Evaluating the vegetation restoration potential achievement of ecological projects: A case study of Yan’an, China

Land Use Policy 90 (2020) 104293

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Evaluating the vegetation restoration potential achievement of ecological projects: A case study of Yan’an, China

T

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Xin Xua, Daojun Zhanga,b, , Yu Zhanga, Shunbo Yaoa,b, Jinting Zhangc a

College of Economics and Management, Northwest A&F University, Yangling 712100, China Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, China c School of Resources and Environmental Sciences, Wuhan University, Wuhan 430079, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Ecological policy Habitat similarity Potential achievement evaluation Vegetation restoration Spatial analysis Yan’an

Evaluating the effects of ecological projects is important for summarizing past experience and for exploring more effective ways to implement land use policies in the future. The base of ecological restoration is vegetation restoration. Consequently, previous studies have predominantly focused on ecological restoration from the view of vegetation coverage improvement (i.e., the growth of a vegetation index). However, vegetation coverage also reflects geographical differences in natural environmental factors. Thus, vegetation index growth rates reflect, to a large extent, differences in resource endowment, rather than in human effort. Using habitat theory and a spatial sliding window model, this study proposes the concept of vegetation restoration potential achievement (VRPA). Taking ecological restoration practices in Yan’an, China, since 1999 as an example, we evaluate the effect of ecological projects in terms of both a vegetation index and VRPA. The results show that the latter can effectively weaken the impact of resource endowment differences and highlight human factors (i.e., the ecological policy itself and its implementation). This approach improves land use policy evaluation by constructing a novel indicator. It is expected that this method will provide better support for regulating ecological restoration through land use policies.

1. Introduction With rapid economic development, China has experienced unprecedented industrialization and urbanization (Liu and Li, 2017). This has inevitably led to a series of environmental problems, including land degradation, scarcity of resources, and ecological destruction (Liu et al., 2008b, 2014; Lyu et al., 2018; Wieland et al., 2019; Xing et al., 2019). These can be attributed to human-earth relationship issues such as contradictions in the land use structure (Cheng et al., 2019), rapid expansion and inefficient use of urban and rural construction land (Liu et al., 2018), and the placing of greater value on development than on protection in the land use process (Wang et al., 2012; Jin et al., 2019). However, in the long term, unsustainable use of land restricts the sustainability of economic development (Liu, 2018). In the Loess Plateau region of northwest China, the pressures of population growth and economic development have resulted in extensive deforestation and land reclamation since the 1970s (Shi and Li, 1999; Li et al., 2008; Liu et al., 2013). Most of the newly exploited arable land has poor natural conditions for agriculture. These conditions include steep slopes, low soil fertility, and high probability of generating ecological problems

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such as soil erosion (Shi and Li, 1999; Zhang et al., 2003). To protect and improve the ecological environment and to promote sustainable land use, so as to promote sustainable economic and social development, the Chinese government has responded to the national land-system sustainability emergency through an integrated portfolio of natural-scale land use programs (Yin, 2009; Yin et al., 2014a; Bryan et al., 2018). The most high-profile of these projects include the Grain for Green Program (GFGP) and the Natural Forest Conservation Program (NFCP) (Yin and Yin, 2010). These projects have rapidly changed land use/cover patterns (Yin et al., 2010; Yin and Zhao, 2012; Bryan et al., 2018; Cao et al., 2018), reversed large-scale arable land reclamation activities (Shi and Li, 1999; Li et al., 2008; Liu et al., 2013), and effectively promoted the construction of ecological civilizations. However, vegetation restoration still has a long way to go, as the 2012 United Nations Rio+20 Conference on Sustainable Development put forward a target of restoring 350 million hectares of deforested and degraded land by 2030 (International Union for Conservation of Nature (IUCN, 2012). Therefore, it is of great significance to accurately evaluate past ecological policies with the aim of improving future outcomes.

Corresponding author at: College of Economics and Management, Northwest A&F University, Yangling 712100, China. E-mail address: [email protected] (D. Zhang).

https://doi.org/10.1016/j.landusepol.2019.104293 Received 13 May 2019; Received in revised form 20 September 2019; Accepted 8 October 2019 0264-8377/ © 2019 Elsevier Ltd. All rights reserved.

