Sampling and Mapping a Soil Erosion Cover Factor by Integrating Stratification,Model Updating and Cokriging with Images

Cost-efficient sample designs for collection of ground data and accurate mapping of variables are required to monitor natural resources and environmental and ecological systems. In this study, a sample design and mapping method was developed by integrating stratification, model updating, and cokriging with Landsat Thematic Mapper (TM) imagery. This method is based on the spatial autocorrelation of variables and the spatial cross-correlation among them. It can lead to sample designs with variable grid spacing, where sampling distances between plots vary depending on spatial variability of the variables from location to location. This has potential cost-efficiencies in terms of sample design and mapping. This method is also applicable for mapping in the case in which no ground data can be collected in some parts of a study area because of the high cost. The method was validated in a case study in which a ground and vegetation cover factor was sampled and mapped for monitoring soil erosion. The results showed that when the sample obtained with three strata using the developed method was used for sampling and mapping the cover factor, the sampling cost was greatly decreased, although the error of the map was slightly increased compared to that without stratification; that is, the sample cost-efficiency quantified by the product of cost and error was greatly increased. The increase of cost-efficiency was more obvious when the cover factor values of the plots within the no-significant-change stratum were updated by a model developed using the previous observations instead of remeasuring them in the field.     

Cost-efficiency in packaging waste management: The case of Belgium

In order to exploit economies of scale Belgian municipalities regularly cooperate in the provision of waste related services. In particular for the collection and separation of household packaging waste, municipalities appear to seek technical and cost efficiency gains by cooperating via municipal waste joint ventures. Although most Belgian municipal waste joint ventures can present excellent recycling and recovery rates for household packaging waste, their performance in terms of cost-efficiency has never been assessed. Using a unique dataset comprising of the costs for all 35 Belgian municipal waste joint ventures in 2010, this paper present the first assessment of the cost efficiency of household packaging waste collection in Belgium. As we are not sure on the relative importance of the separate cost efficiency scores for the three selectively collected household packaging waste fractions when determining the overall cost efficiency, this paper draws on the Benefit-of-the-Doubt approach. Our results indicate that, despite the substantial cooperation between municipalities, still considerable differences in cost efficiency for household packaging waste collection exist.     

Environmental service payments: evaluating biodiversity conservation trade-offs and cost-efficiency in the Osa Conservation Area, Costa Rica

The cost-efficiency of payments for environmental services (PES) to private landowners in the Osa Conservation Area, Costa Rica, is evaluated in terms of the trade-off between biodiversity representation and opportunity costs of conservation to agricultural and forestry land-use. Using available GIS data and an 'off-the-shelf' software application called TARGET, we find that the PES allocation criteria applied by authorities in 2002-2003 were more than twice as cost-efficient as criteria applied during 1999-2001. Results show that a policy relevant assessment of the cost-effectiveness of PES relative to other conservation policies can be carried out at regional level using available studies and GIS data. However, there are a number of data and conceptual limitations to using heuristic optimisation algorithms in the analysis of the cost-efficiency of PES. Site specific data on probabilities of land-use change, and a detailed specification of opportunity costs of farm land, labour and capital are required to use algorithms such as TARGET for ranking individual sites based on cost-efficiency. Despite its conceptual soundness for regional conservation analysis, biodiversity complementarity presents a practical challenge as a criterion for PES eligibility at farm level because it varies depending on the set of areas under PES contracts at any one time.     

Optimal spatial resolution for collection of ground data and multi-sensor image mapping of a soil erosion cover factor

Military training activities disturb ground and vegetation cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion cover factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps.