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Comparing forest and harvested wood product carbon (C) stocks and accumulation among forest management treatments commonly applied in managed forests is needed to inform planning and policy decisions for C objectives. Therefore, pre- and post-harvest C stocks were quantified and C accumulation was projected over a 31-year period (to ∼2050) among forest management treatments that were applied on a subset (n = 3) of the Maine Adaptive Silviculture Network installations in northern Maine, USA. These installations included mature, second-growth forests composed of northern hardwood and hardwood-dominated mixedwood stands. Before treatments were initiated, average aboveground live tree C stocks ranged from 67.1 to 99.7 Mg ha−1. For the aboveground portions of live trees, dead wood and harvested wood products, the projected average annual net change in C (AAC) was 0.232 ± 1.164 Mg ha−1 year−1 (mean ± standard deviation). Models of projected AAC indicated that less biomass removal during harvests and greater representation of tree species with low tolerance of shade were associated with positive AAC values. The results emphasize the importance of leveraging multiple harvesting strategies to achieve C objectives, including consideration of forest reserves and using targeted yet operationally feasible silvicultural treatments that promote forest resilience relative to climate change. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate change is impacting forest ecosystems, which support key ecosystem services and the general well-being of natural resource-dependent communities in Northeastern, USA. Understanding the determinants of climate change risk perceptions among forest resource stakeholders is critical to eliciting broad support for adaptation. We examined social-psychological drivers of climate change risk perceptions using hierarchical regression based on an online survey of 211 forest stakeholders, representing a wide range of subsectors, in Maine, USA. Using the climate change risk perceptions model (CCRPM), we explained 70% of the variance in risk perception. Political orientation, belief in climate change, social norms, affect, and experience with weather-related impacts were all significant predictors of perceived risk. Mediation results demonstrate that experience with weather-related impacts influences risk perceptions indirectly via attribution and holistic affect. This study advances our understanding of the social-psychological determinants of climate change risk perceptions, with implications for communication and outreach strategies. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate change will have a significant impact on the forest industry and will require strategies that promote sustainable forest management. Understanding perceptions of climate change impacts is critical to supporting the use of adaptation strategies, informing future research, and supporting decision-making. We describe a multi-method approach using nominal group technique and semi-structured interviews to identify and understand experts' concerns in regards to future climate change impacts on the forest industry in Maine, USA. A review of the existing scientific literature helped inform the development of the interview and nominal group techniques. Experts prioritized the greatest and most likely climate change impacts on the forest industry as: forest health threats imposed by insects and pathogens, extreme precipitation events, shifts in forest composition, invasive species, and changes in forest productivity. Interviews and current scientific understanding largely support the prioritization of the impacts, but also elucidate uncertainties in regards to climate change perceptions (e.g., timing and magnitude of future impacts), highlight the need for continued research that specifically addresses how climate change will affect the forest industry, and demonstrate that climate change presents not only a perceived threat but also an opportunity in the form of increased forest productivity and economic growth. [ABSTRACT FROM AUTHOR]

We compared long-term C sequestration in the pools of aboveground portions of live trees, dead wood, and harvested wood products among highly contrasting forest management scenarios on a rotation (30-100 years) and 100-year basis. Average annual net change in C (AAC) and the cumulative sum of net changes in C were calculated using 65 years of data from permanent plots representing contrasting approaches to managing mixed-species stands dominated by shade-tolerant coniferous species on the Penobscot Experimental Forest in Maine, USA. Simulations of tree growth and mortality were used to estimate C pools to 100 years. On a rotation basis and for all pools combined, scenarios with selection cutting had greater AAC than those with shelterwood cutting followed by thinning or with diameter-limit cutting (p < 0.05). For combined pools, the cumulative sum of net changes in C for the unmanaged, selection, and guiding diameter-limit stands was positive for most of the study period. Our results suggest that strategies that maintain overstory stocking levels necessary to regenerate desired species and promote the development of sawlog-sized trees can enhance long-term C sequestration in mixed-species, naturally regenerated northern temperate forests. [ABSTRACT FROM AUTHOR]

This research compares the capabilities of various Sentinel-2-derived spectral vegetation indices (SVIs) in particular red-edge SVIs to detect and classify spruce budworm (Choristoneura fumiferana) (SBW) defoliation using Support Vector Machine (SVM) and Random Forest (RF) models. The results showed the superiority of RF in model building for defoliation detection and classification into three classes (nil, light, and moderate) with overall errors of 17% and 32%, respectively. The most important variables for the best model were Enhanced Vegetation Index 7 (EVI7), Modified Chlorophyll Absorption in Reflectance Index (MCARI), Inverted Red-Edge Chlorophyll Index (IRECI), Normalized Difference Infrared Index 11 (NDII11) and Modified Simple Ratio (MSR). Red-edge SVIs were more effective variables for light defoliation detection compared to traditional SVIs such as Normalized Difference Vegetation Index (NDVI) and EVI8. These findings can help improve current remote sensing-based SBW defoliation detection and monitoring. [ABSTRACT FROM AUTHOR]

To overcome existing knowledge gaps regarding altering harvest activities in the mixed species and heavily forested USA state of Maine, we used continuous forest inventory data to develop statistical models that predict harvest probability of occurrence and intensity. Among the three modeling approaches examined, the first one directly predicted stand-level basal area removal as a percentage of initial total stand basal area. The second approach first predicted stand-level harvest probability and then the stand-level basal area removal of the harvested plots only. The third approach used the stand-level harvest probability equation of the second approach, while subsequently predicting individual tree harvest probability for only the harvested plots. Among the most influential stand-level attributes were quadratic mean diameter, stand density, elevation, and ownership type, while the most influential tree-level attributes were diameter at breast height, basal area in larger trees, and species. Differences in prediction accuracy between modeling approaches were small with the third approach performing slightly better. Our findings suggest that harvesting in Maine might be less opportunistic and short-term driven than generally perceived. Overall, the analysis highlights the complex array of factors that influence harvesting patterns and provides a framework for better representing contrasting harvest behavior in future wood supply projections. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Brazilian Atlantic Forest is a biologically and structurally diverse biome which covers a large area; however, few tree-level biomass models exist for this region. The objective of this study was to evaluate alternative individual tree biomass models for the Atlantic Forest using contrasting prediction approaches, and evaluate their implications for stand-level biomass and carbon. The data comprised 106 destructively sampled trees (dbh; 5.4 - 68.5 cm) from the 16 most abundant species. Different approaches, namely species-specific, functional-trait based, generalized, and previously developed equations, as well as alternative model forms using differing variables including diameter at breast height (dbh), wood specific gravity (wsg), and height to crown base (hcb) were examined. Species-specific models were developed using both ordinary least squares and linear mixed effects with species as a random effect. The various approaches resulted in statistically different estimates of biomass and carbon at both the individual tree- and stand-levels. Compared to a generalized biomass equation for Pan-tropical forests, our results indicate differences of over 46% and 52% for biomass and carbon, respectively. Overall, if no species-specific model is available, we suggest the functional plant trait based on dbh and wsg. [ABSTRACT FROM AUTHOR]

Many communities rely on tourism spending, so it is important to understand any potential changes to tourist flows resulting from changing climate and weather patterns. However, tourists are not a homogenous group, as they have different motivations, values, and goals. Therefore, the purpose of this investigation is to better understand potentially varying perceptions and behavior of different tourist types, specifically in regards to their weather sensitivity, climate change concern, and behavioral intention for climate change mitigation. Tourists were randomly surveyed at 20 locations throughout the state of Maine in the United States (n = 704). Segmentation analysis on the activities tourists participated in yielded three segments of Maine tourists: non-nature-based tourists (50.6%), nature-based generalists (16.2%), and nature-based specialists (33.2%). Differences across segments were explored for perceptions of weather impacts, climate change concern, and mitigation intent. Additionally, weather sensitivity was analyzed based on type of overnight accommodations to better understand if this also had a role in differences. Non-nature-based tourists thought that weather variables were less influential during their travels in Maine than the other segments, while nature-based generalists perceived weather to have the highest influence. Additionally, nature-based specialists had the highest level of climate change belief, while nature-based generalists had the highest willingness to engage in climate change mitigation behavior. Results are useful to understand how segments of tourism demand may be altered with a changing climate, such as increased temperatures, precipitation, and storms, and what groups may be the most beneficial to target for marketing or educational efforts to reduce the impact of climate change. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

LiDAR has become a common means for providing area-based estimates of forest attributes. An important, yet understudied, factor influencing forest attribute estimation is the prediction cell size. In this analysis, the influence of 6 prediction cell sizes (2-, 4-, 10-, 25-, 50-, and 100-m squares) and 2 prediction methods (parametric vs. nonparametric) on LiDAR-derived, area-based, stand- and forest-level estimates of total volume at 2 separate locations in the Acadian Forest were evaluated and compared to field-based measurements. Statistically significant differences (p< 0.05) were observed between the field-based and some of the LiDAR-derived volumes. The stand-level percent differences between the various cell sizes ranged from < 0.1 to >38%, and were generally greatest at the 2 extreme cell sizes examined (2 m and 100 m). At the forest-level, all LiDAR-estimates were within the 95% confidence interval for the ground-based estimate, except for a few values from smaller cell sizes. There was also a general trend of smaller cell sizes producing lower values of volume, whereas larger cell sizes tended to give higher estimates. Overall, this study highlights the importance cell size influence on stand- and forest-level estimates and indicates that additional work is needed to better understand the trends identified in this analysis. [ABSTRACT FROM PUBLISHER]