David G. Rossiter

A theoretical framework for land evaluation

Abstract Land evaluation is the process of predicting the use potential of land on the basis of its attributes. A variety of analytical models can be used in these predictions, ranging from qualitative to quantitative, functional to mechanistic, and specific to general. This paper classifies land evaluation models by how they take time and space into account, and whether they use land qualities as an intermediate between land characteristics and land suitability. Temporally, models can be of a static resource base and static land suitability, a dynamic resource base but static land suitability, or both a dynamic resource base and dynamic land suitability. spatially, land evaluation models can be of a single area with no interaction between areas, with static inter-area effects, or dynamic inter-area effects. In the most complex case, land suitabilities for several land uses are interdependent.

A Farm-Level Analysis of Economic and Agronomic Impacts of Gradual Climate Warming

The potential economic and agronomic impacts of gradual climate warming are examined at the farm level. Three models of the relevant climatic, agronomic, and economic processes are developed and linked to address climate change impacts and agricultural adaptability. Several climate warming scenarios are analyzed, which vary in severity. The results indicate that grain farmers in southern Minnesota can effectively adapt to a gradually changing climate (warmer and either wetter or drier) by adopting later maturing cultivars, changing crop mix, and altering the timing of field operations to take advantage of a longer growing season resulting from climate warming.

Soil sampling strategies for spatial prediction by correlation with auxiliary maps

The paper evaluates spreading of observations in feature and geographical spaces as a key to sampling optimisation for spatial prediction by correlation with auxiliary maps. Although auxiliary data are commonly used for mapping soil variables, problems associated with the design of sampling strategies are rarely examined. When generalised least-squares estimation is used, the overall prediction error depends upon spreading of points in both feature and geographical space. Allocation of points uniformly over the feature space range proportionally to the distribution of predictor (equal range stratification, or ER design) is suggested as a prudent sampling strategy when the regression model between the soil and auxiliary variables is unknown. An existing 100-observation sample from a 50 by 50 km soil survey in central Croatia was used to illustrate these concepts. It was re-sampled to 25-point datasets using different experimental designs: ER and 2 response surface designs. The designs were compared for their performance in predicting soil organic matter from elevation (univariate example) using the overall prediction error as an evaluation criterion. The ER design gave overall prediction error similar to the minmax design, suggesting that it is a good compromise between accurate model estimation and minimisation of spatial autocorrelation of residuals. In addition, the ER design was extended to the multivariate case. Four predictors (elevation, temperature, wetness index, and NDVI) were transformed to standardised principal components. The sampling points were then assigned to the components in proportion to the variance explained by a principal component analysis and following the ER design. Since stratification of the feature space results in a large number of possible points in each cluster, the spreading in geographical space can also be maximised by selecting the best of several realisations.

Forecasting the pattern and pace of Fagus forest expansion in Majella National Park, Italy

ABSTRACT Question: Can the pattern and pace of spontaneous Fagus forest expansion from 1975 to 2003 be accurately detected with mid-resolution satellite imagery? Can the historical Fagus expansion be modelled on the basis of environmental predictors? If so, where are the highest probabilities for future Fagus expansion? What are the implications for park management? Location: Majella National Park, Italy, > 1000 m a.s.l.; municipalities of S. Eufemia and Pacentro. Methods: Fagus cover change was detected by overlaying three classified sequential satellite images. Historical Fagus expansion was related to environmental variables using ordinary logistic and autologistic regression models. Fagus expansion probabilities were generated with the best predictive model. Results: From 1975 to 2003 Fagus advanced into abandoned farmland and subalpine pastures from the contiguous, mid-altitudinal Fagus forest and from Fagus outliers, at a rate of 1.2 % per year. Substantial spatial and temporal variations in expansion rates were detected. The ordinary and autologistic models based on the single predictor Distance-from-Fagus-1975 forecasted the Fagus expansion well (AUC 0.81 resp. 0.88). Multiple logistic models, including the topo-climatic and substrate predictors, improved prediction insignificantly. The strong predictive power of proximity to historical Fagus presence is explained by the dispersal biology of Fagus combined with the shading impact of the Fagus canopy at the forest fringe. Conclusion: Decade-long Fagus expansion patterns might be reliably forecasted by proximity to historical Fagus distribution. Consequences for park management options are outlined. Nomenclature: Pignatti (1982).ABSTRACT Question: Can the pattern and pace of spontaneous Fagus forest expansion from 1975 to 2003 be accurately detected with mid-resolution satellite imagery? Can the historical Fagus expansion be modelled on the basis of environmental predictors? If so, where are the highest probabilities for future Fagus expansion? What are the implications for park management? Location: Majella National Park, Italy, > 1000 m a.s.l.; municipalities of S. Eufemia and Pacentro. Methods: Fagus cover change was detected by overlaying three classified sequential satellite images. Historical Fagus expansion was related to environmental variables using ordinary logistic and autologistic regression models. Fagus expansion probabilities were generated with the best predictive model. Results: From 1975 to 2003 Fagus advanced into abandoned farmland and subalpine pastures from the contiguous, mid-altitudinal Fagus forest and from Fagus outliers, at a rate of 1.2 % per year. Substantial spatial and temporal variations in expansi...

Proximal spectral sensing to monitor phytoremediation of metal-contaminated soils.

Assessment of soil contamination and its long-term monitoring are necessary to evaluate the effectiveness of phytoremediation systems. Spectral sensing-based monitoring methods promise obvious benefits compared to field-based methods: lower cost, faster data acquisition and better spatio-temporal monitoring. This paper reviews the theoretical basis whereby proximal spectral sensing of soil and vegetation could be used to monitor phytoremediation of metal-contaminated soils, and the eventual upscaling to imaging sensing. Both laboratory and field spectroscopy have been applied to sense heavy metals in soils indirectly via their intercorrelations with soil constituents, and also through metal-induced vegetation stress. In soil, most predictions are based on intercorrelations of metals with spectrally-active soil constituents viz., Fe-oxides, organic carbon, and clays. Spectral variations in metal-stressed plants is particularly associated with changes in chlorophyll, other pigments, and cell structure, all of which can be investigated by vegetation indices and red edge position shifts. Key shortcomings in obtaining satisfactory calibration for monitoring the metals in soils or metal-related plant stress include: reduced prediction accuracy compared to chemical methods, complexity of spectra, no unique spectral features associated with metal-related plant stresses, and transfer of calibrations from laboratory to field to regional scale. Nonetheless, spectral sensing promises to be a time saving, non-destructive and cost-effective option for long-term monitoring especially over large phytoremediation areas, and it is well-suited to phytoremediation networks where monitoring is an integral part.

A double continuous approach to visualization and analysis of categorical maps

A method to visualize multiple membership maps, called ‘Colour mixture’ (CM) is described and compared with alternative techniques: defuzzification and Pixel mixture. Six landform parameters were used to derive the landform classes using supervised fuzzy k-means classification. The continuous categorical map is derived by GIS calculations with colours, where colour values are considered to represent the taxonomic space spanned by the attribute variables. Coordinates of the nine class centres (landform facets) were first transformed from multivariate to two-dimensional attribute space using factor analysis, and then projected on the Hue Saturation Intensity (HSI) colourwheel. The taxonomic value was coded with the Hue and confusion with Saturation. To improve visual impression, saturation was replaced with whiteness. Classes that were closer in attribute space were merged into similar generic colours. The CM technique limits the derived mixed-colour map to seven generic hues independently of the total number of classes, which provides a basis for automated generalization. The confusion index derived from the mixed-colour map was used to derive primary boundaries and to locate areas of higher taxonomic confusion.

Digital Soil Mapping as a Component of Data Renewal for Areas with Sparse Soil Data Infrastructures

This chapter introduces the concepts of data rescue of legacy soil surveys, here defined as a simple conversion to archival format by scanning or direct entry into a database, and data renewal, here defined as the process of bringing these surveys up to modern standards by taking advantage of technological and conceptual advances in geoinformation technology. This is especially important in areas with sparse soil data infrastructure, as it is both more likely that the data will be lost and less likely that a new survey can be commissioned. Digital Soil Mapping (DSM) techniques, although designed for new surveys, can play an important role in data rescue and renewal, in particular as geodetic control for a GIS coverage, as a medium-resolution elevation model (DEM) and derived terrain parameters to adjust terrain-related boundaries, and synoptic satellite imagery to adjust vegetation or landuse-related boundaries. The semantic issues raised by soil-landscape modelling within DSM are especially important for data renewal and integration with supplementary surveys. As with DSM in general, a data renewal exercise may require cultural and institutional change in traditional soil survey organization.

