Soil moisture conditions control nutrient accumulation, carbon storage and tree growth in boreal forest landscapes

Forest and soil properties change across landscapes due to the complex interactions between various environmental factors. In many landscapes, topography exerts a major influence on the variation in soil moisture conditions, which in turn largely affects soil properties and processes. This thesis synthesises the results from four studies (papers I-IV), with the underlying aim to increase the understanding of how environmental factors, in particular, soil moisture, control the variation of nutrient accumulation, carbon storage, and tree growth within boreal landscapes. The four studies were all based on an extensive survey of a 68 km2 boreal forest landscape in northern Sweden. In Paper I, soil moisture conditions were predicted using multiple terrain indices. The results emphasised within-study validation and how digital elevation model resolution together with user-defined thresholds influence prediction accuracy. Paper II focused on how multiple environmental drivers influence the variation in soil carbon-to-nitrogen (C/N) ratios, with a noteworthy result that the ratio decreases as soil moisture conditions increase. Paper III presented how, soil moisture conditions significantly controls the distribution and partitioning of carbon stocks, with large increases in total carbon stock observed as soil moisture conditions increases, which was observed at both plot and landscape scale. The results in paper IV showed that, estimates of forest site quality decrease in response to increased soil moisture conditions. In conclusion, the research discussed in this thesis emphasises the importance of studying forest ecosystems on a landscape scale, an approach that can provide key insights into the factors that influence variation of different attributes of boreal forest ecosystems

Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices

Soil moisture has important implications for drought and flooding forecasting, forest fire prediction and water supply management. However, mapping soil moisture has remained a scientific challenge due to forest canopy cover and small-scale variations in soil moisture conditions. When accurately scaled, terrain indices constitute a good candidate for modelling the spatial variation of soil moisture conditions in many landscapes. In this study, we evaluated seven different terrain indices at varying digital elevation model (DEM) resolutions and user-defined thresholds as well as two available soil moisture maps, using an extensive field dataset (398 plots) of soil moisture conditions registered in five classes from a survey covering a (68 km2) boreal landscape. We found that the variation in soil moisture conditions could be explained by terrain indices, and the best predictors within the studied landscape were the depth to water index (DTW) and a machine-learning-generated map. Furthermore, this study showed a large difference between terrain indices in the effects of changing DEM resolution and user-defined thresholds, which severely affected the performance of the predictions. For example, the commonly used topographic wetness index (TWI) performed best on a resolution of 16 m, while TWI calculated on DEM resolutions higher than 4 m gave inaccurate results. In contrast, depth to water (DTW) and elevation above stream (EAS) were more stable and performed best on 1–2 m DEM resolution. None of the terrain indices performed best on the highest DEM resolution of 0.5 m. In addition, this study highlights the challenges caused by heterogeneous soil types within the study area and shows the need of local knowledge when interpreting the modelled results. The results from this study clearly demonstrate that when using terrain indices to represent soil moisture conditions, modelled results need to be validated, as selecting an unsuitable DEM resolution or user-defined threshold can give ambiguous and even incorrect results

Tree growth potential and its relationship with soil moisture conditions across a heterogeneous boreal forest landscape

Forest growth varies across landscapes due to the intricate relationships between various environmental drivers and forest management. In this study, we analysed the variation of tree growth potential across a landscape scale and its relation to soil moisture. We hypothesised that soil moisture conditions drive landscape-level variation in site quality and that intermediate soil moisture conditions demonstrate the highest potential forest production. We used an age-independent difference model to estimate site quality in terms of maximum achievable tree height by measuring the relative change in Lorey's mean height for a five year period across 337 plots within a 68 km2 boreal landscape. We achieved wall-to-wall estimates of site quality by extrapolating the modelled relationship using repeated airborne laser scanning data collected in connection to the field surveys. We found a clear decrease in site quality under the highest soil moisture conditions. However, intermediate soil moisture conditions did not demonstrate clear site quality differences; this is most likely a result of the nature of the modelled soil moisture conditions and limitations connected to the site quality estimation. There was considerable unexplained variation in the modelled site quality both on the plot and landscape levels. We successfully demonstrated that there is a significant relationship between soil moisture conditions and site quality despite limitations associated with a short study period in a low productive region and the precision of airborne laser scanning measurements of mean height

