Forest Gain Research Articles

Comparison of spatial modelling frameworks for the identification of future afforestation in New Zealand

In 2018, New Zealand announced an ambitious effort to plant one billion trees by 2028 as part of its climate change mitigation plan and Paris Agreement targets for 2030 and 2050. Afforestation will be promoted through a series of incentives, including payments for carbon sequestration linked to New Zealand’s Emission Trading Scheme. Given that afforestation will be pursued voluntarily, the identification of future afforestation areas is paramount to assist policy formulation and avoid unintended land-use outcomes. We identified spatially-explicit drivers of forest gain and the locations most likely to experience afforestation in the country using two distinct spatial modelling frameworks: logistic regressions and artificial neural networks (ANN). Five of the eight most significant drivers of forest gain that have influenced past afforestation patterns according to our logistic regressions (i.e., erosion potential, distance from exotic forests, woody grasslands, grassland productivity, and slope) were also ranked among the ten most influential drivers by the ANN model. These results indicate an overall agreement between the two modelling approaches, despite substantial methodological differences. In the absence of changes in current incentives for afforestation in the country, simulations suggest that the largest afforestation areas would be in the northeast region of the North Island. These findings can assist policymakers and landscape planners to achieve desirable land-use objectives for New Zealand.

Characterizing spatial structure of urban tree cover (UTC) and impervious surface cover (ISC) density using remotely sensed data in Osmaniye, Turkey

Urban trees provide a wide range of significant benefits, and their degradation can result in air pollution and floods, and can damage public health and decrease social welfare. The purpose of this research was to estimate the urban tree cover (UTC) and impervious surface cover (ISC) density and to evaluate how they influence forest gain and loss in Turkey. Accordingly, i-Tree Canopy random point sampling and remote sensing methods were applied using the most current Landsat 7 Enhanced Thematic Mapper Plus images of Osmaniye City. The results indicate that the majority (n = 217) of random points were overlapped with buildings on aerial photograph with a percent cover estimate of 34.1 ± 1.88%. Also, the second highest number of the randomly selected points (n = 166) overlapped with road cover at estimate of 26.1 ± 1.74%. Grass cover (n = 68), ISC (n = 59), and shrub cover (n = 52) percentages were estimated as 10.7 ± 1.22%, 9.26 ± 1.15%, and 8.16 ± 1.08%, respectively. UTC (n = 41) percentage was estimated as 6.44 ± 0.97% in the urban city center. The lowest percent cover of randomly selected points was parking lots (n = 34) estimated as 5.34 ± 0.89%. Also, the forest loss intensity was unevenly distributed and reflected areas with high population density, and forest cover loss was estimated at the highest level on the east side of the city center. The findings of this research suggest that urbanization around the city center of Osmaniye has altered the local vegetative cover due to deforestation activities to create areas for building construction and new developments.

Анализ динамики антропогенных изменений экосистем водосбора р. Кемь (бассейн Белого моря) с использованием данных Global Forest Change

The analysis of changes in the catchment vegetation cover as a result of industrial development of its territory, primarily deforestation, was carried out. For processing Landsat images, the spectral space modeling method was used, which provides higher reliability of the classification results compared to traditional methods. The most significant changes in the catchment occurred from the 1930s (the beginning of large-scale logging), until the end of the 1990s. Significantly, by more than 60 %, the area of old-growth forests decreased, their large fragments remained only in nature protected areas, as well as around large lakes and along large rivers, due to the presence of water protection zones. In the period from 2000 to 2018 due to deforestation, the area of productive forests decreased even more. Nevertheless, by 2019 the occurrence of secondary reforestation on significant areas (up to 70 %) is noted, which indicates a sufficiently high regeneration potential of the catchment ecosystems. To restore the spatial structure of the forest cover in the period before the start of the Landsat 5 operation, the method of tracking the trajectories of reforestation successions in the spectral space was used. A comparison with the results of the Global Forest Change 2000—2018 data (loss/gain) showed almost complete coincidence in cuttings (loss), but the method of reforestation trajectories showed many times higher results in forest gain, since it reveals forest regeneration at a much earlier stage. The developed methodology will be applied on the catchments of other tributaries of the White Sea, and the results will be used as input to an environmental-socio-economic cognitive model that predicts the state of ecosystems under climate changes and economic priorities.

