Conference Agenda

Forest structural complexity (FSC) reflects the three-dimensional arrangement and distribution of forest elements and serves as a key ecological indicator of biodiversity and forest productivity. Decades of overharvesting have transformed many temperate rainforests into young, homogeneous stands. Given the central role of FSC in ecosystem functioning, silvicultural strategies increasingly aim to retain or enhance structural complexity and mitigate the ecological impacts of timber harvesting. Monitoring structural development across silvicultural treatments, environmental gradients, and disturbance regimes is therefore essential. However, large-scale assessments of FSC remain limited. In this study, we evaluate the scalability of canopy entropy (CE), a LiDAR-derived FSC index, using deep learning applied to multisensor radar and optical imagery. We trained a U-Net convolutional neural network using airborne LiDAR-derived CE as the reference variable and Sentinel-1 and Sentinel-2 data as wall-to-wall predictors. The model demonstrated strong overall predictive performance (R² = 0.80, MAE = 0.09, bias = 0.02, nRMSE = 12.2%). However, the horizontal complexity component of CE (CExy) exhibited substantially lower accuracy. Although aspects of horizontal complexity may be indirectly inferred from vertical structure or canopy cover, CE should be interpreted with caution. Future work should focus on improving the representation of horizontal complexity. Despite these limitations, the resulting CE map provides a foundation for evaluating silvicultural practices and identifying structurally complex forests with high conservation value.

Wildfire severity, often correlated with biomass loss, has increased since the 1980s, driving greater biomass depletion across landscapes. Canada's 2023 wildfire season burned over 15 million hectares and released 647 TgC of carbon, surpassing most nations' annual emissions. This trend underscores the need for scalable aboveground biomass (AGB) monitoring for greenhouse gas estimation. While LiDAR has improved AGB estimation, airborne systems remain costly with limited spatial coverage. Researchers have addressed this by scaling LiDAR-derived estimates to satellite imagery for broader monitoring. However, current scaling paradigms are developed predominantly for closed-canopy forests, with limited evaluation in open-canopy ecosystems like peatlands, despite their high fire severity and disproportionate carbon contributions when burned. Peatlands pose unique challenges: low and spatially heterogeneous AGB, open canopies that allow soil and water to obfuscate satellite signals, and non-linear structural-biomass relationships in sparse vegetation.

This systematic review and meta-analysis examines the accuracy of scaling LiDAR-derived AGB estimates to optical and radar satellite imagery across peatlands and structurally analogous ecosystems, including tropical savannas, floodplain forests, mangroves, and arctic shrublands. We searched Scopus and Google Scholar using a four-block query, yielding 271 peer-reviewed studies. Using a random-effects model, R² values were transformed to Fisher's Z scores, and heterogeneity was quantified using the I² statistic.

Preliminary analysis revealed no significant difference between modelling approaches and target ecosystem. Heterogeneity was minimal, indicating model type and ecosystem type exert limited influence on accuracy outcomes. Full dataset analysis is ongoing.

Habitat suitability models are central to conservation

planning, species management, and landscape-level decision

support. Continuous Landscape Inventory (CLI)

datasets provide stand-level forest attributes (species mix,

height, basal area, crown closure, age, disturbance history)

that are rarely used at scale together with satellitederived

biophysical indicators for operational habitat mapping.

This work proposes a replicable workflow that fuses

provincial CLI with multisensor satellite data (Sentinel-

2 MSI, Landsat series, and SAR-derived structure proxies)

and environmental layers (elevation, distance-to-water,

road density) to produce fine-scale habitat suitability surfaces

across New Brunswick, Canada.

Wildfires significantly impact forest ecosystems by disrupting carbon cycles, with effects varying based on fire intensity and forest bio-physical characteristics such as vegetation types, structures, topography, and climate. These factors collectively influence fire spread, biomass reduction, and post-fire vegetation regrowth, making it crucial to accurately quantify wildfire impacts on forest carbon dynamics for understanding terrestrial-atmosphere interactions and global climate implications.

