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.
Highlights
Monitoring forest biomass dynamics is known as one of the important factors for environmental modeling [1], and obtaining information on forest biomass dynamics has become a necessity for forest management [2]
Nine models were established to identify the best relationship between the airborne light detection and ranging (LiDAR) and Landsat data for 2005 from 4771 points that were extracted from all variables at the same location
Where is the biomass in Mg·H−1 based on the Jenkins allometric equation, is the biomass in Mg·H−1 based on the Lambert and Ung allometric equation, and is the canopy height in meters
Summary
Monitoring forest biomass dynamics is known as one of the important factors for environmental modeling [1], and obtaining information on forest biomass dynamics has become a necessity for forest management [2]. The recent advances in ground and airborne light detection and ranging (LiDAR) remote sensing enable more accurate data acquisition for the vertical and horizontal vegetation structure and better estimation of forest biomass [7,8]. Many investigations have used LiDAR data to estimate biomass by considering the statistical relationship between field measurements and airborne LiDAR [7,9,10]. Multi-spectral optical remote sensing, such as Landsat Thematic Mapper (TM) satellite images, is a valuable data source in ecological research for estimating multi-temporal biomass dynamics and has been widely used for monitoring vegetation cover [14,15,16,17]. Pflugmacher et al [3] discussed the limitations of estimating biomass using multi-spectral sensors, namely, the misrepresentation of the actual spatial distribution of the biomass
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