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factors. To avoid these issues, it is necessary to replace the global spatial statistical model with a local spatial statistical model (Brunsdon et al., 1996; Fotheringham et al., 1996). As an important solution/tool to overcome spatial differentiation, the spatial sliding window technique is often used to construct local statistical models that address the shortcomings of global statistical models by dealing with spatially heterogeneous data (Zhang et al., 2016, 2018b). Here, we use spatial sliding windows to better construct similar habitat units and propose a novel model for vegetation restoration potential achievement (VRPA) evaluation. The main contribution of this study is a new indicator, the vegetation restoration potential achievement degree (VRPAD), which is suitable for the evaluation of ecological projects and land use policy. Compared with traditional indicators, VRPAD can effectively overcome the impact of resource endowment differences, and more objectively reflects the ecological restoration effects resulting from implementation of ecological projects and land use policy.

Vegetation restoration is an important indicator for evaluating the implementation of ecological projects (Li et al., 2016; Zhou et al., 2018). Furthermore, vegetation restoration is a precondition for ecological restoration. Because only vegetation is restored, ecological functions such as soil and water conservation, carbon sequestration, and biodiversity can be realized. There are numerous methods for evaluating the effects of vegetation restoration. For example, field observations can be performed to obtain corresponding evaluation indicators, such as vegetation coverage, species richness, and biomass to ultimately determine recovery effects (Fill et al., 2017; Halik et al., 2019). Alternatively, a comprehensive index can be developed by constructing a system with different observed indicators, with the weight for each indicator determined either qualitatively or quantitatively (Yan et al., 2014). The development of detection technology has spurred the availability of remote sensing images with high spatial and temporal resolutions that provide new means for evaluating the restoration effects of ecological projects. Through the analysis and mining of remote sensing data, ground features such as vegetation coverage and biomass productivity can be measured quantifiably (Wu et al., 2014; Riva et al., 2017; Li et al., 2018b). Compared with point observations conducted in the field, remote sensing observations provide rapid, efficient, highly objective, and reliable data. Therefore, remote sensing data have been widely used for vegetation coverage monitoring and evaluation (Wang et al., 2018; Yin et al., 2018). Remote sensing vegetation indices (VI) can effectively reflect a region’s vegetation coverage (Hilker et al., 2015). Thus, they have been widely used to assess ecosystem governance and vegetation restoration (Li et al., 2017a; Tong et al., 2017). The most commonly used VIs in vegetation restoration evaluation include the Normalized Difference Vegetation Index (NDVI; Costantini et al., 2012; Shen et al., 2018) and the Enhanced Vegetation Index (EVI; Dubovyk et al., 2015; Zhang et al., 2018a). Although the purpose of ecological policies is the restoration of vegetation and ecology, changes in vegetation coverage result from a combination of factors, including those controlled by natural conditions (Li et al., 2015; Cao et al., 2017; Li et al., 2017b) and their responses to global environmental change (Liu et al., 2019). As natural variables always show spatial variability, theoretical maximum potential values that can be achieved by vegetation restoration vary from place to place. Therefore, the improvement of a vegetation index reflects, to a large extent, differences in resource endowment, rather than human effort in ecological restoration. Only by eliminating the impact of natural resources can we reflect the effect of vegetation restoration resulting from ecological policies. Under the framework of existing ecological project and land use policy evaluation theories and methods, constructing a more objective and accurate evaluation indicator is the goal of this study. In ecology, vegetation growth potential areas are usually divided based on similar habitats (i.e., locations with similar natural conditions show similar landscapes). For example, under the same terrain, climate, soil, and hydrology, vegetation coverage should also be similar; any differences among locations with similar natural conditions show the potential for further growth. We define this kind of potential as the similar habitat based vegetation restoration potential (SHBVSP). Differences in SHBVSP lead to differences in the difficulties of vegetation restoration; the greater the SHBVSP, the easier it is to restore vegetation, and vice versa (Bisson et al., 2008; Arianoutsou et al., 2011). To evaluate vegetation restoration in the gully region of the Loess Plateau, Gao et al. (2017) used soil, topography, and meteorological condition indicators to delineate similar habitat units, measured their vegetation coverage using SPOTVEG NDVI data, and then used statistical methods and geospatial analysis techniques to build the SHBVSP model. The only drawback was that similar habitat sets were divided across the study area, ignoring the spatial variability of environmental variables affecting vegetation growth. Consequently, it is hard to overcome the evaluation risk caused by lack of important influencing

