Endre Dobos

Use of combined digital elevation model and satellite radiometric data for regional soil mapping

Abstract Previous reports demonstrated that data from air- and spaceborne sensors are appropriate for delineation of soil patterns. Also, many attempts have been made to use digital elevation model (DEM) for deriving soil information. However, little is known about the potential use of low spatial resolution satellite and digital elevation data in small-scale soil mapping. A case study was conducted to assess the use of integrated terrain and Advanced Very High Resolution Radiometer (AVHRR) databases for small-scale soil pattern delineation. The main objective was to test the effect of the addition of terrain descriptor data to the AVHRR data set on the classification results. Two database were used for the study. The first one was purely AVHRR data and contained the five basic AVHRR channels and the normalized difference vegetation index (NDVI) of five different dates, while in the second database the AVHRR data was complemented with a DEM, a curvature, a slope, an aspect and the potential drainage density layers. The performance of these two databases when employed to derive soil information was compared. These databases were then further processed using the Discriminant Analysis Feature Extraction (DAFE) function (which is based on a canonical analysis procedure), and were then classified using the Fisher linear discriminant, and the ECHO spectral–spatial classifiers. Based on the results, it was concluded that the two reflective bands, the middle infrared, the two thermal bands and the NDVI provided a relatively wide range of detectable soil information. The use of single images or small dimensional AVHRR data sets (less then 10 layers) does not result in acceptable performances, while the use of multispectral and multitemporal databases improved the classification performance very significantly. However, the purely AVHRR-based model could not always delineate soil variations related to terrain differences, and resulted in an overall classification performance of 49.1%. Digital elevation and terrain descriptor data were essential in the model for achieving acceptable results. In the second part of the study an integrated AVHRR-terrain database was used, where five terrain layers were added to the 30 AVHRR channels. Two different spatial resolutions were compared, 500 m and 1 km, respectively. The use of elevation, slope, aspect and curvature as differentiating criteria often lead to a satisfactory result in terrain characterization, particularly in large-scale mapping. However, with those variables extracted from DEM of physiographically complex areas, e.g., — where plain areas and mountainous/hilly regions occur together in the same study — often lose their ability to delineate soil variations of the level lands. Beyond these terrain descriptors we implemented a new function, called potential drainage density (PDD) to improve the performance of the model on the plain areas. The classification accuracy of the integrated AVHRR-terrain database was improved significantly over the case when only AVHRR data was in the model. The classification performances of the three different resolution images were 87.3% for the 500-m resolution image and 70.1% for the 1-km resolution image.

A regional scale soil mapping approach using integrated AVHRR and DEM data

Abstract There is an increasing need for reasonably accurate small-scale soil databases. The compilation of a continental or global-scale soil database requires a lot of spatially and thematically accurate soil data. The aim of this study was to test a method for small-scale soil mapping in Italy using Advanced Very High Resolution Radiometer (AVHRR) and digital elevation data. This method was employed in an earlier study in Hungary for a much smaller area and a significantly different soil-forming environment. An integrated, 45-layer AVHRR-terrain database was used for the study, including a digital elevation model (DEM), slope, curvature, aspect, potential drainage density, and the five bands of AVHRR data for eight different dates. The data were processed using the Discriminant Analysis Feature Extraction (DAFE) function, which is based on a canonical analysis procedure. Two types of images (basic and transformed) were classified using the maximum likelihood classifier. Two training sets were chosen that have identical geographic coverage, but differ in the level of soil classification. One set was based on the soil units (SU) of the FAO-revised legend, while the other set represented major soil groupings (MSG). The best 10, 15, 20, 25, 30, 35, 40 and 45 layers were selected using the Bhattachryya feature selection method and were then classified. The results of the different sets were compared. The performance of the purely AVHRR and purely terrain-data-based images, respectively, were also interpreted. The results indicate that the terrain descriptors alone are not sufficient for soil classification. However, the feature selection algorithms always selected the DEM and its derivatives among the first ones, highlighting their importance for soil-landscape characterization. When using AVHRR data alone, test class performances of 49.8 percent (MSG) and 48.6 percent (SU) were achieved. Integration of terrain data into the AVHRR database produced relatively small improvements (4.6 and 2.8 percent). The best test class performances were achieved when all available channels were used for the classification, namely 51.4 for the FAOs SUs and 54.4 for the MSGs on the basic image, and 51.7 and 54.4 respectively on the DAFE-transformed images. The most informative AVHRR bands were found to be from the spring period (April-May), while the most abundant bands were the visible-red (band 1) and bands 3 and 4.