Decision Support Tool for Tree Species Selection in Forest Regeneration Based on Harvester Data

Precision forestry—i.e., the division of a stand to smaller units and managing of the stand at a micro-stand level—provides new possibilities to increase forest growth, arrange forest stand structure and enhance forest health. In the regeneration phase by adjusting the tree species selection, soil preparation, intensity of regeneration measures (method, planting density, and material), and young stand management procedures according to precise information on soil properties (e.g., site fertility, wetness, and soil type) and microtopography will inevitably lead to an increase in growth of the whole stand. A new approach to utilizing harvester data to delineate micro-stands inside a large forest stand and to deciding the tree species to plant for each micro-stand was piloted in central Finland. The case stands were situated on Finsilva Oyj forest property. The calculation of the local growth (m3/ha/year) for each 16 × 16-m grid cell was based on the height of the dominant trees and the stand age of the previous tree generation. Tree heights and geoinformation were collected during cutting operation as the harvester data, and the dominant height was calculated as the mean of the three largest stems in each grid cell. The stand age was obtained from the forest management plan. The estimated local growth (average of nine neighboring grid cells) varied from 3 to 14 m3/ha/year in the case stands. When creating micro-stands, neighboring grid cells with approximately the same local growth were merged. The minimum size for an acceptable micro-stand was set to 0.23 ha. In this case study, tree species selection (Scots pine or Norway spruce) was based on the mean growth of each micro-stand. Different threshold values, varying from 6 to 8 m3/ha/year, were tested for tree species change, and they led to different solutions in the delineation of micro-stands. Further stand development was simulated with the Motti software and the net present values (NPVs (3%)) for the next rotation were estimated for different micro-stand solutions. The mixed Norway spruce–Scots pine stand structure never produced a clearly economically inferior solution compared to the single species stand, and in one case out of six, it provided a distinctly better solution in terms of NPV (3%) than the single species option did. Our case study showed that this kind of method could be used as a decision support tool at the regeneration phase

Decision Support Tool for Tree Species Selection in Forest Regeneration Based on Harvester Data

Precision forestry—i.e., the division of a stand to smaller units and managing of the stand at a micro-stand level—provides new possibilities to increase forest growth, arrange forest stand structure and enhance forest health. In the regeneration phase by adjusting the tree species selection, soil preparation, intensity of regeneration measures (method, planting density, and material), and young stand management procedures according to precise information on soil properties (e.g., site fertility, wetness, and soil type) and microtopography will inevitably lead to an increase in growth of the whole stand. A new approach to utilizing harvester data to delineate micro-stands inside a large forest stand and to deciding the tree species to plant for each micro-stand was piloted in central Finland. The case stands were situated on Finsilva Oyj forest property. The calculation of the local growth (m3/ha/year) for each 16 × 16-m grid cell was based on the height of the dominant trees and the stand age of the previous tree generation. Tree heights and geoinformation were collected during cutting operation as the harvester data, and the dominant height was calculated as the mean of the three largest stems in each grid cell. The stand age was obtained from the forest management plan. The estimated local growth (average of nine neighboring grid cells) varied from 3 to 14 m3/ha/year in the case stands. When creating micro-stands, neighboring grid cells with approximately the same local growth were merged. The minimum size for an acceptable micro-stand was set to 0.23 ha. In this case study, tree species selection (Scots pine or Norway spruce) was based on the mean growth of each micro-stand. Different threshold values, varying from 6 to 8 m3/ha/year, were tested for tree species change, and they led to different solutions in the delineation of micro-stands. Further stand development was simulated with the Motti software and the net present values (NPVs (3%)) for the next rotation were estimated for different micro-stand solutions. The mixed Norway spruce–Scots pine stand structure never produced a clearly economically inferior solution compared to the single species stand, and in one case out of six, it provided a distinctly better solution in terms of NPV (3%) than the single species option did. Our case study showed that this kind of method could be used as a decision support tool at the regeneration phase

