857 presentation 1

560 views
495 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
560
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • 857 presentation 1

    1. 1. Remote SensingCharacterization ofTundra Vegetation Sarah Allux M.Sc. Candidate Supervisor: Dr. Paul Treitz
    2. 2. Study Site: Melville
    3. 3. Study Site: MelvilleSource: Google Maps, 2011. Imagery ©2011 TerraMetrics
    4. 4. Study Site: MelvilleSource: Google Maps, 2011. Imagery ©2011 TerraMetrics
    5. 5. Study Site: MelvilleSource: Google Maps, 2011. Imagery ©2011 TerraMetrics
    6. 6. Study Site: MelvilleSource: Google Maps, 2011. Imagery ©2011 TerraMetrics
    7. 7. Three simple questions...
    8. 8. Three simple questions... 1. How much vegetation?
    9. 9. Three simple questions... 1. How much vegetation? 2. What kind?
    10. 10. Three simple questions... 1. How much vegetation? 2. What kind? 3. Where?
    11. 11. But WHY?
    12. 12. But WHY?Source: Cape Bounty, 2009
    13. 13. But WHY?Source: Cape Bounty, 2009Source: NASA, 2011
    14. 14. But WHY?Source: Cape Bounty, 2009 Source: CAVM Team, 2003 Circu 0° 45 Cryptoga W ° ° E 45 Cryptoga Noncarbo Carbonat Prostrate Prostrate Rush/gra Graminoi Nontusso Tussock s Erect dwa Low-shru 90° E 180° Sedge/grSource: NASA, 2011 Sedge, m Sedge, m Nunatak c Glaciers N Water 80 ° Lagoon Ar ct Non-Arcti ic Cir cle 13 5° E W 5° 13 La Longi Derived from: C Vegetation Map Flora and Faun 180° Service, Ancho
    15. 15. Research Objectives
    16. 16. Research Objectives1. Model spatial variability in plant composition and percent cover for the Sabine Peninsula, Melville Island, Nunavut using multispectral satellite imagery
    17. 17. Research Objectives1. Model spatial variability in plant composition and percent cover for the Sabine Peninsula, Melville Island, Nunavut using multispectral satellite imagery2. Develop and test new broad-band spectral vegetation indices well-suited for characterizing biophysical properties of Arctic tundra vegetation
    18. 18. Source: Jensen, 2007
    19. 19. The 8 Spectral Bands of WorldView-2!,./0"&123%"4%#$&%:.4#%+,<<&.+"(%$"7$2.&4,)#",*%4(#&"#&%#,%;.,E"/&%@%4;&+#.(%4&*4,.4%"*%#$&%E"4"9&%#,% *&(.2"*-.(.&/% .(*7&A% T(+$% 4&*4,.% "4% *(..,1?% -,+)4&/% ,*% (% ;(.#"+)(.% .(*7&% ,-% #$&% &&+#.,<(7*&#"+%4;&+#.)<%#$(#%"4%4&*4"#"E&%#,%(%;(.#"+)(.%-&(#).&%,*%#$&%7.,)*/5%,.%(%;.,;&.#?%,-%#$&%(#<,4;$&.&A%J,7&#$&.%#$&?%(.&%/&4"7*&/%#,%"<;.,E&%#$&%4&7<&*#(#",*%(*/%+(44":+(#",*%,-%(*/%(*/%(H)(#"+%-&(#).&4%9&?,*/%(*?%,#$&.%4;(+&29(4&/%.&<,#&%4&*4"*7%;(#-,.<ASource: DigitalGlobe, 2009 QuickBird Panchromatic Multispectral WorldView-1 Panchromatic WorldView-2 Panchromatic Multispectral 400 500 600 700 800 900 1000 1100 Wavelength (nm)
