Use of High Resolution Satellite Images for Agricultural Land Use Assessment in Romania

1. 1 Use of High Resolution Satellite Images for Agricultural Land Use Assessment in Romania Dr. G. STANCALIE, Dr. A. MARICA, Dr. E. SAVIN, S.CATANA, C. FLUERARU National Meteorological Administration Bucharest, Romania

2. 2 Presentation outlook

3. 3 Introduction

4. 4 The Present Romanian Agrometeorological Monitoring System

5. 5 The Improved Integrated Support System for the Agrometeorological Warning and the Identification of the Areas with Agricultural Risk in Romania

6. 6 Interoperable System for Meteorology

7. 7 The Information Flowchart of the Integrated Support System for Agrometeorological Warning and Identification of Agricultural Hazard Areas

8. 8

9. 9 Satellite � Derived Information for Agricultural Monitoring

10. 10

11. 11 The Land Cover/Land Use Mapping Requirements for the achievement of the land cover/land use from high resolution images: The structure of this type of information must be at the same time cartographic and statistic; It must be suited to be produced at various scales, so as to supply answers adapted to the different decision making levels; Up-dating of this piece of information must be performed fast and easily. The developed methodology implies the following main stages: Preliminary activities for data organizing and selection; Computer-assisted photo-interpretation and quality control of the obtained results; Vectorisation of the obtained maps (optional); Database validation at the level of the studied geographic area; Obtaining the final documents, in cartographic, statistic and tabular form.

12. 12 Satelite Data Processing and Analysis Optical satellite data (LANDSAT�ETM, IRS�PAN/LISS, SPOT-AN/XS, ASTER) have been used to perform the analysis for land use inventory purposes. A series of specific processing operations for the images were performed, using the ERDAS Imagine and ENVI softwares: Geometric correction and geo-referencing in different map projection system; Image improvement (contrast enhancing, slicking, selective contrast, combinations between spectral bands, re-sampling operation); Classifications and grouping; Statistic analyses (for the characterization of classes, the selection of the instructing samples, conceiving classifications).

13. 13 General Methods for the Generation of the Land Use Map

14. 14 Semi-automatic Generation of the Land Use Map

15. 15 Spatial Resolution Enhancement by Fusion Procedure(Band substitution +IHS transformation)

16. 16 Exemple of fusion for IRS images

17. 17 Statistics of the Land Use Classes

18. 18 Multispectral Classification Pixel based classification Non-supervised classification � �n� classes Regrouping � following interpretation rules Emphases cultivated zones Supervised classification based on training areas Regrouping Parcel based classification Uses neo-channels (derived from PAN) : texture and variance Non-supervised classification based on dynamic clusters (mobile centers) Emphases cultivated areas vs. urban zones Regrouping Combination of the two type of classifications

19. 19 The result of pixels based classification Land use/cover map

20. 20

21. 21 Statistic Validation of the Land Use Classes

22. 22 Land use mapping based on TERRA/ASTER data

23. 23 Land Use Obtained by Un-supervised Classification of TERRA/ASTER Data

24. 24 Land Use Obtained by Un-supervised Classification of TERRA/ASTER Data (cont.)

25. 25 Land Use Obtained from Supervised Classification Based on Training Areas

26. 26 Unsupervised vs Supervised Classifications for the land use mapping

27. 27 Land Use Obtained from TERRA/ASTER Data (Detail)

28. 28 Improving Classification Using Multi-temporal Images

29. 29 Use of Multi-temporal & Multi-sensor Images for Crop Identifications

30. 30 Use of Multi-temporal & Multi-sensor Images for Crop Identifications

31. 31 Conclusions