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Forestry: An International Journal of Forest Research
Journal Prestige (SJR): 1.133

Citation Impact (citeScore): 3
Number of Followers: 15

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0015-752X - ISSN (Online) 1464-3626
Published by Oxford University Press

[424 journals]

Framework for near real-time forest inventory using multi source remote
sensing data
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  Pages: 1 - 19
  Abstract: AbstractForestry inventory update is a critical component of sustainable forest management, requiring both the spatially explicit identification of forest cover change and integration of sampled or modelled components like growth and regeneration. Contemporary inventory data demands are shifting, with an increased focus on accurate attribute estimation via the integration of advanced remote sensing data such as airborne laser scanning (ALS). Key challenges remain, however, on how to maintain and update these next-generation inventories as they age. Of particular interest is the identification of remotely sensed data that can be applied cost effectively, as well as establishing frameworks to integrate these data to update information on forest condition, predict future growth and yield, and integrate information that can guide forest management or silvicultural decisions such as thinning and harvesting prescriptions. The purpose of this article is to develop a conceptual framework for forestry inventory update, which is also known as the establishment of a ‘living inventory’. The proposed framework contains the critical components of an inventory update including inventory and growth monitoring, change detection and error propagation. In the framework, we build on existing applications of ALS-derived enhanced inventories and integrate them with data from satellite constellations of free and open, analysis-ready moderate spatial resolution imagery. Based on a review of the current literature, our approach fits trajectories to chronosequences of pixel-level spectral index values to detect change. When stand-replacing change is detected, corresponding values of cell-level inventory attributes are reset and re-established based on an assigned growth curve. In the case of non–stand-replacing disturbances, cell estimates are modified based on predictive models developed between the degree of observed spectral change and relative changes in the inventory attributes. We propose that additional fine-scale data can be collected over the disturbed area, from sources such as CubeSats or remotely piloted airborne systems, and attributes updated based on these data sources. Cells not identified as undergoing change are assumed unchanged with cell-level growth curves used to increment inventory attributes. We conclude by discussing the impact of error propagation on the prediction of forest inventory attributes through the proposed near real-time framework, computing needs and integration of other available remote sensing data.
  PubDate: Wed, 04 May 2022 00:00:00 GMT
  DOI: 10.1093/forestry/cpac015
  Issue No: Vol. 96, No. 1 (2022)
Differences in epiphytic trunk communities in secondary forests and
plantations of southern Ecuador
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  Pages: 20 - 36
  Abstract: AbstractDeforestation is the most important cause of biodiversity loss in tropical ecosystems. Epiphytic species, lichens and bryophytes, are very sensitive to environmental changes, including those produced by conversion of primary forests into secondary vegetation. However, little is known about the differences between different secondary forests and plantations regarding the epiphytic biota. We compared epiphytic communities among different secondary forests and non-native plantations in southern Ecuador. Four forest types were considered: non-native Pinus patula plantations, monospecific secondary forests of Alnus acuminata, monospecific secondary forests of Andesanthus lepidotus and mixed secondary forests. Within each forest type, two stands were surveyed, establishing a total of 80 plots and analyzing four trees per plot. We estimated lichen and bryophyte cover in four inventories per tree and calculated and compared different metrics for taxonomic and functional diversity, as well as community composition. The results revealed forest type as the major predictor for the species and functional traits richness, and for diversity and composition. In total, 422 taxa were identified (312 lichens and 110 bryophytes), with mixed secondary forests having the richest communities (194 species) and non-native plantations having the lowest richness (105 species). Bryophyte richness was highest in A. lepidotus forests. Taxonomic and functional diversity, and species composition differed greatly among forest types and followed a different pattern depending on the organism considered. Lichens were the most sensitive indicators of environmental conditions associated with different tropical forest types.
