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CHANGE DETECTION METHODS IN THE BOUNDARY WATERS CANOE AREA
Thomas Juntunen
Objectives
To examine some change features and change detection methods beyond what was covered in class.
The methodology was adopted from a 1994 study by Pol Coppin and Marv Bauer.
Coppin, Pol R., and Marvin E. Bauer. 1994. Processing of Multitemporal Landsat TM Imagery to Optimize Extraction of Forest Cover Change Features, IEEE Transactions on Geoscience and Remote Sensing, 32(4):918-927
Coppin and Bauer (1994) found:
Per-pixel classifiers processing spectral-radiometric data were most common
Image differencing and linear transformations generally perform better than other methods
Vegetation indices are more strongly related to changes than the response in single bands
Multidimensional methods seem best for natural environment, but provide little information about the nature of the changes
Standardized differencing minimized identical change values depicting different events
Anniversary Dates & Windows
Minimize discrepancies in reflectance from seasonal vegetation changes and sun angle differences
Mid-summer imagery worked best for disturbance monitoring in northern Minnesota
A four to six year cycle was optimal for disturbances such as thinning, cutting and dieback
Removed the Thermal Band (TM6)
Coppin and Bauer (1994) found that "other investigators have shown that, for identification of surface types, thermal identification is not readily associated with that in the reflective part of the spectrum..."
Some tools in IMAGINE require that all bands have the same spatial resolution
Atmospheric Correction
Coppin and Bauer (1994) asserted the lack of sufficiently detailed atmospheric data for remote wilderness areas usually left dark subtraction (of spectrally stable features from across the time series of images) as the most viable means of atmospheric correction
Geometric Correction & Sub-setting
EROS processing applied terrain and other correction
Selected same row and path numbers for before and after scenes
After sub-setting, extents and world files had identical values
Area of Interest based on NAIP 3.75-minute quadrangles
Uncorrected Images
July 14, 2004 August 10, 2008
After Dark Correction
July 14, 2004 August 10, 2008
Data Enhancement for Interpretability
Crippen’s NDVI:
TM4
TM4 + TM3
Data Enhancement for Interpretability
Crippen’s NDVI:
TM4
TM4 + TM3
Data Enhancement for Interpretability
Crippen’s NDVI:
TM4
TM4 + TM3
Cavity Lake Burn
10 pct 20 pct 40 pct0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
NDVI Decrease NDVI Some Decrease NDVI Some Increase NDVI Increase
In AOI but Outside Burns
10 pct 20 pct 40 pct0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
NDVI Decrease NDVI Some Decrease NDVI Some Increase NDVI Increase
Entire Area of Interest
10 pct 20 pct 40 pct0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
NDVI Decrease NDVI Some Decrease NDVI Some Increase NDVI Increase
Data Enhancement for Interpretability - 2
Tasseled Cap
Greenness
Data Enhancement for Interpretability - 2
Tasseled Cap
Greenness
Data Enhancement for Interpretability - 2
Tasseled Cap
Greenness
Cavity Lake Burn
10 pct 20 pct 40 pct0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
TC Green Decrease TC Green Some Decrease TC Green Some Increase TC Green Increase
In AOI but Outside Burns
10 pct 20 pct 40 pct0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
TC Green Decrease TC Green Some Decrease TC Green Some Increase TC Green Increase
Entire Area of Interest
10 pct 20 pct 40 pct0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
TC Green Decrease TC Green Some Decrease TC Green Some Increase TC Green Increase
Data Enhancement for Interpretability - 3
Second Principal
Component of
Greenness
Techniques described in Poppin and Bauer (1994) are reliable and objective enough for forest change detection with Landsat TM imagery, but generally only as a means of delineating areas as unchanged or requiring further study.
Conclusions
