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 Processing of Multitemporal Landsat TM Imagery to Optimize Extraction of Forest Cover Change Features, IEEE Transactions on Geoscience and Remote Sensing, 32(4):

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 minute quadrangles

Uncorrected Images July 14, 2004August 10, 2008

After Dark Correction July 14, 2004August 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

In AOI but Outside Burns

Entire Area of Interest

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

In AOI but Outside Burns

Entire Area of Interest

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