Forest Canopy Cover Research Articles

An indicator approach to capture impacts of white-tailed deer and other ungulates in the presence of multiple associated stressors.

Management of ungulates is contested ground that lacks stakeholder agreement on desirable population sizes and management approaches. Unfortunately, we often miss information about extent of local impacts, for example on plant communities, to guide management decisions. Typical vegetation impact assessments like the woody browse index do not assess herbaceous plants, and differences in browse severity can be a function of deer density, deer legacy effects, localized deer feeding preferences and/or differences in plant community composition. Furthermore, in heavily affected areas, few remnant plants may remain for assessments. We used a sentinel approach to assess impact of white-tailed deer (Odocoileus virginianus), rodent attack, invasive earthworms and three invasive plants on survival and growth of 3-month-old red oak (Quercus rubra) individuals. We planted cohorts in 2010 and 2011 into deer accessible and fenced 30 × 30 m plots at 12 forests in New York State. We found year and site-specific effects with high deer herbivory of unprotected individuals (70-90 % of oaks browsed by deer versus none in fenced areas) far exceeding importance of rodent attacks. Oaks planted at low earthworm density sites were at significantly higher risk of being browsed compared with oaks at high earthworm density sites, but there was no detectable negative effect of invasive plants. Surviving oaks grew (~2 cm per year) under forest canopy cover, but only when fenced. We consider planting of oak or other woody or herbaceous sentinels to assess deer browse pressure a promising method to provide quantifiable evidence for deer impacts and to gauge success of different management techniques. The strength of this approach is that typical problems associated with multiple stressor impacts can be avoided, areas devoid of forest floor vegetation but under heavy deer browse pressure can still be assessed and the method can be implemented by non-specialists. Implementation of regular assessments can guide ungulate management based on meaningful evidence.

Fuel moisture in Mountain Ash forests with contrasting fire histories

Fuel moisture is a key driver of forest flammability as it influences ignition likelihood, fire intensity and resultant fire severity. Changes to forest canopy cover following disturbances like wildfire or logging may alter forest flammability by changing the microclimatic conditions that influence fine fuel moisture. Wet forests dominated by Mountain Ash (Eucalyptus regnans) are highly valued for their flora and fauna, timber, carbon and water. Wildfires are an important part of the lifecycle of these forests, but too frequent fire can threaten post-fire regeneration. With large tracts of Mountain Ash forest recovering from recent wildfires (in 2009 and 1983) there is a need to understand the mechanisms driving flammability in these forests, particularly as the forest structure changes following fire. This study sought to understand the effects of fire history on the flammability of Mountain Ash forests by specifically considering fuel moisture for different times since fire and fire severities. We measured canopy cover (plant area index) and fuel moisture within 8 forest sites last burnt between 7 and 200years ago by wildfires of low or high severity. Fuel moisture and fuel availability (i.e. number of days when fine fuels are dry enough to ignite and sustain spreading fire) were strongly associated with canopy cover; with denser canopied forests having higher fuel moisture. The largest differences in canopy cover occurred between the recently burnt high and low severity forests. For the longer-unburnt forests there were no systematic differences evident in canopy cover with time since fire or fire severity. The fuel moisture was higher and fuels only available to burn on one day in the forest recently burnt by high severity fire (in association with a dense canopy). In contrast, fuels were drier and available to burn on 238days in the forest recently burnt by low severity fire (in association with a sparser canopy). For the longer-unburnt forests (33 or more years since fire) there were no clear trends between fuel moisture and time since fire and fire severity suggesting that fires do not have a lasting impact on fuel moisture within these wet forests. Overall, this study shows that wildfires have immediate impacts on fuel moisture in Mountain Ash forests but as the time since fire increases, moisture appears to be more a function of canopy properties than fire history.

