Forest attribute maps: a support for small area estimation of forest disturbances

Abstract Key Message Forest attribute maps generated using national forest inventory and remote sensing data can be used to quantify forest attributes (i.e., growing stock volume, basal area) affected by disturbance events (like bark-beetle induced damage) through small area estimation techniques. Our results showed that mature stands were more sensitive to bark-beetle infestation than younger ones. Context Recurrent drought episodes in Europe weakened trees and promoted biotic disturbances such as bark-beetle outbreaks, heavily affecting spruce ( Picea abies (L.) H. Karst.) and fir stands (Abies alba Mill.). While remote sensing data make it possible to assess and monitor the spatial extent of outbreaks, the assessment of the actual impact on forest attributes is still lacking. Aims We propose a small area estimation approach to assess the amount of forest attributes impacted in outbreak areas during the period 2021–2022 using forest attribute maps. Methods We produced forest attribute maps using National Forest Inventory (NFI) plots, and auxiliary data from Light Detection and Ranging (LiDAR) and Sentinel-2, acquired in 2021. The maps were generated using a general k-nearest neighbour model for all forest species (general model) and a model specific to spruce and fir only (specific model). Small estimation domains, defined as bark-beetle outbreak areas, were obtained from an independent source using time series of Sentinel-2 data (2018–2022). Small area estimations were generated using bootstrapped model-predictions with reliability assessment. Results In total, 1137 ha of forest area was affected by bark-beetle outbreaks during 2021–2022, corresponding to 494,000 m 3 (general model) and 505,000 m 3 (specific model) of growing stock volume, respectively. The associated errors were 2.2% for the general model and 2.6% for the specific model. In the specific model, the estimated mean growing stock volume in outbreak areas for 2021 and 2022 was 441.5 m 3 ha⁻ 1 , which was 64.5 m 3 ha⁻ 1 higher than the estimated mean volume of spruce and fir for the whole area (377 m 3 ha⁻ 1 ). This suggests that mature stands were more sensitive to infestation. Conclusion Regular updates to forest attribute maps, possibly every third year using photogrammetric methods, could improve monitoring of forest attributes impacted by disturbances such as drought and bark-beetle outbreaks and contribute to forest decision making and planning.

Authors

Institutions

Publication Details

Journal
Annals of Forest Science
Published
2025-06-16
DOI
https://doi.org/10.1186/s13595-025-01293-8
Citations
2
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
1.60

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Forest attribute maps: a support for small area estimation of forest disturbances

Christian Piedallu, Olivier Bouriaud, Cédric Vega, Jean-Pierre Renaud et al.
2 citations
Annals of Forest Science
Remote Sensing and LiDAR Applications
1.60
article

Forest attribute maps: a support for small area estimation of forest disturbances

Christian Piedallu, Olivier Bouriaud, Cédric Vega, Jean-Pierre Renaud, Ankit Sagar
article en
2 citations

Abstract

Abstract Key Message Forest attribute maps generated using national forest inventory and remote sensing data can be used to quantify forest attributes (i.e., growing stock volume, basal area) affected by disturbance events (like bark-beetle induced damage) through small area estimation techniques. Our results showed that mature stands were more sensitive to bark-beetle infestation than younger ones. Context Recurrent drought episodes in Europe weakened trees and promoted biotic disturbances such as bark-beetle outbreaks, heavily affecting spruce ( Picea abies (L.) H. Karst.) and fir stands (Abies alba Mill.). While remote sensing data make it possible to assess and monitor the spatial extent of outbreaks, the assessment of the actual impact on forest attributes is still lacking. Aims We propose a small area estimation approach to assess the amount of forest attributes impacted in outbreak areas during the period 2021–2022 using forest attribute maps. Methods We produced forest attribute maps using National Forest Inventory (NFI) plots, and auxiliary data from Light Detection and Ranging (LiDAR) and Sentinel-2, acquired in 2021. The maps were generated using a general k-nearest neighbour model for all forest species (general model) and a model specific to spruce and fir only (specific model). Small estimation domains, defined as bark-beetle outbreak areas, were obtained from an independent source using time series of Sentinel-2 data (2018–2022). Small area estimations were generated using bootstrapped model-predictions with reliability assessment. Results In total, 1137 ha of forest area was affected by bark-beetle outbreaks during 2021–2022, corresponding to 494,000 m 3 (general model) and 505,000 m 3 (specific model) of growing stock volume, respectively. The associated errors were 2.2% for the general model and 2.6% for the specific model. In the specific model, the estimated mean growing stock volume in outbreak areas for 2021 and 2022 was 441.5 m 3 ha⁻ 1 , which was 64.5 m 3 ha⁻ 1 higher than the estimated mean volume of spruce and fir for the whole area (377 m 3 ha⁻ 1 ). This suggests that mature stands were more sensitive to infestation. Conclusion Regular updates to forest attribute maps, possibly every third year using photogrammetric methods, could improve monitoring of forest attributes impacted by disturbances such as drought and bark-beetle outbreaks and contribute to forest decision making and planning.

Annals of Forest ScienceVol. 82(1)
Ştefan cel Mare University of Suceava (RO), National University of Singapore (SG), AgroParisTech (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Silva (FR), Office National des Forêts (FR), Université Gustave Eiffel (FR), Université de Lorraine (FR)
Université de Lorraine, Recherches Avancées sur la Biologie de l’Arbre et les Ecosystèmes Forestiers, National Council for Higher Education, Agence Nationale de la Recherche, Centre National d’Etudes Spatiales, Council for Higher Education
Openalex Percentile: Top 18%
Remote Sensing and LiDAR Applications
1.60
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.