Forestry Applications

Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies to provide data for research and operational applications in a wide range of disciplines related to management of forest ecosystems. This book provides a comprehensive, state-of-the-art review of the research and application of ALS in a broad range of forest-related disciplines, especially forest inventory and forest ecology. However, this book is more than just a collection of individual contributions – it consists of a well-composed blend of chapters dealing with fundamental methodological issues and contributions reviewing and illustrating the use of ALS within various domains of application. The reviews provide a comprehensive and unique overview of recent research and applications that researchers, students and practitioners in forest remote sensing and forest ecosystem assessment should consider as a useful reference text.

Nanomaterials for Agriculture and Forestry Applications explores how major nanomaterials are being specially used in the agriculture, forestry, and other associated sectors. Plants and their products are used for synthesis of nanoparticles as they contain primary and secondary metabolites, which reduce the metal salts and metal oxides into their nanoparticles. Exposure of these particles has been examined for their sustainable role and/or interaction with agricultural crops in terms of growth and yields. Nanomaterials accumulation and translocation have shown interaction with cellular organelles, DNA, RNA, proteins, or other biomolecules; and affect various functions of cell organelles. Application of nanosensors holds a significant promise in monitoring signaling pathways, metabolism, detection of crop/soil diseases, and specific pollutants or pesticides. Nanomaterials have also been used in soil and water quality management. In forestry sector, the nanotechnology is considered as the potential platform, which can transform the forest materials into value-added products, such as smart paper, nano-packaging, coating material, building construction, and biomedical and other sectors. This book is an important resource, showing how nanotechnology is being used to enhance large-scale agricultural and/or industrial application and production.

In 2012, a Forestry Special Interest Group (FSIG) was founded within the Canadian Operational Research Society (CORS). Besides a general commitment to promoting the application of operational research (OR) to forest management and forest products industry problems, the FSIG has two concrete mandates: organizing the forestry cluster at the annual CORS conference, and managing the editorial process for forestry-themed special issues of INFOR. The FSIG has been very successful in the first of these two mandates, with record attendance at the forestry cluster over the last four years, hosting of several special sessions, financial and in-kind support from the NSERC Strategic Network on Value Chain Optimization (VCO), and the inauguration of the David Martell Student Paper Prize in Forestry (DMSPPF). This is the first compilation of forestry-themed papers since the inauguration of the CORS FSIG. The six pieces selected for the special issue, now published as a book, feature applications of OR to a wide range of forest management and forest products industry contexts, including supply-chain planning, lumber production planning, demand-driven harvest and transportation planning, and fire-aware wood supply planning. This book was originally published as a special issue of the INFOR: Information Systems and Operational Research journal.

Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral, and thermal infrared. LiDAR sensors are becoming commonly used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees, and the primary objective is the conservation and protection of forests. Nevertheless, forestry and agriculture involve the cultivation of renewable raw materials, with the difference that forestry is less tied to economic aspects and this is reflected by the delay in using new monitoring technologies. The main forestry applications are aimed toward inventory of resources, map diseases, species classification, fire monitoring, and spatial gap estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry.

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion.

Forest management is a complex process that now incorporates information obtained from many sources. It is increasingly obvious that the physiological status of the trees in a forest has a dramatic impact on the likely success of any particular management strategy. Indeed, models described in this book that deal with forest productivity and sustainability require physiological information. This information can only be obtained from an understanding of the basic biological mechanisms and processes that contribute to individual tree growth. This valuable book illustrates that physiological ecology is a fundamental element of proficient forest management. Provides essential information relevant to the continuing debate over sustainable forest management Outlines how modern tools for physiological ecology can be used in planning and managing forest ecosystems Reviews the most commonly used forest models and assesses their value and future

This book presents recent developments in statistical methodologies with particular relevance to applications in forestry and environmental sciences. It discusses important methodologies like ranked set sampling, adaptive cluster sampling, small area estimation, calibration approach-based estimators, design of experiments, multivariate techniques, Internet of Things, and ridge regression methods. It also covers the history of the implementation of statistical techniques in Indian forestry and the National Forest Inventory of India. The book is a valuable resource for applied statisticians, students, researchers, and practitioners in the forestry and environment sector. It includes real-world examples and case studies to help readers apply the techniques discussed. It also motivates academicians and researchers to use new technologies in the areas of forestry and environmental sciences with the help of software like R, MATLAB, Statistica, and Mathematica.