This book has been developed as a forest inventory textbook for students and could also serve as a handbook for practical foresters. We have set out to keep the mathematics in the book at a fairly non-technical level, and therefore, although we deal with many issues that include highly sophisticated methodology, we try to present first and foremost the ideas behind them. For foresters who need more details, references are given to more advanced scientific papers and books in the fields of statistics and biometrics. Forest inventory books deal mostly with sampling and measurement issues, as found here in section I, but since forest inventories in many countries involve much more than this, we have also included material on forestry applications. Most applications nowadays involve remote sensing technology of some sort, so that section II deals mostly with the use of remote sensing material for this purpose. Section III deals with national inventories carried out in different parts of world, and section IV is an attempt to outline some future possibilities of forest inventory methodologies. The editors, Annika Kangas Professor of Forest Mensuration and Management, Department of Forest Resource Management, University of Helsinki. Matti Maltamo Professor of Forest Mensuration, Faculty of Forestry, University of Joensuu. ACKNOWLEDGEMENTS

Designed to aid readers in gathering the most reliable quantitative information on forests for the least cost. Thoroughly explains the interrelationships between sampling strategies; discusses forestry techniques of efficient tactics; examines new developments in statistics having immediate applications in forestry and describes related developments that should have relevance in the future. Includes practical methods for dealing with forest data such as tree number, height, diameter and marketable wood. Also contains problem sets.

Sound forest management planning requires cost-efficient approaches to optimally utilize given resources. Emphasizing the mathematical and statistical features of forest sampling to assess classical dendrometrical quantities, Sampling Techniques for Forest Inventories presents the statistical concepts and tools needed to conduct a modern for

The Forest Inventory and Analysis (FIA) program of the United States Department of Agriculture Forest Service collects forest inventory data that provide estimates with reasonable accuracy at the national scale. However, for smaller domains, these estimates are often not as accurate due to the small sample size. Small area estimation improves the accuracy of the estimates at smaller domains by relying on auxiliary information. This study compared direct (FIA estimates), indirect (multiple linear regression), and composite estimators (Fay-Herriot) using auxiliary information derived from Landsat and Global Ecosystem Dynamics Investigation (GEDI) to obtain county-level estimates of forest attributes namely total and merchantable volume (m3 ha-1), aboveground biomass (Mg ha-1), basal area (m2 ha-1), and Lorey's mean height (m). Compared with FIA estimates, the composite estimator reduced error by 75-78% for all the variables of interest. This shows that a reasonable amount of precision can be achieved with auxiliary information from Landsat and GEDI, improving FIA estimates at the county level.

Forest inventories throughout the world have evolved gradually over time. The content as well as the concepts and de?nitions employed are constantly adapted to the users’ needs. Advanced inventory systems have been established in many countries within Europe, as well as outside Europe, as a result of development work spanning several decades, in some cases more than 100 years. With continuously increasing international agreements and commitments, the need for information has also grown drastically, and reporting requests have become more frequent and the content of the reports wider. Some of the agreements made at the international level have direct impacts on national economies and international decisions, e. g. , the Kyoto Protocol. Thus it is of utmost importance that the forest information supplied is collected and analysed using sound scienti?c principles and that the information from different countries is comparable. European National Forest Inventory (NFI) teams gathered in Vienna in 2003 to discuss the new challenges and the measures needed to get data users to take full advantage of existing NFIs. As a result, the European National Forest Inventory Network (ENFIN), a network of NFIs, was established. The ENFIN members decided to apply for funding for meetings and collaborative activities. COST– European Cooperation in Science and Technology - provided the necessary ?n- cial means for the realization of the program.

One of the most common practices regarding estimation of forest attributes is the partitioning of large forested subpopulations into smaller areas of interest to coincide with specific objectives of present and future forest management. New estimators are needed to improve estimation of selected forest attributes in small areas where the existing sample is insufficient to obtain precise estimates. This dissertation assessed the strength of light detection and ranging (LiDAR) as auxiliary information for estimating plot-level forest attributes (trees/ha, basal area/ha, volume/ha, quadratic mean diameter, Lorey's height) using intensity and nonintensity area-level LiDAR metrics and single tree remote sensing (STRS). LiDAR intensity metrics were useful for increasing precision for trees/ha. With the exception of Lorey's height, STRS did not significantly improve precision for most of the attributes. Small area estimation (SAE) techniques were assessed for precision and bias in estimating stand-level forest attributes (trees/ha, basal area/ha, volume/ha, quadratic mean diameter, mean height of 100 largest trees/ha) assuming a localized subpopulation using LiDAR auxiliary information. Selected estimation methods included area-level regression-based composite estimators and indirect estimators based on synthetic prediction and nearest neighbor imputation. The composite estimators produced lower bias and higher precision than synthetic prediction and imputation. The traditional composite estimator outperformed empirical best linear unbiased prediction for bias but not for precision. SAE methods were compared for precision and bias in estimating county-level forest attributes (trees/ha, basal area/ha, volume/ha, quadratic mean diameter, mean height of 100 largest trees/ha) assuming a regional subpopulation using Landsat auxiliary information. Selected estimation methods included unit-level mixed regression-based indirect and composite estimators, and imputation-based indirect and composite estimators. The indirect and composite estimators based on linear mixed effects models generally outperformed those based on imputation. The composite estimators performed the best in terms of bias for all attributes.