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

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 (r 2 = 0.78; root mean square error (RMSE) = 44 Mg/H, r 2 = 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. OPEN ACCESSRemote Sens. 2015, 7 2833Additionally, 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.

Changes in Global Grassland Productivity during 1982 to 2011 Attributable to Climatic Factors

“…Stepwise multiple regressions: We determined the relationship between the single response variable (dependent variable) and two or more controlled variables (independent variables) using multiple linear regression [23]. Stepwise Multiple Regressions (SMR), which combine forward selection and backward elimination methods, were used to identify climatic factors that controlled productivity changes in the OGFD ecosystems [23,24].…”

“…Stepwise multiple regressions: We determined the relationship between the single response variable (dependent variable) and two or more controlled variables (independent variables) using multiple linear regression [23]. Stepwise Multiple Regressions (SMR), which combine forward selection and backward elimination methods, were used to identify climatic factors that controlled productivity changes in the OGFD ecosystems [23,24]. Since the annual meteorological cycles in the Northern and Southern Hemisphere are different, we used stepwise multiple regressions to determine the relationship between the NDVI and bioclimatic indices in all pixels of the OGFD types and major OGFD ecosystems, aggregated over time for the various types and regions.…”

Estimating aboveground woody biomass change in Kalahari woodland: combining field, radar, and optical data sets

“…Here, we apply a trend estimation method to the Landsat seasonal NDVI composite time-series and map trends in vegetation greenness (Forkel et al 2013). Trend calculation is dependent on the time-series length, its temporal and spatial resolution, data quality and method of analysis (Sulkava et al 2007;Badreldin and Sanchez-Azofeifa 2015). The various approaches for calculating trends produce comparable results with regard to significant trends, although differences occur for weaker trends (de Jong et al 2011).…”

Estimating the “Forgone” ESVs for Small-Scale Gold Mining Using Historical Image Data

“…The defects are as a result of atmospheric instability, geographic positioning, sensor shortcomings etc. These defects must be rectified in order to increase the confidence placed in the classified images [29]. First, the radiometric correction was performed on all images using the image calibration algorithms (FLAASH and Band Math) for atmospheric correction in the Environment of Visual Image (ENVI v5.0) software.…”