Flooded-area satellite monitoring within a Ramsar wetland Nature Reserve in Argentina

Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are currently operational for routine Earth observation. There are substantial differences between instruments onboard both satellites. The enhancements achieved with Landsat-8 refer to the scanning technology (replacing of whisk-broom scanners with two separate push-broom OLI and TIRS scanners), an extended number of spectral bands (two additional bands provided) and narrower bandwidths. Therefore, cross-comparative analysis is very necessary for the combined use of multi-decadal Landsat imagery. In this study, 3,311 independent sample points of four major land cover types (primary forest, unplanted cropland, swidden cultivation and water body) were used to compare the spectral bands of ETM+ and OLI. Eight sample plots with different land cover types were manually selected for comparison with the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), the Land Surface Water Index (LSWI) and the Normalized Burn Ratio (NBR). These indices were calculated with six pairs of ETM+ and OLI cloud-free images, which were acquired over the border area of Myanmar, Laos and Thailand just two days apart, when Landsat-8 achieved operational obit. Comparative results showed that: (1) the average surface reflectance of each band differed slightly, but with a high degree of similarities between both sensors. In comparison with ETM+, the OLI had higher values for the near-infrared band for vegetative land cover types, but lower values for non-vegetative types. The new sensor had lower values for the shortwave infrared (2.11–2.29 µm) band for all land cover types. In addition, it also basically had higher values for the shortwave infrared (1.57–1.65 µm) band for non-water land cover types. (2) The subtle differences of vegetation indices derived from both sensors and their high linear correlation coefficient (R2 > 0.96) demonstrated that ETM+ and OLI imagery can be used as complementary data. (3) LSWI and NBR performed better than NDVI and MNDWI for cross-comparison analysis of satellite sensors, due to the spectral band difference effects.

The surface environment and the thermal infrared information of remote sensing have been widely used to study urban climate. In this paper, the Landsat Thematic Mapper (TM) data acquired in 2008 were applied to study the relationship between urban surface temperature and surface characteristics within the Beijing 5th ring road area of China. The thermal band data of TM combined with classification-based surface emissivity were utilized to estimate land surface temperature (LST). Meanwhile, surface characteristics parameters, such as the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), the Normalized Difference Building Index (NDBI) and the Normalized Difference Bareness Index (NDBaI) were calculated according to related arithmetic respectively. The quantitative relationship between LST and NDVI, MNDWI, NDBI and NDBaI were investigated according to urban main land use/cover types (water body, vegetation and built-up surfaces). The results showed there were negative correlations between LST and NDVI, MNDWI for vegetation and built-up land use/cover types, positive correlations between LST and NDBI, NDBaI for vegetation and built-up land use/cover types. In general, in the area 5th ring road of Beijing the distribution of NDVI, MNDWI and NDBI directly defined the distribution of LST. For built-up land use/cover type, the NDVI was small, However, NDBI and LST were high. While in the area with more water and vegetation, the NDVI and MNDWI were high and LST was small. There were obvious correlation between LST and urban surface characteristics.

The normalized difference built-up index (NDBI) has been useful for mapping urban built-up areas using Landsat Thematic Mapper (TM) data. The applicability of this index to the newer Landsat-8 Operational Land Imager (OLI) data was examined during this study, and a new method for built-up area extraction has been proposed. OLI imagery of urban areas of Lahore, Pakistan, was used to extract built-up areas through a modified NDBI approach and the proposed built-up area extraction method (BAEM). Instead of using individual bands, BAEM employed principal component analysis images of the highly correlated bands pertinent to NDBI computation. Through integration of temperature data, normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI), BAEM was able to improve the overall accuracy of built-up area extraction by 11.84% compared to the modified NDBI approach. Rather than employing the binary NDBI, NDVI and MNDWI images, continuous images of these indices were used, and the final output was recoded by determining the threshold value through a double-window flexible pace search (DFPS) method. Results indicate that BAEM was more accurate at mapping urban built-up areas when applied to OLI imagery as compared to the modified NDBI approach; omission and commission errors were reduced by 75.96% and 33.36%, respectively. Moreover, the use of DFPS improved robustness of the proposed approach by enhancing user control over the segmentation of the output.