Allometric modelling of crown width for white spruce by fixed- and mixed-effects models

Crown width is an important predictor for tree growth, crown surface area, forest canopy cover, tree-crown profiles and wildlife habitat indices. This paper developed crown width models for white spruce (Picea glauca (Moench) Voss) in Alberta using allometric fixed and mixed models with varying degrees of model complexity. Diameter at breast height was the most important predictor and was used in the base model. Crown ratio, height-diameter ratio and two competition indices (CIs) were additional predictors added to the base model to form four expanded models. At each level of complexity, a fixed model and a mixed model were fitted. Improved fits were achieved for both model types as model complexity increased, and all mixed models provided much better fits than their fixed model counterparts. Population-averaged (PA) predictions by fixed models, and typical mean (TM), PA and plot-specific (PS) predictions by mixed models were compared on both model fitting and validation data. TM and PA predictions by each mixed model were almost identical, and they were less accurate than PA predictions by the fixed model counterpart, especially for simpler models. Much better PS predictions by mixed models were observed on both datasets. Although the distancedependent CI was slightly better than the distance-independent CI, both were not recommended due to their marginal contributions to crown width predictions.

Habitat Patch Use by Fishers in the Deciduous Forest-Dominated Landscape of the Central Appalachian Mountains, USA

Abstract Fishers (Pekania pennanti) are often associated with the coniferous and mixed forests of the northern United States and central Canada, and their ecology has been studied extensively in portions of their distributional range. Recently, natural range expansion and reintroductions have led to recolonization by fishers to portions of the central Appalachian Mountains, where deciduous forest is the dominant vegetation type. We used noninvasive hair-snare surveys and microsatellite genetic analysis to detect fishers in the central Appalachian Mountains of Pennsylvania. We used these detections within an occupancy modeling framework to explore habitat patch use by fishers and the forest characteristics and land use features that influenced it. We found that the likelihood of patch use by fishers was related to forests with higher proportions of low-density residential areas. Our results also suggested that lower road densities might be related to higher likelihood of fisher patch use. Fishers in Pennsylvania tolerated some forms of land development. Patch use was not driven by forest type or canopy cover, at least within our deciduous forest-dominated study areas. Future research identifying threshold values at which forest cover and land development affect patch use by fishers in the central Appalachian Mountains will better inform management decisions with respect to sites for future reintroduction of fishers.

Assessing the performance of aerial image point cloud and spectral metrics in predicting boreal forest canopy cover

Canopy cover (CC) is a variable used to describe the status of forests and forested habitats, but also the variable used primarily to define what counts as a forest. The estimation of CC has relied heavily on remote sensing with past studies focusing on satellite imagery as well as Airborne Laser Scanning (ALS) using light detection and ranging (lidar). Of these, ALS has been proven highly accurate, because the fraction of pulses penetrating the canopy represents a direct measurement of canopy gap percentage. However, the methods of photogrammetry can be applied to produce point clouds fairly similar to airborne lidar data from aerial images. Currently there is little information about how well such point clouds measure canopy density and gaps.The aim of this study was to assess the suitability of aerial image point clouds for CC estimation and compare the results with those obtained using spectral data from aerial images and Landsat 5. First, we modeled CC for n=1149 lidar plots using field-measured CCs and lidar data. Next, this data was split into five subsets in north-south direction (y-coordinate). Finally, four CC models (AerialSpectral, AerialPointcloud, AerialCombi (spectral+pointcloud) and Landsat) were created and they were used to predict new CC values to the lidar plots, subset by subset, using five-fold cross validation.The Landsat and AerialSpectral models performed with RMSEs of 13.8% and 12.4%, respectively. AerialPointcloud model reached an RMSE of 10.3%, which was further improved by the inclusion of spectral data; RMSE of the AerialCombi model was 9.3%. We noticed that the aerial image point clouds managed to describe only the outermost layer of the canopy and missed the details in lower canopy, which was resulted in weak characterization of the total CC variation, especially in the tails of the data.

Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index

We present the first comparison of Sentinel-2A (S2) MSI (Multi-Spectral Instrument) and Landsat 8 (L8) OLI (Operational Land Imager) data in the retrieval of forest canopy cover (CC), effective canopy cover (ECC), and leaf area index (LAI). We used S2 and L8 images obtained from Suonenjoki, Finland on 17 and 22 August 2015, respectively. A combination of airborne lidar data and field plots was used to calculate CC, ECC and LAI for a set of 746 systematically placed lidar plots. Generalized additive models were used to link these variables with various types of spectral indices. Our results indicated that instead of using the native image resolution, the model accuracies were better for 60 m lidar plots comprising of nine (S2) or four (L8) image pixels. The best-case absolute root mean square errors of prediction (RMSEPs) (relative RMSEPs shown in parentheses) obtained from ten-fold cross validation of multivariate models for S2 CC, ECC and LAI were 0.126 (24.0%), 0.100 (20.8%), and 0.596 (19.6%), respectively. For L8, the corresponding RMSEPs were 0.128 (24.5%), 0.108 (22.4%), and 0.614 (20.2%). The marginally better performance of S2 models may be related to the 705 nm red edge band, which frequently occurred among the selected predictors. When testing indices that used bands available for both sensors, there were no systematic differences between S2 and L8.

Uneven-aged silviculture can reduce negative effects of forest management on beetles

Decline in biodiversity have increased the interest in alternative forest management approaches. Uneven-aged silviculture has been proposed as a mean to maintain continuity of forest canopy cover, mimic small-scale disturbances and provide a stratified forest structure similar to that of old-growth forests and therefore better maintain species associated with unmanaged forest. We used a large-scale chronosequence study spanning 50years to study beetle diversity in uneven-aged silviculture compared with both short-term impacts and the longer-term legacy of even-aged silviculture. We compared: (1) even-aged recently clear-felled stand, (2) even-aged recently thinned stand, (3) uneven-aged stands subjected to selective felling with (4) uneven-aged reference stands to evaluate whether abundance, species richness and composition of beetles (Coleoptera) were affected differently by even-aged than by uneven-aged management. We collected 15,147 beetles from 461 species using flight interception traps in 30 stands. Beetle composition was maintained in uneven-aged managed stands; composition did not differ from unmanaged reference stands, the exception being cambium consumers. Both even-aged silviculture treatments (clear-felling and thinning) had different beetle composition compared to the reference stands, indicating that assemblages had yet to recover even 50years into the rotation. However, beetle composition did not differ between uneven-aged managed and thinned stands. The result supports our prediction that uneven-aged silviculture better maintains beetles assemblages associated with semi-natural mature forest than even-aged silviculture. The greater temporal continuity in selectively felled stands could benefit species dependent of mature or old growth forest since some of the needed habitat qualities are continuously available. Uneven-aged silviculture could therefore serve as an important tool for landscape planning to benefit biodiversity and thus help fulfil environmental commitments. However, uneven-aged silviculture may still alter the forest and should therefore be viewed as an alternative to even-aged silviculture, rather than to set-asides.

Interactive effects of climate and forest canopy cover on Goshawk productivity

In the current scenario of human-induced environmental changes, boreal forest biodiversity appears to be threatened by both deforestation for timber production and climate change. A northern species which has experienced a decline due to ongoing habitat degradation in Finland is the Northern Goshawk (Accipiter gentilis), a forest-dwelling avian predator that inhabits mostly mature coniferous forests. In order to uncover possible interactive effects of climate and landscape structure on this species, we tested whether temperature and precipitation affect Goshawk reproductive performance differentially in closed (i.e. forest) or open (mainly forest–farmland mosaics) habitats, accounting for land cover at different distances from nests (250, 500, 1000 and 2500 m). Long-term data on brood size and individual chick weight were used to describe breeding success over an 18 year period. Brood size was negatively associated with March and May temperature and positively with June temperature, but no trends related to landscape structure were identified. Chick body condition was negatively affected by high forest cover closest to the nest (250 m), but negative effects of June precipitation on this variable proved to be significantly greater in open than in closed habitats, with results consistent at different scales, i.e. precipitation had greater negative effects on chick body condition in open than in closed landscapes. Precipitation is forecast to increase in the region as a result of climate change, hence forest cover could exert a positive role in mitigating adverse effects of unfavorable climatic conditions. Outcomes of this study may be used to inform sustainable timber harvest management strategies.