Machine Learning and Initial Nursing Assessment-Based Triage System for Emergency Department.

ObjectivesThe aim of this study was to develop machine learning (ML) and initial nursing assessment (INA)-based emergency department (ED) triage to predict adverse clinical outcome.MethodsThe retrospective study included ED visits between January 2016 and December 2017 that resulted in either intensive care unit admission or emergency room death. We trained four classifiers using logistic regression and a deep learning model on INA and low dimensional (LD) INA, logistic regression on the Korea Triage and acuity scale (KTAS) and Sequential Related Organ Failure Assessment (SOFA). We varied the outcome ratio for external validation. Finally, variables of importance were identified using the random forest model's information gain. The four most influential variables were used for LD modeling for efficiency.ResultsA total of 86,304 patient visits were included, with an overall outcome rate of 3.5%. The area under the curve (AUC) values for the KTAS model were 76.8 (74.9–78.6) with logistic regression and 74.0 (72.1–75.9) for the SOFA model, while the AUC values of the INA model were 87.2 (85.9–88.6) and 87.6 (86.3–88.9) with logistic regression and deep learning, suggesting that the ML and INA-based triage system result more accurately predicted the outcomes. The AUC values for the LD model were 81.2 (79.4–82.9) and 80.7 (78.9–82.5) for logistic regression and deep learning, respectively.ConclusionsWe developed an ML and INA-based triage system for EDs. The novel system was able to predict clinical outcomes more accurately than existing triage systems, KTAS and SOFA.

Dynamics of Land Use and the Evolution of Agroforestry Practices in the Dja Biosphere Reserve (DBR) Southeast Cameroon

The study had as objective to investigate the land use/land cover change from 1980 to 2019 in the Dja Biosphere Reserve using GIS and remote sensing techniques and the evolution of agroforestry practices. Semi structured questionnaires were administered to 320 farmers who were randomly selected from 35 villages of the Dja Biosphere Reserve characterized by the presence of agroforestry systems. The land use map revealed six classes which were dense humid forest, perennial crop-based agroforestry practices, river, wetland, built up and bare areas. Between 1980 and 2008, dense humid forest lost -4.9% of its area to the benefits of perennial crop-based agroforestry practices, built up and bare land. Between 2008 and 2019, dense humid forest gain 1.77% of its area due to increase in perennial crop-based agroforestry practices and a subsequent increase in vegetation cover. Perennial crop-based agroforestry practices and built up increase progressively from 1980 to 2019. Farmers perceived hunting (36.3%), slash and burn agriculture (43.3%) and harvesting of tree-based products (20.3%) as the anthropogenic activities impacting the reserve negatively. The evolution of agroforestry practices were observed on the field, but the precise area under agroforestry practices in the study area need to be estimated. As a mitigation strategy to livelihood needs as well as the rehabilitation of degraded land, the conversion of pure cultivated agricultural land into agroforestry is a major opportunity.

Determining a Carbon Reference Level for a High-Forest-Low-Deforestation Country

Research Highlights: A transparent approach to developing a forest reference emissions level (FREL) adjusted to future local developments in Southern Cameroon is demonstrated. Background and Objectives: Countries with low historical deforestation can adjust their forest reference (emission) level (FREL/FRL) upwards for REDD+ to account for likely future developments. Many countries, however, find it difficult to establish a credible adjusted reference level. This article demonstrates the establishment of a FREL for southern Cameroon adjusted to societal megatrends of strong population—and economic growth combined with rapid urbanization. It demonstrates what can be done with available information and data, but most importantly outlines pathways to further improve the quality of future FREL/FRL’s in light of possibly accessing performance-based payments. Materials and Methods: The virtual FREL encompasses three main elements: Remotely sensed activity data; emission factors derived from the national forest inventory; and the adjustment of the reference level using a land use model of the agriculture sector. Sensitivity analysis is performed on all three elements using Monte Carlo methods. Results: Deforestation during the virtual reference period 2000–2015 is dominated by non-industrial agriculture (comprising both smallholders and local elites) and increases over time. The land use model projections are consistent with this trend, resulting in emissions that are on average 47% higher during the virtual performance period 2020–2030 than during the reference period 2000–2015. Monte Carlo analysis points to the adjustment term as the main driver of uncertainty in the FREL calculation. Conclusions: The available data is suitable for constructing a FREL for periodic reporting to the UNFCCC. Enhanced coherence of input data notably for activity data and adjustment is needed to apply for a performance-based payment scheme. Expanding the accounting framework to include forest degradation and forest gain are further priorities requiring future research.

