University of Twente Student Theses


Forest net primary productivity response to an extreme climate event

Islam, M.S. (2023) Forest net primary productivity response to an extreme climate event.

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Embargo date:20 January 2024
Abstract:The Net Primary Productivity (NPP) is an essential indicator of monitoring vegetation health, carbon exchange, and the impact of extreme climate events, such as drought. Light Use Efficiency (LUE)-based models utilise remote sensing data for NPP estimation. However, estimation of NPP through these models has been mainly performed using low/moderate resolution remote sensing data. In this respect, this study utilised two LUE-based models, i.e., MOD17 and GLO-PEM and temporal high-resolution remote sensing (i.e. Sentinel-2) data to evaluate the performance of these models in estimating NPP and to detect the impacts of the 2018 drought on NPP in Bavaria Forest National Park (BFNP), Germany. The necessary data for NPP estimation, including Sentinel-2 imagery, climate data (i.e., daily mean and minimum temperature, vapour pressure deficit, soil moisture deficit), and land cover map, have been collected from Copernicus Open Access Hub, European Centre for Medium-Range Weather Forecasts, National Snow and Ice Data Center, and Copernicus Land Monitoring Service. The forest inventory data, MODIS- NPP, and Standardised Precipitation and Evapotranspiration Index (SPEI) values have been used for NPP verification and drought impact assessment. The two LUE models were used first to estimate the Gross Primary Productivity (GPP), and then NPP was estimated using a coefficient derived from GPP and NPP ratio. Different statistical analyses, including the Coefficient of Determination (R2), Person's Correlation Coefficient (r), Linear Trend, and t-test, have been performed to verify the model outputs and their temporal change as well as to detect the drought and associated impacts. Analysis showed that the results obtained using the GLO-PEM model agree better with forest inventory NPP than the NPP estimated using the MOD17 model. When MODIS products were used for verification, both models' outputs were observed to behave almost similarly to MODIS products. Time series analysis of NPP showed a slight decrease in NPP in 2018 than the previous two years (i.e., 2016 and 2017), and then a significant reduction was found in 2019, 2020, and 2021. In 2022, a slight increase was noted in NPP values, indicating restoration and the recovery's start. However, an overall downward trend in NPP values was found as the slope values of the linear trend were negative in different sample points. The lagged correlation results revealed the delayed impacts of Vapor Pressure Deficit (VPD) and soil moisture deficit on NPP. Besides, a lagged correlation was also found between SPEI and NPP, indicating the legacy impacts of drought. The substantial reduction of NPP and negative NPP values may be attributed to the drought and associated disturbances, such as environmental stress, immature leaf senescence, defoliation, crown dieback, tree mortality, insect infestation, and logging. In a nutshell, the LUE-based models using Sentinel-2 data showed good prospects in NPP estimation and extreme climate impact detection. The study encountered a few limitations, such as an image gap in the Sentinel-2 time series due to cloud cover and insufficient reference data to validate and verify the model outputs. Besides, the downscaling of climate data, atmospheric distortion in the Sentinel-2 images, and inherent limitations of NDVI, like soil background reflectance and saturation problems, may have caused errors in data. Future research may require increasing the satellite image frequency using the harmonized data of Sentinel-2 and Landsat data and improving the models’ ability to estimate autotrophic respiration at daily timestep.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Subject:38 earth sciences, 43 environmental science
Programme:Geoinformation Science and Earth Observation MSc (75014)
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