- Email: [email protected]
Forest cover change and its drivers in the upstream area of the Minjiang River, China
Ecological Indicators 46 (2014) 121–128
Contents lists available at ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Forest cover change and its drivers in the upstream area of the Minjiang River, China Xisheng Hu a , Chengzhen Wu b,c , Wei Hong b,d, Rongzu Qiu a, *, Jian Li b,d, Tao Hong b,d a
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China c Department of Ecology and Resource Engineering, Wuyi University, Nanping 354300, China d Key Laboratory for Forest Ecosystem Process and Management of Fujian Province, Fuzhou 350002, China b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 8 January 2014 Received in revised form 2 June 2014 Accepted 4 June 2014
Rapid human-driven conversion of global forest cover is contributing to the loss of habitat, biodiversity, and climate change. Even in a relatively short time interval and a small local region with relatively homogeneous biophysical interference, forest cover may be changed considerably by human disturbances. To better understand human influences on the pattern of forest cover change (FCC), we investigated the factors of FCC from 2007 to 2012 by combining the broad socio-economic information at the census block group level with the site-specific information measured at the pixel level. Taking the upstream area of the Minjiang River, China, as a case study, the result indicated that the major forest cover classes had a high rate of persistence in area size during the study period, while conversions among forest covers and into non-forest land occurred frequently, accounting for 5.4% of the landscape. The change of mixed forest was among the greatest one, decreasing sharply from 24.0% to 21.6% of the entire landscape, which converted predominantly into coniferous forest from 2007 to 2012. Additionally, 90.0% of the net gain to non-forest land was largely supplied by coniferous forest and mixed forest. The findings corroborate that human-driven conversions of forest covers have deleterious effects on biodiversity conservation in the upstream area of the Minjiang River. Furthermore, a binary logistic regression model was used to observe the biophysical/socio-economic drivers of FCC. We identified a FCC pattern during the period of study that was associated with collective- or enterprise-owned forests, low level of protection intensity forests, and the regions with high growth rates of fiscal revenue and far away from the city center. These results confirm that as a whole the relevant governments play an important role in the FCC in the region. This study is important for the relevant policy-makers and planners to better understand the underlying patterns and causes of this landscape change, to develop effective strategies for conserving biodiversity. ã 2014 Elsevier Ltd. All rights reserved.
Keywords: Land use Forest cover change Biodiversity Binary logistic regression Forest Resources Inventory Database
1. Introduction Forest covers four billion hectares (31.0%) of the Earth's landmass (FAO, 2010). They can provide renewable raw materials and natural amenities, protect land and water resources, conserve biological diversity and mitigate climate change (Mayer et al., 2005). Forest area is a valuable indicator of the relative importance of forests in a country or region, can embody these functions. The estimations of changes in forest area over time and space can characterize deforestation and reforestation/afforestation (Shi
* Corresponding author. Tel.: +86 591 83769536. E-mail address: [email protected] (R. Qiu). http://dx.doi.org/10.1016/j.ecolind.2014.06.015 1470-160X/ ã 2014 Elsevier Ltd. All rights reserved.
et al., 2011), which are among the world's most pressing landchange problems (Redo et al., 2012). An increasingly number of studies have been devoted to investigating land use/cover changes (LUCC) and their related impacts on the environment (Islam and Weil, 2000; Burnside et al., 2003; Ramdani and Hino, 2013). It has been recognized that LUCC benefits human livelihood; however, it also causes various problems (Cai et al., 2013). Forest cover change (FCC) is a significant type of LUCC that occurs in tropical and temperate areas (Bremer and Farley, 2010; Lindenmayer, 2010). Worldwide, forest plantations increased from 178.3 million hectares in 1990 to 264 million hectares in 2010, and the relative rate of annual expansion is predicted to be more than 1.0% over the next 10 years (FAO, 2010). This prediction is based on the world's increasing demand for timber products (Brockerhoff et al., 2008)
122
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
and the attempts to mitigate climate change effects by sequestering carbon via plantations (Strengers et al., 2008; Bremer and Farley, 2010; Nahuelhual et al., 2012). However, these rapid human-driven transitions of forest cover are perhaps the most ecologically significant for the global environment (Wimberly and Ohmann, 2004). The potential consequences include habitat loss, biodiversity disruption, and carbon discharge (Sala et al., 2000; Foley et al., 2005; Gardner et al., 2009). Empirical studies have consistently demonstrated that changes in forest habitats can impact communities of plants (Matlack, 1994; Halpern and Spies, 1995), birds (McGarigal and McComb, 1995; Drapeau et al., 2000), and mammals (Hargis et al., 1999; Lomolino and Perault, 2000). An increasing potential of extinction is also an inherent result of habitat fragmentation (Loehle and Li, 1996). At the macro scale, forest conversion and degradation consistently reduce the biodiversity of forest landscapes (Gibson et al., 2011). In many places, native species will become extinct as old growth forests are converted into fields, and invasive species will establish themselves rapidly, low levels of biodiversity as a result will persist in the disrupted habitats (Rudel et al., 2005). Moreover, it is estimated that harvests and shifting cultivation of forest land cover have added an additional 32–35% of carbon to the global emissions (Shevliakova et al., 2009). Thus, the rapid expansion of plantations has become one of the most contentious issues in contemporary sustainable development due to the significant environmental and societal conflicts it causes in many countries (Gerber, 2011). However, recent studies have indicated that it often takes decades or longer to perceive the potential negative effects that follow FCC (Jackson and Sax, 2010). Because of these potentially delayed responses, the ultimate impacts of FCC on the environment may not be realized immediately. In this context, understanding the possible trajectories and the proximate drivers of FCC is essential to grasp the underlying patterns and causes of the forest landscape changes. Further research will contribute to better identify areas where the risk of habitat loss is particularly high and ultimately to devise more effective measures to prevent adverse environmental and socio-economic impacts (Nahuelhual et al., 2012; Redo et al., 2012). Causes for forest landscape dynamics have been studied extensively in many forested regions (Wimberly and Ohmann, 2004; Onojeghuo and Blackburn, 2011). The studies reveal that FCC is associated with environmental, demographic and economic development (Redo et al., 2012). Among them, land ownership has been shown to correlate to land cover change in many of these studies (Turner et al., 1996; Nagaike and Kamitani, 1999; Radeloff et al., 2000). However, it is still uncertain how much of this variable could explain the patterns of FCC in an exclusive regime of land property in China. Spatial variables, such as topography (Turner et al., 1996), soils (Radeloff et al., 2000), population density (Black et al., 2003), urbanization (Zhang and Nagubadi, 2005), and distance from roads and urban centers (Turner et al., 1996; Rheinhardt et al., 2012), also constrain the rates and pathways of land cover change. Despite all this, the nature and magnitude of these relationships can vary greatly among landscapes (Wimberly and Ohmann, 2004). These various relationships are often conducted by regression models (Alonso, 1964; Thünen and Heinrich, 1966), such as the maximum entropy model (Miller and Plantinga, 1999), linear regression model (Seto and Kaufmann, 2003) and multinomial logistic model (Poudyal et al., 2008). Despite this large body of research, our ability to generalize these results is constrained by the inherent limitations of previous studies. First, the satellite-based studies have been typically conducted at a fixed temporal extent (typically from the early 1970s through 2000s) due to restrictions by the available satellite data (Wimberly and Ohmann, 2004; Zheng et al., 2013). However, the rates of landscape change and the relative influences of
environmental and socio-economic constraints can fluctuate considerably over relatively short (less than a decade) time intervals (Turner et al., 1996). Second, the spatial extent selected for studies may be based on physical feature (e.g., watersheds) or administrative border (e.g., state or county). However, the former can not capture the socioeconomic influences due to the data unavailable in the correspondent scope (Nahuelhual et al., 2012); the latter fails to detect the information in a spatially explicit framework (Redo et al., 2012). Furthermore, sensor limitations often restrict the number of cover classes for which changes can be tracked using satellite imagery (Wimberly and Ohmann, 2004). For example, many landscape-scale studies have emphasized the shifts between forested and non-forested land or open versus closed canopy forests (Spies et al., 1994). Hence, previous studies have largely disregarded that forest cover includes significant heterogeneity within forest types, tree species compositions, and environmental and socio-economic characteristics. In this context, this study aimed to overcome the above limitations by conducting a recent (2007–2012) FRID-based, quantitative analysis of trends in the FCC in a small local region with relatively homogeneous biophysical interference. The term “FCC” used in this study is a land cover designation, not a land use designation. Thus, FCC includes the following: forest conversion to agricultural land, construction land, and unused land and the harvesting of native forest cover or old growth (e.g., replacement by plantations or plantation rotations), which produce temporary, short-term land cover change. In this study, we combined the broad socio-economic information (the authorized statistics) at the census block group level with site-specific information the Forest Resources Inventory Database, (FRID) measured at the pixel level. The study was conducted for the Sanyuan District, Sanming City, which is located in the Southeastern China. The region is one of the major national sources of timber, fiber, and other forest products. Contrasted to developed countries, which have entered into a relatively more stable stage in forest conversion dynamics (Redo et al., 2012), the study area is experiencing rapid deforestation of secondary forests, active plantation practices and significant afforestation (Zhang et al., 2010). Such significant alteration of the forest cover has potential negative consequences for habitat loss and biodiversity conservation. Therefore, the objectives of this research are to (1) quantitatively estimate the FCC between 2007 and 2012; (2) determine the relative importance of biophysical and socio-economic variables [e.g., slope, elevation, road density, accessibility, population, income, owner, and protection policy] in explaining the patterns of the FCC. 2. Study area The study area covers 67,226.011 ha in Sanyuan District, Fujian Province, China. It is located in a catchment basin between the Wuyi Mountains and Daiyun Mountains, the skeleton of the terrain in Fujian Province. The district is upstream of the Minjiang River, the primary river in Fujian Province and has the seventh highest annual runoff in the country (by recent estimates). The elevations range from 100 m to over 1500 m at the highest peaks. The physiography is characterized by highly dissected terrain with steep slopes and high stream densities. The area is covered by the well-drained yellow-red soil derived from a variety of parent materials (e.g., sandstone, slate, shale, tuff, and granite) (Su et al., 2012). The climate is generally moist and mild; most of the precipitation occurs between March and August (Yang et al., 2007). Forest is the predominant land use in the area. Major conifer species are Cunninghamia lanceolata and Pinus massoniana; hardwood species are Castanopsis kawakamii and Phoebe bournei; and Phyllostachys pubescens is the most common bamboo species. The area size of C. kawakamii forest in the study area is currently
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
the largest one among the secondary forest of C. kawakamii in the world. Moreover, the study area is named “The home of Sarcandra glabra in China” by the State Forestry Bureau of China, because it possesses a higher active content of Isofraxidin in S. glabra in Sanyuan District than the other areas of the country (www.smsy. gov.cn). Therefore, it is important to preserve the present forests, especially the secondary forest of C. kawakamii and the surroundings and the S. glabra habitats in this area. The Sanyuan District was chosen as the case study here for three reasons. First, the area is associated with a growing concern over forest transitions, plantation practices, and fragmentation of large tracts of contiguous forest (Zhang et al., 2010). Second, the nature reserve of C. kawakamii and the habitats of S. glabra have experienced increasing artificial interference in recent decades (Liu and Hong, 1998), due to the facts that the demand for timber, fiber, and other forest products from commercial forests and the demand for income-producing orchards for farmers are increasing. Third, forests in the study area serve as the headwaters for the downstream rivers, i.e., the Minjiang River. Therefore, the water quality in these rivers highly depends on the conservation of the forests.
