Analysis of Landsat-8 OLI imagery for land surface water mapping

Du, Zhiqiang; Li, Wenbo; Zhou, Dongbo; Tian, Liqiao; Ling, Feng; Wang, Hailei; Gui, Yuanmiao; Sun, Bingyu

doi:10.1080/2150704x.2014.960606

Cited by 193 publications

(123 citation statements)

References 12 publications

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“…This may be because the difference in the amount of infrared light reflected from pure water and from vegetation is small relative to the difference in short-wave infrared light reflected [27], and crops and other waterline vegetation are prevalent around dryland reservoirs. The results further indicate that bands with shorter wavelengths slightly out-perform those with longer as short-wave infrared inputs to the MNDWI for mapping surface water extents from Landsat satellite imagery, as consistent with [14,29]. The higher uncertainty in GSW-derived area estimates indicates that the water classification algorithm used in creating the GSW datasets is less effective at distinguishing water from non-water pixels than NDWI or MNDWI using Landsat 8 OLI imagery, for our West African study site.…”

Section: Accuracy Of Reservoir Extents Derived From Landsat-based Surmentioning

confidence: 57%

“…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%

“…Ground-based assessments of small reservoir locations, capacities, and seasonal volumes are time-consuming to conduct because of the spatial dispersion of these reservoirs and decentralized decision making regarding reservoir investments and maintenance [12]. Many previous studies have characterized inland surface water resources using free satellite imagery, such as from Landsat [13][14][15][16][17] and the Moderate Resolution Imaging Spectroradiometer (MODIS) [18][19][20][21][22]. Where the spatial and temporal resolution are sufficiently high, remotely sensed imagery provides a practical approach to small water body mapping and monitoring [23,24].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Accuracy Of Reservoir Extents Derived From Landsat-based Surmentioning

confidence: 57%

Section: Landsat-derived Surface Water Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…Based on the annual MNDWI image, a threshold segmentation method was performed to separate the land and water. For this process, it was critical to determine the threshold for the MNDWI image [35,36]. The frequency distribution of the annual MNDWI image was then counted.…”

Section: Methodsmentioning

confidence: 99%

Continuously Tracking the Annual Changes of the Hengsha and Changxing Islands at the Yangtze River Estuary from 1987 to 2016 Using Landsat Imagery

Jia

Ding

et al. 2018

Water

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Abstract:The evolution of estuarine islands is potentially controlled by sediment discharge, tidal currents, sea level rise, and intensive human activities. An understanding of the spatial and temporal changes of estuarine islands is needed for environmental change monitoring and assessment in estuarine and coastal areas. Such information can also help us better understand how estuarine islands respond to sea level rise in the context of global warming. The temporal changes of two estuarine islands in Shanghai near the Yangtze River Estuary were obtained using Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper) images from 1987 to 2016 on an annual scale. First, a composite image was generated by using the multi-temporal Landsat images for each year. Then, a modified normalized difference water index (MNDWI) was applied to the annual estuarine island maps using a threshold segmentation method. Finally, we obtained the temporal changes of the estuarine islands in Shanghai during the period 1987-2016. The results suggest that (1) Landsat TM/ETM+ images can be used for estuarine island mapping and change detection; (2) the two estuarine islands have expanded significantly during the past three decades; (3) human activities are the main driving factor that caused the expansion of the estuarine islands; and (4) the sea level can also partly explain the change in the estuarine islands. This study demonstrates that Landsat data are useful for determining the annual variations in the land area of two estuarine islands in Shanghai during the past 30 years. In the future, other factors and their contributions to estuarine island changes should be further investigated.

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“…Previous studies [20,[36][37][38][39][40] have proved that water index method has advantages of universality, user friendliness and low computation cost in coastline extraction, and the common used water index is the normalized difference water index (NDWI) proposed by McFeeters [39]. In this paper, downscaling, pansharpening and water index approaches were used to extract coastlines from Landsat-8 OLI imagery.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

Liu

Wang

Ling

et al. 2017

Water

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Abstract:Coastline extraction is a fundamental work for coastal resource management, coastal environmental protection and coastal sustainable development. Due to the free access and long-term record, Landsat series images have the potential to be used for coastline extraction. However, dynamic features of different types of coastlines (e.g., rocky, sandy, artificial), caused by sea level fluctuation from tidal, storm and reclamation, make it difficult to be accurately extracted with coarse spatial resolution, e.g., 30 m, of Landsat images. To access this problem, we analyze the performance of coastline extraction by integrating downscaling, pansharpening and water index approaches in increasing the accuracy of coastline extraction from the latest Landsat-8 Operational Land Imager (OLI) imagery. In order to prove the availability of the proposed method, we designed three strategies: (1) Strategy 1 uses the traditional water index method to extract coastline directly from original 30 m Landsat-8 OLI multispectral (MS) image; (2) Strategy 2 extracts coastlines from 15 m fused MS images generated by integrating 15 m panchromatic (PAN) band and 30 m MS image with ten pansharpening algorithms; (3) Strategy 3 first downscales the PAN band to a finer spatial resolution (e.g., 7.5 m) band, and then extracts coastlines from pansharpened MS images generated by integrating downscaled spatial resolution PAN band and 30 m MS image with ten pansharpening algorithms. Using the coastline extracted from ZiYuan-3 (ZY-3) 5.8 m MS image as reference, accuracies of coastlines extracted from MS images in three strategies were validated visually and quantitatively. The results show that, compared with coastline extracted directly from 30 m Landsat-8 MS image (strategy 1), strategy 3 achieves the best accuracies with optimal mean net shoreline movement (MNSM) of −2.54 m and optimal mean absolute difference (MAD) of 11.26 m, followed by coastlines extracted in strategy 2 with optimal MNSM of −4.23 m and optimal MAD of 13.54 m. Further comparisons with single-band thresholding (Band 6), AWEI, and ISODATA also confirmed the superiority of strategy 3. For the various used pansharpening algorithms, five multiresolution analysis MRA-based pansharpening algorithms are more efficient than the component substitution CS-based pansharpening algorithms for coastline extraction from Landsat-8 OLI imagery. Among the five MRA-based fusion methods, the coastlines extracted from the fused images generated by Indusion, additive à trous wavelet transform (ATWT) and additive wavelet luminance proportional (AWLP) produced the most accurate and visually realistic representation. Therefore, pansharpening approaches can improve the accuracy of coastline extraction from Landsat-8 OLI imagery, and downscaling the PAN band to finer spatial resolution is able to further improve the coastline extraction accuracy, especially in crenulated coasts.

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Analysis of Landsat-8 OLI imagery for land surface water mapping

Cited by 193 publications

References 12 publications

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

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

Continuously Tracking the Annual Changes of the Hengsha and Changxing Islands at the Yangtze River Estuary from 1987 to 2016 Using Landsat Imagery

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

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