Improved water extraction using Landsat TM/ETM+ images in Ebinur Lake, Xinjiang, China

Zhang, Fei; Tiyip, Tashpolat; Kung, Hsiang‐te; Johnson, Verner Carl; Wang, Juan; Nurmemet, Ilyas

doi:10.1016/j.rsase.2016.08.001

Cited by 13 publications

(10 citation statements)

References 29 publications

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“…In recent years, the drawdown of dry lakebed (playa) has exposed broad hard salt crusts and saline desert, which might have a range of negative effects on the local fragile environment ( Liu et al, 2011 ). To protect the important wetland ecosystems in arid areas, the Chinese government has designated the adjoining of the Ebinur Lake wetland as a National Nature Reserve in April 2007 ( Zhang et al, 2016b ).…”

Section: Methodsmentioning

confidence: 99%

Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China

Wang

Ding

Abulimiti

et al. 2018

PeerJ

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Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS–NIR) spectroscopy. The soil samples (n = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0–2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R2 (0.93), RMSE (4.57 dS m−1), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity.

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Section: Methodsmentioning

confidence: 99%

Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China

Wang

Ding

Abulimiti

et al. 2018

PeerJ

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“…Maximum likelihood algoritm is one of the most common used in land use/cover classification, therefore could be applied eficiently to classify the water bodies (Du, Teillet, et al, 2002;Zhang et al, 2016).…”

Section: Remote Sensing Image Processingmentioning

confidence: 99%

Historic flood events in NE Romania (post-1990)

et al. 2017

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Using open-source satellite imagery like Landsat TM, ETM+ and Sentinel 2 can lead to accurate cartographic products. We mapped flood events from Siret and Prut river basins in the last 30 years based on the availability of Landsat data archive. In this area were recorded historical values in flow rates for the entire Romanian territory: 4650 m³/s on the Siret River in 2005 -the maximum value ever recorded for Romania; 4240 m³/s on the Prut in 2008 -second maximum value recorded for Romania. The most powerful floods that took place in Romania in the last years were in

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“…However, such cases are not identical with the cloud free and atmospherically corrected Landsat 8 OLI-TIRS and ETM+ images. Hence, reliable and accurate extent of flooded areas and open water bodies can be easily extracted from Landsat images using normalised indices (Kumar and Acharya, 2016;Zhang et al, 2016;Li et al, 2013;Xu, 2006;McFEETERS, 1996).…”

Section: Data Usedmentioning

confidence: 99%

Flood hazard assessment of 2014 floods in Sonawari sub-district of Bandipore district (Jammu&Kashmir): An application of geoinformatics

Kumar

2016

Remote Sensing Applications: Society and Environment

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Massive flooding in the Kashmir valley was induced by heavy rainfall due to combined effects of western disturbances and Indian summer monsoon during September 04-10, 2014. The populous Sonawari sub-district was severely affected by flood waves that caused complete damage to standing crops and disrupted man-made linear structures (embankment, canal, and road network). High value land use classes (cropped land, built-up and plantation), which encroached on swamps and wetlands between 1972 and 2014, were badly damaged. The main aim of this study is to assess flood hazard at village level in the sub-district. Village-wise standardized flood hazard indicators were flood affected areas, mean turbidity, mean flood depth and duration. These indicators were derived from the Landsat 7 ETM+, Landsat 8 OLI-TIRS images and SRTM (30 m) DEM. Linear combination of these standardised indicators in GIS environment formulated Flood Hazard Index (FHI). Furthermore, the computed FHI were divided into four zones. Low, moderate, high and very high flood hazard zones occupied 16, 32, 34 and 19 per cent of the total area, respectively. Further, these zones accounted for 21, 23, 33 and 23 per cent of the total number of villages and towns (43), respectively. FHI was validated with overall changes in NDVI values with the help of linear regression model that showed a fair association between village-wise FHI and changes in mean NDVI values as the coefficient of determination (R 2) was 0.75 with a least standard error of 0.05. Village-wise flood hazard assessment has immense importance to plan future land use and to implement flood management strategies during pre and post-floods in the sub-district.

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Improved water extraction using Landsat TM/ETM+ images in Ebinur Lake, Xinjiang, China

Cited by 13 publications

References 29 publications

Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China

Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China

Historic flood events in NE Romania (post-1990)

Flood hazard assessment of 2014 floods in Sonawari sub-district of Bandipore district (Jammu&Kashmir): An application of geoinformatics

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