# Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
## 1 Introduction  
With improvement in the level of software and hardware for remote sensing acquisition along with the increase in the quantity of data, the need for change detection is also increasing, and the requirements for precision are getting higher and higher. Remote sensing plays an important role in water environment monitoring. In disaster prevention and mitigation, water conservation involves flood and waterlogging disasters, drought and wading geological disaster monitoring and assessment, flood forecasting and early warning. In water resource monitoring and protection, it involves satellite remote sensing precipitation forecasts, soil moisture and evapotranspiration remote sensing estimation, surface water monitoring, groundwater monitoring, water environment monitoring and surface water body monitoring. With respect to ecological protection, water conservation involves the monitoring of the environment, the investigation of soil and water conservation and irrigation area, the monitoring and control of river course and estuary change. In lake water area change detection, remote sensing technology is used to monitor lake area change. On the one hand, the law along with the trend of annual and interannual changes in the lake area should be analyzed from a natural point of view to better protect lakes through watershed management. On the other hand, we must supervise the illegal occupation of water in real time to provide clues for law enforcement. In the Hubei Province, China, from January to July 2015, nearly 48 suspected illegal spots were found in lake monitoring by remote sensing. Law enforcement departments checked 15 suspected illegal spots in the field and confirmed seven real illegal events. This work will be further extended to the monitoring of reservoirs and rivers. In addition, surface water monitoring is also an important part of drought monitoring. In the remote sensing of water resources, [PERSON] et al. [1] used the NDWI model to extract the water bodies of the Tangjiashan barrier lake from the multisource satellite images to detect the changes in the barrier lake, pre- and post-earthquake. [PERSON] et al. [2] compared paleo lakes with modern lakes, showing that lakes on the Tibetan Plateau have shrunk significantly since the great lake period, which provides fundamental information to support research on both global paleo-climatology and paleo-hydrology change. [PERSON] et al. [3], by combining ecological quantity analysis with GIS technology based on land use data and remote sensing imagery, analyzed the changes in land use and land cover as well as the driving force in the mainstream of the Tarim River from 1973 to 2005. [PERSON] et al. [4] used Landsat and Systeme Probatoire d'Observation de la Terre (SPOT) images to estimate the Normalized Difference Vegetation Index (NDVI) and land-use changes at the Plastira artificial lake catchment for the period 1984-2009. [PERSON] et al. [5] focused on change detection on the Jebba Lake Basin between 1978 (five years before the dam was established) and 1995 (twelve years after the dam impoundment). [PERSON] et al. [6] applied high-resolution, X-band, and stripmap Cosmo-SkyMed data to the monitoring of flood events in the Basilicata region (Southern Italy). By using the mapping results with post-earthquake high-resolution images from Google Earth, [PERSON] et al. [7] showed that the pixel-based landslide mapping method was able to identify landslides with relatively high accuracy. [PERSON] et al. [8] used Cosmo-Skymed ScanSAR mode (HH) data for one-year monitoring of seasonal changes in the water surface areas of Poyang Lake from January 2014 to December 2014. [PERSON] et al. [9] proposed a change detection method based on Gabor wavelet features for very high resolution (VHR) remote sensing images. The fuzzy c-means cluster algorithm was employed to obtain the final change map. [PERSON] et al. [10] implemented the canny edge detection method, combining with Otsu thresholding to detect the edges, achieved good edge detection results. [PERSON] et al.