Modeling the Suitability of Potential Wetland Mitigation Sites with a Geographic Information System

Wetland mitigation is frequently required to compensate for unavoidable impacts to wetlands. Site conditions and landscape context are critical factors influencing the functions that created wetlands perform. We developed a spatial model and used a geographic information system (GIS) to identify suitable locations for wetland mitigation sites. The model used six variables to characterize site conditions: hydrology, soils, historic condition, vegetation cover, adjacent vegetation, and land use. For each variable, a set of suitability scores was developed that indicated the wetland establishment potential for different variable states. Composite suitability scores for individual points on the landscape were determined from the weighted geometric mean of suitability scores for each variable at each point. These composite scores were grouped into five classes and mapped as a wetland mitigation suitability surface with a GIS. Sites with high suitability scores were further evaluated using information on the feasibility of site modification and project cost. This modeling approach could be adapted by planners for use in identifying the suitability of locations as wetland mitigation sites at any site or region.     

Disturbance of natural vegetation by camping: Experimental applications of low-level stress

Previously undisturbed sites in four different vegetation types were camped on for one night and for four nights. Changes in vegetation cover and vegetation height were measured after camping and one year later. Results are presented separately for different campsite zones—parts of the site where campers slept, cooked meals, and stored their packs. Just one night of camping was sufficient to cause evident impact in all four vegetation types, although the amount of impact varied significantly between zones and between vegetation types. Vegetation impact on campsites used four nights was generally less than twice as severe as impact on the sites used one night. The effects of camping on vegetation were also predicted for 12 other vegetation types on the basis of vegetational responses to experimental trampling. These results suggest that impact can almost always be minimized by confining camping to a small number of campsites instead of dispersing use across many campsites.     

Landscape design,planning, and management: An approach to the analysis of vegetation

The process of analyzing vegetation in terms of its suitability for various non-consumptive uses is primitive in comparison with systems for evaluating other resources such as soil and topography. This paper proposes a framework for developing a standardized, quantitative vegetation analysis system based on plant ecology methods. A tentative scheme under study in Wisconsin is presented for discussion purposes.     

Selecting suitable sites for animal waste application using a raster GIS

Rapid growth of intensive animal industries in southeast Queensland, Australia, has led to large volumes of animal waste production, which posses serious environmental problems in the Murray Darling Basin (MDB). This study presents a method of selecting sites for the safe application of animal waste as fertiliser to agricultural land. A site suitability map for the Westbrook subcatchment within the MDB was created using a geographic information system (GIS)-based weighted linear combination (WLC) model. The factors affecting the suitability of a site for animal waste application were selected, and digital data sets derived from up to 1:50,000 scale maps were acquired. After initial preprocessing, digital data sets were clipped to the size of the delineated subcatchment boundary producing input factors. These input factors were weighted using the analytical hierarchy process (AHP) that employed an objectives-oriented comparison (OOC) technique to formulate the pairwise comparison matrix. The OOC technique, which is capable of deriving factor weight independently, formulated the weight derivation process by making it more logical and systematic. The factor attributes were classified into multiple classes and weighted using the AHP. The effects of the number of input factors and factor weighting on the areal extent and the degree of site suitability were examined. Due to the presence of large nonagricultural and residential areas in the subcatchment, only 16% of the area was found suitable for animal waste application. The areal extent resulting from this site suitability assessment was found to be dependent on the areal constraints imposed on each input factor, while the degree of suitability was principally a function of the weight distribution between the factors.     

Resource Selection Probability Functions for Gopher Tortoise: Providing a Management Tool Applicable Across the Species’ Range

The gopher tortoise (Gopherus polyphemus) is protected by conservation policy throughout its range. Efforts to protect the species from further decline demand detailed understanding of its habitat requirements, which have not yet been rigorously defined. Current methods of identifying gopher tortoise habitat typically rely on coarse soil and vegetation classifications, and are prone to over-prediction of suitable habitat. We used a logistic resource selection probability function in an information-theoretic framework to understand the relative importance of various environmental factors to gopher tortoise habitat selection, drawing on nationwide environmental datasets, and an existing tortoise survey of the Ft. Benning military base. We applied the normalized difference vegetation index (NDVI) as an index of vegetation density, and found that NDVI was strongly negatively associated with active burrow locations. Our results showed that the most parsimonious model included variables from all candidate model types (landscape features, topography, soil, vegetation), and the model groups describing soil or vegetation alone performed poorly. These results demonstrate with a rigorous quantitative approach that although soil and vegetation are important to the gopher tortoise, they are not sufficient to describe suitable habitat. More widely, our results highlight the feasibility of constructing highly accurate habitat suitability models from data that are widely available throughout the species’ range. Our study shows that the widespread availability of national environmental datasets describing important components of gopher tortoise habitat, combined with existing tortoise surveys on public lands, can be leveraged to inform knowledge of habitat suitability and target recovery efforts range-wide.     

