Managing Understory Vegetation for Maintaining Productivity in Black Spruce Forests: A Synthesis within a Multi-Scale Research Model

Sustainable management of boreal ecosystems involves the establishment of vigorous tree regeneration after harvest. However, two groups of understory plants influence regeneration success in eastern boreal Canada. Ericaceous shrubs are recognized to rapidly dominate susceptible boreal sites after harvest. Such dominance reduces recruitment and causes stagnant conifer growth, lasting decades on some sites. Additionally, peat accumulation due to Sphagnum growth after harvest forces the roots of regenerating conifers out of the relatively nutrient rich and warm mineral soil into the relatively nutrient poor and cool organic layer, with drastic effects on growth. Shifts from once productive black spruce forests to ericaceous heaths or paludified forests affect forest productivity and biodiversity. Under natural disturbance dynamics, fires severe enough to substantially reduce the organic layer thickness and affect ground cover species are required to establish a productive regeneration layer on such sites. We succinctly review how understory vegetation influences black spruce ecosystem dynamics in eastern boreal Canada, and present a multi-scale research model to understand, limit the loss and restore productive and diverse ecosystems in this region. Our model integrates knowledge of plant-level mechanisms in the development of silvicultural tools to sustain productivity. Fundamental knowledge is integrated at stand, landscape, regional and provincial levels to understand the distribution and dynamics of ericaceous shrubs and paludification processes and to support tactical and strategic forest management. The model can be adapted and applied to other natural resource management problems, in other biomes

Cartographie des éricacées (Kalmia angustifolia, Ledum) en forêts d'épinette noire (Picea mariana) cas de la Côte-Nord

L'établissement et la croissance de l'épinette noire en régénération après une coupe en forêts boréales sont fréquemment affectés par la prolifération de plantes éricacées telles le Kalmia angustifolia . La compétition éricacées-épinette noire est fréquente au point d'entraîner une baisse significative du potentiel forestier dans la forêt boréale de l'est du Canada. Il est toujours difficile de proposer des scénarios sylvicoles qui garantissent la résilience des peuplements propices à l'envahissement. Ceci découle du manque de connaissance des impacts de l'aménagement sur la dynamique des éricacées à l'echelle du paysage ; ainsi que du manque de compréhension des mécanismes écologiques qui font qu'une pessière coupée se transforme en pessière à éricacées et non en pessière dense.L'objectif général de cette étude est de cartographier la distribution spatiale des éricacées au niveau régional. Après l'acquisition des données et de leur prétraitement, une interprétation experte a permis de produire les polygones pour l'entraînement des algorithmes de classification pour les images IKONOS. Deux séries de polygones (couverture de surface et strate arborescente) découlent de ces opérations. Chaque serie est associée à l'une des deux stratifications et contient toutes les classes thématiques de cette stratification. Une première segmentation fut appliquée sur une mosaïque de sept images IKONOS pour créer des objets spatiaux. Ces objets ont ensuite étés assignés à une classe thématique en faisant appel à la logique floue disponible dans le logiciel eCognition. Deux types de cartes thématiques (strate arborescente et couverture du sol) sont créés à l'aide des sites d'entraînement issus de la photo-interprétation experte.Les résultats furent validés à l'aide des placettes de sondage terrain (précision globale de 80 % pour les deux thématiques). 70% de l'étendue des cartes produites sur la mosaïque IKONOS furent ensuite utilisée pour son application à la classification de l'image Landsat-TM qui couvre toute la zone d'étude. Le 30% non utilisé des cartes de la mosaïque IKONOS ont servi à valider les résultats cartographiques de la classification de l'image Landsat-TM (précision globale de 88.0% pour la carte arborescente et de 78.4% pour la carte de la couverture du sol).Les méthodes et cartes résultantes seront utiles pour la gestion de la ressource forestière, en particulier pour la productivité de l'épinette noire dans les régions nordiques

A multi-scaled approach to vegetation and landscape assessment in the Barents region: Reindeer habitat in a climate of change

The circumpolar Arctic is predicted to be particularly affected by global change, including climate change. Likely ecological vulnerabilities include flora and dependent fauna. Reindeer (Rangifer tarandus), a keystone Arctic herbivore, are of particular importance, ecologically, as well as culturally and socio-economically. Many Arctic regions, particularly in Russia, lack critical data thereby limiting current understanding and the ability to assess consequences of change. This research develops new botanical and spatial vegetation data for a much understudied region, increases understanding of the landscape and its relationships to reindeer habitat and climate change through multi-scale assessments, and creates the potential for analysing consequences of change. Three primary scales of vegetation analysis and their limitations, uses and value are compared within the Barents region. More detailed examinations are undertaken within study regions in the Nenets Autonomous Okrug, Russia. Plant communities, community structure and environmental influences are described. The most detailed level of analysis uses new data derived from traditional field-based, species-level botanical assessment. Statistical analyses show distinct community divisions and the importance of particular vegetation species or species groupings in defining these communities. The intermediate scale of analysis defines vegetation communities through development of a thirty-metre resolution map, based on Landsat ETM+ satellite sensor imagery. The study region is unusually heterogeneous, complicating classification development. The third and coarsest scale comprises existing regional and global land-cover assessments, with resolutions of one to five kilometres. Multi-scale comparison shows that the intermediate level is the most suitable for reindeer pasture and habitat assessment, and that climate-related shifts could be observed on any of the three scales, depending on the objectives, enabling assessment of patterns of change in species, communities or landscape. Long-term monitoring, however, is currently limited to coarser-scale analysis. Replication and broad regional expansion of finer-scale analysis, as developed here, are restricted by data availability, computing power, logistics and cost, amongst other factors. The thesis ends with a discussion of the potential for habitat assessment that includes additional environmental factors critical to reindeer habitat. All botanical and vegetation-mapping data and about 500 vegetation species, community and landscape photographs, geo-referenced and with metadata, should be archived in DSpace@Cambridge under the Scott Polar Research Institute archive

Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic

Warming induced shifts in tundra vegetation composition and structure, including circumpolar expansion of shrubs, modifies ecosystem structure and functioning with potentially global consequences due to feedback mechanisms between vegetation and climate. Satellite-derived vegetation indices indicate widespread greening of the surface, often associated with regional evidence of shrub expansion obtained from long-term ecological monitoring and repeated orthophotos. However, explicitly quantifying shrub expansion across large scales using satellite observations requires characterising the fine-scale mosaic of Arctic vegetation types beyond index-based approaches. Although previous studies have illustrated the potential of estimating fractional cover of various Plant Functional Types (PFTs) from satellite imagery, limited availability of reference data across space and time has constrained deriving fraction cover time series capable of detecting shrub expansion. We applied regression-based unmixing using synthetic training data to build multitemporal machine learning models in order to estimate fractional cover of shrubs and other surface components in the Mackenzie Delta Region for six time intervals between 1984 and 2020. We trained Kernel Ridge Regression (KRR) and Random Forest Regression (RFR) models using Landsat-derived spectral-temporal-metrics and synthetic training data generated from pure class spectra obtained directly from the imagery. Independent validation using very-high-resolution imagery suggested that KRR outperforms RFR, estimating shrub cover with a MAE of 10.6 and remaining surface components with MAEs between 3.0 and 11.2. Canopy-forming shrubs were well modelled across all cover densities, coniferous tree cover tended to be overestimated and differentiating between herbaceous and lichen cover was challenging. Shrub cover expanded by on average + 2.2 per decade for the entire study area and + 4.2 per decade within the low Arctic tundra, while relative changes were strongest in the northernmost regions. In conjunction with shrub expansion, we observed herbaceous plant and lichen cover decline. Our results corroborate the perception of the replacement and homogenisation of Arctic vegetation communities facilitated by the competitive advantage of shrub species under a warming climate. The proposed method allows for multidecadal quantitative estimates of fractional cover at 30 m resolution, initiating new opportunities for mapping past and present fractional cover of tundra PFTs and can help advance our understanding of Arctic shrub expansion within the vast and heterogeneous tundra biome

Climate-induced changes in ecological dynamics of the Alaskan boreal forest: a study of fire-permafrost interactions

