Integrating remote sensing and climate data with process ... - Forests

Integrating remote sensing and climate data with process-based models to map forest productivity within west-central Alberta’s boreal forest: Ecoleap-West1 by R.J. Hall2,3, F. Raulier4, D.T. Price2, E. Arsenault2, P.Y. Bernier5, B.S. Case2,6, X. Guo3

ABSTRACT

Forest yield forecasting typically employs statistically derived growth and yield (G&Y) functions that will yield biased growth estimates if changes in climate seriously influence future site conditions. Significant climate warming anticipated for the Prairie Provinces may result in increased moisture deficits, reductions in average site productivity and changes to natural species composition. Process-based stand growth models that respond realistically to simulated changes in climate can be used to assess the potential impacts of climate change on forest productivity, and hence can provide information for adapting forest management practices. We present an application of such a model, StandLEAP, to estimate stand-level net primary productivity (NPP) within a 2700 km2 study region in western Alberta. StandLEAP requires satellite remotesensing derived estimates of canopy light absorption or leaf area index, in addition to spatial data on climate, topography and soil physical characteristics. The model was applied to some 80 000 stand-level inventory polygons across the study region. The resulting estimates of NPP correlate well with timber productivity values based on stand-level site index (height in metres at 50 years). This agreement demonstrates the potential to make site-based G&Y estimates using process models and to further investigate possible effects of climate change on future timber supply. Key words: forest productivity, NPP, climate change, process-based model, StandLEAP, leaf area index, above-ground biomass RÉSUMÉ

Les prévisions de rendement forestier utilisent généralement des fonctions de croissance et de rendement statistiquement dérivées qui engendreront des estimés de croissance biaisés dans le cas où les changements climatiques influenceraient sérieusement les conditions de stations à venir. Le réchauffement significatif du climat prévu pour les Provinces des Prairies pourrait provoquer des déficits hydriques accrus, des réductions au niveau de la productivité moyenne des stations et des changements dans la composition naturelle des espèces. Les modèles de croissance des peuplements basés sur des processus et qui répondent de façon réaliste aux changements climatiques simulés, peuvent être utilisés pour évaluer les impacts potentiels du climat sur la productivité forestière et, ainsi, peuvent apporter des informations pour adapter les pratiques d’aménagement forestier. Nous présentons une utilisation de tel modèle, le StandLEAP, pour estimer le niveau net de productivité primaire d’un peuplement situé dans une région de 2 700 km2 de l’Ouest de l’Alberta. Stand LEAP requiert des estimés dérivés par télédétection spatiale de l’absorption de la lumière par le couvert forestier ou l’indice de surface foliaire, en plus des données sur le climat, la topographie et les caractéristiques physiques du sol. Le modèle a été utilisé pour quelque 80 000 polygones d’inventaire des peuplements répartis dans toute la région sous étude. Les estimés obtenus de productivité primaire sont bien corrélés aux valeurs de productivité de matière ligneuse basées sur les indices de station pour les peuplements (hauteur en mètres à 50 ans). Cette corrélation illustre le potentiel d’établir des estimés de croissance et de rendement basés sur la station au moyen de modèles provenant de processus et d’étudier plus en profondeur les effets potentiels des changements climatiques sur les approvisionnements en matière ligneuse de l’avenir. Mots clés : productivité forestière, productivité primaire nette, changements climatiques, modèle tiré des processus, Stand LEAP, indice de surface foliaire, biomasse au-dessus du sol

1Presented

at “One Forest Under Two Flags,” Canadian Institute of Forestry / Institut forestier du Canada and the Society of American Foresters Joint 2004 Annual General Meeting and Convention held October 2–6, 2004, Edmonton, Alberta, Technical Session on Remote Sensing for Forestry. 2Northern Forestry Centre, Canadian Forest Service, Edmonton, Alberta T6H 3S5. 3Corresponding author. E-mail: [email protected] 4Faculté de foresterie et de géomatique, Université Laval, Québec G1K 7P4. 5Laurentian Forestry Centre, Canadian Forest Service, Ste-Foy, Quebec G1V 4C7. 6Now with Environment, Society & Design Division, PO Box 84, Lincoln University Canterbury, New Zealand.

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Introduction Empirical growth and yield (G&Y) models are used to project future wood volumes based on measurements obtained on permanent or temporary sample plots (Avery and Burkhart 1994, Tickle et al. 2001). While G&Y models are fundamental to forest management (Avery and Burkhart 1994), they are based on an implicit assumption that future climate and future average growth rates will resemble those of the past. Projections of future climate derived from general circulation models (GCM) now raise serious questions about the validity of this assumption (e.g., Houghton et al. 2001, Hengeveld 2006). Evidence of climate change can be seen in historical climate trends, particularly in central Canada, that appear to support the GCM projections. Hence, climate change is likely to affect the future composition, health and productivity of Canada’s forest’s and forest sector (e.g., Ohlson et al. 2005). Our ability to predict forest productivity within the framework of a changing climate is limited unless we understand how environmental factors interact with the processes leading to the capture and allocation of carbon to foliage and roots as well as to stemwood (Landsberg and Gower 1997). While it is certainly possible to quantify these interactions experimentally, the only practical way to estimate forest productivity at scales useful to forest managers is by the application of spatial models that capture key growth processes over large heterogeneous landscapes (Kimmins 1997, Landsberg and Gower 1997). Process-based models may therefore complement traditional growth and yield models by providing estimates of tree growth responses under new combinations of environmental conditions. Process-based productivity models typically produce estimates of Net Primary Productivity (NPP) that serve as a direct link between the environmental drivers of tree growth and the production of wood fibre. NPP represents the net capture of energy through photosynthesis after accounting for energy consumption through plant respiration. From a stand perspective, NPP represents the gross accumulation of biomass by the vegetation (typically reported in units of g m-2 year-1), before shedding of components (leaves, fine roots, etc.) and before consumption by herbivores or other losses such as fire (Peng and Apps 1999, Clark et al. 2001). Because a proportion of NPP is allocated to stem growth, differences in NPP between years or between stands will reflect interannual variations in stem growth, both in mass and in volume. Most spatial NPP models use relatively coarse resolution data sets to produce estimates at a regional or national scale (Coops et al. 1998, Field et al. 1998, Reich et al. 1999, Liu et al. 2002). To be more receptive to those in operational forestry, finer resolution NPP models need to estimate productivity at the scale of forest stands. Process-based models have also largely been directed at advancing understanding of ecosystem function rather than to predict wood volume productivity or site indices relevant to forest management (Tickle et al. 2001). NPP models, however, can play an important role through their ability to forecast growth under new management options or climatic conditions. A major challenge is the validation of process-based model estimates, owing to a relative lack of actual measurements of NPP over extensive landscapes (Coops and Waring 2001b). Recent efforts have therefore focused on expressing model

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outputs that are more comparable to productivity information of interest to forest managers (Bernier et al. 1999, Raulier et al. 2000, Tickle et al. 2001, Raulier et al. 2003). Recent examinations of plot-level growth data in the Foothills Model Forest (west-central Alberta) have suggested that site indices of regenerated pine stands are generally higher than those of the stands they replaced. Subsequent investigation has indicated there are some differences in site index between older and younger stands, but the trends are as yet unclear (D. Lyder and D.T. Price, 2005, unpublished data). It was hypothesized that increases in annual temperature observed for Canada during the 20th century, particularly in western Canada, could be a significant factor contributing to this apparent increase. One approach to testing this hypothesis would be to validate estimates of forest productivity expressed as NPP obtained from a process-based stand-level model against more conventional forestry measures of productivity. This approach would then offer the potential to estimate climate impacts on productivity through the introduction of climate scenarios. In the current study, we have focused on a model called StandLEAP, derived from the 3PG model of Landsberg and Waring (1997), which has been designed to estimate forest productivity over large areas (Bernier et al. 1999). The objectives of this study were, therefore, a) to determine, for a contiguous 2700 km2 region of the Foothills Model Forest, how estimates of NPP from StandLEAP would compare with more conventional estimates of productivity based on site index used in the Alberta Vegetation Inventory (AVI) (Huang et al. 1994, Nesby 1997); and b) to assess the influence of input data and modeling errors on above-ground NPP prediction. Forest productivity in the AVI is expressed as a timber productivity rating, which is the potential timber productivity of a stand based on the site index computed from the height and age of dominant and codominant trees (Alberta Environmental Protection 1991). Simulated NPP, as an estimate of forest productivity, was therefore compared to the timber productivity rating of stand polygons. The aim of the overall effort was the development of methodologies whose application could be extended to the boreal forest of west-Central Alberta (Ecological Stratification Working Group 1995).

Methods Study region

The study region lies south of Hinton, Alberta, with corners located approximately at 53.35°N, 116.50°W on the northeast and at 53.00°N, 117.50°W on the southwest (Fig. 1). Four ecoregions are present, classified as Lower Foothills, Upper Foothills, Subalpine and Montane (Achuff 1992), comprising 68%, 21%, 10% and 1% of the total area, respectively. Elevations range from 1070 m above sea level (ASL) in the east to 1725 m (ASL) at the extreme west. The forest stands are dominated by lodgepole pine (Pinus contorta Dougl. var. latifolia Engelm.) and white spruce (Picea glauca (Moench) Voss). In the Lower Foothills, pure or mixed stands of trembling aspen (Populus tremuloides Michx.) and balsam poplar (Populus balsamifera L.) are interspersed with lodgepole pine and white spruce, respectively, while black spruce (Picea mariana (Mill.) BSP) and tamarack (Larix laricina (Du Roi) K. Koch) dominate in poorly drained areas. Mean monthly tem-

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Fig. 1. Map of study region within Foothills Model Forest and its location within Alberta.

peratures range from approximately –13.0°C (January) to +14.5°C (July), while mean total annual precipitation reaches 540 mm. Average growing season is about 1050 growing degree days (5°C base, GDD), with 740 GDD typically occurring during June, July and August. Spatial datasets

The primary data sources included existing climate, inventory and remote sensing data sets along with other ancillary site-level data such as soil texture (Table 1). Spatial data generated from remote sensing included above-ground biomass, Leaf Area Index (LAI) and fraction of incoming Photosynthetically Active Radiation absorbed by the vegetation (fPAR). All coverages were projected to UTM Zone 11, NAD83 using data clipped to the study region limits. Climate surfaces Climate surfaces were created using the daily weather generation program described in Régnière (1996), a component of the BioSIM Pest Management Planning Decision Support program (Régnière et al. 1995). The program interpolates monthly climate normals from a database of local climate station data. Geographic variation, vertical lapses and microclimatic effects are taken into account during creation of the climate surfaces. Input data (latitude, longitude, elevation, slope, aspect) were derived from the Alberta provincial 25metre digital elevation model (DEM) coverage. The output was a complete set of simulated monthly climate records including mean daily and monthly temperature maxima and minima (°C), and rainfall and precipitation totals (mm) for each DEM grid-point. In addition, monthly incoming photosynthetically active radiation (PAR) was computed for each pixel from monthly means of maximum and minimum temperatures and total


monthly precipitation using the algorithm of Nikolov and Zeller (1992). With this algorithm, extra-terrestrial shortwave radiation as attenuated by atmospheric effects is computed as a function of latitude, elevation and cloudiness to produce the total shortwave radiation on a horizontal surface. Monthly cloud cover was estimated from mean monthly temperature, relative humidity and precipitation. This radiation was then decomposed into its diffuse and direct components and subsequently adjusted for pixel slope and exposition. PAR was estimated as one half of total shortwave radiation (Ross 1975). Alberta Vegetation Inventory (AVI) Relevant forest stand data were extracted from the Alberta Vegetation Inventory v.2.1 (AVI) database (Alberta Environmental Protection 1991) to assess NPP modeling at the stand level. The AVI is based on aerial photographs acquired during 1988 and 1993. Key attributes of interest include species composition, crown closure, stand height, year of origin, moisture regime and timber productivity. Species composition was estimated from the reported proportions of each species in 10% increments. Crown closure was classified to four crown closure classes — A: 6–30%, B: 31–50%, C: 51–70%, and D: 71–100%. Stand height was calculated as the average height of the dominant and codominant trees rounded to the nearest metre. Stand age was estimated by subtracting the year of inventory from the midpoint of the decade of origin. Stand-level moisture regimes for all vegetated land cover types were based on an interpreter’s assessment using plant or environmental indicators. The modeling task was simplified by first aggregating the numerous forest cover types found within the AVI into five forest cover groups in a way that permitted the representation of the largest proportion of forest area within the fewest pos-

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Table 1. Sources of data used to develop input for spatial modeling in the Alberta study region Information

Dataset Title

Record Type

Source

Extent

Description

Elevation

DEM grid

raster

AEP*

Study area

“Digital Elevation Model”: 25m resolution elevation

NTDB

point, line, polygon

NRCan

NTS mapsheets for soils

“National Topographic Database”: 1:50,000 scale contours and point elevations, water lines and polygons

Vegetation

AVI 2.1

polygon

Weldwood

Study area

“Alberta Vegetation Inventory”: year of origin, 5-level overstory species and percentage, height, crown closure, natural region, volume per ha, year of inventory

Biomass

PGS

point

Weldwood

Weldwood FMA

“Permanent Ground Sample (PGS)”: tree level species, dbh, height, status Tree biomass measurements Tree biomass measurements

SINGH non-spatial report MANNING non-spatial report

Prairies and NWT Yukon

ESIS PGS MSE

point point point

West-central Alberta Ecological Site Information System Weldwood FMA Permanent Ground Sample

PLUTH

point

Remote sensing Landsat

raster

Landsat Study area Thematic Mapper (TM )

Summer satellite image used to model and map leaf area and biomass

Leaf Area Index LAI

point

Optical data from field

Optical LAI-2000 and TRAC data collected at selected PGS plots.

Soils

*AEP, Alberta

AEP Weldwood Foothills Model Forest Dr. D. Pluth

West-central Alberta Managed Stand Ecosites Northwest-central Alberta Soil sample data collected by Dr. Pluth

Study area

Environmental Protection.

sible number of classes. The species groups were lodgepole pine (Pl), white spruce/balsam fir (Sw/Fb), black spruce/larch (Sb/Lt), “deciduous” (predominantly trembling aspen, Aw, and balsam poplar, Pb), and “mixed” (i.e., stands consisting of a mixture of conifer and deciduous species in proportions of 50/50, 60/40 and 70/30 or vice versa). These combinations accounted for nearly 95% of the forest polygons within the study area. A georeferenced Permanent Growth Sample (PGS) plot database was also provided by Weldwood of Canada (now Hinton Forest Products). These PGS plots had been established by Weldwood to represent the dominant softwood, dominant hardwood and mixedwood stand types throughout their managed land base (Weldwood of Canada Limited 1999). This database contained individual measurements of tree height and diameter at breast height (DBH) at each PGS plot. We used these data to estimate above-ground biomass. Some of these plots were also visited for field measurement of Leaf Area Index. Remote sensing imagery We used a Landsat-5 TM image (track 45, frame 23), acquired on 8 September 1999, for this study, and applied a correction algorithm developed by the Canada Centre for Remote Sensing (CCRS) to produce a top-of-atmosphere corrected reflectance image. Further pre-processing included ortho-

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rectification of the image based on ground control points identified from National Topographic Data Base (NTDB) digital maps at 1:50 000 scale and on elevation data collected from the Alberta provincial 25-metre resolution digital elevation model (DEM). The root mean square error (RMS) of the ortho-rectified image was less than 1 pixel. The image scene covered an area approximately 33 000 km2 in size, with bounding coordinates at: northwest: 54°03’42”N, 118°57’28”W; northeast: 53°38’33”N, 116°07’15”W; southwest: 52°31’35”N, 119°38’07”W; and southeast: 52°07’17”N, 116°53’35”W. The remote sensing image was used with the field and inventory data to produce maps of above-ground biomass and leaf area index over the study area. a) Above-ground biomass map used for NPP allocation Accurate estimation of forest biomass is needed for studies of ecosystem productivity and in models for calculating and forecasting carbon budgets (Penner et al. 1997; Price et al. 1997, 1999; Kurz and Apps 1999; Parresol 1999). Biomass data from the boreal forest regions of the Prairie Provinces, Northwest Territories (Singh 1982, 1984) and Yukon (Manning et al. 1984) were used to develop functions relating tree height (m) and diameter at breast height (DBH) to total above-ground tree biomass (kg tree-1) by species. These functions were then applied to all trees in each PGS plot, and the individual tree biomass values summed to estimate stand-


level biomass density (Mg ha-1). We then derived non-linear functions to express stand biomass as a function of stand height and the mid-point of the crown closure class from the PGS plot database (Hall et al. 2002). We applied these standlevel functions of biomass to the stand attributes in the AVI database to assign a biomass estimate to each stand polygon. b) Leaf Area Index (LAI) map Leaf area index (half-total surface area of leaves per unit of ground area) is related to stand productivity because it defines the exchange surfaces available for light absorption and CO2 uptake (Monteith and Unsworth 1990). The three major steps in estimating and mapping LAI were (a) collect and process optical (i.e., non-destructive) LAI measurements at a sample of PGS plot locations; (b) relate the optical LAI measurements to satellite spectral response values; and (c) develop a statistical model to produce a satellite-image based LAI map of the study area. Ground-based optical LAI measurements were made in summer 2001 at 27 PGS plots chosen randomly throughout the study area, during the same phenological period as the satellite image. The optical method used two field-portable electronic light sensitive instruments, TRAC “Tracing Radiation and Architecture of Canopies” (Chen and Cihlar 1995) and the Li-Cor LAI-2000 (Li-Cor Inc. 1991), which measure the proportion of solar radiation transmitted through the canopy. The TRAC measures LAI of “clumped” canopies typical of stands that are either dominated by conifers or contain mixtures of species (Chen and Cihlar 1995). Combining the canopy clumping index derived from the TRAC with the LAI measurement of the LAI-2000 has been shown to produce significantly more accurate estimates of LAI than the use of the LAI-2000 alone, particularly in conifer-dominated forest canopies (Chen et al. 1997). The TRAC measurements were collected along two parallel 50-m transects. LAI-2000 readings were obtained at 1.3 m above ground and at 10 locations within the stand defined by 10-m intervals along the two transects used for TRAC data collection. This spatial sampling strategy was similar or identical to that reported by Chen and Cihlar (1996), Dantec et al. (2000), Nackaerts et al (2000) and Leblanc and Chen (2001). This data collection process resulted in an oversampling of LAI2000 measurements within a single plot that is common when multiple in-stand measurements are used to derive an average of LAI-2000 measurements. Oversampling increases the signal to noise of average LAI-2000 measurements and therefore helps to reduce variance and the occurrence of anomalous high or low LAI values that could result from recording 10 single measurements at different locations within the area of the plot. The reduced simple ratio (RSR), following the recommendations of White et al. (1997) and of Brown et al. (2000) was considered the preferred vegetation index for LAI retrieval in boreal forests due to a) reported increased sensitivity to LAI compared to other indices, b) greater potential to unify responses from coniferous and deciduous species, c) minimizing the effect of background vegetation, and d) reducing the likelihood of saturation over a broad range of vegetation types. The RSR is computed as the simple ratio multiplied by a short-wave infrared (SWIR) adjustment factor:


