Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China

Du, Zhiqiang; Linghu, Bin; Ling, Feng; Li, Wenbo; Tian, Weidong; Wang, Hailei; Gui, Yuanmiao; Sun, Bingyu; Zhang, Xiaoming

doi:10.1117/1.jrs.6.063609

Cited by 136 publications

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References 33 publications

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“…LSW mapping, using remote sensing techniques, plays an important role in wetland monitoring [1][2][3], flood monitoring [4][5][6], flood disaster assessment [7][8][9][10], surface water area estimation [11][12][13][14], and water resources management [15][16][17]. Over three decades, multi-resource remote sensing data, such as data from the Advanced Very High Resolution Radiometer (AVHRR) [5,6,18], Moderate-resolution Imaging Spectroradiometer (MODIS) [16,19], Small Satellite Constellation For Environment and Disaster Monitoring And Forecasting A/B Satellites (HJ-1A/B) [12,20], Multispectral Scanner System (MSS) [6,11], Thematic Mapper (TM) [13,20,21], and Enhanced Thematic Mapper Plus (ETM+) [7,11] have been employed to extract information on land surface water bodies. One widely used remote sensing dataset for LSW mapping are the TM and ETM+ images provided by the Landsat series satellites [11].…”

Section: Introductionmentioning

confidence: 99%

“…Over three decades, multi-resource remote sensing data, such as data from the Advanced Very High Resolution Radiometer (AVHRR) [5,6,18], Moderate-resolution Imaging Spectroradiometer (MODIS) [16,19], Small Satellite Constellation For Environment and Disaster Monitoring And Forecasting A/B Satellites (HJ-1A/B) [12,20], Multispectral Scanner System (MSS) [6,11], Thematic Mapper (TM) [13,20,21], and Enhanced Thematic Mapper Plus (ETM+) [7,11] have been employed to extract information on land surface water bodies. One widely used remote sensing dataset for LSW mapping are the TM and ETM+ images provided by the Landsat series satellites [11]. The Landsat-7 mission was flawless until May, 2003, when a hardware component failure left wedge-shaped spaces of missing data on either side of ETM+ images.…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI

et al. 2013

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Abstract:Remote sensing has more advantages than the traditional methods of land surface water (LSW) mapping because it is a low-cost, reliable information source that is capable of making high-frequency and repeatable observations. The normalized difference water indexes (NDWIs), calculated from various band combinations (green, near-infrared (NIR), or shortwave-infrared (SWIR)), have been successfully applied to LSW mapping. In fact, new NDWIs will become available when Advanced Land Imager (ALI) data are used as the ALI sensor provides one green band (Band 4), two NIR bands (Bands 6 and 7), and three SWIR bands (Bands 8, 9, and 10). Thus, selecting the optimal band or combination of bands is critical when ALI data are employed to map LSW using NDWI. The purpose of this paper is to find the best performing NDWI model of the ALI data in LSW map. In this study, eleven NDWI models based on ALI, Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) data were compared to assess the performance of ALI data in LSW mapping, at three different study sites in the Yangtze River Basin, China. The contrast method, Otsu method, and confusion matrix were calculated to OPEN ACCESS Remote Sens. 2013, 5 5531 evaluate the accuracies of the LSW maps. The accuracies of LSW maps derived from eleven NDWI models showed that five NDWI models of the ALI sensor have more than an overall accuracy of 91% with a Kappa coefficient of 0.78 of LSW maps at three test sites. In addition, the NDWI model, calculated from the green (Band 4: 0.525-0.605 μm) and SWIR (Band 9: 1.550-1.750 μm) bands of the ALI sensor, namely NDWI A4,9 , was shown to have the highest LSW mapping accuracy, more than the other NDWI models. Therefore, the NDWI A4,9 is the best indicator for LSW mapping of the ALI sensor. It can be used for mapping LSW with high accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI

et al. 2013

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“…Land surface water mapping, using remote sensing techniques plays an important role in wetland monitoring, flood monitoring, flood disaster assessment, surface water area estimation [26], and water resources management [27]. In Figures 4-6 different surface water levels of the 3 main lakes are selected on the Rwandan side of the wetland.…”

Section: Selected Lakes' Surface Water Extent Dynamicsmentioning

confidence: 99%

Mapping and Monitoring the Akagera Wetland in Rwanda

et al. 2017

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Wetland maps are a prerequisite for wetland development planning, protection, and restoration. The present study aimed at mapping and monitoring Rwanda's Akagera Complex Wetland by means of remote sensing and geographic information systems (GIS). Landsat data, spanning from 1987 to 2015, were acquired from different sensor instruments, considering a 5-year interval during the dry season and the shuttle radar topographic mission (SRTM) digital elevation model (30-m resolution) was used to delineate the wetland. The mapping and delineation results showed that the wetland narrowly extends along the Rwanda-Tanzania border from north to south, following the course of Akagera River and the total area can be estimated at 100,229.76 ha. After waterbodies that occupy 30% of the wetland's surface area, hippo grass and Cyperus papyrus are also predominant, representing 29.8% and 29%, respectively. Floodplain and swamp forest have also been inventoried in smaller proportions. While the wetland extent has apparently remained stable, the inhabiting waterbodies have been subject to enormous instability due to invasive species. Lakes, such as Mihindi, Ihema, Hago and Kivumba have been shrinking in extent, while Lake Rwanyakizinga has experienced a certain degree of expansion. This study represents a consistent decision support tool for Akagera wetland management in Rwanda.

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“…The Qingjiang River is one of the main tributaries of Yangtze River below the Three Gorges Dam [46], and its basin area is 17,600 km 2 . Its mean annual rainfall, runoff depth, and annual average discharge are approximately 1460 mm, 876 mm, and 423 m 3 /s, respectively.…”

Section: Basic Datamentioning

confidence: 99%

Two Dimension Reduction Methods for Multi-Dimensional Dynamic Programming and Its Application in Cascade Reservoirs Operation Optimization

Jiang

Hui

et al. 2017

Water

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An efficient reservoir operation technique plays a very important role in improving the water resources and energy efficiency of reservoirs. In order to effectively avoid or alleviate the "curse of dimensionality" of Multi-dimensional Dynamic Programming (MDP) in the application of cascade reservoirs operation optimization (CROO) and keep a global convergence at the same time, two dimension reduction methods are proposed in this paper. One is a hybrid algorithm of MDP and a Progressive Optimality Algorithm (POA), named MDP-POA, which combines the global convergence of MDP and the strong local search ability of POA. MDP-POA first takes the global optimal trajectory of MDP in a low discrete degree as the initial trajectory of the POA, and then implements further optimization to the obtained initial trajectory by the POA with a high discrete degree, so as to avoid the "curse of dimensionality" of MDP in high discrete degree and the dependency of the POA for the initial trajectory. The other is an improved MDP (IMDP), which first constructs a corridor by the optimal trajectory of MDP in a lower discrete degree, and then implements further optimization in the corridor by MDP with a relatively high discrete degree, so as to avoid a large number of unnecessary calculations, and shorten the run-time effectively. In a case study, the results of MDP-POA, IMDP, and MDP are compared and analyzed from the aspects of power generation and run-time. The analysis indicates that the proposed MDP-POA and IMDP both have a good application effect and are worthy of further promotion.

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Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China

Abstract: The Qingjiang River

Cited by 136 publications

References 33 publications

A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI

A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI

Mapping and Monitoring the Akagera Wetland in Rwanda

Two Dimension Reduction Methods for Multi-Dimensional Dynamic Programming and Its Application in Cascade Reservoirs Operation Optimization

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