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Remote Sens. 2018, 10(6), 807; https://doi.org/10.3390/rs10060807

Random Forest Variable Importance Spectral Indices Scheme for Burnt Forest Recovery Monitoring—Multilevel RF-VIMP

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China
2
University of Chinese Academy of Science, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Received: 19 March 2018 / Revised: 26 April 2018 / Accepted: 19 May 2018 / Published: 23 May 2018
(This article belongs to the Special Issue Optical Remote Sensing of Boreal Forests)
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Abstract

Burnt forest recovery is normally monitored with a time-series analysis of satellite data because of its proficiency for large observation areas. Traditional methods, such as linear correlation plotting, have been proven to be effective, as forest recovery naturally increases with time. However, these methods are complicated and time consuming when increasing the number of observed parameters. In this work, we present a random forest variable importance (RF-VIMP) scheme called multilevel RF-VIMP to compare and assess the relationship between 36 spectral indices (parameters) of burnt boreal forest recovery in the Great Xing’an Mountain, China. Six Landsat images were acquired in the same month 0, 1, 4, 14, 16, and 20 years after a fire, and 39,380 fixed-location samples were then extracted to calculate the effectiveness of the 36 parameters. Consequently, the proposed method was applied to find correlations between the forest recovery indices. The experiment showed that the proposed method is suitable for explaining the efficacy of those spectral indices in terms of discrimination and trend analysis, and for showing the satellite data and forest succession dynamics when applied in a time series. The results suggest that the tasseled cap transformation wetness, brightness, and the shortwave infrared bands (both 1 and 2) perform better than other indices for both classification and monitoring. View Full-Text
Keywords: random forest variable importance; forest fire; forest succession; spectral index; boreal forest random forest variable importance; forest fire; forest succession; spectral index; boreal forest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Boonprong, S.; Cao, C.; Chen, W.; Bao, S. Random Forest Variable Importance Spectral Indices Scheme for Burnt Forest Recovery Monitoring—Multilevel RF-VIMP. Remote Sens. 2018, 10, 807.

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