Localization of growth estimates using non-parametric imputation methods

Sironen, Susanna; Kangas, Annika; Maltamo, Matti; Kalliovirta, Jouni

doi:10.1016/j.foreco.2008.05.013

Cited by 17 publications

(12 citation statements)

References 20 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the western part of Finland the growth was only slightly overestimated or underestimated in some areas. In southern Finland our tree-simulator results are in line with study by Sironen et al (2008), where performance of non-parametric estimation method was compared with the same tree-level models of Hynynen et al (2002) in different parts of Finland. In their study the tree-level models overestimated the basal area growth in the southern parts of Finland, while in the northern parts the basal area growth was mainly underestimated.…”

Section: Discussionsupporting

confidence: 85%

Evaluation of forest growth simulators with NFI permanent sample plot data from Finland

Härkönen

Mäkinen

Tokola

et al. 2010

Forest Ecology and Management

View full text Add to dashboard Cite

Section: Discussionsupporting

confidence: 85%

Evaluation of forest growth simulators with NFI permanent sample plot data from Finland

Härkönen

Mäkinen

Tokola

et al. 2010

Forest Ecology and Management

View full text Add to dashboard Cite

“…Growth predictions with empirical growth models can contain large errors, which also vary between different geographic areas. Sironen et al (2008) showed that the root-mean-square errors of 5-year estimates of diameter growth using a simulator similar to the tree simulator we used here varied from 38.2 to 66.1%, depending on the geographic region across Finland. Mäkinen et al (2008) reported that the SDs of prediction errors after a 20-year simulation caused by errors in the growth models were 12, 6, 18 and 14% for the attributes H plot , D plot , BA plot and N plot , respectively.…”

Section: Discussionmentioning

confidence: 93%

Propagating the errors of initial forest variables through stand- and tree-level growth simulators

et al. 2009

View full text Add to dashboard Cite

Developments in the field of remote sensing have led to various cost-efficient forest inventory methods at different levels of detail. Remote-sensing techniques such as airborne laser scanning (ALS) and digital photogrammetry are becoming feasible alternatives for providing data for forest planning. Forest-planning systems are used to determine the future harvests and silvicultural operations. Input data errors affect the forest growth projections and these effects are dependent on the magnitude of the error. Our objective in this study was to determine how the errors typical to different inventory methods affect forest growth projections at individual stand level during a planning period of 30 years. Another objective was to examine how the errors in input data behave when different types of growth simulators are used. The inventory methods we compared in this study were stand-wise field inventory and single-tree ALS. To study the differences between growth models, we compared two forest simulators consisting of either distance-independent tree-level models or stand-level models. The data in this study covered a 2,000-ha forest area in southern Finland, including 240 sample plots with individually measured trees. The analysis was conducted with Monte Carlo simulations. The results show that the tree-level simulator is less sensitive to errors in the input data and that by using single-tree ALS data, more precise growth projections can be obtained than using stand-wise field inventory data.

show abstract

“…To mitigate this problem, several different options are available: (1) to use models that can be calibrated using local data such as kriging/mixed models where data are available (Räty et al 2011;Räty and Kangas 2012), (2) to use geographically weighted regression (Zhang and Gove 2005), or (3) to use a spatial range when selecting the observations in non-parametric approaches like k-nn (Sironen et al 2008). The relative efficiency of such approaches remains to be studied.…”

Section: Field Data Collectionmentioning

confidence: 99%

Remote sensing and forest inventories in Nordic countries – roadmap for the future

Kangas

Astrup

Breidenbach

et al. 2018

Scandinavian Journal of Forest Research

140

103

View full text Add to dashboard Cite

The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field. ARTICLE HISTORY

show abstract

Localization of growth estimates using non-parametric imputation methods

Cited by 17 publications

References 20 publications

Evaluation of forest growth simulators with NFI permanent sample plot data from Finland

Evaluation of forest growth simulators with NFI permanent sample plot data from Finland

Propagating the errors of initial forest variables through stand- and tree-level growth simulators

Remote sensing and forest inventories in Nordic countries – roadmap for the future

Contact Info

Product

Resources

About