Unbalanced species losses and gains lead to non‐linear trajectories as grasslands become forests

AbstractQuestionsRates of plant community shifts after environmental changes depend on how quickly affected species are gained and lost. Understanding how the balance between extinction and colonisation varies over time, and how it is influenced by local and landscape factors, is essential to understanding overall change trajectories. Investigating change requires data at several time steps over sufficient periods, and the paucity of such data represents an important knowledge gap. We ask: (a) how variation over time in the rates of species’ extinction and species’ colonization controls the trajectory of biodiversity change in abandoned semi‐natural grasslands? and (b) can landscape composition and habitat history modify change trajectories by acting independently on groups within plant communities?LocationSweden, Stockholm Archipelago.MethodsWe use data on plant composition, management history and landscape context in former grasslands, abandoned at different points since 1901, in a space‐for‐time analysis, comparing rates of grassland species loss and forest species establishment and investigating resulting biodiversity trajectories.ResultsGrassland species declined steeply in recently abandoned habitats before levelling off, while the accumulation of forest species was linear, with no plateau reached even at the longest time since abandonment. Hence, we observed a trough in biodiversity, with an initial decline in overall species richness followed by a partial recovery. Only forest species gain was enhanced by nearby habitat availability.ConclusionsInformation on community compositional changes over short time periods may be misleading about the extent and even direction of ongoing biodiversity gains and losses. Moreover, the non‐linear changes observed suggest thresholds in time, after which succession to the forest community accelerates and the ability to manage a return to the grassland community diminishes. Accounting for the combined influence of landscape composition and history is key to fully understanding community shifts over time.

Spatio–Temporal Changes of Forests in Northeast China: Insights from Landsat Images and Geospatial Analysis

Dramatic changes of forests have strong influence on regional and global carbon cycles, biodiversity, and ecosystem services. Understanding dynamics of forests from local to global scale is crucial for policymaking and sustainable development. In this study, we developed an updating and object-based image analysis method to map forests in Northeast China using Landsat images from 1990 to 2015. The spatio–temporal patterns of forests were quantified based on resultant maps and geospatial analysis. Results showed that the percentage of forested area occupying the entire northeast China was more than 40%, about 94% of initial forest cover remained unchanged (49.37 × 104 km2) over the course of 25 years. A small net forest loss (1051 km2) was observed during 1990–2015. High forest gain (10,315 km2) and forest loss (9923 km2) both occurred from 2010 to 2015. At the provincial level, Heilongjiang demonstrated the highest rate of deforestation, with a net loss of 1802 km2 (0.89%). Forest changes along elevation, slope, and distance from settlements and roads were also investigated. Over 90% of forest changes occurred in plains and low mountain areas within the elevation of 200–1000 m and slope under 15°. The most dramatic forest changes can be found within the distance of 2000 m from settlements and roads. The reclamation of sloping land, construction of settlements and roads, and possible smallholder clearing contributed more to forest loss, while ecological projects and related government policies play an important role on afforestation and reforestation. These results can provide useful spatial information for further research on the driving forces and consequences of forest changes, which have critical implications for scientific conservation and management of forests.

Extensive land cover change across Arctic-Boreal Northwestern North America from disturbance and climate forcing.

