Forest Ecosystem Classification Research Articles

Klasifikace vegetační stupňovitosti v České republice: review

Considering the changing climatic conditions, the Czech and Slovakian vegetation has been vertically divided into Forest Vegetation Zones (FVZs) units. Each FVZ is represented by a specific tree species, i.e., the oaks (Quercus sp.), European beech (Fagus sylvatica L.), silver fir (Abies alba Mill.), Norway spruce (Picea abies (L.) H. Karst.) and dwarf pine (Pinus mugo Turra). The FVZ concept has been applied in the Czechoslovakian forestry classification practice since the 1950s. Due to the broad time span of the studied topic, this article is divided into five chapters regarding the evolution of the FVZ concept. Its currently applied form was established primarily in the 1970s on the grounds of data gathered during two decades of forest typology research. An FVZ unit has become an upper unit of the Czech Forest Ecosystem Classification since its legislative codification in the Forest Act (289/1995) in 1995. To this day, however, the FVZ concept has not been reassessed by advanced, multi-dimensional statistical methods. This paper aims to describe key moments of the development of this concept and provide a perspective on its limitations, also in connection with current trends in ecological research. This review aims to provide a better understanding of the forest vegetation-environment relationship in the Czech Republic in relation to climate change, indirect anthropogenic environmental impact, and prediction of forest and landscape ecosystems response.

On the Ways of Forest Typology Researches in Siberia in the 1960s–2000s

The article presents a brief overview of the works on forest typology and zoning of Siberia conducted at the Forest Typology Laboratory of the Institute of Forest and Timber of the USSR Academy of Sciences, later Sukachev Institute of Forest of the Siberian Branch of the Russian Academy of Sciences. The ecological and phytocoenotic line of research and the regional approach to the creation of diagnostic tables of forest types were adopted as a basis for complex field work carried out jointly with forest management throughout Siberia. The principal features of forest typological work in mountains required the improvement of the system of classification units and mapping of forest cover with determining their diagnostic indicators. The significance of these works for the development of the concept of systemic structure of forest cover and its deep connection with the concept of hierarchical structure of landscape cover through the idea of biogeocenosis and terrestrial ecosystem are meaningful for today. The author speaks in favor of the regional approach to classification of forest types but does not close the way to the creation of a generalized classification of forest ecosystems based on climatic ordination and information modeling. The practical use of ecological-phytocenotic regional classifications of forest cover is substantiated for creating medium-scale maps for various purposes and solving environmental management problems, with the access to ecosystem management of forest territories.

Classification and Risk Identification of Forest Ecology Based on Statistical Learning

In this paper, the forest ecosystem classification and risk identification are studied based on statistical learning methods such as cluster analysis and discriminant analysis, taking Daxinganling Forest in Inner Mongolia, Beijing Forest, Jilin Changbai Mountain Forest, Hubei Huitong Forest, Guangdong Heshan Forest, and Linzhi Forest as the research objects. The results show that: (1) The classification of forest ecosystem activity is mainly determined by the forest species. At the same time, the composition and growth of forest species is an important basis for risk classification. (2) Climate factors and soil factors affect forest ecosystem stability and vitality, which is an important basis for testing models after risk identification. Therefore, according to the results obtained in this article, the key to study forest ecology is to plant the most suitable forest species and tree species on various site types and implement corresponding afforestation measures so that the entire region can achieve “suitable trees” and “rational management”, so that the potential of land production can be brought into full play and the goal of “use the land as much as possible” is achieved.

