The United Nations adopted the Agenda 2030 with its core element, the 17 Sustainable Development Goals (SDGs), in September 2015. In order to achieve these goals within the coming years, intense efforts are required by all political and societal actors. Although the first definitions of sustainable development referred to the forest sector, the question remains: what contribution can forestry make to achieving the Sustainable Development Goals? Therefore, the direct positive and negative effects of forestry itself on sustainability are analyzed, and it is discussed how sustainable forest management could contribute to achieving other Sustainable Development Goals in addition to SDG 15. This analysis reveals that forestry plays a dual role, i.e., forestry can achieve positive sustainability effects but can also have negative impacts. It is thus recommended to use integrated assessment approaches to analyze whether a specific forest-related policy or strategy is contributing to sustainable development. Beside quantitative integrated assessments, the use of qualitative frameworks like the Framework for Strategic Sustainable Development is proposed. It is also suggested to operationalize the concept of second-order sustainability performance for the forest sector in future research.

Several initiatives have been proposed to mitigate forest loss and climate change through tree planting as well as maintaining and restoring forest ecosystems. These initiatives have both inspired and been inspired by global assessments of tree and forest attributes and their contributions to offset carbon dioxide (CO) emissions. Here we use data from more than 130,000 national forest inventory plots to describe the contribution of nearly 1.4 trillion trees on forestland in the conterminous United States to mitigate CO emissions and the potential to enhance carbon sequestration capacity on productive forestland. Forests and harvested wood products uptake the equivalent of more than 14% of economy-wide CO emissions in the United States annually, and there is potential to increase carbon sequestration capacity by ∼20% (-187.7 million metric tons [MMT] CO ±9.1 MMT CO) per year by fully stocking all understocked productive forestland. However, there are challenges and opportunities to be considered with tree planting. We provide context and estimates from the United States to inform assessments of the potential contributions of forests in climate change mitigation associated with tree planting.Copyright © 2020 the Author(s). Published by PNAS.

O manejo de áreas florestais com base em princípios de ecologia da paisagem é uma tendência do setor florestal, que tem como proposta o gerenciamento integrado dos aspectos econômicos, sociais e ambientais da atividade produtiva, envolvendo decisões complexas que podem ser auxiliadas pelas técnicas de geoprocessamento. No presente trabalho objetivou-se o desenvolvimento de uma metodologia para aplicação dos conceitos de ecologia de paisagem no planejamento do uso da terra em áreas de reflorestamento, por meio da utilização de um sistema de informações geográficas. Para tal, utilizou-se como área de estudo uma fazenda de reflorestamento da International Paper do Brasil, tendo sido estabelecidos cinco critérios para determinação de áreas para recomposição: fertilidade dos solos, mata nativa existente, corpos d'água, declividade e suscetibilidade à erosão. Esses fatores foram analisados, empregando-se os recursos de decisão multicritérios, em ambiente SIG. Como resultado foi obtido um mapa com áreas adequadas à recomposição florestal, segundo os critérios adotados. Com este mapa realizou-se uma simulação, alocando uma nova área de floresta nativa, e o resultado foi avaliado em nível de paisagem por meio de índices apropriados.

The "Grain for Green" project has been initiated in the Loess Plateau since 1999, and would be continuously promoted in the future. Therefore, it is of important significance to assess the vegetation restoration and its potential in the Loess Plateau. In this paper, based on the SPOT VEG NDVI dataset, the trend analysis, Hurst exponent method, statistical methods and geographical spatial analysis technology were adopted. Results showed that NDVI from 1999 to 2013 had a significant upward trend and the vegetation of 2/3 of the area would continue to improve. In loessal areas, the analysis of vegetation response curve indicated that vegetation coverage had a significant exponential relationship with drought index. Such relationship of gentle slope was more obvious than that of steep slope. The best vegetation response function of soil and rock-mountainous areas was linear function. Its correlation coefficient was lower than that of loessal areas. In the future, the average vegetation restoration potential of the Loess Plateau could reach 69.75%, which was high in the southeast and low in the northwest of the plateau. The region with better vegetation restoration would have lower vegetation restoration potential index. The vegetation restoration potential was mainly concentrated in the northern sandy land as well as in the western hilly and gully area. Subsequently, the differences of vegetation restoration rate for this region under different precipitation thresholds were remarkable, among which the area with precipitation of 375-450 mm had fast vegetation restoration. The measures "adaptation to water conditions" should be taken so as to avoid soil drying for afforestation. The results provided scientific support for the construction of ecological civilization on the Loess Plateau.

