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Describes the structure of the Forest Inventory and Analysis Database (FIADB) 4.0 for phase 3 indicators. The FIADB structure provides a consistent framework for storing forest health monitoring data across all ownerships for the entire United States. These data are available to the public.
New knowledge from wildlife-habitat relationship models is often difficult to implement in a management context. This can occur because researchers do not always consider whether managers have access to information about environmental covariates that permit the models to be applied. Moreover, ecosystem management requires knowledge about the condition of habitats over large geographic regions, whereas most research projects have limited spatial inference. For example, research has revealed much about the habitat of fishers (Martes pennanti) at various research sites in California, yet this work has not been translated into practical tools that managers can use to monitor fisher habitat regionally, or to evaluate and mitigate the effects of proposed forest management on fisher habitat. This led us to create new habitat models that are intimately linked to agency approaches to forest monitoring and software tools used by forest managers to plan timber harvests and vegetation management. We created habitat models that were integrated with these approaches and tools that forest managers use for two purposes: to inventory forest resources (i.e., Forest Inventory and Analysis [FIA] plots) and to simulate the response of stands to harvest, fire, insects, disease, and other disturbances (i.e., Forest Vegetation Simulator [FVS]). In this paper we provide an example of how to assess and monitor wildlife habitat using FIA vegetation monitoring protocols. We also provide an example of how to integrate an existing FIA-based model of fisher resting habitat into FVS, software that simulates the effect of alternative silvicultural treatments on vegetation data collected from field plots. Using these tools we produce quantitative predictions of the status of resting habitat quality for fishers, and describe how it can be monitored over time. We also provide an example of the effect of vegetation treatments on predicted fisher resting habitat, which illustrates a process that can be used to understand, reduce, or mitigate the effects of these activities on fisher habitat. This work on the fisher provides one example of how habitat assessments for wildlife could be advanced if they were developed with management applicability and implementation success as a goal.
The annual national report of the Forest Health Monitoring (FHM) Program of the Forest Service, U.S. Department of Agriculture, presents forest health status and trends from a national or multi-State regional perspective using a variety of sources, introduces new techniques for analyzing forest health data, and summarizes results of recently completed Evaluation Monitoring projects funded through the FHM national program.
The application of internet of things (IoT) technologies and artificial intelligence (AI)-enabled IoT solutions has gradually become accepted by business and production organizations as an effective tool for automating several activities effectively and efficiently and developing and distributing products to the global market. Within this book, the reader will learn how to implement IoT devices, IoT-equipped machines, and AI-equipped IoT applications using models and methodologies along with an array of case studies. Advanced IoT Technologies and Applications in the Industry 4.0 Digital Economy covers the basics of IoT-equipped machines in developing and managing various activities in many industries. It discusses all of the key points of an AI-enabled IoT solution, which includes predictive analytics, robotic process automation, predictive maintenance, automated processes, IoT technologies and IoT-equipped sensors related to machines and processes, production testing systems, and product assessment processes in the production environment. The book presents the concepts and interactive methods using datasets, processing workflow charts, and architectural diagrams along with additional real-time systems for easy and fast understanding of the application of IoT-equipped machines and AI-enabled solutions in organizations and includes many case studies throughout the book to enforce reader comprehension. This book is an ideal read for industry specialists, practitioners, researchers, scientists, and engineers working or involved in the fields of Robotics, IT, Computer Science, Soft Computing, IoT, AL/ML/DL, Data Science, the Semantic Web, Knowledge Engineering, and other related fields.
The second edition of Ecological Forest Management Handbook continues to provide forestry professionals and students with basic principles of ecological forest management and their applications at regional and site-specific levels. Thoroughly updated and revised, the handbook addresses numerous topics and explains that ecological forest management is a complex process that requires broad ecological knowledge. It discusses how to develop adaptive management scenarios to harvest resources in a sustainable way and provide ecosystem services and social functions. It includes new studies on ecological indicators, the carbon cycle, and ecosystem simulation models for various forest types: boreal, temperate, and tropical forests. NEW IN THE SECOND EDITION Provides a comprehensive collection of sustainable forest management principles and their applications Covers new ecological indicators that can be applied to address forest environmental issues Includes all types of models: empirical, gap, and process-based models Explains several basic ecological and management concepts in a clear, easy-to- understand manner This handbook is intended for researchers, academics, professionals, and undergraduate and graduate students studying and/or involved in the management of forest ecosystems. Chapters 16 and 18 of this book are available for free in PDF format as Open Access from the individual product page at www.taylorfrancis.com. They have been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.