Évaluation de la productivité actuelle et potentielle de la pessière à épinette noire dans la forêt boréale de la ceinture d’argile à l’aide de différentes approches de cartographie à haute résolution

La forêt boréale canadienne a une importance écologique et économique considérable. Toutefois, les forêts d’épinettes noires situées dans la ceinture d’argile, une région boréale de l’est de l’Amérique du Nord, sont sujettes à la paludification. Ce phénomène est un processus naturel par lequel une couche organique s’accumule sur le sol forestier conduisant à une diminution importante de la productivité de ces forêts. Théoriquement, il existe deux types de paludification, à savoir la paludification permanente et réversible. La paludification permanente se produit dans des endroits où les conditions d’humidité du sol sont élevées (ex., reliefs plats, dépressions topographiques); alors que la paludification réversible intervient dans des sites à pente faible ou moyennement forte au fil du temps en réponse à une perturbation telle qu’un feu peu sévère. L’épaisseur de la couche organique (ECO) et la topographie constituent des paramètres clefs de la présence de la paludification dans cette région et y affectent négativement la productivité. La recherche proposée dans cette thèse consiste à approfondir la compréhension et la détection du phénomène de paludification dans les forêts d’épinette noire dans une perspective de maintien ou d’accroissement de la productivité des arbres. Cette thèse a pour objectif principal de déterminer et de sélectionner les variables permanentes des site permettant d’expliquer les écarts de productivité de la pessière à épinette noire observés dans la ceinture d’argile à l’aide des méthodes à haute résolution et des données recueillies sur le terrain. L’expression de ces variables a été ensuite utilisée pour prédire la productivité actuelle et potentielle des sites soumis à la paludification. Les objectifs spécifiques de cette thèse étaient de (1) détecter et identifier d’une manière continue l’interface sol minéral/couche organique afin de cartographier la topographie du sol minéral à l’échelle des sites paludifiés; (2) étudier d’une façon quantitative les relations entre l’ECO et la topographie (au niveau du sol minéral et de la surface) afin de caractériser ces relations à l’échelle du paysage, notamment la distribution et la variabilité spatiale de l’ECO; (3) identifier les variables topographiques permettant de distinguer et de cartographier la paludification réversible et la paludification permanente à l’échelle du paysage; et (4) évaluer l’effet de l’ECO et des variables topographiques, exprimées à différentes résolutions spatiales, sur la productivité forestière des forêts paludifiées de la ceinture d’argile. Le but ultime est d’améliorer notre compréhension de la façon dont ces variables ainsi que leurs résolutions influencent la productivité des arbres dans les forêts d’épinette noire. Les résultats du premier chapitre de la thèse ont démontré que la méthode géophysique géoradar, ayant une bonne corrélation de ses résultats avec les données du terrain (r = 0,93; P < 0,001), a permis d’obtenir une cartographie précise, continue et fiable de l’interface couche organique/sol minéral dans des sites faiblement à modérément paludifiés. Cependant, en dépit de son incapacité à cartographier l’interface couche organique/sol minéral dans les sites hautement paludifiés, le recours au géoradar s’est révélé pertinent dans la mise en évidence de l’interface horizon fibrique/couche organique et de sa continuité spatiale. Cela rend le géoradar particulièrement intéressant dans la détection des niveaux d’entourbement constituant ainsi une méthode de détection indirecte prometteuse pour l’aménagement des forêts paludifiées. Le deuxième objectif a été abordé dans deux différents chapitres (II et III) à l’aide d’une approche quantitative de modélisation de l’ECO par arbre de régression. Différentes variables topographiques (élévation, pente, exposition, indice topographique d’humidité (TWI), courbure totale, courbure transversale, et courbure horizontale) ont été utilisées dans les modèles sélectionnés des deux chapitres. D’une façon générale, nous avons démontré que les topographies de surface et du sol minéral influencent l'accumulation de la couche organique à l'échelle du paysage dans la ceinture d’argile. Les résultats du deuxième chapitre ont permis de délimiter les principaux patrons de l’ECO et d’élucider trois relations spatiales entre l’ECO et les variables explicatives: (i) les zones avec une couche organique