Dr. david caballero (meteogrid) “ground truth survey in spain”

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  • 1. Ground truth survey in Spain D. Caballero ArcFuel Workshop Thessaloniki 2013
  • 2. Spanish study area • Provinces of Córdoba and Málaga, Andalucía, Spain • Córdoba is one of the largest provinces in Spain. • Both hold forestlands in a rough terrain, shrublands and agricultural lands
  • 3. Spanish study area
  • 4. Survey points • • • • LUCAS points (P) Alternative points (ALT) Complementary points (PC) Other review activity (Fotos)
  • 5. Survey points
  • 6. Method • Geo-tagged pictures • Recordings and annotations for every photo set • GPS antenna linked (BT) to tablet with OruxMaps and Google Earth (cached) • Taken 8 pics (N, NE, E, SE, S, SW, W, NW), Upslope, Dwnslope, Canopy, Ground • Extra pictures showing the environment
  • 7. Navigation to survey points Geo-One GPS receiver attached to a Nikon D7000 camera
  • 8. Navigation to survey points
  • 9. Difficulties • Private properties! Almost no access, everything with fences! • Topography, very steep slopes • Vegetation: dense vegetation with thorns (Ulex sp) • Hunting activty (specially at dusk), bullfighting bulls • Winter time, short daylight hours • Look for alternative points, jump fences and a lot of walking -> reduced performance
  • 10. Survey - Spain • Poor accessbility -> alternative points of the same structure • A total of 56 points, 58 surveyed, one outside study area (Seville), one missing information
  • 11. Survey - Spain • • • • • • • • 1 EG Broad Scrub 2 EG Broad Open 3 EG Broad Dense 7 EG Conif Scrub 15 EG Mix Dense 19 Shrubs 20 Grasses 24 No fuel 1 5 4 1 2 30 5 4
  • 12. ArcFuel Classification • Classification of points according ArcFuel Map: depends on precision and resolution • Complicated in highly fragmented fuels • Suggestion: adaptative resolution (according fuel fragmentation) • Suggestion: use of LiDAR
  • 13. Classification results • Good for urban areas, it should be tested in dense intermix areas (i.e. North of Cordoba city) • EG Broad Scrub vs. Open: no so clear the difference sometimes. Required data on height (LiDAR) • In general good classifying dense forests
  • 14. Classification results • Missing EG Broad Dense for EG Conif Scrub (PC03A) • Missing EG Conif Scrub for Shrub! (P4) • Missing Shrub for EG Broad Dense (PC05A) • Missing EG Conif Dense for Agrofor (P27) • Missing EG Mix Dense for EG Broad Dense (PC38A, PC04A)
  • 15. Classification results • Good classifying shrubs (80%), but many different formations and structures included as shrubs (see comments) Missing Shrubs for Grasses, Shrubs for Agrofor -> abandoned lands PC10A, P35ALT, PC35A, P52ALT • Missing Grasses for Shrubs, difficult to differenciate (shrub density) P45, P47ALT
  • 16. Conclusions • 34 points well classified (60%) • 3 points very badly classified, wrong or inconsistent data sources • 8 points badly classified, difficult to differentate heights, densities, species • 11 with reasonable classification, particularly abandoned agro lands, outdated data sources (19%)
  • 17. Conclusions • Need for more information on shrubs • Design and apply a robust method to classify shrubs in the understory • Use of temporal and thematically consistent data sources • Use of LiDAR (where available) may help in future classifications