Spatio-temporal heterogeneity of riparian soil morphology in a restored floodplain (original) (raw)
Landscape and Urban Planning, 2007
A framework for landscape monitoring based on a functional approach is presented. Landscape monitoring is defined as regular, long-term surveillance of landscapes. Suitable monitoring resulting in the early recognition of crucial changes in the environment is a prerequisite if timely counteractions are to be carried out. Such monitoring provides spatially and temporally homogeneous data sets, and allows for adjustment to and validation of ecological models. The peculiarities of the framework presented are as follows. Landscape functions, and ecological conditions and processes are scrutinized from a strong human perspective. The procedure employs various methods based on models, balances, calculations and estimations. Thus, far-reaching data integration is possible, which allows for the drawing of conclusions which are important for human society. The landscape monitoring proposed also follows a differentiated scale-dependent approach (at local, sub-regional and regional levels) with a stepwise integration of data. Data sampling and analysis are organized according to a scale-dependent set of assumptions allowing a problem-oriented approach. The framework is exemplified by changes in soils in a small (16.1 km 2 ) floodplain in Saxony (Germany). The assessment proposed also takes into account historical data. Essential changes in soils have been established, e.g. losses in humus, deposition of soils by excavation matter and peat degradation. It was found that the percentage of arable land increased, and the groundwater level decreased in several areas. This finds expression in several physical and chemical parameters, e.g. in reduced field and sorption capacities. Therefore, the performances of soil protection functions and of production functions became worse.
Quantifying the proportion of tile-drained land in large river basins.
The structure of a landscape is highly relevant for research and planning (such as fulfilling the requirements of the Water Framework Directive -WFD -and for implementation of comprehensive catchment planning). There is a high potential for restoration of linear landscape elements in most European landscapes. By implementing the WFD in Germany, the restoration of linear landscape elements could be a valuable measure, for example to reduce nutrient input into rivers.
A spatial approach to soil-ecological experimentation at landscape scale
Journal of Plant Nutrition and Soil Science, 2008
The upscaling of soil-ecological processes to larger landscape units represents a special challenge to soil ecology. Results from micro-or mesocosms cannot easily be transferred to other scales because effects are often scale-dependent. In this context, field experiments which take into account the heterogeneity of the landscape may be promising. Therefore, we carried out an experiment based on a transect study in the agrolandscape of NE Germany on heterogeneous sandy soil in which the feeding activity of the soil-organism community was assessed by means of the bait-lamina test at each of the 101 transect locations. At every 4th position, prior to the measurement the soil biota were stimulated by a treatment consisting of adding easily available C and water to the soil. Our aim was to test whether this kind of spatial approach enables to separate effects induced by treatments from landscape effects. The results showed a highly variable feeding activity along the transect after 4 weeks. Despite this variability, a basic trend could be identified which was related to a landscape factor, i.e., the relief. On upper-slope positions, the feeding activity tended to be less in comparison to positions down-slope. At every 4th position of the transect, the stimulating effect of the substrate and water addition could be clearly detected and quantified with spectral and cross-spectral analysis. It is concluded that effects of treatments in heterogeneous landscapes may be distinguished from site effects when the signal-to-noise ratio is high and soil and treatment effects on the variable of interest are sufficiently different from one another. In a heterogeneous landscape with gradients of site properties, a treatment based on the frequency domain and applied in regular intervals can be distinguished with spectral analysis techniques.
