Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery

Rokni, Komeil; Ahmad, Anuar; Selamat, Ali; Hazini, Sharifeh

doi:10.3390/rs6054173

Cited by 564 publications

(305 citation statements)

References 31 publications

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“…The study supports the view of Tuan and Duong (2009) that flood mapping with radar images is appreciable as it is cloud cover free and has advantage of mapping flooded areas during floods. The study is also in agreement with Rokni et al (2014) that BT method produces high quality outputs. In addition to past literatures, this study classifies Northern Peninsula Malaysia as a high risk zone for economic loss being the major paddy rank in the country.…”

Section: Flood Extent Mapsupporting

confidence: 78%

“…5. The first three components of a principal component analysis are considered to have large data variance and much lesser noise, even between the first three principal components one is selected as the best with better information characteristics (Rokni et al, 2014). In this case the PC2 (Fig.…”

Section: Principal Component Analysis (Pca) Outputsmentioning

confidence: 99%

“…It is a method which is developed in order to produce images with RGB bands and that is why only 3 bands are able to be joined at a time (Mandhare et al, 2013). Additionally, in this method, higher resolution images are multiplied by each lower resolution image using mathematical combinations and this is recognized in the production of high quality outputs (Rokni et al, 2014) This paper is as a follow up to a recent study by Dutsenwai et al (2015), in which classification of different fusion methods was tested for quality information extraction from the integration of three single band SAR images, where BT method has the highest percentage accuracy (Table 1). The objective of this study is to compare BT and PCA of three SAR images of different spatial resolution (RadarSat-1 preflood image, RadarSat-1 post flood image and TerraSAR-X during flood image) to increase the confident level of using the BT for flood extent mapping and determine which composite image contains more interpretable information as well as improvement of boundaries.…”

Section: Introductionmentioning

confidence: 99%

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37 Fusion of SAR images for flood extent mapping in northern peninsula Malaysia

Dutsenwai

Mijinyawa

Ahmad

et al. 2016

Int. j. adv. appl. sci

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This study aimed at mapping the flood extents in the northern peninsula Malaysia in order to contribute to the flood disaster eradication by extracting more and better information through the fusion of RadarSat 1 and TerraSAR-X images. Principal Component Analysis and Brovey Transform (BT) techniques were used. The best principal component of the PCA, which is the PC2 was classified and compared with the classified BT image using Maximum likelihood (ML) and support Vector Machine (SVM). The results indicated that the classification of the BT image using SVM has higher accuracy with an overall of 70.9615% as well as kappa coefficient of 0.3418. This method showed relative improvement on the classification of the flooded and non-flooded areas which were used to produce the flood extent Map that was further verified with the DEM of the area. The final results in this study showed more information on the areas that are affected by the floods especially the extents which became more visible after the classification of the fused images.

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Section: Flood Extent Mapsupporting

confidence: 78%

Section: Principal Component Analysis (Pca) Outputsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

37 Fusion of SAR images for flood extent mapping in northern peninsula Malaysia

Dutsenwai

Mijinyawa

Ahmad

et al. 2016

Int. j. adv. appl. sci

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“…We sourced 1291 Landsat 8 OLI images that were acquired over the Volta basin (Landsat paths 192 to 197; rows 050 to 056) between 1 May 2013 and 31 October 2015, corresponding to the earliest complete month of data available in GEE and the temporal limit of GSW data. We used imagery that was pre-processed by USGS to Surface Reflectance and for each image, computed the Normalised Difference Water Index (NDWI) [40], Modified NDWI [27] using band 6 (referred to here as MNDWI1), and using band 7 (MNDWI2), indices that are commonly applied in peer-reviewed literature for surface water mapping [14,[41][42][43]. The relevant bands in Landsat 8 OLI imagery used to compute the three water indices are:…”

