Graciela Metternicht

Remote sensing of soil salinity: potentials and constraints

Abstract Soil salinity caused by natural or human-induced processes is a major environmental hazard. The global extent of primary salt-affected soils is about 955 M ha, while secondary salinization affects some 77 M ha, with 58% of these in irrigated areas. Nearly 20% of all irrigated land is salt-affected, and this proportion tends to increase in spite of considerable efforts dedicated to land reclamation. This requires careful monitoring of the soil salinity status and variation to curb degradation trends, and secure sustainable land use and management. Multitemporal optical and microwave remote sensing can significantly contribute to detecting temporal changes of salt-related surface features. Airborne geophysics and ground-based electromagnetic induction meters, combined with ground data, have shown potential for mapping depth of salinity occurrence. This paper reviews various sensors (e.g. aerial photographs, satellite- and airborne multispectral sensors, microwave sensors, video imagery, airborne geophysics, hyperspectral sensors, and electromagnetic induction meters) and approaches used for remote identification and mapping of salt-affected areas. Constraints on the use of remote sensing data for mapping salt-affected areas are shown related to the spectral behaviour of salt types, spatial distribution of salts on the terrain surface, temporal changes on salinity, interference of vegetation, and spectral confusions with other terrain surfaces. As raw remote sensing data need substantial transformation for proper feature recognition and mapping, techniques such as spectral unmixing, maximum likelihood classification, fuzzy classification, band ratioing, principal components analysis, and correlation equations are discussed. Lastly, the paper presents modelling of temporal and spatial changes of salinity using combined approaches that incorporate different data fusion and data integration techniques.

Spatial discrimination of salt- and sodium-affected soil surfaces

Abstract Salinization-alkalinization is a time- and space-dynamic soil degradation process in semiarid regions. This study implements a synergistic approach to map salt- and sodium affected surfaces, combining digital image classification with field observation of soil degradation features and laboratory determinations. Salinity-alkalinity classes were established using the electrical conductivity (EC) and pH values. A neighbourhood operator, with spatial and spectral user-defined constraints determined the spectral objects constituting the training set. Six combined Landsat TM bands (1,2,4,5,6,7) provided the highest separability between salt- and sodium-affected soil classes. Although the overall accuracy was slightly low (64 per cent), accuracies of 100 per cent were obtained for some classes. Main causes of spectral confusions, masking different salinity-alkalinity degrees were the type and abundance of salt-tolerant vegetation cover, the topsoil textures, and the mixture of topsoil properties under f...

Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system

Abstract This paper presents the foundations of an expert system to map landscape features related to salinity, based in a South American case study. Salinity distribution is mapped using an approach that integrates multi-temporal classification of remotely sensed data, physical and chemical soil properties and landform attributes. Change detection maps are derived in a post-classification change detection. These maps represent the changes in the distribution of salinity type and its severity, and are used as inputs to model the nature, magnitude and reliability of salinity-related changes that occurred in the area. Three rule-based expert systems using fuzzy sets and fuzzy linguistic rules to formalise the expert knowledge about the actual possibility of changes to occur are designed and implemented within a geographical information system (GIS). The systems use a fuzzy semantic import approach that enables the integration of multi-disciplinary knowledge in basic sets of fuzzy rules. The outputs of the fuzzy knowledge-based system are three maps representing ‘likelihood of changes’, ‘nature of changes’ and ‘magnitude of changes’. These maps are then combined with landscape information and analysis of the spatial association among these variables, represented in different GIS layers, is undertaken to derive an exploratory hazard prediction model. The coefficient of areal correspondence is used to assess the degree of association among the variables and landscape positions. The sum of the coefficients of areal association between the variables considered represents evidence of salinity hazard within a specific landscape unit. Higher coefficient values indicate higher hazard to salinity processes. Because the classification model differentiates among saline and alkaline areas, it is possible to evaluate the nature of the salinity changes, i.e. whether an area may become more saline, alkaline, or saline–alkaline. This information is important for decision-makers and land planners, because different reclamation measures can be adopted according to the salinity type. The approach provides a fast way of assessing the likely extent of salinity at regional level, enabling the integration of a variety of data sources and knowledge. Moreover, this monitoring model can help to evaluate the effectiveness of salinity control and management action plans.

Evaluating the information content of JERS-1 SAR and Landsat TM data for discrimination of soil erosion features

A digital classification using Landsat TM and JERS-1 SAR data covering the visible, infrared, thermal and microwave regions of the spectrum was implemented to detect and map soil erosion features in the Sacaba Valley (Bolivia). Three objectives were pursued: (1) soil erosion mapping by using the visible, infrared and thermal bands of the Landsat TM scene; (2) class allocation by merging JERS-1 microwave and Landsat TM data sets; and (3) evaluation of the accuracy improvements in the detection of soil erosion features by merging the two data sets. The synergy of Landsat TM and JERS-1 data provided a unique combination that allowed more accurate identification of badlands, slightly eroded areas, miscellaneous land, fallow land and moderately eroded areas, as compared to the results obtained by Landsat TM alone. Differences in the surface roughness determined variations in the amount of energy returning to the radar antenna, thus improving discrimination of classes exhibiting similar spectral behaviour in the visible and infrared wavelengths.

Change detection assessment using fuzzy sets and remotely sensed data: an application of topographic map revision

Abstract This paper explores a methodology for computing the amount of changes that have occurred within an area by using remotely sensed technologies and fuzzy modelling. The discussion concentrates on the formulation of a standard procedure that, using the concept of fuzzy sets and fuzzy logic, can define the likelihood of changes detected from remotely sensed data. Furthermore, an example of how fuzzy visualisation of areas undergoing changes can be incorporated into a decision support system for prioritisation of areas requiring topographic map revision and updating is presented. By adapting the membership function of the fuzzy model to fit the shape of the histogram characterising the change image (derived from any of the common pre-classification methods of change detection), areas can be identified according to their likelihood of having undergone change during the period of observation.