MAPPING SPATIAL ACCURACY OF FOREST TYPE CLASSIFICATION IN JAXA’s HIGH-RESOLUTION LAND USE AND LAND COVER MAP

Abstract. Accuracy assessment of forest type maps is essential to evaluate the classification of forest ecosystems quantitatively. However, map users do not understand in which regions those forest types are well classified from conventional static accuracy measures. Hence, the objective of this study is to unveil spatial heterogeneities of accuracies of forest type classification in a map. Four forest types (deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF)) found in the JAXA’s land use / cover map of Japan were assessed by a volunteered Site-based dataset for Assessment of Changing LAnd cover by JAXA (SACLAJ). A geographically weighted (GW) correspondence matrix was applied to them to calculate the degree of overall agreements of forest type classes (forest overall accuracy), and the degree of accuracy for each forest class (forest user’s and producer’s accuracies) in a spatially varying way. This study compared spatial surfaces of these measures with static ones of them. The results show that the forest overall accuracy of the forest map tends to be relatively more accurate in the central Japan, while less in the Kansai and Chubu regions and the northern edge of Hokkaido. Static forest user’s accuracy measures for DBF, DNF, and ENF are better than forest producer’s accuracy ones, while the GW approach tells us such characteristics vary spatially and some areas have opposite trends. This kind of spatial accuracy assessment provides a more informative description of the accuracy than the simple use of conventional accuracy measures.

People or place? An exploration of social and ecological drivers of urban forest species composition

Urban forests have garnered increasing attention as providers of an array of beneficial ecosystem services. However, urban forest ecosystems are highly complex and heterogeneous systems whose structure are shaped by interacting social and ecological processes. Approaches to reliably identify and differentiate these processes could be valuable for addressing complexity and reducing uncertainty in decision-making in urban forestry. The purpose of this study is to identify and quantify a range of social and ecological drivers of urban forest species composition, distribution, and diversity. This was done using hierarchical cluster analysis and discriminant analysis with empirical plot data describing the tree species composition in Toronto, Canada. Tree density and imperviousness were by far the most influential drivers of species composition. Increasing imperviousness saw not just reduced tree density but a decline in native species abundance. Additionally, single-detached housing, homeownership, and income were closely associated and explained higher tree densities and abundances of native species. However, income had a lower than expected influence on urban forest species composition given its importance in canopy cover research. Continuous forest patches were highly distinct compared to the remainder of the urban landscape, which highlights the ecological distinctiveness of residual forests within cities and lends support to their conservation. Increasing the understanding of social and ecological drivers of tree species composition, distribution, and diversity within cities is an integral part of urban forest ecosystem classification, which can be a valuable decision-support tool for ecosystem-based management in urban forestry.

Production of high-resolution forest-ecosite maps based on model predictions of soil moisture and nutrient regimes over a large forested area

Forest ecosite reflects the local site conditions that are meaningful to forest productivity as well as basic ecological functions. Field assessments of vegetation and soil types are often used to identify forest ecosites. However, the production of high-resolution ecosite maps for large areas from interpolating field data is difficult because of high spatial variation and associated costs and time requirements. Indices of soil moisture and nutrient regimes (i.e., SMR and SNR) introduced in this study reflect the combined effects of biogeochemical and topographic factors on forest growth. The objective of this research is to present a method for creating high-resolution forest ecosite maps based on computer-generated predictions of SMR and SNR for an area in Atlantic Canada covering about 4.3 × 106 hectares (ha) of forestland. Field data from 1,507 forest ecosystem classification plots were used to assess the accuracy of the ecosite maps produced. Using model predictions of SMR and SNR alone, ecosite maps were 61 and 59% correct in identifying 10 Acadian- and Maritime-Boreal-region ecosite types, respectively. This method provides an operational framework for the production of high-resolution maps of forest ecosites over large areas without the need for data from expensive, supplementary field surveys.

Neighbourhood-scale urban forest ecosystem classification

Urban forests are now recognized as essential components of sustainable cities, but there remains uncertainty concerning how to stratify and classify urban landscapes into units of ecological significance at spatial scales appropriate for management. Ecosystem classification is an approach that entails quantifying the social and ecological processes that shape ecosystem conditions into logical and relatively homogeneous management units, making the potential for ecosystem-based decision support available to urban planners. The purpose of this study is to develop and propose a framework for urban forest ecosystem classification (UFEC). The multifactor framework integrates 12 ecosystem components that characterize the biophysical landscape, built environment, and human population. This framework is then applied at the neighbourhood scale in Toronto, Canada, using hierarchical cluster analysis. The analysis used 27 spatially-explicit variables to quantify the ecosystem components in Toronto. Twelve ecosystem classes were identified in this UFEC application. Across the ecosystem classes, tree canopy cover was positively related to economic wealth, especially income. However, education levels and homeownership were occasionally inconsistent with the expected positive relationship with canopy cover. Open green space and stocking had variable relationships with economic wealth and were more closely related to population density, building intensity, and land use. The UFEC can provide ecosystem-based information for greening initiatives, tree planting, and the maintenance of the existing canopy. Moreover, its use has the potential to inform the prioritization of limited municipal resources according to ecological conditions and to concerns of social equity in the access to nature and distribution of ecosystem service supply.

