Urban Black-Odor Water Remote Sensing Mapping Based on Shadow Removal: A Case Study in Nanjing

Abstract

Urban black-odor water is a serious water environmental problem worldwide that seriously affects the living environment and the health of residents. The detection of black-odor water is a primary problem of centralized governance. Remote sensing is an effective means for water-quality monitoring. However, in most urban areas, with their complex underlying conditions, black-odor water always shows an uncertain distribution and small area. Furthermore, because of similar spectral characteristics, it is easily confused with dark objects such as asphalt roads, dark roofs, bridges, river levees and building shadows. In this paper, we propose a new black-odor water detection method, without shadow misjudgment, for complex urban conditions. A remote sensing image from the Chinese high-resolution satellite Gaofen-2 (GF-2) was used for detection. Using in situ water-quality measurement data for verification, the results showed that the proposed method achieved an accuracy of 85.7% while reducing the false alarm rate to 3.5%. Compared to four other existing methods, the proposed method proved to be more practical because it can reduce the false extraction of non-black-odor water to the greatest extent possible while ensuring accuracy. The method proposed in this paper has a good effect on the extraction of black-odor water in Nanjing City. Therefore, our method can be applied widely to screening and management of large-scale urban black-odor water and can provide reliable data for centralized treatment.

Channel Activity Remote Sensing Retrieval Model: A Case Study of the Lower Yellow River

“…This is the main difference between the water and other ground objects in the spectrum. Therefore, many water indexes enhance water information and suppress non-water information based on the relationship between bands [32][33][34][35][36]. The normalized difference water index (NDWI) is the first proposed water index.…”

Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery

“…The use of satellite imagery to identify flooded areas has been widely adopted with different data-processing approaches [22,23]. Many water extraction models make use of the Normalized Difference Water Index (NDWI), which is given as the difference of reflectance between green and NIR, divided by the summation of green and NIR [24].…”