123
center of the city, then the distances from the pixel location to the city center were measured using ArcGIS 10.0. The demographic and economic data of 2007 and 2012 in census block groups were obtained from statistical yearbooks for Sanyuan District, including variables of population density, per capita income and fiscal revenue. 3.2. Land use/forest cover classification To describe the FCC between 2007 and 2012, we used the existing inventory records to classify landscape into land use types and forest cover types. The classes are as follows: (1) coniferous forest, (2) broadleaf forest, (3) bamboo forest, (4) mixed forest, (5) economic forest, (6) shrubwood land, (7) construction land, (8) agriculture land, and (9) unused land (Table 1). The final land use/cover maps were transformed to a uniform resolution (30 m 30 m) to avoid biases that arise from differences in resolution among images (e.g., Fig. 1). Hereafter, the patches changed from 2007 to 2012 were identified using spatial overlapping of the two images with the application of the ArcGIS (Fig. 2). Then, the transition matrices were calculated to compare the landscapes between years (Table A.1).
3. Materials and methods 3.3. Binary logistic model 3.1. Data sources The data include a set of two FRID images and supporting biophysical and socio-economic data in 2007 and 2012. In China, FRID is gathered as a basis for forest management activities, management unit design, and planning functions and are organized at the county level, and it is continually updated at the end of each year (Xie et al., 2011). The data are in vector format, measuring forest characteristics and other ecological factors at the landscape patch level (e.g., land type, tree species type, vegetation type, protection classes, forest owner, terrain factors) (Piao et al., 2005). The structure and survey methodology of the database is prescribed by the standards of the “Technical Regulations of National Forest Resource Continuous Inventory” (State Forest Administration, 2003). According to the Regulations (see Xie et al., 2011 also), a two stage stratified sampling design which combines field investigation and remote sensing has been employed in the inventory. Most sample plots (0.0667 ha each, one Chinese mu) are permanent, but temporary can be added for supplemental data. The sampling precision for the inventory was more than 95%. Two resources were used to enhance the biophysical variable collection: (1) based on the transportation maps of Sanyuan District in 2012, the road network (including expressway, national roads, provincial roads, county roads, country roads, rail roads, and other main roads) was digitized as vector data, then the road densities of each census block group were calculated; (2) assuming that the administrative building location of the district was the
The binary logistic model, as a nonlinear regression model, is a special case of a generalized linear model (Schumacher et al., 1996). It is to find the best model to describe the relationship between a dependent variable and multiple independent variables (Lee, 2005; Ozdemir, 2011). Through the addition of an appropriate link function to the usual linear regression model, the variables performed in the logistic model may be either continuous or discrete, or any combination of both types and they do not necessarily have normal distributions (Lee, 2005). Hence, the binary logistic regression is applicable to our work with the combination of both continuous and discrete variables (Adnan, 2011; Peeters et al., 2012). The binary logistic regression analysis was performed using the statistical package for the social sciences (SPSS 20.0). The binary dependent variable was a discrete choice of either “no conversion” or “conversion” between 2007 and 2012. Boolean (0 and 1) was applied to account for the unchanged and changed pixels. For example, if a coniferous forest pixel in 2007 remained coniferous forest in 2012, then the dependent variable for that particular pixel had the value of 0; otherwise, the value of the pixel was 1. Based on the FCC map, we systematically selected a sample of 2817 points altogether (Fig. 2). This was performed with the stipulation that a distance of 500 m must separate the sample points to lessen the effect of spatial autocorrelation (Nahuelhual et al., 2012). In this study, the socioeconomic information at the census block group level data was assembled with the inventory data into the
Table 1 Land use and forest cover classes. Land use types
Forest cover classes
Main species and description
Forest land (FL)
Coniferous forest (CONI) Broadleaf forest (BROA) Bamboo forest (BAMB) Mixed forest (MIXE) Economic forest (ECON) Shrubwood land (SHRU)
C. lanceolata and P. massoniana C. kawakamii and P. bournei P. pubescens Mixed forest with Coniferous and Broadleaf species Camellia oleifera, Citrus reticulata and C. mollissima Quercus spp. and Rhododendron spp.,
Non-forest land (NFL)
Construction land (CONS) Agricultural land (AGRI) Unused land (UNUS)
Industrial and mining, urban and rural residential and transportation land Rice, wheat and vegetable Burned area and barren land
124
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
Fig. 1. Spatial distribution of forest covers in 2007.
Fig. 2. Spatial distribution of FCC between 2007 and 2012 and sample plots.
corresponding pixel locations, which together formed the vectors of explanatory variables. The variables were grouped into three categories: (i) biophysical factors related to topographic conditions (slope, elevation), which account for the costs of conversion
because they influence the degree of mechanization and on-foot accessibility; (ii) accessibility factors (road density, distance to the city center, accessibility), which account for the access and opportunity costs of farm labor and the access to output and
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
125
Table A.1 Transition matrices of FCC from 2007; to 2012 (%). 2007 Classes
2012 CONI
BROA
BAMB
MIXE
ECON
SHRU
NONF
% (2007)
CONI BROA BAMB MIXE ECON SHRU % (2012)
94.8 0.4 0.3 8.7 2.7 0 32.3
0.1 97.3 0 1.2 0.7 0 7
0.6 0.1 99.1 0.4 0.3 0 31.6
1.6 1.7 0.2 87.0 0.4 0 21.6
0 0 0.1 0 95.5 0 5.4
0 0 0 0 0 99.7 0.5
2.9 0.5 0.3 2.7 0.4 0.3 1.6
31.5 6.9 31.6 24.0 5.6 0.5 100
input markets, and the potential effects of urban sprawl on land use and forest management; and (iii) demographic and socioeconomic factors (population density, per capita income, fiscal revenue, ownership, protection class), which account for the effects of human dimensions on forest plantation (thereby affecting the rates of harvest and types of forest management applied). The description of the independent variables was provided in Table 2. 4. Results 4.1. FCC analysis The total forest cover of the Sanyuan District after image classification was 67,226.011 ha in 2007 and 66,127.260 ha in 2012, declined by 1.6% during the study period. In terms of percentages, the highest forest cover classes were bamboo forest and coniferous forest. They accounted for more than 30.0% of the total forest cover area respectively in both of the periods. While economic forest and shrubwood accounted for a small and relatively consistent proportion of the landscape during the periods (Table A.1). Changes in the forest landscape structure from 2007 to 2012 were observed in all the six forest cover classes of the Sanyuan District. Although the major forest cover classes had a high rate of persistence in area size between 2007 and 2012, a rapid transition among these cover categories occurred. Overall, 3648.945 ha of forest cover changed during the study period, which accounted for 5.4% of the entire landscape. Among the changes, 1098.749 ha (30.1%) were converted from forest cover to non-forest cover, and 2550.196 ha (69.9%) were transformed among the forest cover types. The change was unevenly distributed across the forest cover classes, with the greatest change rate (13.0%) of mixed forest, which converted predominantly into coniferous forest from 2007 to 2012. The result also confirmed that the net gain to non-forest land was largely supplied by coniferous forest and mixed forest, together accounting for more than 90.0% of the gains (Fig. 3 and Table A.1).
4.2. Model The correlations between the variables were listed in Table A.2. However, no significant correlations were identified, indicating no multi-collinearity between independent factors in current research. Table 3 lists the results of the binary logistic regression for the study period. The value of Chi square of omnibus test (p < 0.001), Hosmer and Lemeshow test (p > 0.05) and the odds ratios of the covariates (most of values >1) indicated the good fit of the model, the explanatory variables can explain the dependent variable in a way. According to the modeling results, the topographic conditions measured by slope and elevation were found negatively while insignificantly associated with the FCC. The distance to the city center was among the significant (p < 0.05) accessibility driver that increased the probability of FCC. Among the socio-economic factors, ownership, protection class and fiscal revenue were examined to be all statistically significant (p < 0.01), which confirms that between 2007 and 2012 the FCC were more likely to occur on collective or enterprise-owned forests or those less protected or un-protected area, or regions with higher growth rates of fiscal revenue. 5. Discussion 5.1. Patterns of FCC Our research corroborated previous work that demonstrated that the loss of forest to development had not been a major driver of land cover change in the study area (Zhang et al., 2010). We also detected only small changes occurred in the broader landscape structure between 2007 and 2012; however, conversions among different forest cover classes occurred frequently. Our finding indicated that the conversion rate of forests to non-forest land, timber production, and renewed forest stands was 5.4% from 2007 to 2012, which is much higher than the rate (0.5% per year)
Table 2 Dependent and independent variables used in the regression model of forest cover change. Variables
Description
Mean
Std. Dev.
SLOPE ELEVATION CTYDIST ROAD ACCESS POP INCP FIRE OWNERS PROCLS
Average slope at the pixel location (degree) Average elevation at the pixel location (m) Distance from the pixel location to the city center (km) Average road density in census block group (m/ha) Accessibility to the pixel location: 1 = accessible currently, 2 = accessible in the near future, 3 = inaccessible Change in population density in census block group between 2007; and 2012 (person/km2) Growth rate of per capita income in census block group between 2007; and 2012 (%) Growth rate of average fiscal revenue in census block group between 2007; and 2012 (%) Forest ownership: 1 = state, 2 = collective, 3 = enterprise, 4= individual, 5 = uncertain Degree of protection in each pixel location: 1 = special protection, 2 = key protection, 3 = general protection, 4 = unprotected
26.010 524.459 15.365 591.777 1.069 2.251 65.669 144.800 1.987 3.662
6.016 276.072 6.745 522.393 0.290 7.945 33.637 260.125 0.695 0.741
126
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
1.7 0.1 1.6
0.4
ECON
MIXE
95.5
87.0 2.7
2.7 8.7 CONI
0.4
0.4
0.3 2.9
0.3
NFL
0.3
94.8
BAMB 99.1
0.4
0.5
0.3
0.1
0.6
0.1
0.7
0.2
BROA
SHRU
97.3
99.7
1.2
Fig. 3. Forest covers persistence and change between 2007 and 2012 in the upstream area of the Minjiang River, China. Values are % of each forest cover class that experienced persistence (inside boxes) or transition among forest cover classes (on the right or above arrowheads).
