No abstract is provided for this article.
We consider practicality of a verified account of Net Ecosystem Carbon Budget for forest ecosystems (CA) that supposes reliable assessment of uncertainties, i.e. understanding uncertainty of The FCA is a fuzzy (underspecified) system, of which membership function is inherently stochastic. Thus, any individually used of FCA is not able to estimate structural and usually reported method are inevitably partial. Attempting an estimation of full uncertainties of the studied system we follow the requirements of applied systems analysis integrating the major methods of terrestrial ecosystems carbon account, assessing the method for intermediate and final indicators of CA with their following mutual constraints. Landscape-ecosystem approach (LEA) 1) serves for strict systems designing the account, 2)contains all relevant spatially distributed empirical and semi-empirical data and models, and 3) is presented in form of an Integrated Land Information System (ILIS). By-pixel parametrization of forest cover is provided by utilizing multi-sensor remote sensing data (1 RS products used) within GEO-wiki platform and other relevant information based on special optimization algorithms. Major carbn fluxes within the LEA (NPP, HR, disturbances etc.) are estimated based on fusion of empirical data with process-based elements by sets of regionally distributed models. Uncertainties within LEA are assessed for each module and at each step of the account. Withi method results and (including LEA, process-based models, eddy covariance, and inverse modelling) are harmoized based on the Bayesian approach. The above methodology have been applied to carbon account of Russian forests for 2000-2010;uncertainties of the FCA for individual years were estimated in limits of 25%. We discussed strengths and weaknesses of the paproach, system requirements to different methods of FCA, information and research needs, obtained and potential levels of uncertainties.
The most comprehensive dataset of in situ destructive sampling measurements of forest biomass in Eurasia have been compiled from a combination of experiments undertaken by the authors and from scientific publications. Biomass is reported as four components: live trees (stem, bark, branches, foliage, roots); understory (above- and below ground); green forest floor (above- and below ground); and coarse woody debris (snags, logs, dead branches of living trees and dead roots), consisting of 10,351 unique records of sample plots and 9,613 sample trees from ca 1,200 experiments for the period 1930–2014 where there is overlap between these two datasets. The dataset also contains other forest stand parameters such as tree species composition, average age, tree height, growing stock volume, etc., when available. Such a dataset can be used for the development of models of biomass structure, biomass extension factors, change detection in biomass structure, investigations into biodiversity and species distribution and the biodiversity-productivity relationship, as well as the assessment of the carbon pool and its dynamics, among many others.
Поступила в редакцию 24
No abstract is provided for this article.
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.
There is a critical need for accurate land cover information for resource assessment, biophysical modeling, greenhouse gas studies, and for estimating possible terrestrial responses and feedbacks to climate change. However, practically all existing land cover datasets have quite a high level of uncertainty and suffer from a lack of important details that does not allow for relevant parameterization, e.g., data derived from different forest inventories.