  PubDate: Tue, 30 Aug 2022 00:00:00 GMT
  DOI: 10.1093/forestry/cpac034
  Issue No: Vol. 96, No. 1 (2022)
Tree height-growth trajectory estimation using uni-temporal UAV laser
scanning data and deep learning
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  Pages: 37 - 48
  Abstract: AbstractInformation on tree height-growth dynamics is essential for optimizing forest management and wood procurement. Although methods to derive information on height-growth information from multi-temporal laser scanning data already exist, there is no method to derive such information from data acquired at a single point in time. Drone laser scanning data (unmanned aerial vehicles, UAV-LS) allows for the efficient collection of very dense point clouds, creating new opportunities to measure tree and branch architecture. In this study, we examine if it is possible to measure the vertical positions of branch whorls, which correspond to nodes, and thus can in turn be used to trace the height growth of individual trees. We propose a method to measure the vertical positions of whorls based on a single-acquisition of UAV-LS data coupled with deep-learning techniques. First, single-tree point clouds were converted into 2D image projections, and a YOLOv5 (you-only-look-once) convolutional neural network was trained to detect whorls based on a sample of manually annotated images. Second, the trained whorl detector was applied to a set of 39 trees that were destructively sampled after the UAV-LS data acquisition. The detected whorls were then used to estimate tree-, plot- and stand-level height-growth trajectories. The results indicated that 70 per cent (i.e. precision) of the measured whorls were correctly detected and that 63 per cent (i.e. recall) of the detected whorls were true whorls. These results translated into an overall root-mean-squared error and Bias of 8 and −5 cm for the estimated mean annual height increment. The method’s performance was consistent throughout the height of the trees and independent of tree size. As a use case, we demonstrate the possibility of developing a height-age curve, such as those that could be used for forecasting site productivity. Overall, this study provides proof of concept for new methods to analyse dense aerial point clouds based on image-based deep-learning techniques and demonstrates the potential for deriving useful analytics for forest management purposes at operationally-relevant spatial-scales.
  PubDate: Tue, 05 Jul 2022 00:00:00 GMT
  DOI: 10.1093/forestry/cpac026
  Issue No: Vol. 96, No. 1 (2022)
Circular or square plots in ALS-based forest inventories—does it
matter'
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  Pages: 49 - 61
  Abstract: AbstractIn airborne laser scanning (ALS)-based forest inventories, there is commonly a discrepancy between the plot shape used for model fitting (typically circular) and the shape of population elements (typically square) where predictions are needed. Circular plots are easy to establish, locate and have the smallest number of edge trees on average. Therefore, a circle is the most common plot shape in both traditional and remote sensing-based forest inventories. In contrast, the shape of population elements used for remote sensing-based predictions is nearly always a square because it enables division of the target population into a grid of non-overlapping plots. In this study, we investigate shape effects for ALS-based forest inventories using circular and square plot shapes. This has not been examined earlier. Aboveground biomass was used as the response variable. The sampling design was created in a way that the probability of selection for any location inside a stem-mapped 30 m × 30 m plot was the same for the circular (radius 7.95 m) and square (side length 14.09 m) plot. This configuration enabled us to compare circular and square plots with the same areas and identical sampling probabilities for every tree in the population. Our primary finding is that for equal area square and circular plots, there is no evidence of systematic prediction error when a model fitted to one shape is used to predict for the other shape. Our secondary finding is that root mean square error (RMSE) value is slightly underestimated (1.2 per cent) when a model fitted to circular plots is used to predict for square plots. A small underestimation of RMSE due to plot shape effect has hardly practical significance in stand-level forest management inventories, but the plot shape effect may be problematic in large area forest surveys.
  PubDate: Fri, 02 Sep 2022 00:00:00 GMT
  DOI: 10.1093/forestry/cpac032
  Issue No: Vol. 96, No. 1 (2022)
Handling uncertainties in forest information: the hierarchical forest
planning process and its use of information at large forest companies
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  Pages: 62 - 75
  Abstract: AbstractThis qualitative study aimed to map what information is used in the forest planning process at large forest-owning companies, how it is used, its level of uncertainty and currently employed strategies to handle forest information uncertainty. An additional aim was to assess the status of the paradigm of the forest planning hierarchy in forestry. We used data from semi-structured interviews with representatives of six large forest-owning companies in Sweden, representing 30 per cent of the productive forest land in the country. Our results show that the forest planning process is a hierarchical system of decisions where the information used in the different planning stages is of varying quality and that the traditional hierarchical planning paradigm still plays a vital role in forestry. The most central source of information in the whole forest-planning process is the forest stand database (forest inventory). This includes uncertain information from various sources, including subjective field measurements and aerial image interpretation. However, the use of remote sensing estimates to feed the databases is increasing, which will probably improve the overall quality. Another important finding is that forest companies tend not to use decision support systems or optimization models to solve planning problems outside the scope of strategic planning; thus, most planning is done manually, e.g. in a geographic information system (GIS) environment. Apart from the hierarchical division of the planning process itself, we identified six main strategies that the companies use to control information uncertainty, namely locking the future by making a decision, utilizing a surplus of available harvests, updating information before a decision is made, replanning when the plan is found to be infeasible, planning by looking back and ignoring the uncertainty, either intentionally or unintentionally. The results from this study increase our understanding of contemporary forest-planning practices and will be helpful in the development of decision support systems and methods for information collection.
