ALBERT

Description: Surfaces of potential vegetation growth in this paper represent the spatial distribution of growing conditions (habitat) for six deciduous tree species native to the Clyburn River valley watershed of northeastern Cape Breton Island, Nova Scotia. Development of potential growth surfaces is based on integrating point calculations of (i) net potential solar radiation, (ii) net long-wave radiation, (iii) growing season degree-day accumulation, and (iv) mean summer soil water content with species-specific evaluations of long-term species environmental response. Functions describing potential species response to available environmental resources are based on generalised mathematical functions that scale species response values between 0 and 1, where 0 represents unsuitable growing conditions and 1, optimal growing conditions. Limitation effects of resource deficits on potential growth are addressed as a multiplication of individual environmental responses. Derived species distributions of potential growth are compared with aerial photo-interpreted distributions of forest vegetation found within the Clyburn River valley watershed. Modelled and photo-interpreted valley distributions demonstrate nearly similar geographic ranges. Actual percent cover for shade-tolerant species displays a positive correlation with modelled potential growth (r2 = 0.5). This is not the case for shade-intolerant species considered, whereby r2 [Formula: see text] 0.

Description: Net ecosystem productivity (NEP) during August 2003 was measured by using eddy covariance above 17 forest and 3 peatland sites along an eastwest continental-scale transect in Canada. Measured sites included recently disturbed stands, young forest stands, intermediate-aged conifer stands, mature deciduous stands, mature conifer stands, fens, and an open shrub bog. Diurnal courses of NEP showed strong coherence within the different ecosystem categories. Recently disturbed sites showed the weakest diurnal cycle; and intermediate-aged conifers, the strongest. The western treed fen had a more pronounced diurnal pattern than the eastern shrub bog or the Saskatchewan patterned fen. All but three sites were clearly afternoon C sinks. Ecosystem respiration was highest for the young fire sites. The intermediate-aged conifer sites had the highest maximum NEP (NEPmax) and gross ecosystem productivity (GEPmax), attaining rates that would be consistent with the presence of a strong terrestrial C sink in regions where these types of forest are common. These results support the idea that large-scale C cycle modeling activities would benefit from information on the age-class distribution and disturbance types within larger grid cells. Light use efficiency followed a pattern similar to that of NEPmax and GEPmax. Four of the five recently disturbed sites and all three of the peatland sites had low water use efficiencies.

Description: Growth and yield models are critically important for forest management planning. Biophysical factors such as light, temperature, soil water, and nutrient conditions are known to have major impacts on tree growth. However, it is difficult to incorporate these biophysical variables into growth and yield models due to large variation and complex nonlinear relationships between variables. In this study, artificial intelligence technology was used to develop individual-tree-based basal area (BA) and volume increment models. The models successfully account for the effects of incident solar radiation, growing degree days, and indices of soil water and nutrient availability on BA and volume increments of over 40 species at 5-year intervals. The models were developed using data from over 3000 permanent sample plots across the province of Nova Scotia, Canada. Model validation with independent field data produced model efficiencies of 0.38 and 0.60 for the predictions of BA and volume increments, respectively. The models are applicable to predict tree growth in mixed species, even- or uneven-aged forests in Nova Scotia but can easily be calibrated for other climatic and geographic regions. Artificial neural network models demonstrated better prediction accuracy than conventional regression-based approaches. Artificial intelligence techniques have considerable potential in forest growth and yield modelling.

Description: Zhao, Z., MacLean, D. A., Bourque, C. P.-A., Swift, D. E. and Meng, F.-R. 2013. Generation of soil drainage equations from an artificial neural network-analysis approach. Can. J. Soil Sci. 93: 329–342. Soil properties, especially soil drainage, are known to be related to topo-hydrologic variables derived from digital elevation models (DEM), such as vertical slope position, slope steepness, sediment delivery ratio, and topographic wetness index. Such relationships typically are strongly non-linear and thus difficult to define with conventional statistical methods. In this study, we used artificial neural network (ANN) models to establish relationships between soil drainage classes and DEM-generated topo-hydrologic variables and subsequently formulated the relationships to generate soil drainage equations for soil mapping. A high-resolution field soil map of the Black Brook Watershed in northwest New Brunswick, Canada, was used to calibrate/validate the ANN models, and the obtained equations. Independent data from an experimental farm, about 180 km away, were also used for validation. Results indicated that vertical slope position was the best predictor of soil drainage classes (r=0.55), followed by slope steepness (r=0.44), sediment delivery ratio (r=0.39), and topographic wetness index (r=0.38). The obtained soil drainage equations fitted well to the ANN model predictions (r 2=0.78–0.99; root mean squared error=0.39–4.55). Analyses indicated that soil drainage equations clearly reflected the actual relationships between soil drainage classes and DEM-generated topo-hydrologic variables, and have the potential to minimize bias originated from over-training the ANN models when applied outside the area of calibration, especially when the ranges of input variables were outside of the range of calibration data.

Description: Forestland classification is central to the sustainable management of forests. In this paper, we explore the possibility of classifying forestland from species–habitat–suitability indices and a hybrid classification of modeled data. Raster-based calculations of species–habitat–suitability were derived as a function of landscape-level descriptions of incident photosynthetically active radiation (PAR), soil water content (SWC), and growing degree-days (GDD) for southwestern Nova Scotia, Canada. PAR and SWC were both generated with the LanDSET model and GDD from thermal data captured with the space-borne MODIS sensor. We compared the distribution of predicted forestland types with the natural range of target species as found in the provincial permanent sample plots (PSPs). Reasonable agreement (≥50% accuracy) existed between some forestland types (e.g., red maple – white birch – red oak and balsam fir – red maple) and PSP-based assessments of species presence–absence. Agreement was noticeably lower for other forestland types, such as sugar maple – beech – yellow birch (

Description: A technique was developed to obtain predictions of potential solar radiation and temperature for a prescribed, mostly unmonitored, area in the Cape Breton Highlands region of northeastern Nova Scotia (46°39′N 60°57′W to 46°40′N 60°24′W). Hourly predictions of incoming solar radiation are based on relations of sun-earth geometry, clear-sky atmospheric transmittance, and land-surface attributes resolved from digital terrain and vegetation models. The digital vegetation model characterizes vegetation cover and is used to define the average midday albedoes for the area in question. Hourly albedoes are calculated according to assigned mid-day albedo and sun-illumination angles. Land-surface characteristics (elevation, slope, aspect, horizon angles, terrain configuration factor, and view factor) affect total incident solar radiation by affecting the direct, diffused, and reflected energy components. Hourly spatial variability in above-ground daytime temperature is captured by way of a fully trained artificial neural network (ANN) that describes hourly fluctuations of interior highland temperatures according to i) reference temperatures taken at two lowland locations, one at Ingonish Beach and the other at Grande Anse; ii) distance from a north-south line representing the east coast of the study area and from the Grande Anse location; iii) time of day; and iv) land-surface attributes. Training the ANN involves supplying the network with actual data and having the network adjust its internal weights iteratively so that the output values are sufficiently close to the supplied target values. Comparison of predicted and observed hourly spring-summer (1997) temperatures revealed that the constructed ANN explained over 88% of the variability exhibited in the observed temperatures and that the standard error of estimate was 2.0 °C (mean absolute error = 1.5 °C). Key words: Sun-earth geometry, radiation laws, variable surface albedo, clear-sky atmospheric transmissivity, digital terrain, vegetation models, artificial neural networks