A multitude of disturbance agents, such as wildfires, land use, and climate-driven expansion of woody shrubs, is transforming the distribution of plant functional types across Arctic-Boreal ecosystems, which has significant implications for interactions and feedbacks between terrestrial ecosystems and climate in the northern high-latitude. However, because the spatial resolution of existing land cover datasets is too coarse, large-scale land cover changes in the Arctic-Boreal region (ABR) have been poorly characterized. Here, we use 31years (1984-2014) of moderate spatial resolution (30m) satellite imagery over a region spanning 4.7×106 km2 in Alaska and northwestern Canada to characterize regional-scale ABR land cover changes. We find that 13.6±1.3% of the domain has changed, primarily via two major modes of transformation: (a) simultaneous disturbance-driven decreases in Evergreen Forest area (-14.7±3.0% relative to 1984) and increases in Deciduous Forest area (+14.8±5.2%) in the Boreal biome; and (b) climate-driven expansion of Herbaceous and Shrub vegetation (+7.4±2.0%) in the Arctic biome. By using time series of 30m imagery, we characterize dynamics in forest and shrub cover occurring at relatively short spatial scales (hundreds of meters) due to fires, harvest, and climate-induced growth that are not observable in coarse spatial resolution (e.g., 500m or greater pixel size) imagery. Wildfires caused most of Evergreen Forest Loss and Evergreen Forest Gain and substantial areas of Deciduous Forest Gain. Extensive shifts in the distribution of plant functional types at multiple spatial scales are consistent with observations of increased atmospheric CO2 seasonality and ecosystem productivity at northern high-latitudes and signal continental-scale shifts in the structure and function of northern high-latitude ecosystems in response to climate change.

Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico

MODIS-based NDVI time series (2000–2016) was applied to monitor sub-annual forest disturbance in the Mexican state of Michoacán, with an algorithm that decomposes the time-series data into a harmonic function and a trend. To detect change, a moving sum of residuals between the observed and predicted NDVI values was compared with that from the reference period. Magnitude of change was computed by subtracting the predicted NDVI from the observed one. By comparing the detected changes with reference data through visual interpretation, a threshold of |0.05| was established as the magnitude of change for forest disturbance detection. The method detected more forest gain than loss for 2013–2016, a result which is supported by recent findings from the national forest inventory. Forest loss decreases yearly for 2013–2016, and forest gain peaks at 2014 and 2015. We verified the findings with data from the global forest cover change project.

Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017

The data, information and knowledge on the tropical forest area and its dynamics in the Brazilian Amazon remain contentious. We use time-series satellite images to quantify annual forest area, loss and gain in the Brazilian Amazon during 2000–2017. We find that forest area was ~15% higher than the estimate by the official Brazilian forest dataset (PRODES), but annual forest-loss rates were twice the PRODES estimates (~0.027 × 106 km2 yr–1 during 2001–2016). Forest-loss rates increased again after 2013. The El Nino and drought year (2015/2016) drove large forest area loss. The cumulative forest-loss area within the protected areas (which include ~50% of forests in the region) was ~11% of the total forest-loss area, which highlights the roles of protected areas in forest conservation. A new estimation of forest cover and loss finds more area but also double the loss rates in recent years, relative to previous estimates. Deforestation increased after a low peak in 2013.

Land‐based and climatic stressors of mangrove cover change in the Auckland Region, New Zealand

Abstract Changes in mangrove forest cover (increases and decreases) have been observed globally as a result of the influence of environmental changes and human impacts. Although mangrove forest increases are globally uncommon, in New Zealand mangroves are increasing in distribution at the expense of other intertidal estuarine habitats. This study quantified the change in mangrove area over a period of 74 years (1940–2014), and investigated the influence of land‐based (e.g. land use conversion in the catchments), climatic, and oceanic factors on the change in temperate mangrove forest cover in the Auckland Region of New Zealand over that period. An improved mapping approach using aerial photographs and Landsat images was applied to quantify changes in tall and dwarf mangrove cover, and to identify the mechanism of change (expansion, gap filling, and loss), across 38 estuaries in the Auckland Region, New Zealand. Mangrove area increased from 2313 ha in 1940 to 10 483 ha in 2014 (on average 3.2% yr−1), with higher rates in small estuaries (<1000 ha) than in large estuaries (>1000 ha). The area of the intertidal mudflat habitat, i.e. potential area for mangrove colonization, has doubled from 14 193 ha in 1974 to 28 764 ha in 2014. The increase in total mangrove area was explained primarily by gap filling followed by expansion. The proportion of expansion‐related increase was predominantly seaward (82%) but mangroves also expanded upstream (16%). Boosted regression tree analysis revealed that catchment forest cover, sea level, and mean annual air temperature explained around 85% of the variation in mangrove area. Periods of catchment forest clearing coincided with gains in mangrove forest area. Dwarf mangroves were more sensitive to changes in sea level, in particular over the past 25 years, when the annual sea‐level rise exceeded the sediment accumulation rates. Catchment land use, especially the clearing of forests, associated with increased sediment supply into estuaries is the strongest driver of mangrove forest gain. Thus, land‐use management is a necessary aspect of the management of New Zealand estuaries to conserve the diversity of estuarine habitats (including mangroves) and the ecosystem services that each habitat provides.