2. Study area and data 2.1. Study area Yan'an is located in northern Shaanxi Province, on the middle reach of the Yellow River (Fig. 1; 35°21′–37°31′N and 107°41′–110°31′E). Its total area is ∼37,000 km2. Yan’an is a typical hilly area of the Loess Plateau that comprises many crisscrossing gullies and valleys of various sizes. The terrain is high in the northwest and low in the southeast, with an average elevation of ∼1200 m. Yan'an belongs to a warm temperate, semi-humid, and drought-prone climate zone, with annual climate change subject to monsoon circulation. The annual average temperature is ∼9.5 °C and its annual average precipitation is ∼506.5 mm. As of 2016, the total resident population was 2,252,800. In 1999, Yan'an took the lead in carrying out the GFGP, and thus presents nearly 20 years of vegetation restoration. As shown in Fig. 1, there are 13 counties in Yan’an. 2.2. Data acquisition and preprocessing This study focuses on the realization of vegetation restoration potential, and thus VI data were needed to calculate the vegetation restoration potential (VRP) and the degree to which it has been achieved (i.e., VRPAD). As the VRP calculation is based on similar habitats, it is necessary to obtain topographic and soil type data to determine habitat similarity. The local window spatial statistical model was adopted to deal with the spatial heterogeneity of environmental variables. Given that environmental variables show different spatial heterogeneity intensities, the elevation, soil type, terrain slope, and aspect, which all influence vegetation growth at the local window scale (18 km), were selected to create divisions for similar habitats. Climate variables could be considered to change only marginally within the local 18-km window; therefore, their influences on vegetation growth were ignored. Besides, the model does not consider global climate change, including changes in temperature and rainfall, although studies have shown that climate change affects the suitability of vegetation growth. However, whereas the impact of climate change on phenology requires relatively long-term observations (Liu et al., 2019), the vegetation coverage data of this work only covered 16 years. In addition, meteorological factors, road data, and nighttime light imagery were also collected to demonstrate the advantages of VRPAD compared with traditional indicators. All of these variables were obtained in the form of spatial data. To perform the modeling required for this study, it was necessary to unify the format, resolution, and spatial references of the different source data. To this end, we used the World Geodetic System 1984 (WGS-84) ellipsoid to establish reference coordinated data. Then, we developed a projected coordinate system with projection mode and central meridian set to the Universal Transverse 2

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Fig. 1. Location of the Study area.

of the study area (Fig. 2g).

Mercator and 109 °E, respectively. Considering that areas with terrain slopes of 0°–6° are not involved in vegetation restoration, they were masked from the study area.

2.2.3. Socio-economic data In China, a village is the most fundamental administrative region, as well as the most basic unit of land ownership; villages function as collective economic organizations. A village is generally defined by topographical or natural features; thus, it is an ideal area for studying vegetation restoration. However, extensive and publicly accessible village-level socio-economic statistical data are not currently available. The development of spatial information technology and the improvement of the basic land information database has enabled us to collect relatively detailed nighttime light imagery in the form of spatial grid data (with the cell size of 1 km × 1 km); data for rural road networks are in the form of vectors. Studies have revealed that night-time light data is strongly correlated with economic activity (Sutton and Costanza, 2002). Some researchers even produced pixel-level maps using the night-time light data, for finding the intensity of economic activity (Zhao et al., 2017). As for road network data, these represent the development level of infrastructure; this data can be regarded as an important indicator of social development (Baum-Snow, 2007; Li et al., 2018a). In conclusion, the night-time light data and rural road network data were used to depict the intensity of village-level economic activities and the level of social development in the area studied, respectively. Rural road network data at a scale of 1:1,000,000 was obtained from the National Fundamental Geo-information Database, which was shared by the National Geomatics Center of China (http://ngcc.sbsm.

2.2.1. Meteorological data The meteorological data used in this study were obtained from the meteorological data center of the China Meteorological Administration (http://data.cma.cn/site/index.html). Data for these indices (annual average values of precipitation, humidity, air temperature, and days below 0 °C) were sourced from ground monitoring stations from 1981 to 2010. After spatial interpolation processing in ArcGIS 10.2, four grid layers were obtained for the study area, with a grid size of 90 × 90 m. The results are shown in Fig. 2(a)–(d). 2.2.2. Topographic and soil data Elevation data (ASTER GDEM, Version 2) were downloaded from the Geospatial Data Cloud Platform of the Chinese Academy of Sciences (http://www.gscloud.cn). The data were in raster format with a cell size of 90 × 90 m. Through image mask processing, the downloaded elevation data were cropped to fit the range of the study area. We obtained terrain slope and aspect data using the Surface Toolset provided in the ArcToolbox of the ArcGIS 10.2 platform; these are shown in Fig. 2(e) and (f), respectively. A vector soil type map of the study area at a scale of 1:1,000,000 was derived from the Institute of Soil Science, Chinese Academy of Sciences (Shi et al., 2004, 2006). The map was converted to raster dataset with a spatial resolution of 90 × 90 m, and masked to the range 3