[1] where RED, NIR, and SWIR terms represent the pixel reflectance values in the red, near and middle infrared portions of the electromagnetic spectrum, and SWIRmin and SWIRmax are the minimum and maximum values of SWIR in forested pixels of the image, respectively. These latter terms represent average reflectance values from completely closed and open canopies, as determined from inventory data contained in the PGS plot database. The number of plot samples needed to extract the minimum and maximum SWIR values was computed to provide an estimate within 20% of the mean at the 90% probability level (Freese 1962). From these computations, SWIR band image values were extracted from the locations of 14 plots in each of open (6–30%) and closed (71–100%) crown closure stands. The average minimum and maximum SWIR values were then used in eq. 1 to generate a RSR for the forested vegetation within the study area. The average RSR values within a 33 pixel window were then correlated with the optical LAI values measured in the field, and the resulting relationships were used to generate the LAI map. To ensure the RSR was the most appropriate model predictor of LAI for the study area, models based on the normalized difference vegetation index (NDVI) and simple ratio (SR) (Chen 1996a) were also generated and compared to the RSR in terms of empirical relationship and model performance (coefficient of determination – R2 and root mean square error – RMSE). Soil texture map Weldwood of Canada (now Hinton Forest Products) provided a digital soils map, based on the Hinton-Edson Area Soil Survey Report of Dumanski et al. (1972). Texture class codes were assigned to map polygons using the generalized “modal profile” texture corresponding to each polygon’s primary soil association and soil variant attributes, as reported by Dumanski et al. (1972). This provided soil texture coverage for the top 15 cm of mineral soil across the study region, with a typical polygon area of 2.5 to 3.0 km2, as compared to the average 3.5-ha area for individual AVI polygons. We therefore developed a higher-resolution map of surface soil texture using data taken at PGS plots by forest inventory survey teams and artificial neural networks to estimate soil texture classes (sand, silt and clay fractions) from elevation, slope angle and other topographic factors. This proved moderately successful and was further used to derive high resolution texture data for the surface layer (top 15 cm) in each AVI polygon (D. Lyder and D.T. Price, unpublished data). Modeling forest productivity using StandLEAP

Modeling total above-ground NPP (NPPa) StandLEAP is based on the 3-PG model of Landsberg and Waring (1997). In StandLEAP, absorbed photosynthetically active radiation (APAR) is transformed into gross primary productivity (GPP, mol m-2 month-1), using a radiation use efficiency (RUE) coefficient (): [2]

GPP=APAR

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where , and where represents a species-specific mean value of RUE and f1…n are speciesspecific environmental modifiers that further modulate the relationship between APAR and GPP. These modifiers are used to represent the impact of soil drought (f as in Landsberg and Waring 1997, Bernier et al. 2002) and of frost (fF - as in Aber et al. 1995) and of mean air temperature, vapour pressure deficit, monthly radiation, and leaf area index. In most cases, the modifiers take on a value of 1 when conditions are average, and values either greater or less than one when conditions are either above or below average for the transformation of APAR into GPP. For deciduous species, monthly APAR was adjusted throughout the growing season for changes in leaf area due to phenological development, as in the PnET model (Aber and Federer 1992). Respiration was computed as a fixed proportion of GPP (Waring et al. 1998, Malhi et al. 1999), and NPP was computed monthly as GPP minus respiration. As in 3PG, total NPP was initially allocated to fine roots (Landsberg and Waring 1997, eq. 13), and then to replacement of carbon biomass that was due to leaf and fine woody litter turnover. The remaining NPP was then allocated to stand carbon compartments (foliage, branches, stems and coarse roots). StandLEAP represents stand carbon allocation with the help of allometric equations in 3-PG but stand carbon compartments are related to stand above-ground biomass instead of mean tree DBH. Canopy light absorption and photosynthesis parameters were derived from metadata generated using a more detailed multi-layer, hourly time step model of canopy photosynthesis and transpiration called FineLEAP (Raulier et al. 2000; Bernier et al. 2001, 2002). This required foliar gas exchange measurements, including response curves for photosynthesis to PAR, temperature and vapour pressure deficit (VPD), the characterization of light absorption, and changes in these

properties with shoot age. FineLEAP results were then aggregated to the monthly time step and used to estimate parameters for radiation interception, radiation- and water-use-efficiency using non-linear ordinary least-squares (NOLS) regression (Raulier et al. 2000). Actual measurements were used to calibrate the model applied to the dominant tree species present in the study area and completed with data obtained from other published studies (Table 2). Parameters for stand allometric equations were also derived from metadata developed for PGS plots classified as “pure” (i.e., where more than 80% of plot basal area was contributed by the dominant species). Above-ground tree biomass was estimated using national biomass equations (Lambert et al. 2005), while tree coarse root biomass was estimated using data from the literature (Table 2). These biomass estimates were then summed to the stand level and used to estimate parameters of the stand allometric equations with simultaneous NOLS regression. Total above-ground NPP (NPPa) was calculated as total NPP less the fractions allocated to fine and coarse roots. The NPPa modeling was undertaken with the input data layers in a raster (pixel) format. Each pixel was treated as a binary system with two species, as identified in the forest cover maps. Since the inventory maps provided information about forest cover as classes, all pixels were considered as mixed, with percentages of each of the species assigned randomly within the class limits of canopy coverage. NPPa estimates for each pixel was then the sum of NPPa for the two dominant species, estimated independently, and pro-rated to their respective canopy coverage. Spatial inputs to StandLEAP included soil texture, slope and aspect, stand properties, and monthly mean daily maximum and minimum air temperature and precipitation. Stand properties included forest type, above-ground biomass (Mg ha-1) and LAI. Modeled values of NPPa and AVI-based estimates of timber productivity were averaged over a 33 pixel window and

Table 2. Summary of sources used in the parameterization of StandLEAP for the five dominant tree species found in the Foothills Model Forest Parameter

Pinus contorta

Picea glauca

Pinus banksiana

Light absorption

Oker-Blom et al. (1991), Sampson and Smith (1993)

Marten Hills, AB (Jim Stewart pers. comm.), present study

BOREAS study TE-06 (Gower et al. 1997) and TE-23 (Fournier et al. 1997), Chen (1996b)

Photosynthesis, transpiration and leaf energy balance

BOREAS study TE-09 (Dang et al. 1997), Weldwood 1956 Gregg Burn, AB (Jim Stewart, pers. Comm.), Smith (1980)

Marten Hills, AB (Jim Stewart, pers. comm.), Landsberg and Ludlow (1970)

BOREAS study TE-09 (Dang et al. 1997)

Respiration

Ryan et al. (1997)

Phenology and frost effects

Burton and Cumming (1995), Aber et al. (1995)

Burton and Cumming Burton and Cumming Frolking et al. (1996), (1995), Scheller and (1995), Aber et al. Aber et al. (1995) Mladenoff (2005) (1995)

Allocation and allometry

Lambert et al. (2005), Comeau and Kimmins (1989), Ryan (1989)

Lambert et al. (2005), Ker and van Raalte (1981)

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Picea mariana

BOREAS study TE-09 (Dang et al. 1997), Landsberg and Ludlow (1970)

Populus tremuloides

BOREAS study TE-09 (Dang et al. 1997), Middleton et al. (1997)

Hogg (1999), Aber et al. (1995)

