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<p>The concept of artificial intelligence has been discussed in society and science for a long time, yet until recently the application of these methods in forestry has mainly focused on heuristic search and simulation for the development of management plans, and on classification methods to describe resource conditions and classes. However, over the last 20&#xa0;years a number of advancements have been offered in the forestry literature that suggest highly complex resource management issues can be more closely examined using sophisticated algorithms and data processing techniques. This review involved a bibliographic search of peer-reviewed literature and a condensed summary of six main areas of forest resource management where advancements have taken place. Included in the analysis is the use of natural language processing for identifying trends in the literature and the development of a lifetime recognition index (λ) related to citation frequency over the life of the published work. Brief, salient examples are provided to illustrate the complexity of efforts. While the recent hype surrounding artificial intelligence has captured the attention of society and industry, there remain significant challenges for its seamless and smooth application to contemporary forest management issues. These challenges center on the development of timely and efficient estimates of forest composition, on the identification of forest health issues and forest disturbances, on the integration of large amounts of data from non-traditional sensors, and on the administration of forests and management of forest operations.</p>

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AI Technology Use in North American Forestry

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Krista Merry,
  • Pete Bettinger,
  • Charles Merritt,
  • Khaled Rasheed,
  • Roger C. Lowe III,
  • Bruno da Silva

摘要

The concept of artificial intelligence has been discussed in society and science for a long time, yet until recently the application of these methods in forestry has mainly focused on heuristic search and simulation for the development of management plans, and on classification methods to describe resource conditions and classes. However, over the last 20 years a number of advancements have been offered in the forestry literature that suggest highly complex resource management issues can be more closely examined using sophisticated algorithms and data processing techniques. This review involved a bibliographic search of peer-reviewed literature and a condensed summary of six main areas of forest resource management where advancements have taken place. Included in the analysis is the use of natural language processing for identifying trends in the literature and the development of a lifetime recognition index (λ) related to citation frequency over the life of the published work. Brief, salient examples are provided to illustrate the complexity of efforts. While the recent hype surrounding artificial intelligence has captured the attention of society and industry, there remain significant challenges for its seamless and smooth application to contemporary forest management issues. These challenges center on the development of timely and efficient estimates of forest composition, on the identification of forest health issues and forest disturbances, on the integration of large amounts of data from non-traditional sensors, and on the administration of forests and management of forest operations.