorithms could be broadly categorized into two types: threshold method and model method. The former chooses a threshold value of the digital number (DN) to determine shadow areas from non-shadow areas, while the latter applies the prior information to simulate shadow areas according to a mathematical model method. In mountainous areas, several ancillary data is necessary to reduce the effects of shadows such as Digital Elevation Models (DEM), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In addition, topographic correction models are also able to decrease the influence of shadows such as C correction (Tokola et al., 2001), cosine topographic correction (Ekstrand, 1996) and Minnaert correction (Bishop et al., 2003). In urban areas, several researchers have applied object-based approach (Zhan et al., 2005), linear-correlation and histogram matching (Sarabandi et al., 2004), and gamma correction techniques (Nakajima et al., 2002) to reduce the effects of shadows or dark surfaces. To reduce the impacts of other low reflectance surfaces, instead of utilizing the shadow correction or other radiometric correction methods (Shahtahmassebi et al., 2013; Xie et al., 2016; Yang et al., 2015a, 2015b), we propose the new water index (MBWI) in accordance with the band difference between surface water and other low reflectance surfaces of these surfaces. To validate the effectiveness of MBWI, we compare MBWI with other six widely used water indices surrounded by different low reflectance surfaces, such as built-up areas, shadows, and soils. The results in Fig. 11 show that the MBWI has the highest accuracies facing with different impacts, the WI2006 and AWEInsh are followed, and the MNDWI, AWEIsh, TCW, and MNDWI are the least accurate. We also find that soils have a relatively lower impact than built-up areas and shadows for surface water extraction because of the higher accuracies. Although the new index has been validated in a wide range of the complicated environmental conditions

6. Conclusion The study proposes a novel index MBWI (Multi-Band Water Index) for surface water extraction from Landsat 8 OLI imagery through maximizing the spectral difference between water and non-water surfaces. Accuracy for surface water extraction in different surface covers, climatic and seasonal conditions are compared with six widely used water indices (i.e. AWEInsh, AWEIsh, MNDWI, NDWI, TCW, and WI2006). It is shown that the performance of the proposed MBWI is significantly better than all other indices in identifying surface water from low reflectance surfaces in areas where shadows and dark built-up areas are the primary sources of surface water extraction errors. The comprehensive evaluation in the selected 29 test sites with different climatic zones and water types reveals that MBWI is more accurate in surface water extraction under the complicated surface water surroundings in comparison with other six water indices. The results of kappa coefficient from MBWI in most test sites are above 0.90. Seven groups of climate experiments show that the MBWI could effectively identify surface water from low reflectance surfaces with respect to different climatic zones and humid conditions. In addition, seasonal tests indicate that the MBWI is able to mitigate seasonal and daily influence resulted from the variations of the solar condition. Therefore, it would be a useful tool for surface water detection studies using optical images.

87

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 12. Surface water mapping results are extracted by the seven water indices in two test sites with different turbidities. The commission and omission errors are marked as red and yellow circles. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

88

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

Fig. 12. (continued) design on signal detection in flash flood forecasting. Int. J. Hum.-Comput. Stud. 99, 48–56. Ayeni, A.O., Cho, M.A., Mathieu, R., Adegoke, J.O., 2016. The local experts’ perception of environmental change and its impacts on surface water in Southwestern Nigeria. Environ. Dev. 17, 33–47. Bharathi, M.D., Patra, S., Sundaramoorthy, S., Madeswaran, P., Sundaramanickam, A., 2017. Elucidation of seasonal variations of physicochemical and biological parameters with statistical analysis methods in Puducherry coastal waters. Mar. Pollut. Bull. 122, 432–440. Bishop, M., Shroder, J.J., Colby, D.J., 2003. Remote sensing and geomorphometry for studying relief production in high mountains. Geomorphology 55 (1–4), 345–361. Brown, S., Versace, V.L., Laurenson, L., Ierodiaconou, D., Fawcett, J., Salzman, S., 2012. Assessment of spatiotemporal varying relationships between rainfall: land cover and surface water area using geographically weighted regression. Environ. Model. Assess. 17, 241–254. Capó, M., Pérez, A., Lozano, J.A., 2017. An efficient approximation to the K-means clustering for massive data. Knowl.-Based Syst. 117, 56–69. Chander, G., Markham, B.L., Helder, D.L., 2009. Summary of current radiometric calibration coefficients for Landsat MSS TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113, 893–903. Chang, N.B., Imen, S., Vannah, B., 2015. Remote sensing for monitoring surface water quality status and ecosystem state in relation to the nutrient cycle: a 40-year

Acknowledgements This research is supported by the National Natural Science Foundation of China (No.41671390, No.41371044, No. 41601366, and No. 5170917), the Natural Science Foundation of Jiangsu Province (Grant No.BK20160623), and the Project funded by Nanjing Hydraulic Research Institute (No.Y916032). The authors would like to thank the editors and three anonymous reviewers for their comments, which have significantly improved the manuscript. References Alamgir, A., Khan, M.A., Manino, I., Shaukat, S.S., Shahab, S., 2016. Vulnerability to climate change of surface water resources of coastal areas of Sindh, Pakistan. Desalin. Water Treat. 57, 18668–18678. Araral, E., Wu, X., 2016. Comparing water resources management in China and India: policy design: institutional structure and governance. Water Policy 18, 1–13. Argyle, E.M., Gourley, J.J., Ling, C., Shehab, R.L., Kanga, Z., 2016. Effects of display

89

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

quality assessment using satellite-derived chlorophyll-a within the European directives, in the southeastern Bay of Biscay. Mar. Pollut. Bull. 64, 739–750. Palmer, S.C.J., Kutser, T., Hunter, P.D., 2015. Remote sensing of inland waters: challenges: progress and future directions. Remote Sens. Environ. 175, 1–8. Phung, D., Huang, C.R., Rutherford, S., Chu, C., Wang, X.M., Nguyen, M., 2015. Climate change water quality, and water-related diseases in the mekong delta basin: a systematic review. Asia-Pac. J. Public Health 27, 265–276. Pires, A., Moratoa, J., Peixoto, H., Boteroc, V., Zuluaga, L., Figueroa, A., 2017. Sustainability assessment of indicators for integrated water resources management. Scie. Total Environ. 578, 139–147. Prigent, C., Papa, F., Aires, F., Jimenez, C., Rossow, W.B., Matthews, E., 2012. Changes in land surface water dynamics since the 1990 and relation to population pressure. Geophys. Res. Lett. 39, L08403. Puertas, O.L., Brenning, A., Meza, F.J., 2013. Balancing misclassification errors of land cover classification maps using support vector machines and Landsat imagery in the Maipo river basin (Central Chile, 1975–2010). Remote Sens. Environ. 137, 112–123. Qin, Y.W., Xiao, X.M., Dong, J.W., Zhou, Y.T., Zhu, Z., Zhang, G.L., Du, G.M., Jin, G., Kou, W.L., Wang, J., Li, X.P., 2015. Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery. ISPRS J. Photogramm. Remote Sens. 105, 220–233. Richards, J.A., 1993. Remote Sensing Digital Image Analysis: An Introduction, 2nd. Springer-Verlag, Berlin. Rokni, K., Ahmad, A., Solaimani, K., Hazini, S., 2015. A new approach for surface water change detection: integration of pixel level image fusion and image classification techniques. Int. J. Appl. Earth Obs. Geoinf. 34, 226–234. Sallo, F.D.S., Sanches, L., Dias, V.R.D.M., Palácios, R.D.S., Nogueira, J.D.S., 2017. Stem water storage dynamics of Vochysia divergens in a seasonally flooded environment. Agric. For. Meteorol. 232, 566–575. Sarabandi, P., Yamazaki, F., Matsuoka, M., Kiremidjian, A., 2004. Shadow detection and radiometric restoration in satellite high-resolution images. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Ancorage Alaska, 20–24 Septmeber. pp. 3744–3747. Schneider, V.E., Marques, R.V., Bortolin, T.A., Cemin, G., Santos, G.M.D., 2016. Monitoring and assessment of surface water quality in taquari-antas watershed: south Brazil-region with intensive pig farming. Environ. Monit. Assess. 188, 617–630. Senay, G.B., Friedrichs, M.K., Singh, R.K., Velpuri, N.M., 2016. Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin. Remote Sens. Environ. 185, 171–185. Shahtahmassebi, A.Z., Yang, N., Wang, K., Moore, N., Shen, Z.Q., 2013. Review of shadow detection and de-shadowing methods in remote sensing. Chin. Geogr. Sci. 23 (4), 403–420. Sharma, A.K., Grant, A.L., Grant, T., Pamminger, F., Opray, L., 2009. Environmental and economic assessment of urban water services for a greenfield development. Environ. Eng. Sci. 26, 921–934. Shen, Y., Xu, L., Zhao, Q., Lan, C., 2013. Relationship between mosquito communities and land use: a study using 3S technology and redundancy analysis. Chin. J. Vector Biol. Control 24, 503–505. Sheng, Y.W., Song, C.Q., Wang, J.D., Lyons, E., Knox, B.R., Cox, J.S., Gao, F., 2016. Representative lake water extent mapping at continental scales using multi-temporal Landsat-8 imagery. Remote Sens. Environ. 185, 129–141. Shi, K., Zhang, Y.L., Xu, H., Zhu, G.W., Qin, B.Q., Huang, C.C., Liu, X.H., Zhou, Y.Q., Lv, H., 2015. Long-term satellite observations of microcystin concentrations in lake taihu during cyanobacterial bloom periods. Environ. Sci. Technol. 49, 6448–6456. Shi, K., Zhang, Y.L., Zhou, Y.Q., Liu, X.H., Zhu, G.W., Qin, B.Q., Gao, G., 2017. Long-term modis observations of cyanobacterial dynamics in lake taihu: responses to nutrient enrichment and meteorological factors. Sci. Rep. 7, 40326. Soundharajan, B.S., Adeloye, A.J., Remesan, R., 2016. Evaluating the variability in surface water reservoir planning characteristics during climate change impacts assessment. J. Hydrol. 538, 625–639. Tewkesbury, A.P., Comber, A.J., Tate, N.J., Lamb, A., Fisher, P.F., 2015. A critical synthesis of remotely sensed optical images change detection techniques. Remote Sens. Environ. 160, 1–14. Tokola, T., Sticklen, J., Linden, M.V.D., 2001. Use of topographic correction in Landsat TM-based forest interpretation in Nepal. Int. J. Remote Sens. 22 (4), 551–563. Tourian, M.J., Elmi, O., Chen, Q., Devaraju, B., Roohi, S., Sneeuw, N., 2015. A space borne multi sensor approach to monitor the desiccation of Lake Urmia in Iran. Remote Sens. Environ. 156, 349–360. Tulbure, M.G., Broich, M., Stehman, S.V., Kommareddy, A., 2016. Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sens. Environ. 178, 142–157. USGS, 2012. United States Geological Survey. http://earthexplorer.usgs.gov (accessed 10th Oct 2014). Urbanski, J.A., Wochna, A., Bubak, I., Grzybowski, W., Matuszewska, K.L., Lacka, M., et al., 2016. Application of Landsat 8 imagery to regional-scale assessment of lake water quality. Int. J. Appl. Earth Obs. Geoinf. 51, 28–36. Viswanathan, V.C., Jiang, Y.J., Berg, M., Hunkeler, D., Schirmer, M., 2016. An integrated spatial snap-shot monitoring method for identifying seasonal changes and spatial changes in surface water quality. J. Hydrol. 539, 567–576. Wilson, C.O., 2015. Land use/land cover water quality nexus: quantifying anthropogenic influences on surface water quality. Environ. Monit. Assess. 187, 424–447. Wolski, P., Hudson, M.M., Thito, K., Gassidy, L., 2017. Keeping it simple: monitoring flood extent in large data-poor wetlands using MODIS SWIR data. Int. J. Appl. Earth Obs. Geoinf. 57, 224–234. Xie, H., Luo, X., Xu, X., Pan, H.Y., Tong, X.H., 2016. Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction. Int. J. Remote Sens. 37 (8), 1826–1844. Xu, H.Q., 2006. Modification of normalized difference water index (NDWI) to enhance

