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Canada Centre for Remote Sensing
Field measurements and remote sensing-derived maps of vegetation around two arctic communities in Nunavut
F. Zhou, W. Chen, B. A. Marriner
Canada’s Natural Resources – Now and for the Future 2
Introduction
Objectives Quantitatively assess spatial distribution of
vegetation around two northern communities using remote sensing technology
Provide vegetation information for Community planning/development Impact assessment of climate warming on
Ecosystems Environment Wildlife habitat
Canada’s Natural Resources – Now and for the Future 3
Community Locations of the Study
Canada’s Natural Resources – Now and for the Future 4
Field Measurements
Vegetation parameters Leaf Area Index (LAI) Cover Percent Aboveground Biomass (all species)
Total 73 plots at 7 sites measured 34 plots of 3 sites at Iqaluit 39 plots of 4 sites at Clyde River Each plot has a size of 0.5m by 0.5m, separated by 5 meters
along a 100 meter transect Include sparse, medium, and dense vegetation coverage Remote sensing images used for site selection
Geographic Coordinates
Canada’s Natural Resources – Now and for the Future 5
Harvest
Canada’s Natural Resources – Now and for the Future 6
Vegetation Species
Arctic willow Labrador tea
Arctic white heather
Mountain Avens
Lingonberry
Blueberry
Net-veined willow
lichen
Saxifrage Sedge Arctic oxytrope
Blue grass
moss
Pepper grass Forbs
Mountain sorrel
Pepper grass
Crowberry
Canada’s Natural Resources – Now and for the Future 7
Sorting
Separate species
Separate live and dead
Separate leave and stems
Canada’s Natural Resources – Now and for the Future 8
Weight (wet and dry)
Canada’s Natural Resources – Now and for the Future 9
Measurement Results
Veg.
Param.
Site
(No. of Plots)
Iqaluit Clyde River
1 (20) 2 (7) 3 (7) 1 (8) 2 (5) 3 (6) 4 (20)
Biomass Mean 8.0 53.6 78.2 34.9 9.0 52.9 15.8
SE 3.5 8.0 9.3 3.1 2.2 5.7 4.6
RSE 44% 15% 12% 9% 24% 11% 29%
LAI Mean 0.049 0.274 0.479 0.168 0.093 0.375 0.063
SE 0.022 0.039 0.033 0.013 0.020 0.028 0.014
RSE 44% 14% 7% 8% 21% 8% 22%
Cover % Mean 5.1 31.3 53.9 19.7 11.4 33.3 7.9
SE 2.4 7.5 6.7 4.5 3.1 7.3 2.1
RSE 46% 24% 12% 23% 27% 22% 26%
Canada’s Natural Resources – Now and for the Future 10
Images and Processes
Landsat 7 ETM+ Multiple bands covering from blue to near Infrared Resolution: 30 by 30 meters Geometrical correction Atmospheric correction (6s)
true reflectance from vegetation (+background) for result comparison of the two communities
Plot coordinates superimposed on the images Reflectance at the measurement sites extracted
from the images for regression analysis
Canada’s Natural Resources – Now and for the Future 11
Regression between RS Images (Reflectance) and Vegetation Parameters
Three groups of linear regression model are experimented1. Single band (3, 4 and 5)
2. Band combination (3 & 4; 4 & 5; and 3, 4 & 5)
3. Vegetation Index Normalized Difference Vegetation Index (NDVI)
(b4-b3)/(b4+b3) Soil Adjusted Vegetation Index (SAVI)
(b4-b3)*(1+L)/(b4+b3+L) Ratio Vegetation Index -- (RVI)
b4/b3
Canada’s Natural Resources – Now and for the Future 12
Regression Statistics
Single band vs. Vegetation parameter
Linear regression
Adjusted correlation coefficient R2 for sample size bias
Vegetation parameter Cover % Biomass LAI
band 3 0.0106 -0.0705 -0.1003
band 4 0.9396 0.9099 0.9528
band 5 0.1574 0.2123 0.0817
Canada’s Natural Resources – Now and for the Future 13
Regression Statistics (cont.)
Band combination vs. Vegetation parameter
Linear regression
Adjusted correlation coefficient R2 for sample size bias
Vegetation parameter Cover % Biomass LAI
bands 3 and 4 0.9527 0.8907 0.9409
bands 4 and 5 0.9285 0.9046 0.9417
bands 3, 4 and 5 0.9498 0.8822 0.9224
Canada’s Natural Resources – Now and for the Future 14
Regression Statistics (cont.)
Vegetation Indices vs. Vegetation parameter
Linear regression
Adjusted correlation coefficient R2 for sample size bias
Vegetation parameter Cover % Biomass LAI
Normalized Difference Vegetation Index (NDVI)
0.8785 0.8080 0.8197
Ratio Vegetation Index
(RAI) 0.9418 0.8475 0.8545
Soil Adjusted Vegetation Index (SAVI) 0.9524 0.8977 0.9239
Canada’s Natural Resources – Now and for the Future 15
Regression Statistics (cont.)
Band combination P-value of the coefficient for Bands 3 and 5, and the
intercept is very big. Band 4
P-value for variable coefficient and intercept is small intercept (<0.001).
SAVI (Soil Adjusted Vegetation Index) has the similar magnitude of P-values to those of
Band 4. has an advantage that background soil contribution
to image reflectance is reduced.
Canada’s Natural Resources – Now and for the Future 16
Regression Equations
Leaf Area Index Aboveground Biomass Cover %
Soil Adjusted Vegetation Index ~ Vegetation Parameters
Canada’s Natural Resources – Now and for the Future 17
Vegetation Cover % Map
Iqaluit Clyde River
Canada’s Natural Resources – Now and for the Future 18
Aboveground Biomass Map
Iqaluit Clyde River
Canada’s Natural Resources – Now and for the Future 19
Vegetation LAI Map
Iqaluit Clyde River
Canada’s Natural Resources – Now and for the Future 20
Vegetation Cover Statistics
Clyde River ~50% of lands -- polar desert ~30% -- polar semi-desert Ecosystem with vegetation
percent cover > 50% only
accounts for ~1.5% of the lands
Iqaluit ~30% of lands -- polar deserts ~30% -- polar semi-deserts Ecosystem with vegetation
percent cover > 50% accounts
for about 5% of the landmass
Canada’s Natural Resources – Now and for the Future 21
Discussion
Relative sample errors of measurements reduce with increases in plot number and vegetation coverage.
For the two communities, SAVI has a good correlation coefficient with the measured vegetation parameters.
Vegetation in Arctic varies locally and regionally. Remote sensing is an efficient tool to investigate the spatial distribution and temporal variation of the vegetation in Arctic region. It usually could produce more accurate results.
The vegetation maps around the two communities are derived from the field measurements at 7 sites and RS images. More field measurements may be required to improve the accuracy of the results.
Canada’s Natural Resources – Now and for the Future 22
The team in the field … Thank you