  PubDate: Sat, 30 Jul 2022 00:00:00 GMT
  DOI: 10.1093/forestry/cpac028
  Issue No: Vol. 96, No. 1 (2022)
Effects of low phosphorus availability on root cambial activity, biomass
production and root morphological pattern in two clones of Chinese fir
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  Pages: 76 - 86
  Abstract: AbstractPhosphorous (P) is a crucial limiting nutrient for plant growth and development in terrestrial ecosystems. As one of the most important subtropical coniferous tree species, Chinese fir (Cunninghamia lanceolata) plays a major role in timber supply, climate mitigation and forest recreation. In this study, two Chinese fir clones (020 and 061) with a high growth rate were subjected to two levels of P supply in a greenhouse pot experiment: P-deficient P0 (no P supply) and P-replete P1 (1.0 mmol L−1 KH2PO4). Our objectives were to study the differences in morphology and growth patterns, cambial development mechanism and secondary root growth. Root sampling was performed at 40, 80 and 120 days after treatment initiation. Results indicated that the P-replete condition produced more root cambial cells (RCCs) in third class (larger) roots of clone-061 than in those of clone-020; concomitantly, clone-061 showed significantly greater stem height (42.87 ± 1.33 cm), root collar diameter (5.45 ± 0.126 mm), root average diameter (RAD, third class = 1.27 ± 0.04 mm) in different root classes and whole-plant biomass (7.55 ± 0.69 g) compared with clone-020 under the P-replete condition. Root diameter and biomass increase due to higher cambial activity resulted in greater seedling quality. In addition, a significant positive correlation was observed between the number of RCCs and root morphological traits (root length, root surface area, RAD and root volume). Root development was significantly promoted by cambium activity in the P-replete environment, thereby enhancing plant secondary growth. Based on these outcomes, we suggest that clone-061 would be more useful for enhancing production. Regarding commercial purposes, these findings will contribute to the improvement of P fertilization efficiency.
  PubDate: Thu, 18 Aug 2022 00:00:00 GMT
  DOI: 10.1093/forestry/cpac030
  Issue No: Vol. 96, No. 1 (2022)
Modelling tree diameter of less commonly planted tree species in New
Zealand using a machine learning approach
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  Pages: 87 - 103
  Abstract: AbstractA better understanding of forest growth and dynamics in a changing environment can aid sustainable forest management. Forest growth and dynamics data are typically captured by inventorying a large network of sample plots. Analysing these forest inventory datasets to make precise forecasts on growth can be challenging as they often consist of unbalanced, repeated measures data collected across large geographic areas with corresponding environmental gradients. In addition, such datasets are rarely available for less commonly planted tree species, and are often incomplete and even more unbalanced. Conventional statistical approaches are not able to deal with such datasets and identify the different factors that interactively affect forest growth. Machine learning approaches offer the potential to overcome some of the challenges with modelling complex forest dynamics in response to environmental and climatic factors, even with unbalanced inventory data. In this study, we employed a widely used machine learning algorithm (random forests) to model individual tree diameter at breast height (DBH, 1.4 m) in response to age, stocking, site and climatic factors for the following five less commonly planted tree species groups in New Zealand: Cupressus lusitanica (North Island); Cupressus macrocarpa (South Island); Eucalyptus nitens; Sequoia sempervirens; Podocarpus totara; and Leptospermum scoparium. Data to build machine learning models were extracted and combined from three national level databases, and included stand variables, information about sites and climate features. The random forest models were able to predict tree DBH with high precision for the five-tree species (R2 > 0.72 and root-mean-square error ranged from 2.79–11.42 cm). Furthermore, the random forest models were interpretable and allowed us to explore the effects of site, environmental and climate factors on forest growth. To our knowledge, this is the first attempt to utilize machine learning approaches to model tree diameter of less common planted forest tree species in New Zealand. This approach can be used to forecast more precise forest growth and carbon sequestration to help us understand how different forest types and species are affected by the changing climate.
