Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.

Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009

Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial prediction

Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.

Remote Sensing of Grassland Production and Management—A Review

China is facing an unprecedented set of challenges in balancing the effects of economic development and global climate change with environmental protection and maintaining biodiversity Although positive steps have been undertaken to remedy this situation, currently 80% of China’s 25 extant primate species are threatened, 15–18 species have population sizes of less than 3000 individuals, and two species of gibbons and one species of langur have been extirpated over the past few decades Today, virtually all species of primates in China inhabit fragmented landscapes and are distributed in small isolated subpopulations with limited opportunities to exchange individuals or genetic information Here we present a historical framework examining how human-induced environmental changes, particularly since the second half of the 20th century, accelerated primate population decline in China In addition, we modeled the expected spatial conflict between agricultural expansion and primate distributions over the next 25–75 years and assessed the current overlap between protected areas and primate distributions Depending on the assumptions of the spatial conflict model, primate distributions are expected to decline by an additional 51–87% by the year 2100 Thus, unless large-scale conservation policies are implemented immediately the current trend of primate population decline, local extirpation, and species extinctions will accelerate To mitigate against such extinction scenarios, we advocate the creation of a Chinese national agency and repository of environmental information focused on public awareness and education, the implementation of targeted programs of habitat restoration designed to return impacted forests to a more natural state especially within and at the boundaries of nature reserves, the establishment of additional protect areas, and the construction of a latticework of corridors connecting isolated primate subpopulations This comprehensive approach offers the most effective way to protect China’s animal and plant biodiversity, including its endangered primate populations

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The primate extinction crisis in China: immediate challenges and a way forward

Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm

Grasslands are an important component of terrestrial ecosystems that play a crucial role in the carbon cycle and climate change. In this study, we collected aboveground biomass (AGB) data from 223 grassland quadrats distributed across the Loess Plateau from 2011 to 2013 and predicted the spatial distribution of the grassland AGB at a 100-m resolution from both meteorological station and remote sensing data (TM and MODIS) using a Random Forest (RF) algorithm. The results showed that the predicted grassland AGB on the Loess Plateau decreased from east to west. Vegetation indexes were positively correlated with grassland AGB, and the normalized difference vegetation index (NDVI) acquired from TM data was the most important predictive factor. Tussock and shrub tussock had the highest AGB, and desert steppe had the lowest. Rainfall higher than 400 m might have benefitted the grassland AGB. Compared with those obtained for the bagging, mboost and the support vector machine (SVM) models, higher values for the mean Pearson coefficient (R) and the symmetric index of agreement (λ) were obtained for the RF model, indicating that this RF model could reasonably estimate the grassland AGB (65.01%) on the Loess Plateau.

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Wenyi Sun

Papers

Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm