SYNTHESIS IN CONTRIBUTION TO NATIONAL ASSESSMENT OF FOREST ECOSYSTEM

RESPONSE TO CLIMATE CHANGE

March 31, 2005

Contributors: S. Wang1, D. Pouliot1, A. Davidson2, A. Trishchenko1,

R. Latifovic1, G. Pavlic2, R. Fraser1, S. Leblanc1,

R. Fernandes1, W. Chen1, Y. Zhang1, I. Olthof2

1 Canada Centre for Remote Sensing, Ottawa, ON, Canada. 2 Noetix Research Inc., Ottawa, ON, Canada (under contract to CCRS).

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TABLE OF CONTENTS

1. INTRODUCTION......................................................................................................... 1

2. NATIONAL DESCRIPTION OF FOREST CONDITIONS.................................... 1

2.1 FOREST COVER .......................................................................................................... 2 2.2 CONIFER AND DECIDUOUS FOREST FRACTIONS ......................................................... 4 2.3 TREE SPECIES DISTRIBUTION ..................................................................................... 6 2.4 AGE CLASS ................................................................................................................ 8 2.5 CROWN CLOSURE AND LEAF AREA INDEX ................................................................. 9

3. FOREST ECOSYSTEM PRODUCTIVITY ............................................................ 11

3.1 DEFINING FOREST PRODUCTIVITY ........................................................................... 11 3.2 CLIMATE CHANGE AND FOREST ECOSYSTEM PRODUCTIVITY .................................. 12 3.3 FOREST ECOSYSTEM PRODUCTIVITY AND SENSITIVITY TO CLIMATIC FACTORS ...... 15

3.3.1 Productivity of Different Forest Ecosystems ................................................... 15 3.3.2. Annual Carbon Balances for Canadian Forest Ecosystems........................... 17 3.3.3. National Carbon Budget ................................................................................. 19 3.3.4 Forest Ecosystem Sensitivity to Climatic Change ........................................... 20

4. FOREST DISTURBANCE ........................................................................................ 28

4.1 FOREST DISTURBANCES AND CLIMATE CHANGE...................................................... 28 4.2 REMOTE SENSING BASED FOREST DISTURBANCE MONITORING .............................. 30

4.2.1 Forest Fire Burned Area Mapping .................................................................. 30 4.2.2 Forest Disturbance Mapping........................................................................... 32

5. FOREST HYDROLOGY........................................................................................... 37

5.1 FORESTS AND GROUNDWATER DYNAMICS............................................................... 38 5.2 CLIMATE AND FOREST STRUCTURAL FACTORS AFFECTING EVAPOTRANSPIRATION 39

5.2.1 Aspen Forests................................................................................................... 40 5.2.2 Black Spruce Forests ....................................................................................... 41

5.3 REMOTE SENSING BASED SNOW MELT MONITORING .............................................. 42

6. CONCLUSION ........................................................................................................... 43

7. APPENDIX.................................................................................................................. 43

7.1 SENSOR DESCRIPTIONS ............................................................................................ 43 7.2 LIST OF DATA PRODUCTS AVAILABLE FOR CLIMATE CHANGE STUDIES .................. 45

8. REFERENCES............................................................................................................ 46

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1. INTRODUCTION Forest ecosystems are of great importance to Canadians. They provide economic revenue from timber and non-timber products, social benefits such as recreational use and visual appeal, and ecosystem services including water and air filtration, carbon sequestration, wildlife habitat, and biodiversity maintenance. Canadian forests are expected to come under considerable stress from climate change because they are located in a region where substantial warming is expected to occur (CCIA, 2002). Further, their long life cycle will create forest ecosystems that are poorly adapted to the changing environmental conditions. This, along with potentially greater forest disturbance occurring from fire, insects, disease, permafrost degradation, extreme weather and more intensive human use, may place forest ecosystems under high levels of stress. Ensuring that forest ecosystems remain a productive and a stable resource for Canadians requires careful management supported by effective monitoring. Remote sensing provides cost effective regional to national scale monitoring that cannot be accomplished through field assessments, and provides a spatially extensive view of forest ecosystem structure and function. However, to effectively utilize remote sensing data for monitoring requires considerable effort for methodological development in order to provide the data products needed as inputs to higher-level products and models. These products and models are critical to evaluate climate change impacts on forests for effective policy development. Utilization and analysis of these products for the purpose of evaluating climate change impacts are in their relative infancy. Future research will focus on this aspect. This report summarizes the contributions of Natural Resources Canada (NRCan), Earth Science Sector (ESS), Canada Centre for Remote Sensing (CCRS) under the Reducing Canada’s Vulnerability to Climate Change Program to understand and monitoring the potential effects of climate change on forest ecosystems. The approach taken is to address key issues concerning climate change and forests where the ESS\CCRS has contributed new and unique knowledge. The first section looks at describing forest structure from ESS remote-sensing products required to determine baseline conditions for future monitoring or comparison with historical data. Subsequent sections examine climate change issues concerning forest productivity, disturbance, and hydrology. 2. NATIONAL DESCRIPTION OF FOREST CONDITIONS A well developed understanding of forest structure and its distribution is important to evaluate potential climate change impacts and to monitor these changes through time. Due to the remote and vast expanse of forests in Canada, remote sensing is the only feasible means to acquire this information with suitable repeat frequencies for timely monitoring. Another advantage of remote sensing is that the data collected represents the entire area at a constant spatial sampling unit, thereby allowing for easier comparison

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between data products. Use of remote sensing and integration with other field and spatial data has enabled mapping of the following forest attributes for the whole country: forest cover type, conifer and deciduous fraction, dominant tree species, forest age class, leaf area index, and canopy closure. These products have been developed from coarse resolution satellite data with a pixel size of ~1×1 km2. The appendix provides detailed descriptions of the different sensors used to create these national coverage products. The following gives a brief description and their spatial trends at the national scale.

2.1 Forest Cover Satellite data are an important part of the Global Climate Observing System (GCOS) contributing to atmospheric, oceanic and terrestrial information needs. The GCOS implementation plan released by the World Meteorological Organization (WMO) in October 2004 in support of the United Nations Framework Convention on Climate Change (UNFCCC) identifies satellite data as a “means of obtaining observations globally for comparing climate variability and change over different parts of the earth” (WMO, 2004). Compared to other information sources satellite remote sensing observations have the unique advantage of complete global coverage and high repeat frequency of observations. A large number geophysical products regarding the earth-climate system can be derived from remote sensing data. In this section we focus on surface characteristics, particularly on land cover types representing forest classes. Land cover, i.e. composition and characteristics of the land surface, provide critical environmental information used for assessing ecosystem processes and functions. Changes in land cover affect exchanges of energy, water and momentum between the biosphere and atmosphere such as altering water flow through the terrestrial portion of the hydrological cycle. Forest cover types or classes are defined based on forest structural characteristics such as dominant species, tree density, or age. It provides the most basic information describing where forests are and what types are present across the landscape. For this reason, it is also widely used as an input for generating other products and in ecosystem processes models. Figure 1 shows the forest cover classes taken from the Global Land Cover 2000 map at 1 km spatial resolution and with a thematic resolution of 4 classes (Latifovic et al., 2004). This product was generated through a joint effort by ESS\CCRS and the US Geological Survey (USGS) using SPOT4/VEGETATION data. A new mapping approach was used that included 1) conversion of daily data into 10-day image composites, 2) post-seasonal image radiometric correction, and 3) extraction of land cover information from the seasonal composite image. The pre-processing methods used were very effective at removing data artifacts caused by cloud contamination, sensor noise and viewing geometry effects. For extraction and land cover classification, a multi-phase approach was employed. The first phase included data correction, initial image clustering and cluster agglomeration. The second phase involved determining an agreed upon legend between ESS/CCRS and the USGS/ Earth Resource and Observation Science (EROS) data center to represent the land cover of North and Central America. The final product

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has 28 land cover types based on a modified Federal Geographic Data Committee/ Vegetation Classification Standard at the regional scale as well as a 22 class legend based on the Land Cover Classification System of the Food and Agriculture Organization. Figure 1 shows that conifer dominated forest cover extends across the middle latitude of Canada and is the main forest type occupying 25 % of Canada’s total land area. Mixed forests are the next most abundant forest type at 10 % and serve as the transition zone between the coniferous forest in the north and deciduous dominated forests in the south. Deciduous forests occupy only a small percentage of 5 %. The total forested area in Canada is 43.5 %. Shrub cover is most abundant towards the north, but occurs in small patches throughout Canada. Shrub cover occupies 19 %.

Figure 1. Forest cover distribution in Canada. Taken from Global Land Cover 2000 generalized to 4 classless Classification accuracy of this map was high for entirely homogeneous pixels and ~50 % accurate for mixed pixels. The map was assessed against 40 LANDSAT based land cover maps with 30 m resolution (Figure 2). The accuracy in the mixed pixel comparison appears low, because 1 km resolution pixels contain more than one cover type within the pixel and therefore it is argued in Latifovic and Olthof (2004) that the maximum achievable accuracy is equal to the average dominant fraction within the pixel. The fraction of the dominant land cover type is related to the ratio of dominant patch size to spatial resolution, while patch size is linked to thematic resolution, i.e. the level of generalization. Thus, the maximum accuracy is limited by homogeneity, which is dependant on spatial and thematic resolution and on landscape characteristics. However, accuracy in separating forest from non-forest was high, 91 %. The overall accuracy was 80 % in separating the six elementary land cover types based on the Intergovernmental Panel on Climate Change (IPCC) legend, indicating that coarse resolution data can provide sufficient accuracy for this level of reporting.

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Figure 2. a) Satellite land cover data base over Canada; b) An example of data Boreal Land Cover Mosaic.

2.2 Conifer and Deciduous Forest Fractions In the forest cover map, a pixel classified as coniferous (or deciduous) forest with a closed canopy could contain a substantial amount of deciduous (or coniferous) trees (Latifovic and Olthof, 2004). Directly labeling these forest classes into dominant tree species will result in significant error, because the deciduous (or coniferous) forest component in a coniferous (or deciduous) forest class will be mistakenly labeled. An improvement would be to divide each 1 km pixel of forest into its fractions of coniferous, deciduous and other classes such as rock, water, and soil. The fractions of coniferous forest, deciduous forest, and other land classes within a 1 km pixel can be estimated using

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higher resolution land cover products to determine the fractions for sampled areas and interpolated spatially to obtain a regional coverage. There are several approaches for which the sample information can be spatially extended. The approach used to generate conifer and deciduous fractions for Canada was to develop statistical relations (i.e. Closed canopy coniferous shown in Figure 3) between sample forest type fractions and climate variables within each forest class (Pavlic et al., 2005).

0%

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Mean May-Sept. air temperature during 1900-98 (oC)

Con

ifero

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Closed-canopy coniferous

Figure 3. Relationship between coniferous/deciduous fractions and the May-September mean air temperature averaged over 1901-98, for the closed-canopy coniferous forest class in the 1998 Canada land cover map. It is well established that vegetation spatial distribution corresponds to climate at large scales (Odum, 1983) enabling this approach to give the best results in comparison to other approaches based on spatial aggregation alone. Figure 6 shows coniferous and deciduous fractional coverage’s obtained for Canada using this method. Overall, coniferous forests represent a much higher proportion than deciduous forests in Canada. In the Pacific coast regions, the coniferous fraction may reach over 80%. A relatively high fraction (up to 70%) of coniferous forests is also found in the western mountain ranges and along the Atlantic coast. The boreal forest zone is typically in the range of 30-60 % coniferous. The highest fractions of deciduous forests occur along the St. Lawrence River, and to a lesser extent at the southern edge of the western boreal forest zone, typically in the range 40-60 %. For the remainder of Canada’s forest land, deciduous forests typically contribute less than 15 % of the land cover.

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Figure 6: Coniferous/deciduous fractional coverage of Canada.

The relations developed for deriving the fractional coverage revealed the importance of temperature and precipitation in controlling the distribution of forests at the national scale. Figure 7 shows the distribution of humid and dry areas in Canada. In humid areas in Eastern and Western Canada, temperature is the critical factor controlling forest cover and composition. In dry areas in Central and Northern Canada, precipitation becomes much more important in association with temperature (Pavlic et al., 2005).

2.3 Tree Species Distribution The composition of tree species over a defined spatial unit is required to evaluate wood and non-wood products available from forested areas as well as the ecological services they may provide. Based on the forest cover fraction maps, forest inventory data, ecoregion descriptions, water body fractions, topography and drainage class information, a rule-based system was used to develop 17 dominant species group distribution maps for Canada (Pavlic et al., 2005). Table 1 shows the proportions of each species group by province and for the whole of Canada.

Figure 7: Distribution of humid and dry areas in Canada.

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Table 1: Area proportions of dominant tree species groups by province and territory for Canada (in 1000 ha). BC AB SK MB ON QC NB PE NF NS YK NT NU Canada BS 1,150 1,557 3,101 5,614 16,791 17,808 869 16 6,602 916 844 2,079 67 57,420 OS 6,671 6,065 1,564 2,056 2,494 2,780 217 18 530 239 3,794 4,833 61 31,327 WP 1094 1 0 97 6657 3996 561 13 596 1264 0 0 0 1,428 JP 11,409 5,543 3,562 3,295 5,141 2,377 59 1 1 12 1,416 957 11 33,791 OP 413 50 0 10 434 167 27 0 0 19 0 0 0 1124 BF 2,540 691 188 265 2,398 2,267 907 19 2,935 589 782 147 1 18,736 HK 4,575 0 0 0 208 183 29 1 0 43 0 0 0 5,042 DF 4129 308 1 2 0 0 0 0 0 0 0 0 0 4,442 LA 204 311 220 729 1,764 1,783 47 1 319 42 167 637 48 6,279 OC 2,009 10 0 9 680 433 147 1 0 3 0 0 0 3,296 AS 2,959 5,669 2,619 2,721 3,298 1,446 178 4 310 46 1,003 2,131 43 22,405 PO 527 718 420 1,283 3,517 1,560 59 0 12 1 214 215 11 8,544 YB 0 0 0 0 168 609 351 2 226 156 0 0 0 1,514 OB 517 462 567 936 3,003 6,034 77 0 2,150 62 143 367 4 14,328 SM 0 0 0 20 1,182 1,221 499 6 0 200 0 0 0 3,132 OM 54 0 25 59 306 623 500 25 78 596 0 0 0 2,270 OH 764 18 68 267 4178 2662 309 1 153 113 0 0 0 8,538 All 38,0.37 21,407 12,343 17,281 46,235 47,360 4,338 100 13,380 3,170 8,368 11,370 221 223,616

Black or red spruce (BS); Other spruce (OS); White pine (WP); Jack, lodgepole or shore pine (JP); Other pine (OP); Balsam, amabilis, grand, or subalpine fir (BF); Hemlock (HK); Douglas fir (DF); Larch (LA); Cedar or other coniferous (OC); Trembling aspen (AS); Other poplar (OP); Yellow birch (YB); Other birch (OB); Sugar or black maple (SM); Other maple (OM); Other broadleaf species (OH) The black spruce group covers the largest forest area among the 17 dominant tree species groups, at ~57 M ha (million hectares). The fractions of black spruce are highest (30-50 %) in the eastern boreal shield, located in Ontario, Quebec, and Newfoundland (including Labrador). The western boreal shield and taiga plains ecozones, extending from Manitoba to Yukon, also have significant fractions of BS, accounting for 10-30 % of the forested area. Trembling aspen is the most widely distributed deciduous species, covering ~22 M ha, with the greatest abundance found in the boreal plains of the Prairie Provinces and in the Montane Cordillera ecozone in central and northern British Columbia. White pine occurs in British Columbia and in the east from Manitoba to Newfoundland, with a total area of ~1.4 M ha. Yellow birch can only be found in the mixed wood plains from Ontario to the Maritime Provinces, being most abundant in Quebec. Figure 8 shows the distribution of the most common coniferous (black spruce) and deciduous (trembling aspen) species in Canada.

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Figure 8. Distribution of black spruce and trembling aspen in Canada.

2.4 Age Class The age of a forest stand is important for forest management and ecological monitoring, as forest structure is related to age. It is also a reflection of disturbance history important in terrestrial carbon dynamics and understanding landscape processes. An age class map was compiled from coarse resolution imagery based on relations between known stand age and coarse resolution near infrared (NIR) and short-wave infrared (SWIR) reflectance combined into a Short Wave Vegetation Index (SWVI) and constrained by forest cover type (Zhang et al., 2004). The information contained in NIR and SWIR reflectance allows for age estimation of about 10 years in the south and up to about 60 years in the north (Figure 8). Thus, at a current point in time, the map largely reflects the disturbance history over the last 10-60 years depending on location. However, continual fire and disturbance monitoring will eventually lead to a complete forest age map for Canada. Figure 9 shows the age class map derived from disturbance history. Examination of this age map shows areas of young forest are caused by frequent disturbance from fire in Central Canada and to a lesser extent in Western Quebec. Fewer recent disturbances have occurred in the more southerly areas and in Western Canada.

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Figure 8. SWVI and stand age relationships Figure 9: Stand age map for Canada.

2.5 Crown Closure and Leaf Area Index Leaf area index (LAI) is defined as half of the total leaf area per unit horizontal ground area. It is a measure of foliage distribution and structure important for characterizing energy and mass interactions between forests and the atmosphere. It has been used for monitoring vegetation status (Cayrol et al., 2000; Waring and Running, 2000), insect defoliation (Hall et al., 2003) and for modeling fluxes of radiation (Wang, 2005a), water (Band et al., 1991; Nouvellon et al., 2000; Wang et al., 2002a), energy (Sellers et al., 1986; Bonan, 1995; Wang et al., 2002a), and greenhouse gasses between the atmosphere

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and the land surface (Liu et al., 1997; Nouvellon et al., 2000; Coops et al., 2001; Wang et al., 2001, 2002b; Frank, 2002). LAI estimates from coarse resolution satellite imagery have been successfully made using statistical relations between spectral reflectance and field measurements (Chen et al., 2002a; Fernandes et al., 2003). Validation of these products have shown estimates to be 50-75 % accurate in Chen et al. (2002a) and 67 % in Fernandes et al. (2003). Fernandes et al. (2003) also pointed out that considerably greater error occurs in mountainous areas due to the effects of topography on spectral reflectance. Figure 10A shows the LAI map derived from SPOT/VEGETATION data for Canada. A similar measure to LAI is canopy crown closure in that it is also representative of foliage abundance and distribution. It is defined as the percentage of the ground surface that would be covered by a downward vertical projection of foliage. This measure has been developed in response to a government policy need to annually report on six indicators that track Canada’s overall wealth in the form of natural and human capital. Forest cover is one of these six indicators. The present methodology used to derive crown closure is based on existing LAI theory and uses LAI and clumping index estimates to determine crown closure (Chen et al., 2005). Figure 10B shows the crown closure product generated from SPOT/VEGETATION LAI and a clumping index derived from POLDAR data. The LAI can be used to assess canopy porosity under assumptions of foliage randomness, while the clumping index is used to correct the deviation of LAI distribution from the random case. Trends evident in both Figures 10A and 10B include a decrease in vegetation density measured using crown closure and LAI, respectively towards the north across Canada with the highest levels of both measures occurring in the southern areas of Eastern Canada and British Columbia. For the purposes of Kyoto reporting, Canada has proposed that a forest is any area covering at least 1 ha, has greater than 10-30 % crown cover, and trees with a potential to reach a height greater than 2 m at maturity. Some areas in northern Canada where the calculated crown closure was greater than 10% were removed based on a land cover map (Cihlar et al. 2002). Calibration of the crow closure map is underway in northern Canada where the large amount of small lakes influences the results within the 1 km satellite field of view. Based on the crown closure map, 392 million of hectares, which is about 44 % of Canada’s landmass meets these criteria, not accounting for the height criterion. The Canadian Forest Service reports 417 million hectares. The slight difference in this total forest area estimate compared to that obtained from the forest cover map is due to differences in the definition of forests for these two products, the inclusion of regenerated forests that have less than 10% crown closure in CFS estimate, and binary accounting at 1 km resolution for the satellite based estimate. The boreal shield ecozone, where forest fires are frequent, is where the largest discrepancy is found.

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3. FOREST ECOSYSTEM PRODUCTIVITY

3.1 Defining Forest Productivity From an ecological perspective, forest productivity is concerned with the rate of carbon fixation through photosynthesis and losses from respiration and disturbances. There are several terms used to describe productivity and respiration based on the components of the forest ecosystem being considered. Gross Primary Production (GPP) is the result of photosynthesis and is a measure of the total carbon influx (g m-2) from the atmosphere into green plants per unit time. Net Primary Production (NPP) is the ‘net carbon flux’ into green plants per unit time, where net carbon flux is the difference between carbon uptake from photosynthesis and carbon loss due to plant respiration (autotrophic respiration Ra). Respiration oxidizes previously fixed carbohydrate, releasing carbon in the reverse reaction to photosynthesis. Plant respiration or autotrophic respiration is the loss of carbon used in plant growth and maintenance. Net Ecosystem Production (NEP) represents the complete net flux for a given ecosystem, which also includes carbon loss from soil respiration (heterotrophic respiration Rh). Soil or heterotrophic respiration is the loss of carbon by microbial decomposition of organic matter contained in soil and/or plant litterfall on the soil surface. Total respiration for an ecosystem (Re) is the sum of Ra and Rh. NEP minus other losses of carbon from disturbances (Ld) such as harvesting, fires, insects and disease damage is referred to as Net Biome Production (NBP). For an ecosystem in a steady state, carbon losses would be balanced by NPP and NBP would be zero. However, the effects of climate and disturbance leads to non-zero NBP and the potential for terrestrial ecosystems to be carbon sources or sinks. In summary, these terms are more formally defined as: GPP = photosynthetic production, NPP = GPP - Ra, NEP = GPP - Ra - Rh, and NBP = NEP - Ld.

Figure 10: Distribution of forest LAI (A) and crown closure (B) in Canada.

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3.2 Climate Change and Forest Ecosystem Productivity The increase of atmospheric greenhouse gas concentrations from anthropogenic sources can directly affect plant physiological processes. The increase of plant leaf photosynthetic rate under higher carbon dioxide (CO2) concentrations has been observed across a wide range of plant species. An improvement in growth and NPP under higher CO2 concentrations has been identified, although it might be limited by other environmental factors such as soil moisture and nutrients. There is also evidence to support improved water use efficiency with higher CO2 concentration (IPCC, 2001) due to the higher stomatal resistance. The impact of higher CO2 concentrations on forest ecosystem productivity can be affected by the climate and ecosystem condition. For example, Kienast and Luxmoore (1988) found that the effect of CO2 was more significant in open forest stands with moderate climatic stress than stands under low or high stress. Increased CO2 was also found to trigger a nutrient sequestering mechanism in soil bacteria and fungi leading to nutritional limitations on plant growth. Long (1991) noted that the increase of CO2 may cause the optimum atmospheric carbon dioxide absorption temperature to rise by as much as 5°C, depending on the interaction between temperature and carbon dioxide concentration. Such interactions may alter both the magnitude and direction (biomass gain or loss) of the CO2 fertilization effect. It has also been shown that in many cases the gains due to increased CO2 will be, to some degree, offset by higher ozone (O3 ) concentrations, which will damage leaf tissues reducing GPP and thus NEP (Percy et al., 2002). Climate change will influence the key variables that control photosynthesis and respiration, most notably air temperature and precipitation. The response of forest ecosystems to climate change is highly dynamic and the results can be very different among different ecosystems. As such, evaluations at local scales based on specific site conditions are essential. However, it is generally expected that increased temperatures will result in increased growth of the temperate and boreal forests due to longer growing seasons and accelerated leaf photosynthesis. Increased temperatures will also lead to increased ecosystem respiration, which offsets the increase of carbon fixation. For some forest ecosystems, this may possibly lead to the decrease of NEP or even change the forest ecosystem from a carbon sink to a carbon source. Some studies have observed this negative impact in temperate and boreal conifers. For example, Morgenstern et al. (2004) found that high air temperatures during an El Niño event reduced NEP of a temperate coastal Douglas fir stand by raising respiration more than GPP. Griffis et al. (2003) found that boreal conifers experienced a pronounced mid-season reduction in NEP due to greater respiration and reduced GPP. Grant et al. (2001) showed that boreal black spruce changed from a sink to a source of CO2 as daily maximum/minimum temperatures rose above 25/15 °C. For some forests, warmer winter temperatures were linked to tree dieback due to early bud break, leading to bud frost damage (Johnson et al., 1988; Hänninen, 1991), which will affect NBP. Changes in the hydrologic cycle and ecosystem water conditions caused by temperature and precipitation changes can also affect forest ecosystem productivity. Jacoby and D'Arrgio (1995) found that moisture stress in high-latitude sites appeared to be a factor limiting growth increases in response to warmer weather. However, due to the complicated interactions between the CO2 induced water

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use efficiency change and climate induced water balance change, understanding of the actual response of forest ecosystems to projected future climates are still very limited. Changes in solar radiation caused by changing cloud cover can positively influence NEP in dry areas with high solar radiation, but will be negative in areas with low solar radiation (IPCC, 2001).

Climate change also causes changes of other ecosystem conditions such as snow cover and soil thermal and moisture conditions, which affect the plant root growing conditions and soil carbon and nitrogen cycles. Several field studies have shown strong interactions between snow pack and soil microclimate conditions. For example, snow removal in a maple tree stand caused more than 10ºC decrease in soil temperature, which resulted in severe physiological responses including increased canopy dieback and earlier leaf senescence in the following growing season (Pilon et al., 1994). Even for a temperate forest in a mild winter, snow removal was found to cause significant fine root mortality and nutrient (NO3¯ ) loss, although it lowered the root zone temperature only by 2-4ºC

How Does Climate Change Affect Forest Ecosystem Carbon Processes?

� Leaf level photosynthetic physiology • Leaf intercellular CO2 concentration • Leaf temperature • Leaf water potential • Leaf N conditions

� Canopy level carbon fixation • Growing season • Leaf area index • Canopy radiation profile

� Autotrophic respiration • Biomass and growth • Leaf and soil temperatures

� Heterotrophic respiration • Litter fall: quantity and quality • Soil freeze/thaw • Soil microclimate and N conditions

� Disturbances • Forest fire • Insect and disease damage

� Dynamic vegetation change

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(Groffman et al., 2001). Under certain climate change conditions, it may be possible that reductions of snow pack due to climate warming might cause soil temperature to decrease, i.e. ‘colder soils in a warmer world’. Compared with temperature, the current climate change projections for future precipitation change still involve high uncertainties in both quantity and spatial and temporal distributions. How the different scenarios of climate change will affect the snow and soil conditions still remains largely uncertain. However, it is generally expected that the magnitude of soil temperature changes will be smaller than the corresponding air temperature changes. Table 2 and Table 3 give Ecological Assimilation of Land and Climate Observations (EALCO) model results on snow cover and soil temperature changes conducted for a boreal aspen ecosystem (Zhang et al., 2005a). Table 2. The simulated changes of average snow depth (cm) during the non-growing season under different combinations of air temperature change (�Ta) and precipitation change (�P) (reference value is 13.0 cm based on the time period of October 1, 2000 to May 31, 2001).

�P (%) �Ta (

oC) -60 -40 -20 0 20 40 60

0 -8.8 -5.8 -3.1 0.0 3.2 6.1 9.3 1 -9.5 -7.0 -4.6 -2.4 0.3 3.0 5.3 2 -10.1 -7.9 -6.1 -3.9 -2.0 0.3 2.2 3 -10.6 -8.6 -7.1 -5.3 -3.6 -1.7 0.0 4 -11.1 -9.5 -7.9 -6.6 -5.1 -4.0 -2.3 5 -11.7 -10.5 -9.0 -8.0 -6.9 -5.9 -4.8 6 -11.9 -11.0 -9.8 -8.6 -7.7 -6.7 -5.8 7 -12.2 -11.6 -10.7 -9.8 -8.9 -8.0 -7.3 8 -12.3 -11.9 -11.2 -10.5 -9.9 -9.1 -8.4 9 -12.4 -12.1 -11.7 -11.1 -10.6 -10.0 -9.5 10 -12.5 -12.2 -11.9 -11.5 -11.1 -10.6 -10.1

Table 3. The simulated changes of average soil temperature (oC) at 15cm soil depth during the non-growing season under different combinations of air temperature change (�Ta) and precipitation change (�P) (reference value is 1.0 oC based on the period of October 1, 2000 to May 31, 2001).

�P (%) �Ta (

oC) -60 -40 -20 0 20 40 60

0 -1.1 -0.6 -0.2 0.0 0.1 0.2 0.3 1 -0.8 -0.4 0.0 0.2 0.4 0.5 0.6 2 -0.5 -0.2 0.2 0.5 0.7 0.8 0.8 3 -0.1 0.1 0.4 0.6 0.8 1.0 1.0 4 0.2 0.4 0.6 0.8 0.9 0.9 1.1 5 0.6 0.7 0.9 1.0 1.1 1.2 1.2 6 1.1 1.1 1.2 1.4 1.5 1.6 1.6 7 1.6 1.6 1.5 1.7 1.8 1.9 1.9 8 2.2 2.2 2.1 2.2 2.3 2.5 2.5 9 2.8 2.8 2.8 2.8 2.8 2.8 2.8 10 3.4 3.4 3.4 3.4 3.4 3.4 3.3

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3.3 Forest Ecosystem Productivity and Sensitivity to Climatic Factors

3.3.1 Productivity of Different Forest Ecosystems Assessment of productivity in different ecosystems is important to evaluate the total carbon storage and potential carbon source-sink capacity. Such knowledge is required so that human use and interaction with forest environments is carried out in manner to minimize carbon losses in order to reduce human CO2 input to the atmosphere and to maintain the goods and services forests provide. It also allows for more rigorous evaluation of mitigation options, such as using forests to uptake and store a portion of the carbon released to the atmosphere from past human activities. Productivity of different forest ecosystems can differ largely, both in magnitude and temporal distributions. Diurnal and seasonal carbon fluxes between the ecosystem and the atmosphere indicate the magnitude of NEP. Figure 11 shows an example of the seasonal distribution of the diurnal carbon flux for a boreal aspen and a boreal black spruce forest. Generally, diurnal patterns of productivity during the growing season are highly correlated to incoming solar radiation, where forests are small sources of carbon at night and strong carbon sinks during the day. In winter, the carbon fluxes between the ecosystem and the atmosphere is close to zero. One point of interest is that spruce has a higher NEP in spring than aspen, but the aspen is much higher in the summer and fall. This reflects the differences in maintaining and shedding leaves annually, where the spruce is able to take advantage of the early season growth resources while the aspen is still developing leaves. In summer, when aspen leaves are fully developed much higher production than spruce is achieved and is maintained the remainder of the growing season. These differences in annual production optimums have important implications for seasonal climate changes, as these species will be influenced by such changes differently (Wang et al., 2002b).

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Figure 11: Half-hourly ecosystem CO2 fluxes for an aspen and black spruce forest in (A) winter, (B) spring (about two weeks after aspen leaf emergence), (C) middle growing season, and (D) late growing season. Lines represent model simulations and dots represent field measurement using eddy correlation technique. (Wang et al., 2002b).

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The productivity of forest ecosystems can change significantly with stand age. Figure 12 shows the relations between stand age and NPP for black spruce forests of different site qualities. It provides some indication as to the influence of age and site quality on productivity (Chen et al., 2002b). Marked differences in NPP are evident for the different sites. The annual incremental growth starts to stabilize towards a steady state a little over 100 years of age. In general, for other major tree species found in the boreal forest in Canada (jack pine, aspen, white birch), NPP begins to stabilize after approximately 100 years (Pouliot, 2003). Before this, NPP is more variable and decreases from peak productivity at around 50 years for many species. In evaluating the differences between productivity for different forest ecosystems, it is important to understand the potential influences of climate factors and forest structural characteristics. Repeated annual productivity measurements give some indication as to the variability due to climatic factors, but this variability is tied to the climate anomalies for the observation period. Clearly structural characteristics will have a strong influence on forest productivity depending on factors such as the dominant tree species, stand density, soil conditions, and topographic position. Further, as pointed out above, the non-linear growth of trees also has a substantial effect.

3.3.2. Annual Carbon Balances for Canadian Forest Ecosystems Annual carbon balances showing the various components of productivity and respiration for the different forest ecosystems found in Canada are shown in Table 4 along with selected site parameters (Amthor, 2001; Grant et al. 2005a, 2005b). The results are from several modeling inter-comparison studies where cross validations using field measurements were made. The results are based on model averages for the year 2000 except the northern old black spruce site where the values were based on the average of 1994-1996. Five different forest sites are presented in the table. Three sites represent the boreal biome including northern black spruce, southern jack pine and southern trembling aspen. Two sites are from the temperate biome of costal Douglas fir and eastern oak. Each of these sites is considered to be representative of the types of forests commonly found within each of these biomes.

Figure 12: Age-NPP relation for black spruce forests for different site qualities.

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Table 4: Annual carbon balances for different forest ecosystems found in Canada. Age

year Understory Soil Mean

T oC Total P. mm

GPP gC m-2

Ra gC m-2

NPP gC m-2

Rh gC m-2

Re gC m-2

NEP gC m-2

Boreal Ecosystems

Northern Old Black Spruce

120 moss Organic -2.8 448 797 594 206 176 770 27

Southern Old Jack Pine

70 lichen Sandy 1.0 379 868 510 358 265 775 93

Southern Old Aspen

80 hazelnut 1.4 484 1170 700 470 320 1020 150

Temperate Ecosystems

Costal Douglas Fir

53 Moss and fern

Organic 8.2 950 2300 1413 886 542 1955 345

Eastern Oak 60 shrub 14.5 1214 1880 1000 880 510 1510 370

Comparison of values in Table 4 reveals that the Douglas fir forest was the most productive and had the highest respiration rates. The oak forest had similarly high productivity and respiration rates. Both of these ecosystems were at an age and under climate conditions conducive of high productivity. In these forests NEP was approximately 15-20 % of GPP. Much lower productivity was seen for the boreal ecosystem sites with the lowest NEP found in the black spruce site (NEP=3 % of GPP). Overall, all of these forest ecosystems acted as a net carbon sink in the years studied. The small NEP of the northern black spruce forest suggests that it had reached a state that is close to equilibrium where production was generally equal to respiration. The jack pine site was a stronger carbon sink than the black spruce site, where NEP was 11 % of GPP. Compared to the conifer ecosystems of the jack pine and black spruce sites, much higher GPP was seen at the deciduous aspen site. The annual GPP took about 13 % of its NEP. Forest ecosystem productivities vary significantly among different years with climate variations. This variation at least partially reflects the impact of climate change and climate variations on forest ecosystems. An example is given in Figure 13, which shows the NPP variations simulated by the EALCO model for the southern old aspen forest for nine years from 1994-2002 (Wang, 2005a). The year 1996 has the lowest annual mean temperature and the lowest NPP. The year 2001 has high annual temperature and radiation intensity, and it was simulated to have the highest annual NPP. Both modeling studies and field measurement reveals that the earlier leaf emergence induced by the higher early spring temperature was found to significantly contribute to the annual carbon flux (Wang et al., 2005a; Barr et al., 2004). The low annual NPP in year 2002 was mainly caused by drought during the growing season, indicating the impact of precipitation and water balance on the ecosystem productivity. These modeling results are consistent with field measurements (Barr et al., 2004).

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0.0

0.3

0.6

0.9

1.2

1.5

1.8

1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

C B

udge

t (kg

C m

-2)

GPP NPP NEP

Figure 13. Inter-annual variation of gross primary productivity (GPP), net primary productivity (NPP), and net ecosystem productivity (NEP) due to climate variations for the southern boreal old aspen ecosystem.

3.3.3. National Carbon Budget A national level carbon budget for Canada’s forest has been estimated for the period 1895-1996 using the Integrated Terrestrial Ecosystem C-budget (INTEC) model (Chen et al., 2000a; Chen et al. 2000b). From 1895-1910, Canada’s forests were simulated as small carbon sources of 30±15 Tg C yr-1, caused mainly by large disturbances (fires, insect damage, and harvesting). The forests were simulated to become large carbon sinks of 170±85 Tg C yr-1 during 1930-1970, mainly due to forest regrowth and nondisturbance factors such as climate, atmospheric CO2 concentration, and N deposition. In recent decades (1980-1996), Canada’s forests were simulated as moderate carbon sinks of 50±25 Tg C yr-1 as a result of a tradeoff between losses due to disturbance and gains from the nondisturbance factors. These non-disturbance factors in order of importance were 1) atmospheric N deposition, 2) net N mineralization and fixation, 3) growing season length increase, and 4) CO2 fertilization. Figure 14 compares three sources of carbon budget estimates for Canada’s forest and shows that reasonable estimates were obtained by the INTEC model.

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Figure 14: Comparison of carbon balances for Canada’s forest estimated from Kurz and Apps (1996), Auclair and Bedford (1997) and Chen et al. (2000).

3.3.4 Forest Ecosystem Sensitivity to Climatic Change An important advantage of using ecosystem models for climate change studies is to assess the influence of changes to model input parameters (e.g. temperature and precipitation) on model outputs (e.g. GPP, Rh, NEP). This allows for exploration of a variety of potential scenarios that may be expected in the future. It also enables identification of components of the modeled system that may be affected by change and the magnitude of these effects. Using models in this manner is a form of sensitivity analysis. The following presents the results of sensitivity analysis to climatic factors for aspen and black spruce ecosystems based on modeling scenarios. Aspen Forests Sensitivity analysis for several climatic variables including air temperature, precipitation, atmospheric CO2 concentration and clear and cloudy conditions was evaluated for the southern old aspen site presented in Table 4 (Wang et al, 2001). The sensitivity of this ecosystem to climate factors is shown in Figure 15. Variation in these factors was set to be large enough to identify and isolate the major differences in the modeled response, while also remaining within the range of variability that the boreal forest ecosystem may be exposed to over a time period of about 50–100 years. The changes included: (i) temperature ± 2.0°C; (ii) precipitation ± 50 %; and (iii) CO2 concentration ± 100 µmol mol-1. Model outputs of plant annual GPP, Ra and NPP were selected as the prognostic variables to represent the model responses (Wang et al., 2001).

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The greatest response of annual GPP, Ra and NPP was due to temperature changes, suggesting that the plant carbon fluxes for this aspen forest are constrained strongly by temperature. A rise in temperature by 2°C increased both modeled annual GPP and Ra by a similar extent of 18 %. While under the - 2°C conditions, modeled GPP decreased more than Ra (28 % vs. 25 %). As a result, NPP increased by 18 % in the model under the +2°C conditions and decreased by 32 % under the -2°C conditions. This change was consistent with NPP observations of aspen in the southern and northern study sites. The mean July air temperature at the southern site (48 year average) was 17.6°C, which was about 2°C higher than that of the northern site (15.7°C, 23 year average). Aboveground NPP was observed to be higher in the southern site compared to the northern site (25% higher in 1993 and 1% higher in 1994; Gower et al., 1997). The major influence of temperature on the productivity of this ecosystem was due to its effects on photosynthesis, changes in growing season length and vegetation N conditions. Plants under higher temperature tend to have longer growing seasons and uptake more N due to the higher mineralization rate of soil organic matter. Modeled GPP, Ra and NPP were all least sensitive to the variations in precipitation, indi-cating that plant carbon dynamics were not predicted to be highly water constrained during near-average rainfall years represented by 1994 and 1996. An increase of precipitation by 50 % increased GPP, Ra and NPP in the model by only about 1–2 %. Another reason for the small rise was the delay in plant leaf emergence caused by increased snow during wintertime, which led to a late snowmelt thereby shortening the growing season. On the other hand, a decrease of precipitation by 50 % caused water stress in August and September in the model and, even though less precipitation led to an earlier snow melt date and longer growing season, overall decreases of 4–5 % in GPP, Ra and NPP were still predicted by the model. Increase in CO2 concentration by 100 µmol mol-1 resulted in the increases of GPP, Ra and NPP by 5–7 %, and decrease in CO2 concentration by 100 µmol mol-1 resulted in the

-35

-25

-15

-5

5

15

25

35 Ta+2 Ta-2 Pt+50% Pt-50% CO2+100 CO2-100

GPPRaNPP

Figure 15. Percent change in modeled response to climate drivers for an aspen ecosystem. Changes include temperature (Ta) of ±±±± 2°°°°C, precipitation (Pt) ±±±± 50 %, and atmospheric CO2 concentration (CO2) ±±±± 100 ppm (Wang et al., 2001).

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decreases of GPP, Ra and NPP by 9–13 %. These responses are consistent with observations from many field CO2

enrichment experiments (Idso and Idso, 1994). Changes of Ra and NPP with CO2 concentrations in the model were mainly induced by the changes of GPP.

The effect of incoming solar radiation has a significant impact on ecosystem productivity. Figure 16 shows the effects of clear and cloudy days on ecosystem productivity for the southern aspen site. On the clear day, GPP is much higher and respiration was similar between the clear and cloudy days leading to much higher NPP and NEP on the clear day. This effect is important as climate change will have a strong influence on cloud patterns and abundance, which as shown here will dramatically impact ecosystem productivity.

It is important to note that the model results presented here only represent changes in a single climate variable. Climate change will cause differences in all these variables simultaneously. The exact changes that occur will determine forest productivity in a give local area. Wang et al. (2005b) investigated the integrated responses of the boreal aspen forest to different climate change scenarios as shown in Figure 17. The model simulated changes of forest GPP, NPP and NEP are shown in Figure 18.

0

1

2

3

4

5

6

7

GPP Ra NPP Rh NEP GPP Ra NPP Rh NEP

g C

m-2

d-1

Clear Cloudy

Figure 16: Aspen ecosystem productivity for a clear day and cloudy day.

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T+3°°°°CCO2 350P. unch.

T+3°°°°CCO2 550

P. de. 30%

T+3°°°°CCO2 550

P. in. 30%

T+3°°°°CCO2 550P. unch.

T+3°°°°CCO2 350

P. in. 30%

T+3°°°°CCO2 350

P. de. 30%

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Figure 17. Climate change scenarios used for model simulation.

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T+3oCCO2=550

P=30% incr.T+3oC

CO2=550P=unch. T+3oC

CO2=550P=30% decr.

T+3oCCO2=350

P=30% decr.

T+3oCCO2=350

P=30% incr.T+3oCCO2=350P=unch.

0

10

20

30

40

50

60

C0P0 C0P+ C0P- C+P0 C+P+ C+P-

Climate Change Scenarios

GP

P C

hang

e (%

)

T+3oCCO2=350P=unch.

T+3oCCO2=350

P=30% incr.T+3oC

CO2=350P=30% decr.

T+3oCCO2=550

P=30% decr.

T+3oCCO2=550P=unch.

T+3oCCO2=550

P=30% incr.

-200

-150

-100

-50

0

50

100

150

200

C0P0 C0P+ C0P- C+P0 C+P+ C+P-

Climate Change Scenarios

NE

P C

han

ge (g

C m

-2)

T+3oCCO2=350P=unch.

T+3oCCO2=350

P=30% incr.

T+3oCCO2=350

P=30% decr.

T+3oCCO2=550

P=30% decr.

T+3oCCO2=550P=unch.

T+3oCCO2=550

P=30% incr.

-60

-40

-20

0

20

40

60

80

C0P0 C0P+ C0P- C+P0 C+P+ C+P-

Climate Change Scenarios

NP

P C

han

ge (%

)

Figure 18. Simulated changes of GPP, NPP, and NEP of the boreal aspen ecosystem under different climate change scenarios.

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Black Spruce Forests Black spruce sensitivity to several climatic variables was assessed for the northern black spruce site in Table 4. For this site, 7 different models were compared for their response to changes in temperature of ±2°C, precipitation ±50 %, incoming solar radiation ±10 %, dew point temperature ±2°C, and atmospheric CO2 concentration ±100 ppm (Potter et al., 2001). The major advantage of evaluating the response of several models to a given change in model input is that there is greater support for a given response if outputs show similar values or trends. Figure 19A shows the effects of temperature on model outputs. As a general trend, productivity and respiration all increase with a temperature increase of +2°C and decrease with a decrease in temperature of -2°C. Increased growing season length and improved early season carbon uptake are likely the most significant environmental changes driving these results. The disagreement between model results for NPP is due the relative sensitivities of GPP and Ra to temperature. If Ra is more sensitive than GPP, then a decrease will be observed, while an increase will be observed if the sensitivities are reversed. Otherwise the models all show the same trends, but have different sensitivities to temperature changes. A decrease in precipitation had a greater effect on productivity and respiration than an increase (Figure 19B). In this case, the black spruce site is not strongly limited by water and thus there was little increase in production (GPP) due to greater water availability. Of the model outputs, Rh appeared to be the most sensitive to changes in precipitation. Some of the models reduced soil microbial decomposition under more saturated soils to reflect changes in soil oxygen availability, whereas other models increased Rh with greater soil water content. The amount of solar radiation was found to be directly related to GPP and NPP (Figure 19C). An increase in GPP and NPP occurs due to improved springtime growth conditions when incoming radiation is presumed to be the limiting factor. In mid summer when solar radiation is high, increases in productivity are much smaller because of a reduced ability to use the extra light available. Respiration was not highly sensitive to changes in incoming solar radiation levels. The response of models to dew point temperature (a measure of air moisture) was consistent in magnitude and direction (Figure 19D). Increased dew point temperature led to increases in GPP and NPP and a decrease in Rh, while decreases had the opposite effect. The explanation for these trends is that water stress is lessened at higher dew point temperatures. Further, higher dew point temperatures can increase soil moisture, which can decrease soil microbial activity and thus decrease Rh. Changes in CO2 concentration have a substantial impact on productivity and respiration, with increases in CO2 generally resulting in greater productivity and respiration and decreases having the opposite effect (Figure 19E). The disagreement in the NPP

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estimates for the models results from differences in GPP and Ra sensitivity to CO2 concentrations. Rh was least influenced by changes in CO2. To summarize these effects, Figure 20 shows NEP model estimates for the various climate variables tested. NEP represents the combined effects for several ecosystem carbon fluxes, and therefore provides a suitable summary measure for evaluating the overall response to environmental factors. For this site, all models indicated it as a carbon sink. NEP generally increases with lower temperature or higher precipitation, dew point temperature, solar radiation and CO2. NEP decreases for the opposite of these climatic changes. It is important to note that the model results presented here only represent changes in a single climate variable and climate change will cause differences in all these variables simultaneously. The exact changes that occur will determine forest productivity in a give local area. As seen here, considerable differences exist between the magnitudes of the response to the different environmental factors. Thus, changes in one highly sensitive variable can be offset by changes in a few less sensitive variables. The important information from this model sensitivity analysis was the relative effects of different climatic factors tested. Information of this nature allows for better evaluation of the potential effects that may occur to forest ecosystems of this type under future climate change.

Response of Boreal Forest Ecosystem Carbon Processes to Climate Change

� Warmer climate increases GPP moderately but likely decreases NEP due to significant increase in ecosystem respiration.

� Higher CO2 could offset the negative impact of higher temperature and lower precipitation on NEP.

� Inter-annual variations of precipitation have less impact on ecosystem C dynamics under higher CO2 conditions.

� The impact of CO2 on forest C dynamics needs to be adequately addressed in assessing the C sequestration capability of the future forest ecosystems.

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Figure 19: Percent change in modeled productivity and respiration for changes in A) temperature (Ta) of ±±±± 2°°°°C, B) precipitation (Pt) ±±±± 50 %, C) incoming solar radiation (Srad) ±±±± 10 %, D) dew point temperature (Td) ±±±± 2°°°°C, and E) atmospheric CO2 concentration (CO2) ±±±± 100 ppm.

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Figure 20: Percentage change in modeled NEP for changes in temperature (Ta) of ±±±± 2°°°°C, precipitation (Pt) ±±±± 50 %, incoming solar radiation (Srad) ±±±± 10 %, dew point temperature (Td) ±±±± 2°°°°C, and atmospheric CO2 concentration (CO2) ±±±± 100 ppm. 4. FOREST DISTURBANCE

4.1 Forest Disturbances and Climate Change Forest disturbance may affect climate by changing the carbon, energy, and water balances between the terrestrial environment and the atmosphere. This, in turn, can result in feedback changes to disturbance regimes, as many disturbances are strongly linked to climatic factors. Disturbances such as fires, flooding, storm damage, forest decline, and insect/disease damage are strongly dependent on climate factors. Disturbances resulting from human activity such as urban expansion, forest harvesting, and other forms of resource extraction are not causally related. Approximately 2 % of the forest area in Canada is disturbed annually. The majority of this disturbance occurs from fires, insect and disease damage and harvesting (NRC, 2001). In the boreal forest region, fire represents the major stand-replacing disturbance. Historical fire trends based on fire occurrence and area burned since the 1920’s suggest that the recorded number of fires has increased steadily over the last eight decades, while the area burned decreased from 1920-1950, and increased steadily since with an average annual burn rate of 2.75 million ha/year in the 1990’s (Stocks et al., 2003). There is strong evidence that this increase is linked to climate change and is not the result of improved reporting capabilities. First, the increasing trend in area annual burnt started

29

before monitoring capabilities were greatly enhanced by satellite imaging in the 1970’s. Second, increases in fire suppression during this same period should have helped reduced the number of fires and total area burnt in the absence of a climate influence (Gillet et al., 2004). Recently, Gillet et al. (2004) have shown that human-induced climate changes have had a significant effect on the area burned in Canada. In regards to future fire disturbance, it is expected that climate change will increase the occurrence and severity of fires, as increases in temperature are likely to promote fire disturbance. The latest predictions suggest that the area burnt will double by the end of the century (Flannigan et al., 2004) and the length of the fire season will also increase (Wotton and Flannigan, 1993; Stocks et al., 1998). This will have important implications for further climate changes due to changes to the carbon budget. It will also impact our ability to emulate natural disturbance in forest management, as Bergeron et al. (2004) show that this can only be accomplished in regions where the current fire frequency (~1950-1999) is lower than the historical fire frequency (~1700-1900) such that a substitution of fire with forest management can take place. In their work, the majority of sampled sites throughout Canada show that this substitution is realistic, but climate change will reduce the capacity for substitution by bringing past and current fire frequencies closer together. The present difference in past and current fire frequencies is likely due to the effects fire suppression and climate change. Insects and disease play important roles in ecosystem nutrient cycling and biomass decomposition, but outbreaks can cause significant mortality and growth loss. Annual biomass lost from forest mortality due to insect attack in Canada is estimated at 51 million m3 yr-1. These losses amount to 1/3 of the annual harvest volume and do not reflect loss when mortality does not occur (Hall et al., 1994). Disease also affects a large area, but is only monitored at the provincial level and is not available for national level reporting. This is a concern, as both Manion (1991) and Callan (2001) suggest that disease has a greater impact on forest volume growth than insects. The warmer winter temperatures associated with global warming are expected to increase insect and disease populations resulting in greater damage (Parker et al., 2000). Insects and disease have distinct temperature preferences. Therefore, climate change may extend their ranges into new areas (Papadopol, 2000). Climate warming is also expected to increase winter temperatures more than summer temperatures, increasing survival of insects and diseases over winter (Coakley, 1999; Porter et al., 1991 reported in Boland 2004). Further, insect induced damage may be more severe as studies have shown that insect damage to plants grown in high CO2 environments can be greater because insects have to increase consumption to compensate for the reduced protein content of the plant tissues (Binkley et al., 1997). There are also important interactions amongst these disturbance agents. Insect damage can increase the suitability for disease infection and vise versa (Boland, 2004). Both types of disturbances increase the content of dry organic material in forests suitable for burning. Thus, increases in insect damage will improve conditions for wildfire and subsequently fire occurrence (Fleming et al., 2002).

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While fire and insect and disease damage are the dominant disturbances in Canada’s forests, harvesting has become increasingly important. Forest harvesting is also the most significant human impact on the forest in regards to the amount of area disturbed. However, it may not be the most significant in regards to the severity of disturbance. Other forms of human disturbance such as road building, mining, and agriculture result in more drastic changes to the local vegetation, soil, and hydrology than harvesting. Currently, a total of 1 million hectares of forests are harvested every year, of which 90 % are clear-cut (http://nfdp.ccfm.org/). Other harvesting systems such as strip cutting and forms of partial cutting make up the remainder (Taylor, 1999). Forest companies are required by license agreements with provincial governments to reforest harvested areas. This can take two forms, either natural or artificial regeneration. Natural regeneration involves re-establishing forest cover on depleted lands utilizing local on site resources such as seed, residual post harvest roots and stumps, and advanced regeneration. Artificial regeneration typically involves planting or seeding treatments to re-establish the new forest (Smith et al., 1997). In Canada, artificial regeneration is used to reforest half of the annually harvested area, with the remainder being regenerated by natural means (NRC, 2001).

4.2 Remote Sensing Based Forest Disturbance Monitoring Incomplete records regarding disturbance both spatially and temporally have significant limitations in determining climate change effects on disturbance regimes. Forest ecosystems are in a constant state of flux caused by natural and human factors, making it difficult to identify non-natural changes from those considered to be natural. More spatially and temporally extensive records are needed and in many cases are being acquired in part by satellite remote sensing. The following sections present the current capabilities and disturbance data records derived from remote sensing at ESS\CCRS.

4.2.1 Forest Fire Burned Area Mapping Of the remote sensing based disturbance monitoring applications, active fire detection and burned area mapping have been the most widely explored. Substantial research using coarse resolution imagery has developed algorithms to map burned areas based on changes in the reflectance of red and NIR channels following fire. One method, dubbed Hotspots and NDVI Differencing Synergy (HANDS; Fraser et al., 2000), exploits these reflectance changes in addition to information on the locations of satellite-detected active fires. Areas burnt during the time period covered by the imagery can be reasonably dated (Fraser et al., 2000; Kasischke and French, 1995; Kasischke et al., 1993). Recent studies have shown a stronger signal of burnt areas in the SWIR band that is effective to discriminate vegetation types and age of fire scares (Eastwood et al., 1998; Fraser and Li, 2002). Fraser et al. (2003) found that fires greater than 10 km2 can be accurately detected. Interestingly, Stocks et al. (2003) examining complied records of fire from 1959-1997 found that fires greater than 200 ha in size accounted for only 3.1 % of the total number of fires in this period, but their area accounted for 96.9 % of the total area burned. Thus,

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the omission of fires less than 10 km2 (1000 ha) will likely contribute a very small error to the total burn area estimates from remote sensing. This type of satellite based fire mapping provides useful information for defining the general fire regime, the amount of area burned, seasonality of fire, and spatial patterns at daily, monthly, and annual time scales. Such information is valuable for large-scale, spatially explicit modeling of forest fire emissions and carbon budget. Figure 21 presents the spatial distribution of burned areas detected from 1994-2004 based on coarse resolution satellite imagery (Fraser et al., 2000). It shows that the most frequently disturbed areas over this time period were in central Canada, mostly northern Saskatchewan. The Yukon Territories did not have much disturbance until 2004, when considerable burning occurred. Fire size tends to be smallest towards the south and larger towards the north, which could be a reflection of fire suppression activity. British Columbia has a moderate amount of small fires distributed throughout due to the topographic influences on the spread of fire. High fire years are also clearly evident in 1995, 1998, 2002, and 2004 for the northwest.

Figure 21: Detected fires using course resolution satellite imagery and methods for Canada from 1994-2004. For the earlier time periods 1921-1998, a similar fire database was generated using historical fire records and coarse resolution satellite imagery. In satellite imagery, forest patches resulting from disturbance are often distinct and can be used to determine the approximate spatial extent of past disturbances. For this a specialized segmentation methodology was developed that used the fire size and location from fire records to determine the fire extent in the satellite imagery (Zhang et al., 2005b). The temporal patterns of burnt area and fire frequency have been reported in Van Wagner (1988) and Stocks et al. (2003). The advantage of this fire database is that it improves the spatial

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characteristics of the fire record. As with the 1994-2004 data, a similar pattern of fire frequency and size distributions can be seen, but some additional information is present (Figure 22). Older fires (blue shades) appear to have occurred more frequently in the south than more recent fires. An indication of the frequency of the spatial pattern of large fires is also evident, with most large fires occurring in central and northern Canada. As note of caution, these results have to be interpreted with care because of differences in the reporting periods and methods used by provinces and territories for compiling fire records.

Figure 22: Detected fires using course resolution satellite imagery combined with historical fire records for Canada from 1921-1998.

4.2.2 Forest Disturbance Mapping Detecting and mapping multiple types of forest disturbance using coarse resolution satellite imagery is more complex than fire detection because of the differences among disturbances. Some disturbances such as fire result in abrupt changes, while others such as forest regeneration or insect damage, are much more subtle. In general, disturbance detection is carried out by comparing image data between two time periods. Numerous methods have been developed, ranging from simple image subtraction to incorporation of physically based reflectance models or auxiliary data sources (Coppin et al., 2004). Fraser et al., (2005) have developed a disturbance mapping technique that fuses the spectral, temporal, and spatial information of coarse resolution imagery into a summary change probability measure. The method has been applied to SPOT/VEGETATION data for the period 1998-2004 to detect changes in forest cover. Many types of disturbance were identified including fire, harvesting, insect defoliation, mining, flooding, and storm damage. In total 1.6 % of Canada’s forest was found to have changed in this period. Fire accounted for most of the disturbance at 0.8 % followed by forest harvesting at 0.4 %,

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insect defoliation at 0.3 %, and flooding at 0.1 %. The accuracy of the change product based on an independent validation dataset was high (94 %). However, because of the short time period for which these changes have been monitored it is not possible to identify if any one of them is increasing or decreasing consistently through time, but continued monitoring will allow for this assessment to be made once several decades of satellite data have been collected and processed. The complex feedback relationship between climate variability and vegetation dynamics is a subject of intense investigation. Land cover change is identified by the IPCC among the causes, or key forcing factors of global change. It is also representative of the means by which human adaptation to change will occur. The changes in land cover distribution may result from natural causes or human activities (Figure 23). The former include competition, predation, pathogens, extreme weather, forest fire and climatic cycles. Human activities include physical disturbances and changes due to water and air pollution from acids, oxidants, toxic organic compounds and trace metals. Physical disturbances or abrupt changes are obvious and relatively easy to quantify using remote sensing change detection techniques, while quantification of impacts caused by dispersion of pollution by air and water leading to transitional changes is often more complex.

Figure 23. Land cover disturbances

In Latifovic and Pouliot (2005) satellite remote sensing data were used for evaluating the general pattern of land use and land cover over Canada. The analysis was approached from three complementary perspectives: 1) monitoring, 2) providing earth observation based spatially explicit input for modeling of processes and 3) assessment of impact on

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ecological functions. Four land cover maps were produced from enhanced surface reflectance time series data that identify change as well as temporal land cover dynamics showing forest removals and re-growth over the observed period. The base map derived for 1995 was updated backward to produce 1985, 1990 and forwarded to produce 2000 land cover maps using a change detection and local classification methodology. Areas of significant change were extracted using region specific thresholds set by comparison with Landsat and provincial fire databases (Figure 24). The approach taken for producing these land cover data ensures its consistency and continuity while the unique classification legend allows for comparison and monitoring. These land cover products are used for evaluating: 1) climate impacts, by coupling remote sensing-based land cover change data to atmospheric data – through Soil-Vegetation-Atmosphere thrasher models; 2) impact of fires on vegetation structure and phenology; and 3) environmental impact of industrial and infrastructure development.

Figure 24 Change derived from multi temporal earth observation data. Change detection accuracy in this dataset has limitations resulting from the coarse resolution data used, but the information on the land cover temporal dynamics and greater temporal range provides additional information to existing change products. The accuracy of these maps is similar to other global land cover products for the Canadian region. Figure 25 shows the spatial locations of changes in each change period. Area estimates by class for each ecozone are given in Table 5. This product reveals the disturbance dynamics in each ecozone. Like all of the disturbance datasets discussed, fire is dominant and thus the same general trends are evident. Overall land cover appears relativity stable at this scale of observation. Ecozones 5, 16 and 17 have the most change in forest cover, which lead to large increases in the disturbed cover class. The trend in disturbed area shows differences between regeneration rates in the north and the south with forest cover increasing more quickly for ecozones 16 and 17 in the south and a slight decrease for ecozone 5 in the north. Average annual change was found to be less than 5 % in each five-year period. Considering all of Canada’s landmass, change between 1985 to1990 was 3.7 %, 1990 to 1995 was 4.6 % and 1995 to 2000 was 4.4 %.

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Figure 25: Temporal pattern of disturbance, blue – 1985, green – 1990, yellow – 1995, red – 2000.

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Table 5: Percent land cover area to the total ecozone area.

Monitoring subtle changes in forest productivity has also shown promise using course resolution satellite imagery. In a study of the effects of surface mining on the surrounding forest, a slight decrease in a climate normalized indicator of vegetation productivity was observed closer to a surface mining operation (Latifovic et al., 2005). Figure 26 shows the trend in this indicator from 1990-2001 using course resolution satellite imagery for rings surrounding the mining area at increasing distances from the mine centre. The indicator value calculated using just climate data is also shown to identify the trend if environmental conditions were to exert no influence on the indicator value. All rings show a slight decline in vegetation productivity since 1997, but the decline is more pronounced in the ring closest to the mine. This is opposite of the expected trend based on the indicator derived from climate data only. It also correlates with the increase in

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mining production quantified by the amount of CO2 equivalent emissions. However, the observed decline is relatively small and within the range of natural variation. Therefore, it cannot be attributed with a high degree of certainty to surface mining activities only. Continued monitoring in coming years when the mining development is going to be intensified will provide more opportunity to evaluate the potential for this type of productivity assessment.

Figure 26: Surface mining impacts on the surrounding vegetation as function of mine proximity. Difference in the productivity indicator estimated from the remote sensing measurements and predicted by the model simulation are attributed to mining impacts. Ring PAI 0.75 is closet to the mine and ring PAI 0.25 is furthest away from the mine. 5. FOREST HYDROLOGY Climate change will have a strong influence on future water supplies due to changes in temperature and precipitation, which are key controls on the hydrological cycle. Water availability is dependent on moisture input from rain and snow and outputs from runoff and evapotranspiration. Evapotranspiration is the mechanism by which water is returned to the atmosphere from transpiration (plant water loss through plant leaf stomata) and evaporation/sublimation from soil/snow and plant surfaces. The difference between these water inputs and outputs is the water supply or water surplus for an area contained in lakes, rivers, groundwater, snow, permafrost, or glaciers. Because of the localized changes in temperature and precipitation due to climate change, it is difficult to determine how water supplies will be affected for a specific region. In general, climate change is expected to increase precipitation, evaporation, water body temperatures and overall hydrologic variability. Extreme low and high flows are expected, increasing flooding and drought events (CCIA, 2002). Warmer winter temperatures will also have a significant impact due to increased snow loss resulting in

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reduced water supplies and changes to temporal water-yield dynamics (Allen et al., 2004). Some predictions for regional effects include reduced Great Lake water levels, reduced soil moisture in southern Canada, and wetland disappearance in the prairies (CCIA, 2002).

5.1 Forests and Groundwater Dynamics A growing concern regarding water availability has been groundwater supply. Groundwater is becoming a crucial source of water use and its quality and quantity has a tremendous impact on people and surface water ecosystems. In Canada the use of ground water has recently increased by 3-fold (Riviera, 2002). This increase in demand requires long term conservation to prevent reductions in recharge and storage. Forests are an important component of groundwater recharge, influencing interception of precipitation, infiltration, and evapotranspiration. A reduction in forest cover generally leads to less evapotranspiration, reduced losses to canopy interception, wetter soils, and increased stream flow. The most significant effect is the reduction in evapotranspiration. In addition to influencing water supply, forest cover helps buffer against flooding and erosion due to canopy interception, reduction of available water through evapotranspiration and from structural impediments to surface and stream flow. Erosion in a forest watershed can be 1000 times less than that of a bare ground watershed (Chang, 2003). For these reasons, factors influencing evapotranspiration need to be better understood in order to evaluate how climate change may influence local hydrology and how we can optimize vegetation conditions to maintain sufficient and stable ground water supplies. Numerous field studies have shown that any reduction in forest canopy will cause an increase in snow accumulation and water yield (Chang, 2003). Snow surveys have shown increases of 30-45 % in seasonal accumulation after removal of conifer forest cover (Pomeroy and Gray, 1995; Pomeroy and Granger, 1997). Hornbeck et al. (1997) did not find evidence to support increased snow accumulation due to deforestation, but did observe earlier snowmelt. Buttle et al. (2000) point out that changes in snow accumulation are specific to forest regions. A comparison of two studies showed less accumulation in the more humid atlantic ecozone than the boreal shield ecozone. The reduced accumulation associated with deforestation is due to interception and associated losses from sublimation (Buttle et al., 2000). Andreassian (2004) compiled results of 137 paired watershed studies regarding the effects of deforestation and reforestation and found that deforestation resulted in earlier snowmelt by 7-12 days. For much of western Canada, winter snow accumulation combined with glacial melt is the main water supply and therefore changes in annual accumulation and melt dynamics can have significant implications for water supply in these areas (Environment Canada, 2004). Deforestation increases annual water yields by reducing evapotranspiration. In the review by Andreassian (2004), deforestation was found to increase annual stream flow and flood peak volume. Hornbeck et al. (1997) found that clear-cut harvest removal increased water

39

yield by 32 % and less intrusive strip cutting increased it by 8 %. Much of the increased flow occurred during low flow, which is beneficial to stream habitat and water supplies. The observed increases dissipated within 7-9 years of cutting due to regeneration. In terms of groundwater recharge, Simic et al. (2004a) found that recharge was dependent on LAI and soil type. In this case high LAI represented forests (> 4) and lower LAI shrubs and other low vegetation covers. In regards to soils, loam, silt, and clay type soils reduced recharge the most. The effects of temperature and precipitation have also been shown to be highly correlated with groundwater levels. Allen et al. (2004) evaluated several temperature and precipitation scenarios on water table levels for a study site in southern British Columbia based on model simulations. Simulations included: high recharge (lower temperature and higher precipitation) and low recharge (higher temperature and lower precipitation). The difference in water level for these two scenarios was +0.05 and –0.025 for the high and low scenario respectively. Chen et al. (2004) studied changes in groundwater levels for an aquifer in southern Manitoba from 1986-2000 and found a greater change in water level due to low recharge conditions with an average drop of 1.7 m. They also note that the change was spatially dynamic, ranging from 1-6 m. In European forests, model simulations suggest that recharge rates may drop by as much as 50 % leading to longer term water table changes (Eckhardt and Ulbrich, 2003; Lasch et al. 2003).

5.2 Climate and Forest Structural Factors Affecting Evapotranspiration Evapotranspiration represents the most substantial loss of water from an area due to forest cover. Canada receives approximately 537 mm/yr of precipitation of which 285 mm/yr leaves surface watersheds as runoff and the balance 250mm/yr constitutes losses through evapotranspiration (UN FAO, 2005). For this reason, understanding the factors that affect evapotranspiration are important to better evaluate the influence of climate change on terrestrial water loss and so that land management can optimize groundwater storage. Evapotranspiration is difficult to measure and there are few field methods for its measurement and no direct methods for measurement at regional scales. One useful indirect method is the use of numerical land surface models that relate climate and surface parameters to evapotranspiration in a physically based manner 3.1.3. This approach offers the additional ability to assess the interactions between carbon-water-energy-nutrient cycles and to include feedback loops useful both to constrain estimates and to explore scenarios under changing land use or climate. The following presents results of sensitivity analysis using the EALCO model to evaluate climate and forest structural factors on evapotranspiration.

40

5.2.1 Aspen Forests For the southern aspen site in Table 4, analyses of model sensitivity to the environmental variables air temperature, precipitation, and atmospheric CO2 concentration show that evapotranspiration from this boreal ecosystem is most sensitive to changes in temperature and least sensitive to precipitation. Increases in air temperature significantly increased the ecosystem water loss due to the change of the humidity gradient between the air inside the stomata and the atmosphere, the lengthening of the plant growing season, and the acceleration of plant growth. CO2 concentrations also affect the water budgets of ecosystems by easing plant CO2 absorption, reducing the number of open stomata and thus transpiration. However, photosynthesis is also increased under higher CO2, which increases transpiration. In addition, high CO2 tends to cause higher canopy temperatures, which increases transpiration. The integrated effects simulated by the model show that elevated atmospheric CO2 can help this aspen forest conserve water. This impact may benefit water-limited ecosystems. These trends of simulated effects have been observed on some CO2 enrichment experiments on field plants (Strain and Cure, 1985). Figure 27 shows the impact of climate change on forest evapotranspiration simulated by the EALCO model (Wang et al., 2005b).

How Does Climate Change Affect Forest Evapotranspiration?

� Leaf level transpiration • Stomatal resistance and leaf physiology • Leaf energy balance and intercellular water vapor

pressure • Leaf water potential

� Canopy level transpiration • Growing season • LAI and canopy resistance • Root growth and hydraulic resistance • Canopy radiation profile • Canopy energy balance

� Soil and snow • Snow melt and sublimation • Soil freeze/thaw and water movement • Soil evaporation • Ground surface energy balance

41

Figure 27. Model simulated change of forest evapotranspiration under different climate change scenarios as described in Figure 17.

5.2.2 Black Spruce Forests For the southern black spruce site several climate and forest structural factors were evaluated for their influence on evapotranspiration. Seven different models were compared for their response to changes in temperature, precipitation, incoming solar radiation, dew point temperature, and atmospheric CO2 concentration. As with the aspen site, evapotranspiration was most sensitive to changes in temperature, with increased temperature increasing evapotranspiration. Increases in precipitation resulted in only a

Response of Boreal Forest Evapotranspiration to Climate Change

� Warmer climate increases ET and drought impact significantly. It could completely offset the effect of moderate precipitation increase.

� Higher CO2 could significantly increase plant water use efficiency and reduce the drought impact caused by higher temperature.

� Decrease of precipitation could remarkably increase drought impact particularly on the aspen ecosystem even under higher CO2 conditions.

T+3oCCO2=350P=unch.

T+3oCCO2=350

P=30% incr.

T+3oCCO2=350

P=30% decr.

T+3oCCO2=550

P=30% decr.

T+3oCCO2=550P=unch.

T+3oCCO2=550

P=30% incr.

-15-10

-505

1015202530

C0P0 C0P+ C0P- C+P0 C+P+ C+P-

Climate Change Scenarios

ET

Ch

ang

e (%

)

42

small increase in evapotranspiration whereas a decrease produced a much greater change. Increased solar radiation increased evapotranspiration with the opposite effect of similar magnitude for a decrease. Increases in both dew point temperature and CO2 led to decreases in evapotranspiration. In regards to CO2, the richer CO2 environment allowed stomata to close to conserve water and maintain similar rates of carbon absorption (Potter at al., 2001). Forest structural factors evaluated at this site included LAI, foliage clumping, maximum stomatal conductance (water requirements at stomata for CO2 absorption), and leaf nitrogen content. Increases in LAI influenced evapotranspiration the most. Maximal stomatal conductance was the next most influential variable. Increases in stomatal water conductance increased evapotranspiration because water can more easily move from roots to leaves whereas decreases limit water movement and therefore reduce evapotranspiration. Both foliage clumping and leaf nitrogen content had only a small influence on evapotranspiration. A greater effect was seen for a decrease in the clumping index than an increase. Greater clumping reduces canopy light interception increasing the light available for surface water evaporation. Increases in leaf nitrogen increased evapotranspiration due to greater respiration and decreased evapotranspiration with lower leaf nitrogen levels (Potter et al., 2001). Another factor affecting evapotranspiration from a site is the species composition. Due to differences in the structural and functional characteristics of plants such as rooting structures, foliage dynamics, stomata responses and stem heights different species have different transpiration rates. Ratios of annual transpiration for Engelmann spruce, subalpine fir, lodgepole pine, and aspen stands of equal basal area were found to be 3.2, 2.1, 1.8, and 1 respectively for a study area in the central Rocky. As a general rule, hardwoods transpire less than conifers (Chang, 2003).

5.3 Remote Sensing Based Snow Melt Monitoring The timing of snow melt and vegetation green-up are important indicators of climate variability as well as being potentially useful for validating ecosystem models. The period between the on-set of snow melt and leaf-out conditions is of critical importance to ecosystem functioning and management in northern biomes. There is increasing evidence that annual net ecosystem productivity is related to the date of onset of the growing season. Additionally, the rate and pattern of snow melt controls runoff processes and understory vegetation activity. Both the onset of understory emergence following snow pack ablation and the green-up of overstory canopies is related to habitat suitability. Finally, the period between snow melt and leaf-out often corresponds to increased forest fire danger (Simic et al. 2004b). Remote sensing is well suited for snow cover mapping because of the unique spectral signature of snow compared to other landcover types (Xiao et al., 2002; Maurer et al., 2003). The major drawback is confusion between snow and clouds. Certain types of clouds such as cirrus, low stratus, and small cumulus, are hard to discriminate from snow

43

and ice-covered surfaces (Simpson et al., 1998). Forest areas represent another obstacle for accurate snow mapping in remote sensing applications. Forest canopies obscure snow from the view of both visible and passive-microwave satellite sensors. Ultimately, less snow is detected underneath a forest canopy when the sensors view at off-nadir angles (Hall et al., 1998). Simic et al., (2004b) compared snow cover products derived from AVHRR, SPOT\VEGETATION, and MODIS satellite sensors. The results showed considerable differences in the relative magnitudes of omission and commission errors between products. The two best products, MODIS and AVHRR, had similar accuracies ranging from 80%-100% for monthly estimates. Lower accuracies were observed for the month of January, when solar zenith angles are large, suggesting that better correction for tree and surface shadow effects is required in current algorithms. The lowest accuracies occurred during snow melt mostly in forest areas. In another study, Fernandes et al. (2004) used spectral signatures calibrated using historical data and field based snow cover measurements. The accuracy of this method is similar to other snow cover products, but there are two significant improvements. First, the omission and commission errors are more evenly balanced. Second, this method was able to provide snow cover estimates for a much larger number of days than other products, which only produced estimates for 25-40 % of the days in a month due to cloud contamination. 6. CONCLUSION Climate change will have numerous impacts on forest environments in Canada. The uncertainty associated with projected climate changes and forest ecosystem response to these changes makes it difficult to predict the magnitude of change and where impacts will be the most severe. This report has focused on the latest research findings of the NRC\ESS\CCRS in regards to forest monitoring, modeling and understanding forest ecosystem processes. The capabilities and products developed allow for continued assessment of potential climate change effects providing for early detection and the required knowledge needed for effective adaptation and mitigation. Future research will focus on evaluation of developed datasets and validation of models for better understanding of climate impacts on forests. 7. APPENDIX

7.1 Sensor Descriptions Remote sensing is concerned with the spectral reflectance of surface objects. At the sensor reflected electromagnetic radiation is broken into classes or bands based on radiometric wavelengths measured in microns (µm). The received energy can be partitioned in a multitude of ways based on sensor design and purpose. For remote sensing of vegetation useful bands are commonly defined by wavelengths in the blue

44

(0.4-0.5 µm), green (0.5-0.6 µm), red (0.6-0.7 µm), near infrared (NIR, 0.7-1.30 µm), and short-wave infrared (SWIR, 1.3-3.0 µm). Partitioning the wavelengths in this manner is useful because vegetation absorbs and reflects the radiometric energy in each wavelength differently. In general, vegetation absorbs incoming radiation in the blue and red wavelengths but reflects it in the green, NIR, and SWIR wavelengths. The intensity of the response of the target object in each band determines what is known as the spectral signature of that object, which can be used to determine various object properties (Lillesand and Kiefer, 2000). Coarse resolution and moderate resolution sensors are two general categories of sensors used by the NRC\ESS\CCRS for national monitoring applications. Coarse resolution sensors have a pixel size in the range ~ 0.2 to 10 km. Here pixel size refers to the area on the ground of the smallest sampled unit (a pixel) in the image captured by the satellite sensor. The National Oceanic and Atmospheric Administration/Advanced High Resolution Radiometer (NOAA/AVHRR), SPOT/VEGETATION, and the Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used coarse resolution satellite sensors for forestry applications. These sensors have a pixel size of ~ 1 km, but have different historical data records and acquire imagery in different wavelengths. The AVHRR data record extends from ~1981 to present. It acquires imagery in the red (0.58-0.68 µm), NIR (0.73 - 1.10 µm), and two thermal wavelengths (3.55 - 3.93 µm and 10.30 - 11.50 µm). The SPOT/VEGETATION data record covers a much shorter temporal period from 1998 to present. It acquires imagery in the blue (0.43-0.47 µm), red (0.61-0.68 µm), NIR (0.79-0.89 µm), and SWIR (1.58-1.75 µm). The MODIS data record is the shortest, extending from 2000 to present. It acquires 36 image bands ranging from the blue to thermal wavelengths and at different spatial resolutions of 250 m, 500 m and 1000 m. Not all wavelengths are acquired at each resolution. A complete description of the wavelengths acquired can be found at: http://modis.gsfc.nasa.gov/about/specs.html. Moderate resolution satellite imagery is mostly used in the development of coarse resolution products and for validation. The Landsat sensor is the most commonly used with a pixel resolution of 30 m. Data records for Landsat extend as far back as the 1970’s to present. It acquires imagery in the blue (0.45-0.52 µm), green (0.52-0.60 µm), red (0.63-0.69 µm), NIR (0.76-0.90 µm), thermal (10.4-12.5 µm), and has two SWIR wavelengths (1.55-1.75 µm and 2.08-2.35 µm). Each scene covers an area of approximately 180x180 km.

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7.2 List of Data Products Available for Climate Change Studies

Product Description Contact NOAA/AVHRR historical archive of reflectance products

Archive of NOAA/AVHRR data 1 km resolution covering Canada from 1985-present. Corrections applied for atmosphere, viewing geometry, inter-sensor calibration, and cloud contamination.

Rasim.Latifovic@nrcan-rncan.gc.ca

SPOT/VEGETATION historical archive of reflectance products

Archive of SPOT/VEGETATION data 1 km resolution covering Canada from 1998-2004. Corrections applied for atmosphere, viewing geometry, inter-sensor calibration, and cloud contamination.

Rasim.Latifovic@nrcan-rncan.gc.ca

MODIS historical archive of reflectance products.

Archive of MODIS data covering Canada from 2000-2004. Corrections applied for atmosphere, viewing geometry, and cloud contamination.

Alexander.Trichtchenko@nrcan-rncan.gc.ca

Albedo for 1985-2004. Surface albedo derived from NOAA/AVHRR for 1985-2004 for Canada.

Alexander.Trichtchenko@nrcan-rncan.gc.ca

1995 land cover 31-class land cover produced from NOAA/AVHRR data. 1 km resolution.

Rasim.Latifovic@nrcan-rncan.gc.ca

1998 land cover 22-class land cover produced from SPOT/VEGETATION data. 1 km resolution.

Rasim.Latifovic@nrcan-rncan.gc.ca

2000 land cover 28-class land cover based on the a modified Federal Geographic Data Committee/Vegetation Classification Standard at the regional scale as well as a 22 class legend based on the Land Cover Classification System of the Food and Agriculture Organization. Product produced from SPOT/VEGETATION data at 1 km resolution.

Rasim.Latifovic@nrcan-rncan.gc.ca

Multi-temporal land cover 1985, 1990, 1995, and 2000.

31-class land cover produced from NOAA/AVHRR data at 1 km resolution.

Rasim.Latifovic@nrcan-rncan.gc.ca

Coniferous and Deciduous Fractions

Fractions of coniferous and deciduous area in each 1 km forest pixel in Canada. Derived from SPOT/VEGETATION.

Goran.Pavlic@nrcan-rncan.gc.ca

Species maps Fraction of species area in each 1 km forest pixel in Canada. Derived from SPOT/VEGETATION.

Goran.Pavlic@nrcan-rncan.gc.ca

LAI 1998-2004 Leaf area index at 1 km resolution derived from SPOT/VEGETATION data.

Richard.Fernandes@nrcan-rncan.gc.ca

Crown closure of Canada Crown closure derived from SPOT/VEGETATION data at 1 km resolution.

Sylvain.Leblanc@nrcan-rncan.gc.ca

Evapotranspiration 1970-2004.

Evapotranspiration of Canada based on the EALCO model.

Richard.Fernandes@nrcan-rncan.gc.ca

Forest disturbance 1998-2004

National coverage disturbance map showing fire, harvesting, insect damage and flooding. Derived from 1 km resolution SPOT/VEGETATION data.

Robert.Fraser@nrcan-rncan.gc.ca

Forest stand age National coverage stand age map for Canada produced from 1 km resolution SPOT/VEGETATION data.

Goran.Pavlic@nrcan-rncan.gc.ca

Daily snow coverage 2000

Daily snow coverage produced from SPOT/VEGETATION data at 1 km resolution.

Richard.Fernandes@nrcan-rncan.gc.ca

Landsat coverage of Canada’s north

Radiometrically normalized bands 3, 4, and 5 of Landsat for northern Canada. Scenes range temporally from 1999-2001 at 30 m resolution

Ian.Olthof@nrcan-rncan.gc.ca

Land cover of northern Canada

43-class land cover for Canada’s north at 90 m resolution derived from the Landsat coverage for the north.

Ian.Olthof@nrcan-rncan.gc.ca

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