Journal of Geographical Sciences ›› 2017, Vol. 27 ›› Issue (6): 681-696.doi: 10.1007/s11442-017-1400-x
• Orginal Article • Previous Articles Next Articles
Yingjie LI1,*(), Liwei ZHANG1(
), Junping YAN1, Pengtao WANG1, Ningke HU1, Wei CHENG2, Bojie FU2
Received:
2016-03-15
Accepted:
2016-07-29
Online:
2017-06-10
Published:
2017-06-10
Contact:
Yingjie LI
E-mail:lyj@snnu.edu.cn;zlw@snnu.edu.cn
About author:
Author: Li Yingjie (1990-), specialized in environmental change and ecosystem services. E-mail:
*Corresponding author: Zhang Liwei (1985-), Assistant Professor, specialized in landscape ecology and ecosystem services. E-mail:
Supported by:
Yingjie LI, Liwei ZHANG, Junping YAN, Pengtao WANG, Ningke HU, Wei CHENG, Bojie FU. Mapping the hotspots and coldspots of ecosystem services in conservation priority setting[J].Journal of Geographical Sciences, 2017, 27(6): 681-696.
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Table 1
The datasets sources"
Data | Type | Resolution | Time period | Sources |
---|---|---|---|---|
Meteorological data | Point | — | 2000-2013 | http://cdc.cma.gov.cn/ |
Soil properties | Raster | 1 km | 2000 | http://webarchive.iiasa.ac.at/ |
DEM | Raster | 90 m | 2004 | http://srtm.csi.cgiar.org/ |
LULC | Polygon | 30 m | 2000, 2013 | http://www.landcover.org/data/ |
MODIS NDVI | Raster | 250 m | 2000-2013 | http://ladsweb.nascom.nasa.gov/data/ |
Table 2
Statistics on the hotspots and coldspots of soil conservation service in Shaanxi Province, China"
Annual SC per unit area (t·hm-2·a-1) | Area percentage (%) | Annual SC percentage (%) | |
---|---|---|---|
Coldspots** | 80.85 | 41.40 | 13.61 |
Coldspots* | 175.99 | 4.95 | 3.55 |
Coldspots | 190.68 | 2.41 | 1.87 |
Not Significant | 239.34 | 20.63 | 20.08 |
Hotspots | 296.79 | 1.02 | 1.24 |
Hotspots* | 309.95 | 1.86 | 2.34 |
Hotspots** | 508.08 | 27.73 | 57.31 |
Figure 7
Comparison of three hotspot analysis methods: KDE (1), Local Moran’s I (2), Getis-Ord Gi* statistics (3)Note: When input features are spatially scattered (see Figure 7a), the KDE can only identify spatial cluster, but it mixes the cluster of high values (i.e., hotspots) and the cluster of low values (coldspots); that is, the KDE can neither tell what the cluster is nor whether it is significant. Fortunately, the Local Moran’s I and Getis-Ord Gi* statistics can both make it. The difference is that the Local Moran’s I is more efficient at identifying the outliers (see Figure 7a-(2) above), while the Gi* statistics is even better at identifying statistically significant hotspots and coldspots with different confidence levels (see Figure 7a-(3)). When input features are spatially uniform grid data (see Figure 7b), the KDE becomes inefficient, while the Local Moran’s I and Gi* statistics work well in this case, and the Gi* statistics especially holds its unique superiority."
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