A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model

Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits

The plant area index (PAI) is a structural trait that succinctly parametrizes the foliage distribution of a canopy and is usually estimated using indirect optical techniques such as digital hemispherical photography. Critically, on-the-ground photographic measurements forgo the vertical variation of canopy structure which regulates the local light environment. Hence new approaches are sought for vertical sampling of traits. We present an uncrewed aircraft system (UAS) spherical photographic method to obtain structural traits throughout the depth of tree canopies. Our method explained 89% of the variation in PAI when compared with ground-based hemispherical photography. When comparing UAS vertical trait profiles with airborne laser scanning data, we found highest agreement in an open birch (Betula pendula/pubescens) canopy. Minor disagreement was found in dense spruce (Picea abies) stands, especially in the lower canopy. Our new method enables easy estimation of the vertical dimension of canopy structural traits in previously inaccessible spaces. The method is affordable and safe and therefore readily usable by plant scientists.Peer reviewe

Multitemporal monitoring of plant area index in the Valencia Rice District with PocketLAI

Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective plant area index (PAIeff ) estimates. Nevertheless, these instruments are expensive and have the disadvantages of low portability and maintenance. Recently, a smartphone app called PocketLAI was presented and tested for acquiring PAIeff measurements. It was used during an entire rice season for indirect PAIeff estimations and for deriving reference high-resolution PAIeff maps. Ground PAIeff values acquired with PocketLAI, LAI-2000, and DHP were well correlated (R2 = 0.95, RMSE = 0.21 m2/m2 for Licor-2000, and R2 = 0.94, RMSE = 0.6 m2/m2 for DHP). Complementary data such as phenology and leaf chlorophyll content were acquired to complement seasonal rice plant information provided by PAIeff. High-resolution PAIeff maps, which can be used for the validation of remote sensing products, have been derived using a global transfer function (TF) made of several measuring dates and their associated satellite radiances

Evolutionary Dynamics of Rapid, Microgeographic Adaptation in an Amphibian Metapopulation

Wild organisms can rapidly adapt to changing environments, even at fine spatial scales. This fact prompts hope that contemporary local adaptation may buffer some of the negative anthropogenic impacts to ecosystems. However, there are limits to the pace of adaptation. Understanding the adaptive potential—and limitations—of individual species at fine-resolution is an important task if we hope to accurately predict the repercussions of future climate and landscape change on biodiversity. My dissertation takes advantage of an uncommonly long-observed and closely-studied system to paint a comprehensive picture of evolution over time in association with shifts in ecological contexts. In this dissertation, I show evidence of rapid, microgeographic evolution in response to climate within a metapopulation of wood frogs (Rana sylvatica). Critically, I show that populations separated by tens to hundreds of meters—well within the dispersal ability of the species—exhibited considerable shifts in development rates over a period of two decades, or roughly 6-9 generations. Using historical climate data and new methods of assessing landscape change, I show that these changes were mainly a response to warming climates. The ecological contexts experienced by the metapopulation are associated with the evolution of physiological rates. Specifically, I show that climate change seems to have caused a counter-intuitive delay in spring breeding phenology while drought and warming later in the larval development period correspond with a shift toward earlier metamorphosis. The picture that emerges is of a contracting developmental window, which is expected to select for faster intrinsic development rates. Superimposed on the metapopulation-wide shift to faster development was a pattern of counter-gradient variation reflecting a similar pattern seen two decades prior. Furthermore, I empirically demonstrate a trade-off between faster development and a swimming performance trait that strongly contributes to fitness. This trade-off helps to explain why intrinsic development rates vary spatially with pond temperatures, but in the opposite direction of the relationship with temperature over time. Though the evidence for rapid adaptation to climate change presented in this dissertation reveals that evolution can buffer populations from extinction, it also entreats caution. There is a clear trend of demographic decline among wood frog populations that experienced greater magnitudes of environmental change. In fact, the three populations that suffered local extinctions over the 20-year course of observations inhabited ponds characterized by the greatest change in temperature or canopy

Forest ecology in a changing world: Effective ground-based methods for monitoring temperate broadleaved forest ecosystem dynamics in relation to climate change

