Detecting Change Areas in Mexico Between 2005 and 2010 Using 250 m MODIS Images. Colditz, R. R., Llamas, R. M., & Ressl, R. A. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8):3358–3372, August, 2014. ![Detecting Change Areas in Mexico Between 2005 and 2010 Using 250 m MODIS Images [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The goal of the North American Land Change Monitoring System (NALCMS) is to provide annually updated land cover maps for the North American continent using satellite information and automated data processing. Current activities of the project aim at the development of an automated algorithm to detect areas of change using 250 m MODIS data. This paper shows the methodology developed for Mexico and demonstrates the resulting change map between the years 2005 and 2010. A data-driven algorithm that builds upon the spectral differences of monthly image composites was developed and critical parameters were defined. Results show that only extreme values of difference images indicate change and that change has to be mapped in at least 25% of all features. The total area of change detected between 2005 and 2010 was 702,331 ha (0.36% of the country) which is in line with other change detection studies in Mexico. Accuracy assessment using higher spatial resolution images accounts for the change fraction in the reference data. The overall accuracy of the change/no change mask is approximately 80%. This is similar to decision tree-based change classification that was developed in other studies and applied to Mexico and significantly better than post-classification change detection. The main limitation is the coarse spatial resolution considering the small-patch landscape structure for large portions of the country, which results in a high omission error (50%) but only 20% commission error for change.
@article{colditz_detecting_2014,
title = {Detecting {Change} {Areas} in {Mexico} {Between} 2005 and 2010 {Using} 250 m {MODIS} {Images}},
volume = {7},
issn = {1939-1404},
url = {https://ieeexplore.ieee.org/document/6616672/},
doi = {10.1109/JSTARS.2013.2280711},
abstract = {The goal of the North American Land Change Monitoring System (NALCMS) is to provide annually updated land cover maps for the North American continent using satellite information and automated data processing. Current activities of the project aim at the development of an automated algorithm to detect areas of change using 250 m MODIS data. This paper shows the methodology developed for Mexico and demonstrates the resulting change map between the years 2005 and 2010. A data-driven algorithm that builds upon the spectral differences of monthly image composites was developed and critical parameters were defined. Results show that only extreme values of difference images indicate change and that change has to be mapped in at least 25\% of all features. The total area of change detected between 2005 and 2010 was 702,331 ha (0.36\% of the country) which is in line with other change detection studies in Mexico. Accuracy assessment using higher spatial resolution images accounts for the change fraction in the reference data. The overall accuracy of the change/no change mask is approximately 80\%. This is similar to decision tree-based change classification that was developed in other studies and applied to Mexico and significantly better than post-classification change detection. The main limitation is the coarse spatial resolution considering the small-patch landscape structure for large portions of the country, which results in a high omission error (50\%) but only 20\% commission error for change.},
number = {8},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
author = {Colditz, Rene R. and Llamas, Ricardo M. and Ressl, Rainer A.},
month = aug,
year = {2014},
keywords = {NALCMS},
pages = {3358--3372},
}
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A data-driven algorithm that builds upon the spectral differences of monthly image composites was developed and critical parameters were defined. Results show that only extreme values of difference images indicate change and that change has to be mapped in at least 25% of all features. The total area of change detected between 2005 and 2010 was 702,331 ha (0.36% of the country) which is in line with other change detection studies in Mexico. Accuracy assessment using higher spatial resolution images accounts for the change fraction in the reference data. The overall accuracy of the change/no change mask is approximately 80%. This is similar to decision tree-based change classification that was developed in other studies and applied to Mexico and significantly better than post-classification change detection. 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