Evaluation of guild-indicator species for use in resource management

We followed selection guidelines commonly used by management agencies to select mountain quail (Oreortyx pictus) as an indicator species for an ecological guild of birds. We then evaluated the ability of mountain quail to indicate the presence of other species from the guild and to index the quality of the habitat for other species. The ability of quail to indicate the presence of species from the ecological guild varied widely within and among vegetation types. Species compositions of the ecological guild were more consistent in comparisons of sites within vegetation types than they were in comparisons of sites between vegetation types. Mountain quail habitat was significantly different from the habitats of sympatric species from the guild for 14 of 15 multivariate contrasts. We suggest that managers use indicator species with caution. If indicators are used, they should be applied to guilds composed of species that closely share ecological affinities. The habitat of the indicator species should overlap extensively with those of all other guild members. The use of indicators should be restricted to very similar sites within the same general vegetation type.     

Recently planted vegetation strips reduce Giardia runoff reaching waterways

Current methods for tracking pathogens across farmland and into surrounding waterways via runoff are limited and typically have been developed using artificially created landscapes. No studies have investigated how Giardia in farm runoff moves across the landscape, despite high prevalence rates in dairy cattle (Bos taurus) worldwide. Here, we report the development of a field-based tracking method specific for Giardia movement in runoff and use this technique to compare the pathogen reduction capability of recently planted vegetation strips with bare soil strips cleared of vegetation. Such scenarios represent typical events in schemes to plant vegetation barriers aimed at reducing waterway contamination. A significant treatment effect was identified, with 26% fewer Giardia detected in runoff collected from the planted strip (P = 0.006). These results highlight the immediate benefit of pathogen removal to be gained from vegetation planting. The successful discrimination of treatment effects by this new technique will enable the assessment of different vegetation types on runoff reduction and the effects of plant development over time.     

Dynamic floodplain vegetation model development for the Kootenai River, USA

The Kootenai River floodplain in Idaho, USA, is nearly disconnected from its main channel due to levee construction and the operation of Libby Dam since 1972. The decreases in flood frequency and magnitude combined with the river modification have changed the physical processes and the dynamics of floodplain vegetation. This research describes the concept, methodologies and simulated results of the rule-based dynamic floodplain vegetation model "CASiMiR-vegetation" that is used to simulate the effect of hydrological alteration on vegetation dynamics. The vegetation dynamics are simulated based on existing theory but adapted to observed field data on the Kootenai River. The model simulates the changing vegetation patterns on an annual basis from an initial condition based on spatially distributed physical parameters such as shear stress, flood duration and height-over-base flow level. The model was calibrated and the robustness of the model was analyzed. The hydrodynamic (HD) models were used to simulate relevant physical processes representing historic, pre-dam, and post-dam conditions from different representative hydrographs. The general concept of the vegetation model is that a vegetation community will be recycled if the magnitude of a relevant physical parameter is greater than the threshold value for specific vegetation; otherwise, succession will take place toward maturation stage. The overall accuracy and agreement Kappa between simulated and field observed maps were low considering individual vegetation types in both calibration and validation areas. Overall accuracy (42% and 58%) and agreement between maps (0.18 and 0.27) increased notably when individual vegetation types were merged into vegetation phases in both calibration and validation areas, respectively. The area balance approach was used to analyze the proportion of area occupied by different vegetation phases in the simulated and observed map. The result showed the impact of the river modification and hydrological alteration on the floodplain vegetation. The spatially distributed vegetation model developed in this study is a step forward in modeling riparian vegetation succession and can be used for operational loss assessment, and river and floodplain restoration projects.     