Thirty Years of Change in Subalpine Forest Cover from Landsat Image Analysis in the Sierra Nevada Mountains of California

Landsat imagery was analyzed to understand changes in subalpine forest stands since the mid-1980s in the Sierra-Nevada region of California. At locations where long-term plot measurements have shown that stands are becoming denser in the number of small tree stems (compared to the early 1930s), the 30-year analysis of Landsat greenness index (NDVI) indicated that no consistent increases in canopy leaf cover have occurred at these same locations since the mid-1980s. Interannual variations in stand NDVI closely followed snow accumulation amounts recorded at nearby stations. In contrast, at eastern Sierra whitebark pine stand locations where it has been observed that widespread tree mortality has occurred, decreasing NDVI trends over the past 5-10 years were consistent with rapid loss of forest canopy cover. Landsat imagery was further analyzed to understand patterns of post-wildfire vegetation recovery, focusing on high burn severity (HBS) patches within burned areas dating from the late 1940s. Analysis of landscape metrics showed that the percentage of total HBS area comprised by the largest patch of recovered woody cover was relatively small in all fires that occurred since 1995, but increased rapidly with time since fire. Patch complexity of recovered woody cover decreased notably after more than 50 years of regrowth, but was not readily associated with time for fires that occurred since the mid 1990s. The aggregation level of patches with recovery of woody cover increased steadily with time since fire. The study approach using satellite remote sensing can be expanded to assess the consequences of stand-replacing wildfires in all forests of the region.

Quantifying insect-related forest mortality with the remote sensing of snow

Greenhouse gas emissions have altered global climate significantly, increasing the frequency of drought, fire, and insect- and pathogen-related mortality in forests across the western United States. The accuracy of satellite-based estimates of canopy change has been limited by difficulties associated with discriminating overstory canopy from understory vegetation. To overcome this issue, we developed a method to quantify forest canopy cover using winter-season fractional snow covered area (FSCA) data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) snow covered area and grain size (MODSCAG) algorithm. The method utilizes time series of FSCA data to identify images with continuous ground snow coverage and a snow-free overstory, effectively masking out the influence of understory vegetation. Using this method, we determined that MODSCAG-retrieved viewable gap fraction (VGF; i.e. fraction of pixel sub-canopy viewable area) was significantly correlated with an independent product of yearly crown mortality caused by mountain pine beetles derived from Landsat imagery at 25 high-mortality sites in northern Colorado (r−=0.96±0.03,p<0.03). Additionally, we determined the temporal lag between tree mortality and needlefall, showing that needlefall occurred an average of 2.6±1.2years after year of attack. The canopy change detection method described herein is the first to utilize snow cover to mask understory impacts on overstory detection. The method can be applied anywhere in the seasonal snow zone and therefore has wide applicability given that 30% of the global land surface is seasonally snow covered. In this regard, the approach addresses significant limitations of previously published methods of canopy change detection and has broad implications with regard to understanding forest mortality and the representation of disturbance within hydrologic, land surface, and climate models.

An UV-sensitive anuran species as an indicator of environmental quality of the Southern Atlantic Rainforest

The Southern Atlantic rainforest is continuously suffering from wood extraction activity, which results in the increase of clearings within the forest. Although the direct impacts of deforestation on landscape are already well described, there is an absence of studies focused on the evaluation of its indirect effects, such as the increase of solar UV radiation levels inside forest environment and its consequences for forest specialist anuran species. The results presented in this work clearly show that the threatened tree frog species Hypsiboas curupi presents severe traits of sensitivity to UV wavelengths of sunlight, making it a vulnerable species to this environmental stressor, as well as a biological indicator of the quality of forest canopy coverage. In addition, the measurement of solar UVB and UVA radiation incidence upon H. curupi breeding site and the analyses of a 20-year dataset of satellite images regarding the management of canopy coverage indicate that the photoprotection provided by trees of the Southern Atlantic rainforest is critical for the conservation of this forest specialist anuran species. Therefore, this work demonstrates that the deforestation process enhances the exposure of H. curupi embryos to solar UVB and UVA radiation, negatively affecting their embryonic development, inducing mortality and population decline.

Species’ habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data?

Spatial resolution of environmental data may influence the results of habitat selection models. As high-resolution data are usually expensive, an assessment of their contribution to the reliability of habitat models is of interest for both researchers and managers. We evaluated how vegetation cover datasets of different spatial resolutions influence the inferences and predictive power of multi-scale habitat selection models for the endangered brown bear populations in the Cantabrian Range (NW Spain). We quantified the relative performance of three types of datasets: (i) coarse resolution data from Corine Land Cover (minimum mapping unit of 25ha), (ii) medium resolution data from the Forest Map of Spain (minimum mapping unit of 2.25ha and information on forest canopy cover and tree species present in each polygon), and (iii) high-resolution Lidar data (about 0.5 points/m2) providing a much finer information on forest canopy cover and height. Despite all the models performed well (AUC>0.80), the predictive ability of multi-scale models significantly increased with spatial resolution, particularly when other predictors of habitat suitability (e.g. human pressure) were not used to indirectly filter out areas with a more degraded vegetation cover. The addition of fine grain information on forest structure (LiDAR) led to a better understanding of landscape use and a more accurate spatial representation of habitat suitability, even for a species with large spatial requirements as the brown bear, which will result in the development of more effective measures to assist endangered species conservation.

Joint effects of resources and amphibians on pond ecosystems.

Primary production can be controlled through bottom-up (e.g., resources) or top-down (e.g., predators) constraints. Two key bottom-up resources in small aquatic systems are light and nutrients, and forest canopy cover heavily influences these factors, whereas amphibian and invertebrate colonizers exert top-down pressure as grazers and predators. We designed our experiment to specifically manipulate two different top-down and bottom-up factors. We manipulated resources by altering light (low/high) and nutrient (low/high) availability; omnivores with the presence/absence of southern leopard frog tadpoles (Lithobates sphenocephalus); and predators with the presence/absence of spotted salamander larvae (Ambystoma maculatum) in a full-factorial experiment conducted over 14weeks. We observed that both bottom-up and top-down effects were important in predicting lower trophic level biomass. We found a significant top-down effect of salamanders on Daphnia, but tadpoles had the strongest overall effect on the food web, influencing phytoplankton (+), periphyton (-), and chironomids (-). None of our models were good predictors of phytoplankton biomass, but both shading and nutrient availability relatively equally boosted periphyton biomass. We also found large temporal differences in food-web dynamics. Our results underscore the need for more information into how ecosystem functioning could be altered by land use, amphibian extirpation, and climate change.

The accuracy of large-area forest canopy cover estimation using Landsat in boreal region

Large area prediction of continuous field of tree cover i.e., canopy cover (CC) using Earth observation data is of high interest in practical forestry, ecology, and climate change mitigation activities. We report the accuracy of using Landsat images for CC prediction in boreal forests validated with field reference plots (N=250) covering large variation in latitude, forest structure, species composition, and site type. We tested two statistical models suitable for estimating CC: the beta regression (BetaReg) and random forest (RanFor). Landsat-based predictors utilized include individual bands, spectral vegetation indices (SVI), and Tasseled cap (Tass) features. Additionally, we tested an alternative model based on spectral mixture analysis (SMA). Finally, we carried out a first validation in boreal forests of the recently published Landsat Tree Cover Continuous (TCC) global product.Results showed simple BetaReg with red band reflectance provided the highest prediction accuracy (leave-site-out RMSECV 13.7%; R2CV 0.59; biasCV 0.5%). Spectral transformations into SVI and Tass did not improve accuracy. Including additional predictors did not significantly improve accuracy either. Nonlinear model RanFor did not outperform BetaReg. The alternative SMA model did not outperform the empirical models. However, empirical models cannot resolve the underestimation of high cover and overestimation of low cover. SMA prediction errors appeared less dependent on forest structure, while there seemed to be a potential for improvement by accounting for endmember variability of different tree species. Finally, using temporally concurrent observations, we showed the reasonably good accuracy of Landsat TCC product in boreal forests (RMSE 13.0%; R2 0.53; bias −2.1%), however with a tendency to underestimate high cover.

