Hidden forest loss: challenges in detecting wind- and insect-driven forest disturbances with global forest change landsat-based products in mixed southern boreal forests

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985939%3A_____%2F25%3A00642918" target="_blank" >RIV/67985939:_____/25:00642918 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41320/25:106287 RIV/60076658:12310/25:43910512

Výsledek na webu

<a href="https://doi.org/10.1016/j.rsase.2025.101798" target="_blank" >https://doi.org/10.1016/j.rsase.2025.101798</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rsase.2025.101798" target="_blank" >10.1016/j.rsase.2025.101798</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hidden forest loss: challenges in detecting wind- and insect-driven forest disturbances with global forest change landsat-based products in mixed southern boreal forests

Popis výsledku v původním jazyce

Wind- and insect-induced forest disturbances are becoming increasingly frequent and severe due to climate change, resulting in significant forest loss worldwide. Accurate detection of these disturbances is essential for understanding carbon storage, forest dynamics, ecosystem resilience, and for developing effective climate adaptation strategies. The Landsat-based Global Forest Change (GFC) product and the related Global Forest Watch web service are widely used for largescale forest monitoring. However, its capacity to detect disturbances caused by wind and insect outbreaks remains uncertain. In this study, we assessed the accuracy of GFC forest loss detection by comparing it with U-Net neural network forest loss masks derived from very high-resolution satellite imagery. We analyzed several study areas in natural mixed-species southern boreal forests affected by windthrows and bark beetle outbreaks, evaluating true positive (TP), false negative (FN), and false positive (FP) detection rates. Our results show that GFC substantially underestimates forest loss, with TP detection rates ranging from 1.56 % to 62.18 % and FN errors reaching 37.82 %-98.44 %. In some cases, overestimation occurred due to high FP rates up to 65.55 %. The FPs happen when a small patch of forest loss within a 30 x 30 m Landsat pixel triggers the entire pixel to be classified as forest loss, even though most of the pixel remains undisturbed. The limitation stems from the small-scale nature of windthrows and insect-induced diebacks, which cannot be reliably captured by Landsat's spatial resolution. Our findings suggest that integrating higher-resolution satellite data is crucial for accurate area estimation and improved assessments of forest loss in the face of climate-driven disturbances such as windthrows and diebacks in natural mixed-species forests. Although GFC can be unsuitable for precisely mapping forest losses, it remains a valuable, globally consistent early warning tool due to its annual updates and broad coverage. Practitioners should treat GFC detections as indicative and conduct rapid visual or automated checks with higher-resolution imagery when assessing windthrow or insect-driven mortality, especially when disturbance patches are less than 450 m2.

Název v anglickém jazyce

Hidden forest loss: challenges in detecting wind- and insect-driven forest disturbances with global forest change landsat-based products in mixed southern boreal forests

Popis výsledku anglicky

Wind- and insect-induced forest disturbances are becoming increasingly frequent and severe due to climate change, resulting in significant forest loss worldwide. Accurate detection of these disturbances is essential for understanding carbon storage, forest dynamics, ecosystem resilience, and for developing effective climate adaptation strategies. The Landsat-based Global Forest Change (GFC) product and the related Global Forest Watch web service are widely used for largescale forest monitoring. However, its capacity to detect disturbances caused by wind and insect outbreaks remains uncertain. In this study, we assessed the accuracy of GFC forest loss detection by comparing it with U-Net neural network forest loss masks derived from very high-resolution satellite imagery. We analyzed several study areas in natural mixed-species southern boreal forests affected by windthrows and bark beetle outbreaks, evaluating true positive (TP), false negative (FN), and false positive (FP) detection rates. Our results show that GFC substantially underestimates forest loss, with TP detection rates ranging from 1.56 % to 62.18 % and FN errors reaching 37.82 %-98.44 %. In some cases, overestimation occurred due to high FP rates up to 65.55 %. The FPs happen when a small patch of forest loss within a 30 x 30 m Landsat pixel triggers the entire pixel to be classified as forest loss, even though most of the pixel remains undisturbed. The limitation stems from the small-scale nature of windthrows and insect-induced diebacks, which cannot be reliably captured by Landsat's spatial resolution. Our findings suggest that integrating higher-resolution satellite data is crucial for accurate area estimation and improved assessments of forest loss in the face of climate-driven disturbances such as windthrows and diebacks in natural mixed-species forests. Although GFC can be unsuitable for precisely mapping forest losses, it remains a valuable, globally consistent early warning tool due to its annual updates and broad coverage. Practitioners should treat GFC detections as indicative and conduct rapid visual or automated checks with higher-resolution imagery when assessing windthrow or insect-driven mortality, especially when disturbance patches are less than 450 m2.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40102 - Forestry

Projekt

<a href="/cs/project/GA25-15727S" target="_blank" >GA25-15727S: Může migrace tropických cyklón směrem k pólům podpořit šíření temperátních lesů, a tím vyvážit ústup jižních boreálních lesů způsobený oteplením?</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Remote Sensing Applications

ISSN

2352-9385

e-ISSN

2352-9385

Svazek periodika

40

Číslo periodika v rámci svazku

Nov 2025

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

101798

Kód UT WoS článku

001626171700001

EID výsledku v databázi Scopus

2-s2.0-105022448645