Estimation of Landscape Potential for Construction of Surface-Flow Wetlands for Wastewater Treatment in Estonia

The main aim of this article is to demonstrate a method of complex landscape analysis in order to estimate the landscape suitability for the construction of surface-flow wetlands (SFW) for wastewater treatment. This is a multilevel suitability analysis from a more general regional (landscape) assessment based on a map of landscape types (1:100,000) toward a detailed analysis based on aerial orthophotos and detailed soil maps (1:10,000). The assessment scheme consists of landscape classification according to the physical-chemical properties of landscape factors (soil conditions, landforms, hydrogeology, expert decisions concerning landscape values, and suitability analysis). The partial suitability values of SFWs are derived by summarizing expert values for landscape factors (each ranging from -1 to +1). By multiplying the summarized partial suitability values with nature protection values (ranging from 0 to 1), we obtain the final suitability value for each landscape type. Any kind of nature protection area has been considered nonsuitable and excluded at regional-level analysis. The results of the regional analysis demonstrate that suitability is distributed relatively equally over the study area. The high suitability potential (classified as "very suitable") is relatively evenly distributed in lowland regions throughout the country. The share of "very suitable" and "suitable" areas in different counties varies from 5 to 23% and 7 to 49%, respectively. The detailed analysis based on aerial orthophotos showed that areas suitable for SFWs can also be found within the areas determined to be unsuitable based on the less detailed map of landscape types, whereas differences are much greater between settlements chosen for the detailed suitability analysis.     

Differences in resistance of three subtropical vegetation types to experimental trampling

Experimental trampling trials using a standardized methodology were undertaken in 10 replicate blocks in three vegetation types in an urban reserve in the subtropics of Australia. In each block different intensities of trampling (controls, 10, 25, 50, 100, 150, 200, 250, 300, 400 and 500 passes) were applied, and vegetation parameters were measured pre-trampling, immediately after trampling and 2 weeks later. A Fern understorey had low resistance to trampling intensity, with reductions in relative vegetation height and cover with as few as 10 passes. A Tussock grass understorey showed moderate resistance with reduction in height at 25 passes and cover at 50 passes. A Disturbed grassland dominated by lawn grasses had the highest resistance, with reductions in vegetation height at 100 passes, but cover was affected by as few as 10 passes. The resistance indices (number of passes required to reduce vegetation cover by 50%) of three vegetation types were 210, 360 and 860 passes, respectively. When these values were compared with those for 52 other vegetation types considerable variation was found within life forms, climatic zones and vegetation types indicating that the response of a specific community may not always be predictable.     

Reasonable creatures: rights and rationalities in valuing the countryside

Debate on the economic valuation of the countryside is typically polarized between absolutist critics who would deny it any valid role and equally fervid proponents who see its techniques as the only way of integrating the environment into policy making. Such debate is structured by conflicting notions of rights, responsibilities and values, rather than by consideration of the role of technique in practical policy‐making. This paper attempts to take the debate forward and begins by examining the ways in which rights, responsibilities and values have been historically created. The techniques of economic valuation rest on particular conceptions of these, making them irreducibly political, and at the same time their results are often used to justify political decisions. Yet the proper role of technique ought to be to explore options. Provided that the sort of clarification that economic valuation offers is understood, it may, along with other types of technique, be used to open up the decision making process.     

Comparing the Use of Indigenous Knowledge with Classification and Ordination Techniques for Assessing the Species Composition and Structure of Vegetation in a Tropical Forest

dentification of groups that are similar in their floristic composition and structure (habitat types) is essential for conservation and forest managers to allocate high priority areas and to designate areas for reserves, refuges, and other protected areas. In this study, the use of indigenous knowledge for the identification of habitat types in the field was compared against an ecological characterization of habitat types, including their species composition obtained by using classification and ordination techniques for a tropical landscape mosaic in a rural Mayan area of Quintana Roo, Mexico. Plant diversity data calculated from 141 sampled sites chosen randomly on a vegetation class’s thematic map obtained by multispectral satellite image classification were used for this propose. Results indicated high similarity in the categorization of vegetation types between the Mayan classification and those obtained by cluster and detrended correspondence analysis. This suggests that indigenous knowledge has a practical use and can be comparable to that obtained by using science-based methods. Finally, identification and mapping of vegetation classes (habitat types) using satellite image classification allowed us to discriminate significantly different species compositions, in such a way that they can provide a useful mechanism for interpolating diversity values over the entire landscape.     

