Response of spectral reflectances and vegetation indices on varying juniper cone densities

Juniper trees are widely distributed throughout the world and are common sources of allergies when microscopic pollen grains are transported by wind and inhaled. In this study, we investigated the spectral influences of pollen-discharging male juniper cones within a juniper canopy. This was done through a controlled outdoor experiment involving ASD FieldSpec Pro Spectroradiometer measurements over juniper canopies of varying cone densities. Broadband and narrowband spectral reflectance and vegetation index (VI) patterns were evaluated as to their sensitivity and their ability to discriminate the presence of cones. The overall aim of this research was to assess remotely sensed phenological capabilities to detect pollen-bearing juniper trees for public health applications. A general decrease in reflectance values with increasing juniper cone density was found, particularly in the Green (545-565 nm) and NIR (750-1,350 nm) regions. In contrast, reflectances in the shortwave-infrared (SWIR, 2,000 nm to 2,350 nm) region decreased from no cone presence to intermediate amounts (90 g/m2) and then increased from intermediate levels tothe highest cone densities (200 g/m2). Reflectance patterns in the Red (620-700 nm) were more complex due to shifting contrast patterns in absorptance between cones and juniper foliage, where juniper foliage is more absorbing than cones only within the intense narrowband region of maximum chlorophyll absorption near 680 nm. Overall, narrowband reflectances were more sensitive to cone density changes than the equivalent MODIS broadbands. In all VIs analyzed, there were significant relationships with cone density levels, particularly with the narrowband versions and the two-band vegetation index (TBVI) based on Green and Red bands, a promising outcome for the use of phenocams in juniper phenology trait studies. These results indicate that spectral indices are sensitive to certain juniper phenologic traits that can potentially be used for juniper cone detection in support of public health applications. © 2013 by the authors

A mathematical transformation of multi-angular remote sensing data for the study of vegetation change

Vegetation change is an important factor affecting the global carbon cycle, land-atmosphere interaction, and terrestrial ecology. The study of vegetation change on a global scale can be used to evaluate the impact of global climate change on terrestrial ecosystems. Satellite remote sensing can monitor vegetation change at the global scale, providing continuous samples of radiation reflected by vegetated surfaces with a temporal resolution of days. The MISR instrument offers the potential to sample the specular anisotropy of the Earth from up to 9 angles. Characterization of the specular anisotropy of vegetated surfaces on a global scale will provide information on the physical characteristics of vegetation affecting anisotropy not available from nadir-view only remote sensing. The objective of this study is to develop a Principal Components Analysis (PCA) transformation of multi-angular measurements of the Earth’s surface acquired by the MISR instrument, to examine the feasibility of quantifying the structural characteristics of different vegetation communities at a global scale. This transformation will be applied to a time-series analysis of the Kalmioposis Wilderness in the Siskiyou National Forest in Southwestern Oregon to better understand the changes in spectral and angular reflectance of a forest stand during re-growth after a stand-replacing disturbance. A sample encompassing a full phenologic cycle, of the red bands only from MISR cameras Ca – Cf, at scaled surface reflectance, provided the template on which PCA was performed. The sample of MISR data was created using imagery collected from 2001 – 2005 to provide a wide variety of vegetation and soils reflectance over a phenologic cycle. Sample data was rotated to the principal components as the new axes using the coefficients of rotation from an un-standardized PCA. Samples were evaluated at various latitudes, differing topography, and varying vegetation density and land cover to determine the properties of the scene controlling the range and magnitude of the principal components. Principal component 1 was found to have high negative correlation to NDVI. Principal component 2 was found to have high positive correlation to both the solar zenith angle and the relative azimuth angle between the MISR sensor and the path of incident radiation. Principal component 3 could not be correlated to any available metric, although evidence suggests that component 3 may carry useful information. The PCA transformation proved useful at relating the changes in vegetation after a fire at the Biscuit Complex. The changes in the BRDF as sampled by MISR were expressed through the principal components, but these changes could not be directly related to changing structural characteristics of the vegetation. The goal of assessing structural characteristics of vegetation through the PCA transformation to a single metric of vegetation structure was unsuccessful. The PCA transformation of the MISR sample successfully yielded a transformation where different classes of vegetation occupied distinct and unique regions of PCA space. The first two principal components were successfully correlated to measurable and definable metrics of vegetation and solar illumination. The third principal component, for which a correlation could not be found, was suggestive of carrying unique information and merits further investigation. The transformation of multi-angular red band reflectance as presented in this study may prove to be a valuable method of estimating biomass at a global scale. With principal components correlated to measures of biomass in NDVI and to the shadowing of the ground through the angle of solar illumination, the PCA relates characteristics of vegetated scenes in a minimum of bands

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)

Assesment of biomass and carbon dynamics in pine forests of the Spanish central range: A remote sensing approach

Forests play a dynamic role in the terrestrial carbon (C) budget, by means of the biomass stock and C fluxes involved in photosynthesis and respiration. Remote sensing in combination with data analysis constitute a practical means for evaluation of forest implications in the carbon cycle, providing spatially explicit estimations of the amount, quality, and spatio-temporal dynamics of biomass and C stocks. Medium and high spatial resolution optical data from satellite-borne sensors were employed, supported by field measures, to investigate the carbon role of Mediterranean pines in the Central Range of Spain during a 25 year period (1984-2009). The location, extent, and distribution of pine forests were characterized, and spatial changes occurred in three sub-periods were evaluated. Capitalizing on temporal series of spectral data from Landsat sensors, novel techniques for processing and data analysis were developed to identify successional processes at the landscape level, and to characterize carbon stocking condition locally, enabling simultaneous characterization of trends and patterns of change. High spatial resolution data captured by the commercial satellite QuickBird-2 were employed to model structural attributes at the stand level, and to explore forest structural diversity

Monitoring Pollen Counts and Pollen Allergy Index Using Satellite Observations in East Coast of the United States

Allergic diseases have become increasingly common over the world during the last four decades, and they are affecting millions of people. Pollination is an important process in the life cycle of plants. However, pollen exposure is associated with allergic diseases such as asthma and seasonal allergic rhinitis (hay fever). As a result, the total annual expenditure for asthma-associated morbidity is about $56 billion in the United States, and the overall cost of allergic diseases is over$18 billion annually. For allergic rhinitis, the annual medical cost is approximately $3.4 billion. The intensity and frequency of the pollen exposures can be easily affected by many factors such as climate, vegetation, and topography, which are difficult to predict in large scales. Vegetation is very important as a pollen source, and the amount and time of pollinations depend on the flowering and growth of plants. With optimal water and temperature, vegetation can reach a maximum growth and flowering during a growing season, which means that maximum amount of pollen can be released from the plants. However, if the requirements of water and temperature cannot be met in the specific times within the growing season, pollen dispersal will be affected negatively. It is an urgent need to develop models or systems for predicting pollen events at large scales and providing early warning to prevent pollen effects on people. Unlike manual pollen counting at local sites, remote sensing facilitates the pollen estimates at large scales with temporally and spatially distributed observations, which significantly reduces the time and labor costs. With remotely sensed observations, Artificial Neural Network (ANN) helps us fill the gaps in understanding of the relationships between environmental variables and pollen concentration. At this point, I investigated pollen estimates from satellite observations in the states of East Coast United States with short and long-term data. This region is highly populated with a population of 104 million. In addition, this region has a great variety of temperature, precipitation, and vegetation. The final goal of this project is to investigate the relationships between satellite-derived variables (precipitation, land surface temperature (LST), and enhance vegetation index (EVI2)) and pollen count and further to generate a model for the prediction of pollen counts at high temporal and spatial resolutions. For this purpose, to predict pollen concentration using environmental variables, a Neural Network Analysis was performed. The results showed that strong correlations existed between pollen counts and environmental variables, except for precipitation in most locations. The validation analysis using regression models revealed strongly significant relationships between the observed and predicted pollen concentrations obtained for short and long-term data. The R squares (R2) for long term pollen counts were mostly higher than 0.5, ranging from 0.5542 for Olean, NY to 0.8589 for Savannah, GA. For short term predictions of pollen allergy index, R2 ranged from 0.53 to 0.966 except for a few sites, especially in southern Florida. The pollen distribution was mostly affected by precipitation in the southern part, whereas it was influenced by temperature in the northern part. Moreover, results demonstrated that ANN is a suitable tool for complicated statistical analysis and EVI2 combining with LST and precipitation is a reliable predictor of pollen variation. Overall the results provide a better understanding of pollen variation with vegetation seasonality and climate variables, which could assist an approach towards the establishment of an early warning system for allergy patients

