Canopy structural attributes derived from AVIRIS imaging spectroscopy data in a mixed broadleaf/conifer forest

There is a well-established need within the remote sensing community for improved estimation and understanding of canopy structure and its influence on the retrieval of leaf biochemical properties. The main goal of this research was to assess the potential of optical spectral information from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to discriminate different canopy structural types. In the first phase, we assessed the relationships between optical metrics and canopy structural parameters obtained from LiDAR in terms of different canopy structural attributes (biomass (i.e., area under Vegetation Vertical Profile, VVPint), canopy height and vegetation complexity). Secondly, we identified and classified different âcanopy structural typesâ by integrating several structural traits using Random Forests (RF). The study area is a heterogeneous forest in Sierra National Forest in California (USA). AVIRIS optical properties were analyzed by means of several sets of variables, including single narrow band reflectance and 1st derivative, sub-pixel cover fractions, narrow-band indices, spectral absorption features, optimized normalized difference indices and Principal Component Analysis (PCA) components. Our results demonstrate that optical data contain structural information that can be retrieved. The first principal component, used as a proxy for albedo, was the most strongly correlated optical metric with vegetation complexity, and it also correlated well with biomass (VVPint) and height. In conifer forests, the shade fraction was especially correlated to vegetation complexity, while water-sensitive optical metrics had high correlations with biomass (VVPint). Single spectral band analysis results showed that correlations differ in magnitude and in direction, across the spectrum and by vegetation type and structural variable. This research illustrates the potential of AVIRIS to analyze canopy structure and to distinguish several structural types in a heterogeneous forest. Furthermore, RF using optical metrics derived from AVIRIS proved to be a powerful technique to generate maps of structural attributes. The results emphasize the importance of using the whole optical spectrum, since all spectral regions contributed to canopy structure assessment

Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod

The aims of this study were to assess the dynamics of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI(1) and NDWI(2)) and Shortwave Angle Slope Index (SASI) in relation to rice agricultural practices and hydroperiod, and (2) to assess the capability for these indices to detect phenometrics in rice under different flooding regimes

Leaf spectral clusters as potential optical leaf functional types within California ecosystems

Our ability to measure and map plant function at multiple ecological scales is critical for understanding current and future changes in Earth's ecosystems and the global carbon budget. Conventional plant functional types (cPFTs) based on a few productivity-related traits have been previously used to simplify and represent major differences in global plant functions, but more recent research has directly focused on the use of functional trait information. Still, sampling limitations have constrained efforts to truly understand the variance and covariance of functional traits globally. Reflectance spectra offer a fast, repeatable, simultaneous measurement of a wide variety of leaf functional traits and could be used to optically define leaf functional types. To evaluate this concept, we measured leaf reflectance from a wide range of species in a diverse set of ecosystems across central and northern California, including observations from multiple individuals, sites, and seasons. Using principal components analysis, we analyzed spectral variation in relation to categorical attributes such as species and cPFTs, as well as to a set of functional trait metrics calculated from the spectra. We found the first three principal components (PCs) to be weakly related to categorical attributes and more strongly related to spectrally-derived functional metrics. Each PC was more strongly associated with different portions of the spectrum and contained different functional information. We applied a hybrid clustering algorithm to the PC coordinates of the observations to define potential optical leaf functional types. Twelve spectral clusters were identified, and these did not correspond directly to either single cPFTs or species. However, each cluster had a unique functional metric profile. Clusters represented both inter- and intra-species and cPFT functional differences driven by taxonomy, trait evolution and environmental responses, demonstrating their value as optical leaf functional types and the value of the clustering approach used here for defining optical types from leaf spectra. Our findings support the notion that cPFTs do not adequately capture differences in leaf function. They demonstrate that spectral measurements can be used to improve both the definition of PFTs as well as our knowledge regarding the covariance of functional traits within these classes