Predictive Mapping of Topsoil Organic Carbon in an Alpine Environment Aided by Landsat TM

The objective of this study was to examine the reflectance of Landsat TM imagery for mapping soil organic Carbon (SOC) content in an Alpine environment. The studied area (ca. 3*104 km2) is the upper reaches of the Heihe River at the northeast edge of the Tibetan plateau, China. A set (105) of topsoil samples were analyzed for SOC. Boosted regression tree (BRT) models using Landsat TM imagery were built to predict SOC content, alone or with topography and climate covariates (temperature and precipitation). The best model, combining all covariates, was only marginally better than using only imagery. Imagery alone was sufficient to build a reasonable model; this was a bit better than only using topography and climate covariates. The Lin’s concordance correlation coefficient values of the imagery only model and the full model are very close, larger than the topography and climate variables based model. In the full model, SOC was mainly explained by Landsat TM imagery (65% relative importance), followed by climate variables (20%) and topography (15% of relative importance). The good results from imagery are likely due to (1) the strong dependence of SOC on native vegetation intensity in this Alpine environment; (2) the strong correlation in this environment between imagery and environmental covariables, especially elevation (corresponding to temperature), precipitation, and slope aspect. We conclude that multispectral satellite data from Landsat TM images may be used to predict topsoil SOC with reasonable accuracy in Alpine regions, and perhaps other regions covered with natural vegetation, and that adding topography and climate covariables to the satellite data can improve the predictive accuracy.

Spectral changes in the leaves of barley plant due to phytoremediation of metals—results from a pot study

Abstract This research studied the changes in leaf reflectance spectra (350–2500 nm) due to metal phytoextraction into barley plants grown in metal-spiked soils (3 levels of Cd, Pb, As and their metal-mixture treatments). Growth of barley was adversely affected due to 100 mg As kg-1 and metal-mixture (10 Cd+150 Pb+100 As; mg kg-1) treatments. Metal phytoextraction were in order of: root>straw≥leaves >grains. Results of reflectance spectra of leaves show the influence of As-treatment only, causing spectral changes in visible and infrared domains mostly, as apparent from the significant correlation between leaf-As and leaf-spectra. Chlorophyll and water stress indices and band depths analyses showed significant correlations to leaf-As, and can be used to distinguish metal-stressed plants. Finally, regression models demonstrate the potential use of hyperspectral reflectance data to monitor plant health during phytoremediation process and to estimate leaf-As in barley, particularly in this study.

Applicability of a distributed watershed pollution model in a data-poor environment in Santa Catarina State, Brazil

Intensification of agricultural production without a sound management and regulations can lead to severe environmental problems, as in Western Santa Catarina State, Brazil, where intensive swine production has caused large accumulations of manure and consequently water pollution. Natural resource scientists are asked by decision-makers for advice on management and regulatory decisions. Distributed environmental models are useful tools, since they can be used to explore consequences of various management practices. However, in many areas of the world, quantitative data for model calibration and validation are lacking. The data-intensive distributed environmental model AgNPS was applied in a data-poor environment, the upper catchment (2,520 ha) of the Ariranhazinho River, near the city of Seara, in Santa Catarina State. Steps included data preparation, cell size selection, sensitivity analysis, model calibration and application to different management scenarios. The model was calibrated based on a best guess for model parameters and on a pragmatic sensitivity analysis. The parameters were adjusted to match model outputs (runoff volume, peak runoff rate and sediment concentration) closely with the sparse observed data. A modelling grid cell resolution of 150 m adduced appropriate and computer-fit results. The rainfall runoff response of the AgNPS model was calibrated using three separate rainfall ranges ( 60 mm). Predicted sediment concentrations were consistently six to ten times higher than observed, probably due to sediment trapping along vegetated channel banks. Predicted N and P concentrations in stream water ranged from just below to well above regulatory norms. Expert knowledge of the area, in addition to experience reported in the literature, was able to compensate in part for limited calibration data. Several scenarios (actual, recommended and excessive manure applications, and point source pollution from swine operations) could be compared by the model, using a relative ranking rather than quantitative predictions.