This study aims at identifying the carbon dioxide reduction (CDR) potential of large-scale and multi-purpose afforestation/reforestation at the global level with special emphasis on the Mid-Latitude Region (MLR). Applying a combined remote sensing/GIS approach coupled with biophysical forest and disturbance modeling under various climate change scenarios, we identify potential afforestation locations, inter-alia on abandoned agricultural land and on areas burnt from wild land fires. With the help of IIASA’s biophysical global forestry model (G4M), we calculate the associated land-based CDR potentials through carbon sequestration in afforested biomass and through climate risk-resilient and sustainable forest management dedicated to the supply of bioenergy plants coupled with carbon capture and storage (BECCS) facilities. Finally, three promising scenarios have been identified including I) afforestation; II) reforestation; and III) BECCS. In all scenarios, priority is put on sustainable forest management and nature/biodiversity conservation. Forest modeling results have been combined with recent data sets which have been overlayed in order to provide a unique basis to estimate the land-based CDR technologies’ potential to mitigate climate change and contribute to reaching the goals of the Paris Agreement. In the case of afforestation, preliminary results indicate a total potential afforestation area greater than 1 billion ha.  The largest area potential for afforestation have been identified in the USA. Given the higher productivity (combined with large area available), Brazil is the country with the highest total CDR potential of close to 500 MtC/yr.
No abstract is provided for this article.
Extreme droughts, heat waves, frosts, precipitation, wind storms and other climate extremes may impact the structure, composition, and functioning of terrestrial ecosystems, and thus carbon cycling and its feedbacks to the climate system. Yet, the interconnected avenues through which climate extremes drive ecological and physiological processes and alter the carbon balance are poorly understood. Here we review literature on carbon-cycle relevant responses of ecosystems to extreme climatic events. Given that impacts of climate extremes are considered disturbances, we assume the respective general disturbance-induced mechanisms and processes to also operate in an extreme context. The paucity of well-defined studies currently renders a quantitative meta-analysis impossible, but permits us to develop a deductive framework for identifying the main mechanisms (and coupling thereof) through which climate extremes may act on the carbon cycle. We find that ecosystem responses can exceed the duration of the climate impacts via lagged effects on the carbon cycle. The expected regional impacts of future climate extremes will depend on changes in the probability and severity of their occurrence, on the compound effects and timing of different climate extremes, and on the vulnerability of each land-cove type modulated by management. Though processes and sensitivities differ among biomes, based on expert opinion we expect forests to exhibit the largest net effect of extremes due to their large carbon pools and fluxes, potentially large indirect and lagged impacts, and long recovery time to re-gain previous stocks. At the global scale, we presume that droughts have the strongest and most widespread effects on terrestrial carbon cycling. Comparing impacts of climate extremes identified via remote sensing vs. ground-based observational case studies reveals that many regions in the (sub-)tropics are understudied. Hence, regional investigations are needed to allow a global upscaling of the impacts of climate extremes on global carbon-climate feedbacks.
No abstract is provided for this article.
No abstract is provided for this article.
The paper presents a short analysis of state and accuracy of forest inventory in Russia during 1961-2009. Major numerical characteristics for all forests of the country, which were obtained by system integration of remote sensing products and on-ground information are discussed and compared with data of official forest inventory. Necessity of development a new forest inventory system in Russia is discussed.
The paper considers the specifics, strengths and weaknesses of available remote sensing products within major steps and modules of a verified terrestrial ecosystems full carbon account (FCA) of Russia's land. The methodology used is based on system integration of all available information sources and major methods of carbon accounting using IIASA's landscape-ecosystem approach for overall designing of the account. A multi-sensor remote sensing concept is a corner stone of the methodology being substantially used for (1) georeferencing and parametrization of land cover and its change, (2) assessment of important biophysical and ecological parameters of ecosystems and landscapes, and (3) assessment of the impacts of environmental conditions on ecosystem productivity and disturbance regimes. System integration and mutual constraints of remote sensing and ground information allow for substantially decreasing uncertainty of the FCA. In the Russian case-study, the net ecosystem carbon balance of Russia for an individual year (2009) is estimated with uncertainty at 25-30% (CI 0.9), that presumably should satisfy current requirements to the FCA at the national (continental) scale.
An automated information system making it possible to estimate spatial distribution of soil organic carbon pool with a high spatial resolution (1 km2) has
Terrestrial biomass is considered as an essential indicator for the monitoring of the Earth's ecosystem and climate. In recent years, many regional biomass datasets have been produced. These were obtained using a wide range of methods - from pure remote sensing RS to the collection of field measurements. The Biomass Geo-Wiki is a new tool from the family of Geo-Wiki.org, which has been launched to bring together different biomass datasets so that they can be viewed and compared with high resolution imagery on Google Earth. The ultimate goal is to perform gap analysis, cross-product validation, harmonization and hybrid product development leading to improved global biomass datasets in the future.
Indicators of biological productivity of forests (live and dead biomass, net primary production, net and grossgrowth) are crucial for both assessment of the impacts of terrestrial ecosystems on major biogeochemical cyclesand practice of sustainable forest management. However, different information and the diversity of methods usedin the assessments of forests productivity cause substantial variation in reported estimates. The paper containsa systems analysis of the existing methods, their uncertainties, and a description of available information. Withrespect to Northern Eurasian forests, the major reasons for uncertainties could be categorized as following: (1)significant biases that are inherent in a number of important sources of available information (e.g., forest inventorydata, results of measurements of some indicators in situ); (2) inadequacy and oversimplification of models ofdifferent types (empirical aggregations, process-based models); (3) lack of data for some regions; and (4) upscalingprocedure of “point” observations.Based on as comprehensive as possible adherence to the principles of systems analysis, we made an attemptto provide a reanalysis of indicators of forests productivity of Russia aiming at obtaining the results for whichuncertainties could be estimated in a reliable and transparent way. Within a landscape-ecosystem approach it hasrequired (1) development of an expert system for refinement of initial data including elimination of recognizedbiases; (2) delineation of ecological regions based on gradients of major indicators of productivity; (3) transitionto multidimensional models (e.g., for calculation of spatially distributed biomass expansion factors); (4) use ofprocess-based elements in empirical models; and (5) development of some approaches which presumably do nothave recognized biases. However, taking into account the fuzzy character of the problem, the above approach (aswell as any other individually used method) is not able to recognize structural uncertainties. In order to assess those,a special statistical procedure for harmonizing the multiple constraints of the estimates obtained by independentmethods (landscape-ecosystem approach; flux measurements; process-based vegetation models; inverse modeling)was used to estimate uncertainty of the final results. Application of the above methodology resulted in a reliableassessment of major indicators of productivity. For instance, live biomass at the country’s level is estimated withuncertainty of 4-6%, net primary production – 7-10% (confidential interval 0.9). It was recognized the tendencyof increasing productivity of Russian forests during the last four decades at level of 0.5 0.2% year