Thesis (Ph.D.) University of Alaska Fairbanks, 2016A warming climate is expected to cause widespread thawing of discontinuous permafrost, and the co-occurrence of wildfire may function to exacerbate this process. Here, I examined the vulnerability of permafrost to degradation from fire disturbance as it varies across different landscapes of the Interior Alaskan boreal forest using a combination of observational, modeling, and remote sensing approaches. Across all landscapes, the severity of burning strongly influenced both post-fire vegetation and permafrost degradation. The thickness of the remaining surface organic layer was a key control on permafrost degradation because its low thermal conductivity limits ground heat flux. Thus, variation in burn severity controlled the local distribution of near-surface permafrost. Mineral soil texture and permafrost ice content interacted with climate to influence the response of permafrost to fire. Permafrost was vulnerable to deep thawing after fire in coarse-textured or rocky soils throughout the region; low ice content likely enabled this rapid thawing. After thawing, increased drainage in coarse-textured soils caused reductions in surface soil moisture, which contributed to warmer soil temperatures. By contrast, permafrost in fine-textured soils was resilient to fire disturbance in the silty uplands of the Yukon Flats ecoregion, but was highly vulnerable to thawing in the silty lowlands of the Tanana Flats. The resilience of silty upland permafrost was attributed to higher water content of the active layer and the associated high latent heat content of the ice-rich permafrost, coupled with a relatively cold continental climate and sloping topography that removes surface water. In the Tanana Flats, permafrost in silty lowlands thawed after fire despite high water and ice content of soils. This thawing was associated with significant ground surface subsidence, which resulted in water impoundment on the flat terrain, generating a positive feedback to permafrost degradation and wetland expansion. The response of permafrost to fire, and its ecological effects, thus varied spatially due to complex interactions between climate, topography, vegetation, burn severity, soil properties, and hydrology. The sensitivity of permafrost to fire disturbance has also changed over time due to variation in weather at multi-year to multi-decadal time scales. Simulations of soil thermal dynamics showed that increased air temperature, increased snow accumulation, and their interactive effects, have since the 1970s caused permafrost to become more vulnerable to talik formation and deep thawing from fire disturbance. Wildfire coupled with climate change has become an important driver of permafrost loss and ecological change in the northern boreal forest. With continued climate warming, we expect fire disturbance to accelerate permafrost thawing and reduce the likelihood of permafrost recovery. This regime shift is likely to have strong effects on a suite of ecological characteristics of the boreal forest, including surface energy balance, soil moisture, nutrient cycling, vegetation composition, and ecosystem productivity.Introduction -- Chapter 1: Interactive effects of wildfire and climate on permafrost degradation in Alaskan lowland forests -- Chapter 2: Edaphic and microclimatic controls over permafrost response to fire in interior Alaska -- Chapter 3: Landscape effects of wildfire on permafrost distribution in interior Alaska derived from remote sensing -- Conclusions

Bridging Arctic pathways: integrating hydrology, geomorphology and remote sensing in the North

Dissertation (Ph.D.) University of Alaska Fairbanks, 2015This work presents improved approaches for integrating patterns and processes within hydrology, geomorphology, ecology and permafrost on Arctic landscapes. Emphasis was placed on addressing fundamental interdisciplinary questions using robust, repeatable methods. Water tracks were examined in the foothills of the Brooks Range to ascertain their role within the range of features that transport water in Arctic regions. Classes of water tracks were developed using multiple factor analysis based on their geomorphic, soil and vegetation characteristics. These classes were validated to verify that they were repeatable. Water tracks represented a broad spectrum of patterns and processes primarily driven by surficial geology. This research demonstrated a new approach to better understanding regional hydrological patterns. The locations of the water track classes were mapped using a combination method where intermediate processing of spectral classifications, texture and topography were fed into random forests to identify the water track classes. Overall, the water track classes were best visualized where they were the most discrete from the background landscape in terms of both shape and content. Issues with overlapping and imbalances between water track classes were the biggest challenges. Resolving the spatial locations of different water tracks represents a significant step forward for understanding periglacial landscape dynamics. Leaf area index (LAI) calculations using the gap-method were optimized using normalized difference vegetation index (NDVI) as input for both WorldView-2 and Landsat-7 imagery. The study design used groups to separate the effects of surficial drainage networks and the relative magnitude of change in NDVI over time. LAI values were higher for the WorldView-2 data and for each sensor and group combination the distribution of LAI values was unique. This study indicated that there are tradeoffs between increased spatial resolution and the ability to differentiate landscape features versus the increase in variability when using NDVI for LAI calculations. The application of geophysical methods for permafrost characterization in Arctic road design and engineering was explored for a range of conditions including gravel river bars, burned tussock tundra and ice-wedge polygons. Interpretations were based on a combination of Directcurrent resistivity - electrical resistivity tomography (DCR-ERT), cryostratigraphic information via boreholes and geospatial (aerial photographs & digital elevation models) data. The resistivity data indicated the presence/absence of permafrost; location and depth of massive ground ice; and in some conditions changes in ice content. The placement of the boreholes strongly influenced how geophysical data can be interpreted for permafrost conditions and should be carefully considered during data collection strategies

Status and trends of wetland studies in Canada using remote sensing technology with a focus on wetland classification: a bibliographic analysis

A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research.Peer ReviewedPostprint (published version

