Forest Disturbance History Research Articles

Climatic and disturbances influence on tree-rings of Larix gmelinii in the southeast coastal area

The vast region of North East Asia is covered by primary mixed conifer-broadleaved forests which include a range of different tree species. A key factor in the dynamics of these ecosystems is wind disturbance. According to reports, this factor will be increasingly important as a result of the poleward migration of tropical cyclones, a characteristic of which is strong wind. At the same time, global climate change may reduce the recovery potency of some species and lead to new combinations of species. Gmelin Larch is one of the key early succession species in the north of the region. In the southeastern part of its range (Russian Far East), this species has not been widely studied by dendroclimatologists and there are no dendroecological studies available. This study shows how the seasonal growth of Larix gmelinii responds to changes in climate, specifically to variations in precipitation and temperature. The study has established that these trees are more affected by rising temperatures than by consistent precipitation levels, provided there is no shortage of water. Interestingly, after the 1970s there was a change in the climate sensitivity of Larix gmelinii in June (the month with the strongest correlation). It went from a negative correlation with temperature to a positive correlation with precipitation. By using tree ring data, we were able to reconstruct the past history of forest disturbance. The peaks in this chronology coincide with those in earlier published dendroecological reconstructions for other species. They indicate that the radial growth of larch is sensitive to canopy changes. We recorded two release peaks in the 1960s and 2000s, during the period of meteorological observations. We attributed the first peak to the most powerful tropical cyclone Emma (1956). We assume also that the second peak reflects the recent intensification of tropical cyclones in the region. In our opinion, the change in microclimate as a result of disturbance has altered the relationship to climate in such a way that disturbance may cause tree growth to be dependent on precipitation. At the same time, such low correlations do not support a conclusion about the strong negative influence of the current climate on the trees.

The impacts of ecological disturbances on the diversity of biosynthetic gene clusters in kauri (Agathis australis) soil

BackgroundThe ancient kauri (Agathis australis) dominated forests of Aotearoa New Zealand are under threat from a multitude of ecological disturbances such as forest fragmentation, biodiversity loss, climate change, and the spread of the virulent soil pathogen Phytophthora agathidicida. Taking a wider ecosystem-level approach, our research aimed to explore the impacts of forest disturbance and disease outbreaks on the biosynthetic potential and taxonomic diversity of the kauri soil microbiome. We explored the diversity of secondary metabolite biosynthetic gene clusters (BGCs) in soils from a range of kauri forests that varied according to historical disturbance and dieback expression. To characterise the diversity of microbial BGCs, we targeted the non-ribosomal peptide synthetase (NRPS) and polyketide synthetase (PKS) gene regions for sequencing using long-read PacBio® HiFi sequencing. Furthermore, the soil bacterial and fungal communities of each forest were characterized using 16 S rRNA and ITS gene region sequencing.ResultsWe identified a diverse array of naturally occurring microbial BGCs in the kauri forest soils, which may offer promising targets for the exploration of secondary metabolites with anti-microbial activity against P. agathidicida. We detected differences in the number and diversity of microbial BGCs according to forest disturbance history. Notably, soils associated with the most undisturbed kauri forest had a higher number and diversity of microbial NRPS-type BGCs, which may serve as a potential indicator of natural levels of microbiome resistance to pathogen invasion.ConclusionsBy linking patterns in microbial biosynthetic diversity to forest disturbance history, this research highlights the need for us to consider the influence of ecological disturbances in potentially predisposing forests to disease by impacting the wider health of forest soil ecosystems. Furthermore, by identifying the range of microbial BGCs present at a naturally high abundance in kauri soils, this research contributes to the future discovery of natural microbial compounds that may potentially enhance the disease resilience of kauri forests. The methodological approaches used in this study highlight the value of moving beyond a taxonomic lens when examining the response of microbial communities to ecosystem disturbance and the need to develop more functional measures of microbial community resilience to invasive plant pathogens.

Extending Canadian forest disturbance history maps prior to 1985

AbstractAn accurate depiction of wildfire, harvesting, and insect outbreak disturbances is essential for sustainable ecosystem management of forests in Canada. Even though the advent of temporally consistent 30‐m resolution Landsat data has enabled the detailed mapping of forest disturbances in Canada from 1985 onward, the disturbance record prior to 1985 remains sparse. This study aimed to extend the existing pre‐1985 disturbance history record by mapping wildfire, harvest, and insect outbreaks in Canadian forests between 1965 and 1984. Our geospatial data processing methodology relied on multilayer perceptrons (MLP) trained on spectral recovery signatures to map and age these disturbances. Our model detected approximately 4.8, 7.3, and 3.8 million ha of burnt, harvested, and insect‐ravaged forest areas, respectively, that were absent from national and provincial disturbance databases and forest inventories. Results were validated using both internal and external validation datasets. Our disturbance detection methodology was highly effective, with an internal validation kappa score of 0.91 and an external score of 0.81. The fire and harvest age disturbance MLPs, whose predictions can also be used as a proxy of forest stand age, performed adequately on the internal (fire R2 = 0.675; root mean squared error [RMSE] = 4.42; harvest R2 = 0.723; RMSE = 3.17) and external validation datasets (fire R2 = 0.242; RMSE = 4.69; harvest R2 = 0.257; RMSE = 5.46), outperforming existing forest age disturbance products. Finally, we relied on several open data products, such as provincial forest inventories, to correct our disturbance type and year prediction whenever these more reliable, but incomplete, data sources were available. Specific years were not assigned to insect outbreaks due to the lack of dependable training and validation data. We also illustrate how extending the existing forest disturbance record by 20 years may provide a more in‐depth understanding of landscape‐disturbance dynamics with a case study of the 2023 Canadian wildfire season.

The distribution of tree biomass carbon within the Pacific Coastal Temperate Rainforest, a disproportionally carbon dense forest

Spatially explicit global estimates of forest carbon storage are typically coarsely scaled. While useful, these estimates do not account for the variability and distribution of carbon at management scales. We asked how climate, topography, and disturbance regimes interact across and within geopolitical boundaries to influence tree biomass carbon, using the perhumid region of the Pacific Coastal Temperate Rainforest, an infrequently disturbed carbon dense landscape, as a test case. We leveraged permanent sample plots in southeast Alaska and coastal British Columbia and used multiple quantile regression forests and generalized linear models to estimate tree biomass carbon stocks and the effects of topography, climate, and disturbance regimes. We estimate tree biomass carbon stocks are either 211 (SD = 163) Mg C ha−1 or 218 (SD = 169) Mg C ha−1. Natural disturbance regimes had no correlation with tree biomass but logging decreased tree biomass carbon and the effect diminished with increasing time since logging. Despite accounting for 0.3% of global forest area, this forest stores between 0.63% and 1.07% of global aboveground forest carbon as aboveground live tree biomass. The disparate impact of logging and natural disturbance regimes on tree biomass carbon suggests a mismatch between current forest management and disturbance history.

Stand dynamics of old-growth hemlock forests in central Bhutan are shaped by natural disturbances

Understanding how past disturbances have influenced the development of forests is critical for deciphering their current structure and composition and forecasting future changes. In this study, dendrochronological methods were applied to uncover the disturbance history of old-growth hemlock-dominated forests in central Bhutan. Analysis of tree-ring samples from two old-growth hemlock stands, located in two different topographic settings, identified the importance of gap-phase dynamics in facilitating recruitment and growth releases and producing complex, multi-aged structures over time. One site showed evidence of a near stand-replacing disturbance in the late 1700s, while the other showed no evidence of high-severity disturbance at any time over the last 400 years. At both sites low-to medium-severity disturbances, some of which appear to be associated with cyclones originating in the Bay of Bengal, dominated the disturbance regime. The hemlock stands exhibited a significant positive association between cyclone occurrence and growth release events and between recruitment pulses and growth release events. From 1800 to 1970 there was an increase in recruitment of angiosperm tree species at most sites and a corresponding decline in conifer recruitment. Over the past 50 years there has been little new recruitment; this may be due to light limitation in the understory from shade-tolerant angiosperms and bamboo in the lower strata of these stands. Significant variations in disturbance dynamics and recruitment were observed across the study sites, suggesting that other factors, such as topography and climate, may be influencing long-term stand development patterns. This study highlights the complex interplay between historical disturbance regimes and tree recruitment in shaping the age and size structures of old-growth hemlock forests in central Bhutan. It also provides new insights into the dynamics of these forests that can be used to support effective forest conservation and management in the future.

History of forest disturbance recorded by old trees in Xishan Mountain, Beijing

History of forest disturbance recorded by old trees in Xishan Mountain, Beijing

Comparing harmonic regression and GLAD Phenology metrics for estimation of forest community types and aboveground live biomass within forest inventory and analysis plots

The Landsat multispectral time series is a valuable source of moderate spatial resolution data to support forest mapping and monitoring tasks. Using United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) plots within the states of Michigan, Oregon, and West Virginia, two methods to summarize time series observations, harmonic regression coefficients and Global Land Analysis & Discovery (GLAD) Phenology metrics, are compared for predicting forest community type, total aboveground live biomass (AGLBM), and species-specific AGLBM. Harmonic regression coefficients, which provided mean overall accuracies (OAs) between 62.8% and 73.1% and map image classification efficacies (MICEs) varying between 0.455 and 0.566 for the three studied states and calculated using multiple model replicates, generally provided better predictive performance for differentiating forest community types in comparison to GLAD phenology metrics. However, differences were not always statistically significant. DTM-derived terrain variables improved classification performance in some landscapes when using machine learning, random forest classification (for example, the highest obtained mean OA of 78.5% (MICE = 0.557) was obtained for West Virginia when using the combined harmonic regression and terrain variables). For the regression-based estimation of total and species-specific AGLBM, the spectral data used and the incorporation of terrain variables had less of an impact on model performance. Further, the regression models in Oregon provided larger mean R-squared values (0.452 to 0.490) in comparison to those in Michigan and West Virginia, where all R-squared values were lower than 0.200, suggesting that AGLBM prediction may be more or less challenging in different landscapes depending on forest characteristics, terrain, management practices, and disturbance histories.

Estimating density of forest bats and their long-term trends in a climate refuge.

For many species, estimating density is challenging, but it is important for conservation planning and understanding the functional role of species. Bats play key ecological roles, yet little is known about their free-ranging density. We used a long-term banding study of four species caught in an extensively forested climate refuge and spatial capture-recapture models (SCR) to estimate density and its change over time. Between 1999 and 2020, there were 3671 captures of four bat species, which were all edge-space foragers. Recaptures represented 16% (n = 587) of all captures, of which 89 were between-trap-cluster movements. Closed spatial mark-recapture models estimated plausible densities that varied with elevation. Preferred elevations differed between species, with density averaging 0.63 ha-1 for Vespadelus darlingtoni (high elevation), 0.43 ha-1 for V. pumilus (low elevation), 0.19 ha-1 for Chalinolobus morio (high elevation), and 0.08 ha-1 for V. regulus (high elevation). Overall, densities were higher than most previous published estimates for bats. Forest disturbance history (past timber harvesting) had no detectable effect on density. Density also varied substantially across years, and although annual maximum temperature and rainfall were not supported in models, some time periods showed an apparent relationship between density and annual rainfall (+ve) and/or annual maximum temperature (-ve). The most notable change was an increase in the density of V. pumilus after 2013, which tracked an increase in annual temperature at the site, reflecting a warming climate. Bat densities in forests outside of climate refugia are likely to be more sensitive to climate change, but more studies are needed in different habitats and continents and outside climate refugia to place the densities we estimated into a broader context.

Development and long-term dynamics of old-growth beech-fir forests in the Pyrenees: Evidence from dendroecology and dynamic vegetation modelling

Ecological knowledge on long-term forest dynamics and development has been primarily derived from the study of old-growth forests. Centuries of forest management have decreased the extent of temperate old-growth forests in Europe and altered managed forests. Disentangling the effects of past human disturbances and climate on current species composition is crucial for understanding the long-term development of forests under global change. In this study, we investigated disturbance and recruitment dynamics in two forests in the Western Pyrenees (Spain) with contrasting management history: an old-growth forest and a long-untouched forest, both dominated by the two shade-tolerant species Fagus sylvatica (European beech) and Abies alba (Silver fir). We used dendroecological methods in seven plots to analyse forest structure, growth patterns and disturbance histories in these forests. We benchmarked these data with the dynamic vegetation model ForClim to examine the effects of natural and human-induced disturbances on forest development, structure and species composition. Disturbance regimes differed between the study forests, but none showed evidence of stand replacing disturbances, either natural or human induced. Low disturbance rates and continuous recruitment of beech and fir dominated the old-growth forest over the last 400 years. In contrast, the long-untouched forest was intensively disturbed in 1700–1780, probably by logging, with lower natural disturbance rates thereafter. Beech and fir recruitment preferentially occurred after more intense disturbances, despite the high shade tolerance of both beech and fir. Higher fir abundance in the long-untouched forest than in the old-growth forest appeared to be related to its human-induced disturbances. ForClim closely simulated forest potential natural vegetation with a dominance of beech over fir, but overestimated the presence of less shade-tolerant species. Previously observed local fir decline may result from natural forest successional processes after logging. Within ∼200 years after logging cessation, some long-untouched forest structural attributes converged towards old-growth forest, but legacy effects still affected species composition and structure. Natural disturbance regimes in beech-fir forests of the Western Pyrenees induce temporal fluctuations between beech and fir abundance, with a natural tendency for beech dominance in advanced developmental stages with low disturbance rates.

Human impacts as the main driver of tropical forest carbon.

Understanding the mechanisms controlling forest carbon storage is crucial to support “nature-based” solutions for climate change mitigation. We used a dataset of 892 Atlantic Forest inventories to assess the direct and indirect effects of environmental conditions, human impacts, tree community proprieties, and sampling methods on tree above-ground carbon stocks. We showed that the widely accepted drivers of carbon stocks, such as climate, soil, topography, and forest fragmentation, have a much smaller role than the forest disturbance history and functional proprieties of the Atlantic Forest. Specifically, within-forest disturbance level was the most important driver, with effect at least 30% higher than any of the environmental conditions individually. Thus, our findings suggest that the conservation of tropical carbon stocks may be dependable on, principally, avoiding forest degradation and that conservation policies focusing only on carbon may fail to protect tropical biodiversity.

Decoupling the impact of biodiversity and environmental factors on the biomass and biomass growth of trees in subtropical forests

Abstract The relationship between biodiversity and ecosystem functioning has become a central issue in any forest ecosystem. However, there are few studies on the interaction of environmental factors based on the history of subtropical forest disturbance. In this study, we intended to disentangle the relationship between different aspects of biodiversity and biomass or biomass change when considering the environmental factors of 34 subtropical forest plots in Zhejiang Province, eastern China. We used linear models to analyze the effects of taxonomic, functional and phylogenetic diversity at the plot level of tree biomass and its growth with or without environmental factors. Taxonomic diversity and functional diversity, rather than phylogenetic diversity, showed significant correlations with biomass and biomass growth. We further found that there was a positive linear relationship between biomass or biomass growth and mean annual temperature and altitude. In addition, the relationship between biomass growth and functional diversity was significantly stronger than the relationship between biomass growth and phylogenetic diversity or taxonomic diversity when environmental factors and stand developmental stages were considered. Our results suggested that the relationship between biodiversity and ecosystem functioning is dependent upon the selection of diversity index and environmental conditions.

Towards accurate mapping of forest in tropical landscapes: A comparison of datasets on how forest transition matters

Tropical forests represent half of the Earth's remaining forest area, but they are shrinking at high rates, which poses a threat to their multiple ecosystem services. As a response, international environmental agreements and related programs require information about tropical forested landscapes. Despite the increasing quantity and quality of remote sensing-based data, the effective monitoring of forests in the tropics still faces operational challenges: (a) applicability at local levels, with lack of reference or cloud-free information; (b) overcoming geographical, ecological, or biophysical variability; (c): stratification, distinguishing forest categories related to functionality and disturbance history.We conducted an extensive ground verification campaign through 36 landscapes in 9 regions of Zambia, Ecuador and Philippines, which constitute a gradient of pantropical deforestation contexts or forest transitions. We collected over 16,000 ground control points and digitized over 18,000 ha with details on land use and forest disturbance history. We trained a random forest algorithm and generated high-resolution (30 m) binary forest maps covering ~15 Mha, building on 39 optical (Landsat-8), radar (Sentinel-1) and elevation bands, indices and textures. We validated the quality of the outputs across the studied deforestation gradient and compared them to (a): 3 national land cover maps used for international reporting, (b): 4 global forest datasets (Global Forest Change, Copernicus Land Cover, JAXA and TanDEM-X Forest/Non-Forest).Our method generated highly accurate (92%) forest maps for the studied regions when compared to the global datasets, which generally overestimated forest cover. We achieved accuracies similar to the national maps, following a standardized method for all countries. The difficulties in delineating forest increased in more advanced stages of deforestation, with recurring struggles to distinguish non-forest tree-based systems (e.g. perennials, palms, or agroforestry), shrublands and grasslands. Regrowth forests were repeatedly misclassified across contexts, countries and datasets, in contrast to reference or degraded forests. Our results highlight the importance of in situ verification as accompanying method to establish efficient forest monitoring systems, especially in areas with higher rates of forest cover change and in tropical regions of advanced deforestation or early reforestation stages. These are precisely the areas where current REDD+ or Forest Landscape Restoration initiatives take place.

Active forest management accelerates carbon storage in plantation forests in Lishui, southern China

China has committed to achieving peak CO2 emissions before 2030 and carbon neutrality before 2060; therefore, accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial ecosystems. The carbon sink capacity of plantation forests contributes to the mitigation of climate change. Plantation forests throughout the world are intensively managed, and there is an urgent need to evaluate the effects of such management on long-term carbon dynamics. We assessed the carbon cycling patterns of ecosystems characterized by three typical plantation species (Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), oak (Cyclobalanopsis glauca (Thunb.) Oerst.), and pine (Pinus massoniana Lamb.)) in Lishui, southern China, by using an integrated biosphere simulator (IBIS) tuned with localized parameters. Then, we used the state-and-transition simulation model (STSM) to study the effects of active forest management (AFM) on carbon storage by combining forest disturbance history and carbon cycle regimes. 1) The carbon stock of the oak plantation was lower at an early age (<50 years) but higher at an advanced age (>50 years) than that of the Chinese fir and pine plantations. 2) The carbon densities of the pine and Chinese fir plantations peaked at 70 years (223.36 ​Mg·ha‒1) and 64 years (232.04 ​Mg·ha‒1), respectively, while the carbon density in the oak plantation continued increasing (>100 years). 3) From 1989 to 2019, the total carbon pools of the three plantation ecosystems followed an upward trend (an annual increase of 0.16–0.22 ​Tg ​C), with the largest proportional increase in the aboveground biomass carbon pool. 4) AFM increased the recovery of carbon storage after 1996 and 2009 in the pine and Chinese fir plantations, respectively, but did not result in higher growth in the oak plantation. 5) The proposed harvest planning is reasonable and conducive to maximizing the carbon sequestration capacity of the forest. This study provides an example of a carbon cycle coupling model that is potentially suitable for simulating China's plantation forest ecosystems and supporting carbon accounting to monitor peak CO2 emissions and reach carbon neutrality.

Floodplain forest structure and the recent decline of Carya illinoinensis (Wangenh.) K. Koch (northern pecan) at its northern latitudinal range margin, Upper Mississippi River System, USA

Floodplain hardwood forests that exist on islands and immediately adjacent to channels along the Upper Mississippi River System (UMRS) are ecologically important due to high floral and faunal biodiversity. The recent decline and regeneration failure of hard mast species—e.g. Quercus spp. and Carya spp.—along the UMRS has been well documented, but causes of these trends have not been widely examined and are thus poorly understood. To investigate the mechanisms of this decline, we installed three 0.20-ha plots within the floodplain forests of the UMRS amongst communities known to contain the northernmost documented individuals of Carya illinoinensis (Wangenh.) K. Koch, a regionally imperiled, yet ecologically, economically, and culturally important hard mast species. Tree density (>5 cm dbh) averaged 355 stems ha−1 (n = 239, 12 representative species across 9 genera) and mean basal area was 77 m2 ha−1 across the three sites. Acer saccharinum L. was the most dominant species (49.0 m2 ha−1). The diameter distribution of trees demonstrated a negative exponential pattern, generally indicative of uneven-aged, regenerating forests. Over the past ca. 120 years, decadal-scale pulses of tree establishment across species show linkages with riparian engineering installments and flood and drought events. C. illinoinensis demonstrated a consistent establishment pattern since at least the 1860s through the 1950s, but experienced regeneration failure over the past ca. 50 years. Widespread ecological growth releases in C. illinoinensis are linked with known large-scale flood events along the UMRS (1965, 1993, 2001, 2011 CE). Correlation analysis revealed relationships between C. illinoinensis growth rings and drought, precipitation, maximum temperature, and river flow to be temporally unstable with an inflection point at the beginning of the lock and dam era (ca. 1930s–1940s). The coincidental decoupling of climate and flow variability with tree growth in the post-lock and dam era suggests that peripheral, disjunct populations of C. illinoinensis are sensitive to impacts from anthropogenic land and hydrologic modifications. Forest structure, disturbance history, and climate analyses demonstrate that without management intervention, C. illinoinensis will no longer have a significant presence within floodplain forests of the UMRS in the near future, contributing to loss in diversity. Direct forest restoration techniques, such as planting and creation of canopy gaps via regeneration-focused harvesting, are needed if C. illinoinensis is to return to historical abundance and distribution.

Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record

Information on forest disturbance is crucial for tropical forest management and global carbon cycle analysis. The long-term collection of data from the Landsat missions provides some of the most valuable information for understanding the processes of global tropical forest disturbance. However, there are substantial uncertainties in the estimation of non-mechanized, small-scale (i.e., small area) clearings in tropical forests with Landsat series images. Because the appearance of small-scale openings in a tropical tree canopy are often ephemeral due to fast-growing vegetation, and because clouds are frequent in tropical regions, it is challenging for Landsat images to capture the logging signal. Moreover, the spatial resolution of Landsat images is typically too coarse to represent spatial details about small-scale clearings. In this paper, by fusing all available Landsat and Sentinel-2 images, we proposed a method to improve the tracking of small-scale tropical forest disturbance history with both fine spatial and temporal resolutions. First, yearly composited Landsat and Sentinel-2 self-referenced normalized burn ratio (rNBR) vegetation index images were calculated from all available Landsat-7/8 and Sentinel-2 scenes during 2016–2019. Second, a deep-learning based downscaling method was used to predict fine resolution (10 m) rNBR images from the annual coarse resolution (30 m) Landsat rNBR images. Third, given the baseline Landsat forest map in 2015, the generated fine-resolution Landsat rNBR images and original Sentinel-2 rNBR images were fused to produce the 10 m forest disturbance map for the period 2016–2019. From data comparison and evaluation, it was demonstrated that the deep-learning based downscaling method can produce fine-resolution Landsat rNBR images and forest disturbance maps that contain substantial spatial detail. In addition, by fusing downscaled fine-resolution Landsat rNBR images and original Sentinel-2 rNBR images, it was possible to produce state-of-the-art forest disturbance maps with OA values more than 87% and 96% for the small and large study areas, and detected 11% to 21% more disturbed areas than either the Sentinel-2 or Landsat-7/8 time-series alone. We found that 1.42% of the disturbed areas indentified during 2016–2019 experienced multiple forest disturbances. The method has great potential to enhance work undertaken in relation to major policies such as the reducing emissions from deforestation and forest degradation (REDD+) programmes.

Estimating Time Since the Last Stand-Replacing Disturbance (TSD) from Spaceborne Simulated GEDI Data: A Feasibility Study

Stand-level maps of past forest disturbances (expressed as time since disturbance, TSD) are needed to model forest ecosystem processes, but the conventional approaches based on remotely sensed satellite data can only extend as far back as the first available satellite observations. Stand-level analysis of airborne LiDAR data has been demonstrated to accurately estimate long-term TSD (~100 years), but large-scale coverage of airborne LiDAR remains costly. NASA’s spaceborne LiDAR Global Ecosystem Dynamics Investigation (GEDI) instrument, launched in December 2018, is providing billions of measurements of tropical and temperate forest canopies around the globe. GEDI is a spatial sampling instrument and, as such, does not provide wall-to-wall data. GEDI’s lasers illuminate ground footprints, which are separated by ~600 m across-track and ~60 m along-track, so new approaches are needed to generate wall-to-wall maps from the discrete measurements. In this paper, we studied the feasibility of a data fusion approach between GEDI and Landsat for wall-to-wall mapping of TSD. We tested the methodology on a ~52,500-ha area located in central Idaho (USA), where an extensive record of stand-replacing disturbances is available, starting in 1870. GEDI data were simulated over the nominal two-year planned mission lifetime from airborne LiDAR data and used for TSD estimation using a random forest (RF) classifier. Image segmentation was performed on Landsat-8 data, obtaining image-objects representing forest stands needed for the spatial extrapolation of estimated TSD from the discrete GEDI locations. We quantified the influence of (1) the forest stand map delineation, (2) the sample size of the training dataset, and (3) the number of GEDI footprints per stand on the accuracy of estimated TSD. The results show that GEDI-Landsat data fusion would allow for TSD estimation in stands covering ~95% of the study area, having the potential to reconstruct the long-term disturbance history of temperate even-aged forests with accuracy (median root mean square deviation = 22.14 years, median BIAS = 1.70 years, 60.13% of stands classified within 10 years of the reference disturbance date) comparable to the results obtained in the same study area with airborne LiDAR.

Spider assemblage structure and functional diversity patterns in clear-cut, logged, and undisturbed areas in a large Atlantic Forest remnant

Habitat loss is one of the main consequences of landscape transformation by humans. Monitoring biodiversity changes in areas under different management strategies is fundamental for species conservation. Our study is the first to assess the role of forest disturbance history on spider (Araneae) biodiversity in the westernmost portion of the Atlantic Forest. We analyzed taxonomic and functional aspects of spider assemblages in understories in a large forest fragment in southwestern Brazil. Spiders were sampled in five 30 m × 5 m plots over three seasons in three areas with different management histories: clear-cutting, selective logging, or native plots. We also characterized tree basal area, tree density, and canopy openness. The clear-cut plots showed more canopy openness and low habitat heterogeneity due to the high density of one pioneer native tree species. Forest structure in selective logging and native plots was similar. Spider richness, abundance, and functional richness were affected only by the season. Species composition also differed among the areas depending on the season. The abundance of web-building species was mainly associated with clear-cut areas in winter and spring. These results highlight the importance of natural regeneration in the Atlantic Forest after disturbance for the conservation of regional spider biodiversity.

Spatially-Explicit Prediction of Wildfire Burn Probability Using Remotely-Sensed and Ancillary Data

Wildfire is a critical process shaping the structure and composition of forest landscapes of western Canada. Spatially-explicit forest disturbance history and forest structure estimated using remotely-sensed data enables the characterization of burn probability, defined as the susceptibility of landscapes to fire hazard over time. In this research, we leveraged the Landsat archive to determine the capacity of land cover, forest structure, and forest disturbance information, together with ancillary data, to estimate burn probability. We analyzed the interactions between a number of contributing factors and identified landscapes with high probability to burn across forested ecosystems of Saskatchewan, Canada. Overall, we found that forests composed of coniferous species, with shorter trees (<3 m), low canopy height variability, an open stand structure (<10% canopy cover), and low timber volumes (<50 m3/ha), had higher burn probabilities. A 2015 burn probability map indicated that forests that did burn in 2015 (determined using independently mapped wildfires) had a median predicted burn probability of 81%, while the median burn probability for unburned forest area was 19%. This paper demonstrates the potential to generate detailed and spatially-explicit burn probability maps from time series remote sensing data to inform wildland fire risk modeling, management, and mitigation.

Mapping Forest Disturbances between 1987–2016 Using All Available Time Series Landsat TM/ETM+ Imagery: Developing a Reliable Methodology for Georgia, United States

Forest resources have a high economic value in the State of Georgia (USA) and the landscape is frequently disturbed as a part of forest management activities, such as plantation forest management activities. Thus, tracking the stand-clearing disturbance history in a spatially referenced manner might be pivotal in discussions of forest resource sustainability within the State. The two major objectives of this research are (i) to develop and test a reliable methodology for statewide tracking of forest disturbances in Georgia, (ii) to consider and discuss the use and implications of the information derived from the forest disturbance map. Two primary disturbance detection methods, a threshold algorithm and a statistical boundary method, were combined to develop a robust estimation of recent forest disturbance history. The developed model was used to create a forest disturbance record for the years 1987–2016, through the use of all available Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+) data. The final product was a raster database, where each pixel was assigned a value corresponding to the last disturbance year. The overall accuracy of the forest disturbance map was 87%, and it indicated that 4,503,253 ha, equivalent to 29.2% of the total land area in Georgia, experienced disturbances between 1987 and 2016. The estimated disturbed area in each year was highly variable and ranged between 84,651 ha (±36,354 ha) to 211,780 ha (±49,504 ha). By combining the use of the disturbance map along with the 2016 database from the National Land Cover Database (NLCD), we also analyzed the regional variation in the disturbance history. This analysis indicated that disturbed forests in urban areas were more likely to be converted to other land-uses. The forest disturbance record created in this research provides the necessary spatial data and address forest resource sustainability in Georgia. Additionally, the methodology used has application in the analysis of other resources, such as the estimation of the aboveground forest biomass.

Optimizing Landsat time series length for regional mapping of lidar-derived forest structure

The value of combining Landsat time series and airborne laser scanning (ALS) data to produce regional maps of forest structure has been well documented. However, studies are often performed over single study areas or forest types, preventing a robust assessment of the approaches that produce the most accurate estimates. Here, we use Landsat time series data to estimate forest attributes across six Canadian study sites, which vary by forest type, productivity, management regime, and disturbance history, with the goal of investigating which spectral indices and time series lengths yield the most accurate estimates of forest attributes across a range of conditions. We use estimates of stand height, basal area, and stem volume derived from ALS data as calibration and validation data, and develop random forest models to estimate forest structure with Landsat time series data and topographic variables at each site. Landsat time series predictors, which were derived from annual gap-free image composites, included the median, interquartile range, and Theil Sen slope of vegetation indices through time. To investigate the optimal time series length for predictor variables, time series length was varied from 1 to 33 years. Across all six sites, increasing the time series length led to improved estimation accuracy, however the optimal time series length was not consistent across sites. Specifically, model accuracies plateaued at a time series length of ~15 years for two sites (R2 = 0.67–0.74), while the accuracies continued to increase until the maximum time series length was reached (24–29 years) for the remaining four sites (R2 = 0.45–0.70). Spectral indices that relied on shortwave infrared bands (Tasseled Cap Wetness and Normalized Burn Ratio) were frequently the most important spectral indices. Adding Landsat-derived disturbance variables (time since last disturbance, type of disturbance) did not meaningfully improve model results; however, this finding was largely due to the fact that most recently disturbed stands did not have predictions of forest attributes from ALS, so disturbed sites were poorly represented in the models. As model accuracies varied regionally and no optimal time series length was found, we provide an approach that can be utilized to determine the optimal time series length on a case by case basis, allowing users to extrapolate estimates of forest attributes both spatially and temporally using multispectral time series data.