Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories

For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed.For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed.Peer reviewe

Habitat effects on the breeding performance of three forest-dwelling hawks

PLoS ONE 10(9): e0137877Habitat loss causes population declines, but the mechanisms are rarely known. In the European Boreal Zone, loss of old forest due to intensive forestry is suspected to cause declines in forest-dwelling raptors by reducing their breeding performance. We studied the boreal breeding habitat and habitat-associated breeding performance of the northern goshawk (Accipiter gentilis), common buzzard (Buteo buteo) and European honey buzzard (Pernis apivorus). We combined long-term Finnish bird-of-prey data with multi-source national forest inventory data at various distances (100–4000 m) around the hawk nests. We found that breeding success of the goshawk was best explained by the habitat within a 2000-m radius around the nests; breeding was more successful with increasing proportions of old spruce forest and water, and decreasing proportions of young thinning forest. None of the habitat variables affected significantly the breeding success of the common buzzard or the honey buzzard, or the brood size of any of the species. The amount of old spruce forest decreased both around goshawk and common buzzard nests and throughout southern Finland in 1992–2010. In contrast, the area of young forest increased in southern Finland but not around hawk nests. We emphasize the importance of studying habitats at several spatial and temporal scales to determine the relevant species-specific scale and to detect environmental changes. Further effort is needed to reconcile the socioeconomic and ecological functions of forests and habitat requirements of old forest specialists.Peer reviewe

Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories

For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed

A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data

The k-Nearest Neighbors (k-NN) technique is a popular method for producing spatially contiguous predictions of forest attributes by combining field and remotely sensed data. In the framework of Working Group 2 of COST Action FP1001, we reviewed the scientific literature for forestry applications of k-NN. Information available in scientific publications on this topic was used to populate a database that was then used as the basis for a meta analysis. We extracted qualitative and quantitative information from 260 experimental tests described in 148 scientific papers. The papers represented a geographic range of 26 countries and a temporal range from 1981 to 2013. Firstly, we describe the literature search and the information extracted and analyzed. Secondly, we report the results of the meta-analysis, especially with respect to estimation accuracies reported for k-NN applications for different configurations, different forest environments, and different input information. We also provide a summary of results that may reasonably be expected for those planning a k-NN application using remotely sensed data from different sensors and for different forest attributes. Finally, we identify some methodological publications that have advanced the state of the science with respect to k-NN.201