Abstract
Flood plains, which are commonly distributed in flat river or lake basins, often contain large tracts of farmland. Therefore, flood plains require precise and detailed information on the role played by farmland in flood routing simulations, flood risk evaluation, and flood loss evaluation. In farmland, cultivated land parcels are not directly adjacent. The intervening non-cultivable land, which might include trails and ditches, can cover large areas. Currently, the area of non-cultivable land between cultivated land parcels is usually measured by artificial visual interpretation or by fieldwork. This study focused on the extraction of uncultivable trails, ditches, and cultivated field parcels within farmland on the basis of a Light Detection and Ranging-derived (LiDAR-derived) high-resolution gridded Digital Elevation Model (DEM). The proposed approach was applied to generate polygons of individual land parcels in a flood storage and detention area. The DEM was first smoothed and then subtracted. To remove small spots and to smooth the boundaries of the land parcels, inner and outer buffers were created to generalize the extracted polygons. Experiments proved that this approach is applicable in flood plain farmland and demonstrated that the chosen parameters were appropriate. This approach is more efficient than traditional surveying methods. For field parcel extraction, the accuracy achieved was 93.42%, using official statistics for comparison, and the Cohen’s kappa coefficient was 0.90, using a visual interpretation of an aerial image for comparison. The kappa coefficients were 0.87 and 0.77 for trail and ditch extraction, respectively.
Highlights
Flooding is one of the most serious natural disasters, putting lives at risk and causing damage to buildings and other infrastructure
It is relevant to the surface roughness of 2D flood routing simulations, the generation of computational meshes in high-resolution flood simulations, and the prediction of crop yield and flood loss
After the original Digital Elevation Model (DEM) was subtracted from the smoothed surface, a raster was generated, as shown in Figure 9, where red areas represent convex trails, blue areas represent concave ditches, and yellow areas represent flat fields
Summary
Flooding is one of the most serious natural disasters, putting lives at risk and causing damage to buildings and other infrastructure. Flood plains are flat areas of land next to rivers or lakes; they are usually rural. Farmland cover large tracts of flood plains and can be inundated by floodwaters, which prevents crops from being harvested. The loss of harvests can sometimes lead to long-term effects even after floodwaters recede. -affected information for farmland is a basic data source for flood simulation, flood-loss evaluation, and flood risk evaluation [1]. It is relevant to the surface roughness of 2D flood routing simulations, the generation of computational meshes in high-resolution flood simulations, and the prediction of crop yield and flood loss. Spatial-tanporal distribution of flood and water-logging disasters in dongting lake area and control strategies.
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