Journal of Geographical Sciences >
Multi-scale analysis of trade-off/synergistic effects of forest ecosystem services in the Funiu Mountain Region, China
Zhang Jingjing (1991-), PhD Candidate, specialized in development and utilization of natural resources in mountainous areas. E-mail: zhang1126@henu.edu.cn |
Received date: 2021-12-20
Accepted date: 2022-02-21
Online published: 2022-07-25
Supported by
National Natural Science Foundation of China(41671090)
National Basic Research Program (973 Program)(2015CB452702)
The trade-offs and synergies of forest ecosystem service are important research topics for several disciplines. The multi-scale analysis of service trade-offs and synergies assists in the implementation of more effective forest resource management. Based on multi-source data including forest distribution, topography, NDVI, meteorology and soil conditions, key forest ecosystem services, including total forest volume, carbon storage, water yield, soil retention and habitat quality were mapped and evaluated for the Funiu Mountain Region through integrated deployment of the CASA model, the InVEST3.2 model and the ArcGIS10.2 software. The characteristics of trade-offs and synergies among different ecosystem services were then mapped and considered across multiple spatial scales (i.e., by region, north and south slopes, vertical belt) using the spatial overlay analysis method. The main results are as follows: (1) Mean forest volume is 49.26 m3/ha, carbon density is 156.94 t/ha, water yield depth is 494.46 mm, the unit amount of soil retention is 955.4 t/ha, and the habitat quality index is 0.79. (2) The area of forests with good synergy is 28.79%, and the area of forests with poor synergy is 10.15%, while about 61.06% of forests show severe trade-offs and weak trade-offs. The overall benefits of forest ecosystem services in the study area are still low. In the future, bad synergy and severe trade-off areas should be the focus of forest resource management and efficiency regulation. (3) Synergy between ecosystem services is better for forest on south slope than that on north slope. Deciduous broad-leaved forest belt at moderate elevations on south slope in the mountains (SIII) has the highest synergies, while that at low elevations on north slope (NI) exhibits the lowest synergy levels.
ZHANG Jingjing , ZHU Wenbo , ZHU Lianqi , LI Yanhong . Multi-scale analysis of trade-off/synergistic effects of forest ecosystem services in the Funiu Mountain Region, China[J]. Journal of Geographical Sciences, 2022 , 32(5) : 981 -999 . DOI: 10.1007/s11442-022-1981-x
Figure 1 Location and the elevation map of the Funiu Mountain Region, China |
Figure 2 Spatial distribution of forest types in the Funiu Mountain Region |
Table 1 Computational methods of model parameters used in ecosystem service assessment |
Service type | Computation model or idea | Major parameters and processing |
---|---|---|
Forest volume | The net primary productivity (NPP) of forest vegetation was computed using the CASA model (Zhang et al., 2014), and the conversion formula of NPP and forest volume for different types of forests in the Funiu Mountain Region was determined according to the literature (Fang et al., 1996; Guang et al., 2006). Finally, the spatial data of forest volume were obtained. | The vegetation index was MODIS NDVI. Air temperature, precipitation, and solar radiation were acquired by spatial interpolation using ANUSPLIN. Solar radiation was estimated from sunshine hours (Allen et al., 1998). |
Carbon storage | Carbon storage module of the InVEST model (Sharp et al., 2014). | Carbon density was obtained mainly according to previous studies (Cui et al., 2015; Hu et al., 2017). The above- and below-ground carbon density was estimated based on the root-to-shoot ratio of different tree species (Zhu et al., 2019). |
Water yield | Water yield module of the InVEST model (Sharp et al., 2014). | Potential evapotranspiration was calculated using the Penman-Monteith formula on a daily scale (Zhang et al., 2014) and interpolated using ANUSPLIN. Soil depth was obtained by rasterization of soil data. Vegetation available water content was calculated using a nonlinear fitting estimation model of soil available water content (Zhou et al., 2005). Watershed and sub-watershed maps were extracted using the Hydrology toolset in ArcGIS 10.2 based on DEM data. |
Soil retention | Soil retention module of the InVEST model (Sharp et al., 2014). | Rainfall erosivity factor was obtained using Wischmeier’s formula on a monthly scale (Wischmeier et al., 1965). Soil erodibility factor was determined using an estimation model of soil erodibility (Williams et al., 1997). Engineering measure factor was assigned 1 in all cases, indicating no implementation of soil and water conservation measures. Vegetation cover and management factor was calculated using the previously reported computation method of forest vegetation management factor (Jiang et al., 1996). |