Table A.2 Correlation matrix of variables. Variables SLOPE ELEVATION CTYDIST ROAD ACCESS POP INCP FIRE OWNERS PROCLS
SLOPE 1.000 0.325 0.053 0.002 0.005 0.038 0.026 0.024 0.205 0.057
ELEVATION 0.325 1.000 0.226 0.378 0.019 0.084 0.028 0.118 0.110 0.051
CTYDIST 0.053 0.226 1.000 0.399 0.162 0.039 0.177 0.104 0.015 0.082
ROAD 0.002 0.378 0.399 1.000 0.019 0.147 0.085 0.045 0.006 0.079
ACCESS 0.005 0.019 0.162 0.019 1.000 0.066 0.061 0.007 0.060 0.004
POP 0.038 0.084 0.039 0.147 0.066 1.000 0.027 0.009 0.033 0.020
INCP 0.026 0.028 0.177 0.085 0.061 0.027 1.000 0.059 0.017 0.013
FIRE 0.024 0.118 -0.104 0.045 0.007 0.009 0.059 1.000 0.109 0.007
OWNERS 0.205 0.110 0.015 0.006 0.060 0.033 0.017 0.109 1.000 0.028
PROCLS 0.057 0.051 0.082 0.079 0.004 0.020 0.013 0.007 0.028 1.000
Note: SLOPE: slope; ELEVATION: elevation; ROAD: road density; CTYDIST: distance to the city center; ACCESS: accessibility; POP: population density; INCP: per capita income; FIRE: fiscal revenue; OWNERS: ownership; PROCLS: protection class.
between 1977 and 2003 at the national scale in China (Shi et al., 2011). The spatiotemporal dynamics resulted from the interaction of natural and socio-economic drivers have important implications for the biodiversity. First, planted forests (i.e., bamboo forest and coniferous forest) were the dominant forest cover classes in the Table 3 Parameter estimation from the binary logistic regression (N = 2817). Variables
Coeff.
p value
7.597 <0.001 Constant SLOPE 0.001 0.976 ELEVATION 0.001 0.089 CTYDIST 0.030 0.044 ROAD <0.001 0.064 ACCESS 0.387 0.257 POP 0.001 0.959 INCP 0.002 0.495 FIRE 0.001 0.009 OWNERS 0.753 <0.001 PROCLS 1.310 <0.001 Chi square of Omnibus test 98.020 <0.001 Chi square of Hosmer and Lemeshow test 12.535 0.129 > 0.05
Odds ratios 0.001 0.999 0.999 1.030 1.000 1.473 0.999 1.002 1.001 0.471 3.706
study area for both of the studied landscapes. Second, similar to many of other countries, such as North America (Powell and Hansen, 2007), Central America (Redo et al., 2012), and Europe (Feranec et al., 2010), the area of coniferous forest increased steadily, while the area of mixed forest decreased sharply in the study area during the period of study. Moreover, forest cover transitions occurred even near the location of the nature reserve of the endangered species (C. kawakamii) (Fig. 2), which has leaded the endangered population being subjected to artificial interference in recent decades (Liu and Hong, 1998). The forests may degrade from primary and secondary forests to planted forests and non-forest land, and such degraded forests are predicted to expand if corresponding restoration measures are not implemented (Zhang et al., 2010). All these findings confirm that the dominant process of the FCC in the upstream area of the Minjiang River, as a whole is likely to have deleterious effects on biodiversity conservation. 5.2. Proximate forces of FCC The modeling result verified the importance of ownership as a key socio-economic variable that structured landscape patterns
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
and dynamics, as demonstrated in previous studies (Stanfield et al., 2002; Nahuelhual et al., 2012). With a unique land system of the separation between land ownership and land use right in China, the forest ownership discussed in this study referred to the land use right of forest. The variable of ownership turned out to be one of the most important predictors of the FCC. The reasons are that the state-owned forests (i.e., the nature reserve) in the study area were protected by the government; instead the forests collective or enterprise-owned were more likely to be changed. Due to the fact that the latter two forests accounted for a large proportion (74.2% as computed from the FRID of 2012) of the total forest cover in the region, more attention should be paid to these forests in regard of the FCC. In the study area, the government played an important role in the FCC. The evidences were as followed: First, fiscal revenue was examined to be a significant predictor for the FCC. This may relate with the fact that the higher activity in timber production and forest planting contributed more to the fiscal revenue of local economy, especially in the area with forestry and related industries as economic lifeline. Our finding was consistent with the forest transitions analysis (Rudel et al., 2005), which revealed that Chinese government enacted an expansion policy for tree plantations during the past two decades. Second, FCC was more likely to occur on unprotected or less protected forests. This finding also verified the importance of the forest protection policy issued by governments. All the biophysical and accessibility factors except the coefficient of distance to the city center were not significantly related to the FCC in the region. This may be explained by the fact that this study was conducted in a small local region with relatively homogeneous spatial paradigm in terms of biophysical interference and accessibility. However, the modeling results still tend to reveal that the areas more vulnerable to future change in the study area would be those located on lower elevations and which also have the higher road density. Yet, we are aware of the complex interactions among the underlying causes of FCC, which can not always be represented by a single model (Nahuelhual et al., 2012). Two considerations may help improve future versions of the model. The first consideration is to use forest cover trajectories over a time series to analyze the impact of socio-economic factors on FCC, rather than two-year transitions. The second consideration is to use geographic information techniques that combining spatial non-stationarity variables with socio-economic data (as spatial weighting value information becomes available) (Poudyal et al., 2012). 5.3. Extensions of the inventory datasets A major distinction between this research and previous studies of FCC (Nahuelhual et al., 2012; Lira et al., 2012) is that we used a typical method of information collection for the FCC analysis (i.e., systematical sampling from the FRID). Information on tree species and age classes were included in the dataset. Such information is fundamental for forest management. Because the forest age may be directly related to forest successional stage, the change in forest age may impact biodiversity (Guariguata and Ostertag, 2001). The forest cover trajectory and resulting forest age structure are very important to assess the current landscape conditions (Ferraz et al., 2009). However, remote sensing methods, including airborne laser scanning, are generally not able to deliver high resolution information on tree species and age structure (Knoke et al., 2010). Studies on the effects of landscape structure on various taxonomic groups usually focus on landscape characteristics, such as forest cover and forest configuration (Martensen et al., 2008), and rarely consider forest age structure (Wimberly and Ohmann, 2004; Etter et al., 2005).
127
Therefore, the FRID, covering much information of the forest resource at the finer level, provides an excellent and unique opportunity to achieve more precise effects of human disturbances on ecological processes. 6. Conclusion This study has identified the spatial pattern of forest conversions in the upstream area of the Minjiang River using FRID. The annual rate of FCC for the landscape was determined as more than 1.0%. The FCC tended to be concentrated in the conversions from mixed forest to coniferous forest and from coniferous forest and mixed forest to non-forest land. Such forest transitions can be accounted for by the different means of management policy implemented by the relevant governments in various forests. The results confirm that human-driven conversions of forest cover have deleterious effects on biodiversity conservation in the region. Our case also verifies that theoretically consistent estimates can be achieved by carefully combining datasets at two different spatial scales (the census block group level and the sample plot level). These findings are important for the relevant policy-makers and planners to take effective planning plantation practices to avoid negative impacts on conserving biodiversity. Acknowledgements This research was funded by the National Natural Science Foundation of China (No.41201100), to which we are very grateful. We are also very grateful for the support provided by the Natural Science Foundation of Fujian (No. 2012J01071) and the Science and Technology Major Project of the Hall of Science and Technology of Fujian (No. 2012NZ0001). Finally, many thanks to all those who provided the raw data used for model regressions and to the Forestry Bureau of Sanyuan District, Fujian Province for providing us with a validation dataset. References Adnan, O., 2011. Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol. 405 (1–2), 123–136. Alonso, W., 1964. Location and Land Use. Harvard University Press, Cambridge, MA. Black, A.E., Morgan, P., Hessburg, P.F., 2003. Social and biophysical correlates of change in forest landscapes of the interior Columbia Basin, USA. Ecol. Appl. 13 (1), 51–67. Bremer, L.L., Farley, K.A., 2010. Does plantation forestry restore biodiversity or create green deserts? A synthesis of the effects of land-use transitions on plant species richness Biodivers. Conserv. 19 (14), 3893–3915. Brockerhoff, E.G., Jactel, H., Parrotta, J.A., Quine, C.P., Sayer, J., 2008. Plantation forests and biodiversity: oxymoron or opportunity? Biodivers. Conserv. 17 (5), 925–951. Burnside, N.G., Smith, R.F., Waite, S., 2003. Recent historical land use change on the South Downs, United Kingdom. Environ. Conserv. 30 (1), 52–60. Cai, Y.B., Zhang, H., Pan, W.B., Chen, Y.H., Wang, X.R., 2013. Land use pattern, socioeconomic development, and assessment of their impacts on ecosystem service value: study on natural wetlands distribution area (NWDA) in Fuzhou city, southeastern China. Environ. Monit. Assess. 185 (6), 5111–5123. Drapeau, P., Leduc, A., Giroux, J.F., Savard, J.P.L., Bergeron, Y., Vickery, W.L., 2000. Landscape-scale disturbances and changes in bird communities of boreal mixed-wood forests. Ecol. Monogr. 70 (3), 423–444. Etter, A., McAlpine, C., Pullar, D., Possingham, H., 2005. Modeling the age of tropical moist forest fragments in heavily-cleared lowland landscapes of Colombia. Forest Ecol. Manag. 208 (1), 249–260. FAO, 2010. Global Forest Resource Assessment 2010 Main Report. FAO, Rome, Italy. Feranec, J., Jaffrain, G., Soukup, T., Hazeu, G., 2010. Determining changes and flows in European landscapes 1990–2000 using CORINE land cover data. Appl. Geogr. 30 (1), 19–35. Ferraz, S.F.D.B., Vettorazzi, C.A., Theobald, D.M., 2009. Using indicators of deforestation and land-use dynamics to support conservation strategies: a case study of central Rondônia, Brazil. Forest Ecol. Manag. 257 (7), 1586–1595. Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A.,
