Effects of conservation policy on China’s forest recovery
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.
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.
The fate of forests in China, the most populous nation on Earth, has global consequences by virtue of the country’s sheer magnitude and the rapidity of its development (12). According to Chinese government statistics, the country’s forests have been recovering over the past three decades (13, 14). This has taken place in the context of exceptionally rapid economic growth over the same time period (15), which suggests that China is on the economic development pathway to the forest transition (because harvests from many of these forests are not currently allowed, the forest scarcity pathway is not a sufficient explanation in this case). However, during the same period, the Chinese government also implemented other environmental programs (16). In particular, since the beginning of the 21st century, China has been implementing one of the largest forest conservation and restoration programs in the world—the Natural Forest Conservation Program (NFCP). This program bans logging in many natural forests and supports monitoring activities to prevent illegal harvesting. In the first decade of implementation, total investment in the NFCP exceeded 93 billion yuan (US$1 = 6.8 yuan in 2010) (17). Although this program was largely motivated by the need to control soil erosion following massive floods in 1998, the consequent increase in forest cover also has important implications for other ecosystem services such as carbon sequestration. In fact, China’s president pledged to increase forest cover by 40 million hectares by 2020 (from the 2005 level), and the NFCP is a key measure for fulfilling the country’s commitment to global climate change mitigation (18). Although widespread environmental and socioeconomic benefits are anticipated from program implementation (17, 19), actual benefits have so far only been assessed in a few localized areas (20, 21), and not on a national scale. This study evaluates the effectiveness of the NFCP on a national scale during the first decade of the 21st century and its contribution to changes in net primary productivity (NPP).
RESULTS
Over the 2000–2010 period, around 1.6% (ca. 157,315 km2) of China’s territory displayed a significant gain in percent tree cover, whereas ca. 0.38% (ca. 37,268 km2) experienced a significant loss (Fig. 1). Among the areas exhibiting gains and losses, the average percent tree cover in the year 2000 was 24.81% (SD, 12.76%) and 53.57% (SD, 12.94%), respectively (fig. S1), suggesting that forest gain tends to occur more often in areas of lower initial tree cover, whereas forest loss tends to occur more often in areas of higher initial tree cover.
Fig. 1 Forest cover dynamics in China.
(A) Pixel-based (250 m per pixel) percent tree cover across China in 2000. The map was derived from the VCF tree cover product based on surface reflectance data collected by MODIS. Polygons correspond to county boundaries. (B) Pixels exhibiting a significant gain or loss in percent tree cover [that is, changes higher than or equal to |20%| and with statistically significant (P < 0.05) positive and negative monotonic trends in percent tree cover] between 2000 and 2010. Polygons correspond to province boundaries.
Stepwise multilevel models show that, at the pixel level, the initial percent tree cover, elevation, and total annual precipitation were statistically significant in the forest loss model (Table 1), whereas northing, distance to main roads, gridded population density in 2000, elevation, mean annual temperature, and the compound topographic index (CTI) were statistically significant in the forest gain model (Table 2). At the county level, the NFCP evidenced a statistically significant (P < 0.001) positive relationship with forest gain, whereas change in rural labor between 2000 and 2010 exhibited a statistically significant negative relation with both forest loss (P < 0.05) and forest gain (P < 0.01) (Table 3). The gross domestic product (GDP) per capita exhibited a statistically significant (P < 0.05) positive relation with forest loss (Table 3).
| Parameter | Estimate | SE | Wald χ2 | P > χ2 |
|---|---|---|---|---|
| Intercept | −11.1959 | 3.0388 | 13.5744 | 0.0002 |
| Easting | 2.997 × 10−7 | 2.66 × 10−7 | 1.2701 | 0.2598 |
| Northing | 3.768 × 10−7 | 5.27 × 10−7 | 0.5113 | 0.4746 |
| Initial tree cover | 0.0534 | 0.00614 | 75.4652 | <0.0001 |
| Distance to main roads | −0.00233 | 0.00275 | 0.7168 | 0.3972 |
| Population density in 2000 | 0.000012 | 7.466 × 10−6 | 2.7208 | 0.0991 |
| CTI | 0.0591 | 0.0597 | 0.9814 | 0.3219 |
| Elevation | −0.00019 | 0.000086 | 4.9686 | 0.0258 |
| Slope | 0.000960 | 0.0205 | 0.0022 | 0.9627 |
| Aspect | −0.00111 | 0.0179 | 0.0038 | 0.9506 |
| Precipitation | 0.0275 | 0.00846 | 10.5727 | 0.0011 |
| Temperature | −0.0876 | 0.0682 | 1.6494 | 0.1990 |
Table 1 Coefficients of the pixel-based forest loss model.