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Fig. 2. Explanatory variables used in this study: (a)–(i) annual average values of precipitation (mm), humidity (g/kg), temperature (°C), days below 0 °C, terrain slope (°), aspect, soil type, road network density (m/km2), and nighttime light intensity.

through band calculation from remote sensing images. The Moderate Resolution Imaging Spectroradiometer (MODIS) provides both the NDVI and EVI in its series of vegetation index products, with different spatial and temporal resolutions. Of these, we chose to use the synthetic vegetation index product MOD13Q1 V6 (Didan, 2015), which has the highest spatial resolution (250 × 250 m) and a temporal resolution of 16 days. Compared with NDVI, EVI incorporates improvements in algorithms and synthesis methods (Qiu et al., 2013; Dutta et al., 2015) that can better reflect variability in spatial vegetation (Li et al., 2007). Consequently, they were adopted here. To eliminate the influence of accidental factors and better reflect vegetation growth state, we used annual average EVI values during the

gov.cn/). Road network density was calculated using the density analysis tool from the ArcToolbox in ArcGIS10.2 with a search radius of 10 km; the results were in raster form with a cell size of 90 × 90 m (Fig. 2h). The Desaturate Radiation Calibration Nighttime Light Image (DRCNLI) was provided by the National Oceanic and Atmospheric Administration (https://www.ngdc.noaa.gov). The original DRCNLI dataset was global, but was masked to obtain the range of the study area with a cell size of 90 × 90 m (Fig. 2i).

2.2.4. Vegetation coverage data VIs have been widely employed in land use and environmental research as an empirical measure of vegetation state; they are obtained 4

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growing season (97th–289th days of the year) as the final EVI data for each year. The study area lay completely within the MODIS scene H26V05. Preprocessing the EVI data included cutting it to the range of the study area, resampling the raster data with a spatial resolution of 90 × 90 m, unifying the spatial reference to the standard mentioned above, and further masking the area with a terrain slope of less than 6°.

(VRPI) for the ith row and jth column at the current location; V1, V2, ⋯VN represent the environmental variables at the current location, supposing that there are N environmental condition variables; Max (uij, R)EVIkl (V1, V2, ⋯ VN ) finds the maximum EVI from grids with 1≤k≤m 1≤l≤n

the same V1, V2, ⋯VN values as the current location, supposing that there are m rows and n columns in the local window; and EVIij (V1, V2, ⋯VN ) is the EVI value of the current grid. To avoid uncertainty in our data, we used 0.98 of the maximum EVI distribution instead of the maximum value in calculating the VRPI. The EVI maximum layer was constructed with the integration of all years’ EVI layers in order to eliminate interannual variability of meteorological conditions.

2.2.5. GFGP intensity data Utilizing the land-use data obtained through artificial interpretation, we calculated the GFGP intensity within each village according to the area ratio of cultivated land converted into forest and grassland coverage prior to when the land was cultivated (Zhang et al., 2018a, 2019). Thus, GFGP intensity is used to depict the GFGP implementation efforts at the village scale. According to the design of the GFGP policy in China, the ecological compensation paid by the government to the farmers is calculated directly according to the area of cultivated land converted into forest and grassland. Therefore, in theory, there should be no difference in the cost incurred between utilizing the area enrolled and the amount invested by the government.

3.3. Vegetation restoration potential achievement model The ratio of the actual EVI value to the VRPI value is defined as the degree of vegetation restoration potential achievement (VRPAD). Because the 98th percentile value is used to calculate the VRPI (instead of the maximum potential), the ratio value may be greater than one. Under this condition, the ratio should be forced to one (i.e., the potential is 100% achieved). Likewise, some EVI values may be less than zero, indicating that there is no vegetation coverage, which will lead to negative ratio values. Under this condition, the ratio should be forced to be zero. Finally, the VRPAD for the location of row i and column j in year t can be expressed as follows:

3. Modeling methods 3.1. Linear growth rate model The ordinary least-squares (OLS) method was used to estimate growth rate. The objective function of the OLS method for solving for the regression parameter obtains a minimum value for the sum of the squares of the difference between the estimated and observed values of the dependent variable. The slope (regression coefficient) was estimated as the growth rate: n

slope =

n

VRPADt _ ij =

n

n × ∑i = 1 VIi × i − ∑i = 1 VIi ∑i = 1 i n

n

n × ∑i = 1 i 2 − (∑i = 1 i)

2

⎨ MEVIij (V1, V2, ⋯VN ) ⎪ 1 ⎩

if EVIt _ ij ≤ 0 if EVIt _ ij > 0 & EVIt _ ij ≤ MEVIij if EVIt _ ij > MEVIij (3)

(1)

where slope represents the growth rate; and VIi refers to a vegetation index in year i, where i ranges from 1 to n . The growth rate is used to quantify the magnitude of the variation in the dependent variable (i.e., the VI data) with the independent variable (i.e., time), which can reflect trends in vegetation coverage.

4. Results and analysis 4.1. Vegetation growth rate To compare the performance of vegetation restoration at different stages, we divided the data into two stages, 2000–2008 and 2009–2016, as the first round of the GFGP was largely finished in around 2008 in the study area. The EVI growth rate over these two stages can be obtained according to Eq. (1); the mean values were calculated at the village level, as seen in Fig. 3(a) and (b), respectively. The EVI growth rate was calculated through unary linear regression, taking time (in years) as the independent variable and the EVI as the dependent variable. This approach can intuitively reflect changes in vegetation coverage over time. Fig. 3 illustrates that the EVI growth rate was much higher in the first stage than in the second stage, which indicates that the EVI growth rate decreased during vegetation restoration and the VRP approached its limit. The limitations to the VRP are reflected in the maximization function in Eq. (2). In the first stage (Fig. 3a), owing to the implementation of new ecological measures (e.g., returning cultivated land to forest and grassland, and national prohibitions against grazing and logging), comparatively higher EVI growth rates predominantly occurred in regions with relatively higher humidity (Fig. 2b), higher temperature (Fig. 2c), and lower and flatter terrain (Figs. 1 and 2e); that is, central Yan'an and the area along the Yellow River (Fig. 1). This indicates that vegetation coverage is first restored in areas with good resource endowments. Over time, the high EVI growth rate values moved northward during the second stage (Fig. 3b), indicating that in areas with poor resource endowments, vegetation restoration brought about by ecological policies generally takes longer to become apparent. In addition, as a county based on forestry and tourism, Huangling (southwest of the

3.2. Sliding-window-based similar habitat potential model Overexploitation of resources and the environment by human beings, as well as unreasonable economic activities, can degrade the landscape (Simpson et al., 2001; Hammad and Tumeizi, 2012). When these disturbances are eliminated or mitigated, the ecological environment may naturally recover and, after a certain period, can reach or approach their optimal state under natural conditions (Odum, 1969; Ren et al., 2004). Therefore, the gap between the current and optimal vegetation states can be defined as the VRP. In a Geographical Information System (GIS) platform, the current vegetation state at each location can be represented by the current year’s EVI value at that grid location, and its optimal growth state can be represented by the maximum EVI value at a similar habitat location (Gao et al., 2017; Nauman et al., 2017). Considering the spatial variability of environmental variables’ effects on vegetation growth and the lack of important unknown variables (Zhang et al., 2018b), similar habitats can be best constructed using a local window model. Therefore, we obtained the sliding-window-based similar habitat potential model (SWSHPM) as follows:

VRPIij (V1, V2, ⋯VN ) = Max (uijt , R)EVIkl (V1, V2, ⋯VN ) 1≤k≤m 1≤l≤n

− EVIij (V1, V2, ⋯VN )

0 ⎧ ⎪ EVIt _ ij (V1, V2, ⋯VN )

(2)

where VRPIij (V1, V2, ⋯VN ) is the vegetation restoration potential index 5

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Fig. 3. Enhanced Vegetation Index (EVI) growth rates at the village level for (a) 2000–2008 and (b) 2009–2016.

