Forest Dynamics Research Articles

Land-use change, no-net-loss policies, and effects on carbon dioxide removals

BackgroundCarbon dioxide removal from the atmosphere (CDR) is a critical component of strategies for restricting global warming to 1.5°C and is expected to come largely from the sequestration of carbon in vegetation. Because CDR rates have been declining in the United States, in part due to land use changes, policy proposals are focused on altering land uses, through afforestation, avoided deforestation, and no-net-loss strategies. Estimating policy effects requires a careful assessment of how land uses interact with forest conditions to determine future CDR.ResultsWe evaluate how alternative specifications of land use-forest condition interactions in the United States affect projections of CDR using a model that mirrors land sector net emission inventories generated by the US government (EPA). Without land use change, CDR declines from 0.826 GT/yr in 2017 to 0.596 GT/yr in 2062 (28%) due to forest aging and disturbances. For a land use scenario that extends recent rates of change, we compare CDR estimated based on net changes in land use (Net Change model) and estimates that separately account for the distinct CDR implications of forest losses and forest gains (Component Change model). The Net Change model, a common specification, underestimates the CDR losses of land use by about 56% when compared with the Component Change models. We also estimate per hectare CDR losses from deforestation and gains from afforestation and find that afforestation gains lag deforestation losses in every ecological province in the US.ConclusionsNet Change approaches substantially underestimate the impact of land use change on CDR and should be avoided. Component Change models highlight that avoided deforestation may provide up to twice the CDR benefits as increased afforestation—though preference for one policy over the other would require a cost assessment. The disparities in the CDR impacts of afforestation and deforestation indicate that no-net-loss policies could mitigate some CDR losses but would lead to overall declines in CDR for our 45-year time horizon. Over a much longer period afforestation could capture more of the losses from deforestation but at a timeframe inconsistent with most climate change policy efforts.

Spectral response of forest disturbances induced by beaver caused floods: A case study in Estonia using Sentinel-2 MSI image time series

Beavers are known as ecosystem engineers and the habitats that they create can change the landscape greatly. When a new beaver colony establishes itself by building dams, digging canals and burrows and felling trees, the resulting changes are often large enough to be studied with medium resolution earth observation sensors like Sentinel-2 MSI. In this study we compare the spectral signatures of 405 sample areas disturbed by beaver-induced flooding with 261 healthy forest sample areas surrounding the area. We used 28 Sentinel-2 MSI multispectral images from years 2015 to 2018 over the southern part of Estonia in Military Grid Reference System map tile T35VME. In summer the average pixel values for areas that are affected by beavers show the largest differences in reflectance values for shortwave infrared (SWIR) and the visible parts of the spectrum compared to areas that are not affected by beavers. Some smaller differences are present also in the near-infrared (NIR) parts of the spectrum, but sometimes they are not significant according to p-values, being over the 0.05 threshold, of the t-test. In autumn and spring, when deciduous trees do not have their leaves and ground vegetation is not thriving, the differences in visible parts of the spectrum disappear. Multispectral vegetation indices, like the Normalized Difference Vegetation Index (NDVI) and others, also show clear differences in the beaver-disturbed forests and surrounding healthy forests throughout all vegetation phenology stages. Keywords: beavers; flood disturbance; phenology; forest damage; habitat change; Sentinel-2 MSI

An Improved Grid-Based Carbon Accounting Model for Forest Disturbances from Remote Sensing and TPO Survey Data