épaisse (62 cm) avaient des pentes douces (≤ 1,8%); (ii) les zones avec pentes plus raides (> 3,2 %) ont été associées à une couche organique peu profonde (27 cm); et (iii) les résultats les plus significatifs ont été obtenus avec des résolutions 10 et 20 m en comparaison au 1 et 5 m. Le troisième chapitre a permis de mettre en évidence les différentes relations entre la topographie du sol minéral et l’ECO à l’échelle du paysage. La construction d’un modèle numérique d'élévation au niveau du sol minéral à l’échelle du paysage est un élément central de notre démarche. Les modelés développés nous permettent d’affirmer que : (i) la pente du sol minéral, la composition du sol minéral (argile, till et régolithe), le TWI et l’exposition sont les quatre principales variables influençant l’accumulation de la couche organique; (ii) les valeurs seuils de pente du sol minéral > 3,5% et ≤ 2% permettent respectivement de distinguer les zones les plus prometteuses et les plus vulnérables pour l’aménagement forestier; (iii) les zones avec une exposition nord et est étaient associées à une couche organique plus profonde par rapport à celles exposées vers le sud et l’ouest; et (iv) la distinction entre les zones paludifiées et non paludifiées sur la base d’une valeur seuil de la pente du sol minéral de l’ordre de 3,5% constitue un des apports majeurs de cette étude. Afin de répondre au troisième objectif, une approche semi-automatique de subdivision sous SIG du territoire à l’étude en des entités du paysage distinctes a été réalisée en combinant des données topographiques, notamment l’indice topographique de position (TPI), l’indice topographique d’humidité (TWI) ainsi que la pente de surface. Cette approche s’est révélée efficace, car elle a permis de délimiter des entités possédant des caractéristiques géomorphologiques semblables, notamment en terme de susceptibilité à l’accumulation de la couche organique, et par conséquent ont été assignées à l’un ou l’autre type de paludification, soit réversible ou permanente. Un apport majeur de cette approche semi-automatisée est la mise en évidence de deux sous-entités statistiquement différentes (le test HSD de Tukey, P < 0,001), à savoir des dépressions ouvertes préférentiellement drainées (paludification réversible) et des dépressions fermées potentiellement engorgées (paludification permanente) du fait de leurs positions topographiques et conditions d’humidité. Cela rend l'outil développé particulièrement utile pour la mise en oeuvre des stratégies d’aménagement durable dans les forêts paludifiées. Pour atteindre le quatrième objectif, deux modèles ont été explorés pour la modélisation de la productivité (exprimée par l’indice de qualité de station (IQS) dans notre cas) en utilisant une approche par arbre de régression. Le premier modèle contient les variables topographiques et l’ECO, alors que le deuxième modèle inclut seulement les variables topographiques issues des données LiDAR. Les résultats de cette modélisation ont démontré que l’ECO, l'exposition et la pente sont les trois variables les plus importantes pour expliquer la productivité forestière à l’échelle du paysage; et pour déterminer des seuils d’ECO et des variables topographiques qui permettent de caractériser, à la fois, des zones productives et improductives. En effet, les zones avec une productivité élevée étaient associées à une couche organique peu profonde ( 85 cm), favorisant l’invasion de mousses et de sphaignes. Du point de vue de l’échelle (résolutions), le premier modèle semble relativement indépendant de l’échelle, alors que la réponse du deuxième modèle augmentait significativement avec la taille du pixel. Ces résultats pourraient donc être appliqués à des échelles opérationnelles et là où des informations sur l’ECO sont disponibles afin de prédire la productivité. Des cartes thématiques prédictives de la distribution spatiale de la productivité ont été réalisées avec nos deux modèles. Les résultats de cette thèse ont permis d'approfondir les connaissances sur les variables permettant d’expliquer les écarts de productivité dans la pessière à épinette noire ainsi que sur l’importance des variables topographiques dans la modélisation de l’ECO et la productivité à l’échelle du paysage. De plus, cette étude a permis de caractériser la distribution spatiale des deux types de paludification (permanente et réversible) à l’échelle du paysage. Quoique cette étude s’intéresse plus particulièrement à la pessière à épinette noire, les connaissances acquises pourraient être applicables à d’autres territoires paludifiés de la forêt boréale

