GIS and Large-scale Linear Programming: Evolution of a Spatial Decision Support System for Land Use Management

FORPLAN (FORest PLANning) is a large-scale linear programming system used to support national forest land management planning. It is available in two versions, and is used extensively to help interdisciplinary planning teams develop forest-wide plans as dictated by the National Forest Management Act of 1976. Nine years of experience clearly show that while the system is working in a technical sense, troublesome issues remain. This paper begins with an overview of how USDA Forest Service planning has evolved. We then give mathematical formulations for portions of FORPLAN models and examples of how the system is used to aid planners on national forests. We present an evaluation of the use of FORPLAN that addresses five criteria including, problems associated with large-scale models and systematic, comprehensive planning, Forest Service organizational issues, the role of foresters in national forest management, and conflicts over competing land uses. We then consider lessons for operations research practitioners. Finally, we discuss a number of conclusions and recommendations, the most important being the need for the Forest Service to more clearly specify the role of forest planning in the overall agency planning hierarchy and the role of FORPLAN in forest planning.

The Northwest has been at the forefront of forest management and research in the United States for more than one hundred years. In The U.S. Forest Service in the Pacific Northwest, Gerald Williams provides an historical overview of the part the Forest Service has played in managing the Northwest's forests. Emphasizing changes in management policy over the years, Williams discusses the establishment of the national forests in Oregon and Washington, grazing on public land, the Great Depression, World War II, and the rise of multiple-use management policies. He draws on extensive documentation of the post-war development boom to explore its effects on forests and Forest Service workers. Discussing such controversial issues as roadless areas and wilderness designation; timber harvesting; forest planning; ecosystems; and spotted owls, Williams demonstrates the impact of 1970s environmental laws on national forest management. The book is rich in photographs, many drawn from the Gerald W. Williams Collection, housed in University Archives at Oregon State University Libraries. Extensive appendices provide detailed data about Pacific Northwest forests. Chronicling a century of the agency's management of almost 25 million acres of national forests and grasslands for the people of the United States, The U.S. Forest Service in the Pacific Northwest is a welcome and overdue resource.

Aim of the study: Decision support systems for forest management (FMDSS) have been developed world wide to account for a broad range of forest ecosystems, management goals and organizational frameworks (e.g. the wiki page of the FORSYS project reports 62 existing FMDSSs from 23 countries). The need to enhance the collaboration among this diverse community of developers and users fostered the rise of new group communication processes that could capture useful knowledge from past experiences in order to efficiently provide it to new FMDSS development efforts.Material and methods: This paper presents and tests an exploratory process aiming to identify the empirical guidelines assisting developers and users of FMDSS. This process encompasses aDelphi survey built upon the consolidation of the lessons-learned statements that summarize the past experiences of the experts involved in the FORSYS project. The experts come from 34 countries and have diverse interests, ranging from forest planners, IT developers, social scientists studying participatory planning, and researchers with interests in knowledge management and in quantitative models for forest planning.Main results: The proposed 37 empirical guidelines that group 102 lessons-learned cover a broad range of issues including the DSS development cycle, involvement of the stakeholders, methods, models and knowledge-based techniques in use.Research highlights: These results may be used for improving new FMDSS development processes, teaching and training and further suggest new features of FMDSS and future research topics. Furthermore, the guidelines may constitute a knowledge repository that may be continuously improved by a community of practice.Keywords: Forest management; guidelines; guidelines definition process; lessons learned; decision support systems; system architecture; knowledge management; participatory planning; Delphi.

Forest management decision support systems (FMDSS) and geographic information systems have improved the incorporation of spatial information into forest planning. However, most FMDSS have been designed to implement silvicultural treatments rather than prescribed fire and fuel treatments. Results from a survey of 277 Forest Service employees in the western US show FMDSS need modifications to be better adapted to the needs of prescribed fire planners. Survey responses indicate that, on average, prescribed fire planners would like to increase the area treated annually by 12 times current levels. Available time windows for burning provide the most severe constraint for managers, while funding and personnel constraints were rated as comparably less important. Given these results, FMDSS may be most appropriately designed to help managers develop a long-term strategy for prioritizing prescribed fire treatments. Necessary FMDSS modifications may include different methods of delineating treatment boundaries, improved integration with wildlife habitat models, emphasis on short-term costs, and flexible intervals between repeated treatments. West. J. Appl. For. 15(4):200–207.

Residential medical digital technology is an emerging discipline combining computer network technology and medical research. Based on the idea of knowledge discovery, this study was designed to construct a decision support system for remote medical management, analyze the need for utilization rate calculations and obtain relevant modeling elements for system design. Specifically, the model constructs a design method for a decision support system for the healthcare management of elderly residents through the use of a utilization rate modeling method based on digital information extraction. In the simulation process, the utilization rate modeling and system design intent analysis are combined to obtain the relevant functions and morphological characteristics that are essential to the system. Using regular usage slices, a higher-precision non-uniform rational B-spline (NURBS) usage rate can be fitted and a surface model with better continuity can be constructed. The experimental results show that the deviation of the NURBS usage rate generated by the boundary division from the original data model can reach test accuracies of 83, 87 and 89%, respectively. It is shown that the method can effectively reduce the modeling error caused by the irregular feature model in the process of modeling the utilization rate of digital information, and that it can ensure the accuracy of the model.