Forest disturbance is one of the main drivers of forest carbon flux change. How to accurately estimate the carbon flux caused by forest disturbance is an important research problem. In a previous study, the authors proposed a Grid-based Carbon Accounting (GCA) model that used remote sensing data to estimate forest carbon fluxes in North Carolina from 1986 to 2010. However, the original model was unable to track legacy emissions from previously harvested wood products and was unable to consider forest growth conditions before and after forest disturbance. This paper made some improvements to the original GCA model to enable it to track fluxes between all major aboveground live carbon pools, including pre-disturbance growth and growth of undisturbed forests, which were not included in the initial model. Based on existing timber product output (TPO) survey data and annual TPO records inversed from remote sensing data, we also worked to clarify the distribution ratios of removed C between slash and different wood product pools. Specifically, the average slash ratio for North Carolina was calculated from the difference between the C removed and the C flowing into the wood product as calculated from TPO survey data. County- and year-specific ratios were then calculated using the annual TPO records obtained from remote sensing and TPO survey data, dividing the removed remaining C into pools P1, P10, and P100, which were then applied to each 30 m pixel based on the county and year to which the pixel belonged. After compensating for these missing legacy emissions and adjusting forest growth rates from Forest Inventory and Analysis (FIA) data, we estimated a net carbon sink of 218.1 Tg of the flux associated with live aboveground biomass and harvested wood products from North Carolina woodlands over the 25-year study period (1986–2010). This estimate is close to the greenhouse gas emission and sink data provided by the U.S. Department of Agriculture for North Carolina and is comparable to estimates reported by several other studies.

Integrating UAV LiDAR and multispectral data to assess forest status and map disturbance severity in a West African forest patch

Unmanned aerial vehicle (UAV) technologies have emerged as promising tools to improve forest ecosystem assessments. These technologies offer high-resolution data that can significantly enhance evaluations of forest structure, condition, and disturbance severity. UAV sensors such as LiDAR and multispectral provide complementary information about forest attributes, capturing structural and spectral details, yet their integration for comprehensive forest assessment remains understudied. In this paper, we explored the potential of combining UAV LiDAR and multispectral data to assess the disturbance severity of a West African forest patch (Benin). We developed an integrated disturbance index (IDI) that fuses structural properties from LiDAR data and spectral characteristics from multispectral vegetation indices through principal component analysis (PCA). This allowed us to delineate low (> 0.65), medium (0.35–0.65), and high (< 0.35) forest disturbance levels. We applied the IDI to the 560-ha Ewe-Adakplame relict forest in Benin, West Africa, and achieved 95 % overall accuracy in disturbance detection, outperforming both LiDAR-only (80 %) and multispectral-only (75 %) approaches. The IDI revealed that 23 % of the forest area has experienced low disturbance, while 28 % and 49 % face medium and high disturbance levels, respectively. These findings highlight that more than three-quarters of this relict forest is under considerable stress, underscoring the urgent need for tailored conservation strategies to strengthen forest resilience. This method's ability to differentiate disturbance levels can inform resource allocation, prioritize conservation efforts, and guide the development of site-specific management plans. The integration of UAV LiDAR and multispectral data demonstrated here has potential for application across diverse tropical forest patches, providing an effective means to monitor forest health, assess disturbance severity, and support data-driven decision-making in forest conservation and sustainable management.

Climate change and disturbance interact to alter landscape reflectivity (albedo) in boreal forests across a large latitudinal gradient in Siberia

Boreal forests form the largest terrestrial biome globally. Climate change is expected to induce large changes in vegetation of high latitude ecosystems, but there is considerable uncertainty about where, when, and how those changes will occur. Such vegetation change produces major feedback to the climate system, including by modifying albedo (reflectivity). Our study used the LANDIS-II forest landscape model to project forest dynamics on four representative landscapes (1 M ha) for 280 years into the future under a range of climate scenarios across a broad latitudinal gradient in Siberia. The model estimated the albedo of the vegetation and any snow on each landscape grid-cell through time to quantify surface albedo change in response to climate change and disturbances. We found that the shortening of the snow-covered season (winter) decreased annual average albedo dramatically, and climate change facilitated the invasion of tundra by boreal trees in the northernmost landscape (reducing albedo in all seasons). However, in other landscapes, albedo increased in summer due to disturbances (fire, wind, insects, harvest), eliminating or reducing leaf area in the short-term, and in the mid-term by promoting more reflective forest types (deciduous, light conifers). This increased albedo was somewhat ephemeral and under climate change was overwhelmed by the shortening of the snow-covered season that greatly reduced albedo. We conclude that the primary driver of the overall reflectivity of boreal ecosystems is not vegetation, but rather, the length of the snow-covered season. Because climate change is likely to dramatically shorten the snow season, the concurrent reduction of albedo has the potential to act as a powerful positive feedback for climate change. Managing natural and anthropogenic disturbances may be the only tool with potential to mitigate the reduction of albedo by climate change in boreal ecosystems because management to encourage more reflective forest types has relatively small effect.