Maximum likelihood estimates of the coefficients of the predictor variables obtained in a logistic regression model to assess the probability of forest loss during the 2000–2010 period. See Materials and Methods for details on the logistic regression models developed in the study.
| Parameter | Estimate | SE | Wald χ2 | P > χ2 |
|---|---|---|---|---|
| Intercept | 1.2464 | 1.1394 | 1.1966 | 0.2740 |
| Easting | 1.528 × 10−7 | 1.02 × 10−7 | 2.2462 | 0.1339 |
| Northing | −5.61 × 10−7 | 1.652 × 10−7 | 11.5298 | 0.0007 |
| Initial tree cover | 0.00415 | 0.00295 | 1.9723 | 0.1602 |
| Distance to main roads | −0.00488 | 0.00174 | 7.8691 | 0.0050 |
| Population density in 2000 | −0.00005 | 0.000015 | 10.4924 | 0.0012 |
| CTI | −0.2077 | 0.0349 | 35.4714 | <0.0001 |
| Elevation | −0.00023 | 0.000044 | 27.1876 | <0.0001 |
| Slope | −0.00523 | 0.00781 | 0.4485 | 0.5031 |
| Aspect | 0.0114 | 0.00830 | 1.8695 | 0.1715 |
| Precipitation | −0.00257 | 0.00405 | 0.4027 | 0.5257 |
| Temperature | 0.0743 | 0.0216 | 11.7815 | 0.0006 |
Table 2 Coefficients of the pixel-based forest gain model.
Maximum likelihood estimates of the coefficients of the predictor variables obtained in a logistic regression model to assess the probability of forest gain during the 2000–2010 period. See Materials and Methods for details on the logistic regression models developed in the study.
| Parameter | Forest loss | Forest gain |
|---|---|---|
| R2 | 0.478 | 0.742 |
| Intercept | 2.61 × 10−5 (0.0006) | −0.0004 (0.0019) |
| NFCP | −0.0007 (0.0004) | 0.0070* (0.0015) |
| GDP per capita in 2000 | 0.0014† (0.0005) | −0.0023 (0.0018) |
| Change (2010–2000) in GDP | −2.04 × 10−7 (6.109 × 10−7) | 1.36 × 10−7 (2.02 × 10−6) |
| Grain production in 2000 | 4.01 × 10−6 (1.91 × 10−5) | −1.10 × 10−5 (6.32 × 10−5) |
| Change (2010–2000) in grain production | 8.43 × 10−7 (2.20 × 10−6) | −2.77 × 10−6 (7.18 × 10−6) |
| Meat production in 2000 | −2.29 × 10−5 (0.0001) | 6.21 × 10−5 (0.0003) |
| Change (2010–2000) in meat production | −2.17 × 10−6 (1.73 × 10−6) | 4.10 × 10−6 (5.72 × 10−6) |
| Total population in 2000 | 1.34 × 10−5 (2.13 × 10−5) | 8.30 × 10−5 (7.03 × 10−5) |
| Change (2010–2000) in total population | 4.47 × 10−7 (1.48 × 10−5) | −3.10 × 10−5 (4.88 × 10−5) |
| Rural labor in 2000 | −3.94 × 10−5 (4.00 × 10−5) | −0.0002 (0.0001) |
| Change (2010–2000) in rural labor | −1.31 × 10−6‡ (6.21 × 10−7) | −6.45 × 10−6† (2.05 × 10−6) |
| Spatial autoregressive term | 0.6922* (0.0195) | 0.8493* (0.0122) |
Table 3 Coefficients of county-based spatial autoregressive models.
Estimates of the coefficients of the spatial autoregressive models developed to assess the relationship between county-based independent variables and significant gain and loss of forest cover [presented as coefficient (SE)]. See Materials and Methods for details on these spatial autoregressive models.
*P < 0.001.
†P < 0.01.
‡P < 0.05.
Among the pixels exhibiting a gain in forest cover, the average per-pixel relative NPP gain over the 2000–2010 period was 14.7% (SD, 17.4%) (Fig. 2). NPP accumulated across all pixels exhibiting a significant gain in forest cover rose by a total of ca. 0.91 Tg (that is, from 45.12 Tg in 2000 to 46.03 Tg in 2010).
Fig. 2 NPP dynamics.
Per-pixel relative (that is, percent) change in NPP between 2000 and 2010 among pixels exhibiting a significant gain in forest cover (Fig. 1B). Insets: Areas that exhibited particularly wide-ranging positive/negative trends.
