This book demonstrates in detail all phases of the 9th National Forest Inventory of Finland (1996–2003): the planning of the sampling design, measurements, estimation methods and results. The inventory knowledge accumulated during almost one hundred years is consolidated in the book. The purpose of the numerous examples of results is to demonstrate the diversity of the estimates and content of a national forest inventory. The most recent results include the assessment of the indicators describing the biodiversity of forests. The Finnish NFI has been and is a model for many countries worldwide. The methods and results of the book are set in the international context and are applicable globally. The book provides a valuable information source for countries, institutions and researchers planning own inventories as well as modifying the existing ones, or seeking the applicable definitions and estimation methods to use in their own inventories.

Highlighting new technologies, Remote Sensing of Natural Resources explores advanced remote sensing systems and algorithms for image processing, enhancement, feature extraction, data fusion, image classification, image-based modeling, image-based sampling design, map accuracy assessment and quality control. It also discusses their applications for evaluation of natural resources, including sampling design, land use and land cover classification, natural landscape and ecosystem assessment, forestry, agriculture, biomass and carbon-cycle modeling, wetland classification and dynamics monitoring, and soils and minerals mapping. The book combines review articles with case studies that demonstrate recent advances and developments of methods, techniques, and applications of remote sensing, with each chapter on a specific area of natural resources. Through a comprehensive examination of the wide range of applications of remote sensing technologies to natural resources, the book provides insight into advanced remote sensing systems, technologies, and algorithms for researchers, scientists, engineers, and decision makers.

Three options for the sampling design of the field plot clusters of NAFORMA II biophysical survey are compared in this report. Option 1 consists of re-measuring all NAFORMA I field sample plots (3 205 clusters) and Option 2 of re-measuring only those that were established as permanent (848 clusters). The recommended Option 3 is a compromise between these two “extreme” options: Re-measure a subset (1 405 clusters) of NAFORMA I field sample plots including (almost) all permanent clusters and a carefully selected set of other NAFORMA I field plot clusters to obtain a uniform sample within each TFS zone. Design Option 3 has the following features: •Sampling intensity is uniform within each TFS zone. This makes it simple to use the data. For example, mean volumes can be estimated by averages over the plots. •The selected clusters are well-spread over the target population. •The anticipated precision of land-class area and mean wood volume relative to sample size is nearly as good as that of NAFORMA I. •All proposed clusters were measured in NAFORMA I, which enables precise estimation of change based on repeated measurements. The costs and precision were anticipated by utilizing NAFORMA I field data, information about subsequent improvements in the road network, and changes in land-use using satellite imaging derived land-class maps.

Agroforestry (AF) is a dynamic, ecologically based, natural resources management system that, by integrating trees on farms, ranches, and in other landscapes, diversifies and increases production and promotes social, economic, and environmental benefits for land users. Further, it is receiving increasing attention as a sustainable land-management option worldwide because of its ecological, economic, and social attributes. Advances have been achieved by building on past research accomplishments and expanding AF’s stakeholder base, which now includes private/public partnerships, communities, ecologists, farmers, indigenous peoples, and policymakers in both temperate and tropical countries. AF has now been recognized as a valuable problem-solving approach to ensuring food security and rebuilding resilient rural environments. Recent studies have shown that more than 1 billion hectares of agricultural land have more than 10% tree cover. Of this area, 160 million hectares have more than 50% tree cover. Agricultural ecosystems can be further improved through AF to achieve environmental restoration, greater farm productivity, and key ecological services, including climate change mitigation and adaptation for improved rural livelihood. In fact, it is largely considered synonymous with climate smart agriculture and a remedy for many modern environmental challenges. Consequently, AF’s knowledge base is being expanded at a rapid rate, as illustrated by the increasing number and quality of scientific publications on various forms and different aspects of AF. This book offers state-of-the-art information on the fundamental concepts and history of AF and its evolution as a science, presenting a wealth of advanced research results and evaluations relating to different aspects of AF. Accordingly, it will be useful for a broad readership, including students, foresters, farmers, local communities, indigenous peoples, civil society institutions, media, policymakers and the general public.

Building on more than a decade of innovative research into multi-source forest inventory (MS-NFI) this book presents full details of the development, outputs and applications of the improved k-NN method. The method, which was pioneered in Finland in 1990, is rapidly becoming a world standard in forest inventory, having been adopted as standard in Finland and Sweden, and recently introduced in Austria and across the US. The book describes in detail the full MS-NFI process, and the input data used – including field data, satellite images, and digital map data, as well as coarse-scale variation of forest variables. It also presents comprehensive information on the types of outputs which can be derived, including maps and statistics, describing, for example, stock volumes and development, dominant tree species, age-class distribution, and large and small-scale variation. The book will provide an invaluable resource for those involved in forest inventory, including government departments and bodies involved in forest policy, management and monitoring, forest managers, and researchers and graduate students interested in forest inventory, modelling and analysis. It will find an additional market among those interested in Earth observation, ecology and broader areas of environmental and natural resource management. Erkki Tomppo was the winner of the 1997 Marcus Wallenberg Prize for his work on the k-NN method.