perspective. Crit. Rev. Environ. Sci. Technol. 45, 101–166. Crist, E.P., 1985. A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sens. Environ. 17, 301–306. Dambach, P., Machault, V., Lacaux, J.P., Vignolles, C., Sié, A., Sauerborn, R., 2012. Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa. Int. J. Health Geogr. 11. Danaher, T., Collett, L., 2006. Development, optimisation and multi-temporal application of a simple Landsat based water index. In: The 13th Australasian Remote Sensing and Photogrammetry Conference. Canberra. Din, S.U., Dousari, A.A., Ghadban, A.N.A., 2007. Sustainable fresh water resources management in northern Kuwait – a remote sensing view from Raudatain basin. Int. J. Appl. Earth Observ. Geoinf. 9, 21–31. Dou, X.S., 2016. A critical review of groundwater utilization and management in China’s inland water shortage areas. Water Policy 18, 1367–1383. Dube, T., Mutanga, O., 2015. Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgenicatchment, South Africa. ISPRS J. Photogramm. Remote Sens. 101, 36–46. Ekstrand, S., 1996. Landsat TM based forest damage assessment correction for topographic effects. Photogramm. Eng. Remote Sens. 62 (2), 151–161. Exelis, 2010. Exelis Visual Information Solutions. http://www.exelisvis.com (Accessed 6 June 2014). ExelisHelp, 2010. Exelis Visual Information Solutions. http://www.exelisvis.com/ Support/.HelpArticles.aspx (Accessed 6 June 2014). Fan, H., Fu, X.H., Zhang, Z., Wu, Q., 2015. Phenology-based vegetation index differencing for mapping of rubber plantations using Landsat OLI data. Remote Sens. 7, 6041–6058. Feyisa, G.L., Meilby, H., Fensholt, R., Proud, S.R., 2014. Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sens. Environ. 140, 23–35. Fisher, A., Flood, N., Danaher, T., 2016. Comparing Landsat water index methods for automated water classification in eastern Australia. Remote Sens. Environ. 175, 167–182. Gardelle, J., Hiernaux, P., Kergoat, L., Grippa, M., 2009. Less rain, more water in ponds: a remote sensing study of the dynamics of surface waters from 1950 to present in pastoral Sahel (Gourma region, Mali). Hydrol. Earth Syst. Sci. Discuss. 6, 5047–5083. Guttler, F.N., Niculescu, S., Gohin, F., 2013. Turbidity retrieval and monitoring of Danube Delta waters using multi-sensor optical remote sensing data: an integrated view from the delta plain lakes to the western-northwestern Black Sea coastal zone. Remote Sens. Environ. 132, 86–101. Halabisky, M., Moskal, L.M., Gillespie, A., Hannam, M., 2016. Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984–2011). Remote Sens. Environ. 177, 171–183. Hamid, A., Bhat, S.A., Bhat, S.U., Jehangir, A., 2016. Environmentric techniques in water quality assessment and monitoring: a case study. Environ. Earth Sci. 75, 321–334. Hansen, M.C., Loveland, T.R., 2012. A review of large area monitoring of land cover change using Landsat data. Remote Sens. Environ. 122, 66–74. Hardy, S., Schultz, D.M., Vaughan, G., 2017. Early evolution of the 23–26 september 2012 U.K. floods: tropical storm nadine and diabatic heating due to cloud microphysics. Mon. Weather Rev. 145, 543–563. Hawkins, D.M., 2004. The problem of overfitting. J. Chem. Inf. Comput. Sci. 44, 1–12. Jia, S.Y., Zhuang, H.F., Han, H.J., Wang, F.J., 2016. Application of industrial ecology in water utilization of coal chemical industry: a case study in Erdos, China. J. Clean. Prod. 135, 20–29. Ke, Y.H., Im, J., Lee, J., Gong, H.L., Ryu, Y., 2015. Characteristics of Landsat 8 OLIderived NDVI by comparison with multiple satellite sensors and in-situ observations. Remote Sens. Environ. 164, 298–313. Kiselev, V., Bulgarelli, B., Heege, T., 2015. Sensor independent adjacency correction algorithm for coastal and inland water systems. Remote Sens. Environ. 157, 85–95. Kummu, M., Guillaume, J.H.A., Moel, H.D., Eisner, S., Flörke, M., Porkka, M., Siebert, S., Veldkamp, T.I.E., Ward, P.J., 2016. The world’s road to water scarcity: shortage and stress in the 20th century and pathways towards sustainability. Sci. Rep. 6, 1–16. Lam, J.C., Tsang, C.L., Yang, L., Li, D.H.W., 2005. Weather data analysis and design implications for different climatic zones in China. Build. Environ. 40, 277–296. Li, M., Xu, L.Z., Tang, M., 2011. An extraction method for water body of remote sensing image based on oscillatory network. J. Multimed. 6, 252–260. Li, Y.Z., Gong, X.Q., Guo, Z., Xu, K.P., Hu, D., Zhou, H.X., 2016. An index and approach for water extraction using Landsat–OLI data. Int. J. Remote Sens. 37, 3611–3635. Loveland, T.R., Irons, J.R., 2016. Landsat 8: The plans the reality, and the legacy. Remote Sens. Environ. 185, 1–6. Luo, S., Hu, H., Cheng, H., He, F., Wu, Z., 2010. A primary study on species diversity of water birds and its relationship to water environment at lake jinyinhu, wuhan. Resour. Environ. Yangtze Basin 19, 671–677. McFeeters, S.K., 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17, 1425–1432. Mlejnková, H., Sovová, K., 2010. Impact of pollution and seasonal changes on microbial community structure in surface water. Water Sci. Technol. 61, 2787–2795. Morfitt, R., Barsi, J., Levy, R., Markham, B., Micijevic, E., 2015. Landsat-8 operational land imager (OLI) radiometric performance on-orbit. Remote Sens. 7, 2208–2237. Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmitha, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., Ip, A., 2016. Water observations from space: mapping surface water from 25 years of Landsat imagery across Australia. Remote Sens. Environ. 174, 341–352. Nakajima, T., Tao, G., Yasuoka, Y., 2002. Simulated recovery of information in shadow areas on IKONOS image by combing ALS data. Proceeding of Asian Conference on Remote Sensing (ACRS). Novoa, S., Chust, G., Sagarminaga, Y., Revilla, M., Borja, A., Franco, J., 2012. Water

