Scaling up Semi-Arid Grassland Biochemical Content from the Leaf to the Canopy Level: Challenges and Opportunities

Remote sensing imagery is being used intensively to estimate the biochemical content of vegetation (e.g., chlorophyll, nitrogen, and lignin) at the leaf level. As a result of our need for vegetation biochemical information and our increasing ability to obtain canopy spectral data, a few techniques have been explored to scale leaf-level biochemical content to the canopy level for forests and crops. However, due to the contribution of non-green materials (i.e., standing dead litter, rock, and bare soil) from canopy spectra in semi-arid grasslands, it is difficult to obtain information about grassland biochemical content from remote sensing data at the canopy level. This paper summarizes available methods used to scale biochemical information from the leaf level to the canopy level and groups these methods into three categories: direct extrapolation, canopy-integrated approach, and inversion of physical models. As for semi-arid heterogeneous grasslands, we conclude that all methods are useful, but none are ideal. It is recommended that future research should explore a systematic upscaling framework which combines spatial pattern analysis, canopy-integrated approach, and modeling methods to retrieve vegetation biochemical content at the canopy level

Remote sensing tools for monitoring grassland plant leaf traits and biodiversity

Rocchini, Duccio1This project has received funding from the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie Grant No. 721995 (project Trustee).openGrasslands are one of the most important ecosystems on Earth, covering approximately onethird of the Earth’s surface. Grassland biodiversity is important as many services provided by such ecosystems are crucial for the human economy and well-being. Given the importance of grasslands ecosystems, in recent years research has been carried out on the potential to monitor them with novel remote sensing techniques. Improved detectors technology and novel sensors providing finescale hyperspectral imagery have been enabling new methods to monitor plant traits (PTs) and biodiversity. The aims of the work were to study different approaches to monitor key grassland PTs such as Leaf Area Index (LAI) and biodiversity-related traits. The thesis consists of 3 parts: 1) Evaluating the performance of remote sensing methods to estimate LAI in grassland ecosystems, 2) Estimating plant biodiversity by using the optical diversity approach in grassland ecosystems, and 3) Investigating the relationship between PTs variability with alpha and beta diversity for the applicability of the optical diversity approach in a subalpine grassland of the Italian Alps To evaluate the performance of remote sensing methods to estimate LAI, temporal and spatial observations of hyperspectral reflectance and LAI were analyzed at a grassland site in Monte Bondone, Italy (IT-MBo). In 2018, ground temporal observations of hyperspectral reflectance and LAI were carried out at a grassland site in Neustift, Austria (AT-NEU). To estimate biodiversity, in 2018 and 2019 a floristics survey was conducted to determine species composition and hyperspectral data were acquired at two grassland sites: IT-MBo and University of Padova’s Experimental Farm, Legnaro, Padua, Italy (IT-PD) respectively. Furthermore, in 2018, biochemistry analysis of the biomass samples collected from the grassland site IT-MBo was carried out to determine the foliar biochemical PTs variability. The results of the thesis demonstrated that the grassland spectral response across different spectral regions (Visible: VIS, red-edge: RE, Near-infrared: NIR) showed to be both site-specific and scale-dependent. In the first part of the thesis, the performance of spectral vegetation indices (SVIs) based on visible, red-edge (RE), and NIR bands alongside SVIs solely based or NIRshoulder bands (wavelengths 750 - 900 nm) was evaluated. A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), it was demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. In the second part, the applicability of the spectral variability hypothesis (SVH) was questioned and highlighted the challenges to use high-resolution hyperspectral images to estimate biodiversity in complex grassland ecosystems. It was reported that the relationship between biodiversity (Shannon, Richness, Simpson, and Evenness) and optical diversity metrics (Coefficient of variation (CV) and Standard deviation (SD)) is not consistent across plant communities. The results of the second part suggested that biodiversity in terms of species richness could be estimated by optical diversity metrics with an R2 = 0.4 at the IT-PD site where the grassland plots were artificially established and are showing a lower structure and complexity from the natural grassland plant communities. On the other hand, in the natural ecosystems at IT-MBo, it was more difficult to estimate biodiversity indices, probably due to structural and biochemical PTs co-variation. The 18 effects of canopy non-vegetative elements (flowers and dead material), shadow pixels, and overexposed pixels on the relationship between optical diversity metrics and biodiversity indices were highlighted. In the third part, we examined the relationship between PTs variability (at both local and community scales, measured by standard deviation and by the Euclidean distances of the biochemical and biophysical PTs respectively) and taxonomic diversity (both α-diversity and βdiversity, measured by Shannon’s index and by Jaccard dissimilarity index of the species, families, and functional groups percent cover respectively) in Monte Bondone, Trentino province, Italy. The results of the study showed that the PTs variability metrics at alpha scale were not correlated with α-diversity. However, the results at the community scale (β-diversity) showed that some of the investigated biochemical and biophysical PTs variations metrics were associated with β-diversity. The SVH approach was also tested to estimate β-diversity and we found that spectral diversity calculated by spectral angular mapper (SAM) showed to be a better proxy of biodiversity in the same ecosystem where the spectral diversity failed to estimate alpha diversity, this leading to the conclusion that the link between functional and species diversity may be an indicator of the applicability of optical sampling methods to estimate biodiversity. The findings of the thesis highlighted that grassland structural heterogeneity strongly affects the ability to retrieve both LAI and biodiversity, with high uncertainties due to structural and biochemical PTs co-variation at complex grassland ecosystems. In this context, the uncertainties of satellite-based products (e.g., LAI) in monitoring grassland canopies characterized by either spatially or temporally varying structure need to be carefully taken into account. The results of the study highlighted that the poor performance of optical diversity proxies in estimating biodiversity in structurally heterogeneous grasslands might be due to the complex relationships between functional diversity and biodiversity, rather than the impossibility to detect functional diversity with spectral proxiesopenImran, H.A