Section: Landsat-derived Surface Water Mapsmentioning

confidence: 99%

Big Data and Multiple Methods for Mapping Small Reservoirs: Comparing Accuracies for Applications in Agricultural Landscapes

et al. 2017

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Abstract:Whether or not reservoirs contain water throughout the dry season is critical to avoiding late season crop failure in seasonally-arid agricultural landscapes. Locations, volumes, and temporal dynamics, particularly of small (<1 Mm 3 ) reservoirs are poorly documented globally, thus making it difficult to identify geographic and intra-annual gaps in reservoir water availability. Yet, small reservoirs are the most vulnerable to drying out and often service the poorest of farmers. Using the transboundary Volta River Basin (~413,000 sq km) in West Africa as a case study, we present a novel method to map reservoirs and quantify the uncertainty of Landsat derived reservoir area estimates, which can be readily applied anywhere in the globe. We applied our method to compare the accuracy of reservoir areas that are derived from the Global Surface Water Monthly Water History (GSW) dataset to those that are derived when surface water is classified on Landsat 8 OLI imagery using the Normalised Difference Water Index (NDWI), Modified NDWI with band 6 (MNDWI1), and Modified NDWI with band 7 (MNDWI2). We quantified how the areal accuracies of reservoir size estimates vary with the water classification method, reservoir properties, and environmental context, and assessed the options and limitations of using uncertain reservoir area estimates to monitor reservoir dynamics in an agricultural context. Results show that reservoir area estimates that are derived from the GSW data are 19% less accurate for our study site than MNDWI1 derived estimates, for a sample of 272 reservoir extents of 0.09 to 72 ha. The accuracy of Landsat-derived estimates improves with reservoir size and perimeter-area ratio, while accuracy may decline as surface vegetation increases. We show that GSW derived reservoir area estimates can provide an upper limit for current reservoir capacity and seasonal dynamics of larger reservoirs. Data gaps and uncertainties make GSW derived reservoir extents unsuitable for monitoring reservoirs that are smaller than 5.1 ha (holding~49,759 m 3 ), which constitute 674 (56%) reservoirs in the Volta basin, or monitoring seasonal fluctuations of most small reservoirs, limiting its utility for agricultural planning. This study is one of the first to test the utility and limitations of the newly available GSW dataset and provides guidance on the conditions under which this, and other Landsat-based surface water maps, can be reliably used to monitor reservoir resources.

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“…MNDWI was reported as the most reliable index among 11 remotely sensed water indices [40][41][42] to generate reliable information from MODIS images [43,44]. MNDWI was calculated as:…”

Section: Spectral Indicesmentioning

confidence: 99%

Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem

et al. 2017

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Abstract:The response of an ecosystem to external drivers may not always be gradual and reversible. Discontinuous and sometimes irreversible changes, called 'regime shifts' or 'critical transitions', can occur. The likelihood of such shifts is expected to increase for a variety of ecosystems, and it is difficult to predict how close an ecosystem is to a critical transition. Recent modelling studies identified indicators of impending regime shifts that can be used to provide early warning signals of a critical transition. The identification of such transitions crucially depends on the ability to monitor key ecosystem variables, and their success may be limited by lack of appropriate data. Moreover, empirical demonstrations of the actual functioning of these indicators in real-world ecosystems are rare. This paper presents the first study which uses remote sensing data to identify a critical transition in a wetland ecosystem. In this study, we argue that a time series of remote sensing data can help to characterize and determine the timing of a critical transition. This can enhance our abilities to detect and anticipate them. We explored the potentials of remotely sensed vegetation (NDVI), water (MNDWI), and vegetation-water (VWR) indices, obtained from time series of MODIS satellite images to characterize the stability of a wetland ecosystem, Dorge Sangi, near the lake Urmia, Iran, that experienced a regime shift recently. In addition, as a control case, we applied the same methods to another wetland ecosystem in Lake Arpi, Armenia which did not experience a regime shift. We propose a new composite index (MVWR) based on combining vegetation and water indices, which can improve the ability to anticipate a critical transition in a wetland ecosystem. Our results revealed that MVWR in combination with autocorrelation at-lag-1 could successfully provide early warning signals for a critical transition in a wetland ecosystem, and showed a significantly improved performance compared to either vegetation (NDVI) or water (MNDWI) indices alone.

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Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery

Cited by 564 publications

References 31 publications

37 Fusion of SAR images for flood extent mapping in northern peninsula Malaysia

37 Fusion of SAR images for flood extent mapping in northern peninsula Malaysia

Big Data and Multiple Methods for Mapping Small Reservoirs: Comparing Accuracies for Applications in Agricultural Landscapes

Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem

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