Designing and Conducting a Forest Inventory - case: 9th National Forest Inventory of Finland

Abstract

PrefaceNational Forest Inventories in Finland have evolved gradually over a period of one hundred years, first with a trial inventory in the 1910s, and since the 1920s, with operative inventories. The sampling design and estimation methods have been continuously revised to correspond with inventory techniques and the available infrastructure. The content, as well as the collected data and variables employed, are constantly adapted to the users' needs. Co-operation with the inventories of other countries, particularly with those of the Nordic countries, has supported these developments. Assessing the wood production potential was the key aim of the early inventories, and has remained important in the current inventories. Forest health and damage assessments were added in the 1970s and 1980s. The assessment of biodiversity became an important part of the inventory in the 1990s with the UNCBD agreements. National inventories worldwide are vital information sources for greenhouse gas reporting for the UNFCCC and its Kyoto Protocol.This book demonstrates in detail all phases of the 9th National Forest Inventory of Finland (1996Finland ( -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 authors hope that the book will help in designing and conducting any large area forest inventory. Helsinki

A Markov Chain Model for Simulating Wood Supply from Any-Aged Forest Management Based on National Forest Inventory (NFI) Data

“…The NFI10 and NFI11 were carried out in 2004-2008 and 2009-2013, respectively, using the measurement and estimation protocol explained by Korhonen [42] and detailed by Tomppo et al [43]. Sample trees were selected using angle count sampling with a relascope factor and the maximum inclusion zone for the tally trees varying in different sampling regions within the country.…”

“…Sample trees were selected using angle count sampling with a relascope factor and the maximum inclusion zone for the tally trees varying in different sampling regions within the country. The stem and assortment volumes of the sampled trees were estimated and converted to unit-area mean values represented by each tree using methods explained by Tomppo et al [43]. The area represented by a sample plot was computed by dividing the known land area of one sampling region by the number of plot centres located in land and in that sampling region.…”

“…More specifically, A BAU was obtained as the proportion of areas with no management, thinning or final felling observed in the pairwise data from the permanent plots between NFI10 and NFI11. A SB was the proportion of areas, in which a need for thinning or final felling within the next five years according to the forestry guidelines [43] was recorded in the field inventory. In both cases, there was none or a very limited number of observations on the activities for a number of static and dynamic factor combinations, which would have resulted in unrealistic activity probabilities if computed as factor-specific based on the data.…”

“…In the NFI, every seventh tree (continuously over plot clusters) is measured for diameter and height increment, from which a volume growth percent was estimated following Tomppo et al [43]. Due to the low number of observations, the percent was estimated at the level of sampling regions and the percent computed for a sampling region was applied for all plots in the region.…”

Inventory Procedures for Smallholder and Community Woodlots in the Philippines: Methods, Initial Findings and Insights

“…Inventory data are also important input for timber industry planning and development purposes (e.g. Tomppo et al 2011;Cedamon 2012). An important use of inventory data is for developing stand tables and growth models for the more commonly grown species, and especially the lesser known native species for which little information typically exists.…”