Spatial Variation of Leaf Optical Properties in a Boreal Forest Is Influenced by Species and Light Environment

Leaf Optical Properties (LOPs) convey information relating to temporally dynamic photosynthetic activity and biochemistry. LOPs are also sensitive to variability in anatomically related traits such as Specific Leaf Area (SLA), via the interplay of intra-leaf light scattering and absorption processes. Therefore, variability in such traits, which may demonstrate little plasticity over time, potentially disrupts remote sensing estimates of photosynthesis or biochemistry across space. To help to disentangle the various factors that contribute to the variability of LOPs, we defined baseline variation as variation in LOPs that occurs across space, but not time. Next we hypothesized that there were two main controls of potentially disruptive baseline spatial variability of photosynthetically-related LOPs at our boreal forest site: light environment and species. We measured photosynthetically-related LOPs in conjunction with morphological, biochemical, and photosynthetic leaf traits during summer and across selected boreal tree species and vertical gradients in light environment. We then conducted a detailed correlation analysis to disentangle the spatial factors that control baseline variability of leaf traits and, resultantly, LOPs. Baseline spatial variability of the Photochemical Reflectance Index (PRI) was strongly influenced by species and to a lesser extent light environment. Baseline variability of spectral fluorescence derived LOPs was less influenced by species; however at longer near-infrared wavelengths, light environment was an important control. In summary, remote sensing of chlorophyll fluorescence has good potential to detect variation in photosynthetic performance across space in boreal forests given reduced sensitivity to species related baseline variability in comparison to the PRI. Our results also imply that spatially coarse remote sensing observations are potentially unrepresentative of the full scope of natural variation that occurs within a boreal forest.Peer reviewe

An empirical assessment of the potential of post-fire recovery of tree-forest communities in Mediterranean environments

The accumulation of fuel and the homogenization of the landscape in Mediterranean forests are leading to an increasingly hazardous behavior of wildfires, fostering larger, more intense, severe, and frequent wildfires. The onset of climate change is intensifying this behavior, fostering the occurrence of extreme forest fires threatening the persistence of forest communities. In this study we present an assessment of the post-fire recovery potential of the most representative tree-forest communities affected by fire in Spain: Pinus halepensis, Pinus nigra, Pinus pinaster and Quercus ilex. A large database of field data collected during specific campaigns -carried out 25 years after the fire- is used in combination with remote sensing, forest inventory and geospatial data to build an empirical model capable of predicting the chances of recovery. The model, calibrated using Random Forest, combines information on burn severity (remote sensing estimates of the Composite Burn Index), local topography (slope and terrain aspect) and climatic data (mean values and trends of temperature and precipitation) to provide information on the degree of similarity (vegetation height, horizontal cover of the vegetation layer along vertical strata, aboveground biomass and species diversity) between the plots burned in the summer of 1994 and the unburned control. Overall, only 33 out of the 131 burned plots could be considered as recovered, that is, reaching a similar state to unburned stands in neighboring areas. Our results suggest a primary role played by burn severity (the higher the severity the lower the probability of recovery), but strongly modulated by local topographic features (higher probability of recovery on steep north-facing slopes). In turn, increasingly warm and wetter conditions increased the chance of recovery

Assessment of data fusion oriented on data mining approaches to enhance precision agriculture practices aimed at increase of Durum Wheat (Triticum turgidum L. var. durum) yield

In 2050, world population will reach a total of 9 billion inhabitants and their food demand have to be satisfied. Durum wheat (Triticum turgidum L. var. durum) is one of the most important food crop and its consumption is increasing worldwide. Productivity growth in agriculture and profitable returns are strongly influenced by investment in research and development, where Precision Agriculture (PA) represents an innovative way to manage farms by introducing the Information and Communication Technology (ICT) into the production process. It is known that farms activities produce large amounts of data. Today ICT allows, with electronic and software systems, to collect and transfer automatically these data, thus increasing yields and profits. In this direction significant data are processed from agricultural production, and retrieved to extract useful information important to increase the knowledge base. Data from multiple data sources can be processed by a Data Fusion (DF) approach able to combine multiple data sources into an unique database system. Raw data are transformed into useful information, thus DF improves pattern recognition, analysis of growth factors, and relationship between crops and environments. Data Fusion is synonym of Data Integration, Sensor Fusion, and Image Fusion. By means of Data Mining (DM) it is possible to extract useful information from data of the production processes thus providing new outputs concerning product quality and product “health status”. The following literature take into account the DF and DM techniques applied to Precision Agriculture (PA) and to cultivation inputs (water, nitrogen, etc.) management.  We report also last advances of DF and DM in modern agriculture and in precision durum wheat production

Development and Evaluation of Remote Sensing Techniques for Assessing Winter Wheat Growth and Yield

Wheat (Triticum aestivum L.) production can be enhanced through the development of improved cultivars with wider genetic background, capable of producing higher yield under various agro-climatic conditions, biotic and abiotic stresses. Early growth stages in wheat can be influenced by many factors, such as planting date, type of cultivar, and water management, among others. It is essential to monitor the crop performance early by taking accurate measurements of crop growth parameters. Monitoring wheat performance during the growing season will provide information on productivity and prospects for realizing yield potential. However, monitoring conventional methods are time-consuming, labor-intensive and can cause large sampling errors. Remote sensing tools have provided easy and quick measurements of ground cover and aboveground biomass, without destructive sampling. The central objective of this research is to evaluate the performance of wheat genotypes using remote sensors on a ground-based plant sensing system, Greenseeker® , and manned aircraft system, under rainfed and irrigated conditions. Field experiments were conducted in the Texas A&M AgriLife Research Experiment Station at Bushland, Texas, in 2011-2012, 2012-2013, 2014- 2015 and 2015-2016 winter wheat growing seasons. Yield as the major desirable trait for plant breeders was associated with biomass at anthesis and maturity, harvest index, spikes m⁻², seeds m⁻², seeds spike⁻¹ and TKW. Spectral data from the remote sensors were taken during tillering, jointing, and heading stage, and used to compute eleven spectral vegetation indices. Results showed that significant variation exists among the genotypes using the indices at different growth stages. Field data included aboveground biomass, percent ground cover (%GC), and yield. The field data and vegetation indices had a significant relationship (R² = 0.30- 0.99, P˂0.05) with the %GC, aboveground biomass, and yield. %GC had the best estimation among the field data with a single index (R² = 0.84; training and R² = 0.94; validation, P˂.0001). Results indicate that the indices could be used as an indirect selection tool for screening a large number of early-generation lines and advanced wheat genotypes. Overall, this study illustrated the potential use of remote sensing techniques by wheat breeders for highthroughput phenotyping to screen for drought tolerant and high-yielding genotypes

Foliar spectra accurately distinguish the invasive common reed from co-occurring plant species throughout a growing season

Les espèces végétales envahissantes sont l'un des principaux facteurs de changement de la biodiversité dans les écosystèmes terrestres. Une détection précise et précoce des espèces exotiques est donc cruciale pour surveiller les invasions en cours et pour prévenir leur propagation. Présentement, les méthodes de surveillance des invasions biologiques permettent de suivre la propagation des envahisseurs à travers les aires de répartition géographique, mais une attention moindre a été accordée à la surveillance des espèces envahissantes à travers le temps. Les plates-formes de télédétection, capables de fournir des informations détaillées sur les variations des traits foliaires dans le temps et l'espace, sont particulièrement bien placées pour surveiller les plantes envahissantes en temps réel. Les changements temporels des traits fonctionnels sont exprimés dans la signature spectrale des espèces par des caractéristiques d'absorption spécifiques de la lumière associés aux pigments photosynthétiques et aux constituants chimiques tous deux liés à la phénologie. Ainsi, les variations temporelles dans la réponse spectrale des plantes peuvent être utilisées afin de mieux identifier des espèces individuelles. L'un des envahisseurs les plus problématiques au Canada est le roseau commun, Phragmites australis (Cav.) Trin. ex Steudel sous-espèce australis, dont la propagation menace la biodiversité des écosystèmes de zones humides en Amérique du Nord. Déterminer la période de l'année où cet envahisseur se distingue d’avantage, du point de vue spectral et fonctionnel, des autres plantes de la communauté serait centrale dans une meilleure gestion du roseau commun. Pour ce faire, nous avons utilisé des traits fonctionnels et une série temporelle de données spectrales foliaires à haute résolution au cours d'une saison de croissance à Boucherville, Québec, Canada, afin de déterminer la séparabilité spectrale de l'envahisseur par rapport aux espèces co-occurrentes et comment cette dernière varie à travers le temps. Nos résultats ont révélé que la spectroscopie foliaire a permis de distinguer le phragmite des espèces co-occurrentes avec une précision de plus de 95% tout au long de la saison de croissance – un résultat prometteur pour le futur de la télédétection des espèces végétales envahissantes.Invasive plant species are one of the main drivers of biodiversity change in terrestrial ecosystems. Accurate detection of exotic species is critical to monitor on-going invasions and early detection of incipient invasions is necessary to prevent further spread. At present, surveillance methods of biological invasions allow to track the spread of invaders across geographic ranges, but less attention has been given to invasive species monitoring across time. Remote sensing platforms, capable of providing detailed information on foliar trait variations across time and space, are uniquely positioned for monitoring invasive plants in real time. Temporal changes in foliar traits are expressed in a species spectral profile through specific absorption features related to variation in photosynthetic pigments and chemical constituents driven by phenology. Thus, variations in a plant’s spectral response can be used to improve the identification of individual species. One of Canada’s most problematic invaders is the common reed, Phragmites australis (Cav.) Trin. ex Steudel subspecies australis, whose spread threatens biodiversity in wetland ecosystems in North America. Determining the time of year when the invader is spectrally and functionally more distinct from other plants in the community would be central to better management of common reed. To do so, we collected a time-series of foliar traits and high-resolution leaf spectral data over the course of a growing season at Boucherville, Quebec, Canada, to determine the spectral separability of the invader from co-occurring species and how its detection varies over time. Our results revealed that leaf-level spectroscopy distinguished Phragmites and co-occurring species with > 95% accuracy throughout the growing season – a promising result for the future remote detection of invasive plant species

Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions

Although satellite-based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The growth of interest in biodiversity variables observable from space has been partly underpinned by the development of the essential biodiversity variable (EBV) framework by the Group on Earth Observations – Biodiversity Observation Network, which itself was guided by the process of identifying essential climate variables. This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing (SRS) EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space. Progress toward the identification of SRS-EBVs will require a clear understanding of what makes a biodiversity variable essential, as well as agreement on who the users of the SRS-EBVs are. Technological and algorithmic developments are rapidly expanding the set of opportunities for SRS in monitoring biodiversity, and so the list of SRS-EBVs is likely to evolve over time. This means that a clear and common platform for data providers, ecologists, environmental managers, policy makers and remote sensing experts to interact and share ideas needs to be identified to support long-term coordinated actions

A semi-mechanistic model for predicting daily variations in species-level live fuel moisture content

Live Fuel Moisture Content (LFMC) is one of the main factors affecting forest ignitability as it determines the availability of existing live fuel to burn. Currently, LFMC is monitored through spectral vegetation indices or inferred from meteorological drought indices. While useful, neither approach provides mechanistic insights into species-specific LFMC variation and they are limited in the ability to forecast LFMC under altered future climates. Here, we developed a semi-mechanistic model to predict daily variation in LFMC across woody species from different functional types by adjusting a soil water balance model which estimates predawn leaf water potential (Ψpd). Our overarching goal was to balance the trade-off between biological realism, which enhances model applicability, and parameterization complexity, which may limit its value within operational settings. After calibration, model predictions were validated against a dataset comprising 1659 LFMC observations across peninsular Spain, belonging to different functional types and from contrasting climates. The overall goodness of fit for our model (R2 = 0.5) was better than that obtained by an existing models based on drought indices (R2 = 0.3) or spectral vegetation indices (R2 = 0.1). We observed the best predictive performance for seeding shrubs (R2 = 0.6) followed by trees (R2 = 0.5) and resprouting shrubs (R2 = 0.4). Through its relatively simple parameterization, the approach developed here may pave the way for a new generation of process-based models that can be used for operational purposes within fire risk mitigation scenarios.This work was partly founded by the Spanish Government, grant number RTI2018-094691-B-C31 (MCIU/AEI/FEDER, EU) . R.B-R. ac-knowledges the Community of Madrid for the predoctoral contract PEJD-2019-PRE/AMB-15,644 funded by the Youth Employment Initia-tive (YEI) . M. De C. was supported by the Spanish Ministry of Science and Innovation via competitive grant CGL2017-89149-C2-2-R. UNED founding for open access publishing