Habitat quality | Habitat quality module of the InVEST model (Sharp et al., 2014). | Habitat threat sources, including paddy fields, dryland, rural settlements, urban construction land, industrial and mining land, and transportation land, were extracted from a 1:100,000 land cover map (2015) and converted to 30-m raster data. The list of habitat threat sources and sensitivity is available in the literature (Yao, 2017), with values assigned based on the actual situation in the study area. To obtain legal accessibility vectors, the boundary of Funiu National Nature Reserve was extracted using the vegetation map of the reserve and classified according to the level of the reserve, with values assigned of the accessibility in the attribute table. |
Note: Raster data of meteorology, soil, and vegetation were saved in Grid format, with a spatial resolution of 250 m. The WGS_1984_Albers geographic coordinate system was used for all data processing. |
Table 2 Classification of ecosystem services capacity |
Service type | Low | Medium | High |
---|---|---|---|
Forest volume | 0-0.224 | 0.224-0.329 | 0.329-1 |
Carbon storage | 0-0.293 | 0.293-0.619 | 0.619-1 |
Water yield | 0-0.325 | 0.325-0.569 | 0.569-1 |
Soil retention | 0-0.184 | 0.184-0.541 | 0.541-1 |
Habitat quality | 0-0.111 | 0.111-0.362 | 0.362-1 |
Table 3 Classification criteria and statistics of tradeoffs and synergies among the five ecosystem services |
Service relationship | Ratio of area (%) | Sub-class | Ratio of area (%) | Supply capacity mix | Ratio of area (%) | Samples |
---|---|---|---|---|---|---|
Tradeoffs | 61.06 | Strong trade-off | 24.56 | 1 high, 4 low | 1.45 | 11311, 11113 |
1 high, 1 medium and 3 low | 2.07 | 11321, 12113 | ||||
1 high, 2 medium and 2 low | 7.37 | 12312, 12321 | ||||
1 high, 3 medium and 1 low | 13.67 | 22312, 32212 | ||||
Weak trade-off | 36.5 | 2 high, 3 low | 1.89 | 11133, 11313 | ||
2 high, 1 medium and 2 low | 4.72 | 23113, 31123 | ||||
2 high, 2 medium and 1 low | 10.78 | 23213, 23123 | ||||
3 high, 2 low | 3.99 | 33113, 23313 | ||||
3 high, 1 medium and 1 low | 12.41 | 33213, 33133 | ||||
4 high, 1 low | 2.72 | 33313, 33133 | ||||
Synergy | 38.94 | High synergy | 28.79 | 5 high | 0.83 | 33333 |
4 high, 1 medium | 4.52 | 33233, 23333 | ||||
3 high, 2 medium | 7.82 | 33223, 32332 | ||||
2 high, 3 medium | 7.83 | 22332, 32322 | ||||
1 high, 4 medium | 6.54 | 22322, 22232 | ||||
5 medium | 1.24 | 22222 | ||||
Low synergy | 10.15 | 1 medium, 4 low | 0.25 | 11211, 12111 | ||
2 medium, 3 low | 2.11 | 12112, 12211 | ||||
3 medium, 2 low | 3.6 | 12212, 22112 | ||||
4 high, 1 low | 4.2 | 22212, 22122 | ||||
5 low | 0 | 11111 |
Table 4 Total amount and proportion of various ecosystem services of different forest types |
Forest type | Forest volume | Carbon storage | Water provision | Soil protection | Habitat quality | ||||
---|---|---|---|---|---|---|---|---|---|
Total (106 m3) | Ratio (%) | Total (106 t) | Ratio (%) | Total (108 m3) | Ratio (%) | Total (107 t) | Ratio (%) | ||
Q. variabilis | 27.37 | 44.29 | 92.15 | 46.82 | 31.95 | 52.70 | 54.79 | 47.27 | 0.71954 |
Broad-leaved mixed forest | 12.25 | 19.83 | 43.97 | 22.34 | 12.31 | 20.31 | 26.18 | 22.59 | 0.79984 |
Q.glandulifera var. brevipetiolata | 10.12 | 16.38 | 29.63 | 15.05 | 7.45 | 12.28 | 16.18 | 13.96 | 0.88991 |
Q. aliena var. acuteserrata | 6.00 | 9.71 | 16.67 | 8.47 | 3.56 | 5.88 | 7.29 | 6.29 | 0.97995 |
P. tabulaeformis | 2.33 | 3.78 | 5.15 | 2.62 | 1.82 | 3.00 | 3.63 | 3.13 | 0.81979 |
Coniferous and broad-leaved mixed forest | 2.41 | 3.90 | 5.63 | 2.86 | 1.69 | 2.78 | 4.31 | 3.72 | 099998 |
P. massoniana | 0.78 | 1.26 | 2.39 | 1.21 | 1.45 | 2.40 | 2.15 | 1.86 | 0.70949 |
P. armandi | 0.53 | 0.86 | 1.24 | 0.63 | 0.40 | 0.66 | 1.37 | 1.18 | 0.99798 |
Total/Average | 61.78 | 100 | 196.83 | 100 | 60.64 | 100 | 115.9 | 100 | 0.7934 |
Figure 3 Spatial pattern of the five ecosystem services based on the providing capacity classification in the Funiu Mountain Region |
Figure 4 Forest area in different areas of the five ecosystem services in the Funiu Mountain Region |
Figure 5 Total amount and unit amount of different areas for various ecosystem services in the Funiu Mountain Region |
Figure 6 Spatial distribution of trade-offs (severe or weak) and synergies (good or bad) among ecosystem services in the Funiu Mountain Region |
Figure 7 Ecosystem service trade-off and synergy on the north and south slopes and on the vertical belt scale in the Funiu Mountain Region |
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