Christos G. Karydas

Monthly soil erosion monitoring based on remotely sensed biophysical parameters: a case study in Strymonas river basin towards a functional pan-European service

Abstract Currently, many soil erosion studies at local, regional, national or continental scale use models based on the USLE-family approaches. Applications of these models pay little attention to seasonal changes, despite evidence in the literature which suggests that erosion risk may change rapidly according to intra-annual rainfall figures and vegetation phenology. This paper emphasises the aspect of seasonality in soil erosion mapping by using month-step rainfall erosivity data and biophysical time series data derived from remote-sensing. The latter, together with other existing pan-European geo-databases sets the basis for a functional pan-European service for soil erosion monitoring at a scale of 1:500,000. This potential service has led to the establishment of a new modelling approach (called the G2 model) based on the inheritance of USLE-family models. The G2 model proposes innovative techniques for the estimation of vegetation and protection factors. The model has been applied in a 14,500 km2 study area in SE Europe covering a major part of the basin of the cross-border river, Strymonas. Model results were verified with erosion and sedimentation figures from previous research. The study confirmed that monthly erosion mapping would identify the critical months and would allow erosion figures to be linked to specific land uses.

A classification of water erosion models according to their geospatial characteristics

In this article, an extensive inventory in the literature of water erosion modelling from a geospatial point of view is conducted. Concepts of scale, spatiality and complexity are explored and clarified in a theoretical background. Use of Geographic Information Systems (GIS) is pointed out as facilitating data mixing and model rescaling and thus increasing complexity in data-method relations. Spatial scale, temporal scale and spatial methodologies are addressed as the most determining geospatial properties underlying water erosion modelling. Setting these properties as classification criteria, 82 water erosion models are identified and classified into eight categories. As a result, a complete overview of water erosion models becomes available in a single table. The biggest share of the models is found in the category of the mechanistic pathway-type event-based models for watershed to landscape scales. In parallel, geospatial innovations that could be considered as milestones in water erosion modelling are highlighted and discussed. An alphabetical list of all models is also listed in the Appendix. For manipulating scale efficiently, two promising spatial theories are suggested for further exploitation in the future such as hierarchy theory and fractals theory. Regarding erosion applications, uncertainty analysis within GIS is considered to be necessary for further improving performance of erosion models.

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is then resampled to the multispectral’s spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements.

Advances in soil erosion modelling through remote sensing data availability at European scale

Under the European Union’s Thematic Strategy for Soil Protection, the European Commission’s Directorate-General for the Environment (DG Environment) has identified the mitigation of soil losses by erosion as a priority area. Policy makers call for an overall assessment of soil erosion in their geographical area of interest. They have asked that risk areas for soil erosion be mapped under present land use and climate conditions, and that appropriate measures be taken to control erosion within the legal and social context of natural resource management. Remote sensing data help to better assessment of factors that control erosion, such as vegetation coverage, slope length and slope angle. In this context, the data availability of remote sensing data during the past decade facilitates the more precise estimation of soil erosion risk. Following the principles of the Universal Soil Loss Equation (USLE), various options to calculate vegetative cover management (C-factor) have been investigated. The use of the CORINE Land Cover dataset in combination with lookup table values taken from the literature is presented as an option that has the advantage of a coherent input dataset but with the drawback of static input. Recent developments in the Copernicus programme have made detailed datasets available on land cover, leaf area index and base soil characteristics. These dynamic datasets allow for seasonal estimates of vegetation coverage, and their application in the G2 soil erosion model which represents a recent approach to the seasonal monitoring of soil erosion. The use of phenological datasets and the LUCAS land use/cover survey are proposed as auxiliary information in the selection of the best methodology.

Development of an IKONOS image classification rule-set for multi-scale mapping of Mediterranean rural landscapes

In this research, a rule-set of object-based classification of IKONOS imagery for fine-scale mapping of Mediterranean rural landscapes was developed. This study was conducted on the Mediterranean island of Crete (Greece). A three-level classification hierarchy was designed in a bottom-up approach containing a total number of 22 classes. The first level was associated with vegetation physiognomy (6 classes), the second level with linear features (6 classes) and the third level with land uses existing in the area (10 classes). Image objects were created with multiresolution segmentation, an algorithm supplied by eCognition software. The segmentation parameters were selected through a trial-and-error approach after visual evaluation of the resulting image objects. The rule-set comprised 100 classification rules described with the ‘Membership Function’ classifier. The classification stability was found to lie between 0.59 and 0.77, inversely proportional to the complexity of each levels classification. For an accuracy assessment, the error matrix method was used in a set of 250 randomly selected points. The overall classification accuracy achieved at the first level was 74%, at the second level 50% and at the third level 64%. The geometric accuracy of the classification was beyond the scope of this research; and moreover, consistent reference data sets were not available. The conclusion is that the use of rules in an object-based image analysis (OBIA) process has the potential to produce accurate landscape maps even in the case of complex environments, in which ancillary data are not available. Future work should focus on testing the transferability of the rule-set in different Mediterranean study sites, in order to draw a conclusion in relation to its potential operational use.

