Research ArticleECOSYSTEMS

# Effects of conservation policy on China’s forest recovery

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Science Advances  18 Mar 2016:
Vol. 2, no. 3, e1500965

## Abstract

Forest loss is one of the most pervasive land surface transformations on Earth, with drastic effects on global climate, ecosystems, and human well-being. As part of biodiversity conservation and climate change mitigation efforts, many countries, including China, have been implementing large-scale policies to conserve and restore forests. However, little is known about the effectiveness of these policies, and information on China’s forest dynamics at the national level has mainly relied on official statistics. In response to international calls for improved reliability and transparency of information on biodiversity conservation and climate change mitigation efforts, it is crucial to independently verify government statistics. Furthermore, if forest recovery is verified, it is essential to assess the degree to which this recovery is attributable to policy, within the context of other relevant factors. We assess the dynamics of forest cover in China between 2000 and 2010 and evaluate the effectiveness of one of the largest forest conservation programs in the world—the Natural Forest Conservation Program (NFCP). Results indicate that forest cover has significantly increased in around 1.6% of China’s territory and that the areas exhibiting forest gain experienced a combined increase in net primary productivity (ca. 0.9 Tg of carbon). Among the variables evaluated at county level, the NFCP exhibited a significantly positive relation with forest gain, whereas reduction in rural labor showed a negative relationship with both forest loss and gain. Findings such as these have global implications for forest conservation and climate change mitigation efforts.

Keywords
• Conservation policy
• economic development
• forest transition
• land use change
• Natural Forest Conservation Program
• spatio-temporal dynamics

## INTRODUCTION

Human activities have transformed up to half of Earth’s land surface, particularly through the often intertwined processes of agricultural expansion and deforestation (1, 2). Because forests provide crucial services to society, including soil and water conservation, climate regulation, and carbon storage, among many others (3, 4), degradation and outright conversion of forests constitute some of the most detrimental anthropogenic land surface processes (5). Yet, although degradation of forests continues in many parts of the world, an opposite trend has also emerged. This “forest transition” from loss to gain at the national level began to occur during the 18th century in Western Europe (6) and has since emerged in other developed (7) and developing (8) countries around the world. Forest scarcity and economic development are considered two of the most common pathways to forest recovery (9). The former involves the planting of trees in response to a demand for timber and other forest products, whereas the latter is associated not only with industrialization leading to the concentration of population in urban centers and consequent rural depopulation and spatial contraction of increasingly intensive agricultural production, but also with changing ecological or aesthetic goals, as wealthy societies come to value natural landscapes and can afford to invest in biodiversity conservation (10). The forest transition literature also recognizes the important role of government policy, particularly in developing countries (11). The challenge is quantifying the actual role of government programs against the background of other (for example, social, economic, and ecological) factors.

## MATERIALS AND METHODS

### Forest cover dynamics

Although some authors deem necessary the use of medium spatial resolution imagery acquired by satellite sensors such as Landsat TM, Landsat ETM+, or SPOT (Satellite Pour l’Observation de la Terre) for forest cover change assessments (30), many areas around the world experience frequent cloud cover. Thus, insufficient cloud-free medium spatial resolution imagery is available for forest change assessments (31, 32), particularly when evaluating the effects of conservation policy implementation over short temporal windows. In addition, Landsat-based products, such as the Global Forest Change 2000–2013 (3), map forest cover as an internally homogeneous land cover type, which is inadequate for assessing forest cover dynamics that do not necessarily correspond with complete land surface transformations (that is, from forest to nonforest or vice versa). Therefore, we assessed the spatiotemporal dynamics of China’s forest cover using the annual Vegetation Continuous Fields (VCF) tree cover product derived from surface reflectance data acquired by NASA’s MODIS (33). This product represents the percentage of annual per-pixel tree cover at a spatial resolution of approximately 250 m and has successfully been used to quantify forest cover dynamics, exhibiting results comparable with those derived using medium spatial resolution imagery (for example, Landsat-based) (34).

Using the MODIS VCF, we evaluated changes in forest cover from 2000 to 2010. We chose this period to capture the first decade of NFCP implementation and to match the availability of relevant socioeconomic data. Because changes in forest cover at a MODIS VCF pixel may not necessarily account for actual changes in forest cover on the ground, we performed two different procedures to assess the change in forest cover, which were later combined to produce a final change output.

In the first procedure, we assessed the change in forest cover by thresholding the VCF to separate forest from nonforest pixels and estimated the minimum magnitude of the percent change required to assess a significant change in forest cover. To find the optimal VCF threshold to separate forest from nonforest pixels and to validate the MODIS VCF tree cover product, we developed a data set of 4000 “ground-truth” polygons of the same size as a MODIS pixel (ca. 0.0625 km2), randomly distributed throughout China. Within each of these polygons, we randomly distributed 25 points. Using the high spatial resolution imagery available in Google Earth, we visually ascertained the number of points per polygon coinciding with a tree canopy. The horizontal positional accuracy of Google Earth’s high-resolution imagery has been established to vary between 0.4 and 171.6 m, with average accuracies of 24.1 m in developed countries and 44.4 m in developing countries (35). Such horizontal positional accuracies are much lower than the spatial resolution of a MODIS pixel (ca. 250 m per pixel) and thus are suitable for assessing the classification accuracy of the MODIS VCF product.

We considered a polygon to be forested if three or more points (that is, >10%) coincided with a tree canopy, on the basis of the classification of forested areas established by the United Nations Food and Agriculture Organization (36). To assess the reliability of the interpretation of the Google Earth imagery, two image interpreters independently performed the point counts for each polygon. The average point count between the two interpreters was obtained, and only those polygons exhibiting a point count difference of less than 20% between the two interpreters were used in the validation. Google Earth imagery acquired between 2000 and 2005 was used to validate the 2000 MODIS VCF data set, whereas Google Earth imagery acquired between 2006 and 2010 was used to validate the 2010 MODIS VCF data set. Thus, given that not all of China’s territory is covered by Google Earth high-resolution imagery, not all polygons were used in the validation. In addition, because high-resolution image availability between 2000 and 2005 is considerably lower than that between 2006 and 2010, the final number of ground-truth polygons used in the validation was 569 (14.2% of the polygons) for the 2000 MODIS VCF data set and 1973 (49.3% of the polygons) for the 2010 MODIS VCF data set (fig. S2).

With these data, we conducted threshold-dependent and threshold-independent validation procedures. The threshold-dependent procedure was the κ statistic, which is a chance-corrected measure of agreement (37). Using equality in sensitivity and specificity as a criterion (reported to be the most reliable criterion for cumulative threshold selection) (38), we found that a percent tree cover of 24 and 23% in the 2000 and 2010 MODIS VCF data sets, respectively, constitutes the optimal threshold for separating forest from nonforest pixels, and we obtained κ coefficients of 0.64 (overall accuracy, 90%) and 0.60 (overall accuracy, 87%) for the 2000 and the 2010 MODIS VCF data sets, respectively. The threshold-independent procedure was the area under the receiver operating characteristic curve (AUC) (39). The AUC ranges from 0 to 1, where a score of 1 indicates perfect discrimination, a score of 0.5 is expected from a random prediction, and a score lower than 0.5 indicates discrimination that is worse than random. The AUC values obtained were ca. 0.94 for both the 2000 and the 2010 MODIS VCF data sets (fig. S3), which were significantly different (P < 0.0001) from 0.5 (that is, a random prediction). Both of these validation procedures (that is, threshold-dependent and threshold-independent) demonstrate that the MODIS VCF tree cover product constitutes an accurate depiction of forest cover in China.

The per-pixel change in forest cover was then obtained by calculating the Δ in the VCF tree cover between 2000 and 2010 (that is, Tree Cover Δ2010–2000 = Tree Cover2010 − Tree Cover2000). To validate this change, we used the Δ in the percent tree cover of the ground-truth polygons, obtained using Google Earth. Validation of the change in forest cover was based on the threshold-dependent κ statistic, which suggested a percent change in tree cover of ±20% as the optimal threshold for detecting changes in percent tree cover. The κ coefficients obtained were 0.39 (overall accuracy, 68.2%) and 0.40 (overall accuracy, 69.8%) for forest recovery and forest loss, respectively.