The global tree carrying capacity (keynote)

editorial reviewe

Is there a solution to the spatial scale mismatch between ecological processes and agricultural management?

The major limit to develop robust landscape planning for biodiversity conservation is that the spatial levels of organization of landscape management by local actors rarely match with those of ecological processes. This problem, known as spatial scale mismatch, is recognized as a reason of lack of effectiveness of agri-environment schemes. We did a review to describe how authors identify the problem of spatial scale mismatch in the literature. The assumption is made that the solutions proposed in literature to conciliate agricultural management and conservation of biodiversity are based on theoretical frameworks that can be used to go towards an integration of management processes and ecological processes. Hierarchy Theory and Landscape Ecology are explicitly mobilized by authors who suggest multiscale and landscape scale approaches, respectively, to overcome the mismatch problem. Coordination in management is proposed by some authors but with no theoretical background explicitly mentioned. The theory of organization of biological systems and the theories of Social-Ecological Systems use the concept of coordination and integration as well as concepts of organization, adaptive capabilities and complexity of systems. These theories are useful to set up a new framework integrating ecological processes and agricultural management. Based on this review we made two hypotheses to explain difficulties to deal with spatial scale mismatch: (1) authors generally do not have an integrated approach since they consider separately ecological and management processes, and (2) an inaccurate use of terminology and theoretical frameworks partially explain the inadequacy of proposed solutions. We then specify some terms and highlight some ‘rules’ necessary to set up an integrative theoretical and methodological framework to deal with spatial scale mismatch.(Presentation des résumés n°186, p. 95-96, non paginé

Proceedings of the 6th International Workshop of the EARSeL Special Interest Group on Forest Fires Advances in Remote Sensing and GIS Applications in Forest Fire Management Towards an Operational Use of Remote Sensing in Forest Fire Management

During the last two decades, interest in forest fire research has grown steadily, as more and more local and global impacts of burning are being identified. The definition of fire regimes as well as the identification of factors explaining spatial and temporal variations in these fire characteristics are recently hot fields of research. Changes in these fire regimes have important social and ecological implications. Whether these changes are mainly caused by land use or climate warming, greater efforts are demanded to manage forest fires at different temporal and spatial scales. The European Association of Remote Sensing Laboratories (EARSeL)’s Special Interest Group (SIG) on Forest Fires was created in 1995, following the initiative of several researchers studying Mediterranean fires in Europe. It has promoted five technical meetings and several specialised publications since then, and represents one of the most active groups within the EARSeL. The SIG has tried to foster interaction among scientists and managers who are interested in using remote sensing data and techniques to improve the traditional methods of fire risk estimation and the assessment of fire effect. The aim of the 6th international workshop is to analyze the operational use of remote sensing in forest fire management, bringing together scientists and fire managers to promote the development of methods that may better serve the operational community. This idea clearly links with international programmes of a similar scope, such as the Global Monitoring for Environment and Security (GMES) and the Global Observation of Forest Cover/Land Dynamics (GOFC-GOLD) who, together with the Joint Research Center of the European Union sponsor this event. Finally, I would like to thank the local organisers for the considerable lengths they have gone to in order to put this material together, and take care of all the details that the organization of this event requires.JRC.H.3-Global environement monitorin