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion

Spatial Assessment of Boreal Forest Carbon

University of Minnesota Ph.D. dissertation. June 2015. Major: Natural Resources Science and Management. Advisor: Paul Bolstad. 1 computer file (PDF); xi, 204 pages.The ability to accurately map and monitor forest carbon (C) has gained global attention as countries seek to comply with international agreements to mitigate climate change. However, attaining precise estimates of forest C storage is challenging due to the inherent heterogeneity occurring across different scales. To develop cost-effective sampling protocols, there is a need for more unbiased estimates of the current C stock, its distribution among forest compartments and its variability across different scales. As a contribution to this work, this dissertation used high-resolution field measurements of C collected from different forest compartments across a boreal forest stand in South East Norway. In the first paper, we combined the use of airborne scanning light detection and ranging (lidar) systems with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools across the forest. We found that predictor variables from lidar derived metrics delivered precise models of above and belowground tree C, which comprised the largest of the measured C pool in our study. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. In the search for an effective tool to measure and monitor forest C pools, we found the capabilities of lidar to map forest C encouraging. In the second paper, we used a geostatistical approach to analyze the fine-scale heterogeneity of the soil organic layer (forest floor) C storage. Our results showed that the C stocks were highly variable within each plot, with spatial autocorrelation distances < 3 m. Further, we established that a minimum of 20 to 25 inventory samples is needed to determine the organic layer C stock with a precision of �0.5 kg C m-2 in inventory plots of ~2000 m2. In the third paper, we investigated how the short-range spatial variability of organic layer C affects sampling strategies aiming to monitor and detect changes in the C stock. We found that sample repeatability rapidly declines with sample separation distance, and the a priori sample sizes needed to detect a change a fixed change in the organic layer C stock vary by a factor of ~4 over 15 to 125 cm separation distance. Unless care is taken by the surveyor to ensure spatial sampling precision, substantially larger samples sizes, or longer time intervals between baseline sampling and revisit are required to detect a change. In the final paper, we utilized the nested sampling protocol to investigate the spatial variability of organic layer C across different scales and incorporated inventory expenses in the development of a cost-optimal sampling approach. Because precise estimates are costly to obtain, it is of great interest for surveyors to develop cost-efficient sampling protocols aimed at maximizing the spatial coverage, while minimizing the estimate variance. We found that the majority of the estimate variance is confined within small subplots (100 m2) of the forest (25 km2), emphasizing the importance of considering the short-range variability when conducting a large-scale inventory. Further, this chapter demonstrated how optimal allocation of sampling units (plot, subplot and sample) is not only a function of the variance component within that dimension, but also changes with the sampling unit costs and the acceptable margin of error. We found that the costs of conducting an organic layer C inventory could be reduced by more than 60% by increasing the inventory uncertainty from �0.25 Mg C ha-1 to �0.5 Mg C ha-1. Finally, we established that sampling costs can be reduced with as much 80% by conducting a double sampling procedure that utilizes the correlation between organic layer C stock (r = 0.79 to 0.85) and measurements of layer thickness

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques