Abstract
Stand-level maps of past forest disturbances (expressed as time since disturbance, TSD) are needed to model forest ecosystem processes, but the conventional approaches based on remotely sensed satellite data can only extend as far back as the first available satellite observations. Stand-level analysis of airborne LiDAR data has been demonstrated to accurately estimate long-term TSD (~100 years), but large-scale coverage of airborne LiDAR remains costly. NASA’s spaceborne LiDAR Global Ecosystem Dynamics Investigation (GEDI) instrument, launched in December 2018, is providing billions of measurements of tropical and temperate forest canopies around the globe. GEDI is a spatial sampling instrument and, as such, does not provide wall-to-wall data. GEDI’s lasers illuminate ground footprints, which are separated by ~600 m across-track and ~60 m along-track, so new approaches are needed to generate wall-to-wall maps from the discrete measurements. In this paper, we studied the feasibility of a data fusion approach between GEDI and Landsat for wall-to-wall mapping of TSD. We tested the methodology on a ~52,500-ha area located in central Idaho (USA), where an extensive record of stand-replacing disturbances is available, starting in 1870. GEDI data were simulated over the nominal two-year planned mission lifetime from airborne LiDAR data and used for TSD estimation using a random forest (RF) classifier. Image segmentation was performed on Landsat-8 data, obtaining image-objects representing forest stands needed for the spatial extrapolation of estimated TSD from the discrete GEDI locations. We quantified the influence of (1) the forest stand map delineation, (2) the sample size of the training dataset, and (3) the number of GEDI footprints per stand on the accuracy of estimated TSD. The results show that GEDI-Landsat data fusion would allow for TSD estimation in stands covering ~95% of the study area, having the potential to reconstruct the long-term disturbance history of temperate even-aged forests with accuracy (median root mean square deviation = 22.14 years, median BIAS = 1.70 years, 60.13% of stands classified within 10 years of the reference disturbance date) comparable to the results obtained in the same study area with airborne LiDAR.
Highlights
Global and regional maps of forest stand age are required to improve carbon pools and flux estimates, and to understand the impact of forest disturbances on net carbon accumulation [1,2,3,4]
The analysis was performed by considering the six possible combinations of the two stand maps derived from image segmentation and three subsets of the Global Ecosystem Dynamics Investigation (GEDI) simulated footprints, namely (a) the ‘full grid’ defined in Section 2.2; (b) the subset of the ‘full grid’ obtained by selecting only the footprints that are fully enclosed in a single forest stand, i.e., omitting footprints on stand edges ( ‘full grid–within stand’); and (c) the subset of the ‘cloud grid’ obtained by selecting only the footprints that are fully enclosed in a single forest stand ( ‘cloud grid—within stand’)
The number of stands not intersected by any GEDI footprint is always lower than 15%, and represent less than 5% of the study area in all considered scenarios (Table 2); direct estimation of time since disturbance (TSD) and associated uncertainties with the current approach is not possible in these stands. 4.2
Summary
Global and regional maps of forest stand age are required to improve carbon pools and flux estimates, and to understand the impact of forest disturbances on net carbon accumulation [1,2,3,4]. In particular Landsat data, have so far been the best alternative to generate records of stand age (expressed as time since disturbance, TSD) through change detection and multi-temporal time series analysis (e.g., [5,6,7,8,9,10,11]). While these methods can provide spatially explicit information on disturbances at high temporal frequency (i.e., every 16 days in the case of a Landsat satellite), they can only detect disturbances that have happened since the beginning of the Earth Observation satellite data record in 1972 with Landsat 1 [12]. More recent studies have demonstrated that canopy structure attributes characterized by LiDAR have explanatory power and can be used as a proxy to estimate stand temporal attributes, such as age, forest regrowth, forest succession, and long-term TSD (~100 years) [12,13,27,28]
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