Extending Airborne Lidar-Derived Estimates of Forest Canopy Cover and Height Over Large Areas Using kNN With Landsat Time Series Data

Airborne light detection and ranging (lidar) data provide an accurate and consistent means to obtain reliable forest canopy cover (CC) and height measurements, which are important in determining forest stand structure, volume, and biomass. Extending CC and height measurements over larger areas by integration with satellite imagery increases the value of airborne lidar data. A typical approach has been to use multiple regression, machine-learning, or regression tree methods to determine relationships between the forest structure variables measured in the lidar data and available single-date or multitemporal Landsat sensor reflectance data, and if the relationships are strong enough, to extend those variables over much larger areas than is typically covered by lidar. Such methods can be difficult to apply because of the complexity of the extending models and algorithms, and the long processing times, which may become prohibitive. One machine-learning approach, which uses the k-nearest neighbor (kNN) algorithm, is presented here in a British Columbia, Canada forest environment. Our goal was to simplify the estimation of lidar-derived forest structure variables with available Landsat time series data and compare the results of the kNN model to the traditional multiple regression results, and to those obtained with more complex and computationally demanding random forest (RF) methods. We develop and test the kNN model with airborne lidar-derived estimates of forest CC and height in 1846 relatively young and mature forests. The best kNN model produced estimates of airborne lidar-derived CC in validation sample ( ${\text n} = 1132$ ) of mature forest with three Landsat time series variables with an ${R}^{2} = 0.74$ and RMSE of approximately 10%. This result is comparable to the results obtained in earlier work using more complex machine-learning approaches (in approximately 1/10 the time). Younger (i.e., recently disturbed) forest CC estimation was less successful in the kNN model because of the high degree of structural variability in these forests. Extending airborne lidar estimates of forest height to larger areas was also possible though, as expected, less successful than CC estimation using the kNN approach.

Multidecadal analysis of forest growth and albedo in boreal Finland

It is well known that forests serve as carbon sinks. However, the balancing effect of afforestation and increased forest density on global warming due to carbon storage may be lost by low albedo (thus high absorption) of the forests. In the last 30 years, there has been a steady increase in the growing stock of Finnish forests by nearly a quarter while the area of the forests has remained virtually unchanged. Such increase in forest density together with the availability of detailed forest inventories provided by the Multi-Source National Forest Inventory (MS-NFI) in high spatial resolution makes Finland an ideal candidate for exploring the effects of increased forest density on satellite derived estimates of bio-geochemical products e.g. albedo (directional-hemispherical reflectance, DHR), fraction of photosynthetically active radiation absorbed by canopies (fAPAR), leaf area index (LAI) and normalized difference vegetation index (NDVI) in both current and long-term perspective.In this study, we first used MODIS-based vegetation satellite products for Finnish forests to study their seasonal patterns and interrelations. Next, the peak growing season observations are linked to the MS-NFI database to yield the generic relationships between forest density and the satellite-derived vegetation indicators. Finally, long-term GIMMS3g datasets between 1982 and 2011 (2008 for DHR) are analyzed and interpreted using forest inventory data. The vegetation peak growing season NIR DHR and VIS DHR showed weak to moderate negative correlation with fAPAR, whereas there was no correlation between NIR DHR and fAPAR. Next, we show that the spectral albedos in the near-infrared region (NIR DHR) showed weak negative correlation with forest biomass, basal area or canopy cover whereas, as expected, the spectral albedo in the visible region (VIS DHR) correlated negatively with these measures of forest density. Interestingly, the increase in forest density (biomass per ha) of Finnish forests during the last 30 years was not accompanied by trends in the indicators of vegetation ‘greenness’ and photosynthetic productivity (fAPAR, LAI and NDVI) or in forest albedo (DHR). Even though there were small increases in both DHR, fAPAR, LAI and NDVI and from the start to the end of the study period (1982–2011), the pattern and magnitude of change in these variables did not follow the development of forest biomass in the different NFI inventories.

Using environmental features to model highway crossing behavior of Canada lynx in the Southern Rocky Mountains

Carnivores are particularly sensitive to reductions in population connectivity caused by human disturbance and habitat fragmentation. Permeability of transportation corridors to carnivore movements is central to species conservation given the large spatial extent of transportation networks and the high mobility of many carnivore species. We investigated the degree to which two-lane highways were permeable to movements of resident Canada lynx in the Southern Rocky Mountains based on highway crossings (n=593) documented with GPS telemetry. All lynx crossed highways when present in home ranges at an average rate of 0.6 crossings per day. Lynx mostly crossed highways during the night and early dawn when traffic volumes were low. Five of 13 lynx crossed highways less frequently than expected when compared to random expectation, but even these individuals crossed highways frequently in parts of their home range. We developed fine- and landscape-scale resource selection function (RSF) models with field and remotely sensed data, respectively. At the fine scale, lynx selected crossings with low distances to vegetative cover and higher tree basal area; we found no support that topography or road infrastructure affected lynx crossing. At the landscape scale, lynx crossed highways in areas with high forest canopy cover in drainages on primarily north-facing aspects. The predicted crossing probabilities generated from the landscape-scale RSF model across western Colorado, USA, were successful in identifying known lynx crossing sites as documented with independent snow-tracking and road-mortality data. We discuss effective mitigation based on model results.