Mapping protection effects and gaps of forest dynamics in Europe 2012 to 2018

Forest management and conservation can help forests adapt to and minimize the effects of natural and anthropogenic disturbance factors on forest mortality. However, little quantitative information is currently available on the impact of different management and conservation practices on forest dynamics in Europe focusing on forest extent and health simultaneously. Against this background, this paper aims to understand the effectiveness of management and protection practices on two key indicators of forest dynamics under different land use conditions. We analyzed changes in both indicators in Europe between 2012 and 2018, detected spatial explicit mean differences between forest dynamics of protected vis á vis unprotected areas, and examined protected areas of multiple management types.The results show that European forest area increased by 1.2 percentage points pp while canopy density decreased by 2 pp. The average forest area in protected areas increased, but at a slower rate than in their unprotected surroundings indicating no protection effects., In contrast, potential protection gaps can be observed for canopy density changes as density dropped in protected areas by 0.5 pp while it remained stable in unprotected areas. Finally, significant differences between IUCN management and protection categories are quantified against three settlement types and discussed along characteristic examples. In doing so, this paper contributes to the EU Forest Strategy 2030 by providing a robust and spatially explicit monitoring of the effectiveness of managed and protected forests linking observed overall patterns of forest dynamics to conclusions on the role of different management and protection practices.

Dominant Species Composition, Environmental Characteristics and Dynamics of Forests with Picea jezoensis Trees in Northeast China

To describe the characteristics and dynamics of the Picea jezoensis (PJ) community, a survey of 48 forest plots containing PJ trees was conducted in Northeast China. Methods of community grouping, analysis of variance, and linear regression, and indicators of relative basal area (RBA, reflecting the dominance degree of species in the community) and temporary stability (TS, reflecting the stability of forest plant community), were employed. The objectives were to describe the dominant species composition and environment of the PJ community and to quantify the changes in PJ’s dominance status. Communities with PJ trees were divided into four groups: PJ as the first dominant species; PJ as the second dominant species; PJ being co-dominant but ranked third or lower; and PJ as a non-dominant species. Among them, the PJ community (i.e., where PJ is the first dominant species) occupied sites at the highest elevation with a mean value of 1408 m, on gentle slopes ≤ 10°, where the tree species occurring with PJ mainly included Betula costata and Abies nephrolepis. A linear relationship was found between the TS and RBA. The threshold for PJ shifting between being the first and second dominant species in the community was RBA = 0.387. This study provides a scientific basis for judging the attributes and status of PJ community and the protection and restoration of PJ forests. These findings also provide comparable information for ecological research and conservation of PJ forests in other areas.

Climate suitability for the moisture-sensitive conifer species may not be universally declining in a warming world