growth rates were mainly located in areas with better resource endowments, whereas low values were distributed in areas with relatively poor resource endowments. However, there were some exceptions. For example, in Wuqi and Zichang, the vegetation growth rate in the first stage was very low (Fig. 3a), but the VRPAD growth rate was high in many villages. This anomaly occurred because the two counties are both in the northernmost part of Yan'an, where climatic conditions are poor; therefore, the maximum EVI value that can be achieved there is relatively low. Furthermore, ecological policies were implemented very effectively in these two counties, which serve as demonstration counties for the GFGP (http://www.forestry.gov.cn). Additionally, large areas of fast-growing alfalfa were planted in these areas, enabling them to achieve great potential in a short period. In the second stage, the spatial distribution patterns of the VRPAD (Fig. 4b) and the EVI (Fig. 3b) growth rates became more similar. In

study area) had always maintained high vegetation coverage, and thus did not show high EVI growth rates in either stage. Furthermore, despite being located in southeast Yan'an, which has relatively better climatic conditions, the terrain conditions in Huanglong are poor, which also impacted the EVI growth rates, which were lower than those of neighboring regions in the first stage. 4.2. Growth rates for VRPAD Corresponding to the EVI growth rate described in Section 4.1, the VRPAD growth rate was also divided into two stages. First, VRPAD was calculated according to Eqs. (2) and (3). Then, its growth rate was calculated according to Eq. (1). Finally, mean values at the village level were obtained, as shown in Fig. 4(a) and (b). Similar to the vegetation growth rate, in the first stage, high VRPAD

Fig. 4. Vegetation restoration potential achievement degree (VRPAD) growth rates at the village level for (a) 2000–2008 and (b) 2009–2016. 6

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areas with low VRPAD growth rates in the first stage, compensatory growth of EVI could be achieved in the second stage, as seen in Zhidan, Ansai, and Huanglong (Figs. 3 and 4, and Table 2), because the VRP was limited, some extremes were determined by local resource endowment conditions. In some regions, afforestation was implemented for shortterm performance, without considering the potential for ecological restoration, which may exceed the resource endowment limitation. Although it may produce immediate effects, the achievements are not sustainable or the maintenance costs are much higher (Yin et al., 2014b). Conversely, with scientific and reasonable planning, although short-term achievement is not obvious, durable growth can be attained at lower cost.

reflect third-party variables (i.e., ecological policies and measures), which reflect human effort and not resource endowment. Regarding topographic variables, smaller terrain slopes and lower elevations were more favorable to rapid EVI increases; the correlation coefficients were −0.144 and −0.601, respectively, significant at a level of 0.01. As can be seen from Figs. 1 and 2e, locations with higher EVI growth rates are more likely to appear in Yanchuan, Yanchang, and Yichuan, where it is relatively lower and flatter, from 2000–2008. Zhang et al. (2013) found that vegetation on steep slopes is much more easily affected by soil erosion and other natural disasters, which is consistent with this phenomenon. However, the absolute values of the correlations between these variables and the VRPAD growth rate became much smaller (−0.055 and −0.550, respectively). As regards socio-economic factors, the correlation coefficient between nighttime light intensity and the EVI growth rate was −0.165 with a significance of 0.01, indicating that human activities are not conducive to vegetation restoration. This is consistent with the phenomenon that vegetation coverage is generally low in economically developed areas (Hou et al., 2016). Road network density was positively correlated with the EVI growth rate, with a correlation coefficient of 0.200 (at a significance level of 0.01), indicating that road network construction can promote ecological project construction and the maintenance of vegetation restoration achievements. This makes sense because traffic accessibility could benefit the maintenance of ecological projects (Zhang et al., 2018a). Although we made certain discoveries from the analyses and results of this study, which were in agreement with those of former studies, more general conclusions should be made after a large number of cases are studied. However, the correlations between these two variables and the VRPAD were much smaller (−0.034 and 0.074, respectively). Interestingly, in examining policy variables, we found that the correlation coefficients between GFGP intensity (Zhang et al., 2019) and the growth rates of EVI and VRPAD were 0.092 and 0.223, respectively. The latter is much greater than the former, showing that VRPAD can better reflect policy factors and is a suitable index for evaluating the effects of ecological projects. In summary, resource endowments, including both natural and socio-economic conditions, have much greater influences on the growth rate of the EVI than of the VRPAD, whereas policy variables have a greater influence on the growth rate of the VRPAD than of the EVI, which confirms our initial hypothesis.