    20. 20. The 8 Spectral Bands of WorldView-2!,./0"&123%"4%#$&%:.4#%+,<<&.+"(%$"7$2.&4,)#",*%4(#&"#&%#,%;.,E"/&%@%4;&+#.(%4&*4,.4%"*%#$&%E"4"9&%#,% *&(.2"*-.(.&/% .(*7&A% T(+$% 4&*4,.% "4% *(..,1?% -,+)4&/% ,*% (% ;(.#"+)(.% .(*7&% ,-% #$&% &&+#.,<(7*&#"+%4;&+#.)<%#$(#%"4%4&*4"#"E&%#,%(%;(.#"+)(.%-&(#).&%,*%#$&%7.,)*/5%,.%(%;.,;&.#?%,-%#$&%(#<,4;$&.&A%J,7&#$&.%#$&?%(.&%/&4"7*&/%#,%"<;.,E&%#$&%4&7<&*#(#",*%(*/%+(44":+(#",*%,-%(*/%(*/%(H)(#"+%-&(#).&4%9&?,*/%(*?%,#$&.%4;(+&29(4&/%.&<,#&%4&*4"*7%;(#-,.<ASource: DigitalGlobe, 2009 QuickBird Panchromatic Multispectral WorldView-1 Panchromatic WorldView-2 Panchromatic Multispectral 400 500 600 700 800 900 1000 1100 Wavelength (nm) Yellow (585 - 625 nm)
    21. 21. The 8 Spectral Bands of WorldView-2!,./0"&123%"4%#$&%:.4#%+,<<&.+"(%$"7$2.&4,)#",*%4(#&"#&%#,%;.,E"/&%@%4;&+#.(%4&*4,.4%"*%#$&%E"4"9&%#,% *&(.2"*-.(.&/% .(*7&A% T(+$% 4&*4,.% "4% *(..,1?% -,+)4&/% ,*% (% ;(.#"+)(.% .(*7&% ,-% #$&% &&+#.,<(7*&#"+%4;&+#.)<%#$(#%"4%4&*4"#"E&%#,%(%;(.#"+)(.%-&(#).&%,*%#$&%7.,)*/5%,.%(%;.,;&.#?%,-%#$&%(#<,4;$&.&A%J,7&#$&.%#$&?%(.&%/&4"7*&/%#,%"<;.,E&%#$&%4&7<&*#(#",*%(*/%+(44":+(#",*%,-%(*/%(*/%(H)(#"+%-&(#).&4%9&?,*/%(*?%,#$&.%4;(+&29(4&/%.&<,#&%4&*4"*7%;(#-,.<ASource: DigitalGlobe, 2009 QuickBird Panchromatic Multispectral WorldView-1 Panchromatic WorldView-2 Panchromatic Multispectral 400 500 600 700 800 900 1000 1100 Wavelength (nm) Red-Edge Yellow (705 - 745 nm) (585 - 625 nm)
    22. 22. The 8 Spectral Bands of WorldView-2!,./0"&123%"4%#$&%:.4#%+,<<&.+"(%$"7$2.&4,)#",*%4(#&"#&%#,%;.,E"/&%@%4;&+#.(%4&*4,.4%"*%#$&%E"4"9&%#,% *&(.2"*-.(.&/% .(*7&A% T(+$% 4&*4,.% "4% *(..,1?% -,+)4&/% ,*% (% ;(.#"+)(.% .(*7&% ,-% #$&% &&+#.,<(7*&#"+%4;&+#.)<%#$(#%"4%4&*4"#"E&%#,%(%;(.#"+)(.%-&(#).&%,*%#$&%7.,)*/5%,.%(%;.,;&.#?%,-%#$&%(#<,4;$&.&A%J,7&#$&.%#$&?%(.&%/&4"7*&/%#,%"<;.,E&%#$&%4&7<&*#(#",*%(*/%+(44":+(#",*%,-%(*/%(*/%(H)(#"+%-&(#).&4%9&?,*/%(*?%,#$&.%4;(+&29(4&/%.&<,#&%4&*4"*7%;(#-,.<ASource: DigitalGlobe, 2009 QuickBird Panchromatic Multispectral WorldView-1 Panchromatic WorldView-2 Panchromatic Multispectral 400 500 600 700 800 900 1000 1100 Wavelength (nm) Red-Edge Yellow (705 - 745 nm) (585 - 625 nm) Near-IR 2 (860 - 1040 nm)
    23. 23. ProposedFieldMethods
    24. 24. ProposedFieldMethods(Rain and other eventualitiesnotwithstanding.)
    25. 25. 1. Where to sample?
    26. 26. Source (left): Geological Survey of Canada, 1990.Source (right): WorldView-2 image, July 2009. © DigitalGlobe
    27. 27. 2. How to sample?
    28. 28. ?veg. types
    29. 29. ? ×veg. types
    30. 30. ? 10×veg. types plots 1.84
    31. 31. ? 10×veg. types plots 1.84 randomly positioned along transect
    32. 32. ? 10×veg. types plots 1.84 × randomly positioned along transect
    33. 33. ? 10×veg. types plots 1.84 × 4 spectra randomly positioned along transect
    34. 34. ? 10×veg. types plots 1.84 × 4 spectra randomly randomly positioned along positioned transect within quadrat
    35. 35. ProposedAnalytical
    36. 36. Proposed AnalyticalSource: chsh/ii (Flickr), 2006
    37. 37. 1. Plot scale
    38. 38. 8 • G.J. LAIDLER et al.Source: Laidler, Treitz, and Atkinson, 2008 120 100 Percent Cover 80 60 % Cover La NDVI 40 R2 = 0.78 % Cover Ik NDVI R2 = 0.72 20 % Cover Su NDVI R2 = 0.74 0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 NDVI FIG. 6. Linear relations between percent cover and NDVI values for surface (Su), IKONOS (Ik), and Landsat (La) sensors. DISCUSSION Moisture and Percent Cover Field Estimates In the majority of Arctic locations, the environmental
    39. 39. 8 • G.J. LAIDLER et al.Source: Laidler, Treitz, and Atkinson, 2008 120 100 Percent Cover 80 60 % Cover La NDVI 40 R2 = 0.78 % Cover Ik NDVI R2 = 0.72 20 % Cover Su NDVI R2 = 0.74 0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 NDVI FIG. 6. Linear relations between percent cover and NDVI values for surface (Su), IKONOS (Ik), and Landsat (La) sensors. DISCUSSION Moisture and Percent Cover Field Estimates In the majority of Arctic locations, the environmental
    40. 40. 2. Landscape scale
    41. 41. 8 • G.J. LAIDLER et al. 120 100 80Percent Cover 60 % Cover La NDVI 40 R2 = 0.78 % Cover Ik NDVI R2 = 0.72 20 % Cover Su NDVI R2 = 0.74 0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 NDVIFIG. 6. Linear relations between percent cover and NDVI values for surface(Su), IKONOS (Ik), and Landsat (La) sensors. DISCUSSIONMoisture and Percent Cover Field Estimates In the majority of Arctic locations, the environmentalfactor most closely correlated with vegetation type is soilmoisture (Oberbauer and Dawson, 1992). In areas of highelevation, water is a limiting factor and an important determi-nant of vegetation structure, productivity, and composition;in lower areas, these aspects may not be controlled directly bysoil moisture, but rather by factors correlated with or affected FIG. 7. Landsat (top) and IKONOS (bottom) NDVI images for a sub-area (approximately 6.8 km2) within the study area (around study plots P1–P3). Darkby soil moisture, such as nutrient availability, thaw depth, soil areas represent regions of low NDVI (-1), while bright areas indicate highaeration, redox potential, and pH (Oberbauer and Dawson, NDVI (+1), typically lush green vegetation.1992). Micro-scale moisture gradients (across a few metres),such as those in periglacial features (troughs to high-centre al., 1994; Walker et al., 1994; Murray, 1997; Henry, 1998;polygons, frost boils, or stone stripes) or from wet meadows Young et al., 1999). The spatial heterogeneity of the studyto beach ridges, have great influence on the pattern and area, regarded as the irregularity of the physical environ-
    42. 42. 8 • G.J. LAIDLER et al. 120 100 80Percent Cover 60 % Cover La NDVI 40 R2 = 0.78 % Cover Ik NDVI R2 = 0.72 20 % Cover Su NDVI R2 = 0.74 0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 NDVIFIG. 6. Linear relations between percent cover and NDVI values for surface(Su), IKONOS (Ik), and Landsat (La) sensors. DISCUSSIONMoisture and Percent Cover Field Estimates In the majority of Arctic locations, the environmentalfactor most closely correlated with vegetation type is soilmoisture (Oberbauer and Dawson, 1992). In areas of highelevation, water is a limiting factor and an important determi-nant of vegetation structure, productivity, and composition;in lower areas, these aspects may not be controlled directly bysoil moisture, but rather by factors correlated with or affected FIG. 7. Landsat (top) and IKONOS (bottom) NDVI images for a sub-area (approximately 6.8 km2) within the study area (around study plots P1–P3). Darkby soil moisture, such as nutrient availability, thaw depth, soil areas represent regions of low NDVI (-1), while bright areas indicate highaeration, redox potential, and pH (Oberbauer and Dawson, NDVI (+1), typically lush green vegetation.1992). Micro-scale moisture gradients (across a few metres),such as those in periglacial features (troughs to high-centre al., 1994; Walker et al., 1994; Murray, 1997; Henry, 1998;polygons, frost boils, or stone stripes) or from wet meadows Young et al., 1999). The spatial heterogeneity of the studyto beach ridges, have great influence on the pattern and area, regarded as the irregularity of the physical environ-
    43. 43. 10 • G.J. LAIDLER et al.8 • G.J. LAIDLER et al. Source: Laidler, Treitz, and Atkinson, 120 100 80Percent Cover 60 % Cover La NDVI 40 R2 = 0.78 % Cover Ik NDVI R2 = 0.72 20 % Cover Su NDVI R2 = 0.74 0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 NDVIFIG. 6. Linear relations between percent cover and NDVI values for surface(Su), IKONOS (Ik), and Landsat (La) sensors. DISCUSSION FIG. 8. Image of percent cover from IKONOS NDVI, calculated from the following regression equation: Y = 275.5 (IK_NDVI) + 16.07 (R2 = 0.716, p < 0.01). linear, significant, and consistent across scales (i.e., R2 = the initial conditions for patch-scale models by inventory-Moisture and Percent Cover Field Estimates 0.72 – 0.78; p < 0.01) (Fig. 6), corroborating trends re- ing landscape conditions and their relative proportions; ii) ported in other Arctic environments. stratify landscapes into relatively homogeneous response An image of percent cover derived from the IKONOS units for spatially distributed modeling of material and In the majority of Arctic locations, the environmental NDVI data is presented in Figure 8, which portrays the energy transport; iii) extrapolate model simulations byfactor most closely correlated with vegetation type is soil relationship between percent cover variations (Fig. 3), mapping areas that are potentially sensitive to particular topographic trends (Table 2), and associated moisture disturbances; and iv) assess landscape- and regional-scalemoisture (Oberbauer and Dawson, 1992). In areas of high regimes (Fig. 2). Percent cover increases along declining model simulations by comparative spatial pattern analy-elevation, water is a limiting factor and an important determi- elevations and slopes as a reflection of increased vegeta- ses. Here, coefficients of determination for NDVI and tion canopy density in areas of high moisture (i.e., water- percent cover were very similar for the IKONOS andnant of vegetation structure, productivity, and composition; tracks, drainage channels, and areas with moderate to Landsat data. This similarity is a function of averaging thein lower areas, these aspects may not be controlled directly by minimal exposure). Modeling percent cover over the en- IKONOS NDVI data to the plot level for correlationsoil moisture, but rather by factors correlated with or affected FIG. 7. Landsat (top) and IKONOS (bottom)tirevegetationprovidesaan interesting perspective on over- study site NDVI images for sub-area all distribution and cover characteristics that analysis. However, applying the model to the high-resolu- tion data provides a more precise definition of the variabil- (approximately 6.8 km2) within the study area (around study plots P1–P3). Darkby soil moisture, such as nutrient availability, thaw depth, soil would otherwise be difficult to visualize. Although these areas represent regions of low NDVI (-1), while bright areas indicate with caution (i.e., IKONOS ity in vegetation percent cover across the landscape. values must be interpreted high IKONOS data therefore show tremendous potential foraeration, redox potential, and pH (Oberbauer and Dawson, NDVI (+1), typically lush green vegetation. NDVI explains 72% of the vegetation cover variance for tundra vegetation mapping at local scales: they are able to1992). Micro-scale moisture gradients (across a few metres), the study plots), they provide important preliminary re- delineate percent cover trends and microsite variability sults. Stow et al. (1993) suggest that data with high spatial throughout the study area (Fig. 8). At the same time,such as those in periglacial features (troughs to high-centre al., 1994; Walker et al., 1994; Murray, 1997; Henry,would strengthen NDVI correla- resolution (i.e., < 10 m) 1998; similarly accurate estimates of percent cover can be de-polygons, frost boils, or stone stripes) or from wet meadows Young et al., 1999). The spatial heterogeneity ofvariables, making it easier to i) identify tions to biophysical the study rived from Landsat data at intermediate or regional scales.to beach ridges, have great influence on the pattern and area, regarded as the irregularity of the physical environ-
    44. 44. 10 • G.J. LAIDLER et al.8 • G.J. LAIDLER et al. Source: Laidler, Treitz, and Atkinson, 120 100 80Percent Cover 60 % Cover La NDVI 40 R2 = 0.78 % Cover Ik NDVI R2 = 0.72 20 % Cover Su NDVI R2 = 0.74 0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 NDVIFIG. 6. Linear relations between percent cover and NDVI values for surface(Su), IKONOS (Ik), and Landsat (La) sensors. DISCUSSION FIG. 8. Image of percent cover from IKONOS NDVI, calculated from the following regression equation: Y = 275.5 (IK_NDVI) + 16.07 (R2 = 0.716, p < 0.01). linear, significant, and consistent across scales (i.e., R2 = the initial conditions for patch-scale models by inventory-Moisture and Percent Cover Field Estimates 0.72 – 0.78; p < 0.01) (Fig. 6), corroborating trends re- ing landscape conditions and their relative proportions; ii) ported in other Arctic environments. stratify landscapes into relatively homogeneous response Many possible transfer An image of percent cover derived from the IKONOS units for spatially distributed modeling of material and In the majority of Arctic locations, the environmental NDVI data is presented in Figure 8, which portrays the energy transport; iii) extrapolate model simulations byfactor most closely correlated with vegetation type is soil relationship between percent cover variations (Fig. 3), mapping areas that are potentially sensitive to particular topographic trends (Table 2), and associated moisture disturbances; and iv) assess landscape- and regional-scalemoisture (Oberbauer and Dawson, 1992). In areas of high regimes (Fig. 2). Percent cover increases along declining model simulations by comparative spatial pattern analy-elevation, water is a limiting factor and an important determi- functions elevations and slopes as a reflection of increased vegeta- ses. Here, coefficients of determination for NDVI and tion canopy density in areas of high moisture (i.e., water- percent cover were very similar for the IKONOS andnant of vegetation structure, productivity, and composition; tracks, drainage channels, and areas with moderate to Landsat data. This similarity is a function of averaging thein lower areas, these aspects may not be controlled directly by minimal exposure). Modeling percent cover over the en- IKONOS NDVI data to the plot level for correlationsoil moisture, but rather by factors correlated with or affected FIG. 7. Landsat (top) and IKONOS (bottom)tirevegetationprovidesaan interesting perspective on over- study site NDVI images for sub-area all distribution and cover characteristics that analysis. However, applying the model to the high-resolu- tion data provides a more precise definition of the variabil- (approximately 6.8 km2) within the study area (around study plots P1–P3). Darkby soil moisture, such as nutrient availability, thaw depth, soil would otherwise be difficult to visualize. Although these areas represent regions of low NDVI (-1), while bright areas indicate with caution (i.e., IKONOS ity in vegetation percent cover across the landscape. values must be interpreted high IKONOS data therefore show tremendous potential foraeration, redox potential, and pH (Oberbauer and Dawson, NDVI (+1), typically lush green vegetation. NDVI explains 72% of the vegetation cover variance for tundra vegetation mapping at local scales: they are able to1992). Micro-scale moisture gradients (across a few metres), the study plots), they provide important preliminary re- delineate percent cover trends and microsite variability sults. Stow et al. (1993) suggest that data with high spatial throughout the study area (Fig. 8). At the same time,such as those in periglacial features (troughs to high-centre al., 1994; Walker et al., 1994; Murray, 1997; Henry,would strengthen NDVI correla- resolution (i.e., < 10 m) 1998; similarly accurate estimates of percent cover can be de-polygons, frost boils, or stone stripes) or from wet meadows Young et al., 1999). The spatial heterogeneity ofvariables, making it easier to i) identify tions to biophysical the study rived from Landsat data at intermediate or regional scales.to beach ridges, have great influence on the pattern and area, regarded as the irregularity of the physical environ-
    45. 45. Expected Results
    46. 46. ‣ Final products: maps of veg. % cover and fxn group composition
    47. 47. ‣ Final products: maps of veg. % cover and fxn group composition‣ Indices with yellow and red edge bands most effective at identifying moss- dominated veg. types
    48. 48. 1524 M . W I L L I A M S et al.‣ Final products: maps of veg. % cover and fxn group composition‣ Indices with yellow and red edge bands most effective at identifying moss- dominated veg. types‣ Non-linearity may pose difficulties in scaling Source: Williams et al., Fig. 3 Relationships between estimated LAI (using both Skye NDVI and LI-COR LAI-2000 observations at 0.2 m resolution, averaged for upscaling) vs. Skye NDVI at different spatial scales. Exponential model equations, R2, and root-mean-square error are Satellite data. The comparison of ground-based NDVI different to Skye NDVI measurements (Fig. 6 with Landsat NDVI revealed a highly significant linear satellite data showed a peak in frequency to relationship (Fig. 8, r2 5 0.20, Po0.0001). The intercept the low end of the measurement range, whi
    49. 49. 1524 M . W I L L I A M S et al.‣ Final products: maps of veg. % cover and fxn group composition‣ Indices with yellow and red edge bands most effective at identifying moss- dominated veg. types‣ Non-linearity may pose difficulties in scaling Source: Williams et al., Fig. 3 Relationships between estimated LAI (using both Skye NDVI and LI-COR LAI-2000 observations at 0.2 m resolution, averaged for upscaling) vs. Skye NDVI at different spatial scales. Exponential model equations, R2, and root-mean-square error are Satellite data. The comparison of ground-based NDVI different to Skye NDVI measurements (Fig. 6 with Landsat NDVI revealed a highly significant linear satellite data showed a peak in frequency to relationship (Fig. 8, r2 5 0.20, Po0.0001). The intercept the low end of the measurement range, whi
    50. 50. Questions?

    ×