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Browsing by Subject "hyperspectral imaging"

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  • Detecting Spatial Patterns of Tundra Plant Communities Using High-Resolution Multi and Hyperspectral Imagery 
    Putkiranta, Pauli (2023)
    Arctic ecosystems face drastic changes in community structure due to warming, shrubification, permafrost loss, and other environmental changes. Due to the spatial heterogeneity of these ecosystems, understanding such changes on a local scale requires high-resolution data. Earth observation using satellite imagery and aerial photography has become a staple in mapping large areas and general patterns. Advances in sensor technology, the proliferation of unmanned aerial vehicles (UAVs), and increases in processing capacity enable the use of higher spatial and spectral resolutions. As a result, more detailed ecological observations can be made using remote sensing methods. In this thesis, I assess how increased spectral resolution affects the remote-sensing based modelling of plant communities in low-growth oroarctic tundra heaths. Based on a large field observation dataset, I estimate biomass, leaf area index, species richness, Shannon's biodiversity index, and fuzzy community clusters. I then build random forest models of these with image data of varying spectral, spatial, and temporal specifications and topographical data. Finally, I create maps of the vegetation. Leaf area index and biomass are best estimated of the response variables, with R2 values of 0.64 and 0.59, respectively, with multispectral data proving the most important explanatory dataset. Biodiversity metrics are best estimated with R2 values of 0.40–0.50 with the most important explanatory variables being topographical and hyperspectral, and community cluster with R2 values of 0.27–0.53, with the importance of various explanatory variables depending on the cluster being estimated. These results can help choose a suitable high-resolution remote sensing approach for modelling plant communities in similar conditions.
  • Monitoring Scots pine seedlings’ response to drought using hyperspectral imaging 
    Miettinen, Iiro (2023)
    Forests’ exposure to drought is increasing as a result of climate change. Drought increases tree mortality and the likelihood of wildfires. Mitigating damages contributed to by drought is important in order to secure access to ecosystem services. Remote sensing can be applied in drought detection, development monitoring, wildfire risk assessment, and phenotyping for resistance breeding. Hyperspectral imaging (HSI) combines spectral and spatial information, which may be used as a proxy to estimate biochemical and physiological traits of plants, including water content and response to water stress. More affordable and compact HSI cameras have become available in recent years, but their use in remote sensing of forests is still somewhat novel. The photochemical reflectance index (PRI) is an optical vegetation index, that was originally defined based on its diurnal response to the epoxidation state of xanthophyll cycle. PRI has been successfully used to capture drought stress and recovery on various scales on mainly broadleaf species. PRI responds to drought due to stomatal closure leading to downregulation of photosynthesis increasing the need for light energy dissipation. The aim of this thesis was to assess the feasibility of monitoring drought development and recovery of Pinus sylvestris seedlings in ambient greenhouse conditions using hyperspectral imaging. The hypotheses addressed in this thesis were: 1) there is a relationship between physiological variables and optical vegetation indices from HSI, and 2) PRI captures reversible photoprotective energy dissipation and responds to stress, outperforming the chlorophyll-responsive indices over the duration of the drought and recovery period. Imaging and additional measurements were performed on 8 4-year-old Pinus sylvestris seedlings. Pinus sylvestris, is the most common tree species in Finland, and it is widely utilized in the Finnish forestry industry. Half of the seedlings were exposed to 17 days of progressive drought and half were watered regularly. HSI and physiological variables were measured every few days for the duration of pre-drought, drought, and recovery periods. Physiological variables, which were leaf water potential and maximum quantum yield of PSII photochemistry (Fv/Fm), were measured to validate drought stress development. Additionally, meteorological conditions and soil moisture content were monitored. Non-imaging leaf level reflectance was measured on 3 days, which represented pre-drought, height of drought, and end of recovery. This thesis is concerned with the data collected on these three days. The results of this thesis showed that HSI based PRI between drought and control plants differentiated significantly during height of drought, and mostly recovered by the end of the scheduled recovery period. Chlorophyll-responsive red edge index responded to drought but did not show signs of recovery. Relationship between HSI based PRI and physiological variables Fv/Fm and leaf water potential was significant. These results demonstrate that HSI can be used to capture progressive drought stress development and recovery at a seedling canopy level in boreal evergreen saplings under greenhouse conditions.

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