A scalable workflow for ecosystem condition assessment from Earth observation data
Abstract
1. Quantifying ecosystem condition consistently across space and time is a central challenge in biodiversity monitoring, ecosystem accounting, and ecological restoration. Earth observation (EO) data provide extensive coverage, but translating remotely sensed ecosystem characteristics into ecologically meaningful measures of condition requires approaches that account for ecological context, and natural variability across space and time. 2. The Habitat Condition Assessment System (HCAS) addresses this challenge through a reference-based benchmarking framework that evaluates ecosystem characteristics relative to ecologically comparable high-integrity reference ecosystems. However, the absence of a general, reproducible implementation has limited broader application of this approach across regions and datasets. 3. Here, we present a scalable workflow that formalises the HCAS framework as an analytical system, in which the ClassicHCAS R-package implements core ecosystem condition calculations. The workflow integrates remotely sensed ecosystem condition variables, environmental covariates, and reference ecosystem data within a benchmarking framework. Ecological context is addressed through reference ecosystem modelling, while ecosystem condition is inferred by benchmarking observations against multiple ecologically comparable reference ecosystems. The modular workflow supports alternative modelling approaches, analytical configurations and calibration strategies while maintaining consistency within a common reference-based framework. 4. We demonstrate the workflow using a continental-scale application for Australia (Supporting Information), generating spatially explicit ecosystem condition estimates and multi-decadal time series from Landsat data aggregated at 90 m resolution. Results capture broad gradients of ecosystem modification, fine-scale spatial structure and temporal dynamics relative to a consistent long-term reference framework. By providing open-source implementations of the HCAS workflow, including the ClassicHCAS R package and accompanying workflow code, we provide a reproducible and scalable approach to ecosystem condition assessment from EO data that can be readily adapted to different regions, datasets and applications.
Related
- Characterising roadside ecosystems using the SEEA Ecosystem Condition Typology: towards a core set of biophysical variables
- Identifying the thresholds of site condition and stand structure for optimizing multiple ecosystem services in the Liupan Mountains, Northwest China
- Unsupervised ecosystem risk assessment in the Baltic Sea reveals bottom-oxygen depletion and fishing pressure as key threats in species-abundant hotspots
- Probabilistic environmental risk assessment of nondissolvable nanomaterials (titanium dioxide, carbon nanotubes, graphene-based materials, polystyrene nanoplastics) in the terrestrial ecosystem
- SEARAM – Seagrass Bed Rapid Assessment Method: A Spatially Explicit Approach to Assessing Seagrass Ecological Condition with Multiple Applications
BibTeX
@misc{valavi2026scalable,
title = {{A scalable workflow for ecosystem condition assessment from Earth observation data}},
author = {Roozbeh Valavi and Kristen Jennifer Williams and Eric A Lehmann and Simon Collings and Shaun Levick and Kate Gilijohann and Ning Liu and Stephanie Johnson and Samantha EM Munroe and Thomas G Van Niel and Matthew Paget and Cassandra Malley and Peter Lyon and Thomas D Harwood and Simon Ferrier},
howpublished = {EcoEvoRxiv},
year = {2026},
month = oct,
doi = {10.32942/x27104},
url = {https://ecoevorxiv.org/repository/view/13239/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A scalable workflow for ecosystem condition assessment from Earth observation data
AU - Roozbeh Valavi
AU - Kristen Jennifer Williams
AU - Eric A Lehmann
AU - Simon Collings
AU - Shaun Levick
AU - Kate Gilijohann
AU - Ning Liu
AU - Stephanie Johnson
AU - Samantha EM Munroe
AU - Thomas G Van Niel
AU - Matthew Paget
AU - Cassandra Malley
AU - Peter Lyon
AU - Thomas D Harwood
AU - Simon Ferrier
PY - 2026
DA - 2026/10/06
PB - EcoEvoRxiv
DO - 10.32942/x27104
UR - https://ecoevorxiv.org/repository/view/13239/
AB - 1. Quantifying ecosystem condition consistently across space and time is a central challenge in biodiversity monitoring, ecosystem accounting, and ecological restoration. Earth observation (EO) data provide extensive coverage, but translating remotely sensed ecosystem characteristics into ecologically meaningful measures of condition requires approaches that account for ecological context, and natural variability across space and time. 2. The Habitat Condition Assessment System (HCAS) addresses this challenge through a reference-based benchmarking framework that evaluates ecosystem characteristics relative to ecologically comparable high-integrity reference ecosystems. However, the absence of a general, reproducible implementation has limited broader application of this approach across regions and datasets. 3. Here, we present a scalable workflow that formalises the HCAS framework as an analytical system, in which the ClassicHCAS R-package implements core ecosystem condition calculations. The workflow integrates remotely sensed ecosystem condition variables, environmental covariates, and reference ecosystem data within a benchmarking framework. Ecological context is addressed through reference ecosystem modelling, while ecosystem condition is inferred by benchmarking observations against multiple ecologically comparable reference ecosystems. The modular workflow supports alternative modelling approaches, analytical configurations and calibration strategies while maintaining consistency within a common reference-based framework. 4. We demonstrate the workflow using a continental-scale application for Australia (Supporting Information), generating spatially explicit ecosystem condition estimates and multi-decadal time series from Landsat data aggregated at 90 m resolution. Results capture broad gradients of ecosystem modification, fine-scale spatial structure and temporal dynamics relative to a consistent long-term reference framework. By providing open-source implementations of the HCAS workflow, including the ClassicHCAS R package and accompanying workflow code, we provide a reproducible and scalable approach to ecosystem condition assessment from EO data that can be readily adapted to different regions, datasets and applications.
ER -