Application of Vegetation Indices to Estimate Acorn Production at Iberian

The Iberian pig valued natural resources of the pasture when fattened in mountain. The variability of acorn production is not contained in any line of Spanish agricultural insurance. However, the production of arable pasture is covered by line insurance number 133 for loss of pasture compensation. This scenario is only contemplated for breeding cows and brave bulls, sheep, goats and horses, although pigs are not included. This insurance is established by monitoring ten-day composites Normalized Difference Vegetation Index (NDVI) measured by satellite over treeless pastures, using MODIS TERRA satellite. The aim of this work is to check if we can use a satellite vegetation index to estimate the production of acorns

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la dinámica de los procesos medio ambientales está considerado como un punto de gran interés en el campo medioambiental. La cobertura espacio temporal de los datos de teledetección proporciona información continua con una alta frecuencia temporal, permitiendo el análisis de la evolución de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetección ha sido ampliamente probado, en la actualidad solo existe un número reducido de metodologías que permiten su análisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetección, basado en la combinación del análisis estadístico de series de tiempo y la fenometría. El objetivo principal es demostrar el uso de las series temporales de datos de teledetección para analizar la dinámica de variables medio ambientales de una forma cuantitativa. Los objetivos específicos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos empíricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos específicos son: (1) evaluar y cartografiar estados fenológicos del cultivo del algodón mediante análisis espectral y fenometría, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclimáticas mediante modelos dinámicos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos dinámicos y (4) evaluar el funcionamiento de la vegetación en base a la autocorrelación temporal y la fenometría. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los índices espectrales AS1 y AS2. Los parámetros fenológicos derivados del ajuste de funciones permiten la identificación de distintos estados fenológicos del cultivo del algodón. El análisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el índice AS2 y de dos ciclos en el AS1 así como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterránea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la dinámica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelación temporal para definir y caracterizar el funcionamiento de la vegetación a nivel pixel. Finalmente el concepto “Optical Functional Type” ha sido definido, donde se propone que los pixeles deberían ser considerados como unidades temporales y analizados en función de su dinámica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the “Optical Functional Types” concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning

Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California

Accurate information about ecosystem structure and biogeochemical properties is essential to providing better estimates ecosystem functioning. Airborne LiDAR (light detection and ranging) is the most accurate way to retrieve canopy structure. However, accurately obtaining both biogeochemical traits and structure parameters requires concurrent measurements from imaging spectrometers and LiDARs. Our main objective was to evaluate the use of imaging spectroscopy (IS) to provide vegetation structural information. We developed models to estimate structural variables (i.e., biomass, height, vegetation heterogeneity and clumping) using IS data with a random forests model from three forest ecosystems (i.e., an oak-pine low elevation savanna, a mixed conifer/broadleaf mid-elevation forest, and a high-elevation montane conifer forest) in the Sierra Nevada Mountains, California. We developed and tested general models to estimate the four structural variables with accuracies greater than 75%, for the structurally and ecologically different forest sites, demonstrating their applicability to a diverse range of forest ecosystems. The model R2 for each structural variable was least in the conifer/broadleaf forest than either the low elevation savanna or the montane conifer forest. We then used the structural variables we derived to discriminate site-specific, ecologically meaningful descriptions of canopy structural types (CST). Our CST results demonstrate how IS data can be used to create comprehensive and easily interpretable maps of forest structural types that capture their major structural features and trends across different vegetation types in the Sierra Nevada Mountains. The mixed conifer/broadleaf forest and montane conifer forest had the most complex structures, containing six and five CSTs respectively. The identification of CSTs within a site allowed us to better identify the main drivers of structural variability in each ecosystem. CSTs in open savanna were driven mainly by differences in vegetation cover; in the mid-elevation mixed forest, by the combination of biomass and canopy height; and in the montane conifer forest, by vegetation heterogeneity and clumping

Suivi du LAI et du contenu en chlorophylles et caroténoïdes d'une savanne boisée à l'aide d'imagerie hyperspectral et d'un logiciel de transfert radiatif 3D

