In response to ecosystem degradation from rapid economic development, China began investing heavily in protecting and restoring natural capital starting in 2000. We report on China's first national ecosystem assessment (2000-2010), designed to quantify and help manage change in ecosystem services, including food production, carbon sequestration, soil retention, sandstorm prevention, water retention, flood mitigation, and provision of habitat for biodiversity. Overall, ecosystem services improved from 2000 to 2010, apart from habitat provision. China's national conservation policies contributed significantly to the increases in those ecosystem services.

Land cover mapping in mountainous areas is a notoriously challenging task due to the rugged terrain and high spatial heterogeneity of land surfaces as well as the frequent cloud contamination of satellite imagery. Taking Southwestern China (a typical mountainous region) as an example, this paper established a new HC-MMK approach (Hierarchical Classification based on Multi-source and Multi-temporal data and geo-Knowledge), which was especially designed for land cover mapping in mountainous areas. This approach was taken in order to generate a 30 m-resolution land cover product in Southwestern China in 2010 (hereinafter referred to as CLC-SW2010). The multi-temporal native HJ (HuanJing, small satellite constellation for disaster and environmental monitoring) CCD (Charge-Coupled Device) images, Landsat TM (Thematic Mapper) images and topographical data (including elevation, aspect, slope, etc.) were taken as the main input data sources. Hierarchical classification tree construction and a five-step knowledge-based interactive quality control were the major components of this proposed approach. The CLC-SW2010 product contained six primary categories and 38 secondary categories, which covered about 2.33 million km 2 (accounting for about a quarter of the land area of China). The accuracies of primary and secondary categories for CLC-SW2010 reached 95.09% and 87.14%, respectively, which were assessed independently by a third-party group. This product has so far been used to estimate the terrestrial carbon stocks and assess the quality of the ecological environments. The proposed HC-MMK approach could be used not only in mountainous areas, but also for plains, hills and other regions. Meanwhile, this study could also be used as a reference for other land cover mapping projects over large areas or even the entire globe.

The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow fires. This paper describes intra-and inter-annual vegetation restoration trajectories via MODIS time-series and Landsat data. The temporal and spatial characteristics of the natural succession were analyzed from 2000 to 2016. Finally, we regressed post-fire MODIS EVI, LST and LSWI values onto GPP and NPP values to identify the main limiting factors during post-fire carbon exchange. The results show immediate variations after the fire event, with EVI and LSWI decreasing by 0.21 and 0.31, respectively, and the LST increasing to 6.89 • C. After this initial variation, subsequent fire-induced variations were significantly smaller; instead, seasonality began governing the change characteristics. The greatest differences in EVI, LST and LSWI were observed in August and September compared to those in other months (0.29, 6.9 and 0.35, respectively), including July, which was the second month after the fire. We estimated the mean EVI recovery periods under different fire intensities (approximately 10, 12 and 16 years): the LST recovery time is one year earlier than that of the EVI. GPP and NPP decreased after the fire by 22-45 g C·m −2 ·month −1 (30-80%) and 0.13-0.35 kg C·m −2 ·year −1 (20-60%), respectively. Excluding the winter period, when no photosynthesis occurred, the correlation between the EVI and GPP was the strongest, and the correlation coefficient varied with the burn intensity. When changes in EVI, LST and LSWI after the fire in the boreal forest were more significant, the severity of the fire determined the magnitude of the changes, and the seasonality aggravated these changes. On the other hand, the seasonality is another important factor that affects vegetation restoration and land-surface energy fluxes in boreal forests. The strong correlations between EVI and GPP/NPP reveal that the C-flux can be simply and directly estimated on a per-pixel basis from EVI data, which can be used to accurately estimate land-surface energy fluxes during vegetation restoration and reduce uncertainties in the estimation of forests' carbon reserves.that the area of boreal forest fires occupies approximately 35% of the national burn area; thus, the forest-fire frequency in Chinese boreal forests will increase by 100-200% over the next 100 years [5,6]. Currently, it has been confirmed that fires release significant amounts of carbon into the atmosphere through combustion; however, less is known regarding the effects of vegetation succession following fires on the ecosystem carbon flux (C-flux), which has potential feedback effects that may lengthen adjustments to regional and global ecosystem carbon cycles and further influence climate change.Boreal forests landscape is shaped by fire disturbances and the site's environmen...

This paper proposes an effective scheme for detecting the number of buildings in a scene from a single high-resolution Synthetic Aperture Radar (SAR) image. The layover and double bounce echoes in SAR images are detected first as building elements, which are then split, merged, or discarded to make each patch correspond to one building. A model describing the statistical relationship between the number of buildings and the features of the detected building elements is constructed. Based on this model, large building patches are split into the proper number of small patches. This scheme is tested on 3-m and 6-m resolution TerraSAR-X images that cover two sites in different provinces of China, and its advantages and limitations are discussed.

Forest age is significantly correlated with the net primary productivity, biomass, carbon flux, and the community structure of forest ecosystems. A Landsat time series was constructed using archived Landsat data and topographical maps to achieve large-scale spatial data on forest age. An algorithm used to identify forest disturbance based on a Mann-Kendall trend test, Mann-Kendall abrupt change test, and a difference rate index (DRI) was proposed. A forest age estimation scheme was established based on the classification of forest disturbance-recovery scenarios to obtain the spatial distribution data of forest age in the study region. The results show that: (1) through de-clouding and spectral fitting, imagery acquired by the Landsat-5 Thematic Mapper and Landsat-8 Operational Land Imager sensors could be used to construct a Landsat time series over the period of 1987-2018 in subtropical areas with complex topography; (2) a DRI was extracted from the time series as a disturbance indicator, which was subjected to a Mann-Kendall trend test, leading to the identification of five forest disturbance-recovery scenarios: recovery (or no recovery) after complete disturbance, recovery after partial disturbance, sustained recovery after positive disturbance, and non-disturbance; (3) based on identification of disturbance-recovery scenarios, a forest age estimation scheme was further developed by using the mean fractional vegetation cover before disturbance, fractional vegetation cover at the end of disturbance, and the vegetation recovery rate after disturbance in conjunction with Landsat Multispectral Scanner data from 1974 and topographical maps from the 1960 s, which achieved overall accuracy metrics of R 2 =0.72 and RMSE=7.8 years for forest age estimates. Specifically, the accuracy of forest age estimates was high in middle-aged and near-mature forests but low in young and mature forests, regardless of the forest vegetation type. The proposed algorithm for identifying areas of forest disturbance and forest age estimation can allow for forest change monitoring and forest age estimation at a regional scale of subtropical mountainous areas, providing a reference for the remote sensing estimation of forest ecological parameters in those areas.