A comparison of ground-based methods for estimating canopy closure for use in phenology research (Chapter 4), was published in Agricultural and Forest Meteorology in April 2018.The impacts of climate change on temperate forests are predicted to accelerate, with widespread implications for forest biodiversity and function. Remote sensing has provided insights into regional patterns of vegetation dynamics, and experimental studies have demonstrated impacts of specific changes on individual species. However, forests are diverse and complex ecosystems. To understand how different species in different forests respond to interacting environmental pressures, widespread ground-based monitoring is needed. The only practical way to achieve this is through the involvement of non-professional researchers, i.e., with citizen science. However, many techniques used to identify subtle changes in forests require expensive equipment and professional expertise. This thesis aimed to identify practical methods for citizen scientists to collect useful data on forest ecosystem dynamics in relation to climate change. Methods for monitoring tree phenology and canopy-understorey interactions were the main focus, as tree phenology exerts strong control on understorey light and forest biodiversity, and is already responding to climate change. The response of understorey vegetation to canopy closure in four woodlands from a single region of England (Devon) was examined in detail. These geographically close woodlands differed considerably in their composition and seasonal dynamics. The spring period was particularly important for herb-layer development, and small variations in canopy openness had important effects on herb-layer cover and composition. This work highlights the need to monitor a range of different woodlands at the regional scale, with sufficient resolution to pick up small but crucial differences through time. Citizen scientists could help to collect such data by monitoring herb-layer cover and changes in the abundance of key species, alongside monitoring the overstorey canopy. The spring leaf phenology of four canopy trees (ash, beech, oak and sycamore) were monitored intensively in one woodland using a range of methods: counts, percentage estimates and photography. First budburst and leaf expansion dates were compared with estimates of leaf expansion timing and rate, derived from time-series data using logistic growth models. Frequently used first-event dates were potentially misleading due to high variation in leaf development rates within and between species. Percentage estimates and counts produced similar estimates of leaf expansion timing and rate. A photo-derived greenness index produced similar estimates of timing, but not rate, and was compromised by practical issues of photographing individual crowns in closed canopy woodland. Citizen science should collect time-series data instead of frequently-used first event dates―visual observations offer the most practical way to do this, but further work is needed to test reliability with citizen scientists. Given high intra- and inter-species variation in tree phenology, whole forest canopies need to be monitored to infer canopy closure timing. Canopy openness was assessed using sophisticated hemispherical photography and a range of low-cost alternatives, across four Devon woodlands over a year. Visual estimates and ordinary photography were too coarse to identify fine-scale variation in canopies. Smartphone fisheye photography analysed with free software was identified as a reliable surrogate for estimating relative, though not absolute, canopy openness. The method has high potential as a citizen science tool, as different phone models and users gave similar canopy openness estimates. In a detailed follow-up study, smartphone fisheye photography, hemispherical photography and visual observations of leaf expansion were used every other day to characterise spring canopy development. Logistic growth models estimated canopy closure timing and rate. Visual observations identified much earlier canopy development than either photographic method. Smartphone fisheye photography performed comparably to hemispherical photography. There is good potential for practical application of smartphone fisheye photography, as similar canopy closure estimates were gained from photos taken once every two weeks. The research in this thesis identifies a range of methods suitable for widespread monitoring of forest ecosystem dynamics in relation to climate change. Developing a smartphone app for automatic analysis and submission of canopy images will be an important next step to enabling widespread use. A pilot project is underway to begin testing methods with citizen scientists. Further research into data quality with citizen scientists is needed before the methods can be rolled out widely with confidence