DISCUSSION
Our results suggest that, among the factors considered at the county level and after accounting for pixel-level biophysical and demographic variables, the implementation of the NFCP exhibited a significant relationship with forest gain in China during the first decade of the 21st century. This significant relationship suggests that government intervention—in the form of logging bans and monitoring activities to prevent illegal logging—was instrumental in enhancing forest recovery. Other conservation policies implemented at the national level (such as the Grain-to-Green Program, which encourages farmers to convert steep hillside cropland into forest by providing cash, grain, and tree seedlings) (17, 22), as well as other forest management measures (for example, fire prevention and stand thinning), may also have had significant effects on China’s forest cover (for example, promotion of forest regeneration). However, they were not evaluated because their implementation generally concerns small cropland parcels (usually <1 ha) that may be difficult to detect at the 250-m resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and because 10 years may not be sufficient to detect their effects owing to the longer time required for trees to establish sufficient canopies from seeds or seedlings (21).
Many of the areas that exhibited significant gains in forest cover constituted some of the most recently logged areas and thus may be showing the most dramatic responses to NFCP implementation. For instance, an area exhibiting prominent gains in forest cover in central China [located between the Qinling Mountains (in Gansu and Shaanxi provinces to the north) and the Daba Mountains (in Sichuan province to the south)] (Fig. 1B) was the locus of major timber extraction activities before the year 2000. In fact, Sichuan and Gansu provinces exhibited some of the highest rates of deforestation during the 1990s (23). With the implementation of the NFCP, thousands of people employed by the timber industry were laid off and sought alternative employment opportunities. Dramatic abandonment of rural livelihood activities had also been spurred by new employment opportunities in urban centers such as Chengdu in Sichuan province and Xi’an in Shaanxi province. Thus, economic growth (as exemplified by the change in rural labor between 2000 and 2010) may have played a significant role in forest recovery. Nevertheless, the effects of economic growth are not completely clear, given that the other metric of economic growth used in the study (that is, GDP per capita) showed a significant positive relationship with forest loss.
Previous studies have shown that the forest recovery observed during the last two decades of the 20th century had significant effects on standing biomass and NPP in the forests of China (24, 25), but the effects of forest recovery during the first decade of the 21st century are not clear. We analyzed the change in NPP in the pixels exhibiting significant forest recovery, and our results showed a significant increase in NPP across all pixels exhibiting a significant gain in forest cover. Thus, in addition to the local and regional benefits it may accord the country in terms of soil and water conservation, the NFCP seems to significantly contribute to carbon sequestration in China’s forested areas, with potentially important effects on global carbon budgets.
Although China’s forest conservation policy may exert positive effects on China’s forests, it may exert negative effects on other forested areas around the world. At the same time that China’s conservation policy bans logging of natural forests, China has become one of the world’s leading timber importers and has also been importing more food and other agricultural products (13). Thus, China’s conservation policy may be exacerbating forest degradation (through both legal and illegal logging) in other regions such as Southeast Asia, Africa, and Northern Eurasia from which China has been importing forest and agricultural products (13, 26). For example, between 2000 and 2010, China imported forest products from Vietnam (ca. US$695 million), Burma (ca. US$62 million), Indonesia (ca. US$880 million), Madagascar (ca. US$18 million), and Russia (ca. US$887 million) (27). Therefore, at least some of the carbon sequestration in China’s forested areas may have come at the cost of carbon emissions elsewhere (3, 28). In addition, if the associated forest cover losses elsewhere occur in areas with high biodiversity, the expansion of forests in China may also be entailing a significant net loss of biodiversity. These considerations pose important challenges for current territory-based strategies for global climate change mitigation and biodiversity conservation. A systems approach that simultaneously considers the tradeoffs and synergies among conservation, production, and consumption in both importing and exporting countries (29) is urgently needed to address these challenges.
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.
Fig. 3 Methodological steps.
Flow chart depicting the major steps in the procedures used in the study. For details, see Materials and Methods.
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).
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.
Supplementary Material
Summary
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.
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Science Advances
Volume 2 | Issue 3
March 2016
March 2016
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Copyright © 2016, The Authors.
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.
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Received: 20 July 2015
Accepted: 19 January 2016
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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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Response to Pang et al. (May 2016)
We appreciate Pang et al.’s interest in our article, and wholeheartedly agree with their main point: better information will lead to a better understanding of the effectiveness of China’s efforts to increase forest cover across the country. We thank them for their comments on our study. However, in some important aspects they have misinterpreted our results and overstated the limitations of our approach.