黄土高原从1999年开始大规模退耕还林（草）,植被覆盖发生了较大变化,对黄土高原植被恢复现状和恢复潜力进行评估具有重要意义。本文使用1999-2013年SPOT VEG NDVI数据,采用线性回归、Hurst指数分析法、统计学方法以及地理空间分析技术,对黄土高原植被恢复状况和潜力进行了探讨。结论主要为：① 1999年退耕还林（草）以来,黄土高原植被覆盖度呈显著上升趋势,黄土高原三分之二地区的植被将会持续改善;② 植被响应曲线分析表明,黄土区植被覆盖度和干旱指数呈显著的指数关系,且缓坡相关性大于陡坡。土石山区植被响应函数为线性函数,相关系数下降;③ 整个黄土高原地区平均植被恢复潜力为69.75%。植被恢复潜力值东南高而西北低,黄土高原东南地区植被恢复状况较好,其植被恢复潜力指数较小,而植被恢复潜力指数较高的地区主要为北方风沙区及西部丘陵沟壑区;④ 不同降水量条件下,植被恢复速度差别显著,其中降水量在375~575 mm之间的地区,植被恢复最快。植被恢复措施应该“因水制宜”,避免因造林带来的土壤干化加剧。研究结果以期为黄土高原生态文明建设提供科学支撑。

Satellite data show increasing leaf area of vegetation due to direct (human land-use management) and indirect factors (climate change, CO fertilization, nitrogen deposition, recovery from natural disturbances, etc.). Among these, climate change and CO fertilization effect seem to be the dominant drivers. However, recent satellite data (2000-2017) reveal a greening pattern that is strikingly prominent in China and India, and overlapping with croplands world-wide. China alone accounts for 25% of the global net increase in leaf area with only 6.6% of global vegetated area. The greening in China is from forests (42%) and croplands (32%), but in India is mostly from croplands (82%) with minor contribution from forests (4.4%). China is engineering ambitious programs to conserve and expand forests with the goal of mitigating land degradation, air pollution and climate change. Food production in China and India has increased by over 35% since 2000 mostly due to increasing harvested area through multiple cropping facilitated by fertilizer use and surface/ground-water irrigation. Our results indicate that the direct factor is a key driver of the "Greening Earth", accounting for over a third, and likely more, of the observed net increase in green leaf area. They highlight the need for realistic representation of human land-use practices in Earth system models.

Some model experiments predict a large-scale substitution of Amazon forest by savannah-like vegetation by the end of the twenty-first century. Expanding global demands for biofuels and grains, positive feedbacks in the Amazon forest fire regime and drought may drive a faster process of forest degradation that could lead to a near-term forest dieback. Rising worldwide demands for biofuel and meat are creating powerful new incentives for agro-industrial expansion into Amazon forest regions. Forest fires, drought and logging increase susceptibility to further burning while deforestation and smoke can inhibit rainfall, exacerbating fire risk. If sea surface temperature anomalies (such as El Niño episodes) and associated Amazon droughts of the last decade continue into the future, approximately 55% of the forests of the Amazon will be cleared, logged, damaged by drought or burned over the next 20 years, emitting 15–26 Pg of carbon to the atmosphere. Several important trends could prevent a near-term dieback. As fire-sensitive investments accumulate in the landscape, property holders use less fire and invest more in fire control. Commodity markets are demanding higher environmental performance from farmers and cattle ranchers. Protected areas have been established in the pathway of expanding agricultural frontiers. Finally, emerging carbon market incentives for reductions in deforestation could support these trends.