128
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, I.C., Ramankutty, N., Snyder, P.K., 2005. Global consequences of land use. Science 309 (5734), 570–574. Gardner, T.A., Barlow, J., Chazdon, R., Ewers, R.M., Harvey, C.A., Peres, C.A., Sodhi, N. S., 2009. Prospects for tropical forest biodiversity in a human-modified world. Ecol. Lett. 12 (6), 561–582. Gerber, J.F., 2011. Conflicts over industrial tree plantations in the South: who, how and why? Global Environ. Chang. 21 (1), 165–176. Gibson, L., Lee, T.M., Koh, L.P., Brook, B.W., Gardner, T.A., Barlow, J., Peres, C.A., Bradshaw, C.J.A., Laurance, W.F., Lovejoy, T.E., Sodhi, N.S., 2011. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478 (7369), 378–381. Guariguata, M.R., Ostertag, R., 2001. Neotropical secondary forest succession: changes in structural and functional characteristics. Forest Ecol. Manag. 148 (1), 185–206. Halpern, C.B., Spies, T.A., 1995. Plant species diversity in natural and managed forests of the Pacific Northwest. Ecol. Appl. 5 (4), 913–934. Hargis, C.D., Bissonette, J., Turner, D.L., 1999. The influence of forest fragmentation and landscape pattern on American martens. J. Appl. Ecol. 36 (1), 157–172. Islam, K.R., Weil, R.R., 2000. Land use effects on soil quality in a tropical forest ecosystem of Bangladesh. Agr. Ecosyst. Environ. 79 (1), 9–16. Jackson, S.T., Sax, D.F., 2010. Balancing biodiversity in a changing environment: extinction debt, immigration credit and species turnover. Trends Ecol. Evol. 25 (3), 153–160. Knoke, T., Hahn, A., Schneider, T., 2010. Linking inventory and forest optimisation: information and decision-making in forest management. Eur. J. Forest Res. 129 (5), 771–775. Lee, S., 2005. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int. J. Remote Sens. 26 (7), 1477–1491. Lindenmayer, D.B., 2010. Landscape change and the science of biodiversity conservation in tropical forests: a view from the temperate world. Biol. Conserv. 143 (10), 2405–2411. Lira, P.K., Tambosi, L.R., Ewers, R.M., Metzger, J.P., 2012. Land-use and land-cover change in Atlantic Forest landscapes. Forest Ecol. Manag. 278, 80–89. Liu, J.F., Hong, W., 1998. A study on the community ecology of Castanopsis kawakamii – Study on the niche of the main tree population in Castanopsis kawakamii community. Acta Ecologica Sinica 19 (3), 347–352 (in Chinese with English abstract). Loehle, C., Li, B.L., 1996. Habitat destruction and the extinction debt revisited. Ecol. Appl. 6 (3), 784–789. Lomolino, M.V., Perault, D.R., 2000. Assembly and disassembly of mammal communities in a fragmented temperate rain forest. Ecology 81 (6), 1517–1532. Martensen, A.C., Pimentel, R.G., Metzger, J.P., 2008. Relative effects of fragment size and connectivity on bird community in the Atlantic Rain Forest: implications for conservation. Biol. Conserv. 141 (9), 2184–2192. Matlack, G.R., 1994. Plant species migration in a mixed-history forest landscape in eastern North America. Ecology 75 (5), 1491–1502. Mayer, A.L., Kauppi, P.E., Angelstam, P.K., Zhang, Y., Tikka, P.M., 2005. Importing timber, exporting ecological impact. Science 308, 359–360. McGarigal, K., McComb, W.C., 1995. Relationships between landscape structure and breeding birds in the Oregon Coast Range. Ecol. Monogr. 65 (3), 235–260. Miller, D.J., Plantinga, A.J., 1999. Modeling land use decisions with aggregate data. Am. J. Agr. Econ. 81 (1), 180–194. Nagaike, T., Kamitani, T., 1999. Factors affecting changes in landscape diversity in rural areas of the Fagus crenata forest region of central Japan. Landscape Urban Plan. 43 (4), 209–216. Nahuelhual, L., Carmona, A., Lara, A., Echeverría, C., González, M.E., 2012. Land-cover change to forest plantations: proximate causes and implications for the landscape in south-central Chile. Landscape Urban Plan. 107 (1), 12–20. Onojeghuo, A.O., Blackburn, G.A., 2011. Forest transition in an ecologically important region: patterns and causes for landscape dynamics in the Niger Delta. Ecol. Indic. 11 (5), 1437–1446. Ozdemir, A., 2011. Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J. Hydrol. 405 (1), 123–136. Peeters, B., Dewil, R., Smets, I.Y., 2012. Improved process control of an industrial sludge centrifuge dryer installation through binary logistic regression modeling of the fouling issues. J. Process Contr. 22 (7), 1387–1396. Piao, S.L., Fang, J.Y., Zhu, B., Tan, K., 2005. Forest biomass carbon stocks in China over the past 2 decades: estimation based on integrated inventory and satellite data. J. Geophys. Res. 110 (G1), G01006. Poudyal, N.C., Johnson-Gaither, C., Goodrick, S., Bowker, J.M., Gan, J.B., 2012. Locating spatial variation in the association between wildland fire risk and social vulnerability across six southern states. Environ. Manag. 49 (3), 623–635.
Poudyal, N.C., Cho, S.H., Strickland, J.D., Hodges, D.G., 2008. Socio-demographic and market forces of forest land use change on the Northern Cumberland Plateau, Tennessee. Int. J. Ecol. Econ. Stat. 10 (W08), 53–62. Powell, S.L., Hansen, A.J., 2007. Conifer cover increase in the Greater Yellowstone Ecosystem: frequency, rates, and spatial variation. Ecosystems 10 (2), 204–216. Radeloff, V.C., Mladenoff, D.J., Boyce, M.S., 2000. Effects of interacting disturbances on landscape patterns: budworm defoliation and salvage logging. Ecol. Appl. 10 (1), 233–247. Ramdani, F., Hino, M., 2013. Land use changes and GHG emissions from tropical forest conversion by oil palm plantations in Riau Province, Indonesia. PLoS. One. 8 (7), 1–6. Redo, D.J., Grau, H.R., Aide, T.M., Clark, M.L., 2012. Asymmetric forest transition driven by the interaction of socioeconomic development and environmental heterogeneity in Central America. Proceedings of the National Academy of Sciences 109 (23), 8839–8844. Rheinhardt, R., Brinson, M., Meyer, G., Miller, K., 2012. Integrating forest biomass and distance from channel to develop an indicator of riparian condition. Ecol. Indic. 23, 46–55. Rudel, T.K., Coomes, O.T., Moran, E., Achard, F., Angelsen, A., Xu, J., Lambin, E., 2005. Forest transitions: towards a global understanding of land use change. Global Environ. Chang. 15 (1), 23–31. Sala, O.E., Chapin, F.S., Armesto, J.J., Berlow, E., Bloomfield, J., Dirzo, R., HuberSanwald, E., Huenneke, L.F., Jackson, R.B., Kinzig, A., Leemans, R., LODGEDM, Mooney, H.A., Oesterheld, M., Poff, N.L., Sykes, M.T., Walker, B.H., Walker, M., Wall, D.H., 2000. Global biodiversity scenarios for the year 2100. Science 287 (5459), 1770–1774. Schumacher, M., Robner, R., Vach, W., 1996. Neural networks and logistic regression: part I. Computation. Stat. Data An. 21 (6), 661–682. Seto, K.C., Kaufmann, R.K., 2003. Modeling the drivers of urban land use change in the Pearl River Delta, China: integrating remote sensing with socioeconomic data. Land Econ. 79 (1), 106–121. Shevliakova, E., Pacala, S.W., Malyshev, S., Hurtt, G.C., Milly, P.C.D., Casperseon, J.P., Sentman, L., Fisk, J., Wirth, C., Crevoisier, C., 2009. Carbon cycling under 300 years of land use change: importance of the secondary vegetation sink. Global Biogeochem. Cy. 23 (2), 1–16. Shi, L., Zhao, S.Q., Tang, Z.Y., Fang, J.Y., 2011. The changes in China's forests: an analysis using the forest identity. PLoS. One. 6 (6), e20778. Spies, T.A., Ripple, W.J., Bradshaw, G.A., 1994. Dynamics and pattern of a managed coniferous forest landscape in Oregon. Ecol. Appl. 4 (3), 555–568. Stanfield, B.J., Bliss, J.C., Spies, T.A., 2002. Land ownership and landscape structure: a spatial analysis of sixty-six Oregon (USA) Coast Range watersheds. Landscape Ecol. 17 (8), 685–697. State Forest Administration, 2003. Technical Regulations of National Forest Continuous Inventory. Internal Document of Chinese Government, Beijing (in Chinese). Strengers, B.J., Van Minnen, J.G., Eickhout, B., 2008. The role of carbon plantations in mitigating climate change: potentials and costs. Climatic Change 88 (3–4), 343– 366. Su, S.J., Liu, J.F., He, Z.S., Hong, W., Zhang, J.B., 2012. The spatial heterogeneity of soil nutrients in a mid-subtropical Castanopsis kawakamii natural forest. Acta Ecol. Sin. 32 (18), 5673–5682 (in Chinese with English abstract). Thünen von, J.H., Heinrich, J., 1966. The Isolated State. Trans. Carla M. Wartenberg. Pergamon, Oxford. Turner, M.G., Wear, D.N., Flamm, R.O., 1996. Land ownership and land-cover change in the southern Appalachian highlands and the Olympic peninsula. Ecol. Appl. 6 (4), 1150–1172. Wimberly, M.C., Ohmann, J.L., 2004. A multi-scale assessment of human and environmental constraints on forest land cover change on the Oregon (USA) coast range. Landscape Ecol. 19 (6), 631–646. Xie, X.K., Wang, Q.L., Dai, L.M., Su, D.K., Wang, X.C., Qi, G., Ye, Y.J., 2011. Application of China's national forest continuous inventory database. Environ. Manag. 48 (6), 1095–1106. Yang, Y.S., Chen, G.S., Guo, J.F., Xie, J.S., Wang, X.G., 2007. Soil respiration and carbon balance in a subtropical native forest and two managed plantations. Plant Ecol. 193 (1), 71–84. Zhang, D.W., Nagubadi, R.V., 2005. The influence of urbanization on timberland use by forest type in the Southern United States. Forest Policy Econ. 7 (5), 721–731. Zhang, X.H., Huang, Q.L., Zhang, C., 2010. Analysis of forest landscape dynamics based on forest landscape restoration: a case study of Yong'an city, Fujian province. China Eur. J. Forest Res. 129, 975–980. Zheng, D.L., Heath, L.S., Ducey, M.J., Quayle, B., 2013. Assessing double counting of carbon emissions between forest land-cover change and forest wildfires: a case study in the United States 1992–2006. Ecosystems 16, 310–322.