ABSTRACTSurface waterbodies in arid and semi-arid environments are threatened by both natural and anthropogenic pressures. Mapping the distribution of surface waterbodies is crucial for managing their dwindling quantities and quality. In this study, a fast and reliable method of water extraction has been introduced. A remote-sensing index called the simple water index (SWI) was formulated to differentiate waterbodies from vegetation class automatically, and to differentiate waterbodies from shadows or built-up areas (water-like features). Its performance was compared with the automated water extraction index (AWEI) and the modified normalized difference water index (MNDWI) on Landsat 8 Operational Land Imager (OLI) image of South Africa. The robustness of the algorithm was tested on images in Madagascar and the Democratic Republic of Congo (DRC) with different biomes. The overall accuracies and kappa coefficient (κ) were used to compare the performance of each index. The McNemar test was performed to assess the significance of the output map and the validation data set. The SWI showed the highest overall accuracy of 91.9% (κ = 0.83), whereas the AWEI and MNDWI yielded overall accuracies of 83.8% (κ = 0.65) and 78.4% (κ = 0.53), respectively. The McNemar test showed that there was no significant difference between the SWI map (p = 0.248), whereas both AWEI and MNDWI maps were significantly different from the validation data set at p = 0.041 and p = 0.013, respectively. The SWI approach reduces the thresholding problem by 50% over the conventional MNDWI and AWEI. It is expected that the SWI will also be useful for the accurate quantification of waterbodies for large areas.

Background: Recent developments in optical satellite remote sensing have led to a new era in the detection of surface water with its changing dynamics. This study presents the creation of surface water inventory for a part of Pune district (an administrative area), in India using the Landsat 8 Operational Land Imager (OLI) and a multi spectral water indices method.Methods: A total of 13 Landsat 8 OLI cloud free images were analyzed for surface water detection. Modified Normalized Difference Water Index (MNDWI) spectral index method was employed to enhance the water pixels in the image. Water and non-water areas in the map were discriminated using the threshold slicing method with a trial and error approach. The accuracy analysis based on kappa coefficient and percentage of the correctly classified pixels was presented by comparing MNDWI maps with corresponding Joint Research Centre (JRC) Global Surface Water Explorer (GSWE) images. The changes in the surface area of eight freshwater reservoirs within the study area (Bhama Askhed, Bhatghar, Chaskaman, Khadakwasala, Mulashi, Panshet, Shivrata, and Varasgaon) for the year 2016 were analyzed and compared to GSWE time series water databases for accuracy assessment. The annual water occurrence map with percentage water occurrence on a yearly basis was also prepared.Results: The kappa coefficient agreement between MNDWI images and GSWE images is in the range of 0.56 to 0.96 with an average agreement of 0.82 indicating a strong level of agreement.Conclusions: MNDWI is easy to implement and is a sufficiently accurate method to separate water bodies from satellite images. The accuracy of the result depends on the clarity of image and selection of an optimum threshold method. The resulting accuracy and performance of the proposed algorithm will improve with implementation of automatic threshold selection methods and comparative studies for other spectral indices methods.

This paper proposes a technique to extract urban built-up land features from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery taking two cities in southeastern China as examples. The study selected three indices, Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SAVI) to represent three major urban land-use classes, built-up land, open water body, and vegetation, respectively. Consequently, the seven bands of an original Landsat image were reduced into three thematic-oriented bands derived from above indices. The three new bands were then combined to compose a new image. This considerably reduced data correlation and redundancy between original multispectral bands, and thus significantly avoided the spectral confusion of the above three land-use classes. As a result, the spectral signatures of the three urban land-use classes are more distinguishable in the new composite image than in the original seven-band image as the spectral clusters of the classes are well separated. Through a supervised classification, a principal components analysis, or a logic calculation on the new image, the urban built-up lands were finally extracted with overall accuracy ranging from 91.5 to 98.5 percent. Therefore, the technique is effective and reliable. In addition, the advantages of SAVI over NDVI and MNDWI over NDWI in the urban study are also discussed in this paper.

This article first examines three existing methods of delineating open water features, i.e. the normalized difference water index (NDWI), the modified normalized difference water index (MNDWI) and a method combining the near-infrared (NIR) band and the maximum likelihood classification. We then propose two new methods for the fast extraction of water features in remotely sensed imagery. Our first method is a pixel-based procedure that utilizes indices and band values. Based on their characteristic spectral reflectance curves, waterbodies are grouped into three types – clear, green and turbid. We found that the MNDWI is best suited for identifying clear water. Green water has its maximum reflectance in Landsat Thematic Mapper (TM) band 4 (NIR band), whereas turbid water has its maximum reflectance in TM band 5 (mid-infrared band). Our second method integrates our pixel-based classification with object-based image segmentation. Two Landsat scenes in Shaanxi Province, China, were used as the primary data source. Digital elevation models (DEMs) and their derived slope maps were used as ancillary information. To evaluate the performance of the proposed methods, extraction results of the three existing methods and our two new methods were compared and assessed. A manual interpretation was made and used as reference data. Results suggest that our methods, which consider the diversity of waterbodies, achieved better accuracy. Our pixel-based method achieved a producer's accuracy of 92%, user's accuracy of 90% and kappa statistics of 0.91. Our integrated method produced a higher producer's accuracy (95%), but a lower user's accuracy (72%) and kappa statistics (0.72), compared with the pixel-based method. The advantages and limitations of the proposed methods are discussed.

ABSTRACTThis paper combines the principal component analysis (PCA) with a modified normalized difference water index (MNDWI) to improve the accuracy in extracting water bodies from Landsat Thematic Mapper (TM) imagery. To this end, one Landsat TM image of Taiyuan, China, obtained on 23 September 2011 was used to perform PCA and extract MNDWI. The first two principal components of the PCA and extracted MNDWI were combined to compose a false-colour image, which was then used to develop the enhanced water index (EWI). This method was applied to extract water bodies from Landsat TM imagery for both urban areas and surrounding mountain areas of Taiyuan, which resulted in an overall accuracy of 95%. EWI was also compared with other common water indices in extracting water bodies including general water index, MNDWI and new water index. The results show that the proposed method has the highest accuracy and is recommended to extract water bodies using multispectral satellite imagery for urban areas with mountains surrounded.

This paper proposes a technique to detect the change in some dominantly available classes in an urban area such as vegetation, built-up, and water bodies. Landsat Thematic Mapper (TM) and Landsat 8 imageries have been selected for a particular area of NCR (National Capital Region), New Delhi, India. In this study, three spectral indices have been used to characterize three foremost urban land-use classes, i.e., normalized difference built-up index (NDBI) to characterize built-up area, modified normalized difference water index (MNDWI) to signify open water and modified soil-adjusted vegetation index (MSAVI2) to symbolize green vegetation. Subsequently, for reducing the dimensionality of Landsat data, a new FCC has been generated using above mentioned indices, which consist of three thematic-oriented bands in place of the seven Landsat bands. Hence, a substantial reduction is accomplished in correlation and redundancy among raw satellite data, and consequently reduces the spectral misperception of the three land-use classes. Thus, uniqueness has been gained in the spectral signature values of the three dominant land-use classes existing in an urban area. Further, the benefits of using MSAVI2 as compared with NDVI and MNDWI as compared to NDWI for the highly urbanized area have been emphasized in this research work. Through a supervised classification, the three classes have been identified on the imageries and the change between the image pairs has been found. The overall accuracy (OA) of change detection is 92.6 %. Therefore, the study shows that this technique is effective and reliable for detection of change.

Investigating the effectiveness of Landsat-8 OLI and Sentinel-2 MSI satellite data in monitoring the effects of drought on surface water resources in the Western Cape Province, South Africa

Spectral indices are algorithms performed to improve the signal of certain features, such as vegetation, water and soil in satellite images. The objective of this work was to utilize the normalized difference vegetation index (NDVI), the modified normalized difference water index (MNDWI) and the ratio index for bright soil (RIBS) along with band compositing techniques in order to map and delineate the extent of the coastal ecosystems along the coasts of Oman, in terms of mangrove vegetation, wetlands, sabkhas and coral reefs, respectively. Satellite data were acquired from the Landsat-8 Operational Land Imager (OLI) during 2018. Some oceanographic characteristics: tidal range, sea surface temperatures (SST) and the depth of the sea floor of Oman offshore region were also utilized to interpret the spatial extent of these coastal ecosystems. Results showed that the applied indices were efficient to highlight 14 locations of mangroves, 19 locations of wetlands, 2 locations of sabkha and 15 locations of coral reefs. It is observed that mangroves and wetlands are much related to high tidal range coasts, whereas coral reefs are contingent to shallow off-shores with SST of 22-30°C. These corals occur either along the main coast or adjacent to the islands of the country. Sabkha and salt marshes occur along extended coastal flats of low-lying sandy coasts. The present study proved that the spectral indices are good surrogates to map coastal ecosystems.