4.3. Attribution analyses for EVI and VRPAD growth rates As mentioned above, the improvement of vegetation coverage is the result of a combination of factors, and our case study shows that the dominant controlling factor is resource endowment. We hypothesized that VRPAD can effectively reduce the impact of resource endowment and better reflect the impact of policies and their implementation on vegetation restoration. Here, we performed spatial correlation analyses at the village level to verify this hypothesis based on first-stage EVI (Fig. 3a) and VRPAD (Fig. 4a) growth rates, as shown in Table 1. In terms of meteorological variables, fewer days at less than 0 °C and higher annual average temperatures were favorable for increases in EVI. The correlation coefficients for these two variables were −0.275 and 0.510, respectively, at a significance level of 0.01, reflecting that better photothermal conditions can benefit vegetation restoration. For instance, in the southeast part of Yan’an, where days at less than 0 °C shows a low value (Fig. 2d) and the annual average temperature shows a high value (Fig. 2c), the EVI growth rate for 2000–2008 was much higher than in other areas (Fig. 3a). The higher the precipitation and humidity, the faster the EVI increased. The correlation coefficients were 0.135 and 0.233, respectively, which were significant at the 0.01 level, reflecting that vegetation recovered better in relatively wet environments. The study area belongs to arid and semi-arid areas, and moisture is the main ecological factor affecting vegetation growth. Thus, the spatial distribution of water, which is jointly determined by precipitation and humidity, has an important influence on the spatial patterns of vegetation restoration (Sun et al., 2015). However, compared with the relationships between these meteorological variables and EVI growth rates, these variables’ influence on increasing VRPAD were much smaller. It can be seen that either the absolute values of the coefficients between these variables and the growth rates of the target variable were significantly reduced or the signs (+/−) of the coefficients were changed, when the target variable is changed from EVI to VRPAD. The latter may be caused by several factors; for example, regions with poor water conditions that may have suffered more serious ecological degradation, and areas where ecological project investment was much greater. Thus, vegetation restoration was even better. These differences

4.4. Effect of vegetation restoration at the county level As the county is the basic unit for policy implementation in China, we evaluated the effect of vegetation restoration at the county level. Table 2 provides county rankings for both the VRPAD and EVI growth rates. In the first stage, the VRPAD increase rate was relatively high in Yanchang, Yanchuan, Zichang, Yichuan, Baota, Ganquan, and Wuqi. Differing from other counties, whose high rankings mainly result

Table 1 Correlation coefficients between each independent variable and the Enhanced Vegetation Index (EVI; Column 3) and vegetation restoration potential achievement degree (VRPAD; Column 4) growth rates. Variable Climate variable

Terrain variable Socio-economic variable Policy variable

Days below 0 °C Annual average humidity Annual average precipitation Annual average temperature Terrain slope Elevation Nighttime light intensity Road network density GFGP intensity

EVI growth rate

VRPAD growth rate

−0.275** 0.233** 0.135** 0.510** −0.144** −0.601** −0.165** 0.200** 0.092**

0.005 −0.167** −0.272** 0.287** −0.055** −0.550** −0.034* 0.074** 0.223**

* and ** indicate significance at levels of 0.05 and 0.01, respectively. GFGP, Grain for Green Program. 7

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Table 2 Enhanced Vegetation Index (EVI) and vegetation restoration potential achievement degree (VRPAD) growth rate rankings at the county level in Yan’an for 2000–2008 and 2009–2016. County

2000–2008

2009–2016

EVI growth rate

Ansai Baota Fuxian Ganquan Huangling Huanglong Luochuan Wuqi Yanchuan Yanchang Yichuan Zhidan Zichang

VRPAD growth rate

EVI growth rate

VRPAD growth rate

Value

Ranking

Value

Ranking

Value

Ranking

Value

Ranking

0.0768 0.1026 0.0904 0.0953 0.0662 0.0682 0.0893 0.0650 0.0933 0.1114 0.1110 0.0550 0.0891

9 3 6 4 11 10 7 12 5 1 2 13 8

0.0199 0.0239 0.0191 0.0210 0.0132 0.0130 0.0201 0.0207 0.0257 0.0290 0.0247 0.0145 0.0256

9 5 10 6 12 13 8 7 2 1 4 11 3

0.0620 0.0583 0.0263 0.0386 0.0200 0.0565 0.0342 0.0283 0.0723 0.0678 0.0467 0.0642 0.0381

4 5 12 8 13 6 10 11 1 2 7 3 9

0.0161 0.0137 0.0054 0.0084 0.0040 0.0106 0.0075 0.0084 0.0199 0.0177 0.0100 0.0171 0.0107

