TY - JOUR
T1 - Integrating process-based vegetation modelling with high-resolution imagery to assess bark beetle infestation and land surface temperature effects on forest net primary productivity
AU - Abdullah, H.
AU - Neinavaz, E.
AU - Darvishzadeh, R.
AU - Huesca Martinez, M.
AU - Skidmore, A.K.
AU - Lindeskog, Mats
AU - Smith, Benjamin
AU - Heurich, Marco
AU - Steinbrecher, Rainer
AU - Paganini, Marc
PY - 2025/1
Y1 - 2025/1
N2 - The European spruce bark beetle (Ips typographus) is an insect species that causes significant damage to Norway spruce (Picea abies) forests across Europe. Infestation by bark beetles can profoundly impact forest ecosystems, affecting their structure and composition and affecting the carbon cycle and biodiversity, including a decrease in net primary productivity (NPP), a key indicator of forest health. The primary objective of this study is to enhance our understanding of the interplay among NPP, bark beetle infestation, land surface temperature (LST), and soil moisture content as key components influencing the effects of climate change-related events (e.g., drought) during and after a drought event in the Bavarian Forest National Park in southeastern Germany. Earth observation data, specifically Landsat-8 TIR and Sentinel-2, were used to retrieve LST and leaf area index (LAI), respectively. Furthermore, for the first time, we incorporated a time series of high-resolution (20 m) LAI as a remote sensing biodiversity product into a process-based ecological model (LPJ-GUESS) to accurately generate high-resolution (20 m) NPP products. The study found a gradual decline in NPP values over time due to drought, increased LST, low precipitation, and a high rate of bark beetle infestation. We observed significantly lower LST in healthy Norway spruce stands compared to those infested by bark beetles. Likewise, low soil moisture content was associated with minimal NPP value. Our results suggest synergistic effects between bark beetle infestations and elevated LST, leading to amplified reductions in NPP value. This study highlights the critical role of integrating high-resolution remote sensing data with ecological models for advancing the understanding of forest carbon dynamics and improving predictive capabilities to inform forest management under climate change.
AB - The European spruce bark beetle (Ips typographus) is an insect species that causes significant damage to Norway spruce (Picea abies) forests across Europe. Infestation by bark beetles can profoundly impact forest ecosystems, affecting their structure and composition and affecting the carbon cycle and biodiversity, including a decrease in net primary productivity (NPP), a key indicator of forest health. The primary objective of this study is to enhance our understanding of the interplay among NPP, bark beetle infestation, land surface temperature (LST), and soil moisture content as key components influencing the effects of climate change-related events (e.g., drought) during and after a drought event in the Bavarian Forest National Park in southeastern Germany. Earth observation data, specifically Landsat-8 TIR and Sentinel-2, were used to retrieve LST and leaf area index (LAI), respectively. Furthermore, for the first time, we incorporated a time series of high-resolution (20 m) LAI as a remote sensing biodiversity product into a process-based ecological model (LPJ-GUESS) to accurately generate high-resolution (20 m) NPP products. The study found a gradual decline in NPP values over time due to drought, increased LST, low precipitation, and a high rate of bark beetle infestation. We observed significantly lower LST in healthy Norway spruce stands compared to those infested by bark beetles. Likewise, low soil moisture content was associated with minimal NPP value. Our results suggest synergistic effects between bark beetle infestations and elevated LST, leading to amplified reductions in NPP value. This study highlights the critical role of integrating high-resolution remote sensing data with ecological models for advancing the understanding of forest carbon dynamics and improving predictive capabilities to inform forest management under climate change.
KW - UT-Hybrid-D
KW - ITC-HYBRID
UR - http://www.scopus.com/inward/record.url?scp=85218902804&partnerID=8YFLogxK
U2 - 10.1016/j.rsase.2025.101499
DO - 10.1016/j.rsase.2025.101499
M3 - Article
SN - 2352-9385
VL - 37
JO - Remote Sensing Applications: Society and Environment
JF - Remote Sensing Applications: Society and Environment
M1 - 101499
ER -