Comment on “Modeling soil variation: past, present and future” by G.B.M. Heuvelink and R. Webster
Geoderma, 2002
particularly well-crafted review of the state of the art of soil variation modeling provides an opportunity to reflect on future avenues for research in this field. Far from being in any way a put-down of these author's fine overview, the present comment is an attempt to jump-start a (long overdue!) debate about some of the philosophical underpinnings of the current work on soil variation and about the extent to which a new conceptual orientation is necessary. A first issue concerns the ultimate purpose(s) of the research on soil variation. In the soil science literature of the last two decades, soil variation has been presented implicitly as an essential feature of soils, one that cannot be ignored and has to be dealt with, headon, regardless of the practical problem at hand. Great emphasis has been placed and continues to be placed in the literature on the panoply of mathematical, particularly geostatistical, tools available to quantify and model soil variation. The equation of semivariograms and (co)kriging estimates, for example, have been rehashed ad nauseam in the last 20 years. In contrast, little if any attention has been devoted to the questions of when, where and why, from a practical standpoint, one should be concerned with soil variation, and what the appropriate scale is at which to observe soil variation in given circumstances. Ten years ago, Arnold and Wilding (1991) echoed a widely held opinion that the key reason for soil scientists to ''bother with soil variability'' was that, if they did not, others, less qualified, would. Heuvelink and Webster's (2001) review does not indicate that there is much novel perspective in this respect to be found in the literature. Even though soil variation undoubtedly matters in a wide range of situations, there is also convincing evidence to the contrary in specific instances. Outside the traditional soil science literature, several authors have described cases where the spatial variation of soils appears to be of marginal relevance to the problems under consideration. For example, in a recent article, Worall (2001) reviews some of the models traditionally used to predict the
PLOS ONE, 2021
The physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis and some of these attributes have been used as proxies to soil quality. Thus, we investigated: (1) whether appropriate soil quality indicators (SQIs) could be identified in soils of Great Britain, (2) whether conventional soil classification or aggregate vegetation classes (AVCs) could predict SQIs and (3) to what extent do soil types and/ or AVCs act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQI which were named as; soil organic matter (SOM), dissolved organic matter (DOM), soluble N, reduced N, microbial biomass, DOM humification (DOMH). SOM was identified as the most important SQI in the discrimination of both soil types and AVCs. Soil attributes constituting highly to the SOM factor were, microbial quotient and bulk density. The SOM indicator discriminated three soil type groupings and four aggregate vegetation class groupings. Among the soil types, only the peat soils were discriminated from other groups while among the AVCs only the heath and bog classes were isolated from others. However, the peat soil and heath and bog AVC were the only groups that were distinctly discriminated from other groups. All other groups heavily overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. The two-way ANOVA showed that the AVCs were a better regulator of the SQIs than the soil types. We conclude that conventionally classified soil types cannot predict the SQIs defined from large areas with differing climatic and edaphic factors. Localised areas with similar climatic and topoedaphic factors may hold promise for the definition of SQI that may predict the soil types or AVCs.
Effects of Soil Morphology on Hydraulic Properties: I. Quantification of Soil Morphology
Utilization of existing soil survey databases for characterizing water flow and solute transport in field soils has practical value. However, the lack of a proper means for quantifying soil morphology limits the incorporation of soil structural information into models. In this study, we examined basic relationships between five major soil morphological features (texture, initial moisture, pedality, macroporosity, and root density) and steady infiltration rates for 96 soil horizons of varying structure. Based on these relationships, a point scale system was developed as an approach to quantify soil morphology. Descriptive morphological classes were first rated with respect to their potential impacts on soil water flow rate. Points that provided the best correlation with the measured steady infiltration rates were then obtained for each morphological class through a computer optimization program. The optimal points assigned to each morphological feature were divided by the maximum value to yield a morphometric index of 0 to 1. Such an approach permitted the determination of interrelationships among different morphological features that would otherwise be difficult with qualitative descriptors. The proposed morphology quantification system also has potential in facilitating pedotransfer studies of estimating water flow and chemical transport parameters from soil survey databases including structural descriptors.
Soil indicators to assess the effectiveness of restoration strategies in dryland ecosystems
Soil indicators may be used for assessing both land suitability for restoration and the effectiveness of restoration strategies in restoring ecosystem functioning and services. In this review paper, several soil indicators, which can be used to assess the effectiveness of ecological restoration strategies in dryland ecosystems at different spatial and temporal scales, are discussed. The selected indicators represent the different viewpoints of pedology, ecology, hydrology, and land management. Two overall outcomes stem from the review. (i) The success of restoration projects relies on a proper understanding of their ecology, namely the relationships between soil, plants, hydrology, climate, and land management at different scales, which are particularly complex due to the heterogeneous pattern of ecosystems functioning in drylands. (ii) The selection of the most suitable soil indicators follows a clear identification of the different and sometimes competing ecosystem services that the project is aimed at restoring.
2021
Generally, the physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis. However, biological indicators of soil quality play no direct role in traditional soil classification and surveys. To support their inclusion in classification schemes, previous studies have shown that soil type is a key factor determining microbial community composition in arable soils. This suggests that soil type could be used as proxy for soil biological function and vice versa. In this study we assessed the relationship between soil biological indicators with either vegetation cover or soil type. A wide range of soil attributes were measured on soils from across the UK to investigate whether; (1) appropriate soil quality factors (SQFs) and indicators (SQIs) can be identified, (2) soil classification can predict SQIs; (3) which soil quality indicators were more effectively predicted by soil types, and (4) to what extent do soil types and/ or aggregate vegetation classes (AVCs) act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQFs namely; Soil organic matter, Organic matter humification, Soluble nitrogen, Microbial biomass, Reduced nitrogen and Soil humification index. Of these, Soil organic matter was identified as the most important SQF in the discrimination of both soil types and AVCs. Among the measured soil attributes constituting the Soil organic matter factor were, microbial quotient and bulk density were the most important attributes for the discrimination of both individual soil types and AVCs. The Soil organic matter factor discriminated three soil type groupings and four aggregate vegetation class groupings. Only the Peat soil and Heath and bog AVC were distinctly discriminated from other groups. All other groups overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. We conclude that conventionally classified soil .
Geoderma, 2002
An appropriate inclusion of spatial variation of soils is becoming increasingly important for spatially distributed ecological modelling approaches. Even though soils are anisotropic vertically and laterally, most soil spatial variability studies have focused on the lateral variation of soil attributes over the landscape. This study characterizes the complexity of three-dimensional variations of individual soil attributes and examines the possibility of predicting soil property distribution using three different regression approaches: artificial neural networks (ANN), regression trees (RT) and general linear models (GLM). Thirty-two physiochemical attributes of 502 soil samples were collected from 64 soil profiles on a slope at Bicknoller Combe, Somerset, UK. After a principal component analysis, five soil attributes were selected to test for environmental correlation, assuming they reflect dominant pedological processes at the hillslope. Vegetation occurrence, soil types, terrain parameters and soil sample depth were used as predictors. Prediction using environmental variables was most successful for soil attributes whose spatial distribution is strongly influenced by lateral hydrological and slope processes with relatively simple depth functions (e.g. total exchangeable bases, Mn oxides and soil pH). These soil attributes also showed a high mobility, which implies that their spatial distribution quickly reaches an equilibrium with current slope processes. Soil taxonomic information only marginally improved the performance of models constructed from surface information such as vegetation and terrain parameters. On the other hand, soil attributes whose vertical distribution is strongly governed by vertical pedogenesis or unknown factors were poorly modelled by environmental variables due to stronger nonlinearity in their vertical distribution. Soil taxonomic information becomes more important for predicting these soil attributes. As an empirical modelling tool, GLM with interaction terms outperformed the other two methods tested, ANN and RT, in terms of both the simplicity of the model structure and the performance of derived empirical functions.
Quantifying Soil Morphology in Tropical Environments Methods and Application in Soil Classification
Soil Science Society of America Journal, 2000
We tested the hypothesis that readily observed and easily measured morphological variables can be used to characterize the soils sampled and described in southeastern Nigeria for purposes of land use and management. Field tests were developed for estimating soil texture and amount of ironstone nodules. Two new soil color indices provided an immediate means of diagnosing the soil drainage regime in case of the color index (CI) and soil forming processes in tropical soils in case of the redness index (RI). The indices correlated negatively with organic C content (R =-0.39) and positively with dithionite-extracted Fe 2 O 3 (0.44) and A1 2 O 3 (0.51). Inexpensive field tests for color, texture, and ironstone can be quantified using color indices and laboratory measurements. The local soil classification was quantified by means of color indices (RI, CI) and percentages of ironstone, sand, silt, and clay measured in the A horizon. A classification based on soil texture, ironstone, and color was used to define classes for the B horizon. The two first principal components (PC) extracted from soil morphological variables measured on the upper three horizons of 11 pedons explained 64.7% of the total variance. Nonhierarchical clustering performed on the two PCs produced seven clusters that compare well with the great groups of U.S. soil taxonomy. Principal component analysis on 20 soil chemical and morphological variables confirmed that soil texture, ironstone, and soil color account for most of the variation of the soils and provide an efficient means of characterizing tropical soils derived from sedimentary parent material. T HE INCREASING USE of geographical information systems and earth observation techniques in land resources analysis has highlighted the need for quantitative data on the spatial distribution pattern of soil characteristics. However, many soil surveys have concentrated on the vertical sequence of horizons within pedons, paying less attention to the spatial distribution (FAO, 1998). Parametric soil surveys concentrate on measuring single soil characteristics, and provide, in concert with process modeling, a suitable paradigm for spatial prediction across low-surveyed regions (McKenzie and Austin, 1993; Moore et al., 1993). The use of readily observed and quantifiable morphological characteristics to distinguish between different soils as a first step to determining their spatial extent is our major concern here. Soil morphological descriptions are commonly re
Ecological Modelling, 2014
Since the late 1970s, thousands of scholarly articles, books and reports have dealt with the application of the mathematical theory of geostatistics to characterize the spatial "variability" of soils, and to produce soil property maps. Insensibly, this application of geostatistics appears to have become an end in itself, and the reasons why one should be concerned about the spatial heterogeneity of soil properties are rarely if ever made clear any more. In this context, the purpose of the present critical review article is to return to some of the primal questions that motivated this interest in the topic several decades ago. After a brief review of the background behind the application of geostatistics to soils, a number of situations and modeling efforts are described where, even though soils undoubtedly vary spatially, nothing seems to be gained