Validation and application of the MERIS Terrestrial Chlorophyll Index.

Climate is one of the key variables driving ecosystems at local to global scales. How and to what extent vegetation responds to climate variability is a challenging topic for global change analysis. Earth observation provides an opportunity to study temporal ecosystem dynamics, providing much needed information about the response of vegetation to environmental and climatic change at local to global scales. The European Space Agency (ESA) uses data recorded by the Medium Resolution Imaging Spectrometer (MERlS) in red I near infrared spectral bands to produce an operational product called the MERlS Terrestrial Chlorophyll Index (MTCI). The MTCI is related to the position of the red edge in vegetation spectra and can be used to estimate the chlorophyll content of vegetation. The MTCI therefore provides a powerful product to monitor phenology, stress and productivity. The MTCI needs full validation if it is to be embraced by the user community who require precise and consistent, spatial and temporal comparisons of vegetation condition. This research details experimental investigations into variables that may influence the relationship between the MTCI and vegetation chlorophyll content, namely soil background and sensor view angle, vegetation type and spatial scale. Validation campaigns in the New Forest and at Brooms Barn agricultural study site reinforced the strong correlation between chlorophyll content and MTCI that was evident from laboratory spectroscopy investigations, demonstrating the suitability of the MTCI as a surrogate for field chlorophyll content measurements independent of cover type. However, this relationship was significantly weakened where the leaf area index (LAI) was low, indicating that the MTCI is sensitive to the effects of soil background. In the light of such conclusions, this project then assessed the MTCI as a tool to monitor changes in ecosystem phenology as a function of climatic variability, and the suitability of the MTCI as a surrogate measure of photosynthetic light use efficiency, to model ecosystem gross primary productivity (GPP) at various sites in North America with contrasting vegetation types. Changes in MTCI throughout the growing season demonstrated the potential of the MTCI to estimate vegetation dynamics, characterising the temporal characteristics in both phenology and gross primary productivity

Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Abstract available in PDF file

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)