Lambert et al. (2005), Steele et al. (1997), Gower et al. (1997)


randomly sampled at a 30% sample intensity, along with ecoregion, species group and elevation attributes. Of interest was determining whether NPPa values would vary with an independent and more conventional assessment of timber productivity derived from the inventory. Rating the impact of modeling and input errors on NPPa predictions Establishing the accuracy of the NPPa predictions over extensive landscapes is difficult because adequate ground-based validation data are usually unavailable (Coops and Waring 2001b). This problem was addressed for lodgepole pine, the dominant species in the study area, by undertaking a threestep process to compare estimates at PGS plots and to conduct a sensitivity analysis. First, StandLEAP predictions of net above-ground biomass increment (Husch et al. 1982) were compared with those predicted by GYPSY (Huang et al. 2001), a stand growth and yield model currently used in Alberta for management of pure lodgepole stands. This relative comparison was directed at determining if the outputs between these two independent models were similar. A sample of 103 PGS plots whose basal area was more than 80% lodgepole pine was selected. StandLEAP and GYPSY were then used to estimate above-ground biomass at each PGS plot between the first (1957–1959) and second (1962–1966) measurement periods as recorded in the PGS database. GYPSY predicts standing merchantable volume, which necessitated a proportionality coefficient for translation to above-ground biomass. This coefficient was derived from the 103 PGS sample plots, for which above-ground tree biomass was estimated using national biomass equations (Lambert et al. 2005). Plot level biomass was summed to the stand level and the proportionality coefficient for standing volume determined using ordinary least squares regression. Second, StandLEAP estimates of NPPa were compared to corresponding observed values. The mean NPPa of these plots between the first two measurement periods was calculated by adding the mean annual biomass lost in stem mortality and an estimate of leaf and fine woody litter production to the observed standing biomass increment (Gower et al. 1999). Annual leaf litter production was calculated by averaging the standing leaf biomasses reported for both measurement periods and dividing by the average leaf expectancy (Schoettle 1994). Third, the sensitivity of NPPa to environmental factors was estimated as the relative change in predicted NPPa divided by the relative change in one of the input data layers, including stand and soil types, monthly rainfall, monthly mean minimum and maximum temperatures, LAI and above-ground biomass. The assignment of stand types into the five AVI species cover groups described previously constrained the choice of parameters when using StandLEAP to predict NPPa. Five different simulations were therefore performed to assess the effects of incorrect species group assignment by randomly changing the plot cover group (lodgepole pine) to any other cover group for 0, 25, 50, 75 and 100% of the plots. Soil type has an influence on the estimation of the soil water-holding capacity, a property that integrates both soil texture and rooting depth that can affect stand transpiration and NPP. For soil types, instead of changing soil texture, rooting depth was randomly changed between 0 and 2


meters, whereas a constant depth of 1 meter is normally used in StandLEAP. The same approach was used for examining sensitivity to monthly rainfall, minimum and maximum temperatures. We conducted a sensitivity analysis by changing each climate variable, in turn, by a random percentage varying between ± 100% (Matsushita et al. 2004) of the values given by the climate normals (assuming 0°C as a baseline for temperature changes). This random percentage was kept constant for each plot, resulting in the introduction of a constant annual bias into the twelve monthly values of each one of the climate variables. The generation of the spatial LAI and above-ground biomass data from remote sensing imagery involved many complex steps (see above), so their precision and accuracy were subjected to an empirical assessment. The global predicted and observed values were compared with their corresponding calibration data sets, revealing significant heteroscedasticity in both LAI and above-ground biomass errors. Therefore, a weight, derived as a function of the predicted value, was first estimated with feasible generalized least squares (FGLS1, Gregoire and Dyer 1989). A weighted confidence interval was then built around the individual predicted values (e.g., Zorn et al. 1997, eq. 13). Finally, the LAI and above-ground biomass inputs used in StandLEAP were randomly altered (normal distribution) within their approximated confidence intervals. Also, a correlation was expected between the errors of predicted LAI and above-ground biomass for each pixel. This correlation was tested on plots that were used for the calibration of the LAI and above-ground biomass prediction models from remote sensing.

Results Derived data products

Biomass map The independent tree measurement data from Singh (1982, 1984) and Manning et al. (1984) were strongly associated with measured stem volumes (i.e., DBH squared multiplied by tree height) for all four dominant species groups (Fig. 2). Non-linear allometric regression models were highly significant, with relatively low errors (Root Mean Square Error –RMSE, Fig. 2). Scatter plots comparing the validation data for predicted biomass with independent biomass estimates in the literature also suggest that our predicted stem volumes were reasonable for the four species groups (Fig. 3). Estimates of stand-level biomass were computed for a randomly selected sample of AVI polygon data, covering a wide range of stand ages, heights and crown closures (Table 3). Average biomass densities (Mg ha-1; 1 Mg = 1 metric tonne) varied among the species groups, with the highest for lodgepole pine and the lowest for black spruce/larch (Table 4). Based on tests of different statistical models of the relationship between biomass density (B), stand height (H) and crown closure (CC), the overall best-fit model was found to be of the general form: [3]

(B)1/3 = b0 + b1(ln H) + b2(CC)

The transformations of the B and H terms served to improve model fit and decrease heteroscedasticity of variance in the data. The model fits attained by species groups were (fit index calculated by transforming the dependent variable in

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Fig. 2. Relationships between tree biomass (kg tree-1) and D2H compiled from data sets of Manning et al. (1984) and Singh (1982, 1984), for the four “pure” species groups.

Fig. 3. Relationships between observed and predicted biomass data for the four “pure” species groups.

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Table 3. Descriptive statistics for stand structure as represented by provincial inventory (AVI) stand age, height, and crown closure within the study area. Values are based on thirty percent random sample of AVI polygons over the study area. Stand age (years)

Stand height (m)

Crown closure (%)