practically by explicitly accounting for their spatial heterogeneity in order to reach a number of management or research objectives. Contrastedly, whenever the spatial heterogeneity of soil properties in the field might be relevant, it is shown that very different perceptions about it emerge, depending on the type of measurement that is performed. This suggests that the approach one adopts to characterize spatially-varying soil properties should be dictated by whatever goal one pursues. For example, if the objective is to evaluate the "ecosystem services" of soils in a given region and to reach decisions about them, one should probably first consider the (typically large) spatial scale that is most relevant to the decision-making process, then proceed via a top-down approach to characterize the spatial heterogeneity of soil services, if and when appropriate. In other contexts, it is argued that measurements should be patterned after the behavior of plants or microbes present in soils, relative to which, unfortunately, the macroscopic measurements that are now routinely carried out appear largely irrelevant or misleading. The article concludes with a number of potential lessons learned from the analysis of the research on the spatial heterogeneity of soils, which bear relevance to the broader practice of soil science.
Quantifying Soil Morphology in Tropical Environments
Soil Science Society of America Journal, 2000
We tested the hypothesis that readily observed and easily measured phological descriptions have been quantified and commorphological variables can be used to characterize the soils sampled and described in southeastern Nigeria for purposes of land use and bined in a soil profile development index for evaluating management. Field tests were developed for estimating soil texture soil development (Bilzi and Ciolkosz, 1977; Meixner and amount of ironstone nodules. Two new soil color indices provided and Singer, 1981; Harden, 1982). In addition, McKenzie an immediate means of diagnosing the soil drainage regime in case and Jacquier (1997) describe the use of soil morphologiof the color index (CI) and soil forming processes in tropical soils in cal descriptions together with inexpensive field tests to case of the redness index (RI). The indices correlated negatively with derive soil hydraulic properties in low-surveyed regions. organic C content (R ϭ Ϫ0.39) and positively with dithionite-extracted Last, soil color indices present a quantified approach Fe 2 O 3 (0.44) and Al 2 O 3 (0.51). Inexpensive field tests for color, texture, for assigning drainage class to a soil (Megonigal et al., and ironstone can be quantified using color indices and laboratory 1993; Thompson and Bell, 1996) and assessing the Fe measurements. The local soil classification was quantified by means oxide mineralogy (Torrent et al., 1980; Mokma, 1993). of color indices (RI, CI) and percentages of ironstone, sand, silt, and clay measured in the A horizon. A classification based on soil texture, Farmers often describe soils in combinations of single ironstone, and color was used to define classes for the B horizon. The morphological characteristics (e.g. red sand or stone) two first principal components (PC) extracted from soil morphological and often relate their decision-making on land use and variables measured on the upper three horizons of 72 pedons exmanagement to these soil descriptions (Gobin et al., plained 64.7% of the total variance. Nonhierarchical clustering per-1998, 1999). Quantifying these morphological characterformed on the two PCs produced seven clusters that compare well istics opens new perspectives for incorporating farmers' with the great groups of U.S. soil taxonomy. Principal component knowledge into land resources information systems, and analysis on 20 soil chemical and morphological variables confirmed enables statistical modeling to be used. Farmers' knowlthat soil texture, ironstone, and soil color account for most of the edge could benefit scientific understanding of soils, convariation of the soils and provide an efficient means of characterizing tribute to international agricultural development, and tropical soils derived from sedimentary parent material.
Imeson and Prinsen vegetation patterns as indicators etc AEE 2004.pdf
The rationale behind this researcl-r concerns the need to better undcrstand relationships Lretween vegetation characteristics and hydrological processes. Changcs in vegetation patterns could provide sensitive indicators ofboth desertification and water availability. This paper presenis a rnethodology to use vegetzrtion-bare soil patterns as indicators fbr identifying the exteni, distribution and conncctivity ofrunofland sediment source and sink areas. Dunng field studies it has been fbund thatbiological indicators can be used as evidence that areas are I'unctionin-e as a sollrce or sink fbr runoff and sediment. It has also been found that the locations ol such source and sink areas are tightly iinked to the spatial distribution of vegetation patches within the vegetation-bare soil mosaic. Thereforc. patterns in vegctation and soil can be important indicators ior ecosystem health and hillslope hydrolog.v' . Four pattern indices are introduced to describe the degree of contagion and bare area distribution within the vegetation mosaic and the degree olconnectivity between difl'ercnt source areas. Binary pattern maps retrieved fronr a digital aerial photograph are used as examples to illustrate the bchaviour o1'the indices. It rs concluded that the indices yield important intbrmation on the spatial structure of'the patterns studied and can be used to understand the interactions between pattern and its underlying processes.