90

Int J Appl  Earth Obs Geoinformation 68 (2018) 73–91

X. Wang et al.

river–lake interactions: advances in research on the middle Yangtze river. Hydrol. Res. 47, 1–7. Zhan, Q.M., Shi, W.Z., Xiao, Y.H., 2005. Quantitative analysis of shadow effects in highresolution images of urban areas. 3rd International Symposium Remote Sensing and Data Fusion Over Urban Areas (URBAN) and 5th International Symposium Remote Sensing of Urban Areas (URS) 1682–1777. Zhang, Y.S., Balzter, H., Zou, C.C., Xu, H.Q., Tang, F., 2015. Characterizing bi-temporal patterns of land surface temperature using landscape metrics based on sub-pixel classifications from Landsat TM/ETM+. Int. J. Appl. Earth Obs. Geoinf. 42, 87–96.

open water features in remotely sensed imagery. Int. J. Remote Sens. 27, 3025–3033. Yang, K., Li, M.C., Liu, Y.X., Cheng, L., Huang, Q.H., Chen, Y.M., 2015a. River detection in remotely sensed imagery using gabor filtering and path opening. Remote Sens. 7, 8779–8802. Yang, Y.H., Liu, Y.X., Zhou, M.X., Zhang, S.Y., Zhan, W.F., Sun, C., Duan, Y.W., 2015b. Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach. Remote Sens. Environ. 171, 14–32. Yang, G.S., Zhang, Q., Wan, R.R., Lai, X.J., Jiang, X., Li, L., Dai, H.C., Lei, G.C., Chen, J.C., Lu, Y.J., 2016. Lake hydrology, water quality and ecology impacts of altered

91