Kahden varpukasvin spektrien kaksisuuntaiset heijastussuhdetekijämittaukset

Recent studies have shown the benefits of multiangular remote sensing techniques for characterizing vegetation reflection properties. The study of spectral anisotropy of understory vegetation enables methods for improved plant species identification, and provides valuable input data for radiation scattering models of forests. This thesis presents the applied methods and results of a research effort carried out over the growing season of 2017 for the temporal spectral characterization of two of the economically most important wild berry species in Finland: lingonberry (Vaccinium vitis-idaea) and blueberry (Vaccinium myrtillus). The spectral bidirectional reflectance factor (BRF) data on lingonberry and blueberry shrub samples were collected in a multidirectional measurement geometry using the Finnish Geodetic Institute Goniospectrometer (FIGIFIGO) in laboratory conditions. Leaf reflectance and transmittance spectra on both species were collected with SpectroClip-TR spectral probe. The anisotropic characteristics were analysed in the spectral range from 400 to 2200 nm for view angle dependence (-40° to +40°), illumination angle dependence (+40°, +55°), seasonal dynamics over the growing season (2017), and for berry and flower detection. Both lingonberry and blueberry shrubs have strong backward and notable forward scattering characteristics on the principal plane. In the interspecies comparison, lingonberry is brighter into all view direction in the visible and near infrared wavelengths but darker in the short-wave infrared. Increasing the illumination zenith angle by 15° improves the spectral discrimination of the two dwarf shrub species by inducing a 12% ratio of the spectral responses. Vegetation indices that are commonly used in remote sensing of forests (NDVI, NDVI705, MSI, PSRI) show low sensitivity to the changes in the view- and illumination angles. The presence of lingonberries and lingonberry flowers is indicated as a spectral peak around 679 nm in the spectral ratio of samples with berries or flowers to samples without berries or flowers. It was shown that the analysis of spectral data on the reflectance anisotropy improves the spectral discrimination of the dwarf shrub species. The contribution of the berries on the obtained shrub spectra was shown to be notable enough to justify further studies by applying unmanned aerial vehicle (UAV) platforms. Future studies on the aerial spectral data are suggested to evaluate the potential of berry mapping in larger-scale.Viimeaikaiset tutkimukset ovat osoittaneet monisuunta-spektrometrian hyödyt kasvillisuuden heijastusominaisuuksien karakterisoinnissa kaukokartoituksessa. Aluskasvillisuuden spektrien anisotropian tutkiminen edesauttaa kehittämään menetelmiä kasvilajien tunnistamiseksi ja tarjoaa validointiaineistoa metsien sirontamalleihin. Tämä diplomityö esittää menetelmät ja tulokset Suomen kahden taloudellisesti tärkeimmän luonnonmarjoja tuottavan varpukasvin, mustikan (Vaccinium myrtillus) ja puolukan (Vaccinium vitis-idaea), spektrien temporaalisesta karakterisointikampanjasta kasvukauden 2017 yli. Kaksisuuntainen heijastussuhdetekijä spektriaineisto mitattiin mustikan ja puolukan varpunäytteistä monisuuntamittausgeometriassa FIGIFIGO (Finnish Geodetic Institute Goniospectrometer) goniospektrometrillä laboratorio-olosuhteissa. Lehtien heijastus- ja läpäisyspektrit mitattiin molemmista lajeista käyttäen SpectroClip-TR mittalaitetta. Anisotropiset ominaispiirteet analysointiin aallonpituuksien 400 - 2200 nm välillä katselukulmariippuvuudelle (-40° to +40°), valaistuskulmariippuvuudelle (+40°, +55°), vuodenajan aiheuttamille muutoksille (kasvukausi 2017) sekä marja ja kukintojen tunnistamiselle. Sekä puolukka että mustikka osoittavat voimakasta taaksepäin suuntautuvaa ja huomattavaa eteenpäin suuntautuvaa ominaissirontaa päätasossa. Lajien välisessä vertailussa puolukka on kirkkaampi kaikkiin mitattuihin katselukulmiin näkyvän valon ja lähi-infrapunan aallonpituuksilla, mutta tummempi lyhytaaltoisen infrapunan alueella. Valaistuskulman zeniitin kasvattaminen 15° parantaa lajien spektrien erotettavuutta aiheuttamalla 12 %:n eron lajien heijastusvasteisiin. Yleisesti metsän kaukokartoituksessa käytetyt kasvillisuusindeksit (NDVI, NDVI705, MSI, PSRI) osoittavat matalaa herkkyyttä katselu- ja valaistuskulman muutoksille. Näytteessä olevat puolukanmarjat ja -kukat erottuvat spektrissä piikkinä 679 nm:n kohdalla, kun tarkastellaan marjallisten ja kukallisten näytteiden suhdetta marjattomiin ja kukattomiin. Spektriaineiston heijastus-anisotropian analysoinnin näytettiin edesauttavan varpukasvien erotettavuutta. Marjojen vahva kontribuutio varpunäytteistä mitattuihin spektreihin osoitettiin niin selkeästi, että jatkotutkimuksia UAV (unmanned aerial vehicle) -alustalla voidaan pitää perusteltuina. Ilma-aluksilla kerättyä aineistoa ehdotetaan käytettävän marjojen laajemman kartoituksen potentiaalin selvittämiseksi

Olive Crown Porosity Measurement Based on Radiation Transmittance: An Assessment of Pruning Effect

Crown porosity influences radiation interception, air movement through the fruit orchard, spray penetration, and harvesting operation in fruit crops. The aim of the present study was to develop an accurate and reliable methodology based on transmitted radiation measurements to assess the porosity of traditional olive trees under different pruning treatments. Transmitted radiation was employed as an indirect method to measure crown porosity in two olive orchards of the Picual and Hojiblanca cultivars. Additionally, three different pruning treatments were considered to determine if the pruning system influences crown porosity. This study evaluated the accuracy and repeatability of four algorithms in measuring crown porosity under different solar zenith angles. From a 14 to 30 solar zenith angle, the selected algorithm produced an absolute error of less than 5% and a repeatability higher than 0.9. The described method and selected algorithm proved satisfactory in field results, making it possible to measure crown porosity at different solar zenith angles. However, pruning fresh weight did not show any relationship with crown porosity due to the great differences between removed branches. A robust and accurate algorithm was selected for crown porosity measurements in traditional olive trees, making it possible to discern between different pruning treatments

A Simple and Non-destructive Method for Chlorophyll Quantification of Chlamydomonas Cultures Using Digital Image Analysis

Growing interest in the use of microalgae as a sustainable feedstock to support a green, circular, bio-economy has led to intensive research and development initiatives aimed at increasing algal biomass production covering a wide range of scales. At the heart of this lies a common need for rapid and accurate methods to measure algal biomass concentrations. Surrogate analytical techniques based on chlorophyll content use solvent extraction methods for chlorophyll quantification, but these methods are destructive, time consuming and require careful disposal of the resultant solvent waste. Alternative non-destructive methods based on chlorophyll fluorescence require expensive equipment and are less suitable for multiple sampling of small cultures which need to be maintained under axenic growth conditions. A simple, inexpensive and non-destructive method to estimate chlorophyll concentration of microalgal cultures in situ from digital photographs using the RGB color model is presented. Green pixel intensity and chlorophyll a, b and total chlorophyll concentration, measured by conventional means, follow a strong linear relationship (R2 = 0.985–0.988). In addition, the resulting standard curve was robust enough to accurately estimate chlorophyll concentration despite changes in sample volume, pH and low concentrations of bacterial contamination. In contrast, use of the same standard curve during nitrogen deprivation (causing the accumulation of neutral lipids) or in the presence of high quantities of bacterial contamination led to significant errors in chlorophyll estimation. The low requirement for equipment (i.e., a simple digital camera, available on smartphones) and widely available standard software for measuring pixel intensity make this method suitable for both laboratory and field-based work, particularly in situations where sample, qualified personnel and/or equipment is limited. By following the methods described here it should be possible to produce a standard curve for chlorophyll analysis in a wide range of testing conditions including different microalga cultures, culture vessel and photographic set up in any particular laboratory

Development of an earth observation processing chain for crop biophysical parameters at local and global scale

This thesis’ topics embrace remote sensing for Earth observation, specifically in Earth vegetation monitoring. The Thesis’ main objective is to develop and implement an operational processing chain for crop biophysical parameters estimation at both local and global scales from remote sensing data. Conceptually, the components of the chain are the same at both scales: First, a radiative transfer model is run in forward mode to build a database composed by simulations of vegetation surface reflectance and concomitant biophysical parameters associated to those spectrum. Secondly, the simulated database is used for training and testing nonlinear and non-parametric machine learning regression algorithms. The best model in terms of accuracy, bias and goodness-of-fit is then selected to be used in the operational retrieval chain. Once the model is trained, remote sensing surface reflectance data is fed into the trained model as input in the inversion process to retrieve the biophysical parameters of interest at both local and global scales depending on the inputs spatial resolution and coverage. Eventually, the validation of the leaf area index estimates is performed at local scale by a set of ground measurements conducted during coordinated field campaigns in three countries during 2015 and 2016 European rice seasons. At global scale, the validation is performed through intercomparison with the most relevant and widely validated reference biophysical products. The work elaborated in this Thesis is structured in six chapters including an introduction of remote sensing for Earth observation, the developed processing chain at local scale, the ground LAI measurements acquired with smartphones, the developed chain at global scale, a chapter discussing the conclusions of the work, and a chapter which includes an extended abstract in Valencian. The Thesis is completed by an annex which include a compendium of peer-reviewed publications in remote sensing international journals