Evaluation of satellite change detection methods for monitoring forest harvest and silvicultural activity in Maine industrial forests

Humala P. Pangaribuan; Steven A. Sader

doi:10.1117/12.285550

19 September 1997 Evaluation of satellite change detection methods for monitoring forest harvest and silvicultural activity in Maine industrial forests

Humala P. Pangaribuan, Steven A. Sader

Author Affiliations +

Proceedings Volume 3119, Multispectral Imaging for Terrestrial Applications II; (1997) https://doi.org/10.1117/12.285550
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

Change detection is a major application of satellite remote sensing. The idea is to analyze change in spectral patterns over a particular geographic area at different points of time. The information might be gathered by different satellite platforms (multi-sensor), in various wavebands (multi- spectral) and on several acquisition dates (multi-temporal). For forestry field applications, change detection might provide useful information for forest resources management, inventory, evaluation, planning, and monitoring. This study incorporated a multi-temporal approach for detecting forest change due to clearcut, partial cut, and release operation treatments in a Maine study area. Most forest change detection studies include only two dates of imagery. However, in this investigation, three date satellite images from 1983, 1988 and 1991 were examined simultaneously in a single step analysis approach. Two change detection methods, the Normalized Difference Vegetation Index (NDVI) and the Principal Components Analysis (PCA) were evaluated and a new method, Principal Factor Analysis (PFA) was introduced. A maximum likelihood classification algorithm was used to categorize change/no change events and the results were compared to a forest stand exam and history database. The Khat statistic was chosen as the criteria to evaluate the accuracy of each classification method while pairwise significance tests were constructed to compare results between methods. The Standardized variant of Principal Factor Analysis (SPFA) produced the best results followed by Principal Components Analysis and Normalized Difference Vegetation index.

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Humala P. Pangaribuan and Steven A. Sader "Evaluation of satellite change detection methods for monitoring forest harvest and silvicultural activity in Maine industrial forests", Proc. SPIE 3119, Multispectral Imaging for Terrestrial Applications II, (19 September 1997); https://doi.org/10.1117/12.285550

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