Pang et al. state that “MODIS lacks the spatial resolution to accurately evaluate changes in forest cover to the degree required to draw the desired conclusions”. They then suggest that the use of “Landsat data at 30x30 m pixel resolution would generate more accurate forest cover data compared to the authors’ analysis with MODIS”. While we agree that the MODIS-derived vegetation continuous fields (VCF) tree cover product may not be able to detect all afforestation/deforestation processes occurring on the ground due to its coarser spatial resolution (something we stated in our article), we do not agree that the use of Landsat data is necessarily more accurate simply by virtue of its finer spatial resolution. It is important to avoid conflating greater spatial precision with higher accuracy. Previous work has demonstrated that the use of high spatial resolution data can in fact reduce the accuracy of land change models, as there are more opportunities for error (1). Furthermore, the MODIS sensors collect data at a considerably higher frequency than the Landsat systems, enabling a higher temporal precision view of vegetation phenology. By revealing crucial aspects of the development of the vegetative cover through the growing season, MODIS data permit more robust differentiation of forest from other land covers with which it might otherwise be confounded. To reduce uncertainty due to the coarser spatial resolution of the MODIS-VCF tree cover product, in our study we used a combination of procedures (change detection and trend analysis). This combination generated a conservative estimate of change in forest cover, which may partially explain the disparity between our assessment of 1.6% increase in forest cover, compared to the 2.15% increase shown in the government reports mentioned by Pang et al. While we did not directly acknowledge this disparity in our article, we did explicitly state that our results, while conservative, agreed with government estimates in that forest cover in China increased over the study period.
Pang et al. state that our study did not include other conservation programs implemented concurrently with the Natural Forest Conservation Program (NFCP), undermining the contribution of those programs to forest cover gains. However, the significant contribution of NFCP does not mean that other programs are not contributing to forest recovery. In fact, the R2 of our forest gain model was 0.742, which means that there is a substantial amount of variance due to other factors that our model did not capture. In our article we explicitly acknowledged that other conservation programs may have made significant contributions to China’s forest recovery. Pang et al. also opine that our study “should have focused on the locations where NFCP activities were undertaken and excluded areas where the other programs were active”. As stated above, our analysis of forest cover change was conservative. This conservative approach allows a focus on areas exhibiting unambiguous positive or negative changes, as detected by the MODIS-VCF product. As a result, many areas subject to the implementation of other conservation programs, such as those in Qinghai or Inner Mongolia mentioned by Pang et al., appear to have witnessed no significant change, and thus were excluded from our step-wise multilevel models. This does not necessarily mean that afforestation has not happened in some of these excluded areas (perhaps related to the implementation of those programs), but rather that their detection is not sufficiently unambiguous using the MODIS-VCF tree cover product. Our conservative approach focused on the most significant forest cover dynamics, and their spatial variability was found to be significantly related with the spatial variability in the implementation of the NFCP. Furthermore, the contribution of NFCP to forest recovery has been verified in regions without any of the other programs mentioned by Pang et al. (with the exception of the GTGP) (2, 3).
Finally, Pang et al. state that the “parameters used for the county-based spatial autoregressive model are insufficient to evaluate the relation between forest loss and gain and the implementation of the NFCP” and argued that we “should have considered additional parameters related to local livelihoods and industry”, such as “household income, government compensation, change in labor in various industries, and change in non-timber forest products (e.g. tea, fruit)”. Although some or all of these variables may be correlated with the variables that our model has already considered, we would have explored the possible roles of these and other variables had relevant data been available to us across all counties in China. We hope Pang et al. will join efforts to develop, analyze and make freely available such data, accompanied by robust metadata and accuracy assessments. Until such data are available, the approach we pursued is the best we are able to do to address crucial questions about the causes and consequences of land cover dynamics in the most populous nation on Earth.
References
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RE: Effects of Conservation policy on China's forest recovery
Comment on "Effects of conservation policy on China's forest recovery"
Yong Pang1, Guangyu Wang2, Shari L. Mang2, Zhongqi Xu3, Hongbo Zhai4, Zhijian Yang5, and John L. Innes1*
1Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing China
2Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada
3College of Forestry Agriculture University of Hebei, Baoding, China
4Chinese Academy of Forestry Inventory and Planning, Beijing, China
5College of Forestry, Fujian Agriculture and Forestry University
Vina et al. (Reports, 18 March 2016) use MODIS VCF to assess change in forest cover in China in an attempt to verify government statistics regarding forest cover and to evaluate the effectiveness of the government's Natural Forest Conservation Program (NFCP). However, they fail to verify their data against the government's own data in a quantitative way, and their evaluation of NFCP ignores the five other forest conservation programs run simultaneously by the government. Furthermore, they use a limited number of parameters to evaluate success and rely on MODIS, which lacks the spatial resolution to address the authors' objectives.
