Development of a practical forest ecosystem classification from existing biophysical studies: An approach used in northwestern Quebec

While forest ecosystem classification work in Quebec has traditionally concentrated on inventory and mapping, more effort is now being placed on developing field guides similar to those produced in other Canadian provinces. As part of a project to produce a practical forest ecosystem field guide for the Amos Lowlands Ecological Region in northwestern Quebec, existing sub-regional ecological studies were exploited in order to develop a regional classification of forest ecosystems, or forest stations. Review of four fundamental studies provided a list of 107 ecological phases, each representing a particular combination of forest composition, surface deposit type and moisture regime. A series of silvicultural and environmental interpretations were developed and values for each were attributed to the ecological phases. Cluster analysis was then performed to classify phases into 29 broader units. A large, regional biophysical database which became available later in the project provided a means of validating and effectively modifying the classification. The justifications for using the original approach are discussed.     

Forest ecological classification and mapping: Their application for ecosystem management in Newfoundland

A prerequisite to sustaining ecosystems is the inventory and classification of landscape structure and composition. Ecological classification and mapping involves the delineation of landscapes into easily recognizable units. Topography, soils, vegetation, physical landscape form, and successional pathways are delineation criteria commonly used.Damman (1967) developed a forest type classification system for Newfoundland using vegetation, soil and landforms as the defining criteria. Damman's forest types were used in combination with mensurational data to assign forest types to timber volume productivity classes. Since each of the Damman forest types is associated with characteristic soils, parent materials, moisture regime and topographic position, the mapping units are similar to Canada Land Inventory (CLI) mapping units. Field work to confirm the correlation between Damman forest types and CLI capability classes was initiated in 1993. CLI maps were recoded in 1994 and Damman forest types were determined; resulting ecosystem-based maps provide a common framework to assess forestry/wildlife interactions in an ecosystem planning process.     

Monitoring global change: Comparison of forest cover estimates using remote sensing and inventory approaches

Satellite-based remote sensing offers great potential for frequent assessment of forest cover over broad spatial scales, however, calibration and validation using ground-based surveys are needed. In this study, forest cover estimates for the United States from a recently developed land surface cover map generated from satellite remote sensing data were compared to state-level inventory data from the U.S. National Resources Planning Act Timber Database. The land cover map was produced at the U.S. Geological Survey EROS Data Center and is based on imagery from the AVHRR sensor (spatial resolution 1.1 km). Vegetation type was classified using the temporal signal in the Normalized Difference Vegetation Index derived from AVHRR data. Comparisons revealed close agreement in the estimate of forest cover for extensively forested states with large polygons of relatively similar vegetation such as Oregon. Larger forest cover differences were observed in other states with some regional patterns in the level of agreement apparent.Comparisons in inventory- and remote sensing-based estimates of current forested area with potential vegetation maps indicated the magnitude of past land use change and the potential for future changes. The remote sensing approach appears to hold promise for conducting surveys of forest cover where inventory data are limited or where rates of vegetation change, due to human or climatic factors, are rapid.     

Classification and mapping forest sites using geographic information system (GIS): a case study in Artvin Province

The productivity of forest sites has been indirectly determined with solo wood production objective in forest management. Forest site productivity should, however, be determined directly in order to implement ecosystem based multipurpose forest management philosophy. This article tackles the problem in distinguishing and mapping forest sites using both direct method and indirect method in Genya Mountain located in central of Artvin State Forest Enterprise. About 112 sample plots were designed and distributed over the area. In each sample plot, soil samples were collected and the classical timber inventory measurements were taken. According to direct method, Soil Moisture Regime (SMR) method is preferred due to a water deficiency in the study area. Water holding capacity was used as an essential criterion for the classification of the forest site. Forest site classifications were assigned regarding the physiographic factors such as landform, aspect, and slope. Five different forest sites classes; dry, moderate fresh, fresh, humid and hygric were determined. According to direct method, the guiding curve was used to generate anamorphic site index (SI) equations and three site index classes; good (SI=I–II), medium (SI=III) and low (SI=IV–V) were determined. Some important differences between the methods were realized. The forest sites determined with site index estimation method indicate that site index I and II is 505.99 ha, III 1095.79 ha and IV and V 992.95 ha, whereas forest sites determined with direct method related to dry site of 937.58 ha, moderate fresh site of 931.90 ha, fresh site of 1,797.71 ha, humid site of 80.48 ha and hygric site of 356.55 ha. The forest site maps of both methods were created using GIS functions. The forest sites of open and degraded areas should be determined according to direct method.     