In the second procedure, we assessed per-pixel annual trends in the VCF percent tree cover over the decade (that is, 2000–2010). This was performed not only because a change in forest cover in a MODIS VCF pixel may not necessarily represent the same areal change in forest cover on the ground but also because the values of the VCF tree cover product may change from year to year as a result of changes in climate conditions accumulated over time (for example, annual precipitation and incoming radiation). To assess the significance of per-pixel trends (monotonic increases and decreases in the VCF between 2000 and 2010), we used the Spearman rank correlation coefficient. The significance of the Spearman rank correlation coefficient was determined through a permutation analysis in which the order of the ranks was randomly permuted 99 times. The significance measure corresponds to the number of times the correlation coefficient of the permuted data set exceeded the original (that is, nonpermuted) coefficient. When fewer than 5 of 99 permutations yielded higher correlation coefficients, these pixels were determined to exhibit significant positive (for r > 0) or negative (r < 0) trends.

Finally, we combined the two procedures to assess the number of pixels that exhibited a significant change (that is, gain or loss) in forest cover. To this effect, among the pixels exhibiting an absolute change in percent forest cover equal to, or larger than, 20% (assessed through the validation of the change in percent tree cover) between 2000 and 2010, we only selected those exhibiting significant positive/negative trends based on the significance of the Spearman rank correlation coefficient. Although the combination of these two procedures noticeably reduced the number of pixels considered to exhibit significant positive and negative changes in forest cover, it was preferred because it reduces the potential effects of the coarse pixel resolution of the MODIS VCF and accounts for the effects of accumulated climate conditions on the MODIS VCF tree cover product. Our estimates of forest cover dynamics are therefore conservative but reduce potential overestimations of forest gain or loss, yielding more robust analytical findings. The use of such combination of approaches to assessing changes in forest cover takes advantage of the fuzzy classification nature of the VCF tree cover product, together with its annual frequency. This constitutes an alternative to recent procedures designed to incorporate land cover classification errors into areal estimates of land cover change (40). Figure 3 shows a summary of the procedures used.

### Dynamics of NPP

To assess the changes in NPP between 2000 and 2010 among the pixels exhibiting a significant gain in forest cover, we used the Terra/MODIS NPP MOD17A3 product, which was obtained through the online data pool at the NASA Land Processes Distributed Active Archive Center, U.S. Geological Survey/Earth Resources Observation and Science Center, Sioux Falls, SD (https://lpdaac.usgs.gov/data_access). This data set was resampled and coregistered to the MODIS VCF product (that is, 250 m per pixel). Pixels exhibiting a significant gain in forest cover between 2000 and 2010 were evaluated to determine whether they exhibited significant (P < 0.01) trends in decadal (2000–2010) NPP values. Pixels with no significant NPP trends were assumed not to have changes in NPP between 2000 and 2010. Accumulated NPP per year in all pixels exhibiting a significant gain in forest cover was obtained as the cumulative sum of NPP gain across space.

### Statistical analyses

To assess the relationship between forest loss or gain and the implementation of the NFCP, we used a stepwise multilevel approach, which accommodates data acquired at different scales. Figure 3 shows a summary of the procedures used. In the first step, we developed logistic regression models (41) to assess the probability of forest loss and gain at the pixel level during the 2000–2010 period. We randomly selected 30,000 pixels across China that have a minimum sampling distance of 5 km to limit potential spatial autocorrelation effects. Two-thirds of these pixels were used to calibrate a pair of logistic regression models (41), whereas the remainder were reserved for validating them. The dependent binary variable (either forest loss or gain) was set to 1 if the pixel exhibited a significant positive (or negative) change [that is, a percent tree cover Δ equal to, or higher than, 20% (equal to, or lower than, −20%) and with significant positive (negative) trends in percent tree cover] and to 0 if the pixel exhibited significant negative (or positive) change or no change. Biophysical and demographic factors that were shown to be strongly correlated with forest cover dynamics in previous spatially explicit models [for example, Rudel et al. (9), Geist and Lambin (42), and Rudel (43)] were selected for use as predictor variables. These included the geographic position of each pixel (that is, easting and northing), elevation, slope, aspect [converted into soil moisture classes (44)], and the CTI [a measure of soil water accumulation (45)] derived from a digital elevation model constructed from data acquired by the Shuttle Radar Topography Mission (46), initial tree cover (that is, VCF in 2000), distance to main roads [obtained from the Digital Chart of the World Dataset (47)], gridded population density in 2000 (48), and mean annual temperature and total annual precipitation in grid format obtained from the WorldCLIM database (49). We acknowledge that, like almost all other data sets, these global data sets have limitations (for example, lack of uniformity across different regions) that may reduce their reliability. However, given that standardized procedures have been applied in their production and that they are publicly available, their use not only allows replication and verification but also allows rapid updates of the analyses as more data become available. As a result, such data sets have been widely used in numerous publications over the last several years [for example, Yang et al. (50), Zheng et al. (51), and Zhang et al. (52)]. All grids were resampled and coregistered to match the MODIS VCF product (that is, 250 m per pixel). Model validation was conducted using the AUC procedure (39). These logistic regression models registered AUC scores of 0.71 and 0.67, respectively, which were significantly different (P < 0.0001) from 0.5 (that is, a random prediction). Using the coefficients obtained in these logistic regression models, we calculated the probability of forest loss and gain for all pixels comprising mainland China. Logistic regression model residuals were then obtained by subtracting these probability values from the observed forest loss and gain values, with the latter expressed in binary format [that is, 1 for any significant negative (positive) change and 0 for any significant positive (negative) change or no change] (fig. S4). These pixel-level residuals represent the unexplained variance of the logistic regression models.