The authors aim to verify Chinese government statistics on forest recovery, an element they correctly highlight as being crucial in ensuring reliable and transparent information for conservation and climate change action; however, this verification is not performed in depth. Both parties conclude that forests have been recovering over the period of 2000-2010, but the authors do not quantitatively compare their findings against the government's own statistics. Vina et al.'s analysis indicates that 1.6% of China's territory experienced a significant increase in percent tree cover from 2000-2010, which is substantially lower than the value reported by the Chinese government. From 2003-2008 alone, the government reports an increase in total forest cover from 18.21% (1) to 20.36% (2) - a change of 2.15%. The 10-year change presented by Vina et al. is much lower than that reported by the Chinese government, but the authors do not acknowledge this disparity.
The authors relate their findings to the Natural Forest Conservation Program (NFCP). However, from 2000-2010, China had six key programs aimed at addressing forest loss and ecosystem degradation. Four of these programs overlap with NFCP's implementation area – there is 82.4% overlap with Conversion of Cropland to Forest Program (CCFP) (referred to as Grain-to-Green in the paper), 29.4% with Three North Shelterbelt Development Program, 50.8% with Shelterbelt Development Program along the Yangtze River Basin (3Ns&YRB) (3), and 11.8% with Afforestation Projects in the plain areas (4). Although the authors acknowledge the other programs, they do not account for the substantial overlap in the analysis. Based on their methods, it is not feasible to evaluate the success of the NFCP in isolation from other government environmental programs as there are no steps taken to separate the source of forest cover change. This is of concern as the authors aim to "evaluate the effectiveness of… NFCP" and state that "… the implementation of NFCP exhibited a significant relationship with forest gain…". Although NFCP contributed to increased forest cover, it is unreasonable to claim that the changes in forest cover are significantly related to NFCP as the contributions of each program are not evaluated. All programs carried out forest preservation and restoration activities between 2000-2010, such as logging bans and afforestation. However, the authors assume all changes are a result of NFCP activities when the program was responsible for only 18.7% of the area reforested by government programs from 1998-2010 (2). Furthermore, statements such as "NFCP seems to significantly contribute to carbon sequestration…" and claims that the dramatic increase in forest cover is in response to NFCP implementation undermines the contribution of other programs. If the authors wished to evaluate the NFCP, they should have focused on the locations where NFCP activities were undertaken and excluded areas where the other programs were active. As such, the primary objective to 'evaluate the effectiveness of the NFCP at the national scale… and its contribution to changes in net primary productivity…' is not achieved by this research.
The parameters used for the county-based spatial autoregressive model are insufficient to evaluate "the relation between forest loss and gain and the implementation of the NFCP", which is the authors' stated purpose. Although several industry- and demographic-related factors were used, several factors critical for an analysis at the county level are not included. To fully understand county level factors that relate to the NFCP and change in forest cover, the authors should have considered additional parameters related to local livelihoods and industry. The NFCP program led to the layoff and replacement of over 910,000 forest workers during 2001-2010 (5); as such, factors such as household income, government compensation, change in labor in various industries, and change in non-timber forest products (e.g. tea, fruit) need to be considered (6, 7). This analysis misses these critical elements, and thus is not robust enough to draw meaningful conclusions.
The authors' assessment using Vegetation Continuous Fields (VCF) tree cover products derived from MODIS is a significant advance for forestry. However, MODIS lacks the spatial resolution to accurately evaluate changes in forest cover to the degree required to draw the desired conclusions. Forestry activities in China are carried out in management compartments that range from 1-3 ha in size. The spatial resolution of the MODIS data used is 6.25 ha; thus the analysis is unable to capture the diversity of activity occurring within a single pixel, reducing the accuracy of the perceived changes in forest cover across the landscape. Although the authors point out that evaluation using LANDSAT can often be hindered due to cloud cover, there is cloud-free LANDSAT data at 30X30m resolution available for the entire area of mainland China (8, 9). Analyses with these data would yield pixels that are 0.09 ha (smaller than forestry compartments), which would generate more accurate forest cover data compared to the authors' analysis with MODIS.
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2. State Forestry Administration, "Forest Resource Statistics of China, 7th Report" (China Forestry Publishing House, Beijing, China, 2010).
3. G. Wang, J.L. Innes, J. Lei, S. Dai, S.W. Wu, China's Forestry Reforms. Science, 318, 1556-1557 (2007).
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