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Remote Sens. 2015, 7(3), 2832-2849; doi:10.3390/rs70302832

Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada

Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, AB T6G 2E3, Canada
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Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 26 October 2014 / Revised: 12 February 2015 / Accepted: 17 February 2015 / Published: 10 March 2015
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Abstract

Assessing biomass dynamics is highly critical for monitoring ecosystem balance and its response to climate change and anthropogenic activities. In this study, we introduced a direct link between Landsat vegetation spectral indices and ground/airborne LiDAR data; this integration was established to estimate the biomass dynamics over various years using multi-temporal Landsat satellite images. Our case study is located in an area highly affected by coal mining activity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI and EVI2), chlorophyll vegetation index (CVI), and tasseled cap transformations were used as vegetation spectral indices to estimate canopy height. In turn, canopy height was used to predict a coniferous forest’s biomass using Jenkins allometric and Lambert and Ung allometric equations. The biophysical properties of 700 individual trees at eight different scan stations in the study area were obtained using high-resolution ground LiDAR. Nine models (Hi) were established to discover the best relationship between the canopy height model (CHM) from the airborne LiDAR and the vegetation spectral indices (VSIs) from Landsat images for the year 2005, and HB9 (Jenkins allometric equation) and HY9 (Lambert and Ung allometric equation) proved to be the best models (r2 = 0.78; root mean square error (RMSE) = 44 Mg/H, r2 = 0.67; RMSE = 58.01 Mg/H, respectively; p < 0.001) for estimating the canopy height and the biomass. This model accurately captured the most affected areas (deforested) and the reclaimed areas (forested) in the study area. Five years were chosen for studying the biomass change: 1988, 1990, 2001, 2005, and 2011. Additionally, four pixel-based image comparisons were analyzed (i.e., 1988–1990, 1990–2005, 2005–2009, and 2009–2011), and Mann-Kendall statistics for the subsets of years were obtained. The detected change showed that, in general, the environment in the study area was recovering and regaining its initial biomass after the dramatic decrease that occurred in 2005 as a result of intensive mining activities and disturbance. View Full-Text
Keywords: biomass; Landsat; LiDAR; spectral vegetation indices; land cover change biomass; Landsat; LiDAR; spectral vegetation indices; land cover change
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MDPI and ACS Style

Badreldin, N.; Sanchez-Azofeifa, A. Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada. Remote Sens. 2015, 7, 2832-2849.

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