Miro Govedarica

Ontology development for land administration

This paper presents ontology architecture for the land administration domain, which should provide shared vocabulary for semantic interoperability between cadastral systems. This ontology is based on general geospatial ontology which describes generic concepts from geospatial domain such as geometry and topology and is in correspondence with geospatial standards: ISO 19100 series and OGC. Land administration ontology is based on the Land Administration Domain Model specified in ISO 19152 standard which is extended with the concepts from the Serbian national cadastre.

Object based image analysis in forestry change detection

This paper presents methods for land cover change detection, using object based image analysis. Considering that huge area in Serbia is covered with forest, this study is of great value for tracking changes over time. The research involved collecting data. The data used in this study are optical remote sensing images taken by Landsat TM satellite and Landsat ETM+ satellite. These images were processed in order to classify four main land cover classes, namely: forest, agricultural, urban and water areas. The images are analyzed in terms of change detection. The results show the type and the place of land-use changes occurred in these main four classes during those years. Change analyses have been executed based on object image analysis, and results are given in raster and vector format.

Ontology for real estate cadastre

Abstract Standardisation efforts in the geospatial domain, provided by ISO TC 211 and OpenGIS Consortium, have brought many specifications and standards that define structure and encoding rules of data and interfaces of services. These standards provide syntactic interoperability of geospatial data and services, while neglecting their semantics. In order to achieve semantic interoperability, i.e. explicitly define the meaning of data and services to make them understandable both to machines and humans, there are attempts to develop Geospatial Semantic Web, where semantics of data will be expressed explicitly and formally. An important part of this process is the development of domain ontologies, i.e. a common vocabulary in some domain. In order to bring semantics into cadastral systems, it is necessary to develop ontology for real estate cadastre. Therefore, the authors propose the ontology based knowledge model in the field of real estate cadastre based on ISO 19152 international standard and other geospatial standards, as a core ontology for cadastre on top of which a domain ontology for a specific country should be built. The domain ontology presented in this paper refers to the real estate cadastre of Serbia, but a similar approach can be adopted for different cadastral organisations in other countries. In this way, different domain ontologies based on various cadastral organisations will conform to the same standard based core ontology that will provide a basis for the semantic search and integration of cadastral data on national and international level.

Corn yield estimation in Serbia using MODIS 13Q1 product

The aim of our study was to verify the accuracy and the usability of Moderate resolution imaging spectroradiometer (MODIS) 13Q1 product for corn yield estimation on a local level for 2014 year. Product 13Q1 consists of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) 16-day composites with 250 m spatial resolution. The estimation is based on ground truth data (sowing structures for 8 years) which was provided by local agricultural organization in Vojvodina, Serbia. The indices were used in linear regression, where the average yield for corn was the dependent variable, NDVI and EVI were independent variables. Average corn yield was estimated approximately 15 days before the beginning of the harvest and compared with official results. Depending on the used linear method, relative errors ranged from 0.6 % to 7.4 %. Overall, coefficients of determination (R2) ranged from 0.66 to 0.75 and were significant at 0.05. The smallest difference between official results for corn yield and our estimate when using NDVI was 0.59 t/ha, when using EVI the smallest difference was 0.07 t/ha. Paper showed that NDVI and EVI from MODIS follow linear relationship with average corn yield and can be used in estimation of crop yields in Serbia and also that EVI produces better prediction results than NDVI. The crop yield estimation can be used for similar cultivated plants in Serbia and for longer period dataset.

Semantic metadata in spatial information systems

Standardization in the field of GIS has led to a series of specifications and standards that define structure and encoding rules of data and interfaces of services provided by ISO TC 211 (ISO 19100 series) and OpenGIS Consortium. This set of standards provides syntactic interoperability of geospatial data and services, but does not solve semantic problems. In order to achieve semantic interoperability i.e. explicitly define the meaning of data and services to make them understandable both to machines and humans, attempts are made to develop geospatial semantic web, where not only the structure of data will be visible but semantics as well. In this process, particular attention is drawn to various domains such as environmental modeling or observations and measurements, but there are few results in the field of land administration that only address the problem partially and are not coordinated with standards. Therefore, the authors develop the ontology based knowledge model in the field of real estate cadastre coordinated with standards and propose how to reference resources to knowledge model concepts by annotating catalogue metadata and data models.

