Approaches to integrated strategic/tactical forest planning

Traditionally forest planning is divided into a hierarchy of planning phases. Strategic planning is conducted to make decisions about sustainable harvest levels while taking into account legislation and policy issues. Within the frame of the strategic plan, the purpose of tactical planning is to schedule harvest operations to specific areas in the immediate few years and on a finer time scale than in the strategic plan. The operative phase focuses on scheduling harvest crews on a monthly or weekly basis, truck scheduling and choosing bucking instructions. Decisions at each level are to a varying degree supported by computerized tools. A problem that may arise when planning is divided into levels and that is noted in the literature focusing on decision support tools is that solutions at one level may be inconsistent with the results of another level. When moving from the strategic plan to the tactical plan, three sources of inconsistencies are often present; spatial discrepancies, temporal discrepancies and discrepancies due to different levels of constraint. The models used in the papers presented in this thesis approaches two of these discrepancies. To address the spatial discrepancies, the same spatial resolution has been used at both levels, i.e., stands. Temporal discrepancies are addressed by modelling the tactical and strategic issues simultaneously. Integrated approaches can yield large models. One way of circumventing this is to aggregate time and/or space. The first paper addresses the consequences of temporal aggregation in the strategic part of a mixed integer programming integrated strategic/tactical model. For reference, linear programming based strategic models are also used. The results of the first paper provide information on what temporal resolutions could be used and indicate that outputs from strategic and integrated plans are not particularly affected by the number of equal length strategic periods when more than five periods, i.e. about 20 year period length, are used. The approach used in the first paper could produce models that are very large, and the second paper provides a two-stage procedure that can reduce the number of variables and preserve the allocation of stands to the first 10 years provided by a linear programming based strategic plan, while concentrating tactical harvest activities using a penalty concept in a mixed integer programming formulation. Results show that it is possible to use the approach to concentrate harvest activities at the tactical level in a full scale forest management scenario. In the case study, the effects of concentration on strategic outputs were small, and the number of harvest tracts declined towards a minimum level. Furthermore, the discrepancies between the two planning levels were small

A multi-scale method to assess pesticide contamination risks in agricultural watersheds

The protection of water is now a major priority for environmental managers, especially around drinkingpumping stations. In view of the new challenges facing water agencies, we developed a method designedto support their public policy decision-making, at a variety of different spatial scales. In this paper, wepresent this new spatial method, using remote sensing and a GIS, designed to determine the contami-nation risk due to agricultural inputs, such as pesticides. The originality of this method lies in the useof a very detailed spatial object, the RSO (Reference Spatial Object), which can be aggregated to manyworking and managing scales. This has been achieved thanks to the pixel size of the remote sensing, witha grid resolution of 30 m × 30 m in our application.The method – called PHYTOPIXAL – is based on a combination of indicators relating to the environmen-tal vulnerability of the surface water environment (slope, soil type and distance to the stream) and theagricultural pressure (land use and practices of the farmers). The combination of these indicators for eachpixel provides the contamination risk. The scoring of variables was implemented according knowledgein literature and of experts.This method is used to target specific agricultural input transfer risks. The risk values are first calculatedfor each pixel. After this initial calculation, the data are then aggregated for decision makers, accordingto the most suitable levels of organisation. These data are based on an average value for the watershedareas.In this paper we detail an application of the method to an area in the hills of Southwest France. Weshow the pesticide contamination risk by in areas with different sized watersheds, ranging from 2 km2to 7000 km2, in which stream water is collected for consumption by humans and animals. The resultswere recently used by the regional water agency to determine the protection zoning for a large pumpingstation. Measures were then proposed to farmers with a view to improving their practices.The method can be extrapolated to different other areas to preserve or restore the surface water

Public Participation GIS for sustainable urban mobility planning: methods, applications and challenges

Sustainable mobility planning is a new approach to planning, and as such it requires new methods of public participation, data collection and data aggregation. In the article we present an overview of Public Participation GIS (PPGIS) methods with potential use in sustainable urban mobility planning. We present the methods using examples from two recent case studies conducted in Polish cities of Poznań and Łodź. Sustainable urban mobility planning is a cyclical process, and each stage has different data and participatory requirements. Consequently, we situate the PPGIS methods in appropriate stages of planning, based on potential benefits they may bring into the planning process. We discuss key issues related to participant recruitment and provide guidelines for planners interested in implementing methods presented in the paper. The article outlines future research directions stressing the need for systematic case study evaluation

Drive counts as a method of estimating ungulate density in forests: mission impossible?

Although drive counts are frequently used to estimate the size of deer populations in forests, little is known about how counting methods or the density and social organization of the deer species concerned influence the accuracy of the estimates obtained, and hence their suitability for informing management decisions. As these issues cannot readily be examined for real populations, we conducted a series of ‘virtual experiments’ in a computer simulation model to evaluate the effects of block size, proportion of forest counted, deer density, social aggregation and spatial auto-correlation on the accuracy of drive counts. Simulated populations of red and roe deer were generated on the basis of drive count data obtained from Polish commercial forests. For both deer species, count accuracy increased with increasing density, and decreased as the degree of aggregation, either demographic or spatial, within the population increased. However, the effect of density on accuracy was substantially greater than the effect of aggregation. Although improvements in accuracy could be made by reducing the size of counting blocks for low-density, aggregated populations, these were limited. Increasing the proportion of the forest counted led to greater improvements in accuracy, but the gains were limited compared with the increase in effort required. If it is necessary to estimate the deer population with a high degree of accuracy (e.g. within 10% of the true value), drive counts are likely to be inadequate whatever the deer density. However, if a lower level of accuracy (within 20% or more) is acceptable, our study suggests that at higher deer densities (more than ca. five to seven deer/100 ha) drive counts can provide reliable information on population size

Collaborative Storage Management In Sensor Networks

In this paper, we consider a class of sensor networks where the data is not required in real-time by an observer; for example, a sensor network monitoring a scientific phenomenon for later play back and analysis. In such networks, the data must be stored in the network. Thus, in addition to battery power, storage is a primary resource: the useful lifetime of the network is constrained by its ability to store the generated data samples. We explore the use of collaborative storage technique to efficiently manage data in storage constrained sensor networks. The proposed collaborative storage technique takes advantage of spatial correlation among the data collected by nearby sensors to significantly reduce the size of the data near the data sources. We show that the proposed approach provides significant savings in the size of the stored data vs. local buffering, allowing the network to run for a longer time without running out of storage space and reducing the amount of data that will eventually be relayed to the observer. In addition, collaborative storage performs load balancing of the available storage space if data generation rates are not uniform across sensors (as would be the case in an event driven sensor network), or if the available storage varies across the network.Comment: 13 pages, 7 figure