A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits

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.

Advances in Vegetation Structure Modelling Using Remote Sensing to Support the Acquisition of Sustainable Development Goals through Forest Management

“…Entropy and dissimilarity texture indices, derived from the grey-level co-occurrence matrix (GLCM), show the highest correlations with the phenotypic decline index. The aforementioned three papers focus on forest losses after disturbances, while Stoddart et al [12] analyse forest aboveground biomass change, in terms of losses and gains (i.e., regrowth) in the tropical forests of Brazil. The authors propose a conceptual model based on three ecosystem morphological traits (EMTs), namely height, cover, and complexity, to predict aboveground biomass change using multi-temporal LiDAR data.…”

“…Unmanned aerial vehicles, which can mount a variety of remote sensing sensors, provide both spectral and structural information (i.e., point clouds derived from Structure from Motion) and are also a suitable choice for predicting individual tree structural properties such as tree detection (F-score = 0.91), height, or crown area [11]. The applicability of airborne laser scanning, being one of the most widely used sensors for forest structure characterization, was reinforced by its capability to create standardizable models of AGB based on ecosystem morphological traits (EMTs) [12] and to predict conservation status [7]. Though active sensors provide a better fit for complex structural metrics (i.e., vertical structural complexity), the use of medium-resolution multispectral sensors is feasible for predicting canopy cover changes [9] and the presence of invasive alien species [8], nowadays constituting global open-source information for forest monitoring.…”

“…Furthermore, parametric models, such as linear regression, were used for forest inventory metric prediction [4,10], generalized linear models for microclimate prediction [6], and generalized additive mixed models, which combine the flexibility of generalized additive models while accounting for random effects (i.e., plot), were used for predicting yield mushrooms. The choice of different algorithms may be linked to dataset size, feasibility, or even the application, but should be parsimonious and pursue a biological meaning [12], so as to enhance sustainable forest management.…”

Continuous Cover Forestry and Remote Sensing: A Review of Knowledge Gaps, Challenges, and Potential Directions

“…There is an obvious need for more work specific to the overlap of these subjects to further encourage the adoption of CCF [38][39][40][41][42][43][44]. There are a range of remote sensing data sources which could be applied to monitoring CCF; however, they do not all describe the specific forest stand traits.…”

“…Remote sensing-informed multivariate models are already being applied to similarly complex irregular forest systems, such as selectively logged tropical forests, and thus, it may be prudent to apply similar approaches to CCF. Various approaches have been proposed that involve other nonheight morphological traits of forest ecosystems [43•]often one of either cover or vertical structural complexities-to make a biomass prediction that would be better applicable to CCF systems [44,[103][104][105][106]. One example of note is the 'ecosystem morphological trait' (EMT) framework proposed by Valbuena et al which is intended to be applicable across a range of diverse and complex ecosystems and across multiple sources of 3D data [43•].…”