Statistic

Lower Foothills

Upper Foothills

Lower Foothills

Upper Foothills

Lower Foothills

Upper Foothills

Lodgepole pine

N Mean SD* Range Minimum Maximum

434 90.7 33.1 148 6 154

3017 75.2 32.1 186 6 192

434 19.3 5.9 26 2 28

3017 16.6 5.8 26 2 28

434 47.1 16.8 70.5 15.0 85.5

3017 48.8 17.8 70.5 15.0 85.5

White spruce/ Balsam fir


222 89.5 50.2 170 9 179

528 83.4 50.3 210 6 216

222 15.3 7.6 29 2 31

528 16.6 7.8 33 2 35

222 44.2 17.5 70.5 15.0 85.5

528 39.5 17.9 70.5 15.0 85.5

Black spruce/ Larch


855 95.4 26.6 167 8 175

1507 89.8 32.6 263 9 272

855 9.9 3.4 20 3 23

1507 9.8 3.3 19 2 21

855 47.2 15.6 70.5 15.0 85.5

1507 40.4 17.0 70.5 15.0 85.5

Deciduous


207 73.0 37.4 123 7 130

354 67.6 35.7 130 6 136

207 18.5 7.3 24 4 28

354 18.1 7.0 26 2 28

207 52.0 18.8 70.5 15.0 85.5

354 50.2 20.6 70.5 15.0 85.5

Mixedwood


212 67.4 40.7 152 7 159

367 68.5 40.0 170 6 176

212 15.8 8.3 27 2 29

367 17.0 7.6 26 2 28

212 47.4 15.8 70.5 15.0 85.5

367 46.8 16.0 70.5 15.0 85.5

Species

*SD, standard deviation

eq. [3] back to biomass (Parresol 1999) as a measure of R2, RMSE): Pl (0.77, 37.1), SwFir (0.62, 43.5), SbLt (0.60, 31.7), Decid (0.77, 41.2), and Mixed (0.72, 38.8). Paired t-tests for biomass comparing the fitted statistical models and the validation data suggested no significant differences (p < 0.05) in the estimates of stand-level biomass for Pl (p = 0.35), SwFir (p = 0.57), Decid (p = 0.25), and Mixed (p = 0.42). The only exception was for SbLt (p = 0.03), where differences in biomass between model estimates and validation data were statistically significant. Above-ground biomass is larger in the Lower Foothills ecoregion compared to the Upper Foothills, though with similar variability for three of the five species groups. The biomass densities of the lodgepole pine, black spruce/larch and deciduous species groups are larger in the Lower Foothills (Table 4) because of the higher proportion (75–91%) of mature stands (> 100 years) in these species groups (Table 3).


The deciduous species group has the second largest average biomass density, which we attributed to the majority of stands being mature with large basal areas (R.J. Hall, unpublished data). Fig. 4 depicts the spatial variability in above-ground biomass density across the study region. Within this spatial variability, there is a visible trend of increasing biomass density from west to east and from Upper Foothills to Lower Foothills, which is consistent with local knowledge of stand level yields for this region7 (Fig. 1, 4). Biomass densities coincide with the patterns of stand ages in the Upper and Lower Foothills that are influenced, in part, by the fire history in the study area (Table 3, 4).

7Huang, S. Alberta Sustainable Resource Development, Edmonton,

Alberta. May 18, 2005, personal communication.

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Table 4. Descriptive statistics for aboveground biomass, LAI and total aboveground NPP based on thirty percent random sample over the study area Biomass (Mg ha-1)

NPP (g m -2 yr -1)

LAI

Statistic

Lower Foothills

Upper Foothills

Lower Foothills

Upper Foothills

Lower Foothills

Upper Foothills


434 252.2 112.8 474.3 8.3 482.6

3017 200.1 103.8 480.9 1.7 482.6

434 4.5 0.9 6.5 0.5 6.8

3017 4.4 1.0 8.7 0.0 8.7

434 783.5 97.9 830.5 173.3 1003.8

3017 696.7 109.3 916.6 21.6 938.2


222 162.8 110.7 436.4 13.5 449.9

528 179.3 111.3 542.8 13.5 556.3

222 4.4 1.4 6.7 0.7 7.4

528 4.6 1.4 7.6 0.0 7.6

222 345.3 95.0 765.9 104.3 870.1


855 80.9 56.2 396.1 1.7 397.8

1507 72.9 51.9 384.3 0.0 384.3

855 4.3 1.0 7.6 0.0 7.6

1507 4.2 0.9 6.4 0.6 7.0

855 697.0 148.0 1072.2 7.0 1079.3

1507 655.5 143.5 896.8 101.4 998.3

Deciduous


207 203.1 114.8 377.2 9.6 386.8

354 191.9 106.8 415.5 7.7 423.2

207 4.3 1.4 6.7 0.3 6.9

354 4.2 1.3 7.2 0.0 7.2

207 824.7 126.1 756.3 257.9 1014.2

354 814.1 147.2 957.9 42.3 1000.2

Mixedwood


212 186.2 139.0 486.5 3.0 489.5

367 203.5 132.9 454.9 3.0 457.9

212 4.3 0.9 5.8 0.9 6.7

367 4.4 0.9 5.9 0.5 6.5

212 706.9 171.2 689.2 280.5 969.7

367 710.6 145.2 844.5 96.4 940.9

Species Lodgepole pine

White spruce/ Balsam fir

Black spruce/ Larch

528 370.6 89.8 889.6 6.18 895.8

*SD, standard deviation

Leaf Area Index map Estimates of LAI ranged from 0.8 (m2 foliage (half-total surface) area per m2 ground area) in a young regenerating stand of balsam poplar to a maximum of 8.1 in a mature, dense, upland black spruce stand. There was a statistically significant relationship between optical LAI and the satellite remote sensing model based on the Landsat-TM data RSR (r = 0.68, p < 0.0001). The RSR was used to produce a map of LAI for the study region (Fig. 5) (R2 = 0.47, p < 0.0001, RMSE = 1.3), which was a stronger predictor than models based on NDVI (R2 = 0.06, p < 0.0001, RMSE = 1.7) and SR (R2 = 0.05, p < 0.0001, RMSE = 1.7). The LAI map was based on a sample of 27 LAI field measurements, which is well within the target number recommended for mapping LAI at the image scene level by Chen et al. (2002), in a study area that was smaller than a full Landsat scene by a factor of 12. While the model R2 based on RSR was within the range typically reported in the

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boreal forests of Canada (R2 between 0.38 and 0.66) (Brown et al. 2000), improvements may be possible from additional samples and refinements to model development by considering the error structures in these variables (Fernandes and Leblanc 2005). Fig. 5 depicts a spatial pattern of high and low LAI, generally ranging between 3.0 and 5.0, that compared well with variations in biomass density (Fig. 4). Mean LAI values were similar between Lower Foothills and Upper Foothills for each species group, although Upper Foothills data were generally more variable (Table 4). Modeling forest productivity: Total above-ground NPP

Distribution of total above-ground NPP StandLEAP’s estimates of NPP across all species groups (Table 4; Fig. 6) are in the general range of previously published values for boreal and other Canadian interior ecosystems (Comeau and Kimmins 1989, Prescott et al. 1989,


Fig. 4. Estimated aboveground biomass (Mg ha-1) within the Foothills Model Forest study area. While analysis in this study was confined to the forested area, the biomass of non-forested, vegetated land cover was estimated from look-up tables compiled by M.-C. Lambert and H. Ung, Natural Resources Canada, Laurentian Forestry Centre, Quebec.

Fig. 5. Leaf area index (m2 m-2) for the forested land cover within the Foothills Model Forest study area, as estimated from Landsat TM imagery.

Fig. 6. Aboveground net primary productivity (g m-2 yr-1) as simulated by StandLEAP for the forested land cover within the Foothills Model Forest study area.


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a) Lodgepole pine

b) White spruce/Balsam fir

c) Black spruce/Larch

d) Deciduous

e) Mixedwood

Fig. 7. Changes in mean NPP and standard deviation as simulated by StandLEAP, for different species groups and elevation classes.

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Table 5. Average total aboveground NPP values (g m -2 yr -1) summarized by timber productivity rating and stratified by species group and natural sub-regions in study area Lower Foothills Timber productivity rating

Upper Foothills

N

Mean

C.I.*

N

Mean

C.I.*

a) Lodgepole pine Good Moderate Fair Unproductive

138 231 64 1

779.6 785.3 785.7 789.6

(761.7, 797.5) (772.8, 797.7) (764.0, 807.4) (789.6, 789.6)

784 1517 668 48

723.5 699.9 664.6 603.6

(716.5, 730.6) (694.6, 705.3) (656.0, 673.2) (564.3, 642.9)

b) White spruce/Balsam fir Good Moderate Fair Unproductive

15 66 87 54

328.0 382.6 335.2 320.9

(285.2, 370.9) (350.3, 414.9) (318.1, 352.4) (309.3, 332.5)

102 253 116 57

371.1 385.2 355.7 335.6

(352.5, 389.7) (374.1, 396.3) (340.6, 370.9) (313.5, 357.8)

c) Black spruce/Larch Good Moderate Fair Unproductive

41 177 152 485

735.5 731.5 708.9 677.5

(688.4, 782.6) (713.2, 749.8) (685.7, 732.2) (663.8, 691.2)

55 395 301 756

685.8 675.3 670.6 636.9

(644.4, 727.1) (661.0, 689.6) (655.0, 686.2) (626.7, 647.0)

d) Deciduous Good Moderate Fair Unproductive

44 108 46 9

839.3 838.1 791.6 761.0

(801.8, 876.8) (816.2, 860.1) (750.3, 832.9) (633.3, 888.7)

83 204 61 6

848.3 825.8 737.4 721.0

(824.3, 872.3) (804.8, 846.7) (700.5, 774.3) (508.7, 933.3)

e) Mixedwood Good Moderate Fair Unproductive

71 85 47 9

765.1 691.6 676.6 549.9

(734.4, 795.9) (651.9, 731.2) (624.9, 728.4) (423.8, 676.1)

147 160 57 3

720.4 713.8 678.5 669.2

(698.0, 742.8) (690.7, 737.0) (638.2, 718.7) (135.9, 1202.5)

*Confidence interval at the 95% probability level for standard error of the mean

Gower et al. 1997, Goetz and Prince 1998, Smith and Resh 1999, Kimball et al. 2000). Generally, NPPa estimates are higher and less variable in the Lower Foothills than in the Upper Foothills, notably for the lodgepole pine, black spruce/larch and deciduous species groups (Fig. 6). Similar to patterns of above-ground biomass and LAI, modelled NPPa shows a generally increasing trend from west to east across the study region, as reflected by the decrease in mean NPPa with increasing elevation (Fig. 7). Estimated NPPa values are highest for the deciduous species group, which is consistent with other observations of productivity in boreal species (e.g., BOREAS sites in southern Saskatchewan; Black et al. 1999, Barr et al. 2002). There is relatively little difference in estimated NPPa between the two predominant ecological zones across all species groups, which is to be expected given the general similarity in LAI seen for the two ecoregions (Fig. 5; Table 4). Association of total above-ground NPP with timber productivity Higher NPPa values were generally associated with higher timber productivity values across the study region in both


Lower and Upper Foothills (Table 5). There was some variation in this response for the lodgepole pine and white spruce/balsam fir species groups, but this may be attributable to the relatively broad classes of timber productivity recorded in the AVI database. In particular, the timber productivity ratings were generally higher for the deciduous and mixedwood species groups than for the conifer species, consistent with the observation noted earlier for boreal species. There was also a correspondence between high NPPa (Table 5) and the relatively large biomass (Table 4) for the deciduous species group. The results for mixedwood stands merit further investigation as previous reports suggest productivity in mixedwood stands is often higher than that of pure species stands growing on the same sites (MacPherson et al. 2001). Above-ground NPP was generally higher in the Lower Foothills ecoregion, compared to the Upper Foothills, across all timber productivity classes — with the previously noted exception of white spruce/balsam fir. These results were likely attributed to differences in elevation (Fig. 7). Lower elevation sites would generally be warmer during the growing season, and may also have more soil moisture due to drainage from higher elevations. These results confirm a general asso-

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decrease of -2 and -4°C resulted in a NPPa decrease of -18 and -52% (-16 and -26% for minimum temperature). The degradation of the accuracy surrounding stand type assignment by the AVI introduced a slight but significant bias in estimated NPPa (p = 0.004), but more importantly, there was symmetrical variation around the expected values of ± 25%, independent of the accuracy in stand type determination (Fig. 9d).

Discussion

Fig. 8. Relationship between observed (i.e., as estimated from successive inventories) and predicted (i.e., as simulated by StandLEAP) aboveground NPP data for 103 pure lodgepole pine stands.

ciation between productivity predicted by the StandLEAP process model and the timber productivity rating based on direct measurements of site index. Error and sensitivity assessment around predicted aboveground NPP values StandLEAP tended to overpredict above-ground biomass increments in pure lodgepole stands. Observed biomass increments within the data used for this error assessment averaged 1.9 Mg ha-1 y-1 with a standard deviation of 1.9 Mg ha-1 y-1. The average bias of StandLEAP predictions was 1.7 Mg ha-1 y-1 (with a standard deviation of 2.0 Mg ha-1 y-1). This accuracy was, however, comparable to that obtained from GYPSY, which underpredicted above-ground biomass increments for this data set by 0.8 Mg ha-1 y-1, on average (standard deviation of 1.8 Mg ha-1 y-1). Hence, although StandLEAP predictions were more biased than those from GYPSY, overall accuracy was similar. Values of above-ground NPP calculated from the two successive plot inventories were correlated to those predicted by StandLEAP for pure lodgepole pine stands (Fig. 8). This correlation was, however, somewhat artificial given that the “observed” values result from the summation of gross biomass increment and litter losses (Gower et al. 1999). Plot litter production was evaluated as in StandLEAP, so some correlation with litter losses, NPP and LAI were to be expected (e.g., Gower et al. 1997). Predicted NPPa values were moderately sensitive to insensitive to the input data. Ranked in decreasing order, NPPa was most sensitive to maximum monthly temperature, followed by LAI, monthly minimum temperature and rainfall, with virtually no sensitivity to soil depth or monthly rain (Fig. 9a, b, c, respectively). The sensitivity was highly asymmetric for temperature. A mean annual increase of monthly maximum temperature of 2 and 4°C enhanced NPPa on average by 10 and 11% (3 and 1% for minimum temperature), while a

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Various studies have attempted to relate site index directly to biophysical factors such as climate, soils, and topography but observed correlations, have been weak at best (McKenney and Pedlar 2003). Different non-parametric approaches have also been tested to improve such empirical relationships (Mc Kenney and Pedlar 2003, Wang et al. 2005), but processbased models offer an alternative approach, whereby standlevel productivity indicators used by conventional growth and yield models are modified as a function of climate variability. Through relationships developed between aboveground NPP (NPPa) and site index, models such as StandLEAP could be used to complement operational yield forecasting, particularly at the large scale, and particularly when attempting to account for possible future changes in climate. Other studies, notably in Australia and New Zealand, have been able to derive strong relationships between NPPa and measured wood production (Coops et al. 1998). This study demonstrated the feasibility of mapping forest NPP, across a diverse landscape, and showed that simulated NPPa can be related to field-based estimates of timber productivity. Various sources of uncertainty exist in the inputs to StandLEAP and in the procedures for deriving the productivity ratings from its output. All of the spatial data, including the AVI, interpolated climatology, soil mapping, and the derivation of LAI data set from remote sensing imagery, are subject to some level of error. Based on the error analysis, the sensitivity of predicted NPPa to the input data was highly asymmetric for factors that limit NPP (Fig. 9). Simulated NPPa values are most sensitive to a decrease of maximum monthly temperatures; LAI determines interception of radiation by the canopy, minimum temperatures constrain the period of active growth (and tissue damage caused by frost during the growing season), while precipitation, soil depth and texture affect soil water availability. High sensitivity to temperatures and radiation interception in latitudes above 50°N has been shown for many NPP models and is consistent with the compression of the active growing season to a few weeks in the summer (Schloss et al. 1999, Matsushita et al. 2004). Simulated NPPa values were least sensitive to input values of above-ground biomass. In StandLEAP, standing aboveground biomass is only used to allocate NPP into its aboveground and belowground components, and more specifically to coarse roots. A constant allocation coefficient would thus be as accurate as the procedure used in StandLEAP. Climate data interpolations for the study region suggest that the Rocky Mountains cast a rain shadow, such that precipitation increases slightly towards the eastern edge of the study area. Water draining rapidly from the coarse-textured soils in the steeply sloped western areas (the Subalpine and Montane natural regions), will increase summer water deficits, whereas much of the remaining area, being lower and flatter, will tend to receive more rain and store it for longer.


Fig. 9. Relative change in StandLEAP predictions of aboveground NPP for 103 pure lodgepole pine permanent sample plots (PSP), when StandLEAP input data are randomly altered. a) monthly mean daily minimum and maximum temperatures. (As monthly mean daily minima were always negative for these PSPs, temperature changes were multiplied by -1 to have the same interpretation as for the maxima.); b) aboveground biomass and leaf area index; c) monthly precipitation and soil depth; d) stand type (see text for further explanation of these changes).

This effect is captured to a certain extent in the current model as it drives the observed west-to-east trend in NPP. The lack of sensitivity of StandLEAP NPP predictions to precipitation and soil water-holding capacity, however, shows that water deficit effects on NPP are not so well accounted for, even for a drought-prone region such as the study area. Three factors could contribute to this apparent lack of sensitivity: (1) climate normals are not adequate to simulate drought effects because these effects are associated with extreme climate events that are in themselves related to interannual variability, and not to long-term averages. Hence, a better representation of drought effects on NPPa might be achieved in future implementations by injecting natural variability in the dataset of climate normals to create a more realistic climate time series. Also, part of this uncertainty rests on interpolated estimates of precipitation in a region where complex orographic effects may not be captured very well; (2) soil water balance


and its effects on NPP are simulated as in the 3-PG model of Landsberg and Waring (1997). Coops and Waring (2001a) have shown that 3-PG accurately differentiates rates of soil water depletion once a good estimate of soil water-holding capacity (SWHC) can be obtained from local soil texture and rooting depth data; (3) related to (2), is the need for highquality soil texture and depth data to generate good estimates of soil water-holding capacity. Although we believe we have significantly improved the resolution of soil texture data, as described earlier, detailed information about soil depth is seldom available. Vertical stratification with clay layers buried beneath coarser textured horizons, as identified in the Hinton area soil survey report (Dumanski et al. 1972) present an additional difficulty because of the complexity of simulating vertical drainage in heterogeneous profiles. In the latter case, however, the monthly time step in StandLEAP precludes the need for such fine temporal scale modeling.

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The number and distribution of LAI field points may not have been sufficient to capture the full variability in LAI across species and stand types in the Upper and Lower Foothills ecoregions. As a result, errors in LAI predictions may propagate to modeled estimates of NPP. Future implementation would employ recently developed non-parametric (Theil-Sen) regressions to reduce the likelihood of biased predictions in the presence of measurement errors (Fernandes and Leblanc 2005). The approaches used for sampling and measuring LAI at PGS plots followed procedures recommended by Chen et al. (2002) and yielded results consistent with previous reports for boreal forests in Canada (Brown et al. 2000). For further improvements, increasing the number of field measurements of LAI distributed over the range of dominant vegetation types could support the development of specific LAI models for different land cover types and, hence, provide the opportunity for error analyses that ultimately lead to better retrieval algorithms. Results of this study suggest estimates of productivity from process-based models such as StandLEAP, using satellite remote sensing inputs, may offer a landscape-level accuracy of growth prediction comparable to that offered by regular growth and yield models, in addition to being able to assess effects of changes in forest composition and climatic conditions (e.g., to project impacts of climate change). Although it is unlikely that forest management agencies or companies would adopt such models soon in their operational forest planning, the capacity of process models to be applied beyond the empirical data domain that bounds regular growth and yield models will in time ensure them a place in the forester’s tool box. Demonstrated relationships between model predictions and estimates of productivity, applicability at both local and regional scales, and improvements in the availability of pertinent data layers will certainly further enhance their usefulness. Future work on StandLEAP will continue to focus on predicting operational indices such as Site Index as well as to assess the potential effects of a changing climate on forest productivity using projected scenarios of future climate.

Acknowledgements This work was possible due to major contributions from: Prairie Adaptation Research Cooperative (PARC), the Foothills Model Forest, the Sustainable Forest Management Network Center of Excellence (SFM-NCE) based at the University of Alberta, and the Earth Observation for Sustainable Development research initiative funded by the Canadian Space Agency. In addition, support from Hinton Forest Products (formerly Weldwood of Canada, Hinton Division) and, in particular, Sharon Meredith and Hugh Lougheed formerly with this company was greatly appreciated. Additional support and technical assistance from Mark Storie and Tammy Kobliuk, formerly of the Foothills Model Forest, is also appreciated. Within the Canadian Forest Service, important contributions were provided by Marty Siltanen, Michael Gartrell, Jim Stewart, Debbie Mucha (now with Parks Canada), Zoran Stanojevic (now with Global Forest Watch) at the Northern Forestry Centre in Edmonton, Alberta, and Jacques Régnière, and Rémi St-Amant at the Laurentian Forestry Centre in Ste Foy, Québec.

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