This study uses wildfires in California's mountainous forests as a case study, employing two aboveground biomass (AGB) datasets—one derived from remote sensing data and the other from process-based ecological models—to assess wildfire impacts on forest carbon stocks. Remote sensing-based indices, while effective in detecting spectral changes, often fall short in quantifying biophysical alterations, particularly carbon dynamics. Conversely, process-based models adhere to ecological principles but may not fully capture fire-induced carbon changes. Our analysis reveals significant variations in post-fire disturbance and recovery patterns based on fire severity, elevation, and forest type. The remote sensing dataset showed faster initial recovery, likely due to herbaceous vegetation greening, while the ecological model dataset indicated slower, more stable recovery, reflecting delayed tree regeneration. These findings underscore the necessity of integrating multi-source datasets to accurately capture post-fire carbon dynamics.

Wildfire Early Warning Systems (EWS) are increasingly essential as climate-driven extreme fire events grow in frequency

and severity. Yet their maturity and operational robustness vary widely across countries due to differences in

resources, data infrastructure, and institutional capacity. This study conducts a systematic global assessment of wildfire

EWS across high-, middle-, and low-income nations, evaluating how multisensor Earth Observation (EO) data and predictive

intelligence are integrated into functional early warning and decision-support systems. A transparent benchmarking

framework is introduced with two core pillars: (i) multisensor geospatial monitoring—assessing temporal resolution,

spectral sensitivity, spatial detail, and GEO–LEO fusion; and (ii) hotspot intelligence and predictive modeling—evaluating

model class, forecast range, validation practices, and real-time operational performance. These pillars are complemented

by an impact-readiness layer aligned with the Sendai Framework, linking hazard detection to exposure, vulnerability, and

alert dissemination.

Results show strong stratification by income. High-income countries achieve near–real-time hotspot detection, GEO–LEO

data fusion, and validated multi-day behaviour forecasts. Middle-income nations display transitional but uneven progress,

while low-income countries rely almost exclusively on global detection platforms, highlighting institutional, not technological,

bottlenecks. Canada’s EWS landscape is evaluated, highlighting gaps in accessibility, standardization, and

timeliness of EO-derived intelligence. Opportunities for strengthening Canada’s system include adoption of emerging EO

technologies, improved fuel characterization, next-generation hybrid physics–ML/QML behaviour modeling, integrated

national decision-support platforms, and enhanced FireSmart community interfaces.

Overall, the study provides a scalable global framework for comparing national wildfire EWS maturity, identifying

investment priorities, and guiding future improvements.

This project explores how drones and deep learning can help monitor the recovery of former aggregate and mining sites. Traditional methods for assessing land restoration such as field surveys and satellite imagery are often time-consuming, expensive, and limited in detail. Using high-resolution drone imagery and a compact deep learning model, this study offers a faster and more flexible way to track how vegetation and land cover change over time.

The approach classifies ground surfaces into three simple categories: healthy vegetation, stressed vegetation, and bare soil or rock - providing clear indicators of how well a site is recovering after extraction/rehabilitation. Tested at two rehabilitated sites in southern Ontario, the model showed strong and consistent results across different months of the growing season, even using only standard colour drone imagery.

This work highlights how drone-based monitoring can make ecological restoration assessment more efficient, objective, and repeatable. Once trained, the model can quickly analyze new imagery without the need for extensive fieldwork, allowing land managers and regulators to identify problem areas and track recovery in near real time. Ultimately, this research points toward a future where rapid, data-driven drone assessments play a role in supporting sustainable land rehabilitation and environmental stewardship.

Deep, persistent drought in 2023 in the Canadian Boreal Plains was associated with wildfires that persisted underground and re-emerged the following spring, a process known as "overwintering" and sometimes called "zombie fires". We analyzed pre-fire Sentinel-2 multispectral imagery of paired 2023-2024 fires to extract any spectral anomalies, with the goal of characterizing conditions conducive to wildfire overwintering. We assessed several spectral indices, including NDVI, GNDVI, EVI, NDMI, TCW, and others relative to a 2016-2022 baseline using the npphen R package.

We found that sites of overwintering fires exhibited moisture anomalies in the spring of 2024, indicating drought conditions that were conducive to the reemergence of overwintering fires. We show how these anomalies were co-located with early season wildfire with an apparent absence of ignition events. Furthermore, we show how in 2024, 25 overwintering wildfires burned 22.8% of the total area burned, while comprising only 1.3% of the total fire count.