There is profound interest in knowing the degree to which China's institutions are capable of protecting its natural forests and biodiversity in the face of economic and political change. China's 2 most important forest-protection policies are its National Forest Protection Program (NFPP) and its national-level nature reserves (NNRs). The NFPP was implemented in 2000 in response to deforestation-caused flooding. We undertook the first national, quantitative assessment of the NFPP and NNRs to examine whether the NFPP achieved its deforestation-reduction target and whether the NNRs deter deforestation altogether. We used MODIS data to estimate forest cover and loss across mainland China (2000-2010). We also assembled the first-ever polygon dataset for China's forested NNRs (n = 237, 74,030 km(2) in 2000) and used both conventional and covariate-matching approaches to compare deforestation rates inside and outside NNRs (2000-2010). In 2000, 1.765 million km(2) or 18.7% of mainland China was forested (12.3% with canopy cover of ≥70%)) or woodland (6.4% with canopy cover <70% and tree plus shrub cover ≥40%). By 2010, 480,203 km(2) of forest and woodland had been lost, an annual deforestation rate of 2.7%. Forest-only loss was 127,473 km(2) (1.05% annually). In the NFPP provinces, the forest-only loss rate was 0.62%, which was 3.3 times lower than in the non-NFPP provinces. Moreover, the Landsat data suggest that these loss rates are overestimates due to large MODIS pixel size. Thus, China appears to have achieved, and even exceeded, its target of reducing deforestation to 1.1% annually in the NFPP provinces. About two-thirds of China's NNRs were effective in protecting forest cover (prevented loss 4073 km(2) unmatched approach; 3148 km(2) matched approach), and within-NNR deforestation rates were higher in provinces with higher overall deforestation. Our results indicate that China's existing institutions can protect domestic forest cover.

An increasing number of end-users looking for ground data about fire activity in regions where accurate official datasets are not available adopt a free-of-charge global burned area (BA) and active fire (AF) products for applications at the local scale. One of the pressing requirements from the user community is an improved ability to detect small fires (less than 50 ha), whose impact on terrestrial environments is empirically known but poorly quantified, and is often excluded from global earth system models. The newest generation of BA algorithms combines the capabilities of both the BA and AF detection approaches, resulting in a general improvement of detection compared to their predecessors. Accuracy assessments of these products have been done in several ecosystems; but more complex ones, such as regions that are characterized by frequent small fires and steep terrain has never been assessed. This study contributes to the understanding of the performance of global BA and AF products with a first assessment of four selected datasets: MODIS-based MCD45A1; MCD64A1; MCD14ML; and, ESA’s Fire_CCI in a mountainous region of northwest Yunnan; P.R. China. Due to the medium to coarse resolution of the tested products and the reduced sizes of fires (often smaller than 50 ha) we used a polygon intersection assessment method where the number and locations of fire events extracted from each dataset were compared against a reference dataset that was compiled using Landsat scenes. The results for the two sample years (2006 and 2009) show that the older, non-hybrid products MCD45A1 and, MCD14ML were the best performers with Sørensen index (F1 score) reaching 0.42 and 0.26 in 2006, and 0.24 and 0.24 in 2009, respectively, while producer’s accuracies (PA) were 30% and 43% in 2006, and 16% and 47% in 2009, respectively. All of the four tested products obtained higher probabilities of detection when smaller fires were excluded from the assessment, with PAs for fires bigger than 50 ha being equal to 53% and 61% in 2006, 41% and 66% in 2009 for MCD45A1 and MCD14ML, respectively. Due to the technical limitations of the satellites’ sensors, a relatively low performance of the four products was expected. Surprisingly, the new hybrid algorithms produced worse results than the former two. Fires smaller than 50 ha were poorly detected by the products except for the only AF product. These findings are significant for the future design of improved algorithms aiming for increased detection of small fires in a greater diversity of ecosystems.

Remote mountainous regions are among the Earth’s last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions.