Large-scale Forest Disturbance Research Articles

Zásoby uhlíku v lesních půdách a lesní hospodářství – review

The article summarizes and discusses studies focused on the carbon stock in forest soils and the way it is affected by forestry operations with a focus on the Central European (Czech) region. In general, the methods enhancing forests´ vitality and stability are more important than the procedures aimed at carbon sequestration only, as the major risk for carbon stock in forest soils is represented by large-scale disturbances. Important factors that will influence the mitigation potential include the change of tree species composition, as the majority of forests in the Czech Republic is still represented by Norway spruce or Scots pine even-aged monocultures. Careful practices of logging and forest restoration are recommended, with minimal opening of the crown layer. The use of clear-cuts should be reserved mainly for afforestation of light-demanding tree species. Care should be taken of sustainable nutrients content in forest soils, as the soil quality plays substantial role in the future forest vitality, productivity and resilience. Forest protection and all measures preventing large-scale forest disturbances are of key importance for carbon sequestration.

Soil Respiration after Bark Beetle Infestation along a Vertical Transect in Mountain Spruce Forest

In recent decades, large-scale forest disturbances such as windthrow and bark beetle infestations have significantly impacted Earth’s carbon balance and forest ecosystems. This impact alters soil respiration (SR), along with decreased gross primary productivity. To investigate the impact of bark beetle (Ips typographus L.) infestations in mountain spruce forests on SR, we measured SR at sites infested by bark beetles and adjacent undisturbed stands in the Tatra National Park (Slovakia) during the vegetation period (May–September) in 2016–2017 five to six years after the initial bark beetle attack. The measurements were taken along an altitudinal gradient (1100–1400 m a.s.l.). The highest rates of SR were observed during the summer months in both years (from June to August). However, yearly SR from May to September at infested sites showed significantly higher rates than uninfested ones in both years. SR showed a decreasing pattern with elevation gain in 2016 at infested sites, but this pattern was not observed in 2017. This study provides important insights into the impact of bark beetle infestations on SR and emphasizes the need for further research on the long-term effects of forest disturbances on carbon cycling. It also underscores the importance of determining the effect of different components of SR on the changed environment due to bark beetle attacks on mountain spruce forests.

Seasonal patterns of habitat use of resident birds in Białowieża Forest and its links to post-disturbance management

Resident bird species staying in the same area year-round may face very different habitat conditions between seasons in temperate forests. This may cause resident forest birds to use different habitats during winter and spring. Furthermore, habitat use and their seasonal shifts could be additionally affected by large scale forest disturbances (e.g. outbreaks of bark beetles) and post-disturbance management, as they likely affect the availability of crucial resources for reproduction and survival. We investigated the impact of the European spruce bark beetle (Ips typographus) outbreak and post-outbreak management on winter and spring habitat use of 18 resident bird species in Białowieża Forest, Eastern Poland. We showed differences in habitat use of resident birds in winter and spring, at the level of species, community and three foraging guilds (i.e. invertivore bark foragers, invertivore arboreal gleaners and omnivores). First, bird species richness and bird abundance recorded at 111 sites in winter were not related or even showed a negative relationship with richness and abundance in spring, indicating that winter and spring bird diversity hotspots did not spatially overlap. Second, the wintering community as a whole, and the invertivore arboreal gleaners in specific, shifted their density towards more coniferous sites and increased their density at moderately salvage-logged sites, while decreasing their density at strongly salvage-logged sites. The wintering invertivore bark foragers shifted towards more outbreak sites (i.e. both salvage-logged and natural regeneration) and distributed more evenly along the natural regeneration – salvage-logged gradient. The distributions of habitat use were species-specific and there was no single habitat or level of disturbance preferred or avoided by all species or groups in spring or winter. This study highlights the importance of considering habitat use outside of the breeding season, when assessing habitat requirements of resident forest bird communities to evaluate the impact of post-disturbance management. In addition, it shows the significance of maintaining the heterogeneity of forest habitats to the wintering resident bird community, especially when deciding on post-disturbance management actions.

Detection of Forest Disturbances with Different Intensities Using Landsat Time Series Based on Adaptive Exponentially Weighted Moving Average Charts

Forest disturbance detection is important for revealing ecological changes. Long-time series remote sensing analysis methods have emerged as the primary approach for detecting large-scale forest disturbances. Many of the existing change detection algorithms focus primarily on identifying high-intensity forest disturbances, such as harvesting and fires, with only a limited capacity to detect both high-intensity and low-intensity forest disturbances. This study proposes an online continuous change detection algorithm for the detection of multi-intensity forest disturbances such as forest harvest, fire, selective harvest, and insects. To initiate the proposed algorithm, the time series of the Normalized Difference Vegetation Index (NDVI) is fitted into a harmonic regression model, which is then followed by the computation of residuals. Next, the residual time series is entered into the adaptive exponentially weighted moving average (AEWMA) chart. This chart adaptively adjusts the smoothing coefficients to identify both high-intensity and low-intensity disturbances. When the chart value consistently deviates from the control limit, the forest pixel is classified as disturbed. With an overall spatial accuracy of 85.2%, including 86.1% producer’s accuracy and 84% user’s accuracy, along with a temporal accuracy of 96.7%, the algorithm enables precise and timely detection of forest disturbances with multiple intensities. This method provides a robust solution for detecting multi-intensity disturbances in forested regions.

A Combined Deep Learning and Prior Knowledge Constraint Approach for Large-Scale Forest Disturbance Detection Using Time Series Remote Sensing Data

The scale and severity of forest disturbances across the globe are increasing due to climate change and human activities. Remote sensing analysis using time series data is a powerful approach for detecting large-scale forest disturbances and describing detailed forest dynamics. Various large-scale forest disturbance detection algorithms have been proposed, but most of them are only suitable for detecting high-magnitude forest disturbances (e.g., fire, harvest). Conversely, more continuous, subtle, and gradual lower-magnitude forest disturbances (e.g., thinning, pests, and diseases) have been subject to less focus. Deep learning (DL) can distinguish subtle differences in information within time series data, offering new opportunities to capture forest disturbances in a complete and detailed way. This study proposes an approach for analyzing forest dynamics across large areas and long time periods by combining DL time series classification and prior knowledge constraint. The approach consists of two stages: (1) an improved self-attention model used for time series classification to identify sequences with forest disturbance characteristics; (2) developed skip-disturbance recovery index (S-DRI) characterizing the temporal context, using prior knowledge constraint to identify forest disturbance years in time series with disturbance characteristics. In this study, the year of forest disturbances in five study areas located in the United States, Canada, and Poland from 2001 to 2020 was mapped. A total of 3082 manually interpreted test data with different disturbance causal agents (such as fire, harvest, conversion, hurricane, and pests) were sampled from five research areas for validation. Our approach was also evaluated against two forest disturbance benchmark datasets derived from LandTrendr and the Global Forest Change (GFC) dataset. The results demonstrate that our approach achieved an overall accuracy of 87.8%, surpassing the accuracy of LandTrendr (84.6%) and the Global Forest Change dataset (81.4%). Furthermore, our approach demonstrated lower omission rates (ranging from 10.0% to 67.4%) in detecting subtle to severe causal agents of forest disturbance, in comparison to LandTrendr (with a range of 18.0% to 81.6%) and GFC (with a range of 15.0% to 88.8%). This study, which involved mapping large-scale and long-term forest disturbance in multiple regions, revealed that our approach can be applied to new areas without a requirement for complex parameter adjustments. These results demonstrate the potential of our approach in generating comprehensive and detailed forest disturbance data, thus providing a new and effective method in this domain.

Ecological factors affecting the recent Picea abies decline in Slovenia: the importance of bedrock type and forest naturalness

Norway spruce (Picea abies [L.] Karst.) has been at the centre of controversy for many decades. Recent evidence of its profound disturbance-induced damage and consequent stock depletions across forest landscapes in Europe has reinforced doubts regarding the sustainability and prospects of this tree species in the future. Like many other European countries, Slovenia has experienced significant Norway spruce mortality and a decrease in growing stock primarily as the result of several disturbance agents (bark beetle outbreaks, an ice storm, windthrows). We investigated a countrywide spruce growing stock decline based on data between 2010 and 2018. Particular focus was placed on identifying the main ecological drivers of this decline, namely geological conditions, climatic parameters, soil attributes, topographic factors and forest stand characteristics. The effects of potential predictors on the relative change (%) in spruce volume (m3 ha-1) during the period 2010-2018 were analysed with Generalized Additive Models. Based on a national dataset including forest compartments (n = 6355) with a spruce growing stock decline > 10%, we found mixed support for ecology-based hypotheses. While spruce decline responded to bedrock type as predicted (i.e., greater relative decline in carbonate compared to silicate compartments), higher forest naturalness (preservation of tree species composition) was not associated with a lower decline. Spruce decline was amplified by higher potential evapotranspiration and soil clay content but showed a strong negative relationship with spruce proportion in the year 2010. General trends along the gradients of other selected predictors (stoniness/rockiness and heat load index) were less pronounced. The results suggest that most of these ecological predictors interact with geology and forest naturalness in affecting Norway spruce decline. Our analysis reveals that bedrock type can play an important role due to its mitigating effects. However, forest naturalness is of secondary significance as intensified large-scale forest disturbances likely override its buffering potential.

Quantifying mangrove canopy regrowth and recovery after Hurricane Irma with large-scale repeat airborne lidar in the Florida Everglades

Hurricane Irma caused significant damages to mangrove forested wetlands in south Florida, including defoliation, tree snapping, and uprooting. Previous studies have used optical satellite imagery to estimate large-scale forest disturbance and resilience patterns. However, satellite images alone cannot provide measurements of vertical mangrove structure. In this study, we used dense point cloud data collected by NASA Goddard’s LiDAR, Hyperspectral, and Thermal (G-LiHT) airborne imager before (March 2017) and after (December 2017 and March 2020) Hurricane Irma to quantify the recovery, or lack thereof, of the three-dimensional (3D) mangrove forest structure. Recent resilience and vulnerability models developed from Landsat time series following the storm were used to group the lidar data into distinct disturbance-recovery classes. We then analyzed lidar-based forest canopy within each of the recovery classes to test a suite of forest structural characteristics. Our results indicate that 77.0 % of the survey area experienced canopy height loss three months after Hurricane Irma, whereby the majority of canopy height loss occurred in areas with the tallest mangrove forests (i.e., 15–25 m tall). Our analysis shows that the mangrove canopy height in South Florida increased by an average 0.26 m from December 2017 to March 2020, with most of the forest (84.7 % of the survey area) experiencing canopy height regrowth. However, only 38.1 % of the survey area has recovered to pre-storm canopy height. The distribution of canopy height was significantly altered by Hurricane Irma in the low and intermediate resilience classes, but were not significantly different 2.5 years later. Indeed, in areas of low resilience, little to no vertical change has occurred suggesting the absence of canopy regrowth and natural regeneration. Conversely, mangroves in high resilience class, which are dominated by shorter canopies (<5 m), were not heavily damaged by the storm and have maintained the same structural attributes as those before Hurricane Irma. Our findings highlight that hurricane disturbances significantly alter mangrove forest canopy structure, but recovery of vertical structure varies by resilience classes, species composition, and canopy height.

Mapping Large-Scale Forest Disturbance Types with Multi-Temporal CNN Framework

Forests play a vital role in combating gradual developmental deficiencies and balancing regional ecosystems, yet they are constantly disturbed by man-made or natural events. Therefore, developing a timely and accurate forest disturbance detection strategy is urgently needed. The accuracy of traditional detection algorithms depends on the selection of thresholds or the formulation of complete rules, which inevitably reduces the accuracy and automation level of detection. In this paper, we propose a new multitemporal convolutional network framework (MT-CNN). It is an integrated method that can realize long-term, large-scale forest interference detection and distinguish the types (forest fire and harvest/deforestation) of disturbances without human intervention. Firstly, it uses the sliding window technique to calculate an adaptive threshold to identify potential interference points, and then a multitemporal CNN network is designed to render the disturbance types with various disturbance duration periods. To illustrate the detection accuracy of MT-CNN, we conducted experiments in a large-scale forest area (about 990 km2) on the west coast of the United States (including northwest California and west Oregon) with long time-series Landsat data from 1986 to 2020. Based on the manually annotated labels, the evaluation results show that the overall accuracies of disturbance point detection and disturbance type recognition reach 90%. Also, this method is able to detect multiple disturbances that continuously occurred in the same pixel. Moreover, we found that forest disturbances that caused forest fire repeatedly appear without a significant coupling effect with annual temporal and precipitation variations. Potentially, our method is able to provide large-scale forest disturbance mapping with detailed disturbance information to support forest inventory management and sustainable development.

Effect of game browsing on natural regeneration of European beech (Fagus sylvatica L.) forests in the Krušné hory Mts. (Czech Republic and Germany)

Abstract Tree damage by game browsing is one of the biggest threats to forest ecosystems at the time of climate change and large-scale forest disturbances. The aim of the paper was to determine the effect of browsing by ungulates on the diversity, abundance and species composition of natural regeneration in forest stands dominated by European beech (Fagus sylvatica L.). The research was conducted on 10 permanent research plots in the Krušné hory Mts. in the Czech Republic and Germany. The density of natural regeneration was in the range of 23,300–114,100 recruits ha−1. A higher proportion of silver birch (Betula pendula Roth.) and rowan (Sorbus aucuparia L.) was found in the regeneration compared to the mature stands. A total of 78% of recruits was damaged by browsing. The most frequently damaged tree species were sycamore (Acer pseudoplatanus L.; 98%) and black alder (Alnus glutinosa [L.] Gaertn.; 97%), while Norway spruce (Picea abies [L.] Karst; 31%) and sessile oak (Quercus petraea [Matt.] Liebl.; 50%) were the least affected. Seventy-nine percent of European beech recruits were damaged. The game significantly reduced the height of regeneration by up to 40%, especially by terminal browsing. Browsing also negatively affected the quality and abundance of regeneration. For successful dynamics of species-rich natural forest ecosystems, it is necessary to minimize tree damage by game browsing. These main measures include the reduction of ungulate population levels and the optimization of their age structure and sex ratio, an increase in the number of overwintering enclosures and food fields for game and a change in the political approach to game management with sufficient consideration of forestry interests.

Evaluation and Quantification of the Effects of Climate and Vegetation Cover Change on Karst Water Sources: Case Studies of Two Springs in South-Western Slovenia

Karst aquifers hold important water resources such as regional water budgets and freshwater supply. Due to highly dynamic hydrological processes in comparison to other less permeable systems, they are particularly susceptible to environmental changes. However, little research directly characterizes the impacts of climate and vegetation cover changes on karst water sources. In this study, we aimed to evaluate individual long-term impacts and impacts of sudden large-scale forest disturbances on changes in groundwater recharge and in spring discharge. The work is based on temporal analysis of forest cover and a comparison of trend analysis of hydro-meteorological parameters. To investigate and evaluate vegetation cover change impacts on groundwater recharge, we used a soil water balance model and compared estimated actual daily values of effective precipitation to its fictional estimation disregarding the vegetation cover change. The applied methodology enabled quantification of the impacts of climate and vegetation cover change on selected karst water sources. The study suggests that the vegetation cover can have a significant impact on the spring recharge. Large-scale disturbances that occurred in a short-term mitigated the effects expected from the trend analysis of hydro-meteorological parameters. In the long-term, in addition to climate changes, the multi-decadal natural vegetation overgrowth significantly contributed to the reduction in the spring’s discharge values, especially in the warm season when water demand is higher. Therefore, the results are of key importance for developing proper water management and environmental policies.

Spatially Quantifying Forest Loss at Landscape-scale Following a Major Storm Event

Large scale forest disturbances are becoming more frequent across the world, and remote sensing must play a role in informing and prioritizing immediate, short-term and long-term disaster response and recovery. However, such evaluations from remote sensing are currently limited (e.g., burned area severity and change NDVI) and do not always explicitly relate to change in resources of interest. Herein we demonstrate a novel method to predict basal area loss, validated by independent field evaluations. Hurricane Michael made landfall on Mexico Beach in the Florida panhandle as a Category 5 storm on October 10th, 2018. The storm affected roughly 2 million hectares of largely forested land in the area. In this study, we use Sentinel-2 imagery and 248 forest plots collected prior to landfall in 2018 in the forests impacted by Hurricane Michael to build a general linear model of tree basal area across the landscape. The basal area model was constrained to areas where trees were present using a tree presence model as a hurdle. We informed the model with post-hurricane Sentinel-2 imagery and compared the pre- and post- hurricane basal area maps to assess the loss of basal area following the hurricane. The basal area model had an r-squared value of 0.508. Plots were revisited to ground truth the modelled results; this showed that the model performed well at categorizing forest hurricane damage. Our results validate a novel method to create a landscape scale spatial dataset showing the location and intensity of basal area loss at 10-m spatial resolution which can be used for quantifying forest disturbances worldwide.

Juvenile thinning can effectively mitigate the effects of drought on tree growth and water consumption in a young Pinus contorta stand in the interior of British Columbia, Canada

Large-scale forest disturbances including mountain pine beetle infestation and forest fires have generated overstocked lodgepole pine stands in the interior of British Columbia. A critical need is to determine sustainable management strategies to ensure their healthy growth and provision of various ecological functions under increased drought risk due to climate change. In 2016, a field experiment was established to study the effects of juvenile thinning on carbon assimilation and water use at the both tree- and stand-scales in a 16-year old lodgepole pine stand from June to October in 2016 and 2017. This study located northeast of Penticton, British Columbia, included two thinning treatments (T1: 4500 stems per ha; and T2: 1100 stems per ha) and one control (C: 27,000 stems per ha), randomly assigned in three blocks. Sap flow and microclimatic variables were continuously monitered in one plot of each treatment in one block, while tree diameter at breast height were measured across the three blocks. The results showed that C had the lowest tree radial growth (0.14 mm2/d), sap flow velocity (64.61 g/cm2d), and highest stand transpiration (4.36 mm/d), while T2 had the highest tree radial growth (1.28 mm2/d), sap flow velocity (149.14 g/cm2d), and lowest stand transpiration (0.36 mm/d) over the two-year study period. Significant differences of tree radial growth and sap flow velocity between T1 and T2 only occurred under the drought condition (in the summer season of 2017), with T2 having a significant higher resistant index of sap flow velocity than C and T1, by taking advantage of the change in microclimatic conditions following intense thinning. At the stand-level, only the stand transpiration of T1 statistically decreased in the drought year. We conclude that the thinning plays a significant and positive role in maintaining tree growth and water consumption in the short term, and the more heavily thinning (T2) would be more effective to mitigate the drought effect in young overstocking lodgepole pine forests in terms of water consumption. These findings improve our understanding on how thinning can be used to manage ecological responses to forest practices in a changing climate.

Quantification of selective logging in tropical forest with spaceborne SAR interferometry

Tropical forest disturbance (such as selective logging and fire) along with deforestation have significant contributions to the carbon source due to land-use change and anthropogenic CO2 emissions, and thus envisioned by United Nation's REDD+ programme. In previous work, spaceborne single-pass InSAR phase-center height has been shown to have the capability of accurately monitoring the subtle height change due to forest growth and degradation (with meter or even sub-meter level RMSE about the regression curve fit to time). In this paper, a new approach using spaceborne SAR interferometry has been developed to detect and quantify selective logging events. In particular, a quantitative indicator of forest disturbance is first defined, namely disturbance index (DI; from 0 “no disturbance” to 1 “deforestation”). A numerical field data-based InSAR simulation is then performed to study the functional relationship between the field-measured DI and InSAR relative phase-center height change from a modeled perspective. A selective logging event (October 2015 through January 2016) over the Tapajos National Forest in Brazil is used for experimental validation. The InSAR-inverted DI estimates derived from DLR's TanDEM-X time-series data were compared with those measured from a field work over 32 quarter-hectare stands at Tapajos with relative RMSE of 30% for DI up to 0.3 and the disturbance epoch can be determined with an average accuracy of 13 days (constrained by the satellite repeat interval usually on the order of 2 weeks). As a comparison, the repeat-pass InSAR coherences from the concurrent JAXA's ALOS-2 data are shown to qualitatively correspond to the TanDEM-X results, confirming both the location and the epoch of the disturbance event. This new method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring (for UN's REDD+ programme) through using spaceborne single-pass InSAR missions (e.g., DLR's TanDEM-X and in the future, TanDEM-L).

Detection of Forest Disturbance With Spaceborne Repeat-Pass SAR Interferometry

Focusing on open forests and woodlands within the Injune Landscape Collaborative Project research area in central southeast Queensland, Australia, and using dual-pol (HH and HV) ALOS PALSAR repeat-pass InSAR data (temporal baseline of 92 days), this paper explores the detection of forest disturbance from the spaceborne repeat-pass InSAR correlation magnitude by developing a simple and efficient forest disturbance detection approach. In particular, a generic physical InSAR scattering model is derived by accounting for the forest disturbance information as well as the normal temporal decorrelation effects that are later compensated for using the modified Random Volume over Ground model. Based on the generic model, a quantitative indicator of forest disturbance is retrieved, namely, disturbance index that varies from 0 (no disturbance) to 1 (complete deforestation). This index is compared with that identified using a time series of Landsat sensor data over a selective logging area and has a relative root mean square error of 13% at a spatial resolution of 0.8 ha. This paper highlights the use of the co-pol InSAR correlation magnitude for forest disturbance detection, which serves as a complimentary application to using the cross-pol counterpart for forest height inversion in a companion work. Given the global availability of this type of data (e.g., Japanese Aerospace Exploration Agency’s ALOS-1/2 and NASA-ISRO’s NISAR), the method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring.

Detecting Forest Disturbance in Northeast China from GLASS LAI Time Series Data Using a Dynamic Model

Large-scale forest disturbance often leads to changes in forest cover and structure, which imposes a great uncertainty in the estimation of the forest carbon cycle and biomass and affects other applications. In northeastern China, the Daxinganling region has abundant forest resources, where the forest coverage is about 30%. The Global LAnd Surface Satellite (GLASS) leaf area index (LAI) time series data provide important information to monitor the possible change of forests. In this study, we developed a new method to detect forest disturbances using GLASS LAI data over the Daxinganling region of Northeast China. As a dynamic model, the season-trend model has a higher sensitivity toward a seasonal change in LAI. Based on the accumulation of multi-year GLASS LAI products from 1997 to 2002, the dynamic model of LAI time series for each pixel is established first. The time-stepping modeling (TSM) process was designed by using the season-trend method, and sequential tests for detecting disturbances from a time series of pixels. Significant changes in the model parameters were captured as disturbance signals. Then, the near-infrared and shortwave-infrared bands of Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance are used as auxiliary information to distinguish the types of forest disturbances. Here, the algorithm led to the detection of two different types of disturbances: fire and other (e.g., insect, drought, deforestation). In this study, we took the forest region as the study area, used the 8-day composite GLASS LAI data at 1000-m spatial resolution to identify each pixel as a fire disturbance, other disturbance, or non-disturbance. Validation was performed using reference burned area data derived from Landsat 30 m imagery. Results were also compared with the MCD64 product. The validation results were based on confusion matrices showing the overall accuracy (OA) exceeded 92% for our method and the MCD64 product. Statistical tests identified that TSM’s product accuracy is higher than that of MCD64. This study demonstrated that the TSM algorithm using a season-trend model provides a simple and automated approach to identify and map forest disturbance.

Disturbance-grazer-vegetation interactions maintain habitat diversity in mountain pasture-woodlands

Low-intensity livestock grazing is a widespread management tool in order to maintain habitat diversity in mountain pasture-woodlands for nature conservation purposes. Historical photographs indicate that forest disturbance significantly contributed to forest-grassland mosaic pattern formation. Disturbance-grazer interactions are however poorly understood and the effects of logging or windthrow are rarely considered in management plans. Moreover, disturbance-grazer interactions are crucial for the maintenance of open habitats in the upcoming “rewilding” approach of nature conservation.We aimed to understand the effects of forest gap creation by the breakdown of senile trees or by single-tree cutting and of large forest openings by windthrow or logging on mosaic pattern formation in pasture-woodlands that were grazed by cattle and dominated by tree species with distinct regeneration ecology (Picea abies vs. Fagus sylvatica).We used the process-based model of pasture-woodland vegetation dynamics WoodPaM and newly implemented a forest disturbance routine. We simulated disturbance and grazing scenarios in an artificial mountain landscape and analyzed mosaic patterns with landscape metrics.We found that grazing in absence of disturbance promoted simply structured mosaics that were preconditioned by topography. Only large-scale forest disturbance disrupted this pattern and maintained the historical heterogeneous distribution of grassland communities across all habitat conditions (especially species-rich mountain grasslands on poor soil). This prerequisite is stronger in pasture-woodlands where the ecology of the dominant tree species promotes forest-grassland segregation (F. sylvatica in our case) and less in naturally thin-canopy mountain forest close to the tree line (P. abies). In wilderness areas, the very low density of grazers may limit the maintenance of open habitats regardless disturbance.

Nitrogen export from a boreal stream network following forest harvesting: seasonal nitrate removal and conservative export of organic forms

Abstract. Clear-cutting is today the primary driver of large-scale forest disturbance in boreal regions of Fennoscandia. Among the major environmental concerns of this practice for surface waters is the increased mobilization of nutrients, such as dissolved inorganic nitrogen (DIN) into streams. But while DIN loading to first-order streams following forest harvest has been previously described, the downstream fate and impact of these inputs is not well understood. We evaluated the downstream fate of DIN and dissolved organic nitrogen (DON) inputs in a boreal landscape that has been altered by forest harvests over a 10-year period. The small first-order streams indicated substantial leaching of DIN, primarily as nitrate (NO3−) in response to harvests with NO3− concentrations increasing by ∼ 15-fold. NO3− concentrations at two sampling stations further downstream in the network were strongly seasonal and increased significantly in response to harvesting at the mid-sized stream, but not at the larger stream. DIN removal efficiency, Er, calculated as the percentage of "forestry derived" DIN that was retained within the stream network based on a mass-balance model was highest during the snowmelt season followed by the growing season, but declined continuously throughout the dormant season. In contrast, export of DON from the landscape indicated little removal and was essentially conservative. Overall, net removal of DIN between 2008 and 2011 accounted for ∼ 65 % of the total DIN mass exported from harvested patches distributed across the landscape. These results highlight the capacity of nitrogen-limited boreal stream networks to buffer DIN mobilization that arises from multiple clear-cuts within this landscape. Further, these findings shed light on the potential impact of anticipated measures to increase forest yields of boreal forests, such as increased fertilization and shorter forest rotations, which may increase the pressure on boreal surface waters in the future.