This work deals with the design and implementation of a decision support system for flood management. In the society today emergency issues arises due to terrorist attack, flood disaster, epidemics, widespread massive riot etc. In other to handle this situation government has established a number of agents supported by Non-governmental organization (NGO). Due to non-existence of integrated database and communication support system for managing flood emergency has resulted to poor handling, huge losses and reoccurrences. This work involves managing the inter operations/ interlinks of flood incidents sources by the handling agents, design of database report format and communication media for the system automation. In other to solve this problem waterfall methodology was adopted in this study. The programming tool adopted was adobe dreamer. Its host has front end and back end. The front end is the user interface and serves as a channel through which users communicates to the system. Adobe dreamer being the tools used for building such web-based application supports many mark-up languages like HTML, CSS and Java script. The most common available tools for the development of this system is the Adobe Dream waver which is IDE (integrated Development Environment. It is a creation management tool and requires no programming language and good enough for experienced website developers.

Effects of temporal aggregation in integrated strategic/tactical and strategic forest planning

On December 6, 2002, the USDA Forest Service published a proposed planning rule that replaces postdecisional appeals of land and resource management plans with a “predecisional objection process” within a framework of collaborative planning for sustainability. This article examines the Forest Service's rationale for eliminating postdecisional appeals, provides an overview of accountability relationships, and explores the implications for democratic accountability if the proposed rule eliminating postdecisional appeals is adopted. Appeals have provided an important mechanism of accountability in forest planning. There are questions about whether the objection process will provide a comparable means of accountability. The change represents a shift away from traditional legal and hierarchical accountability relationships toward accountability based on public deliberation and scientific input in a collaborative planning process. The success of such a model depends on successfully institutionalizing collaborative planning throughout a large, decentralized organization.

National forest planning and management traditionally have involved addressing a broad spectrum of natural resource issues and ecological conditions to comply with forest management regulations and policies. In 2012, the U.S. Department of Agriculture Forest Service issued a new forest planning rule requiring that national forests and grasslands identify and evaluate information on the ecosystem services provided by plan areas from which people benefit as part of the plan assessment phase of plan revision. Specifically, planning teams are directed now to identify “key” ecosystem services that are both of importance outside the plan area and likely to be affected by plan alternatives. The agency’s intent is to integrate ecosystem services into the planning and management process to help ensure forest plans produce more beneficial outcomes for the public while meeting the needs of present and future generations. In this report, we develop and demonstrate a conceptual framework and process that forest planning teams can use or draw upon to address ecosystem services during the assessment phase of planning. We provide several examples regarding how planning teams might identify key goods and services, as well as a worksheet teams might use in their analysis.

Federal land-management agencies are increasingly developing policies to support multiscale monitoring for land-management planning and decisionmaking. Regulations for national forest planning promulgated in 2012 require US Forest Service Regions to develop “broader-scale” monitoring strategies conducted at scales greater than a single planning unit that can complement forest plan monitoring strategies. Given that this requirement is relatively new, we conducted research to investigate the advantages, challenges, and opportunities associated with implementation. We conducted and analyzed interviews with 95 interviewees from forest and regional levels of the agency, and federal, state, nongovernment, and research organizations who could provide insight on broader-scale monitoring challenges and opportunities. We also drew on findings from four interagency workshops. We found that broader-scale monitoring strategies have the potential to generate efficiencies for forest planning processes and improve coordination and communication across levels of the agency and with external partners. Major challenges for implementation relate to limited human and financial resource capacity and the agency’s decentralized organizational structure and culture. Opportunities for addressing these issues include building capacity for implementation through partnerships and investing in regional capacity for coordination and implementation.

This multimedia short-course presents a range of information on the science, management and policy of forest and grassland carbon. Forests and grasslands worldwide play a critical role in storing carbon and sequestering greenhouse gases from the atmosphere. The U.S. Forest Service, which manages 193 million acres of forests and grasslands, emphasizes the need for management to consider carbon stewardship in conjunction with other sustainable land management goals. This course aims to provide a broad background for public and private land managers who are interested in how ecosystems store and release carbon, how management can affect carbon storage and release, and how carbon can be considered in the context of other management objectives. Experts in the subject matter prepared each presentation and each talk was reviewed by a group of scientists and managers to help improve the clarity and content of the final recording. The DVD includes fifteen presentations, accompanied by references and links, and a set of quizzes on the material. Course material can also be accessed online at http://www.fs.fed.us/ccrc/carboncourse/

Exploiting the PAIN OUT registry with a clinical decision support system for acute pain management

Design of a computerized decision support system for hatchery production management

Within the framework of a technical cooperation project between ACSAD and BGR a Decision Support System (DSS) for water resources management was developed and applied in two pilot areas.

Industrial dairy farms boast highly advanced health monitoring and disease diagnosis systems. But without easily accessible, user-friendly web platforms for real-time decision-making, most dairy farmers cannot proactively manage herd health management and optimize treatments based on disease prediction and prevention. To bridge this gap, we have developed a web application of a Decision support system (DSS) for dairy health management based on machine learning. The system architecture combines a Flask backend with a React frontend and scalable cloud data storage and includes preprocessing, data integration, predictive modelling, and cost analysis. DSS forecasts herd diseases with an accuracy 6.66 mean absolute error and 2.35 median absolute deviation across predictions. Its core predictive capabilities rely on long short-term memory (LSTM) neural networks to forecast disease progression from historical records and on a linear trend model to project cuts in treatment costs. The system calculates medication dosages and cost per disease, streamlines supplier selection, and simulates various treatment scenarios, thereby identifying high-cost diseases with potential savings. In other words, this DSS application processes disease and treatment data by incorporating veterinary records into advanced data analytics and neural networks, thereby predicting diseases, optimizing disease prevention and treatment strategies, and reducing costs. As such, this DSS application provides dairy farmers with a tool for strategic decision-making, veterinary treatment planning, and cost-effective disease management towards improving animal welfare and increasing milk yield.