Are rubber-based agroforestry systems effective in controlling rain splash erosion?

In order to evaluate the influence of different types of rubber-based agroforestry systems on soil erosion processes, rainfall and throughfall erosivity (splash erosion potential) were measured in an open field environment and under different vegetation types using sand-filled Tübingen splash cups. Our results indicate that the splash erosion potential under rubber monoculture was, on average, 3.12 times greater than those in the open environment. Splash erosion potential under agroforestry systems was higher than that of an open environment (ranging from 1.22 to 2.18 times greater), except for the rubber and tea system (0.87 times the open environment). However, in all but one system (the rubber and orange system), there was a significant reduction in splash erosion beneath multiple canopies compared to monoculture, especially for the rubber and tea system (0.27 times the monoculture) where it had high sub-canopy closure and low sub-canopy height. The erosion potential under the forest is closely related to the forest structure, especially height and canopy cover. These results indicate that low canopy height with high sub-canopy coverage is the major control on the amount of splash erosion, regardless of how the splash potential is increased by the canopy above. These results highlight the importance of selecting low near-surface intercrops for constructing rubber-based agroforestry systems. This also accentuates the importance of an intact litter layer in rubber plantations to protect the soil against splash erosion. Disturbance of these forests by latex tapping activities, herbicide application and removal of the litter layer during fertilization, for example, will also lead to higher actual splash erosion rates inside the forests in comparison with the open environment.

A framework for adapting urban forests to climate change

Planting urban trees and expanding urban forest canopy cover are often considered key strategies for reducing climate change impacts in urban areas. However, urban trees and forests can also be vulnerable to climate change through shifts in tree habitat suitability, changes in pests and diseases, and changes in extreme weather events. We developed a three-step framework for urban forest vulnerability assessment and adaptation that scales from regional assessment to local on-the-ground action. We piloted this framework in the Chicago region in 10 locations representing an urban-exurban gradient across a range of socioeconomic capacities. The majority of trees across a seven-county region had low to moderate vulnerability, but many of the least vulnerable species were nonnative invasive species. Urban forests in the 10 pilot locations ranged in vulnerability largely due to differences in economic and organizational adaptive capacity. Adaptation actions selected in these locations tended to focus on increased biodiversity and restoration of natural disturbance regimes. However, adaptation actions in more developed sites also included incorporating new species or cultivars. Lessons learned from the pilot area can be used to inform future efforts in other urban areas.

Estimates of selective logging impacts in tropical forest canopy cover using RapidEye imagery and field data

Abstract: Selective logging is one of the leading causes of forest degradation in the Brazilian Amazon region. The Brazilian Federal government has adopted a forest concession policy as a strategy to mitigate impacts of selective logging and regulate operations of the tropical timber industry in Brazil. This study used fractional forest coverage derived from satellite imagery and field data to assess forest degradation in two selectively logged study sites within the Jamari National Forest, a protected area located in the western Brazilian state of Rondonia. Initially, we estimated the fractional coverage from vegetation indices using RapidEye imagery and compared to gap fraction data derived from hemispherical photos acquired in the field. Subsequently, we estimated the impacts of different types of selective logging activities (log decks, primary and secondary roads, tree fall gaps, and skid trails) on forest cover using the fractional coverage dataset. The NDVI showed the highest R2 (0.56), indicating that 56% of the sample variation in fractional coverage derived from ground measurements can be explained by fractional coverage derived from the NDVI model. Our results also showed that the intensity of canopy impacts may vary according to the selective logging activity, ranging from skid trails to log decks which had the lightest and the heaviest canopy impacts, respectively.