Stress detection in conifer forest with high resolution hyperspectral and thermal remote sensing and radiative transfer modeling

Recently, widespread forest mortality related to drought or temperature stress has been described for drought-prone forests throughout the world. Long-term exposure of water stress to a combination of high light levels and temperatures causes a depression of photosynthesis and photosystem II efficiency that is not easily reversed even for resistant Mediterranean pines. Several authors have demonstrated that declining physiological status is connected with decline in chlorophyll content and with decreasing rate of photosynthesis; whereas the ratio C a+b/C x+c shows a decreasing trend. This thesis evaluates different physiological vegetation indices (SVI) at the canopy level and methods for the estimation of chlorophyll (C a+b) and carotenes (C x+c) pigment content with high spatial resolution sensors and radiative transfer models in heterogeneous conifer canopies. The objective is the early detection of decline processes based on the analysis of the trees physiological status and mapping of the major pigments regulating photosynthesis efficiency. Relationships between spectral vegetation indices and pigment content have been widely analyzed at the leaf level in previous works. However, studies were lacking where these kind of relationships were explored at the canopy level and for heterogeneous forest canopies. The heterogeneous forest canopies are more structurally complex than other vegetation types, therefore previous relationships obtained at the leaf level or on homogeneous canopies might not be applicable in a general way. Consequently, modelling work at leaf and canopy scales is needed to enable an operational use of SVI to map stress levels in non-homogeneous canopies where structural variation plays the main role in the reflectance signature. New formulations of SVI related to Cx+c and xanthophylls cicle were formulated based on radiative transfer simulation and experiemtal data and demonstrated to be more robust at the canopy level. A new modelling method is presented in this thesis based on scalingup methods to estimate Ca+b and Cx+c pigment concentration. The methodology has been tested in two conifer species: Pinus sylvestris and Pinus nigra. This study required extensive field measurements of biophysical paremeters of the canopy, leaf optical and biochemistry laboratory analysis, as well as analysis of highperspectral airborne imagery acquired by a sensor on board and unmanned aerial vehicle (UAV). Moreover, the use of radiative transfer models allowed the evaluation of the influence of different biophysical paramenters; at the leaf level, such us Ca+b and Cx+c as well as the relation between them,and at the canopy level, such as Leaf Area Index (LAI) or tree density.En los últimos años se han descrito procesos de mortalidad en distintos tipos de bosques en todo el mundo, siendo una de las causas más importantes el estrés hídrico y térmico. La exposición a largo plazo de estrés hídrico combinado con altos niveles de radiación y altas temperaturas provoca una depresión de la fotosíntesis y la eficiencia del fotosistema II, que no es fácilmente reversible incluso para especies vegetales resistentes a este tipo de ambientes como las coníferas mediterráneas. Varios autores han demostrado que el estado de estrés fisiológico está relacionado con la disminución en el contenido de clorofila y de la fotosintésis, mientras que la proporción de C a+b / Cx +c muestra una tendencia decreciente. Esta tesis evalúa diferentes índices de vegetación fisiológicos (SVI) a nivel de la cubierta y para la estimación del contenido de clorofila (C a + b) y carotenos (C x + c) con sensores de alta resolución espacial y modelos de transferencia radiativa en bosques de coníferas. El objetivo es la detección temprana de los procesos de decaimiento basados en el análisis del estado fisiológico de los árboles y la cartografía del contenido de los principales pigmentos que regulan la eficiencia de la fotosíntesis. Las relaciones entre los índices espectrales de vegetación y contenido de pigmentos han sido ampliamente analizadas a nivel de hoja en trabajo anteriores. Sin embargo, existe una carencia de conocimiento de este tipo de relaciones a nivel de cubierta, y más concretamente aplicado a doseles de vegetación heterogéneos como los bosques de coníferas. Los doseles en este tipo de masas son estructuralmente más complejos que otros tipos de vegetación, por lo tanto, las relaciones derivadas a nivel de hoja o de cubierta homogénea no se pueden aplicar de una manera generalizada. En consecuencia, la modelización a escala de la hoja y de cubierta es necesaria para permitir un uso operativo de SVI que permitan determinar los niveles de estrés en cubiertas no homogéneos, donde la variación estructural tiene gran efecto sobre la firma espectral de la cubierta. Este trabajo presenta nuevas formulaciones de SVI relacionados con Cx+c y ciclo de las xantofilas (VAZ) obtenidas a partir de la simulación con modelos de transferencia radiativa y datos experimentales, demostrando la fiabilidad de dichas formulaciones a nivel de cubierta. La metodología ha sido probada en dos especies de coníferas mediterráneas: Pinus sylvestris y Pinus nigra. Este estudio ha requerido mediciones de parámentros biofísicos en campo, análisis ópticos y bioquímicos foliares de laboratorio, así como el análisis de imágenes hiperespectrales adquiridas en plataformas tripuladas y de vehículos aéreos no tripulados (UAV)

In situ measurement of Scots pine needle PRI

Abstract Background The Photochemical Reflectance Index (PRI) calculated from narrow-band spectral reflectance data is a vegetation index which is increasingly used as an indicator of photosynthetic activity. The leaf-level link between the status of photosynthetic apparatus and PRI has been robustly established under controlled light conditions. However, when a whole canopy is measured instantaneously, the PRI signal is heavily modified by vegetation structure and local variations in incident light conditions. To apply PRI for monitoring the photosynthesis of whole canopies under natural conditions, these large-scale measurements need to be validated against simultaneous leaf PRI. Unfortunately, PRI changes dynamically with incident light and has a large natural variation. No generally accepted procedure exists today for determining the PRI of canopy elements in situ. Results We present a successful procedure for in situ measurements of needle PRI. We describe, characterize and test an optical measurement protocol and demonstrate its applicability in field conditions. The measurement apparatus consisted of a light source, needle clip, spectroradiometer and a controlling computer. The light level inside the clip was approximately two-thirds of that on sunlit needle surfaces at midday. During each measurement the needle was inserted into the clip for approximately 5 s. We found no near-instantaneous changes (sub-second scale jumps) in PRI during the measurements. The time constants for PRI variation in light to full shade acclimations were approximately 10 s. The procedure was successfully applied to monitor the greening-up of Scots pine trees. We detected both facultative (diurnal) PRI changes of 0.02 (unitless) and constitutive (seasonal) variations of 0.1. In order to reliably detect the facultative PRI change of 0.02, 20 needles need to be sampled from both sunlit and shaded locations. Conclusions We established a robust procedure for irradiance-dependent leaf (needle) PRI measurements, facilitating empirical scaling of PRI from leaf (needle) to full canopy level and the application of PRI to monitoring the changes in highly structured vegetation. The measured time constants, and facultative and constitutive PRI variations support the use of an artificial light for in situ PRI measurements at leaf (needle) level

Deriving Predictive Relationships of Carotenoid Content at the Canopy Level in a Conifer Forest Using Hyperspectral Imagery and Model Simulation

Recent studies have demonstrated that the R570/R515 index is highly sensitive to carotenoid (Cx + c) content in conifer forest canopies and is scarcely influenced by structural effects. However, validated methods for the prediction of leaf carotenoid content relationships in forest canopies are still needed to date. This paper focuses on the simultaneous retrieval of chlorophyll (Ca + b) and (Cx + c) pigments, which are critical bioindicators of plant physiological status. Radiative transfer theory and modeling assumptions were applied at both laboratory and field scales to develop methods for their concurrent estimation using high-resolution hyperspectral imagery. The proposed methodology was validated based on the biochemical pigment quantification. Canopy modeling methods based on infinite reflectance formulations and the discrete anisotropic radiative transfer (DART) model were evaluated in relation to the PROSPECT-5 leaf model for the scaling-up procedure. Simpler modeling methods yielded comparable results to more complex 3-D approximations due to the high spatial resolution images acquired, which enabled targeting pure crowns and reducing the effects of canopy architecture. The scaling-up methods based on the PROSPECT-5+DART model yielded a root-mean-square error (RMSE) and a relative RMSE of 1.48 μg/cm2 (17.45%) and 5.03 μg/cm2 (13.25%) for Cx+c and Ca+ b, respectively, while the simpler approach based on the PROSPECT-5+Hapke infinite reflectance model yielded 1.37 & mug/cm2 (17.46%) and 4.71 μg/cm2 (14.07%) for Cx + c and Ca+b, respectively. These predictive algorithms proved to be useful to estimate Ca + b and Cx + c from high-resolution hyperspectral imagery, providing a methodology for the monitoring of these photosynthetic pigments in conifer forest canopies. © 2013 IEEE.Peer Reviewe

Characteristics of areas affected by fire in 2005 at Parque Nacional de Torres del Paine (Chile) as assessed from multispectral images

El uso de sensores remotos para la evaluación de la severidad es una de los aspectos más importantes en el estudio de grandes incendios, así como la aplicación de los resultados para el proceso de restauración. En este trabajo se ha estudiado la aplicación de imágenes de los sensores Landsat ETM+ y ASTER para evaluar la vegetación previa, la superficie recorrida por el fuego y los daños producidos por el incendio ocurrido en el año 2005 en el Parque Nacional de Torres del Paine (Chile). Los resultados obtenidos indican que el índice delta NBR es bastante versátil para evaluar la superficie afectada, estimada en este caso en 17.138 ha, así como la severidad de los daños (Fiabilidad = 81,5 %; κ = 0,73). Por otro lado, se ha confirmado la adecuación del uso de imágenes Landsat ETM+ para mejorar la calidad de los mapas de vegetación previa a la ocurrencia del fuego (Fiabilidad = 79,5 %; κ = 0,75). La combinación de esta información se ha podido aplicar para apoyar la restauración del área afectada por el incendio. Sin embargo, los resultados también han mostrado algunas limitaciones de los sensores, en particular en la definición de ecosistemas con representaciones superficiales pequeñas y/o fragmentadas, lo cual sugiere que el uso de sensores de mayor resolución espacial puede mejorar los productos cartográficos finales y, por tanto, la calidad de los trabajos de restauración.The use of remote sensors is one of the most important aspects in the study of large fires for an assessment of their severity, as well as the application of the results to the restoration process. This work has studied the application of images from the Landsat ETM + ASTER sensors in order to evaluate the prior vegetation, the surface burned and the damage caused by a fire occurring in 2005 in the National Park of Torres del Paine (Chile). The results obtained indicate that the delta NBR index is reasonably versatile for evaluating the affected surface, in this case estimated at 17.138 ha, as well as the damage severity (Reliability = 81.5 %; κ = 0.73). In addition, the suitability of using Landsat ETM+ images to improve the quality of maps of vegetation prior to the fire (Reliability = 79.5 %; κ = 0.75.) has been confirmed. It has been possible to apply a combination of this information to assist in the restoration of the fire-affected area. However, the results have also shown some limitations in the sensors, particularly in the definition of ecosystems with small and/or fragmented surface representations, which suggests that the use of sensors with a greater spatial resolution could improve the final cartographic products, and, therefore, the quality of the restoration works

Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain

We explored the usefulness of LiDAR for modelling and mapping the stand biomass of two conifer species in southern Spain. We used three different plot sizes and two statistical approaches (i.e. stepwise selection and genetic algorithm selection) in combination with multiple linear regression models to estimate biomass. 43 predictor variables derived from discrete-return LiDAR data (4 pulses per m2 ) were used for estimating the forest biomass of Pinus sylvestris Linnaeus and Pinus nigra Arnold forests. Twelve circular plots – six for each species – and three different fixed-radius designs (i.e. 7, 15, and 30 m) were estab lished within the range of the airborne LiDAR. The Bayesian information criterion and R2 were used to select the best models. As expected, the models that included the largest plots (30 m) yielded the highest R2 value (0.91) for Pinus sp. using genetic algorithm models. Considering P. sylvestris and P. nigra models separately, the genetic algorithm approach also yielded the highest R2 values for the 30-m plots (P. nigra: R2 = 0.99, P. sylvestris: R2 = 0.97). The results we obtained with two species and different plot sizes revealed that increasing the size of plots from 15 to 30 m had a low effect on modelling attempts.European Commission (EC) FP7-315165Ministerio de Economía, Industria y Competitividad QUERCUSAT (CLG2013-40790-R

Forest decline evaluation in Antarctic Beech Forests (Nothofagus antarctica) in Chilean Patagonia by using Landsat TM and ETM+

Antarctic beech forests (Nothofagus antarctica (G. Forst.) Oerst.) have shown a major decline process in the past few decades, together with an important lack of specific studies on this type of forest. The aim of this work was to create cartography of the surface of Antarctic beech forests and to evaluate decline levels in the XII Region of Chile. A study area was selected between the cities of Puerto Natales and Punta Arenas (latitudes 50º40’S to 52º40’S) and from latitudes 60º15’W to 74º15’W, where a random stratified sampling was carried out in 68 plots, in which the forest cover, mortality, height, normal diameter and regeneration were measured. Using two Landsat images (1986-2002), the study area was classified in terms of vegetation cover and forest mortality, by means of the normalized vegetation index (NDVI). The forests in this study area are characterized by their high density, and, in over half their surface (27,873 ha) they exhibit some degree of mortality, with 7,585 ha of forest completely affected. The distribution of the mortality in Antarctic beech on the period 1986-2002 showed an improvement on forests condition, which seems to corroborate the hypothesis of a change on perturbation pattern as the major reason for this forest decline process.Los bosques de ñirre (Nothofagus antarctica) han experimentado en las últimas décadas un importante proceso de mortalidad. El objetivo de este trabajo fue elaborar una cartografía de las masas de ñirre en función de la fracción de cabida cubierta del dosel arbóreo y el nivel de mortalidad en la XII Región de Chile. En una zona entre las ciudades de Puerto Natales y Punta Arenas (50º40’ - 52º40’ S y 69º15’ - 74º15’ O) se realizó un muestreo estratificado en 68 parcelas, donde se midieron: fracción de cabida cubierta, mortandad del arbolado, altura, diámetro normal (DAP) y regeneración. Mediante clasificación de dos imágenes Landsat TM (1986) y ETM+ (2002) se estudiaron el estado de las masas de ñirre y la evolución de la mortandad en un periodo de 16 años, utilizando el índice de vegetación normalizado (NDVI). En el año 2002 los bosques de ñirre se caracterizaban por una elevada fracción de cabida cubierta, tallas y diámetros medios, y una escasa regeneración. Más de la mitad de la superficie de estudio (casi 28.000 ha) presentaba algún grado de mortandad del arbolado, con 7.585 ha de bosques totalmente muertos. El patrón de mortandad, por comparación con el estado del arbolado en 1986, indicó una tendencia a mejorar el estado del arbolado en los últimos 16 años, lo cual parece confirmar la hipótesis de que los procesos de mortandad en esta especie no están asociados a un cambio en el patrón climático en la zona, sino más bien a la modificación del régimen de perturbaciones

Global monitoring of soil multifunctionality in drylands using satellite imagery and field data

Models derived from satellite image data are needed to monitor the status of terrestrial ecosystems across large spatial scales. However, a remote sensing-based approach to quantify soil multifunctionality at the global scale is missing despite significant research efforts on this topic. A major constraint for doing so is the availability of suitable global-scale field data to calibrate remote sensing indicators (RSI) and, to a lesser extent, the sensitivity of spectral data of available satellite sensors to soil background and atmospheric conditions. Here, we aimed to develop a soil multifunctionality model to monitor global drylands coupling ground data on 14 soil functions of 222 dryland areas from six continents to 18 RSI derived from a time series (2006–2013) Landsat dataset. Among the RSI evaluated, the chlorophyll absorption ratio index was the best predictor of soil multifunctionality in single-variable-based models (r = 0.66, P < 0.01, NMRSE = 0.17). However, a multi-variable RSI model combining the chlorophyll absorption ratio index, the global environment monitoring index and the canopy-air temperature difference improved the accuracy of quantifying soil multifunctionality (r = 0.73, P < 0.01, NMRSE = 0.15). Furthermore, the correlation between RSI and soil variables shows a wide range of accuracy with upper and lower values obtained for AMI (r = 0.889, NMRSE = 0.05) and BGL (r = 0.685, NMRSE = 0.18) respectively. Our results provide new insights on assessing soil multifunctionality using RSI that may help to monitor temporal changes in the functioning of global drylands effectively.Field data were obtained with the support of the European Research Council (ERC) grant agreement 242658 (BIOCOM). Hernández-Clemente R was supported by the Ramón y Cajal program (RYC2020-029187-I) and the State Plan for Scientific and Subprogram for Knowledge Generation (PID2021-124058OA-I00) from the Spanish Ministry of Science and Innovation (RYC2020-029187-I). Maestre FT acknowledges support from Generalitat Valenciana (CIDEGENT/2018/041) and the Spanish Ministry of Science and Innovation (EUR2022-134048)

Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling

Outbreaks of Xylella fastidiosa (Xf) in Europe generate considerable economic and environmental damage, and this plant pest continues to spread. Detecting and monitoring the spatio-temporal dynamics of the disease symptoms caused by Xf at a large scale is key to curtailing its expansion and mitigating its impacts. Here, we combined 3-D radiative transfer modelling (3D-RTM), which accounts for the seasonal background variations, with passive optical satellite data to assess the spatio-temporal dynamics of Xf infections in olive orchards. We developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations. Sentinel-2A time series data collected over a two-year period were used to assess the temporal trends in Xf-infected olive orchards in the Apulia region of southern Italy. Hyperspectral images spanning the same two-year period were used for validation, along with field surveys; their high resolution also enabled the extraction of soil spectrum variations required by the 3D-RTM to account for canopy background effect. Temporal changes were validated with more than 3000 trees from 16 orchards covering a range of disease severity (DS) and disease incidence (DI) levels. Among the wide range of structural and physiological vegetation indices evaluated from Sentinel-2 imagery, the temporal variation of the Atmospherically Resistant Vegetation Index (ARVI) and Optimized Soil-Adjusted Vegetation Index (OSAVI) showed superior performance for DS and DI estimation (r2VALUES>0.7, p < 0.001). When seasonal understory changes were accounted for using modelling methods, the error of DI prediction was reduced 3-fold. Thus, we conclude that the retrieval of DI through model inversion and Sentinel-2 imagery can form the basis for operational vegetation damage monitoring worldwide. Our study highlight the value of interpreting temporal variations in model retrievals to detect anomalies in vegetation health.Data collection was partially supported by the European Union's Horizon 2020 research and innovation programme through grant agreements POnTE (635646) and XF-ACTORS (727987). A. Hornero was supported by research fellowship DTC GEO 29 “Detection of global photosynthesis and forest health from space” from the Science Doctoral Training Centre (Swansea University, UK). The authors would also like to thank QuantaLab-IAS-CSIC (Spain) for laboratory assistance and the support provided during the airborne campaigns and image processing. B. Landa, C. Camino, M. Montes-Borrego, M. Morelli, M. Saponari and L. Susca are acknowledged for their support during the field campaigns, as well as IPSP-CNR and Dipartimento di Scienze del Suolo (Università di Bari, Italy) as host institutions

A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data

Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands required for estimating and frequently updating forest data. Here we demonstrate the capability of alternative solutions based on the use of low-cost color infrared (CIR) cameras to estimate tree-level parameters, providing a cost-effective solution for forest inventories. ALS data were acquired with a Leica ALS60 laser scanner and digital aerial imagery (DAI) was acquired with a consumer-grade camera modified for color infrared detection and synchronized with a GPS unit. In this paper we evaluate the generation of a DAI-based canopy height model (CHM) from imagery obtained with low-cost CIR cameras using structure from motion (SfM) and spatial interpolation methods in the context of a complex canopy, as in forestry. Metrics were calculated from the DAI-based CHM and the DAI-based Normalized Difference Vegetation Index (NDVI) for the estimation of tree height and LAI, respectively. Results were compared with the models estimated from ALS point cloud metrics. Field measurements of tree height and effective leaf area index (LAIe) were acquired from a total of 200 and 26 trees, respectively. Comparable accuracies were obtained in the tree height and LAI estimations using ALS and DAI data independently. Tree height estimated from DAI-based metrics (Percentile 90 (P90) and minimum height (MinH)) yielded a coefficient of determination (R2) = 0.71 and a root mean square error (RMSE) = 0.71 m while models derived from ALS-based metrics (P90) yielded an R2 = 0.80 and an RMSE = 0.55 m. The estimation of LAI from DAI-based NDVI using Percentile 99 (P99) yielded an R2 = 0.62 and an RMSE = 0.17 m2/m−2. A comparative analysis of LAI estimation using ALS-based metrics (laser penetration index (LPI), interquartile distance (IQ), and Percentile 30 (P30)) yielded an R2 = 0.75 and an RMSE = 0.14 m2/m−2. The results provide insight on the appropriateness of using cost-effective 3D photo-reconstruction methods for targeting single trees with irregular and heterogeneous tree crowns in complex open-canopy forests. It quantitatively demonstrates that low-cost CIR cameras can be used to estimate both single-tree height and LAI in forest inventories