ORIGINAL RESEARCH article

Front. Sustain. Food Syst.

Sec. Agroecology and Ecosystem Services

Volume 9 - 2025 | doi: 10.3389/fsufs.2025.1581252

This article is part of the Research TopicOptimizing Natural Features and BMPs in Agroecosystems Through a One-Health ApproachView all 7 articles

Detecting forest and linear woody feature change between 2019 and 1954 in South-Eastern Canadian agroecosystems for regional biodiversity assessment

Provisionally accepted
Darren  PouliotDarren Pouliot*Niloofar  AlaviNiloofar AlaviMao  MaoMao MaoJon  PasherJon PasherJason  DuffeJason Duffe
  • Wildlife Research and Landscape Science, Environment and Climate Change Canada (ECCC), Ottawa, Canada

The final, formatted version of the article will be published soon.

Effectively mapping and detecting changes in forests and linear woody features (LWFs) is crucial for assessing their impact on biodiversity and ecosystem services. This study investigates this capability using heterogeneous, high-resolution aerial imagery, in terms of spectral and spatial properties. Mitigating the influence of these factors, arising from differences in sensor specifications and acquisition conditions, is essential for robust detection and analysis of temporal change across historical image datasets. The deep learning model developed here successfully mapped forest and LWFs between 1954 and 2019 using just a single image band, enabling reliable change estimation. Assessment at the pixel scale showed forest mapping achieved an accuracy of 90%, while LWF accuracy was lower at 69%, primarily due to their narrow widths and boundary errors in both the reference and predicted results. For LWF an object-based assessment was undertaken to reduce the effect of precise delineation achieving a higher accuracy of 77%. As a final assessment, comparison of area within 200 by 200 m extents showed good agreement, with a mean absolute error of 1.3% for LWF. For forest this was 2.7%. For change detection the accuracy was greater than 81% for both forest and LWF. Change analysis indicated an 8.5% net increase in forests since 1954, along with a small net loss of less than 1% in LWFs. LWF loss was mainly attributed to forest gains. In areas without significant forest gain, LWFs slightly increased. These changes are generally seen as beneficial for biodiversity and ecosystem services in the region. However, other factors such as urban development and larger agricultural field sizes need to be considered in future studies.Copernicus Land Monitoring Service defines linear features as having a width less than 30 m and length greater than 30 m (EU, CLMS, EEA 2021).LWFs are considered a nature-based solution (Collier, 2021) and contribute various ecosystem services such as providing shelter from winds, reducing soil erosion, improving soil drainage (

Keywords: linear woody features1, Forest2, Remote Sensing3, change detection4, biodiversity5, agriculture6

Received: 21 Feb 2025; Accepted: 12 May 2025.

Copyright: © 2025 Pouliot, Alavi, Mao, Pasher and Duffe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Darren Pouliot, Wildlife Research and Landscape Science, Environment and Climate Change Canada (ECCC), Ottawa, Canada

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