The roles of competition and climate in tree growth variation in northern boreal old‐growth forests

Aakala, Tuomas; Berninger, Frank; Starr, Michael

doi:10.1111/jvs.12687

Cited by 22 publications

(19 citation statements)

References 48 publications

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“…The potential factors driving the canopy cover increase in the four landscapes are less evident compared to canopy cover change dynamics in Pistuacanis that could be attributed to disturbance and the following succession. Recent research conducted in the studied regions suggests that changes in forest structure that have occurred after the late 19 th century could be related to changes in climate (Aakala et al 2018;Hofgaard et al 2018, Sulla-Menashe et al 2018, as annual temperature and precipitation have significantly increased in both regions during this period of time (Yagouti et al 2007;Kivinen and Rasmus 2015). This supports the notion that the observed canopy cover increase could indeed be due to improved growth conditions.…”

Section: Large Scale Processes Drove Canopy Cover Increase In Most Lasupporting

confidence: 68%

“…In I-III, the field-measured canopy covers and tree-ring measurements were used to reconstruct canopy cover in the sampled cells to correspond to the years the aerial photographs used in the particular study were taken. For the canopy cover reconstruction, the full radial growth histories of the field-sampled trees presented in Aakala et al (2018) were utilized. In the reconstruction, the field-measured tree canopy size was set as a starting point.…”

Section: Canopy Cover Reconstruction From the Field Datamentioning

confidence: 99%

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Analyzing spatial variation and change in the structure of boreal old-growth forests

Kulha¹

2020

Diss. For.

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Global environmental change and other anthropogenic changes, such as changes in disturbance regimes alter the structure and dynamics of boreal old-growth forests (boreal forests with negligible human impact, henceforth natural boreal forests). Changes in these forests greatly influence key ecosystem properties such as biodiversity and carbon cycle. Hence, understanding the development of the remaining natural boreal forests is particularly important. This thesis examines how boreal forest structure varies in space and changes over time. Forest structure was examined in three natural boreal forest landscapes (2 km × 2 km each) in northern Fennoscandia and two landscapes in eastern North America. Canopy cover that was visually interpreted from stereopairs of aerial photographs taken between the years 1959 and 2011 was used as a surrogate measure of forest structure to quantify and examine spatial variation and/or temporal change, and Bayesian inference was used to separate credible ecological phenomena from the noise caused by visual interpretation error. This thesis presents and applies a novel methodology to study changes in forest structure. We calibrated visual canopy cover interpretations made from time series of aerial photographs with canopy cover reconstructions that were based on field-and tree-ring measurements. We successfully identified credible changes in forest structure in each studied landscape, but also noted that the visual interpretation of canopy cover was prone to systematic and random error that depended on, e.g., aerial photo quality. Due to this error, changes that occurred at the level of an individual tree could not be credibly discerned. Still, the methodology can be used to detect both abrupt and slow continuous changes in forest ecosystems. The methodology was extended to examine spatial variation in forest structure. The results revealed variation in forest structure at multiple spatial scales which showed similarities despite the differences in dominant tree species and disturbance regimes between the studied landscapes. The variability was connected with scale-dependent driving processes that also showed similarities among the landscapes. Last, the methodology was applied to study how varying scale of observation influences how changes in forest structure are perceived over different periods of time. This multi-scale change analysis revealed a synchronous and prevalent cover increase at large spatial scales in the majority of the studied landscapes, and canopy cover decrease and increase in areas that were subjects to disturbances. Changes of variable direction and magnitude were detected at smaller spatial scales in each studied landscape. The results indicated that historical aerial photographs are a valuable resource in studying how forest ecosystems develop, but the notable errors in their visual interpretation need to be taken into account in analysis of change. The results aligned with the hierarchy theory and the hierarchical patch dynamics concept by showing that the structur...

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Section: Large Scale Processes Drove Canopy Cover Increase In Most Lasupporting

confidence: 68%

Section: Canopy Cover Reconstruction From the Field Datamentioning

confidence: 99%

Analyzing spatial variation and change in the structure of boreal old-growth forests

Kulha¹

2020

Diss. For.