We used the forest fragmentation model to assess forest fragmentation in China based on a 50-m forest cover map in 2010. Six different fragmentation types including interior forest, perforated forest, edge forest, patch forest, transitional forest and undetermined were obtained. We reported the forest fragmentation status in different administration scales (by country, province, and county) based on Chinese administration boundary map of 2010, At the national scale, the patch forest accounted for the largest proportion (49.05%), while the interior forest was the smallest (3.40%). At the regional scale, Northeast China had the lowest forest fragmentation, while Southwest China had moderate forest fragmentation. The highest forest fragmentation areas were mainly distributed in the North China Plain, Central China, Shandong Peninsula and the Huang-Huai-Hai Plain. At the provincial scale, the highest forest fragmentation was located in Shanghai and Tianjin, and the lowest forest fragmentation was located in Yunnan Province and Heilongjiang Province. At the county scale, the lowest forest fragmentation was located in counties in Shaanxi Province. Overall, our results have clearly shown that forest fragmentation occurs extensively and varies substantially over China in 2010. Our study will provide data support for the forestry administrative sector to conduct better forest management and to optimize forestry production and forest spatial patterns, which can improve forest ecosystem services and biodiversity conservation.

基于2010年50 m空间分辨率森林分布图, 我们利用森林破碎化模型制作了中国森林6个不同破碎化类型 (内部森林、孔洞森林、边缘森林、斑块森林、过渡森林和未确定森林)的空间分布图。然后结合2010年中国行政区划图, 对比分析了不同尺度行政区域的森林破碎化情况。结果表明: 在国家尺度上, 斑块森林的比例最大(49.05%), 内部森林的比例最小(3.40%); 在区域尺度上, 东北地区森林破碎化程度最低, 西南地区森林破碎化程度次之, 华北平原、华中地区、山东半岛、黄淮海平原的森林破碎化程度最高; 在省级尺度上, 上海市森林破碎化程度最高, 天津市次之, 云南省及黑龙江省森林破碎化程度较低; 在县级尺度上, 陕西省所属的县森林破碎化程度最低。由此可知, 总体上我国森林破碎化情况较为严重, 但不同森林破碎化类型的空间分布存在较大差异。本研究可为各级林业部门管理森林资源以及优化林业生产和森林空间格局中提供数据支持, 提高森林的生态系统服务功能和生物多样性保护功能。

. Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m land-cover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the CCI_LC (Climate Change Initiative Global Land Cover) land-cover and MCD43A4 NBAR products (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, a local adaptive random forest model was built for each 5∘×5∘ geographical tile by using the multi-temporal Landsat spectral and texture features and the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC_FCS30-2015 achieved an overall accuracy of 82.5 % and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 % and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 % and kappa coefficient of 0.662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI_LC, MCD12Q1, FROM_GLC, and GlobeLand30) indicated that GLC_FCS30-2015 provides more spatial details than CCI_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM_GLC-2015 and GlobeLand30-2010. They also showed that GLC_FCS30-2015 achieved the best overall accuracy of 82.5 % against FROM_GLC-2015 of 59.1 % and GlobeLand30-2010 of 75.9 %. Therefore, it is concluded that the GLC_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system (containing 16 global LCCS land-cover types as well as 14 detailed and regional land-cover types) with high classification accuracy at 30 m. The GLC_FCS30-2015 global land-cover products produced in this paper are free access at https://doi.org/10.5281/zenodo.3986872 (Liu et al., 2020).\n

