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Emergency flood relief needs fast and precise spatial information of flood-inundated area. Because of spectral similarity to some extent, inundated area was often viewed as water body and then derived from remote sensing classification result of the water areas before flood disaster minus that after flood disaster, which often caused difficulties in separating inundated area from true water body (e.g. river, lake or reservoir) and waterlogged cropland. This paper proposed an optimized method for identifying inundated area based on human perceptual visual attention and spatio-temporal variation characteristics of flood. Flood-inundated area is a temporary compound of water and other flooded objects (e.g., crops). It has specific spatio-temporal dynamic property, which differs from that of water body and could be used as a stable basis for remote sense identification of the inundated area. After the specific property was digitalized into numerical and visual data by generating a composite RGB color imagery of NDVI, MNDWI before the flood event and NDWI after the inundation, apparent coloring difference between water body and the inundated area in the imagery was reached. To gain reliable machine-vision detection based results of the flood-inundated area, an imagery of Munsell HLC color was transformed from the RGB imagery and then adopted in unsupervised classification based on coefficient of NBS color distance and K-means clustering algorithm. A case was studied on applying the proposed method to derive information of the inundated area in 2016 flood disaster in middle reaches of Yangtze River Basin and using Landsat OLI images. And an error confusion matrix method was adopted in the accuracy assessment on recognition results of the inundated area. The results showed that the proposed method gained excellent detection of the area with coefficients of Kappa and composite classification accuracy (CCA) equal to 93.4% and 88.5%, respectively. A controlled experiment on the credibility of different remote sensing classification methods for the same identification object (i.e. flooded-inundated area) was also made. It proved the proposed method for detection of flood-inundated area based on the selective visual attention mechanism could effectively improve the accuracy of remote sensing identification on the area with its CCA coefficient of 5% larger than that of the traditional method. The proposed method also had good performance on easily separating the flooded-inundated areas from water and waterlogged cropland, especially helped solving the misclassification between the flooded-inundated area and the water body. These benefited from three keys: 1) The inundated area was viewed as that not equal to water coverage in remote sensing classification, which could be proven by the former’s higher heterogeneity and its different spatio-temporal dynamic property. As a result, the identification uncertainty of the area was reduced. 2) Digital visualization of spatio-temporal variation features of the inundated area was completed through data fusion of NDWI after the inundation and NDVI and MNDWI before the flood event, which enhanced the area’s visual saliency on the remote sensing image and made it possible to identify them using machine-vision color clustering. 3) Difference of the flooded-inundated areas from the water and waterlogged cropland was precisely captured in the NBS image in HLC space that was transformed from the fusion image at an optimized RGB color matching scheme (i.e. red given to NDWI after the inundation, green and blue to NDVI and MNDWI before the inundation, respectively). During summer flooding of the study area in 2016, the waterlogged farmland with crops uncovered by water was about 19 143.35 hm² while the inundated acreage was up to 142 157.5 hm², among which 16.6% (i.e. 23 579 hm²) was being planted with rice paddy. Additionally, most of the inundated areas located in low-lying land along mainstream of the middle Yangtze River and its two branches, namely Hanjiang River and Fuhe River. The most severe flood disaster occurred in Wuhan City with 34 492 hm² of the inundated acreage. [ABSTRACT FROM AUTHOR]

Based on the land use data of five periods during 1995-2015 and water body data of continuous time series, this paper examines the degree of wetland damage in Wuhan Urban Agglomeration and its corresponding characteristics of water body change, and then further analyzes the driving mechanism of wetland damage by logistic model. The results are as follows: (1) From 1995 to 2015, the wetland area in Wuhan Urban Agglomeration increased slightly and then decreased, and the wetland area decreased by 665.15 km2 during this period. Wetland damage areas were mainly distributed in Wuhan, Huangpi, and Xiantao, which was caused by construction land and dryland encroach wetland. Wetland restoration areas were mainly located in cities of Xiantao and Wuhan, which was caused by the transformation of paddy fields to functional wetland. (2) In the past 20 years, the characteristics of wetland change are similar to those of corresponding water decrease or increase. The area of water body in damaged wetland area decreased while that in restored wetland area increased. (3) Logistic regression result shows that construction land, precipitation and GDP are the main factors affecting the risk of wetland degradation, and human activities are the main forces influencing wetland damage. [ABSTRACT FROM AUTHOR]