Automated Extraction for Water Bodies Using New Water Index from Landsat 8 OLI Images

The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to highlight water bodies in remote sensing images.We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images.Firstly,we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction.Subsequently,we apply KT transformation,LBV transformation,AWEI nsh,and HIS transformation to the preprocessed image to calculate a new water index.Then,we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately.Meanwhile,we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies.Finally,we combine small and large water bodies to get complete water bodies.Compared with other traditional methods,our method has apparent advantages in water extraction,particularly in the extraction of small water bodies.

Extracting water body data based on SDWI and threshold segmentation:A case study in permafrost area surrounding Salt Lake in Hoh Xil,Qinghai-Tibet Plateau,China

There are a large number of lakes,rivers,and other natural water bodies distributed in the permafrost area of the Qinghai-Tibet Plateau(QTP).The changes in water bodies will affect the distribution of water resources in sur-rounding areas and downstream areas,resulting in environmental impact and bringing potential flood disasters,which will induce more serious issues and problems in alpine and high-altitude areas with a fragile habitat(such as the QTP in China).Generally,effective,reasonable,and scientific monitoring of large-scale water bodies can not only document the changes in water bodies intuitively,but also provide important theoretical reference for subsequent environmental impact prediction,and disaster prevention and mitigation in due course of time.The large-scale water extraction technology derived from the optical remote sensing(RS)image is seriously affected by clouds,bringing about large differences among the extracted water result products.Synthetic aperture radar(SAR)RS technology has the unique advantage characteristics of all-weather,all-day,strong penetration,and not being affected by clouds,which is hopeful in extracting water body data,especially for days with cloudy weather.The data extraction of large-scale water bodies based on SAR images can effectively avoid the errors caused by clouds that become prevalent at present.In this paper,the Hoh Xil Salt Lake on the QTP and its surrounding five lakes are taken as the research objects.The 2-scene Sentinel-1 SAR image data covering the whole area on 22 August 2022 was used to verify the feasibility of extracting water body data in permafrost zones.Furthermore,on 22 August 2022,the wealth here was cloudy,which made the optical RS images,e.g.,Sentinel-2 images full of clouds.The results show that:using the Sentinel-1 image and threshold segmentation method to extract water body data is efficient and effective with excellent results in permafrost areas.Concretely,the Sentinel-1 dual-polarized water index(SDWI),calculated by combining dual vertical–vertical(VV)polarized and verti-cal–horizontal(VH)polarized data is a useful index for water extraction and the result is better than each of the VV or VH polarized images.

Climate effects on an inland alpine lake in Xinjiang, China over the past 40 years

Inland lakes are important water resources in arid and semiarid regions. Understanding climate effects on these lakes is critical to accurately evaluate the dynamic changes of water resources. This study focused on the changes in Sayram Lake of Xinjiang, China, and addressed the effects of climate fluctuations on the inland lake based on long-term sequenced remote sensing images and meteorological data from the past 40 years. A geo- graphic information system （GIS） method was used to obtain the hypsometry of the basin area of Sayram Lake, and estimation methods for evaporation from rising temperature and water levels from increasing precipitation were proposed. Results showed that： （1）Areal values of Sayram Lake have increased over the past 40 years. （2） Both temperature and precipitation have increased with average increases of more than 1.8~C and 82 mm, respectively. Variation of the water levels in the lake was consistent with local climate changes, and the areal values show linear relationships with local temperature and precipitation data. （3） According to the hypsometry data of the basin area, the estimated lake water levels increased by 2.8 m, and the water volume increased by 12.9×108 m3 over the past 40 years. The increasing area of Sayram Lake correlated with local and regional climatic changes because it is hardly affected by human activities.

FWENet:a deep convolutional neural network for flood water body extraction based on SAR images

As one of the most severe natural disasters in the world,floods caused substantial economic losses and casualties every year.Timely and accurate acquisition of flood inundation extent could provide technical support for relevant departments in the field of flood emergency response and disaster relief.Given the accuracy of existing research works extracting flood inundation extent based on Synthetic Aperture Radar(SAR)images and deep learning methods is relatively low,this study utilized Sentinel-1 SAR images as the data source and proposed a novel model named flood water body extraction convolutional neural network(FWENet)for flood information extraction.Then three classical semantic segmentation models(UNet,Deeplab v3 and UNet++)and two traditional water body extraction methods(Otsu global thresholding method and Object-Oriented method)were compared with the FWENet model.Furthermore,this paper analyzed the water body area change situations of Poyang Lake.The main results of this paper were as follows:Compared with other five water body extraction methods,the FWENet model achieved the highest water body extraction accuracy,its F1 score and mean intersection over union(mIoU)were 0.9871 and 0.9808,respectively.This study could guarantee the subsequent research on flood extraction based on SAR images.