Amid ongoing and escalating climate change, understanding changes in habitat suitability for forest tree species is crucial for anticipating risks to their growth and ecosystem services. Despite advancements in this area, challenges remain, particularly in addressing spatial and temporal discrepancies in suitability change. This study evaluated habitat suitability changes for Qinghai spruce, a moisture-sensitive conifer species, during the warming period from 1960 to 2020, by integrating species distribution modeling with dendrochronological methods. Maxent model outputs revealed a distinct dipole pattern in habitat suitability changes: an increase in the west (56.2%) and a decrease in the east (43.8%). This dipole pattern was corroborated by the spatial pattern of forest population dynamics, highlighting much higher adult-tree mortality frequency, rate, and density decline in regions with decreased suitability, while showing minimal values in most (3 out of 4) regions with increased suitability. Climate factors and their changes explained 80.9% of the variance in suitability changes. Precipitation change, with a significant positive relationship to suitability changes, contributed the most (54.5%) to this explained variance, followed by post-warming temperature, which contributed 34.3% with a significant negative relationship. Thresholds of +20mm for precipitation change and 0°C for post-warming temperature distinguished regions with a high probability of increased suitability from those with a high probability of decreased suitability. This study provides nuanced insights into the responses of moisture-sensitive conifer species to climate change, helping inform and enhance conservation strategies for Qinghai spruce.

Cross-modal fusion approach with multispectral, LiDAR, and SAR data for forest canopy height mapping in mountainous region

The study introduces a technique for integrating multispectral, LiDAR, and Synthetic Aperture Radar (SAR) data within a machine-learning (ML) framework. By leveraging ML models, including Random Forest (RF), Gaussian Process Regression (GPR), and k-Nearest Neighbors (k-NN), successfully provides a comprehensive methodology for mapping forest canopy height (CH) and analyzes seasonal changes from 2019 to 2023 in the mountainous region of Vodno Mountain, North Macedonia. The RF model achieved the highest accuracy (R2 = 0.91, RMSE = 1.2 m), outperforming the other models when trained with Aerial LiDAR data. The forest CH models were validated against field measurements, Aerial LiDAR, and Global Ecosystem Dynamics Investigation (GEDI) data, confirming the accuracy of the approach and showing solid correlations between predicted and observed CH values. This research is significant due to its innovative approach to forest CH modeling in a region with minimal prior studies. Integrating multi-source data enables more accurate and detailed CH mapping, essential for monitoring forest biomass and carbon stocks, detecting forest disturbances, and assessing future forest management activities.

Reconstructing 450 Years of Pollarding Events in Spanish Deciduous Oak Woodlands Using Machine Learning

Pollarding, the practice of pruning tree branches at a specific height, has been crucial for managing open forests in Europe. This practice has supported the persistence of highly biodiverse open woodlands featuring ancient trees. Understanding historical management patterns is essential for interpreting past socioeconomic conditions and developing strategies to mimic traditional practices for biodiversity conservation. Current methods for reconstructing past management in pollarded forests often rely on techniques for large-scale forest disturbances, which may be suboptimal for detecting short-term perturbations like pollarding. To address this gap, we applied a random forest algorithm to detect pollarding events using tree-ring traits, reconstructing the multi-centennial management history of four deciduous oak dehesas in northern Spain. Our analysis revealed that short-term changes in latewood were the most reliable indicator of pollarding events. Pollarding typically reduced latewood production for about three years, with the most pronounced declines occurring toward the end of the pollarding effect period. Pollarding patterns underwent a major shift starting in the last third of the 20th century. Key historical decades of both high and low pollarding pressure were consistently observed across the studied dehesas. These findings enhance our understanding of these unique ecosystems and offer critical insights for their conservation.

Tree demographic drivers across temperate rain forests, after accounting for site-, species-, and stem-level attributes.

Diverse drivers such as climate, soil fertility, neighborhood competition, and functional traits all contribute to variation in tree stem demographic rates. However, these demographic drivers operate at different scales, making it difficult to compare the relative importance of each driver on tree demography. Using c. 20,000 stem records from New Zealand's temperate rain forests, we analyzed the growth, recruitment, and mortality rates of 48 tree species and determined the relative importance of demographic drivers in a multilevel modeling approach. Tree species' maximum height emerged as the one most strongly associated with all demographic rates, with a positive association with growth rate and negative associations with recruitment and mortality rates. Climate, soil properties, neighborhood competition, stem size, and other functional traits also played significant roles in shaping demographic rates. Forest structure and functional composition were linked to climate and soil, with warm, dry climates and fertile soil associated with higher growth and recruitment rates. Neighborhood competition affected demographic rates depending on stem size, with smaller stems experiencing stronger negative effects, suggesting asymmetric competition where larger trees exert greater competitive effects on smaller trees. Our study emphasizes the importance of considering multiple drivers of demographic rates to better understand forest tree dynamics.

