Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the 21st century

During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include: warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land-use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia's role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large scale water withdrawals, land use and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that Integrated Assessment Models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts

Patterns of tree cover loss along the Indonesia-Malaysia border on Borneo

Borneo Island is experiencing rapid tree-cover loss. This loss has been quantified for the Indonesian part of the island at Landsat spatial resolution, but no recent study exists that extends across the border into Malaysia. This research focused on quantifying patterns of tree-cover loss in the IndonesiaMalaysia border zone on Borneo. The methods used for quantifying 20002010 tree-cover loss within 20 km on either side of the border are an internally consistent mapping algorithm used on Landsat imagery and a local indicator of spatial autocorrelation to quantify the concentration of loss. Within the 20 km zone on either side of the border, tree-cover loss rates in lowlands were high in the two countries (19.8% and 14.4%, in Indonesia and Malaysia, respectively), but rates in the Malaysian uplands were an order of magnitude higher than in the Indonesian uplands (2.95% and 0.25%, respectively). Clusters of tree-cover loss in the Malaysian uplands were considerably larger than in the Indonesian uplands

Quantifying forest cover loss in Democratic Republic of the Congo, 2000-2010, with Landsat ETM+ data

Forest cover and forest cover loss for the last decade, 2000â2010, have been quantified for the Democratic Republic of the Congo (DRC) using Landsat time-series data set. This was made possible via an exhaustive mining of the Landsat Enhanced Thematic Mapper Plus (ETM+) archive. A total of 8881 images were processed to create multi-temporal image metrics resulting in 99.6% of the DRC land area covered by cloud-free Landsat observations. To facilitate image compositing, a top-of-atmosphere (TOA) reflectance calibration and image normalization using Moderate Resolution Imaging Spectroradiometer (MODIS) top of canopy (TOC) reflectance data sets were performed. Mapping and change detection was implemented using a classification tree algorithm. The national year 2000 forest cover was estimated to be 159,529.2 thousand hectares, with gross forest cover loss for the last decade totaling 2.3% of forest area. Forest cover loss area increased by 13.8% between the 2000â2005 and 2005â2010 intervals, with the greatest increase occurring within primary humid tropical forests. Forest loss intensity was distributed unevenly and associated with areas of high population density and mining activity. While forest cover loss is comparatively low in protected areas and priority conservation landscapes compared to forests outside of such areas, gross forest cover loss for all nature protection areas increased by 64% over the 2000 to 2005 and 2005 to 2010 intervals

Devastating Decline of Forest Elephants in Central Africa.

African forest elephants– taxonomically and functionally unique–are being poached at accelerating rates, but we lack range-wide information on the repercussions. Analysis of the largest survey dataset ever assembled for forest elephants (80 foot-surveys; covering 13,000 km; 91,600 person-days of fieldwork) revealed that population size declined by ca. 62% between 2002–2011, and the taxon lost 30% of its geographical range. The population is now less than 10% of its potential size, occupying less than 25% of its potential range. High human population density, hunting intensity, absence of law enforcement, poor governance, and proximity to expanding infrastructure are the strongest predictors of decline. To save the remaining African forest elephants, illegal poaching for ivory and encroachment into core elephant habitat must be stopped. In addition, the international demand for ivory, which fuels illegal trade, must be dramatically reduced

Biomass Resources: Agriculture

Bioenergy is the single largest source of renewable energy in the European Union (EU-28); of this, 14% was produced from agricultural feedstocks in 2012. This chapter provides an overview of the current use (for bioenergy) and future potential of agricultural feedstocks for (amongst others) biorefinery purposes in the European Union. The main application of these feedstocks is currently the production of biofuels for road transport. Biodiesel makes up 80% of the European biofuel production, mainly from rapeseed oil, and the remaining part is bioethanol from wheat and sugar beet. Dedicated woody and grassy crops (mainly miscanthus and switchgrass) are currently only used in very small quantities for heat and electricity generation. There is great potential for primary agricultural residues (mainly straw) but currently only part of this is for heat and electricity generation. Agricultural land currently in use for energy crop cultivation in the EU-28 is 4.4 Mio ha, although the land area technically available in 2030 is estimated to be 16–43 Mio ha, or 15–40% of the current arable land in the EU-28. There is, however, great uncertainty on the location and quality of that land. It is expected that woody and grassy crops together with primary agricultural residues should become more important as agricultural feedstocks

Quantifying Uncertainty for Estimates Derived from Error Matrices in Land Cover Mapping Applications:The Case for a Bayesian Approach

The use of land cover mappings built using remotely sensed imagery data has become increasingly popular in recent years. However, these mappings are ultimately only models. Consequently, it is vital for one to be able to assess and verify the quality of a mapping and quantify uncertainty for any estimates that are derived from them in a reliable manner. For this, the use of validation sets and error matrices is a long standard practice in land cover mapping applications. In this paper, we review current state of the art methods for quantifying uncertainty for estimates obtained from error matrices in a land cover mapping context. Specifically, we review methods based on their transparency, generalisability, suitability when stratified sampling and suitability in low count situations. This is done with the use of a third-party case study to act as a motivating and demonstrative example throughout the paper. The main finding of this paper is there is a major issue of transparency for methods that quantify uncertainty in terms of confidence intervals (frequentist methods). This is primarily because of the difficulty of analysing nominal coverages in common situations. Effectively, this leaves one without the necessary tools to know when a frequentist method is reliable in all but a few niche situations. The paper then discusses how a Bayesian approach may be better suited as a default method for uncertainty quantification when judged by our criteria