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Water 2018, 10(3), 252;

Extracting Farmland Features from Lidar-Derived DEM for Improving Flood Plain Delineation

Department of Geographic Information Science, Nanjing University, Nanjing 210023, China
Changjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, China
Department of Surveying and Mapping Engineering, Datong University, 037009 Datong, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Author to whom correspondence should be addressed.
Received: 6 December 2017 / Revised: 16 February 2018 / Accepted: 22 February 2018 / Published: 1 March 2018
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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. View Full-Text
Keywords: flood disaster-forming and -affected information; flood plains; farmland features; LiDAR-derived DEM flood disaster-forming and -affected information; flood plains; farmland features; LiDAR-derived DEM

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Qian, T.; Shen, D.; Xi, C.; Chen, J.; Wang, J. Extracting Farmland Features from Lidar-Derived DEM for Improving Flood Plain Delineation. Water 2018, 10, 252.

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