The positive influence of Tibetan Buddhist monasteries on forest conservation

The Qinghai-Tibetan Plateau (QTP) is vital for stabilizing climate in East and South Asia and its glaciers and snow meltwater nourish key continental rivers. The region’s forests are not only ecologically essential but also fragile. The practices of local herdsmen, shaped by long-standing cultural traditions, significantly affect forest dynamics. This study examines the vital role that Tibetan Buddhist monasteries play in forest conservation on the QTP. We utilize data sources including extensive field surveys and geospatial data to assess the impact of monastery and village locations on forest conditions. Spatial analysis techniques, such as Moran’s I and Ripley’s K function, reveal significant clustering of monasteries and villages, which are closely linked with forest conditions. Our findings indicate that forests near Buddhist monasteries have higher aboveground biomass and better quality compared to other areas, suggesting that their presence supports better forest conservation practices. This positive influence is attributed to the cultural significance of these sites and the environmental consciousness promoted by Buddhist cultural teachings. Additionally, this study employs analytical methods including Random Forest and Partial Least Squares Structural Equation Modeling (PLS-SEM) to explore the drivers of forest quality. Initial aboveground biomass, climatic factors, and monastery density emerge as key influences on forest quality within monastery buffer zones, indicating that cultural factors are crucial in shaping forest landscapes.

Experimental Study on Tree Shape Identification and Interaction Among Trees

Forests play a crucial role in mitigating global warming and maintaining climate balance, necessitating a comprehensive understanding of forest dynamics. However, many optical characteristics related to forests remain poorly understood, particularly models for determining forest parameters such as tree species, shape, and inter-tree distances. Although several models exist for estimating leaf area index and photosynthetically active radiation, fewer models address structural forest parameters. This research aims to develop a model using visible and near-infrared radiometer data from Earth observation satellites to estimate forest parameters, including tree species, shape, height, and spacing. Results show that as the distance between trees increases, the impact of multiple reflections decreases significantly. Elliptical tree shapes exhibit approximately three times higher multiple reflection effects compared to conical shapes, indicating potential for distinguishing tree shapes through radiometric data. For canopy shapes, shorter, thicker trees experience more significant reflection effects than taller, thinner trees, suggesting the feasibility of estimating tree height. Overall, the impact of multiple reflections ranges from a few to 10% of TOA radiance, necessitating its consideration when calculating forest reflectance to ensure accurate forest parameter retrieval from satellite observations.

Can beavers canopy alterations affect managed forests more than natural forests?

Beavers, as ecosystem engineers, significantly affect riparian ecosystems, mostly through foraging and dams construction. This study examines how beaver-related tree cutting altered canopy openness and affected microhabitats and forest floor vegetation in the Northern Poland’s Tuchola Forest. Woody plant characteristics, forest floor vegetation abundance, and canopy openness were assessed at three sites varying in forest naturalness, with canopy openness being measured using hemispherical photography. Results show that selective tree removal by beavers from all diameter classes significantly increased canopy openness. This alteration leads to transformative changes in forest habitat properties, notably increasing light availability, and to nutrient status changes as evidenced by shifts in Ellenberg indicator values. Consequently, these changes result in increased forest floor total vegetation cover, diversity and composition. The cascading effects of beaver tree-cutting on canopy alterations have been comprehensively modelled using structural equation models. The study also reveals distinct spatial patterns in canopy alterations, with the most pronounced effects near riverbanks. The intensified impact of beaver activities, could be linked to the naturalness of the forest, and might be especially pronounced in more altered environments, particularly where pioneer aspen trees dominate the tree stand composition. These findings underscore the role of beavers in shaping forest dynamics, particularly in managed or disturbed forests. By creating canopy gaps, beavers initiate processes that enhance habitat heterogeneity and biodiversity, suggesting that their presence may be crucial for the restoration of natural processes in disturbed ecosystems.

