scholarly journals Survival analyses of individual tree populations in natural forest stands to evaluate the maturity of forest stands: A case study of preserved forests in Northern Japan

Author(s):  
Pavithra Rangani Wijenayake ◽  
Takuya Hiroshima
Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1014
Author(s):  
Pavithra Rangani Wijenayake ◽  
Takuya Hiroshima

Scientifically sound methods are essential to estimate the survival of trees, as they can substantially support sustainable management of natural forest resources. Tree mortality assessments have mainly been based on forest inventories and are mostly limited to planted forests; few studies have conducted age-based survival analyses in natural forests. We performed survival analyses of individual tree populations in natural forest stands to evaluate differences in the survival of two coniferous species (Abies sachalinensis (F. Schmidt) Mast. and Picea jezoensis var. microsperma) and all broad-leaved species. We used tree rings and census data from four preserved permanent plots in pan-mixed and sub-boreal natural forests obtained over 30 years (1989–2019). All living trees (diameter at breast height ≥ 5 cm in 1989) were targeted to identify tree ages using a Resistograph. Periodical tree age data, for a 10-year age class, were obtained during three consecutive observation periods. Mortality and recruitment changes were recorded to analyze multi-temporal age distributions and mean lifetimes. Non-parametric survival analyses revealed a multi-modal age distribution and exponential shapes. There were no significant differences among survival probabilities of species in different periods, except for broad-leaved species, which had longer mean lifetimes in each period than coniferous species. The estimated practical mean lifetime and diameter at breast height values of each coniferous and broad-leaved tree can be applied as an early identification system for trees likely to die to facilitate the Stand-based Silvicultural Management System of the University of Tokyo Hokkaido Forest. However, the survival probabilities estimated in this study should be used carefully in long-term forest dynamic predictions because the analysis did not include the effects of catastrophic disturbances, which might significantly influence forests. The mortality patterns and survival probabilities reported in this study are valuable for understanding the stand dynamics of natural forests associated with the mortality of individual tree populations.


Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.


2013 ◽  
Vol 21 (2) ◽  
pp. 71-84 ◽  
Author(s):  
Guy R. Larocque ◽  
Nancy Luckai ◽  
Shailendra N. Adhikary ◽  
Arthur Groot ◽  
F. Wayne Bell ◽  
...  

Competition in forest stands has long been of interest to researchers. However, much of the knowledge originates from empirical studies that examined the effects of competition. For instance, many studies were focused on the effects of the presence of herbaceous species on the development of tree seedlings or the decrease in individual tree growth with increases in stand density. Several models that incorporate competitive effects have been developed to predict tree and stand growth, but with simplified representations of competitive interactions. While these studies provided guidance useful for forest management, they contributed only partially to furthering our understanding of competitive mechanisms. Also, most competition studies were conducted in single-species stands. As competitive interactions occurring in mixed stands are characterized by a higher degree of complexity than those in single-species stands, a better understanding of these mechanisms can contribute to developing optimal management scenarios. The dynamics of forest stands with at least two species may be affected not only by competition, but also by facilitation or complementarity mechanisms. Thus, knowledge of the mechanisms may provide insight into the relative importance of intra- versus inter-specific competition and whether competition is symmetric or asymmetric. Special attention to the implementation of field experimental designs is warranted for mixed stands. While traditional spacing trials are appropriate for single-species stands, the examination of competitive interactions in mixed stands requires more complex experimental designs to examine the relative importance of species combinations. Forest productivity models allow resource managers to test different management scenarios, but again most of these models were developed for single-species stands. As competitive interactions are more complex in mixed stands, models developed to predict their dynamics will need to include more mechanistic representations of competition.


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