An evaluation of remotely sensed wetland mapping

Landscape management is based on the maintenance of natural ecosystems and recognizes the importance of maintaining the habitat diversity of all ecosystem types. Acquiring information about the size, distribution and location of wetlands is the first step towards evaluating their habitat value in a landscape perspective. An explicit review about the strengths and limitations of any landcover database is critical prior to input into the decision making process. Techniques were developed for characterizing wetland habitat components in a landscape context utilizing remote sensing and geographic information system technologies. A hierarchy of remotely sensed data ranging from 1:5000 colour infrared aerial photography to LANDSAT Thematic Mapper satellite data was employed to compare detail of information available at each scale of data. These techniques included evaluation of ground-based wetland classification systems, air photo interpretation, investigation of approaches to image classification, and development of accuracy assessment techniques. The developed techniques were applied to a Northwestern Ontario landscape to produce a thematic layer of wetland habitat information. The effectiveness of these techniques was evaluated by assessing the accuracy of each remote sensing scale for mapping the broad scale wetland habitat at the physiognomic group level. 1:5,000 and 1:10,000 scale colour infrared aerial photography provided the best thematic accuracy at 94 percent, whereas 1:20,000 scale allowed wetland mapping at 84 percent accuracy. Satellite based mapping using Landsat Thematic Mapper integrated with digital Forest Resource Inventory map data allowed wetlands to be mapped with 72 percent accuracy. Combining physiognomically similar wetland classes increased satellite based mapping accuracy to 81 percent

Investigation of winter habitat selection by woodland caribou in relation to forage abundance and snow accumulation

Imprecision and misclassification of land cover types are two issues commonly encountered in habitat selection studies using satellite land cover classifications and telemetry data. Here, the utility of broad land cover types is explored in a study of habitat selection by woodland caribou (Rangifer tarandus caribou). Broad land cover types have potential to reduce the misclassification error associated with finer land cover types, while remaining relevant to the factors influencing habitat selection in the species of interest. Lichen abundance and snow accumulation are two factors important in explaining the selection of land cover types by woodland caribou in winter, and they are used here to predict the probability of occupation by caribou of land cover types in three regions of the boreal forest in Eastern Canada. Land cover types were initially categorized using Landsat EOSD land cover data, and field surveys were conducted to measure terrestrial and arboreal lichen abundance in each land cover type. The relative accumulation of snow was modeled for land cover types using documented patterns of snow distribution in the boreal forest as well as data collected in the Greater Gros Morne Ecosystem, Newfoundland, and the Côte-Nord region, Quebec. Subsequently, land cover types were collapsed into three (dense forest, sparse-open forest, and non forest) that reflected differences in lichen abundance and snow accumulation while reducing misclassification errors. Resource selection functions were estimated using logistic regression where GPS and Argos satellite telemetry data existed for caribou in the Greater Gros Morne Ecosystem and Middle Ridge regions of Newfoundland and the Côte-Nord region of Quebec. In all regions, telemetry-monitored caribou selected nonforested areas, where lichen abundance was high and snow accumulation was low, more than expected by chance. The similarities in selection of non-forested areas across regions despite variation in landscape composition indicates that there are congruencies both in the factors influencing winter habitat selection and in the relative value of land cover types on a given landscape. These findings support the argument that resource selection functions with parameters based on broadly defined land cover types are applicable among different regions of caribou occurrence and are therefore a valuable tool for understanding patterns of space use in caribou throughout the boreal forest

Combination of optical and SAR remote sensing data for wetland mapping and monitoring

Wetlands provide many services to the environment and humans. They play a pivotal role in water quality, climate change, as well as carbon and hydrological cycles. Wetlands are environmental health indicators because of their contributions to plant and animal habitats. While a large portion of Newfoundland and Labrador (NL) is covered by wetlands, no significant efforts had been conducted to identify and monitor these valuable environments when I initiated this project. At that time, there were only two small areas in NL that had been classified using basic Remote Sensing (RS) methods with low accuracies. There was an immediate need to develop new methods for conserving and managing these vital resources using up-to-date maps of wetland distributions. In this thesis, object- and pixel-based classification methods were compared to show the high potential of the former method when medium or high spatial resolution imagery were used to classify wetlands. The maps produced using several classification algorithms were also compared to select the optimum classifier for future experiments. Moreover, a novel Multiple Classifier System (MCS), which combined several algorithms, was proposed to increase the classification accuracy of complex and similar land covers, such as wetlands. Landsat-8 images captured in different months were also investigated to select the time, for which wetlands had the highest separability using the Random Forest (RF) algorithm. Additionally, various spectral, polarimetric, texture, and ratio features extracted from multi-source optical and Synthetic Aperture Radar (SAR) data were assessed to select the most effective features for discriminating wetland classes. The methods developed during this dissertation were validated in five study areas to show their effectiveness. Finally, in collaboration with a team, a website (http://nlwetlands.ca/) and a software package were developed (named the Advanced Remote Sensing Lab (ARSeL)) to automatically preprocess optical/SAR data and classify wetlands using advanced algorithms. In summary, the outputs of this work are promising and can be incorporated into future studies related to wetlands. The province can also benefit from the results in many ways