Inakwu Odeh

Further results on prediction of soil properties from terrain attributes : heterotopic cokriging and regression-kriging

Several methods involving spatial prediction of soil properties from landform attributes are compared using carefully designed validation procedures. The methods, tested against ordinary kriging and universal kriging of the target variables, include multi-linear regression, isotopic cokriging, heterotopic cokriging and regression-kriging models A, B and C. Prediction performance by ordinary kriging and universal kriging was comparatively poor as the methods do not use covariation of the predictor variable with terrain attributes. Heterotopic cokriging outperformed isotopic cokriging because the former utilised more of the local information from the covariables. The combined regression-kriging methods generally performed well. Both the regression-kriging model C and heterotopic cokriging performed well when soil variables were predicted into a relatively finer gridded digital elevation model (DEM) and when all the local information was utilised. Regression-kriging model C generally performed best and is, perhaps, more flexible than heterotopic cokriging. Potential for further research and developments rests in improving the regression part of model C.

An overview of pedometric techniques for use in soil survey

Quantitative techniques for spatial prediction in soil survey are developing apace. They generally derive from geostatistics and modern statistics. The recent developments in geostatistics are reviewed particularly with respect to non-linear methods and the use of all types of ancillary information. Additionally analysis based on non-stationarity of a variable and the use of ancillary information are demonstrated as encompassing modern regression techniques, including generalised linear models (GLM), generalised additive models (GAM), classification and regression trees (RT) and neural networks (NN). Three resolutions of interest are discussed. Case studies are used to illustrate different pedometric techniques, and a variety of ancillary data. The case studies focus on predicting different soil properties and classifying soil in an area into soil classes defined a priori. Different techniques produced different error of interpolation. Hybrid methods such as CLORPT with geostatistics offer powerful spatial prediction methods, especially up to the catchment and regional extent. It is shown that the use of each pedometric technique depends on the purpose of the survey and the accuracy required of the final product.

Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions

Abstract Fuzzy systems, including fuzzy set theory and fuzzy logic, provide a rich and meaningful improvement, or extension of conventional logic. The mathematics generated by this theory is consistent, and fuzzy set theory may be seen as a generalisation of classic set theory. Applications in soil science, which may be generated from, or adapted to fuzzy set theory and fuzzy logic, are wide-ranging: numerical classification of soil and mapping, land evaluation, modelling and simulation of soil physical processes, fuzzy soil geostatistics, soil quality indices and fuzzy measures of imprecisely defined soil phenomena. Many other soil concepts or systems may be modelled, simulated, and even replicated with the help of fuzzy systems, not the least of which is human reasoning itself.

Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis.

As more than 50% of the human population are situated in cities of the world, urbanization has become an important contributor to global warming due to remarkable urban heat island (UHI) effect. UHI effect has been linked to the regional climate, environment, and socio-economic development. In this study, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery, respectively acquired in 1989 and 2001, were utilized to assess urban area thermal characteristics in Fuzhou, the capital city of Fujian province in south-eastern China. As a key indicator for the assessment of urban environments, sub-pixel impervious surface area (ISA) was mapped to quantitatively determine urban land-use extents and urban surface thermal patterns. In order to accurately estimate urban surface types, high-resolution imagery was utilized to generate the proportion of impervious surface areas. Urban thermal characteristics was further analysed by investigating the relationships between the land surface temperature (LST), percent impervious surface area, and two indices, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The results show that correlations between NDVI and LST are rather weak, but there is a strong positive correlation between percent ISA, NDBI and LST. This suggests that percent ISA, combined with LST, and NDBI, can quantitatively describe the spatial distribution and temporal variation of urban thermal patterns and associated land-use/land-cover (LULC) conditions.

Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley

Estimation and mapping carbon storage in the soil is currently an important topic; thus, the knowledge of the distribution of carbon content with depth is essential. This paper examines the use of a negative exponential profile depth function to describe the soil carbon data at different depths, and its integral to represent the carbon storage. A novel method is then proposed for mapping the soil carbon storage in the Lower Namoi Valley, NSW. This involves deriving pedotransfer functions to predict soil organic carbon and bulk density, fitting the exponential depth function to the carbon profile data, deriving a neural network model to predict parameters of the exponential function from environmental data, and mapping the organic carbon storage. The exponential depth function is shown to fit the soil carbon data adequately, and the parameters also reflect the influence of soil order. The parameters of the exponential depth function were predicted from land use, radiometric K, and terrain attributes. Using the estimated parameters we map the carbon storage of the area from surface to a depth of 1 m. The organic carbon storage map shows the high influence of land use on the predicted storage. Values of 15-22 kg/m 2 were predicted for the forested area and 2-6 kg/m 2 in the cultivated area in the plains.

Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement

Classifying remote sensing imageries to obtain reliable and accurate land use and land cover (LULC) information still remains a challenge that depends on many factors such as complexity of landscape, the remote sensing data selected, image processing and classification methods, etc. The aim of this paper is to extract reliable LULC information from Landsat imageries of the Lower Hunter region of New South Wales, Australia. The classical maximum likelihood classifier (MLC) was first applied to classify Landsat-MSS of 1985 and Landsat-TM of 1995 and 2005. The major LULC identified were Woodland, Pasture/scrubland, Vineyard, Built-up and Water-body. By applying post-classification correction (PCC) using ancillary data and knowledge-based logic rules the overall classification accuracy was improved from about 72% to 91% for 1985 map, 76% to 90% for 1995 map and 79% to 87% for 2005 map. The improved overall Kappa statistics due to PCC were 0.88 for the 1985 map, 0.86 for 1995 and 0.83 for 2005. The PCC maps, assessed by McNemar’s test, were found to have much higher accuracy in comparison to their counterpart MLC maps. The overall improvement in classification accuracy of the LULC maps is significant in terms of their potential use for land change modelling of the region.

Using AVHRR images for spatial prediction of clay content in the lower Namoi Valley of eastern Australia.

Soil information is needed at the regional scale to enable planning of land utilization in accordance with its capacity. Because existing soil maps are inadequate in Australia to meet this demand, there is the need to develop models that could be used to improve soil maps at this scale for aggregation up to the national or continental scale. The most efficient and cheapest means of achieving this is by using remotely sensed data in multivariate spatial prediction models. This study therefore examines the soil spectral properties as depicted by the National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) data, with the main aim of developing prediction models for improved mapping of, for example, topsoil % clay in the Lower Namoi Valley of New South Wales (NSW). The paper compares several prediction models: multiple linear regression (MLR) using an external training set (MLR-ETS), interpolation by MLR — MLR-INT, kriging based on a generalized covariance function of order 1 (IRF-1), and a mixed model of MLR and ordinary kriging, termed as regression/kriging (RK). Comparison was based on an independent validation set (N=40), using the root mean square error (RMSE) of prediction. The MLR-ETS performed very poorly (RMSE=35.0%), due to some degree of contrariety of the external training data with the data from the prediction area. The RK is superior to all the methods in predicting the topsoil % clay with RMSE of 10.2%. This performance by RK, compared with MLR-ITS (RMSE=13.3%) and IRF-1 (RMSE=12.6%), is quite remarkable at the regional scale of consideration. The correlation coefficient (ρ) between the actual and predicted % clay confirms the order of prediction performance. Isarithmic maps of the topsoil % clay, as predicted by the best two methods, indicate that IRF-1 over-smoothed the predicted clay values as it removed both the low and high spikes in the spatial distribution of the % clay. However, the map of predicted topsoil clay by RK, which incorporated the AVHRR bands and indices in the prediction model reflects most of the local variability. Thus this study has demonstrated that a few soil-sampled sites, combined with the AVHRR data, could be adequate for regional soil inventory of good quality and known precision using RK. The basic tenet of this study can be extended to any situation where ancillary attributes have relatively high correlation with soil variables.

Using a legacy soil sample to develop a mid-IR spectral library

This paper describes the development of a diffuse reflectance spectral library from a legacy soil sample. When developing a soil spectral library, it is important to consider the number of samples that are needed to adequately describe the soil variability in the region in which the library is to be used; the manner in which the soil is sampled, handled, prepared, stored, and scanned; and the reference analytical procedures used. As with any type of modelling, the dictum is ‘garbage in = garbage out’ and hopefully the converse ‘quality in = quality out’. The aims of this paper are to: (i) develop a soil mid infrared (mid-IR) diffuse reflectance spectral library for cotton-growing regions of eastern Australia from a legacy soil sample, (ii) derive soil spectral calibrations for the prediction of soil properties with uncertainty, and (iii) assess the accuracy of the predictions and populate the legacy soil database with good quality information. A scheme for the construction and use of this spectral library is presented. A total of 1878 soil samples from different layers were scanned. They originated from the Upper Namoi, Namoi, and Gwydir Valley catchments of north-western New South Wales (NSW) and the McIntyre region of southern Queensland (Qld). A conditioned Latin hypercube sampling (cLHS) scheme was used to sample the spectral data space and select 213 representative samples for laboratory soil analyses. Using these data, partial least-squares regression (PLSR) was used to construct the calibration models, which were validated internally using cross validation and externally using an independent test dataset. Models for organic C (OC), cation exchange capacity (CEC), clay content, exchangeable Ca, total N (TN), total C (TC), gravimetric moisture content θg, total sand and exchangeable Mg were robust and produced accurate results (R2adj. > 0.75 for both cross and test set validations). The root mean squared error (RMSE) of mid-IR-PLSR predictions was compared to those from (blind) duplicate laboratory measurements. Mid-IR-PLSR produced lower RMSE values for soil OC, clay content, and θg. Finally, bootstrap aggregation-PLSR (bagging-PLSR) was used to predict soil properties with uncertainty for the entire library, thus repopulating the legacy soil database with good quality soil information.

GlobalSoilMap: Toward a Fine-Resolution Global Grid of Soil Properties

Abstract Soil scientists are being challenged to provide assessments of soil condition from local through to global scales. A particular issue is the need for estimates of the stores and fluxes in soils of water, carbon, nutrients, and solutes. This review outlines progress in the development and testing of GlobalSoilMap —a digital soil map that aims to provide a fine-resolution global grid of soil functional properties with estimates of their associated uncertainties. A range of methods can be used to generate the fine-resolution spatial estimates depending on the availability of existing soil surveys, environmental data, and point observations. The system has an explicit geometry for estimating point and block estimates of soil properties continuously down the soil profile. This geometry is necessary to ensure mass balance when stores and fluxes are computed. It also overcomes some limitations with existing systems for characterizing soil variation with depth. GlobalSoilMap has been designed to enable delivery of soil data via Web services. This review provides an overview of the systems technical specifications including the minimum data set. Examples from contrasting countries and environments are then presented to demonstrate the robustness of the technical specifications. GlobalSoilMap provides the means for supplying soil information in a format and resolution compatible with other fundamental data sets from remote sensing, terrain analysis, and other systems for mapping, monitoring, and forecasting biophysical processes. The initial research phase of the core project is nearing completion and attention is now shifting toward establishing the institutional and governance arrangements necessary to complete a full global coverage and maintaining the operational version of the GlobalSoilMap . This will be a grand and rewarding challenge for the soil science profession in the coming years.

Comparison of statistical prediction methods for estimating field-scale clay content using different combinations of ancillary variables

The need for spatial information on soil properties at the field level is increasing, particularly for its applications in precision agriculture and environmental management. One important soil property is clay content; however, costs involved with obtaining soil data at the field scale are prohibitive. Geostatistical techniques have been used with some success to improve the accuracy of spatial prediction of soil properties, especially those based on easy-to-obtain ancillary information. There is also, however, the need to determine optimal spacing for generating the ancillary data for spatial prediction. In this paper, we used ancillary variables along with spatial prediction models to determine an optimal method for estimating clay content at the field scale. We also determined the optimal spacing for generating the ancillary data for spatial prediction. The ancillary variables used were apparent soil electrical conductivity (ECa) obtained with EM38 and EM31 and digitized bands (red, green, and blue) of aerial photographs of the bare soil. The spatial prediction models tested are generalized additive models using various combinations of ancillary data (e.g., ECa and red, green, and blue data) and the geostatistical methods of ordinary-, regression- and co-kriging. The results suggest that the linear regression of average clay content with ECa (EM38) data used in combination with kriging of regression residuals was most accurate (RMSE = 3.03). The generation of ECa data on 24-m transect spacing was optimal for prediction. Doubling and tripling the transect spacing (i.e., 48 and 72 m) cause relative reductions in precision of 17% and 12%, respectively.