Abstract
Current LiDAR-based methods for detecting forest change use a host of statistically selected variables which typically lack a biological link with the characteristics of the ecosystem. Consensus of the literature indicates that many authors use LiDAR to derive ecosystem morphological traits (EMTs)—namely, vegetation height, vegetation cover, and vertical structural complexity—to identify small-scale changes in forest ecosystems. Here, we provide a conceptual, biological model for predicting forest aboveground biomass (AGB) change based on EMTs. We show that through use of a multitemporal dataset it is possible to not only identify losses caused by logging in the period between data collection but also identify regions of regrowth from prior logging using EMTs. This sensitivity to the change in forest dynamics was the criterion by which LiDAR metrics were selected as proxies for each EMT. For vegetation height, results showed that the top-of-canopy height derived from a canopy height model was more sensitive to logging than the average or high percentile of raw LiDAR height distributions. For vegetation cover metrics, lower height thresholds for fractional cover calculations were more sensitive to selective logging and the regeneration of understory. For describing the structural complexity in the vertical profile, the Gini coefficient was found to be superior to foliage height diversity for detecting the dynamics occurring over the years after logging. The subsequent conceptual model for AGB estimation obtained a level of accuracy which was comparable to a model that was statistically optimised for that same area. We argue that a widespread adoption of an EMT-based conceptual approach would improve the transferability and comparability of LiDAR models for AGB worldwide.
Highlights
Tropical forests are complex ecosystems which provide important ecosystem services, especially relating to global carbon and water cycles [1,2]
We argue that modelling forest aboveground biomass (AGB) should be based on ecosystem morphological traits (EMTs), and an investigation on LiDAR proxies identified for each of them can help identify forest
In this study we focus on identifying the LiDAR proxies for each EMT on the basis of their sensitivity to small-scale disturbances (e.g., Nunes et al, 2021) rather than on the estimated AGB change itself (e.g., Rex et al, 2020)
Summary
Tropical forests are complex ecosystems which provide important ecosystem services, especially relating to global carbon and water cycles [1,2]. Tropical forests suffer from deforestation and illegal logging which have significant environmental, ecological, and economic impacts locally and globally [3,4]. Remote sensing technologies are becoming increasingly efficient at detecting large-scale disturbances [5]; small-scale clearance activities in the Amazon rainforest account for half of Brazil’s deforestation rate [6]. 2022, 14, 933 degradation includes natural windfall and legal and illegal selective logging and is difficult to detect using most forms of remote sensing due to their effective resolutions [5]. There is a need to further develop current remote sensing technologies into methods that are more sensitive to such selective logging which would be capable of detecting and monitoring the degradation.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