. The amount of impervious surface is an important\nindicator in the monitoring of the intensity of human activity and\nenvironmental change. The use of remote sensing techniques is the only means\nof accurately carrying out global mapping of impervious surfaces covering\nlarge areas. Optical imagery can capture surface reflectance\ncharacteristics, while synthetic-aperture radar (SAR) images can be used to\nprovide information on the structure and dielectric properties of surface\nmaterials. In addition, nighttime light (NTL) imagery can detect the\nintensity of human activity and thus provide important a priori\nprobabilities of the occurrence of impervious surfaces. In this study, we\naimed to generate an accurate global impervious surface map at a resolution\nof 30 m for 2015 by combining Landsat 8 Operational Land Image (OLI) optical images, Sentinel-1 SAR\nimages and Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images based on\nthe Google Earth Engine (GEE) platform. First, the global impervious and\nnonimpervious training samples were automatically derived by combining the\nGlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation\nindex (EVI) imagery. Then, the local adaptive random forest classifiers,\nallowing for a regional adjustment of the classification parameters to take into\naccount the regional characteristics, were trained and used to generate\nregional impervious surface maps for each 5∘×5∘ geographical grid using local training samples and multisource\nand multitemporal imagery. Finally, a global impervious surface map,\nproduced by mosaicking numerous 5∘×5∘\nregional maps, was validated by interpretation samples and then compared\nwith five existing impervious products (GlobeLand30, FROM-GLC, NUACI, HBASE and GHSL). The results indicated that the global\nimpervious surface map produced using the proposed multisource,\nmultitemporal random forest classification (MSMT_RF) method\nwas the most accurate of the maps, having an overall accuracy of 95.1 %\nand kappa coefficient (one of the most commonly used statistics to test\ninterrater reliability; Olofsson et al., 2014) of 0.898 as\nagainst 85.6 % and 0.695 for NUACI, 89.6 % and 0.780 for\nFROM-GLC, 90.3 % and 0.794 for GHSL, 88.4 % and 0.753 for\nGlobeLand30, and 88.0 % and 0.745 for HBASE using all 15 regional\nvalidation data. Therefore, it is concluded that a global 30 m impervious\nsurface map can accurately and efficiently be generated by the proposed\nMSMT_RF method based on the GEE platform. The global\nimpervious surface map generated in this paper is available at https://doi.org/10.5281/zenodo.3505079 (Zhang and Liu, 2019).\n

. High-spatial-resolution and long-term climate data are\nhighly desirable for understanding climate-related natural processes. China\ncovers a large area with a low density of weather stations in some (e.g.,\nmountainous) regions. This study describes a 0.5′ (∼ 1 km)\ndataset of monthly air temperatures at 2 m (minimum, maximum, and mean proxy monthly temperatures, TMPs)\nand precipitation (PRE) for China in the period of 1901–2017. The dataset\nwas spatially downscaled from the 30′ Climatic Research Unit (CRU) time\nseries dataset with the climatology dataset of WorldClim using delta spatial\ndownscaling and evaluated using observations collected in 1951–2016 by 496\nweather stations across China. Prior to downscaling, we evaluated the\nperformances of the WorldClim data with different spatial resolutions and\nthe 30′ original CRU dataset using the observations, revealing that their\nqualities were overall satisfactory. Specifically, WorldClim data exhibited\nbetter performance at higher spatial resolution, while the 30′ original CRU\ndataset had low biases and high performances. Bicubic, bilinear, and\nnearest-neighbor interpolation methods employed in downscaling processes\nwere compared, and bilinear interpolation was found to exhibit the best\nperformance to generate the downscaled dataset. Compared with the\nevaluations of the 30′ original CRU dataset, the mean absolute error of the new dataset (i.e., of the 0.5′ dataset downscaled by bilinear interpolation) decreased by 35.4 %–48.7 % for TMPs and by 25.7 % for PRE. The root-mean-square error decreased by 32.4 %–44.9 % for TMPs and by 25.8 % for PRE. The Nash–Sutcliffe efficiency coefficients increased by\n9.6 %–13.8 % for TMPs and by 31.6 % for PRE, and correlation\ncoefficients increased by 0.2 %–0.4 % for TMPs and by 5.0 % for PRE. The new dataset could provide detailed climatology data and annual trends of all climatic variables across China, and the results could be evaluated well using observations at the station. Although the new dataset was not evaluated before 1950 owing to data unavailability, the quality of the new\ndataset in the period of 1901–2017 depended on the quality of the original\nCRU and WorldClim datasets. Therefore, the new dataset was reliable, as the\ndownscaling procedure further improved the quality and spatial resolution of\nthe CRU dataset and was concluded to be useful for investigations related\nto climate change across China. The dataset presented in this article has\nbeen published in the Network Common Data Form (NetCDF) at\nhttps://doi.org/10.5281/zenodo.3114194 for precipitation (Peng,\n2019a) and https://doi.org/10.5281/zenodo.3185722 for air temperatures at 2 m\n(Peng, 2019b) and includes 156 NetCDF files compressed in zip\nformat and one user guidance text file.\n

