Imaging spectroscopy for ecological analysis in forest and grassland ecosystems

Terrestrial vegetation is an important component of the Earth’s biosphere and therefore playing an essential role in climate regulation, carbon sequestration, and it provides large variety of services to humans. For a sustainable management of terrestrial ecosystems it is essential to understand vegetation responses to various pressures, to monitor and to predict the spatial extent and the rate of ecosystem changes. Remote sensing (RS) therefore offers a unique opportunity for spatially continuous, and for some type of RS data, also frequent monitoring of terrestrial ecosystems. RS of vegetation is a broad research field, where a lot of progress has been made in the last three decades. However, the complexity of interactions between vegetation and solar radiation, constantly modulated by environmental factors, offers room for deeper investigation. Rather than solving one big research problem, this thesis built a few bridges on a way leading towards better understanding of using airborne imaging spectroscopy for ecological analysis in temperate coniferous forests and subalpine grasslands. The research was divided into a theoretical and an applied part. The theoretical part contributed to a critical evaluation of research achievements and challenges in optical RS of plant traits (Chapter 2). The applied part addressed three research topics: i) investigating variability of total to projected leaf area ratio in spruce canopies and its implications on RS of chlorophyll content (Chapter 3), ii) testing chlorophyll retrieval methods based on continuum removal in spruce canopies (Chapter 4), and iii) exploring potentials of imaging spectroscopy to map ecosystem properties and the capacity of subalpine grasslands in providing ecosystem services in comparison with a plant trait-based modelling approach (Chapter 5). In Chapter 2, we reviewed achievements and challenges in RS estimation of key plant traits and we concentrated our discussion on eight traits with the strongest potential to be mapped using RS (plant growth and life forms, flammability properties, photosynthetic pathways and photosynthesis activity, plant height, leaf lifespan and phenology, specific leaf area, leaf nitrogen and phosphorous). The review indicated that imaging spectroscopy facilitates better retrievals of plant traits related to leaf biochemistry, photosynthesis and phenology rather than traits related to vegetations structure. Estimation of the canopy structure related traits (e.g. plant height) can certainly benefit from increasing synergies between imaging spectroscopy and active RS (radar or laser scanning). One of major challenges in RS of plant traits is to effectively suppress the negative influences of water absorption and canopy structure, which would facilitate more accurate retrievals of biochemical and photosynthesis-related traits. Secondly, a successful integration of RS and plant ecology concepts would require careful matching of spatial scales of in-situ trait data with RS observations. In Chapter 3, measurement methods and variability of total to projected leaf area within spruce crowns were investigated. Comparison of six laboratory methods revealed that methods using an elliptic approximation of a needle shape underestimated total leaf area compared to methods using a parallelepiped approximation. The variability in total to projected leaf area was primarily driven by the vertical sampling position and less by needle age or forest stand age. We found that total leaf area estimation has an important implication on RS of leaf chlorophyll content. An error associated with biased estimates of total leaf area can reach up to 30% of the expected chlorophyll range commonly found in forest canopies and therefore negatively influences the validation of RS-based chlorophyll maps. In Chapter 4, potentials of the continuum removal transformation for mapping of chlorophyll content in spruce canopies were investigated. We tested two methods based on continuum removal: artificial neural networks and an optical index. The optical index was newly designed here and it was based on the spectral continuum between 650 and 720 nm. Both continuum removal based methods exhibited superior accuracy in chlorophyll retrieval compared to commonly used narrow-band vegetation indices (e.g. NDVI, TCARI/OSAVI). The newly designed index was equally accurate, but certainly provided a more operational approach as compared to the neural network. In Chapter 5, mapping of ecosystem properties that underline ecosystem services provided by subalpine grasslands using RS methods was tested and further compared with a statistical plant trait-based modelling approach. Imaging spectroscopy in combination with empirical retrieval methods was partly successful to map ecosystem properties. The prediction accuracy at the calibration phase was comparable to the trait-based modelling approach. Spatial comparison between the two approaches revealed rather small agreement. The average fuzzy similarity between the approaches was around 20% for ecosystem properties, but in case of the total ecosystem service supply it decreased below 10%. However, the RS approach detected more variability in ecosystem properties and thereby in services, which was driven by local topography and microclimatic conditions, which could not be detected by the plant trait-based approach. Especially Chapters 2 and 5 indicated that one of the future RS research directions may be in spatial ecology, i.e. spatially explicit mapping of plant traits, ecosystem properties and ecosystem services. High quality RS data are certainly essential building elements for spatial ecology. But in order to address the effects of climate and land use changes on biodiversity and ecosystems, their properties and services, the integration of in-situ and RS data will be ultimately required. Therefore, more coherent experiments, where in-situ and RS data are measured simultaneously at different spatial scales, are needed in the future.