Remote sensing of aquatic vegetation distribution in Taihu Lake using an improved classification tree with modified thresholds

Classification trees (CT) have been used successfully in the past to classify aquatic vegetation from spectral indices (SI) obtained from remotely-sensed images. However, applying CT models developed for certain image dates to other time periods within the same year or among different years can reduce the classification accuracy. In this study, we developed CT models with modified thresholds using extreme SI values (CT(m)) to improve the stability of the models when applying them to different time periods. A total of 903 ground-truth samples were obtained in September of 2009 and 2010 and classified as emergent, floating-leaf, or submerged vegetation or other cover types. Classification trees were developed for 2009 (Model-09) and 2010 (Model-10) using field samples and a combination of two images from winter and summer. Overall accuracies of these models were 92.8% and 94.9%, respectively, which confirmed the ability of CT analysis to map aquatic vegetation in Taihu Lake. However, Model-10 had only 58.9-71.6% classification accuracy and 31.1-58.3% agreement (i.e., pixels classified the same in the two maps) for aquatic vegetation when it was applied to image pairs from both a different time period in 2010 and a similar time period in 2009. We developed a method to estimate the effects of extrinsic (EF) and intrinsic (IF) factors on model uncertainty using Modis images. Results indicated that 71.1% of the instability in classification between time periods was due to EF, which might include changes in atmospheric conditions, sun-view angle and water quality. The remainder was due to IF, such as phenological and growth status differences between time periods. The modified version of Model-10 (i.e. CT(m)) performed better than traditional CT with different image dates. When applied to 2009 images, the CT(m) version of Model-10 had very similar thresholds and performance as Model-09, with overall accuracies of 92.8% and 90.5% for Model-09 and the CT(m) version of Model-10, respectively. CT(m) decreased the variability related to EF and IF and thereby improved the applicability of the models to different time periods. In both practice and theory, our results suggested that CT(m) was more stable than traditional CT models and could be used to map aquatic vegetation in time periods other than the one for which the model was developed.     

Modelling trends in woody vegetation structure in semi-arid Australia as determined from aerial photography

Accounting of carbon stocks in woody vegetation for greenhouse purposes requires definition of medium term trends with accurate error assessment. Tree and shrub cover was sampled through time at randomly located sites over a large area of central Queensland, Australia using aerial photography from 1945 to 1999. Calibration models developed from field data for the same land types as those represented within the study area allowed for the extrapolation of overstorey and understorey cover, basal area and biomass values and these were modelled as trends over the latter half of the 20th century. These structural attributes have declined over the region because of land clearing with values for biomass changing from a mean of 58.0(+/-1.2)t/ha in 1953 to 41.1(+/-1.0)t/ha in 1991. The biomass of Acacia on clay and Eucalypt on texture contrast soils land types has declined most dramatically. Within uncleared vegetation there was an overall trend of increase from 56.1(+/-1.2)t/ha in 1951 to 67.6(+/-1.3)t/ha in 1995. The increase in structural attributes within uncleared vegetation was most pronounced for the Eucalypt on texture contrast soils and Eucalypt on clay land types. It was demonstrated that the sites sampled were representative of their land types and that spatial bias of the photography, undetected tree-killing, sampling error, inherent variability of structural attributes and measurement error should not have impacted greatly on bias or precision of trend estimates for well-sampled land types. Certainly the errors are not likely to be substantial for trends averaged over all land types and they provide an accurate assessment of the magnitude and direction of change. The technique presented here would appear to be a robust means of accounting for the above-ground woody component of woodlands and open forests and will also contribute to a broader understanding of savanna dynamics.     

Rough Set Rule Induction for Suitability Assessment

The data that characterize an environmental system are a fundamental part of an environmental decision-support system. However, obtaining complete and consistent data sets for regional studies can be difficult. Data sets are often available only for small study areas within the region, whereas the data themselves contain uncertainty because of system complexity, differences in methodology, or data collection errors. This paper presents rough-set rule induction as one way to deal with data uncertainty while creating predictive if–then rules that generalize data values to the entire region. The approach is illustrated by determining the crop suitability of 14 crops for the agricultural soils of the Willamette River Basin, Oregon, USA. To implement this method, environmental and crop yield data were spatially related to individual soil units, forming the examples needed for the rule induction process. Next, four learning algorithms were defined by using different subsets of environmental attributes. ROSETTA, a software system for rough set analysis, was then used to generate rules using each algorithm. Cross-validation analysis showed that all crops had at least one algorithm with an accuracy rate greater than 68%. After selecting a preferred algorithm, the induced classifier was used to predict the crop suitability of each crop for the unclassified soils. The results suggest that rough set rule induction is a useful method for data generalization and suitability analysis.     

Demographic Analysis of Tree Colonization in a20-Year-Old Right-of-Way

Past tree colonization dynamics of a powerline-right-of-way (ROW) corridor in the Haut-Saint-Laurent region of Quebec was studied based on the present age distribution of its tree populations. This colonization study spans 20 years, from 1977 (ROW clearance) to 1996. The sampled quadrats were classified into six vegetation types. Tree colonization dynamics were interpreted in each type, and three distinct patterns were identified. (1) Communities adapted to acidic conditions were heavily colonized by Acer rubrum, at least for the last 12 years. (2) Communities adapted to mesic or to hydric conditions were more intensely colonized in the period 1985-1987 than in the following 9 years; this past success in tree colonization may have been caused by herbicide treatments, which could have facilitated tree establishment by damaging the herbaceous and shrub vegetation. (3) Cattail, vine-raspberry, and reed-dominated communities contained few tree individuals, with almost all trees establishing between 1979 and 1990; those three vegetation types appear as the most resistant to tree invasion in the ROW studied. This study supports the need for an integrated approach in ROW vegetation management, in which the selection of vegetation treatment methods would depend on the tree colonization dynamics in each vegetation type. Minimizing disturbances inflicted on ROW herbaceous and shrub covers should be the central strategy because disturbances jeopardize natural resistance to future tree invasion, except in communities adapted to acidic conditions where the existing vegetation does not prevent invasion by A. rubrum. Many trees are surviving the successive cutting operations by producing new sprouts each time, particularly in communities adapted to mesic and hydric conditions. In these cases, mechanical cutting should be replaced by a one-time stump-killing operation, to avoid repeated and unsuccessful treatments of the same individuals over time.     

Cluster analysis of structural stage classes to map wildland fuels in a Madrean ecosystem

Geospatial information technology is changing the nature of fire mapping science and management. Geographic information systems (GIS) and global positioning system technology coupled with remotely sensed data provide powerful tools for mapping, assessing, and understanding the complex spatial phenomena of wildland fuels and fire hazard. The effectiveness of these technologies for fire management still depends on good baseline fuels data since techniques have yet to be developed to directly interrogate understory fuels with remotely sensed data. We couple field data collections with GIS, remote sensing, and hierarchical clustering to characterize and map the variability of wildland fuels within and across vegetation types. One hundred fifty six fuel plots were sampled in eight vegetation types ranging in elevation from 1150 to 2600 m surrounding a Madrean 'sky island' mountain range in the southwestern US. Fuel plots within individual vegetation types were divided into classes representing various stages of structural development with unique fuel load characteristics using a hierarchical clustering method. Two Landsat satellite images were then classified into vegetation/fuel classes using a hybrid unsupervised/supervised approach. A back-classification accuracy assessment, which uses the same pixels to test as used to train the classifier, produced an overall Kappa of 50% for the vegetation/fuels map. The map with fuel classes within vegetation type collapsed into single classes was verified with an independent dataset, yielding an overall Kappa of 80%.     

DESIGNATION OF WETLANDS BY WEIGHTED AVERAGES OF VEGETATION DATA: A PRELIMINARY EVALUATION1

ABSTRACT: Weighted averages (WA) was investigated as a vegetation-based method for wetland designation, to be used in conjunction with the wetland indicator status of plants from Wetland Plants of the United States of America 1986 (Reed, 1986). Ecological indices were assigned to indicator groups and were used to compute weighted averages for quantitative data obtained from four studies of wetland vegetation conducted in various regions of the United States. Weighted averages of vegetation data proved to be a useful tool for assessing wetland status of the vegetation types included in our study: (1) rankings of vegetation stands or types by WA correlated well with their positions on environmental moisture gradients; and (2) the results of WA could be used, together with a wetland/upland break-point, to designate vegetation types as wetland or upland in a way that agreed well, in three of the four studies, with an alternative classification of wetland habitats. The variation of weighted averages among the sampling units representing a vegetation type was generally small relative to the range of ecological indices assigned. However, designations based on weighted average scores close to the break-point should be considered provisional and must be verified with supplementary data on soils and hydrology.     

Computer-aided classification of forest cover types from small scale aerial photography

The US National Park Service must map forest cover types over extensive areas in order to fulfill its goal of maintaining or reconstructing presettlement vegetation within national parks and monuments. Furthermore, such cover type maps must be updated on a regular basis to document vegetation changes. Computer-aided classification of small scale aerial photography is a promising technique for generating forest cover type maps efficiently and inexpensively. In this study, seven cover types were classified with an overall accuracy of 62 percent from a reproduction of a 1120,000 color infrared transparency of a conifer-hardwood forest. The results were encouraging, given the degraded quality of the photograph and the fact that features were not centered, as well as the lack of information on lens vignetting characteristics to make corrections. Suggestions are made for resolving these problems in future research and applications. In addition, it is hypothesized that the overall accuracy is artificially low because the computer-aided classification more accurately portrayed the intermixing of cover types than the hand-drawn maps to which it was compared.