Contents lists available at ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Forest cover change and its drivers in the upstream area of the Minjiang River, China Xisheng Hu a , Chengzhen Wu b,c , Wei Hong b,d, Rongzu Qiu a, *, Jian Li b,d, Tao Hong b,d a
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China c Department of Ecology and Resource Engineering, Wuyi University, Nanping 354300, China d Key Laboratory for Forest Ecosystem Process and Management of Fujian Province, Fuzhou 350002, China b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 8 January 2014 Received in revised form 2 June 2014 Accepted 4 June 2014
Rapid human-driven conversion of global forest cover is contributing to the loss of habitat, biodiversity, and climate change. Even in a relatively short time interval and a small local region with relatively homogeneous biophysical interference, forest cover may be changed considerably by human disturbances. To better understand human influences on the pattern of forest cover change (FCC), we investigated the factors of FCC from 2007 to 2012 by combining the broad socio-economic information at the census block group level with the site-specific information measured at the pixel level. Taking the upstream area of the Minjiang River, China, as a case study, the result indicated that the major forest cover classes had a high rate of persistence in area size during the study period, while conversions among forest covers and into non-forest land occurred frequently, accounting for 5.4% of the landscape. The change of mixed forest was among the greatest one, decreasing sharply from 24.0% to 21.6% of the entire landscape, which converted predominantly into coniferous forest from 2007 to 2012. Additionally, 90.0% of the net gain to non-forest land was largely supplied by coniferous forest and mixed forest. The findings corroborate that human-driven conversions of forest covers have deleterious effects on biodiversity conservation in the upstream area of the Minjiang River. Furthermore, a binary logistic regression model was used to observe the biophysical/socio-economic drivers of FCC. We identified a FCC pattern during the period of study that was associated with collective- or enterprise-owned forests, low level of protection intensity forests, and the regions with high growth rates of fiscal revenue and far away from the city center. These results confirm that as a whole the relevant governments play an important role in the FCC in the region. This study is important for the relevant policy-makers and planners to better understand the underlying patterns and causes of this landscape change, to develop effective strategies for conserving biodiversity. ã 2014 Elsevier Ltd. All rights reserved.
Keywords: Land use Forest cover change Biodiversity Binary logistic regression Forest Resources Inventory Database
1. Introduction Forest covers four billion hectares (31.0%) of the Earth's landmass (FAO, 2010). They can provide renewable raw materials and natural amenities, protect land and water resources, conserve biological diversity and mitigate climate change (Mayer et al., 2005). Forest area is a valuable indicator of the relative importance of forests in a country or region, can embody these functions. The estimations of changes in forest area over time and space can characterize deforestation and reforestation/afforestation (Shi
* Corresponding author. Tel.: +86 591 83769536. E-mail address: [email protected] (R. Qiu). http://dx.doi.org/10.1016/j.ecolind.2014.06.015 1470-160X/ ã 2014 Elsevier Ltd. All rights reserved.
et al., 2011), which are among the world's most pressing landchange problems (Redo et al., 2012). An increasingly number of studies have been devoted to investigating land use/cover changes (LUCC) and their related impacts on the environment (Islam and Weil, 2000; Burnside et al., 2003; Ramdani and Hino, 2013). It has been recognized that LUCC benefits human livelihood; however, it also causes various problems (Cai et al., 2013). Forest cover change (FCC) is a significant type of LUCC that occurs in tropical and temperate areas (Bremer and Farley, 2010; Lindenmayer, 2010). Worldwide, forest plantations increased from 178.3 million hectares in 1990 to 264 million hectares in 2010, and the relative rate of annual expansion is predicted to be more than 1.0% over the next 10 years (FAO, 2010). This prediction is based on the world's increasing demand for timber products (Brockerhoff et al., 2008)
122
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
and the attempts to mitigate climate change effects by sequestering carbon via plantations (Strengers et al., 2008; Bremer and Farley, 2010; Nahuelhual et al., 2012). However, these rapid human-driven transitions of forest cover are perhaps the most ecologically significant for the global environment (Wimberly and Ohmann, 2004). The potential consequences include habitat loss, biodiversity disruption, and carbon discharge (Sala et al., 2000; Foley et al., 2005; Gardner et al., 2009). Empirical studies have consistently demonstrated that changes in forest habitats can impact communities of plants (Matlack, 1994; Halpern and Spies, 1995), birds (McGarigal and McComb, 1995; Drapeau et al., 2000), and mammals (Hargis et al., 1999; Lomolino and Perault, 2000). An increasing potential of extinction is also an inherent result of habitat fragmentation (Loehle and Li, 1996). At the macro scale, forest conversion and degradation consistently reduce the biodiversity of forest landscapes (Gibson et al., 2011). In many places, native species will become extinct as old growth forests are converted into fields, and invasive species will establish themselves rapidly, low levels of biodiversity as a result will persist in the disrupted habitats (Rudel et al., 2005). Moreover, it is estimated that harvests and shifting cultivation of forest land cover have added an additional 32–35% of carbon to the global emissions (Shevliakova et al., 2009). Thus, the rapid expansion of plantations has become one of the most contentious issues in contemporary sustainable development due to the significant environmental and societal conflicts it causes in many countries (Gerber, 2011). However, recent studies have indicated that it often takes decades or longer to perceive the potential negative effects that follow FCC (Jackson and Sax, 2010). Because of these potentially delayed responses, the ultimate impacts of FCC on the environment may not be realized immediately. In this context, understanding the possible trajectories and the proximate drivers of FCC is essential to grasp the underlying patterns and causes of the forest landscape changes. Further research will contribute to better identify areas where the risk of habitat loss is particularly high and ultimately to devise more effective measures to prevent adverse environmental and socio-economic impacts (Nahuelhual et al., 2012; Redo et al., 2012). Causes for forest landscape dynamics have been studied extensively in many forested regions (Wimberly and Ohmann, 2004; Onojeghuo and Blackburn, 2011). The studies reveal that FCC is associated with environmental, demographic and economic development (Redo et al., 2012). Among them, land ownership has been shown to correlate to land cover change in many of these studies (Turner et al., 1996; Nagaike and Kamitani, 1999; Radeloff et al., 2000). However, it is still uncertain how much of this variable could explain the patterns of FCC in an exclusive regime of land property in China. Spatial variables, such as topography (Turner et al., 1996), soils (Radeloff et al., 2000), population density (Black et al., 2003), urbanization (Zhang and Nagubadi, 2005), and distance from roads and urban centers (Turner et al., 1996; Rheinhardt et al., 2012), also constrain the rates and pathways of land cover change. Despite all this, the nature and magnitude of these relationships can vary greatly among landscapes (Wimberly and Ohmann, 2004). These various relationships are often conducted by regression models (Alonso, 1964; Thünen and Heinrich, 1966), such as the maximum entropy model (Miller and Plantinga, 1999), linear regression model (Seto and Kaufmann, 2003) and multinomial logistic model (Poudyal et al., 2008). Despite this large body of research, our ability to generalize these results is constrained by the inherent limitations of previous studies. First, the satellite-based studies have been typically conducted at a fixed temporal extent (typically from the early 1970s through 2000s) due to restrictions by the available satellite data (Wimberly and Ohmann, 2004; Zheng et al., 2013). However, the rates of landscape change and the relative influences of
environmental and socio-economic constraints can fluctuate considerably over relatively short (less than a decade) time intervals (Turner et al., 1996). Second, the spatial extent selected for studies may be based on physical feature (e.g., watersheds) or administrative border (e.g., state or county). However, the former can not capture the socioeconomic influences due to the data unavailable in the correspondent scope (Nahuelhual et al., 2012); the latter fails to detect the information in a spatially explicit framework (Redo et al., 2012). Furthermore, sensor limitations often restrict the number of cover classes for which changes can be tracked using satellite imagery (Wimberly and Ohmann, 2004). For example, many landscape-scale studies have emphasized the shifts between forested and non-forested land or open versus closed canopy forests (Spies et al., 1994). Hence, previous studies have largely disregarded that forest cover includes significant heterogeneity within forest types, tree species compositions, and environmental and socio-economic characteristics. In this context, this study aimed to overcome the above limitations by conducting a recent (2007–2012) FRID-based, quantitative analysis of trends in the FCC in a small local region with relatively homogeneous biophysical interference. The term “FCC” used in this study is a land cover designation, not a land use designation. Thus, FCC includes the following: forest conversion to agricultural land, construction land, and unused land and the harvesting of native forest cover or old growth (e.g., replacement by plantations or plantation rotations), which produce temporary, short-term land cover change. In this study, we combined the broad socio-economic information (the authorized statistics) at the census block group level with site-specific information the Forest Resources Inventory Database, (FRID) measured at the pixel level. The study was conducted for the Sanyuan District, Sanming City, which is located in the Southeastern China. The region is one of the major national sources of timber, fiber, and other forest products. Contrasted to developed countries, which have entered into a relatively more stable stage in forest conversion dynamics (Redo et al., 2012), the study area is experiencing rapid deforestation of secondary forests, active plantation practices and significant afforestation (Zhang et al., 2010). Such significant alteration of the forest cover has potential negative consequences for habitat loss and biodiversity conservation. Therefore, the objectives of this research are to (1) quantitatively estimate the FCC between 2007 and 2012; (2) determine the relative importance of biophysical and socio-economic variables [e.g., slope, elevation, road density, accessibility, population, income, owner, and protection policy] in explaining the patterns of the FCC. 2. Study area The study area covers 67,226.011 ha in Sanyuan District, Fujian Province, China. It is located in a catchment basin between the Wuyi Mountains and Daiyun Mountains, the skeleton of the terrain in Fujian Province. The district is upstream of the Minjiang River, the primary river in Fujian Province and has the seventh highest annual runoff in the country (by recent estimates). The elevations range from 100 m to over 1500 m at the highest peaks. The physiography is characterized by highly dissected terrain with steep slopes and high stream densities. The area is covered by the well-drained yellow-red soil derived from a variety of parent materials (e.g., sandstone, slate, shale, tuff, and granite) (Su et al., 2012). The climate is generally moist and mild; most of the precipitation occurs between March and August (Yang et al., 2007). Forest is the predominant land use in the area. Major conifer species are Cunninghamia lanceolata and Pinus massoniana; hardwood species are Castanopsis kawakamii and Phoebe bournei; and Phyllostachys pubescens is the most common bamboo species. The area size of C. kawakamii forest in the study area is currently
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
the largest one among the secondary forest of C. kawakamii in the world. Moreover, the study area is named “The home of Sarcandra glabra in China” by the State Forestry Bureau of China, because it possesses a higher active content of Isofraxidin in S. glabra in Sanyuan District than the other areas of the country (www.smsy. gov.cn). Therefore, it is important to preserve the present forests, especially the secondary forest of C. kawakamii and the surroundings and the S. glabra habitats in this area. The Sanyuan District was chosen as the case study here for three reasons. First, the area is associated with a growing concern over forest transitions, plantation practices, and fragmentation of large tracts of contiguous forest (Zhang et al., 2010). Second, the nature reserve of C. kawakamii and the habitats of S. glabra have experienced increasing artificial interference in recent decades (Liu and Hong, 1998), due to the facts that the demand for timber, fiber, and other forest products from commercial forests and the demand for income-producing orchards for farmers are increasing. Third, forests in the study area serve as the headwaters for the downstream rivers, i.e., the Minjiang River. Therefore, the water quality in these rivers highly depends on the conservation of the forests.