Explaining heterogeneous afforestation outcomes: How community officials and households mediate tree cover change in China

Afforestation efforts are proliferating as states promote tree-planting to accomplish sustainable rural development and combat forest loss and climate change. China’s Returning Farmland to Forest Program (RFFP), one of the world’s most ambitious afforestation programs, has often been presented as a great success. However, research on the RFFP shows substantial unexplained heterogeneity. Case studies reveal divergent outcomes, but the processes behind local variation are poorly understood. This study examines mechanisms differentiating tree cover change across 12 communities. We join narrative histories with formal case comparison using fuzzy-set qualitative comparative analysis (fsQCA), which applies Boolean analysis to identify combinatorial patterns across cases, to explain variation in remotely sensed land cover change. These analyses identify distinct pathways to vegetation gain and loss linked to livelihood patterns and environmental conditions. The RFFP’s contribution to forest gain depends on how local governance and environmental conditions enable different land use patterns. In particular, whether community officials act in responsive, self-serving, or perfunctory ways shapes options available to other households. Responsive governance does not have a consistent relationship with forest gain; outcomes depend on the particular activities officials enable. Impacts on land cover change of labor outmigration, livestock husbandry, and cash crop expansion are likewise contingent. To make afforestation interventions effective and sustainable, policymakers must be mindful of crosscutting factors that may impede or facilitate forest establishment.

Spatio‐temporal patterns of tree growth as related to carbon isotope fractionation in European forests under changing climate

AbstractAimThe aim was to decipher Europe‐wide spatio‐temporal patterns of forest growth dynamics and their associations with carbon isotope fractionation processes inferred from tree rings as modulated by climate warming.LocationEurope and North Africa (30‒70° N, 10° W‒35° E).Time period1901‒2003.Major taxa studiedTemperate and Euro‐Siberian trees.MethodsWe characterize changes in the relationship between tree growth and carbon isotope fractionation over the 20th century using a European network consisting of 20 site chronologies. Using indexed tree‐ring widths (TRWi), we assess shifts in the temporal coherence of radial growth across sites (synchrony) for five forest ecosystems (Atlantic, boreal, cold continental, Mediterranean and temperate). We also examine whether TRWi shows variable coupling with leaf‐level gas exchange, inferred from indexed carbon isotope discrimination of tree‐ring cellulose (Δ13Ci).ResultsWe find spatial autocorrelation for TRWi and Δ13Ci extending over a maximum of 1,000 km among forest stands. However, growth synchrony is not uniform across Europe, but increases along a latitudinal gradient concurrent with decreasing temperature and evapotranspiration. Latitudinal relationships between TRWi and Δ13Ci (changing from negative to positive southwards) point to drought impairing carbon uptake via stomatal regulation for water saving occurring at forests below 60° N in continental Europe. An increase in forest growth synchrony over the 20th century together with increasingly positive relationships between TRWi and Δ13Ci indicate intensifying impacts of drought on tree performance. These effects are noticeable in drought‐prone biomes (Mediterranean, temperate and cold continental).Main conclusionsAt the turn of this century, convergence in growth synchrony across European forest ecosystems is coupled with coordinated warming‐induced effects of drought on leaf physiology and tree growth spreading northwards. Such a tendency towards exacerbated moisture‐sensitive growth and physiology could override positive effects of enhanced leaf intercellular CO2 concentrations, possibly resulting in Europe‐wide declines of forest carbon gain in the coming decades.

Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition

While many developed countries are increasing their forest cover, deforestation is still rife in the tropics and subtropics. With international trade in forest-risk commodities on the rise, it is becoming increasingly important to consider between-country trade linkages in assessing the drivers of—and possible connections between—forest loss and gain across countries. Previous studies have shown that countries that have undergone a forest transition (and are now increasing their forest cover) tend to displace land use outside their borders. However, lack of comprehensive data on deforestation drivers imply that it has not been possible to ascertain whether this has accelerated forest loss in sourcing countries. To remedy this, we present a land-balance model that quantifies deforestation embodied in production of agricultural and forestry commodities at country level across the tropics and subtropics, subsequently tracing embodied deforestation to countries of apparent consumption using a physical, country-to-country trade model. We find that in the period 2005–2013, 62% (5.5 Mha yr−1) of forest loss could be attributed to expanding commercial cropland, pastures and tree plantations. The commodity groups most commonly associated with deforestation were cattle meat, forestry products, oil palm, cereals and soybeans, though variation between countries and regions was large. A large (26%) and slightly increasing share of deforestation was attributed to international demand, the bulk of which (87%) was exported to countries that either exhibit decreasing deforestation rates or increasing forest cover (late- or post-forest transition countries), particularly in Europe and Asia (China, India, and Russia). About a third of the net forest gains in post-forest transition countries was in this way offset by imports of commodities causing deforestation elsewhere, suggesting that achieving a global forest transition will be substantially more challenging than achieving national or regional ones.

Woody vegetation dynamics in the tropical and subtropical Andes from 2001 to 2014: Satellite image interpretation and expert validation.

The interactions between climate and land‐use change are dictating the distribution of flora and fauna and reshuffling biotic community composition around the world. Tropical mountains are particularly sensitive because they often have a high human population density, a long history of agriculture, range‐restricted species, and high‐beta diversity due to a steep elevation gradient. Here we evaluated the change in distribution of woody vegetation in the tropical Andes of South America for the period 2001–2014. For the analyses we created annual land‐cover/land‐use maps using MODIS satellite data at 250 m pixel resolution, calculated the cover of woody vegetation (trees and shrubs) in 9,274 hexagons of 115.47 km2, and then determined if there was a statistically significant (p < 0.05) 14 year linear trend (positive—forest gain, negative—forest loss) within each hexagon. Of the 1,308 hexagons with significant trends, 36.6% (n = 479) lost forests and 63.4% (n = 829) gained forests. We estimated an overall net gain of ~500,000 ha in woody vegetation. Forest loss dominated the 1,000–1,499 m elevation zone and forest gain dominated above 1,500 m. The most important transitions were forest loss at lower elevations for pastures and croplands, forest gain in abandoned pastures and cropland in mid‐elevation areas, and shrub encroachment into highland grasslands. Expert validation confirmed the observed trends, but some areas of apparent forest gain were associated with new shade coffee, pine, or eucalypt plantations. In addition, after controlling for elevation and country, forest gain was associated with a decline in the rural population. Although we document an overall gain in forest cover, the recent reversal of forest gains in Colombia demonstrates that these coupled natural‐human systems are highly dynamic and there is an urgent need of a regional real‐time land‐use, biodiversity, and ecosystem services monitoring network.

Intensity Analysis and the Figure of Merit’s components for assessment of a Cellular Automata – Markov simulation model

Some popular metrics to evaluate land change simulation models are misleading. Therefore, land change scientists have called for the development of methods to evaluate various aspects of modelling applications. This article answers the call by giving novel methods to compare three types of land change: 1) reference change during the calibration time interval, 2) simulation change during the validation time interval, and 3) reference change during the validation time interval. We compare these changes by using Intensity Analysis’ three levels and the Figure of Merit’s four components: Misses, Hits, Wrong Hits and False Alarms. We illustrate the concepts by applying a Cellular Automata – Markov land change model to a case study in northeast Hungary. We used reference maps of five land categories to calibrate the model during 2000–2006, then to validate the simulation during 2006–2012. Intensity Analysis’ time interval level shows that the simulation change and the reference change decelerated from 2000–2006 to 2006–2012. Intensity Analysis’ category level shows that the simulation losses were less than what a pure Markov chain would have dictated. Intensity Analysis’ transition level shows that the model’s Markov algorithm simulated correctly that the gain of Forest targeted Agriculture and Wetland. The Figure of Merit’s components reveals more allocation error than quantity error. Our collection of metrics show that more error derived from the Cellular Automata algorithm than from the Markov algorithm. We recommend that scientists use Intensity Analysis and the Figure of Merit’s components to reveal various fundamental aspects of modelling applications.

