Summary

The aim of the research is to develop a modeling system for rural environment. The sustainable development of rural areas is a global problem, and monitoring the eco-environmental quality factors is a common goal, but approaches are different depending on the country so that the emphasis is put on specific aspects, although the modeling procedure has same steps; finally, it would be very useful to compare the models for different countries and cities. Therefore we intend to involve Hungary and China.
The project aims to develop quantitative techniques for urban eco-environment assessment using high-resolution remotely sensed data; to construct workflow which can assess the urban eco-environment automatically using remote sensing technique, to promote understanding of the relation between remote sensing data and environmental factors in rural areas
To reach these goals it is especially important to work out registration and geometrical modeling for 3D point clouds of the terrain too. The key point in our research is to integrate information in models with GIS support. The model points are generated by terrestrial laser scanners and by airborne laser scanning (ALS) systems. The data fusion of terrestrial and ALS point clouds together with the 3D models gained by stereo image matching technology has great importance in modeling of rural areas, especially for vegetation and agriculture.
Part of the research will focus on developing new ways of modeling and data storage techniques. Most of the recent research on this area is concentrating more on 3D modeling of different land forms and other land cover objects. The plant segmentation and modeling is more complex and needs more expertise and larger knowledge base.
The theory will be tested on real data with real tasks related to rural environment of micro and macro regions.

Aims

The aim of the project is to change the remote sensing techniques in both countries that means both techniques are suitable in China or in Hungary in eco-environmental monitoring. There are only a few studies available on calibration and accuracy measurement (including LIDAR data) of rural eco environment. The expected results may serve as a reference for further results of inversion and calibration parameters which help to monitor the quality of rural eco-environment. The use of remote sensing data together with the GIS datasets improves the accuracy of eco environmental monitoring.
The planned tasks and goals are

• Data collection from remote sensing, attribute data and existing maps:
  

• WorldView II, RADSARSAT-2, Landsat, Lidar 3D point cloud data and Sunflower 8 satellite data from the demonstration;
• GIS spatial information data(included large scale land use classification map ) from the demonstration;
• Crop Phenology data in the demonstration area;
• Soil texture data in the demonstration area.
  

• Studying of rural eco-environmental products using quantitative inversion and validation techniques. Available datasets are: Relevant Chinese satellite image data, basic elements of agricultural environment such as land use, topography and other series of professional maps and background data. 
  
• Making of vegetation index maps using Chinese and Hungarian satellite images. 
• Modelling of different and cover categories and surfaces can affect the rural and agricultural environment directly and the quality of daily life. The fine classification technology of cultivated land based on the texture features of time series. Multi spectral imaging and multi feature fusion of Lidar data.
• Inversion of agricultural land surface vegetation LAI parameters with remote sensing monitoring technology.  
• Analysis of agricultural areas using hyperspectral satellite images.
• 3D classification of objects of rural environment and crop lands including:

• Basic processing, automatic segmentation, vegetation analysis and visualization technology of LIDAR point cloud data
• Three dimensional image reconstruction and feature extraction technology based on 3D point cloud data
• Multi scale segmentation, classification and change detection technology based on 3D point cloud data
  

• Multi source remote sensing monitoring technology of agricultural surface water content.
• Making of GIS data modelling and visualization of eco-environmental spatial data, thematic maps and reports
• Technical personnel training: publication of the results organizing educational seminar.
• Use of results: common publications, conference lectures, reports, recommendations for local authorities.
  

Indicators for performance checking:

• The precision error of point cloud data and high resolution image fusion is better than that of multi spectral 1-2 pixels. 
• Precision agricultural land classification accuracy is better than 80%. 
• Precision of cultivated land classification based on time series texture features is better than 80%. 
• The accuracy of leaf area index is better than 70%. 
• The retrieval accuracy of soil moisture in agricultural areas is better than 70%
  

The project provides possibility for a stable scientific and technological cooperation between China and Hungary helping the deeper connection between the two countries. The aim of the project is the change of GIS and remote sensing techniques helping the evaluation of the eco-environment in both countries that justifies the usefulness of the study. The expected results may give decision support for the local authorities that are involved in eco-environment which may help in environmental management.

Co-operation basis

1. Comprehensive researches were conducted to study the urban eco-environment indicators inversion using multi-source remotely sensed data such as LiDAR, aerial imageries, HJ-1 or CBERS02-c imageries.
2. The method of remote sensing inversion of urban eco-environment spatial information products were studied systematically to verify the remotely sensed inversion sensitivity and possible error source to screen the appropriate and most suitable remote sense data and the key factors with great effect on model precision.
3. In 2006, 2010, 2012, 2014 and 2015 Prof. Meng Qing-Yan visited the University. Prof. Meng carried out scientific research and signed a scientific cooperation agreement with UWH. 
4. In 2006, the project partners applied for an EU project of ‘Distribution of on-line learning methods with application examples in environmental modeling and monitoring’. 
5. In 2008, partners applied successfully for the Sino-Hungarian inter-governmental scientific and technological cooperation project ‘Study on urban ecological space information products quantitative inversion and validation’ successfully. 
6. In 2009, with the coordination of the Hungarian partner, they successfully applied for a European Union 7th Framework Program. FP7-PEOPLE-2009-IRSES ‘Integrated geo-spatial information technology and its application to resource and environmental management towards the GEOSS’. 
7. In 2012 a cooperation agreement were signed between the two parties for research topics on remote sensing, spatial informatics and eco-environmental monitoring. 
8. In 2012, partners applied successfully for the Sino-Hungarian inter-governmental scientific and technological cooperation project “Urban Eco-environmental Spatial Information Retrieval and Analysis Model with Remote Sensing’ successfully. Recent applications would be a continuation of this project. 
9. In 2014, the Chinese Academy of Sciences – Institute of Remote Sensing and Digital Earth and the Óbuda University bound a cooperation agreement for joint studies and exchange of researchers. 
10. In 2015 a cooperation agreement was signed with the Guangzhou University, the Chinese Academy of Sciences – Institute of Remote Sensing and Digital Earth and the Alba Regia Technical Faculty belonging to the Óbuda University.
    Thus, both sides have conducted fruitful cooperation, and established stable exchange mechanism and good cooperation atmosphere, which laid good foundation to carry out this project.
    
    The Institute of Geoinformatics inside the Alba Regia Technical Faculty based of the Óbuda University has gained a lot of practice in the characteristics and metrics of ecology studies, and participated in several international GIS-related projects.
    
    The Institute of Remote Sensing and Digital Earth belonging to the Chinese Academy of Sciences considers the geospatial research related to rural eco-environment as an immediate target. The Institute achieved fruitful results in the field of remote sensing applications in relation to the urban eco-environment. The Chinese party can provide Chinese satellite images and they have extensive experience in the field of processing and analysis.
    
    The Chinese and Hungarian parties' approaches and solutions complement each other, and in this respect they are worthy of comparison. The integration in a GIS environment provides a better and broader understanding of the results.
    
    With the use of remote sensing technology the goal is to organize monitoring of complex rural environments and to understand the scientific basis to support the development needs and the environment as a science. The project is based on a stable exchange of information between scientific and technological co-operation, between China and Hungary, which deepens and spends the earlier content with a long-term cooperation.
    
    With the help of Chinese-Hungarian cooperation based on the combination of Chinese and European satellite data, the goal is to derive core eco-environmental indicators in rural areas in Sino-Hungarian relations and to carry out eco-environmental assessments and related analyzes using geospatial quantitative data and high-resolution remote sensing data sources.
    

Research Process

• Data providing, algorithmic and adjustment aspects, filtering outliers, preparing materials. Multi source data integration processing. Main content: The acquisition of remote sensing data and Multi-source image fusion technology.
• Extraction of agricultural land surface environmental parameters. Leaf area index extraction. Surface water content extraction. Authenticity verification and application demonstration Application of models in rural environment for micro and macro agricultural regions with feature extraction of different land forms and covers, classification of images using the data fusion and integration techniques. 
• Refining classification techniques for time series crops, technology of agricultural land classification with multi-source data. Testing the algorithms in frame of real applications and datasets.
• Segmentation, processing, modelling and visualization of terrain and other models and maps gained from point clouds and remote sensing data.
• Project communication and achievement sharing. Main content: Technical personnel training for the remote sensing forum between Hungary and China; preparing common research report. 
• Four common publications. 
  

Time schedule:

1. Data providing, algorithmic and adjustment aspects, filtering outliers, preparing materials. - 2022.01-2022.06.
2. Extraction of agricultural land surface environmental parameters. 2022.07 - 2022.12.
3. Refining and testing the algorithms in frame of real applications and datasets. - 2023.01 - 2023.06.
4. Segmentation, processing, modelling and visualization of terrain and other models and maps gained from point clouds and remote sensing data. - 2023.07 - 2023.12
5. Organizing workshops, joint research report and recommendation. - 2023.08 - 2023.09
6. 4 common publications. - 2023.01 - 2023.08 (continuous)