123
center of the city, then the distances from the pixel location to the city center were measured using ArcGIS 10.0. The demographic and economic data of 2007 and 2012 in census block groups were obtained from statistical yearbooks for Sanyuan District, including variables of population density, per capita income and fiscal revenue. 3.2. Land use/forest cover classification To describe the FCC between 2007 and 2012, we used the existing inventory records to classify landscape into land use types and forest cover types. The classes are as follows: (1) coniferous forest, (2) broadleaf forest, (3) bamboo forest, (4) mixed forest, (5) economic forest, (6) shrubwood land, (7) construction land, (8) agriculture land, and (9) unused land (Table 1). The final land use/cover maps were transformed to a uniform resolution (30 m 30 m) to avoid biases that arise from differences in resolution among images (e.g., Fig. 1). Hereafter, the patches changed from 2007 to 2012 were identified using spatial overlapping of the two images with the application of the ArcGIS (Fig. 2). Then, the transition matrices were calculated to compare the landscapes between years (Table A.1).
3. Materials and methods 3.3. Binary logistic model 3.1. Data sources The data include a set of two FRID images and supporting biophysical and socio-economic data in 2007 and 2012. In China, FRID is gathered as a basis for forest management activities, management unit design, and planning functions and are organized at the county level, and it is continually updated at the end of each year (Xie et al., 2011). The data are in vector format, measuring forest characteristics and other ecological factors at the landscape patch level (e.g., land type, tree species type, vegetation type, protection classes, forest owner, terrain factors) (Piao et al., 2005). The structure and survey methodology of the database is prescribed by the standards of the “Technical Regulations of National Forest Resource Continuous Inventory” (State Forest Administration, 2003). According to the Regulations (see Xie et al., 2011 also), a two stage stratified sampling design which combines field investigation and remote sensing has been employed in the inventory. Most sample plots (0.0667 ha each, one Chinese mu) are permanent, but temporary can be added for supplemental data. The sampling precision for the inventory was more than 95%. Two resources were used to enhance the biophysical variable collection: (1) based on the transportation maps of Sanyuan District in 2012, the road network (including expressway, national roads, provincial roads, county roads, country roads, rail roads, and other main roads) was digitized as vector data, then the road densities of each census block group were calculated; (2) assuming that the administrative building location of the district was the
The binary logistic model, as a nonlinear regression model, is a special case of a generalized linear model (Schumacher et al., 1996). It is to find the best model to describe the relationship between a dependent variable and multiple independent variables (Lee, 2005; Ozdemir, 2011). Through the addition of an appropriate link function to the usual linear regression model, the variables performed in the logistic model may be either continuous or discrete, or any combination of both types and they do not necessarily have normal distributions (Lee, 2005). Hence, the binary logistic regression is applicable to our work with the combination of both continuous and discrete variables (Adnan, 2011; Peeters et al., 2012). The binary logistic regression analysis was performed using the statistical package for the social sciences (SPSS 20.0). The binary dependent variable was a discrete choice of either “no conversion” or “conversion” between 2007 and 2012. Boolean (0 and 1) was applied to account for the unchanged and changed pixels. For example, if a coniferous forest pixel in 2007 remained coniferous forest in 2012, then the dependent variable for that particular pixel had the value of 0; otherwise, the value of the pixel was 1. Based on the FCC map, we systematically selected a sample of 2817 points altogether (Fig. 2). This was performed with the stipulation that a distance of 500 m must separate the sample points to lessen the effect of spatial autocorrelation (Nahuelhual et al., 2012). In this study, the socioeconomic information at the census block group level data was assembled with the inventory data into the
Table 1 Land use and forest cover classes. Land use types
Forest cover classes
Main species and description
Forest land (FL)
Coniferous forest (CONI) Broadleaf forest (BROA) Bamboo forest (BAMB) Mixed forest (MIXE) Economic forest (ECON) Shrubwood land (SHRU)
C. lanceolata and P. massoniana C. kawakamii and P. bournei P. pubescens Mixed forest with Coniferous and Broadleaf species Camellia oleifera, Citrus reticulata and C. mollissima Quercus spp. and Rhododendron spp.,
Non-forest land (NFL)
Construction land (CONS) Agricultural land (AGRI) Unused land (UNUS)
Industrial and mining, urban and rural residential and transportation land Rice, wheat and vegetable Burned area and barren land
124
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
Fig. 1. Spatial distribution of forest covers in 2007.
Fig. 2. Spatial distribution of FCC between 2007 and 2012 and sample plots.
corresponding pixel locations, which together formed the vectors of explanatory variables. The variables were grouped into three categories: (i) biophysical factors related to topographic conditions (slope, elevation), which account for the costs of conversion
because they influence the degree of mechanization and on-foot accessibility; (ii) accessibility factors (road density, distance to the city center, accessibility), which account for the access and opportunity costs of farm labor and the access to output and
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
125
Table A.1 Transition matrices of FCC from 2007; to 2012 (%). 2007 Classes
2012 CONI
BROA
BAMB
MIXE
ECON
SHRU
NONF
% (2007)
CONI BROA BAMB MIXE ECON SHRU % (2012)
94.8 0.4 0.3 8.7 2.7 0 32.3
0.1 97.3 0 1.2 0.7 0 7
0.6 0.1 99.1 0.4 0.3 0 31.6
1.6 1.7 0.2 87.0 0.4 0 21.6
0 0 0.1 0 95.5 0 5.4
0 0 0 0 0 99.7 0.5
2.9 0.5 0.3 2.7 0.4 0.3 1.6
31.5 6.9 31.6 24.0 5.6 0.5 100
input markets, and the potential effects of urban sprawl on land use and forest management; and (iii) demographic and socioeconomic factors (population density, per capita income, fiscal revenue, ownership, protection class), which account for the effects of human dimensions on forest plantation (thereby affecting the rates of harvest and types of forest management applied). The description of the independent variables was provided in Table 2. 4. Results 4.1. FCC analysis The total forest cover of the Sanyuan District after image classification was 67,226.011 ha in 2007 and 66,127.260 ha in 2012, declined by 1.6% during the study period. In terms of percentages, the highest forest cover classes were bamboo forest and coniferous forest. They accounted for more than 30.0% of the total forest cover area respectively in both of the periods. While economic forest and shrubwood accounted for a small and relatively consistent proportion of the landscape during the periods (Table A.1). Changes in the forest landscape structure from 2007 to 2012 were observed in all the six forest cover classes of the Sanyuan District. Although the major forest cover classes had a high rate of persistence in area size between 2007 and 2012, a rapid transition among these cover categories occurred. Overall, 3648.945 ha of forest cover changed during the study period, which accounted for 5.4% of the entire landscape. Among the changes, 1098.749 ha (30.1%) were converted from forest cover to non-forest cover, and 2550.196 ha (69.9%) were transformed among the forest cover types. The change was unevenly distributed across the forest cover classes, with the greatest change rate (13.0%) of mixed forest, which converted predominantly into coniferous forest from 2007 to 2012. The result also confirmed that the net gain to non-forest land was largely supplied by coniferous forest and mixed forest, together accounting for more than 90.0% of the gains (Fig. 3 and Table A.1).
4.2. Model The correlations between the variables were listed in Table A.2. However, no significant correlations were identified, indicating no multi-collinearity between independent factors in current research. Table 3 lists the results of the binary logistic regression for the study period. The value of Chi square of omnibus test (p < 0.001), Hosmer and Lemeshow test (p > 0.05) and the odds ratios of the covariates (most of values >1) indicated the good fit of the model, the explanatory variables can explain the dependent variable in a way. According to the modeling results, the topographic conditions measured by slope and elevation were found negatively while insignificantly associated with the FCC. The distance to the city center was among the significant (p < 0.05) accessibility driver that increased the probability of FCC. Among the socio-economic factors, ownership, protection class and fiscal revenue were examined to be all statistically significant (p < 0.01), which confirms that between 2007 and 2012 the FCC were more likely to occur on collective or enterprise-owned forests or those less protected or un-protected area, or regions with higher growth rates of fiscal revenue. 5. Discussion 5.1. Patterns of FCC Our research corroborated previous work that demonstrated that the loss of forest to development had not been a major driver of land cover change in the study area (Zhang et al., 2010). We also detected only small changes occurred in the broader landscape structure between 2007 and 2012; however, conversions among different forest cover classes occurred frequently. Our finding indicated that the conversion rate of forests to non-forest land, timber production, and renewed forest stands was 5.4% from 2007 to 2012, which is much higher than the rate (0.5% per year)
Table 2 Dependent and independent variables used in the regression model of forest cover change. Variables
Description
Mean
Std. Dev.
SLOPE ELEVATION CTYDIST ROAD ACCESS POP INCP FIRE OWNERS PROCLS
Average slope at the pixel location (degree) Average elevation at the pixel location (m) Distance from the pixel location to the city center (km) Average road density in census block group (m/ha) Accessibility to the pixel location: 1 = accessible currently, 2 = accessible in the near future, 3 = inaccessible Change in population density in census block group between 2007; and 2012 (person/km2) Growth rate of per capita income in census block group between 2007; and 2012 (%) Growth rate of average fiscal revenue in census block group between 2007; and 2012 (%) Forest ownership: 1 = state, 2 = collective, 3 = enterprise, 4= individual, 5 = uncertain Degree of protection in each pixel location: 1 = special protection, 2 = key protection, 3 = general protection, 4 = unprotected
26.010 524.459 15.365 591.777 1.069 2.251 65.669 144.800 1.987 3.662
6.016 276.072 6.745 522.393 0.290 7.945 33.637 260.125 0.695 0.741
126
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
1.7 0.1 1.6
0.4
ECON
MIXE
95.5
87.0 2.7
2.7 8.7 CONI
0.4
0.4
0.3 2.9
0.3
NFL
0.3
94.8
BAMB 99.1
0.4
0.5
0.3
0.1
0.6
0.1
0.7
0.2
BROA
SHRU
97.3
99.7
1.2
Fig. 3. Forest covers persistence and change between 2007 and 2012 in the upstream area of the Minjiang River, China. Values are % of each forest cover class that experienced persistence (inside boxes) or transition among forest cover classes (on the right or above arrowheads).
Table A.2 Correlation matrix of variables. Variables SLOPE ELEVATION CTYDIST ROAD ACCESS POP INCP FIRE OWNERS PROCLS
SLOPE 1.000 0.325 0.053 0.002 0.005 0.038 0.026 0.024 0.205 0.057
ELEVATION 0.325 1.000 0.226 0.378 0.019 0.084 0.028 0.118 0.110 0.051
CTYDIST 0.053 0.226 1.000 0.399 0.162 0.039 0.177 0.104 0.015 0.082
ROAD 0.002 0.378 0.399 1.000 0.019 0.147 0.085 0.045 0.006 0.079
ACCESS 0.005 0.019 0.162 0.019 1.000 0.066 0.061 0.007 0.060 0.004
POP 0.038 0.084 0.039 0.147 0.066 1.000 0.027 0.009 0.033 0.020
INCP 0.026 0.028 0.177 0.085 0.061 0.027 1.000 0.059 0.017 0.013
FIRE 0.024 0.118 -0.104 0.045 0.007 0.009 0.059 1.000 0.109 0.007
OWNERS 0.205 0.110 0.015 0.006 0.060 0.033 0.017 0.109 1.000 0.028
PROCLS 0.057 0.051 0.082 0.079 0.004 0.020 0.013 0.007 0.028 1.000
Note: SLOPE: slope; ELEVATION: elevation; ROAD: road density; CTYDIST: distance to the city center; ACCESS: accessibility; POP: population density; INCP: per capita income; FIRE: fiscal revenue; OWNERS: ownership; PROCLS: protection class.
between 1977 and 2003 at the national scale in China (Shi et al., 2011). The spatiotemporal dynamics resulted from the interaction of natural and socio-economic drivers have important implications for the biodiversity. First, planted forests (i.e., bamboo forest and coniferous forest) were the dominant forest cover classes in the Table 3 Parameter estimation from the binary logistic regression (N = 2817). Variables
Coeff.
p value
7.597 <0.001 Constant SLOPE 0.001 0.976 ELEVATION 0.001 0.089 CTYDIST 0.030 0.044 ROAD <0.001 0.064 ACCESS 0.387 0.257 POP 0.001 0.959 INCP 0.002 0.495 FIRE 0.001 0.009 OWNERS 0.753 <0.001 PROCLS 1.310 <0.001 Chi square of Omnibus test 98.020 <0.001 Chi square of Hosmer and Lemeshow test 12.535 0.129 > 0.05
Odds ratios 0.001 0.999 0.999 1.030 1.000 1.473 0.999 1.002 1.001 0.471 3.706
study area for both of the studied landscapes. Second, similar to many of other countries, such as North America (Powell and Hansen, 2007), Central America (Redo et al., 2012), and Europe (Feranec et al., 2010), the area of coniferous forest increased steadily, while the area of mixed forest decreased sharply in the study area during the period of study. Moreover, forest cover transitions occurred even near the location of the nature reserve of the endangered species (C. kawakamii) (Fig. 2), which has leaded the endangered population being subjected to artificial interference in recent decades (Liu and Hong, 1998). The forests may degrade from primary and secondary forests to planted forests and non-forest land, and such degraded forests are predicted to expand if corresponding restoration measures are not implemented (Zhang et al., 2010). All these findings confirm that the dominant process of the FCC in the upstream area of the Minjiang River, as a whole is likely to have deleterious effects on biodiversity conservation. 5.2. Proximate forces of FCC The modeling result verified the importance of ownership as a key socio-economic variable that structured landscape patterns
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
and dynamics, as demonstrated in previous studies (Stanfield et al., 2002; Nahuelhual et al., 2012). With a unique land system of the separation between land ownership and land use right in China, the forest ownership discussed in this study referred to the land use right of forest. The variable of ownership turned out to be one of the most important predictors of the FCC. The reasons are that the state-owned forests (i.e., the nature reserve) in the study area were protected by the government; instead the forests collective or enterprise-owned were more likely to be changed. Due to the fact that the latter two forests accounted for a large proportion (74.2% as computed from the FRID of 2012) of the total forest cover in the region, more attention should be paid to these forests in regard of the FCC. In the study area, the government played an important role in the FCC. The evidences were as followed: First, fiscal revenue was examined to be a significant predictor for the FCC. This may relate with the fact that the higher activity in timber production and forest planting contributed more to the fiscal revenue of local economy, especially in the area with forestry and related industries as economic lifeline. Our finding was consistent with the forest transitions analysis (Rudel et al., 2005), which revealed that Chinese government enacted an expansion policy for tree plantations during the past two decades. Second, FCC was more likely to occur on unprotected or less protected forests. This finding also verified the importance of the forest protection policy issued by governments. All the biophysical and accessibility factors except the coefficient of distance to the city center were not significantly related to the FCC in the region. This may be explained by the fact that this study was conducted in a small local region with relatively homogeneous spatial paradigm in terms of biophysical interference and accessibility. However, the modeling results still tend to reveal that the areas more vulnerable to future change in the study area would be those located on lower elevations and which also have the higher road density. Yet, we are aware of the complex interactions among the underlying causes of FCC, which can not always be represented by a single model (Nahuelhual et al., 2012). Two considerations may help improve future versions of the model. The first consideration is to use forest cover trajectories over a time series to analyze the impact of socio-economic factors on FCC, rather than two-year transitions. The second consideration is to use geographic information techniques that combining spatial non-stationarity variables with socio-economic data (as spatial weighting value information becomes available) (Poudyal et al., 2012). 5.3. Extensions of the inventory datasets A major distinction between this research and previous studies of FCC (Nahuelhual et al., 2012; Lira et al., 2012) is that we used a typical method of information collection for the FCC analysis (i.e., systematical sampling from the FRID). Information on tree species and age classes were included in the dataset. Such information is fundamental for forest management. Because the forest age may be directly related to forest successional stage, the change in forest age may impact biodiversity (Guariguata and Ostertag, 2001). The forest cover trajectory and resulting forest age structure are very important to assess the current landscape conditions (Ferraz et al., 2009). However, remote sensing methods, including airborne laser scanning, are generally not able to deliver high resolution information on tree species and age structure (Knoke et al., 2010). Studies on the effects of landscape structure on various taxonomic groups usually focus on landscape characteristics, such as forest cover and forest configuration (Martensen et al., 2008), and rarely consider forest age structure (Wimberly and Ohmann, 2004; Etter et al., 2005).
127
Therefore, the FRID, covering much information of the forest resource at the finer level, provides an excellent and unique opportunity to achieve more precise effects of human disturbances on ecological processes. 6. Conclusion This study has identified the spatial pattern of forest conversions in the upstream area of the Minjiang River using FRID. The annual rate of FCC for the landscape was determined as more than 1.0%. The FCC tended to be concentrated in the conversions from mixed forest to coniferous forest and from coniferous forest and mixed forest to non-forest land. Such forest transitions can be accounted for by the different means of management policy implemented by the relevant governments in various forests. The results confirm that human-driven conversions of forest cover have deleterious effects on biodiversity conservation in the region. Our case also verifies that theoretically consistent estimates can be achieved by carefully combining datasets at two different spatial scales (the census block group level and the sample plot level). These findings are important for the relevant policy-makers and planners to take effective planning plantation practices to avoid negative impacts on conserving biodiversity. Acknowledgements This research was funded by the National Natural Science Foundation of China (No.41201100), to which we are very grateful. We are also very grateful for the support provided by the Natural Science Foundation of Fujian (No. 2012J01071) and the Science and Technology Major Project of the Hall of Science and Technology of Fujian (No. 2012NZ0001). Finally, many thanks to all those who provided the raw data used for model regressions and to the Forestry Bureau of Sanyuan District, Fujian Province for providing us with a validation dataset. References Adnan, O., 2011. Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol. 405 (1–2), 123–136. Alonso, W., 1964. Location and Land Use. Harvard University Press, Cambridge, MA. Black, A.E., Morgan, P., Hessburg, P.F., 2003. Social and biophysical correlates of change in forest landscapes of the interior Columbia Basin, USA. Ecol. Appl. 13 (1), 51–67. Bremer, L.L., Farley, K.A., 2010. Does plantation forestry restore biodiversity or create green deserts? A synthesis of the effects of land-use transitions on plant species richness Biodivers. Conserv. 19 (14), 3893–3915. Brockerhoff, E.G., Jactel, H., Parrotta, J.A., Quine, C.P., Sayer, J., 2008. Plantation forests and biodiversity: oxymoron or opportunity? Biodivers. Conserv. 17 (5), 925–951. Burnside, N.G., Smith, R.F., Waite, S., 2003. Recent historical land use change on the South Downs, United Kingdom. Environ. Conserv. 30 (1), 52–60. Cai, Y.B., Zhang, H., Pan, W.B., Chen, Y.H., Wang, X.R., 2013. Land use pattern, socioeconomic development, and assessment of their impacts on ecosystem service value: study on natural wetlands distribution area (NWDA) in Fuzhou city, southeastern China. Environ. Monit. Assess. 185 (6), 5111–5123. Drapeau, P., Leduc, A., Giroux, J.F., Savard, J.P.L., Bergeron, Y., Vickery, W.L., 2000. Landscape-scale disturbances and changes in bird communities of boreal mixed-wood forests. Ecol. Monogr. 70 (3), 423–444. Etter, A., McAlpine, C., Pullar, D., Possingham, H., 2005. Modeling the age of tropical moist forest fragments in heavily-cleared lowland landscapes of Colombia. Forest Ecol. Manag. 208 (1), 249–260. FAO, 2010. Global Forest Resource Assessment 2010 Main Report. FAO, Rome, Italy. Feranec, J., Jaffrain, G., Soukup, T., Hazeu, G., 2010. Determining changes and flows in European landscapes 1990–2000 using CORINE land cover data. Appl. Geogr. 30 (1), 19–35. Ferraz, S.F.D.B., Vettorazzi, C.A., Theobald, D.M., 2009. Using indicators of deforestation and land-use dynamics to support conservation strategies: a case study of central Rondônia, Brazil. Forest Ecol. Manag. 257 (7), 1586–1595. Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A.,