The G2 erosion model: An algorithm for month-time step assessments

ABSTRACT A detailed description of the G2 erosion model is presented, in order to support potential users. G2 is a complete, quantitative algorithm for mapping soil loss and sediment yield rates on month‐time intervals. G2 has been designed to run in a GIS environment, taking input from geodatabases available by European or other international institutions. G2 adopts fundamental equations from the Revised Universal Soil Loss Equation (RUSLE) and the Erosion Potential Method (EPM), especially for rainfall erosivity, soil erodibility, and sediment delivery ratio. However, it has developed its own equations and matrices for the vegetation cover and management factor and the effect of landscape alterations on erosion. Provision of month‐time step assessments is expected to improve understanding of erosion processes, especially in relation to land uses and climate change. In parallel, G2 has full potential to decision‐making support with standardised maps on a regular basis. Geospatial layers of rainfall erosivity, soil erodibility, and terrain influence, recently developed by the Joint Research Centre (JRC) on a European or global scale, will further facilitate applications of G2. HIGHLIGHTSG2 is a soil erosion model for developing monthly erosion maps at regional scale.G2 enhances the spatio‐temporal variability of cover management and erosivity factors.The V‐factor models the impact of vegetation cover, land use and imperviousness layer.G2 uses data from ESDAC, LUCAS, COPERNICUS, CORINE, SENTINEL and EU‐DEM databases.G2 has been applied in Strymonas Catchment, Crete, Cyprus and Albania catchments.

Use of LUCAS LC Point Database for Validating Country-Scale Land Cover Maps

In this study, the Land Use/Cover Area frame statistical Survey (LUCAS) of 2009 was used as a reference dataset for validating a Land Cover Map of Greece for 2007, produced with remote sensing by the Greek Office of the World Wildlife Fund (WWF Hellas). First, all class definitions were decomposed in terms of four vegetation parameters (type, height, density, and composition), considered as critical in indicating unconformities between LUCAS and the WWF Hellas map; their inter-class relations were described in a table of correspondence. Then, a two-tier methodology was applied: an “automated” process, where thematic agreement was based exclusively on the main land cover attribute of LUCAS (LC1); and a “supervised” process, where thematic agreement was based on the reinterpretation of LUCAS ground photos and use of ancillary earth observation imagery; non-square error matrix was deployed in both processes. For the supervised process specifically, a decision-tree was designed, using the critical vegetation parameters (mentioned above) as quantified criteria, thus allowing objective labelling of testing points in both systems. The results show that only a small proportion of the reassessed points verified the WWF Hellas map predictions and that the overall accuracy of the supervised process was reduced compared to that of the automated process. In conclusion, the LUCAS point database was found to be supportive, but not fully efficient, for identifying the various sources of error in country-scale land cover maps derived with remote sensing. Synergy with very high resolution satellite images and air photos, or a dedicated ground truth campaign, seems to be inevitable in order to validate their thematic accuracy, especially in highly heterogeneous environments. In this direction, LUCAS could be used as a verification, rather than a validation, dataset.

Development of a rule-based algorithm for rice cultivation mapping using Landsat 8 time series

In the framework of ERMES project (FP7 66983), an algorithm for mapping rice cultivation extents using mediumhigh resolution satellite data was developed. ERMES (An Earth obseRvation Model based RicE information Service) aims to develop a prototype of downstream service for rice yield modelling based on a combination of Earth Observation and in situ data. The algorithm was designed as a set of rules applied on a time series of Landsat 8 images, acquired throughout the rice cultivation season of 2014 from the plain of Thessaloniki, Greece. The rules rely on the use of spectral indices, such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Normalized Seasonal Wetness Index (NSWI), extracted from the Landsat 8 dataset. The algorithm is subdivided into two phases: a) a hard classification phase, resulting in a binary map (rice/no-rice), where pixels are judged according to their performance in all the images of the time series, while index thresholds were defined after a trial and error approach; b) a soft classification phase, resulting in a fuzzy map, by assigning scores to the pixels which passed (as ‘rice’) the first phase. Finally, a user-defined threshold of the fuzzy score will discriminate rice from no-rice pixels in the output map. The algorithm was tested in a subset of Thessaloniki plain against a set of selected field data. The results indicated an overall accuracy of the algorithm higher than 97%. The algorithm was also applied in a study are in Spain (Valencia) and a preliminary test indicated a similar performance, i.e. about 98%. Currently, the algorithm is being modified, so as to map rice extents early in the cultivation season (by the end of June), with a view to contribute more substantially to the rice yield prediction service of ERMES. Both algorithm modes (late and early) are planned to be tested in extra Mediterranean study areas, in Greece, Italy, and Spain.

Accurate multi-source forest species mapping using the multiple spectral–spatial classification approach

This paper proposes an efficient methodology for combining multiple remotely sensed imagery, in order to increase the classification accuracy in complex forest species mapping tasks. The proposed scheme follows a decision fusion approach, whereby each image is first classified separately by means of a pixel-wise Fuzzy-Output Support Vector Machine (FO-SVM) classifier. Subsequently, the multiple results are fused according to the so-called multiple spectral– spatial classifier using the minimum spanning forest (MSSC-MSF) approach, which constitutes an effective post-regularization procedure for enhancing the result of a single pixel-based classification. For this purpose, the original MSSC-MSF has been extended in order to handle multiple classifications. In particular, the fuzzy outputs of the pixel-based classifiers are stacked and used to grow the MSF, whereas the markers are also determined considering both classifications. The proposed methodology has been tested on a challenging forest species mapping task in northern Greece, considering a multispectral (GeoEye) and a hyper-spectral (CASI) image. The pixel-wise classifications resulted in overall accuracies (OA) of 68.71% for the GeoEye and 77.95% for the CASI images, respectively. Both of them are characterized by high levels of speckle noise. Applying the proposed multi-source MSSC-MSF fusion, the OA climbs to 90.86%, which is attributed both to the ability of MSSC-MSF to tackle the salt-and-pepper effect, as well as the fact that the fusion approach exploits the relative advantages of both information sources.