<p>The variations of alpine timberline altitude with latitude and longitude in China were investigated and their relationships with climatic indices were analyzed in this study. The results are summarized as follows: (1) The altitude of alpine timberline in China changes significantly along latitudinal and longitudinal gradients. North of 30^oN, the timberline altitude decreases with the increase of latitude at the rate of 112 m per latitude degree. South of 30^oN, the latitudinal change shows a different pattern: the altitude does not vary significantly with latitude in the eastern part and increases with latitude in the western part. The timberline altitude drops with the increasing of longitude at similar latitudes, resulting from increased temperature conditions from east to west due to large-scale topographic and geographic factors. The highest timberline (4600 m) was observed in southeast Tibet (29^o-32^oN, 94^o-96^oN), the highest timberline in the world. (2) The timberline is limited by growing season temperature, i.e. Annual Biotemperature (ABT) of 3.5^oC, Warmth Index (WI) of 14.2 ^oC&middot;month, and Mean Temperature for Growing Season (MTGS) of 8.2 ^oC. The corresponding altitudes of these temperature thresholds change with geographic factors, and this change has led to the changes in the latitudinal and longitudinal timberline patterns and timberline variations from the oceanic to continental climate. (3) Precipitation affects obviously timberline altitude at the middle-high latitudes in China; timberline is higher in arid regions than in humid areas at the similar latitudes, caused by increase of temperature at the low humid conditions.</p>

In this paper, four calculation algorithms for average wind direction and their comparison are given, these algorithms include arithmetic, scalar, unit vector and vector. The comparison of the results from these algorithms using the observation data was also made. Results are as follows: the results of arithmetic algorithm enlarge the percentage of south wind, the results of scalar algorithm cause a larger error when wind direction rotation cxceeds 360°. The unit vector has similar results with vector, and does not need wind speed. The unit vector is the best.

常用的平均风向4种计算方法分别是算术平均法、标量平均法、单位矢量法和矢量法,利用实测资料对各种方法的计算结果进行比较,结果表明:算术平均法加大了偏南风的比重;标量平均法在风向变化360°时有可能出现较大的误差,在应用时须加以注意;单位矢量法与矢量法的结果比较一致,且不需风速的同期观测资料,是一种比较好的方法。

The terrestrial carbon sink has been large in recent decades, but its size and location remain uncertain. Using forest inventory data and long-term ecosystem carbon studies, we estimate a total forest sink of 2.4 ± 0.4 petagrams of carbon per year (Pg C year(-1)) globally for 1990 to 2007. We also estimate a source of 1.3 ± 0.7 Pg C year(-1) from tropical land-use change, consisting of a gross tropical deforestation emission of 2.9 ± 0.5 Pg C year(-1) partially compensated by a carbon sink in tropical forest regrowth of 1.6 ± 0.5 Pg C year(-1). Together, the fluxes comprise a net global forest sink of 1.1 ± 0.8 Pg C year(-1), with tropical estimates having the largest uncertainties. Our total forest sink estimate is equivalent in magnitude to the terrestrial sink deduced from fossil fuel emissions and land-use change sources minus ocean and atmospheric sinks.