  PubDate: Mon, 19 Sep 2022 00:00:00 GMT
  DOI: 10.1093/forestry/cpac037
  Issue No: Vol. 96, No. 1 (2022)
Estimation of aboveground carbon stock using Sentinel-2A data and Random
Forest algorithm in scrub forests of the Salt Range, Pakistan
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  Pages: 104 - 120
  Abstract: AbstractForest ecosystems play a vital role in the global carbon cycle as forests store ~283 Gt of carbon globally and hence help mitigate climate change. Carbon stock estimation is the key step for assessing the mitigation potential of a given forest. About 5–10 Gt CO2 equivalent emissions come from deforestation and forest degradation annually. Pakistan’s forest resources are currently deteriorating due to deforestation and degradation and resulting in sourcing carbon dioxide emissions. One forest type that has been examined little so far in this context is subtropical scrub forests. This research suggests a workflow to estimate the carbon stock from three carbon pools (aboveground, belowground and litter) in scrub forests of the Salt Range, Pakistan by incorporating remote sensing and geographic information system techniques. The study’s objectives include the estimation of biomass and carbon stocks by using field inventory data and allometric equations, quantifying CO2 sequestration by using the ‘IPCC 2006 Guidelines for National Greenhouse Gas Inventories’ and finally map biomass and carbon by utilizing satellite imagery and statistical analysis. For prediction and mapping of biomass and carbon, field plots data along with vegetation indices and spectral bands of the Sentinel-2A satellite imagery were fed into a Random Forest (RF) algorithm in the cloud computing Google Earth Engine platform. Our results of ground data suggest that the examined scrub forests harbour 243 917 t of biomass, 114 989 t of carbon and 422 009 t of CO2 equivalent in the three carbon pools of the study area with a mean biomass density of 12.04 t ha−1 (±5.31) and mean carbon density of 5.72 t ha−1 (±2.46). The RF model showed good performance with reasonable R2 (0.53) and root mean square error (3.64 t ha−1) values and predicted average biomass at 13.93 t ha−1 (±4.35) and mean carbon density of 6.55 t ha−1 (±2.05). The total predicted and field-measured biomass has a plausible difference in values while the mean values have a minimal difference. The red-edge region and short-wave infrared (SWIR) region of the Sentinel-2A spectrum showed a strong relationship with aboveground biomass estimates from the field. We conclude that the combination of Sentinel-2A data coupled with ground data is a cost-effective and reliable tool to estimate various carbon pools in the scrub forests at a regional scale and may contribute to formulate policies to manage forests sustainably, enhance forest cover and conserve biodiversity.
  PubDate: Sun, 11 Sep 2022 00:00:00 GMT
  DOI: 10.1093/forestry/cpac036
  Issue No: Vol. 96, No. 1 (2022)
Mitigating the risk of wind damage at the forest landscape level by using
stand neighbourhood and terrain elevation information in forest planning
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  Pages: 121 - 134
  Abstract: AbstractWind damage and the bark beetle outbreaks associated with it are major threats to non-declining, long-term wood production in boreal forests. We studied whether the risk of wind damage in a forested landscape could be decreased by using stand neighbourhood information in conjunction with terrain elevation information. A reference management plan minimized the differences in canopy height at stand boundaries and did not utilize information on the topography of the terrain, overlooking the possibility that the risk of windthrow may depend on the elevation of the terrain. Alternative management plans were developed by using four different weighting schemes when minimizing differences in canopy height at stand boundaries: (1) no weight (reference); (2) mean terrain elevation at the stand boundary; (3) deviation of the mean elevation of the boundary from the mean elevation of the terrain within a 100-m radius and (4) multipliers that described the effect of topography on wind speed at the stand boundary. For each management plan, we calculated the total number of at-risk trees and the total area of vulnerable stand edge. These statistics were based on the calculated critical wind speeds needed to uproot trees in stand edge zones. Minimization of the weighted mean of canopy height differences between adjacent stands resulted in homogeneous landscapes in terms of canopy height. Continuous cover management was often preferred instead of rotation management due to smaller canopy height differences between adjacent stands and its economical superiority. The best weighting scheme for calculating the mean canopy height difference between adjacent stands was the deviation between the mean elevation of the boundary and the mean elevation of the terrain within 100 m of the boundary. However, the differences between the weighting schemes were small. It was found that reasonably simple methods, based on a digital terrain model, a stand map, and the canopy heights of stands, could be used in forest planning to minimize the risk of wind damage. Validation against actual wind damages is required to assess the reliability of the results and to further develop the methodology presented.
  PubDate: Sat, 08 Oct 2022 00:00:00 GMT
  DOI: 10.1093/forestry/cpac039
  Issue No: Vol. 96, No. 1 (2022)