The role of plant sociology in the study and management of European forest ecosystems

Abstract: Forest composition is a faithful indicator of the stressors and disturbances that influence forest ecosystems, and it should be accounted for in Sustainable Forest Management policies. Indeed, the classification of forest ecosystems in forest types is considered as a key tool to improve the assessment and monitoring of forest biological diversity, and for the definition of management guidelines. Accordingly, the Ministerial Conference on the Protection of Forests in Europe has recognized the need of developing a pan-European forest classification in forest types, and has identified indicators of Sustainable Forest Management that should be applied by forest types. The classification of vegetation has always been among the main aims of the plant sociology. The quantitative and qualitative analysis of plant species composition, performed through the plant sociological approach, condenses compositional and structural information within a hierarchical system, and expresses all historical, sociological and habitat factors that influence the actual and potential vegetation. In a modern perspective the integration of plant sociology and ecological analysis represents a key to a hierarchical land classification and to the understanding of vegetation dynamics; furthermore the long history of plant sociology determined the availability of large datasets of vegetation data throughout Europe. Starting from these considerations, in this paper we briefly describe how plant sociology could represent a tool for the assessment of the indicators of SFM that should be applied by forest types, giving insights on how this discipline could contribute to the assessment of each of these indicators.

An analysis of the Modeling and Inventory Support Tool: Yield curves vary with Forest Ecosystem Classification

Stand-level growth and yield models are essential to assessing sustainable levels of forest harvest; such assessments are supported in Ontario by the Modeling and Inventory Support Tool (MIST), which combines updated yield estimates and predicted successional trajectories to improve yield forecasts in Ontario. Currently, forest management planning and MIST stratifies the landbase by the Standard Forest Unit (SFU), but not ecosite as defined under the Forest Ecosystem Classification (FEC) system. Here we examined variation in MIST's input and output parameters (site index, top height, and basal area) for ecosites that fall within the definition of the PW1 SFU in Central Ontario (white-pine-dominated sites). Ecosites showed significant differences in site index values and top height, but not basal area, results indicating systematic differences in productivity among ecosites within the SFU. These results show that fine-scale variation in edaphic factors, as indicated by ecosite information, correspond to differences in stand productivity, and suggest the importance of a more harmonized approach between yield modeling, SFUs, and the FEC system in Ontario.

A tool for converting forest ecosystem classifications for permanent or temporary growth plots into the new provincial Ecological Land Classification (ELC) system in the boreal regions of Ontario

Forest Ecosystem Classification (FEC) systems were originally developed and used in the Province of Ontario at regional levels with the objective of classifying forest ecosystems to support silvicultural decision-making in an operational setting. A new provincial Ecological Land Classification (ELC) system has been developed, which integrates the regional systems into a single consistent framework. To make continued use of data and knowledge gained from long-term forest monitoring plots classified under the old FEC system, such as Permanent Growth Plots (PGPs), Permanent Sample Plots (PSPs) and Temporary Sample Plots (TSPs), an ecosite conversion from the existing FEC data to the new ELC system was deemed necessary. We developed a conversion matrix to convert FEC ecosite classifications from the northeast and north-west to the provincial ELC system for the boreal forest region of Ontario. The conversion system is intended to apply at the scale of individual plots, with a special focus on PGP, PSP and TSP networks, and has been limited to forested ecosystems, as FEC systems were originally developed for the forest land base only. The conversion is primarily driven by canopy cover composition derived from plot-based individual tree data, with additional information required on substrate characteristics (e.g., substrate type, depth of mineral material, effective soil texture and moisture regime). It is possible to derive some of the soil variables from the broadly defined soil-type (S-type) categories of the original FEC systems; however, this approach requires making some assumptions that could reduce the accuracy of conversion. We anticipate that this conversion matrix will bridge the gap until active plot networks are re-typed in the field into the ELC system, provide a link to historical TSPs, and would be of general interest to a variety of new ELC users.