Liana distribution and small-scale natural disturbance in an old-growth temperate forest

Liana distribution and small-scale natural disturbance in an old-growth temperate forest

Divergent trajectories of regeneration in early-successional forests after logging and wildfire.

Increases in forest disturbances have altered global forest demography rates, with many regions now characterized by extensive areas of early-successional forest. Heterogeneity in the structure, diversity, and composition of early-successional forests influence their inherent ecological values from immediately following disturbance to later successional stages, including values for biodiversity and carbon storage. Here, using 14 years of longitudinal data, we describe patterns in the structure, richness, and composition of early-successional forests subject to one of three different disturbance types: (1) clearcut logging followed by slash burn, (2) severe wildfire followed by salvage logging, and (3) severe wildfire only, in the Mountain Ash (Eucalyptus regnans) and Alpine Ash (Eucalyptus delegatensis) forests of southeastern Australia. We also documented the influence of disturbance intervals (short, medium, and long) on early-successional forests. Our analyses revealed several key differences between forests that regenerated from wildfire versus two different anthropogenic perturbations. Most ash-type plant communities were resilient to wildfire within historical fire-regimes (75-150 years), exhibiting temporal trends of recovery in plant structure, richness, and composition within the first decade. In contrast, the richness, occurrence, and abundance of some plant lifeforms and life history traits were negatively associated with clearcut logging and salvage logging, relative to forests disturbed by wildfire alone. These included resprouting species, such as tree ferns and ground ferns. However, Acacia spp. and shrubs were more abundant after clearcut logging. Our findings also provide evidence of the pronounced negative impact of salvage logging on early-successional plant communities, relative to that of both clearcut logging and wildfire. Notably, plant richness declined for over a decade after salvage logging, rather than increased as occurred following other disturbance types. Early-successional forests provide the template for the stand structure and composition of mature forests. Therefore, altered patterns of recovery with different disturbance types will likely shape the structure and function of later-successional stages. Predicted increases in wildfire will increase the generation of early-successional forests and subsequent salvage logging. Therefore, it is pertinent that management consider how different disturbance types can produce alternate states of forest composition and structure early in succession, and the implications for mature stands.

Satellite observations indicate slower recovery of woody components compared to upper-canopy and leaves in tropical rainforests after drought

The 2015–2016 El Niño-induced drought caused biomass loss in global tropical forests, yet the recovery duration of different vegetation components (woody components, upper canopies, and leaves) remains unknown. Here, we use satellite remote sensing data of vegetation optical depth and leaf area index, with varying sensitivity to different vegetation components, to examine vegetation recovery during the drought event. We find that the woody component had the slowest recovery compared to the upper canopy and leaves, and displayed greater spatial variability between continents. Key factors influencing woody recovery include drought severity, moisture-related climatic conditions (i.e., vapor pressure deficit, precipitation, and soil moisture), and seasonal variations in temperature and precipitation. Our study highlights the importance of different vegetation components for maintaining ecosystem balance under drought disturbances and indicates the need for further research to explore recovery mechanisms and the long-term impacts of drought on forest dynamics.