Ecological mapping: A framework for delimiting forest management units

Ecological mapping attempts to objectively and spatially delimit and represent the natural organization and structure of the landscape. It offers nested levels of resolution, based upon a regionalization process, and provides an ecological basis for planning activities that may impact upon the environment.The essential principles of ecological mapping, as applied by the Quebec Ministry of Environment and Wildlife, are summarized. A methodological mapping approach is proposed for the determination of significant land portions for forest management using an ecological map at a scale of 1:50 000. At this scale, two nested levels of perception are expressed: 1) the topographic complex, and 2) the topographic entity. The topographic entity can be further subdivided into working units based upon operational criteria oriented to forest management. Within each nested level from topographic complex to working unit, there is a corresponding increase in the amount of detailed information available. Ecological mapping undertaken at 1:50 000 scale can provide a reliable and robust tool for planning forest management activities. In most cases, major ecological variations can be expressed and mapped at this scale; however, a greater degree of generalization must be accepted in the planning process when working at this scale rather than at larger scales.     

Implications of differing input data sources and approaches upon forest carbon stock estimation

Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p?<?0.001). The LIDAR-derived site class was greater than the original site class for 42% of stands; however, 77% of stands were within ±1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare (p?<?0.001); differences for Douglas-fir-dominated stands (representing 85% of all stands) were not significant (p?=?0.288). Overall, the relationship between the two biomass estimates was strong (R ²?=?0.92, p?<?0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping.     

Effects of changing forest land definitions on forest inventory on the West Coast, USA

A key function of forest inventory is to detect changes in the area of forest land over time, yet different definitions of forest land are used in different regions of the world. Changes in the definition of forest intended to improve international consistency can affect the ability to quantify true changes over time. The objective of this study was to evaluate the effects of a definitional change from relative stocking to canopy cover on the area classified as forest land and its relationship to species and forest density in California, Oregon, and Washington. Both western Juniper and ponderosa pine will yield higher estimates of forest land area using a canopy cover definition in comparison to a stocking-based definition, with the difference being most pronounced where land is marginally forested. The change in definition may result in an additional 146,000 ha of forest land identified on the West Coast. Measuring marginal forest lands with both metrics for the first cycle after implementation should make it possible to distinguish real change from definitional change.     

Multiscale satellite and spatial information and analysis framework in support of a large-area forest monitoring and inventory update

Many countries undertake a national forest inventory to enable statistically valid monitoring in support of national and international reporting of forest conditions and change. Canada’s National Forest Inventory (NFI) program is designed to operate on a 10-year remeasurement cycle, with an interim report produced at the 5-year mid-point. The NFI is a sample-based inventory, with approximately 18,850 2 ×2-km photo plots across the country, distributed on a 20×20-km grid of sample points; these photo plots are the primary data source for the NFI. Capacity to provide annual monitoring information is required to keep policy and decision makers apprised of current forest conditions. In this study, we implemented a multistage monitoring framework and used a Moderate Resolution Imaging Spectroradiometer (MODIS) change product to successfully identify 78% of the changes in forest cover area that were captured with a Landsat change detection approach. Of the NFI photo plots that were identified by both the Landsat and MODIS approaches as having changes in forest cover, the proportion of change area within the plots was similar (R ²?=?0.78). Approximately 70% of the Landsat-derived change events occupied less than 40% of a single MODIS pixel, and more than 90% of the change events of this size were successfully detected with the MODIS product. Finally, MODIS estimates of the proportion of forest cover change at the NFI photo plot level were comparable to change estimates for the ecoregions as a whole (R ²?=?0.95). High-temporal, low-spatial resolution imagery such as MODIS, in combination with other remotely sensed data sources, can provide information on disturbance events within a national forest inventory remeasurement cycle, thereby satisfying the interim information needs of policy and decision makers as well as the requirements of national and international reporting commitments.     

