Selecting sites for converting farmlands to wetlands in the Sanjiang Plain, Northeast China, based on remote sensing and GIS.
Wetlands in the Sanjiang Plain are rich in biodiversity and natural resources in the northeast of China. However, this wetland area has decreased in size and deteriorated in quality owing to expanded agricultural activities since the 1950s. Converting farmlands to wetlands is necessary to improve these conditions. Using Remote Sensing (RS) and Geographic Information Systems (GIS) technologies, we derived farmland productivity data and hydrology data for the Sanjiang Plain. The farmland productivity data were derived from land use and net primary productivity (NPP) data of the MODIS products. We obtained three productivity farmland classes (low, medium, and high) through the NPP anomaly percentage method. We were only concerned with the low-productivity farmland. Hydrology data were modeled with a wetness index, which was derived from Digital Elevation Model (DEM) data. Based on these two data layers, we identified and prioritized sites for the conversion of farmlands to wetlands. The areas with low farmland productivity and medium or high wetness values have potential to support the conversion of farmlands to wetlands. Potential sites were prioritized in terms of patch size and proximity to natural wetlands and water bodies. We obtained three priority classes, among which the high-priority class would be used as the areas for the recent conversion of farmlands to wetlands. The area of this class was 75,888 ha and accounted for 1.3% of the total farmland area.
Huang
Ni
N
Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China.
Wang
Zongming
Z
Liu
Dianwei
D
Niu
Zheng
Z
D000383:Agriculture; D044822:Biodiversity; D002681:China; D003247:Conservation of Natural Resources / Q000379:methods*; D005618:Fresh Water / Q000737:chemistry; D040362:Geographic Information Systems; D058998:Remote Sensing Technology; D053833:Wetlands
Ecol Appl. 2010 Mar;20(2):525-38
20405804
Ambio. 2004 Aug;33(6):306-10
15387064
Environ Manage. 2004 Mar;33(3):368-75
15031767
Ann Bot. 2010 Jan;105(1):155-63
19700447
Environ Manage. 2007 Feb;39(2):278-90
17123001
Tree Physiol. 2000 Jun;20(11):761-775
12651512