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 Δ 2010–2000 = Tree Cover 2010 − Tree Cover 2000 ). 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.