Conference Agenda

Accurately representing forest canopies in atmospheric models remains a major challenge due to the complex ways in which trees interact with airflow and modulate surface--atmosphere exchanges. Aerodynamic roughness is a key control variable in modelling frameworks related to air quality, meteorology, and atmospheric transport processes. In this study, we develop a physically based and spatially resolved framework to estimate aerodynamic roughness length from remote sensing observations. Specifically, using AHN (Actueel Hoogtebestand Nederland) airborne laser scanning data over a coniferous forest in Loobos, located within the Veluwe Natura 2000 region in the central Netherlands, we derive geometric roughness parameters and compare them qualitatively against eddy-covariance (EC) tower measurements at the site.

Results show that LiDAR-based roughness captures strong directional and structural variability driven by forest stand height and canopy heterogeneity, patterns that closely align with the anisotropy observed in the EC-derived displacement height and roughness length. Seasonal differences between leaf-on and leaf-off conditions further demonstrate the importance of canopy phenology in shaping aerodynamic behaviour. The spatial patterns resolved by the AHN data underscore the capacity of high-resolution laser scanning to reveal fine-scale canopy--atmosphere interactions that are entirely missed by traditional land-use--based roughness representations.

Additional opportunities remain for integrating complementary remote sensing observations (e.g., multispectral vegetation properties) to enhance the dynamical fidelity of the roughness estimates. The proposed framework provides an observation-driven pathway for parameterizing surface roughness, offering substantial potential for improving land-use representations in wind-flow and chemical transport models such as LOTOS--EUROS.

Forest canopy height (FCH) is a critical biophysical parameter that characterizes forest structure and provides fundamental information for estimating above-ground biomass and carbon stocks. The Global Ecosystem Dynamics Investigation (GEDI) Level 2A (L2A) product offers accurate canopy height observations; however, its point-based nature constrains spatial continuity in FCH mapping. This study integrates the multimodal remote sensing datasets for continuous FCH mapping in Tanzania’s West Usambara (WUSA) forest, recognized globally for its rich biodiversity and ecological significance. Hence, remote sensing data, including Sentinel-1 polarizations (VV and VH), Sentinel-2 spectral bands and vegetation indices, and the SRTM digital elevation model (DEM), were integrated and matched with GEDI canopy height data used as reference for FCH modelling. The optimal feature set was derived by evaluating the performance of several feature selection and extraction methods, including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), Recursive Feature Elimination (RFE), Sequential Feature Selection (SFS), and the Selected K-Best approach using F-value and mutual information scoring functions. The feature set derived from RFE, comprising ten features from all data sources, demonstrated the highest accuracy and reliability in FCH modelling. Subsequently, four machine learning algorithms, including Random Forest (RF), Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Ordinary Least Squares (OLS), were evaluated for FCH modelling. Accordingly, RF achieved higher R² than GBR, SVR, and OLS, with differences of 0.9%, 8.7%, and 16.4%, respectively. Therefore, the RF model, as the most reliable model, was employed for FCH mapping across the WUSA forest.

Canopy height models (CHMs) are raster maps representing normalized tree canopy height above ground and are often used as co-products for estimating carbon storage, forest degradation, and biodiversity at regional to global scales. While airborne LiDAR delivers the most accurate canopy height (CH) measurements, its high cost and limited temporal coverage motivate the use of spaceborne (multispectral) imagery combined with machine learning.

In this study, we compare two distinct deep-learning approaches for continental-scale CHM estimation from 3 m PlanetScope imagery: (1) a CNN-based regression model (DeepLabv3+), and (2) a monocular depth-estimation model (Depth Anything V2) based on a foundation model. We train/fine-tune both models on a curated dataset of 16,973 pairs of airborne point cloud-derived CHMs and PlanetScope imagery of Australia using a stratified sampling scheme to ensure balanced representation of vegetation structural classes. We then evaluate their generalizability on independent validation sets across Australia, across different heights, and under limited-data scenarios.

Through extensive quantitative and qualitative analysis, we show that the DeepLab-based regression model outperforms Depth Anything across all evaluation metrics, partly because it can incorporate additional spectral channels. DeepLab also learns more effectively from less data. On our dataset, the conventional CNN-based regression model performs better than the fine-tuned foundation model.

To address the critical challenge of regional transferability for ALS-based above-ground biomass (AGB) models, we developed and applied a rigorous leave-one-region-out cross-validation (LORO-CV) framework. This protocol integrates a <1 SE “near-zero” bias filter to ensure models are not just accurate, but statistically free of regional bias.

With this framework, we compared two distinct modeling methods: a data-driven Best-Subset Selection (BSS) method and a Functional Regression (FR) method. The analysis was based on 163 field plots and co-located multispectral Titan ALS data from four regions in the Taiga Plains ecozone, Canada.

The BSS method identified a transferable linear model using height skewness, p95, and an intensity-weighted metric, which achieved 19.3% LORO-CV %RMSE and 2.0% mean absolute bias. Crucially, it passed our <1 SE bias screen in all regions. The FR model, relying only on height, achieved 22.4% LORO-CV %RMSE (4.1% bias) but failed the bias screen in two regions.

Our findings demonstrate that a systematic, bias-controlled data-driven method is effective for producing regionally transferable models. The results highlight the critical importance of ALS intensity metrics for this success, while also showing that the data-driven method currently surpasses the functional approach.

The role of forests in carbon sequestration and regulation is important to understand, given the alarming rate of global warming caused by greenhouse gases. Understanding the structural characteristics of trees can help assess the potential of forests for carbon storage. Light Detection and Ranging (LiDAR) has emerged as a powerful remote sensing tool that is capable of providing detailed three-dimensional information of the forest. The increasing availability of aerial LiDAR data has provided opportunities to estimate the forest biomass over a larger extent. This study utilizes the available LiDAR data from the provincial repository of geospatial data to estimate the diameter at breast height (DBH), which is a key parameter in existing biomass allometric models. LiDAR-derived tree metrics were integrated with the optical images to further differentiate the forest type to assess how it influences the aboveground biomass estimates in a heterogeneous mixed-wood forest. This research contributes to improving our understanding of LiDAR's potential for estimating DBH, an area that has not been explored much. It also demonstrates how existing global biomass allometric equations can be utilized in combination with remote sensing technology to provide a pathway to a larger extent and an efficient method of biomass estimation across diverse ecosystems.

Communication towers equipped with cameras are widely distributed across rural landscapes but remain largely unused for scientific observation. This presentation introduces an AI-driven framework that transforms such existing infrastructure into a high-frequency, real-time agricultural monitoring system, complementing traditional satellite and UAV remote sensing.

The proposed system resolves three fundamental challenges that hinder tower-based sensing: (1) precise georeferencing of highly oblique imagery through a quaternion-based spatial transformation; (2) automated delineation of cultivated parcels via a GIS-guided, iterative segmentation process integrating the Segment Anything Model (SAM); and (3) intelligent recognition of crop types, growth stages, and farming activities using a multimodal large language model that fuses time-series imagery with contextual field data.

Validated through deployments in varied agricultural regions of China, the framework demonstrates stable operation and parcel-level accuracy for continuous monitoring within 1–2 km of each tower. The results indicate a practical pathway toward scalable, cost‑efficient, and autonomous agricultural information acquisition at high spatio‑temporal resolution.