Land Use Management Based on GPS Technology, and Satellite and Ground Remote Sensing Technologies

The paper presents an overview of the GPS technology, satellite remote sensing technologies, ground remote sensing technologies, and GIS technologies applications to the land use management. The technological method, developed by the Centre for Geo-Information Technologies and Systems Novi Sad, aimed for but not restricted to agricultural land use management using GPS, satellite remote sensing, geo-radar sensing and GIS technologies is presented as a case study.

Automated data extraction from synthetic and real radargrams of district heating pipelines

The main goal of this paper is to investigate the performance of an algorithm for point extraction from hyperbolic reflections in synthetic and real Ground-Penetrating Radar (GPR) data. The real radargrams that we considered contain hyperbolic reflections due to the presence, in the surveyed area, of district heating pipelines DN250 (250mm inner diameter pipe). These are buried 88 cm deep in a soil trench, and covered by compacted sand and concrete bricks (behaton pavement). The synthetic radargrams result from the simulation of a model representing the real geometry on the location of interest. The simulation was carried out by using gprMax, ver. 3.

Comparison of MODIS 250 m products for early corn yield predictions: a case study in Vojvodina, Serbia

Abstract The aim of the paper is to compare Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) products with different compositing periods (8-day, 16-day and 8-day-dual) at 250 m spatial resolution for early corn yield estimation. In order to achieve this objective, several regression models were used where the average yield was the dependent variable and the different values of NDVI were independent variables. The various inputs in the regression models included: (i) maximum NDVI value (peak value) during the season or heading date value, (ii) NDVI values from the first date after heading date, (iii), NDVI values from the second date after heading date, (iv) Seasonally integrated NDVI values. Results showed that the 16-day composite was better yield predictor than the 8-day composite when using maximum NDVI value during the season, which is the value from the most significant earliest period for yield estimation, which is called the heading date. The 8-day composites were more useful than 16-day composites later in the season for yield estimation when NDVI values from first date after heading date and values from second date after heading were used. However, the 8-day-dual was not useful for yield prediction. In order to validate the results, the authors used the leave-one-year-out approach, which trains the remaining years for the left out year and is used for yield prediction for missing year. It was found that the inverse regression model produced the best yield estimates. After excluding the anomalous 2012 year, the R2 values for the regression model were > 0.5 for all remaining years and products, with statistical significant at 0.05. The smallest difference between predicted and actual corn yield when using 8-day composite was 0.05% while the largest difference was 34.47%, whilst in the case of 16-day composite the smallest difference between predicted and actual yield was 1.67% and the largest difference was 44.12%.

Impact analysis of pansharpening Landsat ETM+, Landsat OLI, WorldView-2, and Ikonos images on vegetation indices

The aim of our study was to verify the impact that pansharpening (PS) methods produce on vegetation indices. We used images with both moderate (Landsat 7, Landsat 8) and high (World View2, Ikonos) spatial resolution on which we performed three methods of PS (Brovey transform, Gram-Schmidt and Principal component). The study is based on the differences of vegetation indices (VI) values before and after the pansharpening method is applied. The difference is quantified as an root mean square error. Vegetation indices used in this study were: NDVI, MSAVI2, EVI2, GNDVI, OSAVI and SAVI. Statistical analysis is carried out by calculating coefficients of correlation, root mean square errors and bias calculations for every vegetation index before and after pansharpening procedure is done. The results imply that the BT gave the most diverse results between original VI values and the PS VI values, while the GS and PC methods preserved the values of pixel bands, and that the effect of any PS method is most evident when using Ikonos bands.

Spatial analysis of high-resolution urban thermal patterns in Vojvodina, Serbia

Main objective of this study was to establish a relationship between land cover and land surface temperature (LST) in urban and rural areas. The research was conducted using Landsat, WorldView-2 (WV-2) and Digital Mapping Camera. Normalised difference vegetation index and normalised difference built-up index were used for establishing the relation between built-up area, vegetation cover and LST for spatial resolution of 30 m. Impervious surface and vegetation area generated from Digital Mapping Camera from Intergraph and WV-2 were used to establish the relation between built-up area, vegetation cover and LST for spatial resolutions of 0.1, 0.5 and 30 m. Linear regression models were used to determine the relationship between LST and indicators. Main contribution of this research is to establish the use of combining remote sensing sensors with different spectral and spatial resolution for two typical settlements in Vojvodina. Correlation coefficients between LST and LST indicators ranged from 0.602 to 0.768.