Wildfire activity and land use drove 20th‐century changes in forest cover in the Colorado front range

AbstractRecent shifts in global forest area highlight the importance of understanding the causes and consequences of forest change. To examine the influence of several potential drivers of forest cover change, we used supervised classifications of historical (1938–1940) and contemporary (2015) aerial imagery covering a 2932‐km2 study area in the northern Front Range (NFR) of Colorado and we linked observed changes in forest cover with abiotic factors, land use, and fire history. Forest cover in the NFR demonstrated broad‐scale changes 1938–2015 and overall cover increased 7.8%, but there was notable spatial variability and many sites also experienced Forest Loss. Recent (1978–2015) wildfire was the largest single driver of Forest Loss, with fires burning 14.3% of the total study area. Recently burned areas showed net losses of 36.9% forest cover. Reasons for Forest Gain were more complex, with elevation, past mining density, fire history, and topographic heat load index being the strongest predictors of increases in forest cover. Historical mining activity is one of the dominant anthropogenic impacts in ecosystems in the NFR and it had a complex, non‐linear relationship with 20th‐century changes in forest cover. Subalpine stands originating after stand‐replacing fires circa mid‐1800s to early 1900s showed some of the greatest gains in forest cover, indicative of slow and continuous post‐fire recovery through the 20th century. We also investigated factors such as land ownership, road density, forest management activities, and development intensity, which played detectable, but more minor roles in observed change. Twentieth‐century changes in forest cover throughout the NFR are a result of ecological disturbances and anthropogenic influences operating at varying timescales and overlaid upon variability in the abiotic environment.

Differences in forest management practices in Primorsky Krai: Case study of certified and non-certified by Forest Stewardship Council forest concessions

ABSTRACTThe Forest Stewardship Council (FSC) aims to promote environmentally responsible forest management globally. However, quantifiable evidence of effects of FSC on forest management practices is lacking. FSC has been present in the Russian forestry arena for over 20 years. In this case study, we compare ecological indicators of forest management in FSC-certified and non-certified companies in Primorsky Krai of the Russian Far East. Those indicators include percent of forest cover loss and gain on forest concessions managed by three certified and three non-certified companies. We also interview a range of stakeholders involved with forest management or in forest policy planning in Russia to provide context for this case study. Results indicate no difference between certified and non-certified companies with regard to forest cover loss from 2008 to 2015 or forest cover gain from 2000 to 2012. Stakeholder interviews reveal that while forest certification is viewed positively and considered an efficient mechanism to improve forest management, the stakeholders doubt its ability to stimulate quantifiable changes in forest management practices in Russia and the Far East specifically. We focus on Primorsky Krai because of its unique geographic location, where its neighbors China and Japan significantly influence Primorsky Krai’s export market.

Forest Carbon Gain and Loss in Protected Areas of Uganda: Implications to Carbon Benefits of Conservation

Uganda designated 16% of its land as Protected Area (PA). The original goal was natural resources, habitat and biodiversity conservation. However, PAs also offer great potential for carbon conservation in the context of climate change mitigation. Drawing on a wall-to-wall map of forest carbon change for the entire Uganda, that was developed using two Digital Elevation Model (DEM) datasets for the period 2000–2012, we (1) quantified forest carbon gain and loss within 713 PAs and their external buffer zones, (2) tested variations in forest carbon change among management categories, and (3) evaluated the effectiveness of PAs and the prevalence of local leakage in terms of forest carbon. The net annual forest carbon gain in PAs of Uganda was 0.22 ± 1.36 t/ha, but a significant proportion (63%) of the PAs exhibited a net carbon loss. Further, carbon gain and loss varied significantly among management categories. About 37% of the PAs were “effective”, i.e., gained or at least maintained forest carbon during the period. Nevertheless, carbon losses in the external buffer zones of those effective PAs significantly contrast with carbon gains inside of the PA boundaries, providing evidence of leakage and thus, isolation. The combined carbon losses inside the boundaries of a large number of PAs, together with leakage in external buffer zones suggest that PAs, regardless of the management categories, are threatened by deforestation and forest degradation. If Uganda will have to benefit from carbon conservation from its large number of PAs through climate change mitigation mechanisms such as REDD+, there is an urgent need to look into some of the current PA management approaches, and design protection strategies that account for the surrounding landscapes and communities outside of the PAs.