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Marguerite Madden Improved Understanding of Landuse Change using Geospatial Technologies and Geovisualization


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Marguerite Madden Improved Understanding of Landuse Change using Geospatial Technologies and Geovisualization

  1. 1. Improved Understanding of Landuse Change using Geospatial Technologies and Geovisualization: Case Studies of Historical Agricultural Landuse and Tornado Damage in a Forest Preserve Marguerite Madden Professor and Director Center for Geospatial Research (CGR) Department of Geography, University of Georgia Athens, Georgia USA INTEREXPO GEO-SIBERIA-2014 - April 16-18, 2014
  2. 2. “As a plot of ground that has for almost three hundred years been devoted to agricultural pursuits or the interpretation of historic agriculture, Wormsloe is an inviting case study to deal with regional themes of land use, the preservation of historic and natural landscapes, and the place of agriculture and people within the environment.” “Wormsloe is a typical southern space, a place where cotton and cornfields were as prevalent as woods and marshes and farm work was the most common way that people interacted with the environment.” Drew Swanson, Remaking Wormsloe Plantation: The Environmental History of a Lowcountry Landscape (2013) The University of Georgia Press Stresses the centrality of agroecosystems, especially in southern landscapes.
  3. 3. Tommy Jordan CGR Associate Director
  4. 4. Objectives: 1) Brief introduction to the Wormsloe Historic Site and interdisciplinary research by the Wormsloe Institute for Environmental History (WIEH)-UGA . 2) Use of LiDAR data and spatio-temporal data sets to assess current landscapes related to historical landuse legacy of disturbance/transition. 3) Demonstrate 3D reconstruction of historic buildings, landscapes and artifacts with cloud- based Surface from Motion photogrammetry and geovisualization
  5. 5. Wormsloe State Historic Site Charleston, SC Savannah, GA Wormsloe Historic Site
  6. 6. Wormsloe Colonial Settlement Slave Cabins Main House Rice Mill Former Cotton Fields Pine Bark Beetle Damage 1920s Dairy Shell Middens Whiskey Stills
  7. 7. Wormsloe Historic Site, Isle of Hope, Georgia “…the story of one family’s continuous land stewardship that has lasted almost three centuries to date.” (Swanson, 2012)
  8. 8. Shell Midden – Native American inhabitants 4000 BC
  9. 9. Tabby ruins of the fortified house - Noble and Sarah Jones, with children Mary and Noble Wimberly among the 22 “Trustees” who left England in 1732 to colonize Georgia
  10. 10. Wormsloe Plantation House 1828
  11. 11. Silo from 1920s dairy barn
  12. 12. Formal gardens at Wormsloe opened for tourists 1930s
  13. 13. 1840s cabin
  14. 14. • 1800 – Isle of Hope map • 1828 – Plan for original Wormsloe house • 1870 – Hand drawn map of roads, fields and buildings • 1871 – Plan for proposed causeway to Skidaway Island • 1890 – Hand drawn map of roads, fields and buildings – Stereo photo cards • 1897 – Hand drawn and colored map of roads, fields, fence lines, drainage ditches and buildings – Trees and plantings with species labeled • 1908 – Crude map of fields and land cover Historical Data
  15. 15. How does 1897 landuse legacy shape vegetation pattern and process today? Clearing Evergreen Mixed Forest Residential
  16. 16. 33 Permanent Vegetation Plots represent current vegetation communities located within the 1897 landuse legacies
  17. 17. North East South West Canopy
  18. 18. Placing a Historical Plantation in an Ecological Context, Geography M.S. Thesis by Carey Burda (2011) determined effects of historical land uses on vegetation structure.
  19. 19. Vegetation Plots Vegetation Plot Landscape perspective
  20. 20. LiDAR return types: Type 1: singular return Type 2: first of many returns Type 3: intermediate returns Type 4: last of many returns Objective 1: Derive canopy stratification & LiDAR metrics according to Miura & Jones (2010)
  21. 21. Used QCoherent LP360 extension to ArcGIS to stratify the canopy Strata # Class Layers Elevation Range (m) 0 Ground ≤0 1 Litter >0.001 and ≤0.5 2 Low Veg1 >0.5 and ≤ 1.5 3 Low Veg 2 >1.5 and ≤ 5 4 Medium 1 >5 and ≤ 10 5 Medium 2 >10 and ≤20 6 High 1 >20 and ≤ 30 7 High 2 >30
  22. 22. Description LiDAR Type Miura & Jones (2010) correlated field variables Formula (adapted from Miura & Jones, 2010) OG Opening above the ground Ground Type 1 Total volume coarse woody debris OL Opening above low vegetation LowVeg Types 1 & 2 Field mean canopy cover VL Presence of understory vegetation Low Veg Types 1,2,3, & 4 LAI for vegetation < 1 meter CC Canopy cover Medium Veg Types 1 & 2 and High Veg Types 1 & 2 Field derived canopy cover OM Opening above medium vegetation Medium Veg Types 1 & 2 Opening above medium vegetation
  23. 23. 1810 Hand drawn map, De Renne Family Collection Hargrett 1897 Hand drawn map, Hargrett Rare Book & Manuscript Library 1908 Hand drawn map, Hargrett Rare Book & Manuscript Library 1912 Topographic map, Army Corps of Engineers/USGS 1933 Hand drawn map, US Coast & Geodetic Survey/Air Photos 1937 Hand drawn Map, US Dept. of Agriculture 1945 Topographic Map, USGS 1957 Topographic Map, USGS 1971 Aerial Photograph, B&W, Skidaway Inst. for Oeanography 1976 Aerial Photograph, B&W, UGA Map Library 1988 Aerial Photograph, B&W, USGS-NHAP 1999 Aerial Photograph, CIR, USGS-NAPP 2009 Aerial Photograph, Color, NAIP-USDA Identified Vegetation at each Longterm Vegetation Plot over 200-year period:
  24. 24. Disturbance & Transition: Antebellum (1810-1860) Postbellum (1870- 1910) Pine Beetle Infestation (1970-present)
  25. 25. Plot No. 1870 1880 1890 1900 1910 Disturbance Score Disturbance Class 1 Evergreen forest Mixed forest Mixed forest Mixed forest Mixed forest 6 low 2 Evergreen forest Evergreen forest Evergreen forest Evergreen forest Evergreen forest 10 medium 3 Field Field Field Field Evergreen forest 14 high 4 Field Field Field Field Field 15 high 5 Field Field Field Field Field 15 high 6 Mixed forest Mixed forest Mixed forest Mixed forest Mixed forest 5 low 7 Mixed forest Mixed forest Mixed forest Mixed forest Mixed forest 5 low Disturbance Score 1. Disturbance level= based on impact land cover would have on current vegetation structure, plots were classified as having low, moderate, high levels of disturbance.
  26. 26. Plot No. Antebellum Postbellum Pine Beetle Transition Score Transition Class 1 high low low 1 moderate 2 high medium low 2 high 3 medium high low 2 high 4 medium high low 2 high 5 high high medium 1 moderate 6 low low low 0 low 7 low low low 0 low Transition Score 2. Transition score = # times shift in disturbance level within the 3 time periods.
  27. 27. DCA Ordination LiDAR metrics input to Detrended Correspondence Analysis (DCA) Ordination – clustered plots are similar Veg Plot 21 DensetoSparseMid-Storey Low Canopy Ht/More Open to High Canopy Ht/Less Open Mid-Storey
  28. 28. Axis 1: P < 0.05 Axis 1: P < 0.05 (Significant Groups!)
  29. 29. Wormsloe: 1909 DeRenne Library
  30. 30. Dr. CJ Jackson, GSU and UGA-SKIO, collected TLS data of existing cabin and building site with Riegl VZ-1000: @ 70 kHz, 29,000 pts/sec, to 1400m @ 300 kHz, 122,000 pts/sec, to 450m One sigma at 100m, 8 mm accuracy, 5 mm precision UGA Architects plan new cabins Stores 32 Gb data, wireless data transfer
  31. 31. X, Y, Z Point Cloud and Return Intensity
  32. 32. Rapid, inexpensive and easy 3D surveys of buildings or small objects Image-based method requires little training and is inexpensive. Online SfM programs available (e.g., 123D Catch) Create high-resolution digital elevation models from ordinary ground/aerial photographs with free and open source software. Process reconstructs objects/landscapes from SfM algorithms based on the derived positions of the photographs in 3D space. Surface from Motion or Structure from Motion (SfM) Fonstad, et al. (2013) Topographic structure from motion: a new development in photogrammetric measurement, Earth Surfaces Processes and Landforms, 38 (4): 421-430.
  33. 33. Surface from Motion using multiple ground photos taken at different angles uploaded to Autodesk 123D Catch Creates 3D point cloud and 3D image model downloaded for free (pay for 3D print), Video
  34. 34. • Historical sites such as Wormsloe offer unique opportunities to explore impacts of historical landuses on current landscapes. • Future conservation and resource management will increasingly draw upon rich, multiscale geospatial data sets for landscape-scale assessments (with variable accuracies, temporal intervals). • Integrated 3D geospatial data and geovisualization techniques can be used effectively to reconstruct historic landscapes shaping current conditions and investigate change. Conclusions
  35. 35. Thank Wormsloe Institute for Environmental History, the Wormsloe Foundation, UGA Graduate School and UGA College of Environmental Design for their support of Wormsloe researchers and students. Craig and Diana Barrow, Sarah Ross, WIEH President Carey Burda, Andrew Parker, Nancy O’Hare, Carrie Jensen Wormsloe Institute for Environmental History (WIEH),