Water scarcity in Egypt requires better management of water resources. The most important parameters needed for lake water management are the lake surface area and volume at any specific time. This study focused on developing an innovative technique that enables the accurate calculation of the lake surface area at any specific time using satellite images. Neal-real time and long-term water body monitoring is essential for the effective management and conservation of Lake Nasser as one of the most important water resources in Egypt, which is enormously benefited from the advent of remotely sensed images. However, an efficient as well as robust method to perform water detection from these images remains challenging due to the various noise sources from heterogeneous backgrounds. A robust methodology is designed in this study to extract free surface water from multi-temporal and multispectral images acquired by Landsat-8 Operational Land Imager (OLI) and sentinel-2 satellites. To achieve the objectives of the study many Water Index image-processing techniques used to delineate the water body boundary. The study shows that the Modified Normalized Difference Water Index (MNDWI) is most useful technique more than the Normalized Difference Water Index (NDWI), to delineate water surface from other land features.

Based on the spectral reflection characteristics analysis of the muddy coastline in Jiangsu, an improved spectral water index (IWI) combined with the Otsu algorithm is proposed to extract muddy coastlines from Landsat Operational Land Imager (OLI) images. The IWI-extracted coastline results are compared with those extracted by the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), enhanced water index (EWI), revised normalized different water index (RNDWI) and automated water extraction index (AWEI). The results show that the IWI is not affected by tidal conditions or sand content in the water, can reduce the “salt-and-pepper” phenomenon in the Otsu algorithm classification, can accurately identify water boundaries and can extract silty mudflats and marine buildings with high accuracy. It can also significantly increase the degree of automation of coastline extraction. The IWI combined with the Otsu algorithm demonstrates high accuracy of over 84% in the extraction muddy coastline data with one-pixel tolerance, which is twice as accurate as other indices. The accuracy of extraction for all other types of coastlines is over 81%. Therefore, the IWI index combined with the Otsu algorithm is reliable for studies of sea–land processes and coastline evolutions.

Accurate and rapid extraction of urban water information is the basis of finding out the temporal and distribution of urban water, surveying water pollution, water environmental protection and scientific development and use of the water environment. However, because of the complexity of the background and environment of urban surface water, different water extraction methods have certain differences in extracting urban water. In addition, different water extraction methods face different types of water objects, and their accuracy also has certain differences. Therefore, it is necessary to explore the adaptability of different water extraction methods in different water types in urban areas. Based on the image of landsat8 OLI, this paper selects four sub areas of water in Xi’an, including two sub areas of landsat8 OLI. The Modified Normalized Difference Water Index (MNDWI) is used to extract water from banded and surface water, and compared with the three methods of Normalized Difference Water Index (NDWI), Support Vector Machine (SVM) and Decision Tree Classification. Four precision evaluations are used: the confusion matrix to calculate k, commission errors, omission errors, and overall precision. The advantages of the Modified Normalized Difference Water Index (MNDWI) in extracting banded water and surface water in urban areas are explored. The results show the extraction accuracy of banded water is higher than that of surface water. For banded water, there is little difference between MNDWI and the other three methods in water extraction. For regular surface water, SVM had better extraction effect than MNDWI.MNDWI has the best extraction effect on irregular surface water. In adaptability and stability, MNDWI has the best effect.

This study compared two remote sensing water indices: the Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI). Both indices were calculated using publically-available data from the Landsat 8 Operational Land Imager (OLI). The research goal was to determine whether the indices are effective in locating open water and measuring surface soil moisture. To demonstrate the application of water indices, analysis was conducted for freshwater wetlands in the Tualatin River Basin in northwestern Oregon to estimate hydrologic connectivity and hydrological permanence between these wetlands and nearby water bodies. Remote sensing techniques have been used to study wetlands in recent decades; however, scientific studies have rarely addressed hydrologic connectivity and hydrologic permanence, in spite of the documented importance of these properties. Research steps were designed to be straightforward for easy repeatability: 1) locate sample sites, 2) predict wetness with water indices, 3) estimate wetness with soil samples from the field, 4) validate the index predictions against the soil samples from the field, and 5) in the demonstration step, estimate hydrologic connectivity and hydrological permanence. Results indicate that both indices predicted the presence of large, open water features with clarity; that dry conditions were predicted by MNDWI with more subtle differentiation; and that NDWI results seem more sensitive to sites with vegetation. Use of this low-cost method to discover patterns of surface moisture in the landscape could directly improve the ability to manage wetland environments.