Ecosystem classification and inventory maps as surrogates for ground beetle assemblages in boreal forest

AIMS: We compare performance of ecosystem classification maps and provincial forest inventory data derived from air photography in reflecting ground beetle (Coleoptera: Carabidae) biodiversity patterns that are related to the forest canopy mosaic. Our biodiversity surrogacy model based on remotely sensed tree canopy cover is validated against field-collected ground data. METHODS: We used a systematic sampling grid of 198 sites, covering 84 km2 of boreal mixedwood forest in northwestern Alberta, Canada. For every site, we determined tree basal area, characterized the ground beetle assemblage and obtained corresponding provincial forest inventory and ecosystem classification information. We used variation partitioning, ordination and misclassification matrices to compare beetle biodiversity patterns explained by alternative databases and to determine model biases originating from air photo-interpretation. IMPORTANT FINDINGS: Ecosystem classification data performed better than canopy cover derived from forest inventory maps in describing ground beetle biodiversity patterns. The biodiversity surrogacy models based on provincial forest inventory maps and field survey generally detected similar patterns but inaccuracies in air photo-interpretation of relative canopy cover led to differences between the two models. Compared to field survey data, air photo-interpretation tended to confuse two Picea species and two Populus species present and homogenize stand mixtures. This generated divergence in models of ecological association used to predict the relationship between ground beetle assemblages and tree canopy cover. Combination of relative canopy cover from provincial inventory with other geo-referenced land variables to produce the ecosystem classification maps improved biodiversity predictive power. The association observed between uncommon surrogates and uncommon ground beetle species emphasizes the benefits of detecting these surrogates as a part of landscape management. In order to complement conservation efforts established in protected areas, accurate, high resolution, wide ranging and spatially explicit knowledge of landscapes under management is primordial in order to apply effective biodiversity conservation strategies at the stand level as required in the extensively harvested portion of the boreal forest. In development of these strategies, an in-depth understanding of vegetation is key.

A comparison of pixel-based and object-oriented approaches to VHR imagery for mapping saltmarsh plants

Object-oriented classification (OOC) has shown many significant advantages over other methods for classification of urban or forest ecosystems. However, it remains unclear if this technology could exhibit these advantages on mapping monospecific plant stands in herbaceous plant dominated ecosystems ( e.g. saltmarshes). In this study, we compared the effectiveness of OOC and pixel-based classification (PBC) methods for mapping plants in a saltmarsh ecosystem. QuickBird was selected for very high resolution (VHR) imagery. Eleven models defined by classification types, feature spaces, classifiers, and hierarchical approaches with multi-scale segmentation were built for comparison. The results showed that the QuickBird imagery efficiently discriminated saltmarsh monospecific vegetation stands and that OOC performed better than PBC in terms of accuracy. We also found that the improvement of OOC was primarily due to employing membership functions and a hierarchical approach with multi-scale segmentation. Although texture and shape features have been deemed as two major advantages of OOC, enhanced performance was not observed in this study. The results of this study demonstrated that OOC would be superior to PBC for classifying herbaceous plant species in terms of accuracy. To improve the classification accuracy, greater concern should be given to exploration of the relationships between features of both objects and classes and to combining information from different object scales, while shape and texture features can be a minor consideration due to their intricately high spatial variability.

Representing site productivity in the basal area increment model for FVS-Ontario

The utility of site index as a predictor variable in models for complex, mixed species stands is limited because the site index concept is not well suited for these stand types. Additionally, there is no standard protocol of estimating site index for uneven-aged mixed species stands, which is evident in permanent sample plot (PSP) and co-operative (COOP) data sets available from the Province of Ontario, Canada. Under such circumstances, an alternative to site index in a basal area increment model was explored, using a combination of climate and Forest Ecosystem Classification (FEC) variables from the Ontario boreal region. Among the four candidate climate variables chosen, mean annual temperature (MAT) explained the most variability in basal area increment for the four selected tree species – trembling aspen (Populus tremuloides Michx.), balsam fir (Abies balsamea (L.) Mill.), jack pine (Pinus banksiana Lamb.), and black spruce (Picea mariana (Mill.) B.S.P.). Our results indicated that a combination of the climate variable, MAT, and FEC explained a substantially higher proportion of variation in the basal area increment than site index alone. Thus, climate and FEC variables are superior substitutes in the basal area increment model even when error-free site index values are possible to obtain.

Uniqueness of limestone soil-forming substrate in the forest ecosystem classification

The uniqueness of limestone soil-forming substrate was assessed with respect to the classification of forest ecosystems. 50 soil profiles from the Bohemian Karst were studied and the results were confronted with previously published works from other karst areas. The evaluation of soil profiles was based on a macroscopic description, on the results of chemical and physicochemical analyses, and on a micromorphological study. The carbonate bedrock was a cause of specific genesis of soils in these sites − both recent and relict ones (terrae calcis). However, it does not always condition the unique characteristics of these sites whose differentiation would call for an <i>a priori</i> special edaphic category. Unique geochemical characteristics of the substrate are modified on the gradient of advanced pedogenesis (Holocene up to mid-Pleistocene soils were assessed) by a very intensive soil-forming process connected with the impact of allochthonous, mostly aeolian material. Two possible approaches were designed for the classification of these sites: (<i>i</i>) wider conception − distinction of the basic edaphic category with small limitations for the quality of soil-forming substrate, (<i>ii</i>) narrower conception − limestone edaphic category with a number of limitations to distinguish this edaphic category.

Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification

Terrestrial Ecosystem Mapping provides critical information to land and resource managers by incorporating information on climate, physiography, surficial material, soil, and vegetation structure. The main objective of this research was to determine the capacity of high spatial resolution satellite image data to discriminate vegetation structural stages in riparian and adjacent forested ecosystems as defined using the British Columbia Terrestrial Ecosystem Mapping (TEM) scheme. A high spatial resolution QuickBird image, captured in June 2005, and coincident field data covering the riparian area of Lost Shoe Creek and adjacent forests on Vancouver Island, British Columbia, was used in this analysis. Semi-variograms were calculated to assess the separability of vegetation structural stages and assess which spatial scales were most appropriate for calculation of grey-level co-occurrence texture measures to maximize structural class separation. The degree of spatial autocorrelation showed that most vegetation structural types in the TEM scheme could be differentiated and that window sizes of 3 × 3 pixels and 11 × 11 pixels were most appropriate for image texture calculations. Using these window sizes, the texture analysis showed that co-occurrence contrast, dissimilarity, and homogeneity texture measures, based on the bands in the visible part of the spectrum, provided the most significant statistical differentiation between vegetation structural classes. Subsequently, an object-oriented classification algorithm was applied to spectral and textural transformations of the QuickBird image data to map the vegetation structural classes. Using both spectral and textural image bands yielded the highest classification accuracy (overall accuracy = 78.95%). The inclusion of image texture increased the classification accuracies of vegetation structure by 2–19%. The results show that information on vegetation structure can be mapped effectively from high spatial resolution satellite image data, providing an additional tool to ongoing aerial photograph interpretation.

Forest site classification of forest ecosystems in Bohemian Karst (Czech Republic)

The study focuses on selected forest site units on water uninfluenced sites on limestone in Bohemian Karst. The species composition of phytocoenoses, their potential production and soil properties were assessed. Studied forest site complexes are correctly determined and they provide important information from the aspect of planning and practical nature conservation management. It is necessary to specify the criteria of categoria calcaria in detail, since it is often difficult to distinguish it from categoria mesotrophica. Water retention capacity of soils, a criterion often neglected so far, presents a useful view of forest site water budget and it is a suitable criterion in forest site classification.

An assessment of expert-based marten habitat models used for forest management in Ontario

We used marten snow tracking data and a previously developed empirical habitat model from northeastern Ontario to validate a number of expert-based, non-spatial marten habitat models. In particular, we tested the non-spatial Ontario Wildlife Habitat Analysis Model, the Boreal East Habitat Suitability Matrix (including tests of both standard forest units and development stages), and Allen's (1982) HSI model. Marten habitat use as measured by tracks in the snow was consistent with predictions of all the expert-based models, suggesting that these models correctly characterized the stand-level forest cover selected by marten in winter. Suitability ranks for individual stands derived from standard forest units and development stages also were consistent with their use by marten. The empirical model was consistent with the expert-based models in that it considered suitable forest stands to be those with tall trees dominated by spruce (Picea spp.) and balsam fir (Abies balsamea) trees, with a large amount of coarse woody debris, and high canopy closure. Our findings suggested that the expert-based models were able to characterize stand structure used by marten despite some of the models using only inputs available from stand inventories. This was accomplished because stand structural elements such as coarse woody debris were integrated into OWHAM and HSM indirectly, through relationships with stand age and species composition. Key words: boreal forest, forest inventory, habitat, habitat suitability, guidelines, Forest Ecosystem Classification, landscape, Martes americana, resource selection, snow tracking, spatial autocorrelation, stand structure

Multifactor classification of forest landscape ecosystems of Jocassee Gorges, southern Appalachian Mountains, South Carolina

Ecosystem classification identifies interrelationships within and among the geomorphology, soils, and vegetation that converge to form ecosystems across forest landscapes. We developed a multifactor ecosystem classification system for a 13 000 ha southern Appalachian landscape acquired in 1998 by the South Carolina Department of Natural Resources. Using a combination of multivariate analyses, we distinguished five ecosystem types ranging from xeric oak (Quercus spp.) to mesic eastern hemlock (Tsuga canadensis (L.) Carrière) ecosystems. Ecosystems segregated along geomorphic gradients influencing potential moisture availability, with soil properties such as solum thickness distinguishing among ecosystems occupying similar topographic positions. Our results suggest that different combinations of geomorphic and soil factors interact to form similar ecosystems across the landscape, and a given environmental factor can impact ecosystem development at some constituent sites of an ecosystem type but not at other sites. A regional comparison of ecosystem classifications indicates that environmental variables important for distinguishing ecosystems in the southern Appalachians vary, with Jocassee Gorges characterized by unique suites of environmental complexes. Our study supports the contention that the strengths of ecosystem classification are providing (i) comprehensive information on the interrelationships among ecosystem components, (ii) a foundation from which to develop ecologically based forest management plans, and (iii) an ecological framework in which to conduct future research on specific ecosystem components or processes.

Czech forest ecosystem classification

Forest classification, made up by Plíva and Průša (Plíva 1971), has been used in the Czech Republic forestry since 1970. This classification is based on ecological factors of the environment. Edaphic conditions are at the first position, then climatic conditions follow (by their vertical [altitudinal] distribution mainly). These two factors form the basic construction called ecological grid (Table 1).

Quantifying Ecosystem Geomorphology of the Southern Appalachian Mountains

Geomorphology is a dominant factor influencing vegetation distribution in the southern Appalachians, and quantifying landform characteristics is increasingly important for forest ecosystem classification. This study used slope gradient and two previously published geomorphic indices, terrain shape index and landform index that quantify landform shape and protection, to develop a field-based landform quantification system at four study areas in the southern Appalachians. Six major landform types (ridgetops, nose slopes, linear hillslopes, coves, stream ravines, and stream bottoms) exhibited quantitatively different characteristics, and these differences among landforms were not evident when using only categorical landform descriptions (e.g., convex, concave) that have been most common in southern Appalachian ecological research. Discriminant function resubstitution based on quantitative geomorphic variables distinguished 78% or more of categorical landform types, and misclassifications partly resulted from inadequacies of categorical data for capturing the continuum of landform characteristics. I applied the geomorphic quantification system by developing a classification tree model to predict the presence or absence of eastern hemlock (Tsuga canadensis) ecosystems in northwestern South Carolina. The quantitative model correctly identified 86% of sites actually supporting a hemlock ecosystem, substantially higher than a model using categorical landform data that correctly identified only 57% of hemlock sites.