|Pu, Ruiliang - UNIV. OF SOUTH FLORIDA|
|Gong, Peng - UNIV. CAL. BERKELEY|
|Tians, Yong - UNIV. MASS|
|Miao, Xin - MISSOURI STATE UNIV.|
Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 17, 2007
Publication Date: July 1, 2008
Citation: Pu, R., Gong, P., Tians, Y., Miao, X., Carruthers, R.I., Anderson, G.L. 2008. Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada, USA.. International Journal of Remote Sensing.29(14):3987-4011. Interpretive Summary: A common vegetation index, NDVI (Normalized Difference Vegetation Index), was used to assess vegetation change on an invasive species (saltcedar) that was being assessed in conjunction with a new USDA biological control project. New assessment techniques were used to compare both visible and near infra-red reflectance bands of hyperspecteral remote sensed data collected from medium altitude aircraft. These data allowed accurate foliage change detection to be conducted over wide areas following the defoliation of saltcedar by a newly released biological control agent (Diorhabda elongata) in Nevada. This beneficial insect continues to spread and impact saltcedar over wide areas where similar airborne remote sensed data have documented control over thousands of acres in 10 western states.
Technical Abstract: A change detection experiment for an invasive species, saltcedar, near Lovelock, Nevada, was conducted with multi-date Compact Airborne Spectrographic Imager (CASI) hyperspectral datasets. Classification and NDVI differencing change detection methods were tested, In the classification strategy, a principal component analysis was performed on single-date CASI imagery separately in the visible bands and NIR bands were used to classify six to either cover types with a maximum likelihood classifier. A complete matrix of change information and change/ no-change maps were produced by overlaying two single-date classification maps. In the NDVI differencing strategy, a linear regression model was developed between two Normalized Difference Vegetation Index (NDVI) images to normalize the index differences caused by factors not related to land cover changes. The actual time 2 NDVI image was subtracted by the predicted time 2 NDVI image to obtain the differencing image. The NDVI differencing image was further processed with a new threshold method into change/ no-change of saltcedar. By testing the single-date classification results and validating the change/ no-change results, both change detection results indicated that CASI hyperspectral data have the potential to map and monitor the change of saltcedar. However, the accuracy assessment n and change/ no-change validation results (overall accuracy 91.56% and kappa value 0.618 for the classification method against corresponding values of 93.02% and 0.684 for the NDVA differencing method) indicate that the NDVI differencing method outperformed the classification method in this particular study. In addition, use of the new method of determining thresholds for differentiating change pixels from no-change pixels from the NDVI differencing image improved the change detection accuracy compared to a traditional method (kappa value increased from 0.813 to 0.888 from a test sample). Therefore, according to the criteria of higher accuracy of change/ no-change maps and fewer spectral bands, the NDVI differencing method is recommend for use in a suitable spectral normalization between multi-temporal images can be carried out before performing image differencing.