Vegetation indices derived from high-resolution airborne videography for precision crop management

This paper examines the potential of airborne videography as a remote sensing tool in a project aimed to develop a rapid and cost-effective system for assessing and monitoring the conditions of crops, pastures and native forest areas in the agricultural region of Western Australia. This implies a close examination of current techniques for low cost/high resolution data capture, and the requirements for image-based precision farming. The research approach comprises reviewing stress indicators that can be used as early signs of changes in the conditions of crops and pasture, and the identification of value-added products, such as vegetation indices, that can be used by farmers and land planners interested to know where field variations occur. Four vegetation indices, Namely Plant Pigment Ratio (PPR), Photosynthetic Vigour Ratio (PVR), the Normalized Difference Vegetation Index (NDVI) and the green NDVI (NDVIg) are tested. Experimental results related to the identification and mapping of crop density variations, crop types, and variations in crop conditions due to the presence of weeds and dead standing vegetation are discussed. Statistical analysis ( T -test and multiple comparisons procedure using the Fisher LSD test, at @ =0.05) suggest the PVR as the best index to detect relative variations (e.g. high, medium, low) in crop density, followed by the NDVI. Results also show the indices ability to separate canola from lupins and wheat. Additionally, the PPR appears to detect the presence of weeds ( Arctotheca calendula ) in pasture and cereal crops, while NDVIg and NDVI can only identify weeds in pasture. The NDVI and NDVIg appear as the more sensitive indices to detect the presence of dead standing vegetation (stubble) in pasture. The status of high-resolution satellite imagery, as compared to airborne videography, for crop management applications, and main findings in regard to the potential of video-based vegetation indices to support precision farming, are presented as well.

Estimating Erosion Surface Features by Linear Mixture Modeling

Abstract Spectral mixture modeling was performed for identification and mapping of land degradation features related to soil erosion processes in the Sacaba Valley, Bolivia. The model allowed use of up to five surface components to characterize the selected area, since six bands (1, 2, 3, 4, 5, and 7) of the Landsat TM sensor were used as inputs. Among the various methods commonly used to determine end-members from the satellite image, three were selected: a) identification of one “pure” pixel representing a particular surface component from false color composites; b) average of “pure” pixels to characterize a particular end-member; and c) a method based on principal components. The best characterization of end-members was achieved by using average pure pixel reflectance. The median of the abundance images showed that, in 95% of the cases, the individual pixel compositions were explained by the selected surface components. The research has demonstrated that regional patterns of soil surface erosion features can be reliably mapped using linear spectral mixture analysis. Extrapolation of this approach to other regions where soil degradation features are correlated with spectrally distinguishable surface characteristics is feasible, provided that an optimization of the unmixing model as a function of local or regional surface component types is completed.

Geomorphometric landscape analysis using a semi-automated GIS-approach

This paper presents LANDFORM, a customized GIS application for semi-automated classification of landform elements, based on topographic attributes like curvature or elevation percentile. These parameters are derived from a Digital Elevation Model (DEM) and used as thresholds for the classification of landform elements like crests, flats, depressions and slopes. With a new method, slopes were further subdivided into upper, mid and lower slopes at significant breakpoints along slope profiles. The paper discusses the results of a fuzzy set algorithm used to compare the similarity between the map generated by LANDFORM and the visual photo-interpretation conducted by a soil expert over the same area. The classification results can be used in applications related to precision agriculture, land degradation studies, and spatial modelling applications where landscape morphometry is identified as an influential factor in the processes under study.

FUERO: foundations of a fuzzy exploratory model for soil erosion hazard prediction

Abstract This paper describes the foundations of FUERO, a FUzzy exploratory model for soil EROsion hazard prediction, which explores cause–effect relationships on the basis of general knowledge about causes and specific relations between processes and indicators of soil erosion. The model was designed to investigate the susceptibility of specific areas of a landscape to erosion by incorporating expert knowledge, in the sense that information on soil properties and/or landscape elements assumed to control accelerated soil erosion could be incorporated into the modelling process. Fuzzification of the landscape elements used in modelling the likelihood of an area to be affected by different degrees of erosion was done using a Fuzzy Semantic Import Modelling approach. Fuzzy min–max operators were used within a GI System for determining the likelihood an area to low, moderate or high erosion hazard. Although the model provides qualitative estimations, it showed very useful to explore indicators–causes–processes relationships. In addition, it allowed testing the importance of individual landscape elements related to soil erosion and selection of those that best predict soil erosion over a particular area.

Mapping and modelling mass movements and gullies in mountainous areas using remote sensing and GIS techniques

Abstract Natural as well as human-induced mass movements and gullies are severe environmental hazards. Remote sensing data offer promising possibilities for identification and monitoring. But their effective use in mountainous areas is hampered by cloud effects and relief-controlled factors, which cause geometric distortions and shadow areas, among other constraints. Nevertheless, aerial photographs and satellite images (visible, infrared and microwave bands), or combinations thereof, have been successfully used to discriminate and delineate landslide and gully types. GIS modelling of mass movements and gullies, using ancillary information in combination with remote sensing data, is rapidly developing. The shortcomings of deterministic modelling of such chaotic phenomena as mass movements and gullies highlight the relevance of GIS-assisted approaches to exploratory and predictive modelling. This paper describes practical applications of remote sensing and GIS for mapping, monitoring, exploring cause-effect relationships and assessing hazards of mass movements and gullies in hilly and mountainous areas.