128
X. Hu et al. / Ecological Indicators 46 (2014) 121–128
Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, I.C., Ramankutty, N., Snyder, P.K., 2005. Global consequences of land use. Science 309 (5734), 570–574. Gardner, T.A., Barlow, J., Chazdon, R., Ewers, R.M., Harvey, C.A., Peres, C.A., Sodhi, N. S., 2009. Prospects for tropical forest biodiversity in a human-modified world. Ecol. Lett. 12 (6), 561–582. Gerber, J.F., 2011. Conflicts over industrial tree plantations in the South: who, how and why? Global Environ. Chang. 21 (1), 165–176. Gibson, L., Lee, T.M., Koh, L.P., Brook, B.W., Gardner, T.A., Barlow, J., Peres, C.A., Bradshaw, C.J.A., Laurance, W.F., Lovejoy, T.E., Sodhi, N.S., 2011. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478 (7369), 378–381. Guariguata, M.R., Ostertag, R., 2001. Neotropical secondary forest succession: changes in structural and functional characteristics. Forest Ecol. Manag. 148 (1), 185–206. Halpern, C.B., Spies, T.A., 1995. Plant species diversity in natural and managed forests of the Pacific Northwest. Ecol. Appl. 5 (4), 913–934. Hargis, C.D., Bissonette, J., Turner, D.L., 1999. The influence of forest fragmentation and landscape pattern on American martens. J. Appl. Ecol. 36 (1), 157–172. Islam, K.R., Weil, R.R., 2000. Land use effects on soil quality in a tropical forest ecosystem of Bangladesh. Agr. Ecosyst. Environ. 79 (1), 9–16. Jackson, S.T., Sax, D.F., 2010. Balancing biodiversity in a changing environment: extinction debt, immigration credit and species turnover. Trends Ecol. Evol. 25 (3), 153–160. Knoke, T., Hahn, A., Schneider, T., 2010. Linking inventory and forest optimisation: information and decision-making in forest management. Eur. J. Forest Res. 129 (5), 771–775. Lee, S., 2005. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int. J. Remote Sens. 26 (7), 1477–1491. Lindenmayer, D.B., 2010. Landscape change and the science of biodiversity conservation in tropical forests: a view from the temperate world. Biol. Conserv. 143 (10), 2405–2411. Lira, P.K., Tambosi, L.R., Ewers, R.M., Metzger, J.P., 2012. Land-use and land-cover change in Atlantic Forest landscapes. Forest Ecol. Manag. 278, 80–89. Liu, J.F., Hong, W., 1998. A study on the community ecology of Castanopsis kawakamii – Study on the niche of the main tree population in Castanopsis kawakamii community. Acta Ecologica Sinica 19 (3), 347–352 (in Chinese with English abstract). Loehle, C., Li, B.L., 1996. Habitat destruction and the extinction debt revisited. Ecol. Appl. 6 (3), 784–789. Lomolino, M.V., Perault, D.R., 2000. Assembly and disassembly of mammal communities in a fragmented temperate rain forest. Ecology 81 (6), 1517–1532. Martensen, A.C., Pimentel, R.G., Metzger, J.P., 2008. Relative effects of fragment size and connectivity on bird community in the Atlantic Rain Forest: implications for conservation. Biol. Conserv. 141 (9), 2184–2192. Matlack, G.R., 1994. Plant species migration in a mixed-history forest landscape in eastern North America. Ecology 75 (5), 1491–1502. Mayer, A.L., Kauppi, P.E., Angelstam, P.K., Zhang, Y., Tikka, P.M., 2005. Importing timber, exporting ecological impact. Science 308, 359–360. McGarigal, K., McComb, W.C., 1995. Relationships between landscape structure and breeding birds in the Oregon Coast Range. Ecol. Monogr. 65 (3), 235–260. Miller, D.J., Plantinga, A.J., 1999. Modeling land use decisions with aggregate data. Am. J. Agr. Econ. 81 (1), 180–194. Nagaike, T., Kamitani, T., 1999. Factors affecting changes in landscape diversity in rural areas of the Fagus crenata forest region of central Japan. Landscape Urban Plan. 43 (4), 209–216. Nahuelhual, L., Carmona, A., Lara, A., Echeverría, C., González, M.E., 2012. Land-cover change to forest plantations: proximate causes and implications for the landscape in south-central Chile. Landscape Urban Plan. 107 (1), 12–20. Onojeghuo, A.O., Blackburn, G.A., 2011. Forest transition in an ecologically important region: patterns and causes for landscape dynamics in the Niger Delta. Ecol. Indic. 11 (5), 1437–1446. Ozdemir, A., 2011. Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J. Hydrol. 405 (1), 123–136. Peeters, B., Dewil, R., Smets, I.Y., 2012. Improved process control of an industrial sludge centrifuge dryer installation through binary logistic regression modeling of the fouling issues. J. Process Contr. 22 (7), 1387–1396. Piao, S.L., Fang, J.Y., Zhu, B., Tan, K., 2005. Forest biomass carbon stocks in China over the past 2 decades: estimation based on integrated inventory and satellite data. J. Geophys. Res. 110 (G1), G01006. Poudyal, N.C., Johnson-Gaither, C., Goodrick, S., Bowker, J.M., Gan, J.B., 2012. Locating spatial variation in the association between wildland fire risk and social vulnerability across six southern states. Environ. Manag. 49 (3), 623–635.
Poudyal, N.C., Cho, S.H., Strickland, J.D., Hodges, D.G., 2008. Socio-demographic and market forces of forest land use change on the Northern Cumberland Plateau, Tennessee. Int. J. Ecol. Econ. Stat. 10 (W08), 53–62. Powell, S.L., Hansen, A.J., 2007. Conifer cover increase in the Greater Yellowstone Ecosystem: frequency, rates, and spatial variation. Ecosystems 10 (2), 204–216. Radeloff, V.C., Mladenoff, D.J., Boyce, M.S., 2000. Effects of interacting disturbances on landscape patterns: budworm defoliation and salvage logging. Ecol. Appl. 10 (1), 233–247. Ramdani, F., Hino, M., 2013. Land use changes and GHG emissions from tropical forest conversion by oil palm plantations in Riau Province, Indonesia. PLoS. One. 8 (7), 1–6. Redo, D.J., Grau, H.R., Aide, T.M., Clark, M.L., 2012. Asymmetric forest transition driven by the interaction of socioeconomic development and environmental heterogeneity in Central America. Proceedings of the National Academy of Sciences 109 (23), 8839–8844. Rheinhardt, R., Brinson, M., Meyer, G., Miller, K., 2012. Integrating forest biomass and distance from channel to develop an indicator of riparian condition. Ecol. Indic. 23, 46–55. Rudel, T.K., Coomes, O.T., Moran, E., Achard, F., Angelsen, A., Xu, J., Lambin, E., 2005. Forest transitions: towards a global understanding of land use change. Global Environ. Chang. 15 (1), 23–31. Sala, O.E., Chapin, F.S., Armesto, J.J., Berlow, E., Bloomfield, J., Dirzo, R., HuberSanwald, E., Huenneke, L.F., Jackson, R.B., Kinzig, A., Leemans, R., LODGEDM, Mooney, H.A., Oesterheld, M., Poff, N.L., Sykes, M.T., Walker, B.H., Walker, M., Wall, D.H., 2000. Global biodiversity scenarios for the year 2100. Science 287 (5459), 1770–1774. Schumacher, M., Robner, R., Vach, W., 1996. Neural networks and logistic regression: part I. Computation. Stat. Data An. 21 (6), 661–682. Seto, K.C., Kaufmann, R.K., 2003. Modeling the drivers of urban land use change in the Pearl River Delta, China: integrating remote sensing with socioeconomic data. Land Econ. 79 (1), 106–121. Shevliakova, E., Pacala, S.W., Malyshev, S., Hurtt, G.C., Milly, P.C.D., Casperseon, J.P., Sentman, L., Fisk, J., Wirth, C., Crevoisier, C., 2009. Carbon cycling under 300 years of land use change: importance of the secondary vegetation sink. Global Biogeochem. Cy. 23 (2), 1–16. Shi, L., Zhao, S.Q., Tang, Z.Y., Fang, J.Y., 2011. The changes in China's forests: an analysis using the forest identity. PLoS. One. 6 (6), e20778. Spies, T.A., Ripple, W.J., Bradshaw, G.A., 1994. Dynamics and pattern of a managed coniferous forest landscape in Oregon. Ecol. Appl. 4 (3), 555–568. Stanfield, B.J., Bliss, J.C., Spies, T.A., 2002. Land ownership and landscape structure: a spatial analysis of sixty-six Oregon (USA) Coast Range watersheds. Landscape Ecol. 17 (8), 685–697. State Forest Administration, 2003. Technical Regulations of National Forest Continuous Inventory. Internal Document of Chinese Government, Beijing (in Chinese). Strengers, B.J., Van Minnen, J.G., Eickhout, B., 2008. The role of carbon plantations in mitigating climate change: potentials and costs. Climatic Change 88 (3–4), 343– 366. Su, S.J., Liu, J.F., He, Z.S., Hong, W., Zhang, J.B., 2012. The spatial heterogeneity of soil nutrients in a mid-subtropical Castanopsis kawakamii natural forest. Acta Ecol. Sin. 32 (18), 5673–5682 (in Chinese with English abstract). Thünen von, J.H., Heinrich, J., 1966. The Isolated State. Trans. Carla M. Wartenberg. Pergamon, Oxford. Turner, M.G., Wear, D.N., Flamm, R.O., 1996. Land ownership and land-cover change in the southern Appalachian highlands and the Olympic peninsula. Ecol. Appl. 6 (4), 1150–1172. Wimberly, M.C., Ohmann, J.L., 2004. A multi-scale assessment of human and environmental constraints on forest land cover change on the Oregon (USA) coast range. Landscape Ecol. 19 (6), 631–646. Xie, X.K., Wang, Q.L., Dai, L.M., Su, D.K., Wang, X.C., Qi, G., Ye, Y.J., 2011. Application of China's national forest continuous inventory database. Environ. Manag. 48 (6), 1095–1106. Yang, Y.S., Chen, G.S., Guo, J.F., Xie, J.S., Wang, X.G., 2007. Soil respiration and carbon balance in a subtropical native forest and two managed plantations. Plant Ecol. 193 (1), 71–84. Zhang, D.W., Nagubadi, R.V., 2005. The influence of urbanization on timberland use by forest type in the Southern United States. Forest Policy Econ. 7 (5), 721–731. Zhang, X.H., Huang, Q.L., Zhang, C., 2010. Analysis of forest landscape dynamics based on forest landscape restoration: a case study of Yong'an city, Fujian province. China Eur. J. Forest Res. 129, 975–980. Zheng, D.L., Heath, L.S., Ducey, M.J., Quayle, B., 2013. Assessing double counting of carbon emissions between forest land-cover change and forest wildfires: a case study in the United States 1992–2006. Ecosystems 16, 310–322.