In the second step, we obtained aggregate model residual values on a per-county basis by averaging the pixel-level residuals (both positive and negative) within the county divisions (including city districts) of China (Fig. 1). These per-county aggregate residuals (county-based aggregate of the unexplained variance of the logistic regression models) were used as the dependent variables in spatial autoregressive models (53, 54). Independent predictor variables included implementation of the NFCP [expressed in binary format; that is, 1 for counties where the NFCP was implemented and 0 for counties where it was not (17)], county-based per-capita GDP for 2000, change in county-based per-capita GDP (that is, GDP 2010 − GDP 2000), agricultural production (in the form of total grain and meat production) for 2000 and its change (that is, 2010 − 2000), total population in 2000 and its change (that is, 2010 − 2000), and rural labor in 2000 and its change (that is, 2010 − 2000). These data were obtained from the China Data Center (55). Spatial weighting matrices at the county level were created, defining a neighbor based on both the Queen and the Rook contiguity approaches (56). We report results obtained using the Queen contiguity approach, although results using the two approaches were similar. Using these data, we developed lag and error spatial autoregressive models (53, 54) for each dynamic (that is, forest loss and gain, respectively). We report the results of the lag model for both forest loss and gain (Table 1) because these exhibited the best (that is, lowest) Akaike information criterion values (53, 54).

## SUPPLEMENTARY MATERIALS

Fig. S1. Histogram of dynamic pixels.

Fig. S2. Distribution of validation polygons.

Fig. S3. Validation of the MODIS VCF.

Fig. S4. Distribution of model residuals.

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

## REFERENCES AND NOTES

Acknowledgments: We thank the NASA Land Processes Distributed Active Archive Center for the data sets used in the study and S. Li for assistance. Funding: This study received financial support from the National Science Foundation and Michigan AgBioResearch. Author contributions: A.V., W.J.M., and J.L. conceived the ideas. A.V. built the models and performed the data analyses. A.V., W.J.M., H.Y., and Z.X. validated the VCF data. A.V., W.J.M., and J.L. wrote the paper. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data used to obtain the conclusions in this paper are publicly available or presented in the paper and/or the Supplementary Materials. NASA & U.S. Geological Survey, Earth Resources Observation and Science Center, Land Processes Distributed Active Archive Center, Sioux Falls, SD. https://lpdaac.usgs.gov/data_access; Center for International Earth Science Information Network (CIESIN) Columbia University, United Nations Food and Agriculture Programme (FAO), and Centro Internacional de Agricultura Tropical (CIAT). Gridded Population of the World, Version 3 (GPWv3): Population Count Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4639MPP; University of Michigan, Beijing Hua tong ren shi chang xin xi you xian ze ren gong si, China Data Center, Ann Arbor, MI, http://chinadatacenter.org; Food and Agriculture Organization of the United Nations – Statistics Division (FAOSTAT), http://faostat3.fao.org/home/E.
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