4 5 12 9 13 7 11 10 1 2 8 3 6

human factors. Our case study supports this theoretical analysis. Therefore, the VRPAD growth rate indicator can well reflect the impact of ecological policy intensity and execution efficiency on vegetation restoration and can be used as an important reference for evaluating differences in the performance of local governments in the implementation of ecological projects. The analysis results obtained using the new model are logical and are consistent with previous studies, especially for areas with poor resource endowments. (2) The growth rates of the EVI and VRPAD are both different from and associated with each other. Generally, after vegetation destruction, areas with better resource endowments have greater restoration potential, and the EVI and VRPAD growth rates should also be greater in these areas. Differences in human factors may cause a deviation between these two indicators. However, this divergence mainly occurs in the short term, and the two metrics tend to be consistent in the long run. For instance, during 2000–2008, the EVI and VRPAD showed the greatest deviations in Wuqi and Zichang, where the VRPAD rank was much higher than that of EVI, and in Fuxian, Ganquan, Huanglong, Baota, and Yichuan, where EVI rank was much higher than that of VRPAD. However, this divergence narrowed or even disappeared during 2009–2016. This result reflects the fact that during ecological restoration, resource endowments should be fully considered. Although a high investment in ecological projects can bring about short-term rapid restoration of vegetation, once the VRP limit is reached or approached, the EVI growth rate will be greatly slowed. Therefore, when planning vegetation restoration, it is necessary to comprehensively consider and develop ecological project measures according to both shortterm and long-term goals in order to achieve optimal benefits. With respect to the study area, Wuqi County has relatively poor resource endowment conditions, but was given the strongest policy support in the application of GFGP. This is reflected in its high deviation between growth rate ranks of EVI (No. 12) and VRPAD (No. 7), which are the largest among the 13 counties during 2000–2008 (i.e., excessive human intervention has caused a rate of vegetation restoration far beyond the level determined by its resource endowment). In this case, the EVI growth rate during 2009–2016 was still relatively low (No. 11). In contrast, Zhidan showed well-matched growth rates of EVI and VRPAD during 2000–2008, ranking No. 13 for EVI and No. 11 for VRPAD. During 2009–2016, Zhindan showed the greatest improvement in EVI growth rate rank, moving to position No. 3. (3) The VRPAD provides a new perspective to understand the process of vegetation restoration. By comparing EVI and VRPAD growth rates,

from the benefits of both resource endowment and ecological policy implementation, Zichang and Wuqi are resource-poor; their high rankings are mainly due to their efficient implementation of the GFGP. In particular, Wuqi is known for its excellent performance in implementing the GFGP, and its input costs and maintenance efforts are much higher than those of other regions (Zhang et al., 2018a). There are many state-owned forests in Fuxian, Huangling, and Huanglong counties, and vegetation destruction in these counties was far less serious than in other counties before the implementation of ecological policies. The VRP was relatively low, and thus, their VRPAD growth rate rankings are low. In contrast, before the implementation of the GFGP in Zhidan, vegetation damage was already serious and its VRP was relatively large. Its low VRPAD growth rate may be caused by the longer growth cycle of the species that were planted, which need a longer time to show vegetation restoration effects. This conjecture is supported by Zhidan’s high growth rate rankings (3rd) in both EVI and VRPAD in the second stage. In summary, EVI growth rates are mainly controlled by resource endowments, whereas VRPAD growth rates better reflect differences caused by human factors. However, in regions with good resource endowments, if the intervention of human factors such as policies and their implementation are similar, EVI and VRPAD growth rates will be similarly high (i.e., there is potential consistency between these two variables). The rank correlation coefficients between the EVI and VRPAD growth rates were calculated for both stages (0.72 in the first stage, and 0.96 in the second stage). The comparatively higher correlation coefficient in the second stage indicates that, although human factors such as policies can perturb vegetation growth trends determined by resource endowments in the short term, the effect of this external force is gradually weakened in the long run, and EVI and VRPAD growth rates will tend to be consistent. 5. Conclusions With a focus on vegetation restoration potential achievement, and based on habitat theory and the spatial sliding window method, we proposed a novel index (i.e., the VRPAD) for evaluating the effects of ecological projects. Empirical analysis was carried out to test the performance and characteristics of the new index, from which we drew the following conclusions. (1) The new model calculates the VRPI based on similar habitats in a local window, and then calculates the VRPAD and its annual growth rate. Thus, it can diminish the impact of resource endowment differences on vegetation restoration and highlight the impact of 8

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we can further understand the relationships between resource endowment and human activity in vegetation restoration and ecological reconstruction. This knowledge will improve our ability to play a positive role in restoring and protecting the environment when making land use policies.

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