Northern Canadian boreal forest is characterised by accumulation of a thick organic soil layer (paludification). Two types of paludification are recognised on the basis of topography and time since the last fire, viz., permanent paludification that dominates in natural depressions within the landscape, and reversible paludification that occurs on flat or sloping terrain over time following fire or mechanical site preparation. Accurate information about the occurrence of permanent or reversible paludification is required for land resource management. Such information is useful for the identification of locations of existing paludified areas where investment after harvesting should help to achieve greater productivity. This study investigated the potential for using a semi-automated method that was based on geomorphological analysis to map and differentiate between the two paludification types at the landscape scale within the Canadian Clay Belt region. For the purposes of this study, slope, topographic position index (TPI), and topographic wetness index (TWI) were generated from a LiDAR digital terrain model. TPI and TWI are, respectively, predictors of surface morphology (i.e., depressions vs flat areas) and moisture conditions (i.e., wet vs dry), and were used to explain paludification occurrence. A semi-automated classification method based on TPI and slope was firstly used to create six initial topographic position classes: deep-depressions, lower-slope depressions, flat surfaces, mid-slopes, upper-slopes, and hilltops. Each of these six classes was then combined with TWI classes (representing moisture conditions: wet, moderately wet, and dry) and this combination assisted in assigning each resulting class to one of the two paludification types. Slope and TWI values were used in sub-dividing the lower slope depression class, based on slope, into significantly different sub-classes, namely open and closed depressions (Tukey's HSD, P < 0.001). The distribution of field data (e.g., tree basal area, organic layer and fibric horizon thicknesses) within each position class provided additional information for corroborating the assignment of each class to a defined paludification type. The proposed semi-automated classification provided a relatively simple and practical tool for distinguishing and mapping permanent and reversible paludification types with an overall accuracy of 74%. The tool would be particularly useful for implementing strategies of sustainable management in remote boreal areas where field survey information is limited.

Mineral soil topography is difficult to describe in boreal regions because of the thick overlying organic layer despite its presumed importance in determining where and at what rate an organic layer will accumulate (paludification). The overall purpose of this study was to examine the relationship between mineral soil topography and OLT at the landscape scale. More specifically, these relationships can be used to map the distribution and spatial variability of paludification across the landscape, thereby exploring the potential to discriminate between the two commonly known paludification types (permanent and reversible). Seven topographic variables (elevation, slope, aspect, mean curvature, plan curvature, profile curvature and topographic wetness index) were generated from a digital elevation model that we developed for the mineral soil surface (MS-DEM). OLT data were collected from field measurements across the landscape by manual probing and values varied from 5 to 150 cm. The MS-DEM was generated by subtracting OLT field values from the corresponding LiDAR-derived elevation values. Most correlations between OLT and individual predictor variables were weak and illustrated that OLT and its landscape-scale distribution cannot be explained by simple bivariate relationships. Consequently, two regression tree-based models were developed using: (1) only the seven mineral soil topographic variables, and (2) all predictor variables (mineral soil topography and surficial deposits). Mineral soil slope was the most important variable for both models and corresponded to the first level of splitting the dataset into homogenous landscape units in terms of organic layer thickness. Surficial deposit, topographic wetness index (TWI) and aspect were also related to OLT and proved to be contributing to the development of the two models. Model 1 explained 0.34 of the OLT variability and offer simple models with few landscape units that are easy to interpret. Model 1 splitting rules allowed the combination of different maps (slope, TWI and aspect) for producing a landscape units map, on which OLT was determined and related to increasing paludification categories. A good overall accuracy of 74% was achieved for this map. Model 2 was the best model in terms of estimate quality (R2adj = 0.52). Both models were successful in discriminating highly paludified landscape units. Except for one landscape unit that was assigned to permanent paludification type, both models were unable to further subdivide more landscape units into reversible and permanent paludification, suggesting that both of these types interact within the same landscape unit. This study demonstrated that the combination of topographic information from remotely sensed LiDAR data and field OLT measurement data has the potential to be useful for defining both promising and vulnerable areas for forest management.

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. Cette étude a évalué l'aptitude du radar-sol (géoradar, ou GPR «Ground Penetrating Radar») à cartographier l'interface «OL–MS» constituée entre la couche organique et le sol minéral (la profondeur de cette interface correspond de fait à l’épaisseur de la couche organique (OLT «Organic Layer Thickness»), paramètre clef de l'apparition de la paludification dans cette région. Deux sites ont été choisis pour cette étude; ils représentent différents types de sol et d'OLT, associées à différents degrés de paludification, soit faible à moyennement paludifié (site A) ou fortement paludifié (site B). Trois profils radar-sol parallèles d'une longueur de 40 m chacun ont été acquis sur chaque site selon un pas d’échantillonnage de 20 cm et une fréquence de 200 MHz. L'interprétation des données GPR a été comparée avec les sondages manuels de l'OLT effectués sur le terrain. La détection de cette interface continue a été fait avec succès sur le site A (r = 0,93; P < 0,001); sur le site B, la forte teneur en argile des horizons mésiques et humiques a limité la pénétration en profondeur des ondes radar, en rendant le sol minéral indétectable. Cependant, nous avons découvert que l'utilisation des données de radar-sol combinée à des mesures de l'OLT sur le terrain du site B, a permis de cartographier l’épaisseur de l'horizon fibrique (r = 0,79; P < 0,001), ce qui pourrait être considéré comme un indicateur de l'OLT dans les zones fortement paludifiées. Cette étude montre ainsi que, le radar-sol apparaît l'instrument efficace pour cartographier l'interface OL–MS dans un site faiblement à modérément paludifié. Ce résultat apparaît prometteur pour la mise en æuvre de stratégies de gestion forestière qui permettront de stopper la progression de la paludification.