A comparative assessment of land cover dynamics of three protected forest areas in tropical eastern Africa

Processes of deforestation, known to threaten tropical forest biodiversity, have not yet been studied sufficiently in East Africa. To shed light on the patterns and causes of human influences on protected forest ecosystems, comparisons of different study areas regarding land cover dynamics and potential drivers are needed. We analyze the development of land cover since the early 1970s for three protected East African rainforests and their surrounding farmlands and assess the relationship between the observed changes in the context of the protection status of the forests. Processing of Landsat satellite imagery of eight or seven time steps in regular intervals results in 12 land cover classes for the Kakamega–Nandi forests (Kenya) and Budongo Forest (Uganda) whereas ten are distinguished for Mabira Forest (Uganda). The overall classification accuracy assessed for the year 2001 or 2003 is similarly high for all three study areas (81% to 85%). The time series reveal that, despite their protection status, Kakamega–Nandi forests and Mabira Forest experienced major forest decrease, the first a continuous forest loss of 31% between 1972/1973 and 2001, the latter an abrupt loss of 24% in the late 1970s/early 1980s. For both forests, the temporally dense time series show short-term fluctuations in forest classes (e.g., areas of forest regrowth since the 1980s or exotic secondary bushland species from the 1990s onwards). Although selectively logged, Budongo Forest shows a much more stable forest cover extent. A visual overlay with population distribution for all three regions clearly indicates a relationship between forest loss and areas of high population density, suggesting population pressure as a main driver of deforestation. The revealed forest losses due to local and commercial exploitation further demonstrate that weak management impedes effective forest protection in East Africa.     

Mapping forest composition from the Canadian National Forest Inventory and land cover classification maps

Canada's National Forest Inventory (CanFI) provides coarse-grained, aggregated information on a large number of forest attributes. Though reasonably well suited for summary reporting on national forest resources, the coarse spatial nature of this data limits its usefulness in modeling applications that require information on forest composition at finer spatial resolutions. An alternative source of information is the land cover classification produced by the Canadian Forest Service as part of its Earth Observation for Sustainable Development of Forests (EOSD) initiative. This product, which is derived from Landsat satellite imagery, provides relatively high resolution coverage, but only very general information on forest composition (such as conifer, mixedwood, and deciduous). Here we link the CanFI and EOSD products using a spatial randomization technique to distribute the forest composition information in CanFI to the forest cover classes in EOSD. The resultant geospatial coverages provide randomized predictions of forest composition, which incorporate the fine-scale spatial detail of the EOSD product and agree in general terms with the species composition summaries from the original CanFI estimates. We describe the approach and provide illustrative results for selected major commercial tree species in Canada.     

Monitoring urban forest health

Renewed interest in urban forestry has resulted in significant public investment in trees during the past few years, yet comprehensive urban forest monitoring programs are uncommon. Monitoring is an integral component of a program to sustain healthy community forests and long term flows of net benefits. Volunteer-based monitoring will promote continued public involvement and support in community forestry. To overcome constraints to monitoring in urban environments, programs must be personally relevant, socially desirable, scientifically credible, and economically feasible. A three-tiered monitoring approach is presented. Canopy cover analysis documents net gains and losses in regional urban forest cover. Simplified detection monitoring uses trained volunteers to better understand tree population dynamics, while intensive monitoring characterizes urban forest functions and stressors. Implementation of an urban forest health initiative to develop, place, and evaluate monitoring programs is advocated.     

Addressing statistical and operational challenges in designing large-scale stream condition surveys

Implementing a statistically valid and practical monitoring design for large-scale stream condition monitoring and assessment programs can be difficult due to factors including the likely existence of a diversity of ecosystem types such as ephemeral streams over the sampling domain; limited resources to undertake detailed monitoring surveys and address knowledge gaps; and operational constraints on effective sampling at monitoring sites. In statistical speak, these issues translate to defining appropriate target populations and sampling units; designing appropriate spatial and temporal sample site selection methods; selection and use of appropriate indicators; and setting effect sizes with limited ecological and statistical information about the indicators of interest. We identify the statistical and operational challenges in designing large-scale stream condition surveys and discuss general approaches for addressing them. The ultimate aim in drawing attention to these challenges is to ensure operational practicality in carrying out future monitoring programs and that the resulting inferences about stream condition are statistically valid and relevant.     

Development of Ecological Land Classification and mapping in support of forest management in northern Newfoundland,Canada

For the sustainable development of forest land, as recently prescribed by the Canadian Forest Strategy, a land classification project in northern Newfoundland was initiated to support the local forest management activities. The method adopted here is a modification of the Canadian Committee for Ecological Land Classification's (CCELC) system, and it applies various levels of mapping to uniform areas based on geomorphology, soils, vegetation, climate, water, and fauna.In this study, all CCELC levels were mapped; resulting maps were digitized and imported into a Geographic Informations System (GIS). The GIS data base contained the following maps: 1) digital terrain model, 2) bedrock geology, 3) surficial geology, 4) forest inventory, and 5) various levels of the ecological land classification, including Vegetation Types at the lowest level. In addition to the mapping, mensurational data were analyzed to provide stand and stock tables for each of the forest types, including growth curves that could be entered into specific forest growth modelling systems to predict wood supply scenarios based upon different management interventions.     

Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches

Discrete trees and small groups of trees in nonforest settings are considered an essential resource around the world and are collectively referred to as trees outside forests (ToF). ToF provide important functions across the landscape, such as protecting soil and water resources, providing wildlife habitat, and improving farmstead energy efficiency and aesthetics. Despite the significance of ToF, forest and other natural resource inventory programs and geospatial land cover datasets that are available at a national scale do not include comprehensive information regarding ToF in the United States. Additional ground-based data collection and acquisition of specialized imagery to inventory these resources are expensive alternatives. As a potential solution, we identified two remote sensing-based approaches that use free high-resolution aerial imagery from the National Agriculture Imagery Program (NAIP) to map all tree cover in an agriculturally dominant landscape. We compared the results obtained using an unsupervised per-pixel classifier (independent component analysis—[ICA]) and an object-based image analysis (OBIA) procedure in Steele County, Minnesota, USA. Three types of accuracy assessments were used to evaluate how each method performed in terms of: (1) producing a county-level estimate of total tree-covered area, (2) correctly locating tree cover on the ground, and (3) how tree cover patch metrics computed from the classified outputs compared to those delineated by a human photo interpreter. Both approaches were found to be viable for mapping tree cover over a broad spatial extent and could serve to supplement ground-based inventory data. The ICA approach produced an estimate of total tree cover more similar to the photo-interpreted result, but the output from the OBIA method was more realistic in terms of describing the actual observed spatial pattern of tree cover.     

A spatial hierarchical framework for the co-management of ecosystems in Canada and the United States for the upper Great Lakes region

Over the past three decades, considerable effort has been invested in the development of complex and comprehensive ecosystem classifications and inventories in many parts of North America. Paralleling this has been an evolution in those hierarchical frameworks guiding the development and application of classifications. However, resource management agencies continue to grapple with the dilemma of applying multiple classification and inventory templates over large jurisdictions, especially as they attempt to address ecosystem management objectives. Given that Canada and the United States share ecosystems and that commitments have been made by all levels of government to make progress towards ecosystem-based approaches to management, there is a need to provide the proper tools. Comprehensive goals will not be achieved without collaboration and cooperation.This paper outlines the range of ecosystem classification approaches that exist in the Upper Great Lakes region. Canadian and American national hierarchical frameworks are briefly examined. Specific information needs and tasks are outlined which must be followed, independent of national boundaries, for the successful integration of planning and monitoring programs for large regional ecosystems.A general model is proposed for the development and application of an integrated, multi-scale and bi-national ecosystem classification, inventory and information system. This approach would facilitate data sharing and communication across jurisdictional boundaries.     

Rational forest productivity decline

A whole forest optimisation model was employed to examine economic behaviour as it relates to long term, forest productivity decline in the boreal forests of Ontario, Canada. Our productivity investment model(PIM) incorporated a choice between productivity decline as representedby a drop in forest Site Class, and a fee to 'maintain' site productivity. Sensitivity analysis was used to determine the point at which these fees exceeded the value of the differential in timber volume between upper and lower site classes. By varying discount rate, 'productivity investment frontiers' were constructed, which highlight the effects of the magnitude in productivity decline, maintenance fees, and harvest flow constraints upon the occurrence and schedule of productivity declines. In presenting this simple approach to exploring the effects of economic choice upon forest productivity decline, the phenomena of 'natural capital divestment' within forestry is described.     

Analyzing riparian forest cover changes along the Firniz River in the Mediterranean City of Kahramanmaras in Turkey

Riparian forests adjacent to surface water are important transitional zones which maintain and enrich biodiversity and ensure the sustainability in a forest ecosystem. Also, riparian forests maintain water quality, reduce sediment delivery, enhance habitat areas for aquatic life and wildlife, and provide ecological corridors between the upland and the downstream. However, the riparian ecosystems have been degraded mainly due to human development, forest operations, and agricultural activities. In order to evaluate the impacts of these factors on riparian forests, it is necessary to estimate trends in forest cover changes. This study aims to analyze riparian forest cover changes along the Firniz River located in Mediterranean city of Kahramanmaras in Turkey. Changes in riparian forest cover from 1989 to 2010 have been determined by implementing supervised classification method on a series of Landsat TM imagery of the study area. The results indicated that the classification process applied on 1989 and 2010 images provided overall accuracy of 80.08 and 75 %, respectively. It was found that the most common land use class within the riparian zone was productive forest, followed by degraded forest, agricultural areas, and other land use classes. The results also indicated that the areas of degraded forest and forest openings increased, while productive forest and agricultural areas decreased between the years of 1989 and 2010. The amount of agricultural areas decreased due to the reduction in the population of rural people. According to these results, it can be concluded that special forest management and operation techniques should be implemented to restore the forest ecosystem in riparian areas.     

Land cover mapping based on random forest classification of multitemporal spectral and thermal images

Thematic mapping of complex landscapes, with various phenological patterns from satellite imagery, is a particularly challenging task. However, supplementary information, such as multitemporal data and/or land surface temperature (LST), has the potential to improve the land cover classification accuracy and efficiency. In this paper, in order to map land covers, we evaluated the potential of multitemporal Landsat 8’s spectral and thermal imageries using a random forest (RF) classifier. We used a grid search approach based on the out-of-bag (OOB) estimate of error to optimize the RF parameters. Four different scenarios were considered in this research: (1) RF classification of multitemporal spectral images, (2) RF classification of multitemporal LST images, (3) RF classification of all multitemporal LST and spectral images, and (4) RF classification of selected important or optimum features. The study area in this research was Naghadeh city and its surrounding region, located in West Azerbaijan Province, northwest of Iran. The overall accuracies of first, second, third, and fourth scenarios were equal to 86.48, 82.26, 90.63, and 91.82 %, respectively. The quantitative assessments of the results demonstrated that the most important or optimum features increase the class separability, while the spectral and thermal features produced a more moderate increase in the land cover mapping accuracy. In addition, the contribution of the multitemporal thermal information led to a considerable increase in the user and producer accuracies of classes with a rapid temporal change behavior, such as crops and vegetation.