Soil legacy data rescue via GlobalSoilMap and other international and national initiatives

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1km in 2014, followed by an update at a resolution of 250m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.

Javaslat talajosztályozási rendszerünk megújítására: alapelvek, módszerek, alapegységek

A jelenlegi, 1960 evekben kidolgozott genetikus alapokon nyugvo magyar talaj-osztalyozasi rendszerunk modernizalasara az elmult evtizedek hazai tapasztalatai, dokumentalt adathalmazai, valamint a nemzetkozi standardok es elvarasok alapjan teszunk javaslatot. Munkank kiindulasi alapja az elődeink altal definialt 29 talaj-kepző folyamat, 39 talajtipus, es az azokhoz kapcsolodo tartalmi leiras es vizsgalati adatokbol nyert informacio. Modszereink az egyszerű elemzesek es statisztikai szamitasok mellet a pedometria modszerere, a taxonomiai tavolsagszamitasra es klaszteranalizisre tamaszkodtak. Eredmenyeink alapjan a jelenlegi hazai talajtipusok egy resze jol elkulonithető, masok pontosabb definiciora szorulnak, vagy egyes esetekben osszevonasra kerul-hetnek. A nemzetkozi gyakorlatban meglevő es hazankban is jellegzetes tipusokat pedig, bevezetesre javaslunk. A szigorubb es mind a melysegi, morfologiai es osszetetelbeli tulajdonsagok te-kinteteben szamszerűbb meghatarozasokon alapulo javasolt struktura kozpont...

Calculation of Potential Drainage Density Index (PDD)

This paper focuses only on the technical details of the usage and creation of PDD. More details on the theory can be found in the papers written by (1998) and (2000). The Potential Drainage Density index, abbreviated as PDD, can be used for geomorphologic, pedologic and geologic characterisation of the landscape.

A Quantitative Procedure for Building Physiographic Units for the European SOTER Database

Soil data of various scales is needed for good management of agricultural and environmental resources. On the European level soil information is used for crop monitoring, yield forecasting, agricultural planning, feasibility studies for rural development, natural hazards forecasting, such as floods and landslides or slowly acting processes such as erosion, acidification and other types of chemical, biological and physical degradation of soils. However, no soil database for the European Union to support these goals had existed before the late eighties. The strong need for policy support has speeded up the database compilation and resulted in the first version of the Soil Geographical Database of Eurasia at scale 1:1 million (SGDBE1M). Despite its limitations, SGDBE1M is still among the few databases which serve as a flagship in the development of the small scale spatial databases in Europe. The version 2.0 of SGDBE1M was published recently, in 2004. The refinement of the database and the extension of its geographic coverage to Eurasia and the Mediterranean Africa are in preparation (ESB 2004)

Soil organic matter map of Hungary derived from digital elevation model and satellite data

One of the major stresses on the soil functions is the decline of soil organic matter (SOM) content. Soil Information and Monitoring Systems were setup to survey the recent situation and estimate the rate and trend of potential changes in different soil properties and components including SOM. These Monitoring systems are profile or point based networks with regular sampling periods, which can provide limited, often insignificant percentage of the country surface. The collected data need to be extrapolated to create continuous coverage of the land area of interest.

FLOOD MODEL FOR THE BÓDVA CATCHMENT

In term of floods the current area of Hungary has extensively been endangered. Modelling of flood processes – mainly following the hydrological events in the riverbed – has recently been developed. As far as protection dykes provide protection of the inhabited and agricultural areas, the flood models can run with acceptable preciseness. However, when dykes cannot withstand against the increasing load and a dyke burst occurs, fast and efficient protection measures shall be taken in the protected areas. The dynamic 4D Flood model presented in this paper makes possible a fast modelling of dyke burst occurring in the protected side and spreading of water mass, based on real parameters. For this reason the features of protected area shall be recognised, for example topology of creeks, features of agricultural and inhabited areas, parameters of roads, railways, rainwater drainage, buildings, natural conditions (soil parameters, meteorological characteristics, etc.). The results satisfy the comprehensive demands of the Directorate General for Disaster Prevention of Borsod-Abauj-Zemplen County. In case of dyke burst, the completed Flood Model can run the expected events of the next hour in a few minutes. This time is enough for the specialists to bring operative decisions to protect the inhabitants and avoid material losses.