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“…Following fieldwork, we determined the full radial growth histories of each sampled tree [38], using standard dendrochronological methods. For this, we glued the increment cores to wooden core mounts, and sanded them to a progressively finer grit (600 for conifers, 1000 for deciduous trees).…”

Section: Field Data For the Calibration Of The Canopy Cover Interpretmentioning

confidence: 99%

How to Calibrate Historical Aerial Photographs: A Change Analysis of Naturally Dynamic Boreal Forest Landscapes

Kulha

Pasanen

Aakala

2018

Forests

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Time series of repeat aerial photographs currently span decades in many regions. However, the lack of calibration data limits their use in forest change analysis. We propose an approach where we combine repeat aerial photography, tree-ring reconstructions, and Bayesian inference to study changes in forests. Using stereopairs of aerial photographs from five boreal forest landscapes, we visually interpreted canopy cover in contiguous 0.1-ha cells at three time points during 1959-2011. We used tree-ring measurements to produce calibration data for the interpretation, and to quantify the bias and error associated with the interpretation. Then, we discerned credible canopy cover changes from the interpretation error noise using Bayesian inference. We underestimated canopy cover using the historical low-quality photographs, and overestimated it using the recent high-quality photographs. Further, due to differences in tree species composition and canopy cover in the cells, the interpretation bias varied between the landscapes. In addition, the random interpretation error varied between and within the landscapes. Due to the varying bias and error, the magnitude of credibly detectable canopy cover change in the 0.1-ha cells depended on the studied time interval and landscape, ranging from −10 to −18 percentage points (decrease), and from +10 to +19 percentage points (increase). Hence, changes occurring at stand scales were detectable, but smaller scale changes could not be separated from the error noise. Besides the abrupt changes, also slow continuous canopy cover changes could be detected with the proposed approach. Given the wide availability of historical aerial photographs, the proposed approach can be applied for forest change analysis in biomes where tree-rings form, while accounting for the bias and error in aerial photo interpretation. that capture both slow and abrupt changes in forest ecosystems are required to understand how forest structure changes in time.Remote sensing provides records over large spatial extent even at remote locations. For example, the Landsat satellites provide observations on global coverage from the early 1970s onwards [6]. However, among remotely sensed records, aerial photographs span the longest time period and currently enable the fine-scale analysis of vegetation dynamics over decades in many locations of the world [7]. Accordingly, aerial photographs have been used to study spatial [8-10] and temporal [3,11,12] vegetation dynamics, even at the scale of an individual tree [13].As with any measurements, data derived from aerial photographs contain bias (systematic error) and error (random variation) [14]. For example, georectification errors [15], the aerial photo scale, defined by the focal length of the camera and the altitude from where the photo was taken [9,16], the quality (e.g., contrast, haziness, and shadowiness) [17,18], and texture and grain size of the photograph [18,19], as well as subjectivity in manual feature quantification [20], potentially produce errors. Hence, da...

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“…Tree growth is influenced by several factors, including climate patterns, site conditions, and competition processes [1][2][3][4] Among them, tree competition measures are the main predictors of individual trees' growth [5]. Competition among trees is defined as the negative effects that neighboring trees have on a subject tree.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Competition Indices in a Norway Spruce and Silver Fir-Dominated Forest Using Lidar Data

et al. 2019

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Competitive interactions are important predictors of tree growth. Spatial and temporal changes in resource availability, and variation in species and spatial patterning of trees alter competitive interactions, thus affecting tree growth and, hence, biomass. Competition indices are used to quantify the level of competition among trees. As these indices are normally computed only over small areas, where field measurements are done, it would be useful to have a tool to predict them over large areas. On this regard, remote sensing, and in particular light detection and ranging (lidar) data, could be the perfect tool. The objective of this study was to use lidar metrics to predict competition (on the basis of distance-dependent competition indices) of individual trees and to relate them with tree aboveground biomass (AGB). The selected study area was a mountain forest area located in the Italian Alps. The analyses focused on the two dominant species of the area: Silver fir (Abies alba Mill.) and Norway spruce (Picea abies (L.) H. Karst). The results showed that lidar metrics could be used to predict competition indices of individual trees (R 2 above 0.66). Moreover, AGB decreased as competition increased, suggesting that variations in the availability of resources in the soil, and the ability of plants to withstand competition for light may influence the partitioning of biomass.

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The roles of competition and climate in tree growth variation in northern boreal old‐growth forests

Cited by 22 publications

References 48 publications

Analyzing spatial variation and change in the structure of boreal old-growth forests

Analyzing spatial variation and change in the structure of boreal old-growth forests

How to Calibrate Historical Aerial Photographs: A Change Analysis of Naturally Dynamic Boreal Forest Landscapes

Prediction of Competition Indices in a Norway Spruce and Silver Fir-Dominated Forest Using Lidar Data

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