Top 10+1 indicators for assessing forest ecosystem conditions: A five-decade fragmentation analysis

Globally, land use change has consistently resulted in greater losses than gains in aboveground biomass (AGB). Forest fragmentation is a primary driver of biodiversity loss and the depletion of natural capital. Measuring landscape characteristics and analyzing changes in forest landscape patterns are essential for accounting for the contributions of forest ecosystems to the economy and human well-being. This study predicts national forest distribution for 2036 and 2054 using a Cellular Automata (CA) system and assesses ecosystem conditions through landscape metrics at the patch, class, and landscape levels. We calculated 130 metrics and applied a Variance Threshold method to remove features with low variance, testing different thresholds. The first filtered-out metrics were further analysed through Principal Component Analysis combined with a Feature Importance technique to select and rank the top 10 indicators: effective mesh size, splitting index, mean radius of gyration, largest patch index, mean core area, core area percentage, Simpson's evenness index, mutual information, Simpson's diversity index, and mean contiguity index. The eleventh selected indicator is the AGB density, a structural measurement for ecosystem condition and a proxy for forest carbon storage and sequestration assessments. From 2000 to 2018, the national AGB forest carbon stock decreased from 131.5 to 91.3 Megatons (Mt) with expected values for 2036 and 2054 being 71.8 and 55.3 Mt., respectively. Landscape measurements quantitatively describe forest dynamics, providing insights into the structure, configuration, and changes characterizing landscape evolution. This research underscores the capability of CA models to map large-scale forest resources and predict future development scenarios, offering useful information for conservation and environmental management decisions. Additionally, it provides measurements to support Ecosystem Accounting by assessing forest extent and indicators of its conditions.

Assessing disturbances in surviving primary forests of Europe.

Primary forests are of paramount importance for biodiversity conservation and the provision of ecosystem services. In Europe, these forests are scarce and threatened by human activities. However, a comprehensive assessment of the magnitude of disturbances in these forests is lacking, due in part to their incomplete mapping. We sought to provide a systematic assessment of disturbances in primary forests in Europe based on remotely sensed imagery from 1986 to 2020. We assessed the total area disturbed, rate of area disturbed, and disturbance severity, at the country, biogeographical, and continental level. Maps of potential primary forests were used to mitigate gaps in maps of documented primary forests. We found a widespread and significant increase in primary forest disturbance rates across Europe and heightened disturbance severity in many biogeographical regions. These findings are consistent with current evidence and associate the ongoing decline of primary forests in Europe with human activity in many jurisdictions. Considering the limited extent of primary forests in Europe and the high risk of their further loss, urgent and decisive measures are imperative to ensure the strict protection of remnants of these invaluable forests. This includes the establishment of protected areas around primary forests, expansion of old-growth zones around small primary forest fragments, and rewilding efforts.

Detection of snow disturbances in boreal forests using unitemporal airborne lidar data and aerial images

Abstract Snow is among the most significant natural disturbance agents in Finland. In silviculture, maps of snow disturbance are needed to recognize severely disturbed forests where the risk of subsequential disturbances, such as insect outbreaks, is high. We investigated the potential of unitemporal airborne lidar (light detection and ranging) data and aerial images to detect snow disturbance at the tree level. We used 81 healthy and 128 snow-disturbed field plots established in a 63 800 ha study area in Eastern Finland. A subset of trees (n = 675) was accurately positioned in the field plots. We carried out individual tree detection (ITD) using airborne lidar data (5 p/m2), and a random forest classifier was used to classify healthy and broken trees. Tree features were extracted from a terrain elevation model, lidar data, and aerial imagery. We compared canopy height model–based (ITDCHM) and point cloud–based (ITDPC) ITD approaches. We explored random forest variable importance scores and evaluated the classification performance by an F1-score and its components (precision and recall). Performance was also evaluated at the plot level to investigate errors associated with the predicted number of broken trees. We achieved F1-scores of 0.66 and 0.85 for the tree- and plot-level classifications, respectively. The variable importance scores showed that elevation above sea level was the most important predictor variable followed by ITD-based features characterizing the neighborhood of trees. The ITDCHM slightly outperformed the ITDPC at the tree level, while they both underestimated the number of broken trees at the plot level. The proposed approach can be carried out alongside lidar-assisted operational forest management inventories provided that a set of positioned broken and healthy trees are available for model training. Since airborne lidar data often have a temporal resolution of several years for the same areas, future research should consider the utilization of other remotely sensed data sources to improve the temporal resolution.