Spatial Variability of Soil Features Affected by Landuse Type using Geostatistics
2014
Since the change of land use accrued in the Iran, especially in northern Iran, this research aims tocompare the spatial variability of soil properties in three adjacent land uses including cultivated by wheat lands, grazing lands and forest Lands covered by juniperus sp, fagus orientalis, quercus castanifolia, and acer velotinum species in kiasar region, Mazandaran Province, northern Iran. Some of soil features, i.e. pH, CaCO3, total nitrogen (TN), soil organic carbon (SOC), electric conductivity (EC), percentage of silt, clay and sand contents and saturation moisture content(SM) were measured at a grid with 20 m sampling distance on the top soil (0 – 30 cm depth). Accordingly, total of 147samples were taken from 49 soil sites. The normality of data was examined by the tests of normality. Then, data were analyzed by using of geostatistics approach. The results showed that spatial distribution of many soil properties could be well described by spherical model in the forest and exponential model in the cultivated and grazing lands. Spatial dependences were the highest for SOC, EC and the lowest for silt, (SOC and silt) in the forest method and grazing lands, respectively. Deforestation and conversion to cultivated and grazing lands decreased spatial dependence of soil properties.
Spatial Variation of Soil Properties Relating to Vegetation Changes
Plant and Soil, 2006
Bekele and Hudnall provide an interesting perspective on the spatial variation of soil chemical properties in a natural area undergoing transition from prairie to forest. Their focus is on the unique calcareous prairie ecosystem of Louisiana where prairie remnants are being encroached upon by the forest, primarily eastern red cedar (Juniperus virginiana L.). Bekele and Hudnall were especially interested in investigating any differences in spatial variability among similar sites and in documenting the scale at which the variability occurs. Geostatistical methods have been used to describe and model spatial patterns in soil data for more than 20 years. The accessibility of user-friendly geostatistical software packages has increased the use . of spatial analysis of soil's data but carries the risk that these tools are used without due consideration of the underlying theory, especially in the field of semivariogram modeling or recommended good practices. The feedback between plant community composition and species distribution and soil properties in natural systems has promise to provide enhanced insight into the short-and long-term relationships between plants and soil properties. This is an intriguing area of research that couples plant ecology and soil science and should provide valuable information on the interaction of soils with the processes of plant succession and competition. Researchers in this area are urged to be cautious in verifying the assumptions behind popular geostatistical methods and explicit in describing the important steps such as trend analysis, which can reveal critical interpretive information.
Journal of Soils and Sediments, 2016
Purpose Land preparation (e.g., leveled ditches, leveled benches, adversely graded tableland, and fish-scale pits) is one of the most effective ecological engineering practices to reduce water erosion in the Loess Plateau, China. Land preparation greatly affects soil physicochemical properties. This study investigated the influence of different land preparation techniques during vegetation restoration on soil conditions, which remained poorly understood to date. Materials and methods Soil samples were collected from depths of 0-10, 10-20, 20-40, 40-60, 60-80, and 80-100 cm, in the typical hilly watershed of Dingxi City, Loess Plateau. Soil bulk density (BD), soil organic matter (SOM), and total nitrogen (TN) were determined for different land preparations and vegetation type combinations. Fractal theory was used to analyze soil particle size distribution (PSD). Results and discussion (1) The effect of land preparation on soil properties and PSD varied with soil depth. For each land preparation category, SOM and TN values showed a significant difference between the top soil layer and the underlying soil depths. (2) The fractal dimension of PSD showed a significant positive correlation with clay and silt content, but a significant negative correlation with sand content. (3) The 20 cm soil layer was a boundary that distinguished the explanatory factors, with land preparation and vegetation type as the controlling factors in the 0-20-and 20-100-cm soil layers, respectively. Conclusions Land preparation and vegetation type significantly influenced soil properties, with 20 cm soil depth being the boundary for these two factors. This study provided a foundation for developing techniques for vegetation restoration in water-limited ecosystems.