International audienceLeaf pigment contents, such as chlorophylls a and b content (Cab) or carotenoids content (Car), and the Leaf Area Index (LAI) are recognized indicators of plants and forests health status that can be estimated through hyperspectral imagery. Their measurement on a seasonal and yearly basis is critical to monitor plant response and adaptation to stress such as droughts. While extensively done over dense canopies, estimation of these variables over tree-grass ecosystems with very low overstory LAI (mean site LAI<1 m²/m²), such as woodland savannas, is lacking. We investigate the use of Look-Up Table (LUT) based inversion of a Radiative Transfer Model to retrieve LAI and leaf Cab and Car from AVIRIS images at an 18 m spatial resolution at multiple dates over a broadleaved woodland savanna during the California drought. We compare the performances of different cost functions in the inversion step. We demonstrate the spatial consistency of our LAI, Cab and Car estimations by using validation data from low and high canopy cover parts of the site, and their temporal consistency by qualitatively confronting their variations over two years with those that would be expected. We conclude that LUT-based inversions of medium-resolution hyperspectral images, achieved with a simple geometric representation of the canopy within a 3D RTM, are a valid mean of monitoring woodland savannas and more generally sparse forests, although for maximum applicability the inversion cost functions should be selected using validation data from multiple dates. Validation revealed that for monitoring use: NDVI outperformed other indices for LAI estimations (RMSE=0.22 m²/m², R²=0.81); the band ratio rho_0.750µm/rho_0.550µm retrieved Cab more accurately than other chlorophylls indices (RMSE=5.94 µg/cm², R²=0.75); RMSE over the 0.5-0.55 µm interval showed encouraging results for Car estimations.Le contenu foliaire en pigments, comme le contenu en chlorphylles a et b (Cab) ou le contenu en caroténoïdes (Car), et le Leaf Area Index (LAI) sont des indicateurs reconnus de la santé des plantes et des arbres qui peuvent être estimés à l'aide de l'imagerie hyperspectrale. Leurs mesures saisonnières et annuelles sont critiques pour suivre la réponse et l'adaptation des plantes à des stress tels que des sécheresses. Bien que très largement appliqués au dessus de couverts denses, l'estimation de ces variables pour des écosystèmes herbe-arbre avec un faible LAI de canopée (LAI moyen du site<1m²/m²), comme les savannes boisées, est rare. Nous investigons l'utilisation d'inversion basées sur des Look-Up Tables (LUT) générées à l'aide de logiciels de transfert radiatif pour estimer le LAI et ainsi que le Cab et Car foliaire à partir d'images AVIRIS d'une savanne boisée de feuillus acquises à différentes dates pendant la sécheresse californienne avec une résolution spatiale de 18 m. Nous comparons les performances de différentes fonctions de coût durant l'étape d'inversion. Nous démontrons la cohérence spatiale de nos estimations de LAI, Cab et Car à l'aide de données de validations issues de parties du site ayant différentes couvertures de canopée, et leur cohérence temporelle de façon qualitative en comparant leurs variations sur des ans à celles qui seraient attendues. Nous concluons que des inversions par LUT d'images hyperspectrales de moyenne résolution, utilisant une représentation géométrique simple de la canopée au sein de logiciels de transfert radiatif 3D, sont appropriées pour le suivi de savannes boisées et plus généralement de forêts éparses, bien que pour une généralisation maximale les fonctions de coût de l'inversion doivent être sélectionnées à l'aide de données de validation provenant de différentes dates. La validation a montré que pour une application de suivi: le NDVI était le plus adapté pour les estimations du LAI (RMSE=0.22m²/m², R²=0.81); le ratio de bandes de réflectance rho_0.750µm/rho_0.550µm estimait Cab plus précisément que les autres indices (RMSE=5.94µg/cm², R²=0.75); le RMSE sur l'intervalle spectral 0.5-0.55 µm montrait des résultats encourageants pour l'estimation de Car