Spatiotemporal patterns of tropical deforestation and forest degradation in response to the operation of the Tucuruí hydroelectric dam in the Amazon basin

The planned construction of hundreds of hydroelectric dams in the Amazon basin has the potential to provide invaluable ‘clean’ energy resources for aiding in securing future regional energy needs and continued economic growth. These mega-structures, however, directly and indirectly interfere with natural ecosystem dynamics, and can cause noticeable tree loss. To improve our understanding of how hydroelectric dams affect the surrounding spatiotemporal patterns of forest disturbances, this case study integrated remote sensing spectral mixture analysis, GIS proximity analysis and statistical hypothesis testing to extract and evaluate spatially-explicit patterns of deforestation (clearing of entire forest patch) and forest degradation (reduced tree density) in the 80,000 km2 neighborhoods of the Brazil's Tucuruí Dam, the first large-scale hydroelectric project in the Amazon region, over a period of 25 years from 1988 to 2013. Results show that the average rates of deforestation were consistent during the first three time periods 1988–1995 (620 km2 per year), 1995–2001 (591 km2 per year), and 2001–2008 (660 km2 per year). However, such rate dramatically fell to half of historical levels after 2008, possibly reflecting the 2008 global economic crisis and enforcement of the Brazilian Law of Environmental Crimes. The rate of forest degradation was relatively stable from 1988 to 2013 and, on average, was 17.8% of the rate of deforestation. Deforestation and forest degradation were found to follow similar spatial patterns across the dam neighborhoods, upstream reaches or downstream reaches at the distances of 5 km–80 km, suggesting that small and large-scale forest disturbances may have been influencing each other in the vicinity of the dam. We further found that the neighborhoods of the Tucuruí Dam and the upstream region experienced similar degrees of canopy loss. Such loss was mainly attributed to the fast expansion of the Tucuruí town, and the intensive logging activities alongside major roads in the upstream reservoir region. In contrast, a significantly lower level of forest disturbance was discovered in the downstream region.

Adaptive Participation in Forest Planning Contingent on a Hypothetical Large-Scale Forest Disturbance

Adaptive Participation in Forest Planning Contingent on a Hypothetical Large-Scale Forest Disturbance

Using ICESat's Geoscience Laser Altimeter System (GLAS) to assess large-scale forest disturbance caused by hurricane Katrina

In 2005, hurricane Katrina resulted in a large disturbance to U.S. forests. Recent estimates of damage from hurricane Katrina have relied primarily on optical remote sensing and field data. This paper is the first large-scale study to use satellite-based lidar data to quantify changes in forest structure from that event. GLAS data for the years prior to and following hurricane Katrina were compared to wind speed, forest cover, and damage data to assess the adequacy of sensor sampling, and to estimate changes in Mean Canopy Height (MCH) over all areas that experienced tropical force winds and greater. Statistically significant decreases in MCH post-Katrina were found to increase with wind intensity: Tropical Storm ∆MCH = − 0.5 m, Category 1 ∆MCH = − 2 m, and Category 2 ∆MCH = − 4 m. A strong relationship was also found between changes in non-photosynthetic vegetation (∆NPV), a metric previously shown to be related to storm damage, and post-storm MCH. The season of data acquisition was shown to influence calculations of MCH and MCH loss, but did not preclude the detection of major large-scale patterns of damage. Results from this study show promise for using space-borne lidar for large-scale assessments of forest disturbance, and highlight the need for future data on vegetation structure from space.