14 research outputs found

    Variability in Surface BRDF at Different Spatial Scales (30 m-500 m) Over a Mixed Agricultural Landscape as Retrieved from Airborne and Satellite Spectral Measurements

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    Over the past decade, the role of multiangle remote sensing has been central to the development of algorithms for the retrieval of global land surface properties including models of the bidirectional reflectance distribution function (BRDF), albedo, land cover/dynamics, burned area extent, as well as other key surface biophysical quantities represented by the anisotropic reflectance characteristics of vegetation. In this study, a new retrieval strategy for fine-to-moderate resolution multiangle observations was developed, based on the operational sequence used to retrieve the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 reflectance and BRDF/albedo products. The algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model to provide estimates of intrinsic albedo (i.e., directional-hemispherical reflectance and bihemispherical reflectance), model parameters describing the BRDF, and extensive quality assurance information. The new retrieval strategy was applied to NASA's Cloud Absorption Radiometer (CAR) data acquired during the 2007 Cloud and Land Surface Interaction Campaign (CLASIC) over the well-instrumented Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site in Oklahoma, USA. For the case analyzed, we obtained approx.1.6 million individual surface bidirectional reflectance factor (BRF) retrievals, from nadir to 75 off-nadir, and at spatial resolutions ranging from 3 m - 500 m. This unique dataset was used to examine the interaction of the spatial and angular characteristics of a mixed agricultural landscape; and provided the basis for detailed assessments of: (1) the use of a priori knowledge in kernel-driven BRDF model inversions; (2) the interaction between surface reflectance anisotropy and instrument spatial resolution; and (3) the uncertain ties that arise when sub-pixel differences in the BRDF are aggregated to a moderate resolution satellite pixel. Results offer empirical evidence concerning the influence of scale and spatial heterogeneity in kernel-driven BRDF models; providing potential new insights into the behavior and characteristics of different surface radiative properties related to land/use cover change and vegetation structure

    Spectrodirectional remote sensing : from pixels to processes

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    Retrieving spectral and biophysical parameters of land vegetation by the Earth Observation Land Data Assimilation System

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    In this thesis, a new methodology for retrieval of land spectral and biophysical parameters from optical remote sensing data has been designed and used. The result of the work was a physically based methodology for Fraction of Photosynthetically Active Radiation (FAPAR) and Leaf Area Index (LAI) retrievals, simulation of hyper-spectral information and estimation of associated uncertainties. The presented methodology is based on the generic Earth Observation-Land Data Assimilation System (EO-LDAS). In the course of the work it was found that EO-LDAS can be used for daily estimation of FAPAR and associated uncertainties without any in-situ information and when the number of available observations is low. The results were in line with the field measurements with r2 varying from 0.84 to 0.92 and Root Mean Square Error (RMSE) from 0.11 to 0.16. This was the highest rate among compared products (Two Stream Inversion Package - JRC-TIP, Medium Resolution Imaging Spectrometer - MERIS FR and Moderate Resolution Imaging Spectro-radiometer - MODIS MCD15). It was shown, that using MISR information, EO-LDAS temporal regularization and generic dynamic prior, it was possible to stabilize results of the retrieval and to obtain better results than MERIS FAPAR or JRC-TIP MISR. In addition, inclusion of generic static and dynamic prior information, decreases posterior uncertainties and can increase accuracies compared to in-situ data. The results showed that proper estimation of LAI and soil parameters were sufficient to simulate a hyper-spectral signal between 400 and 1000 nm with acceptable precision: best RMSE is equal to 0.03 for real data and less than 0.008 for synthetic data. This implies that in case of the given experimental set-up, LAI and soil parameters are the major mechanisms controlling spectral variations in the visible and near infrared regions

    Monitoring of the Biophysical Status of Vegetation: Using Multi-angular, Hyperspectral Remote Sensing for the Optimization of a Physically-based SVAT Mode

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    Diese Arbeit ist das Ergebnis der letzten acht Jahre meines wissenschaftlichen Lebensweges und spiegelt die Schwerpunkte meiner Forschungsinteressen wider: Einen wesentlichen Schwerpunkt bildet das Thema Pflanzen, das nahezu unerschöpfliche Möglichkeiten der Forschung bietet. Der Großteil aller Austauschprozesse zwischen der LandoberflĂ€che und der AtmosphĂ€re werden durch Landpflanzen vermittelt (Schurr et al. 2006). Dabei stellt die Photosynthese den primĂ€ren Energiewandlungsprozess dar, der die Sonnenenergie in chemisch nutzbare Energie ĂŒberfĂŒhrt, der Biomasseproduktion und Wachstum treibt. Photosynthese, Stoffproduktion und Pflanzenwachstum sind dynamische, in hohem Maße geregelte Prozesse, die von den verschiedensten Umweltfaktoren beeinflusst werden und zur Ausbildung vielfĂ€ltiger rĂ€umlicher und zeitlicher Muster – von der Ebene der einzelnen Zelle bis zum Ökosystem – fĂŒhren. Das Verstehen der komplexen Prozesse und ihrer Interaktionen fĂŒhrt dabei ĂŒber die Analyse ihrer raumzeitlichen Dynamik auf verschiedenste Ebenen. Die Zukunft vieler Themen der Menschheit ist eng mit dem VerstĂ€ndnis der raumzeitlichen Dynamik der Entwicklung und Funktion der Landpflanzen verbunden, wozu unter anderem die Sicherung der ErnĂ€hrung und der Versorgung der AtmosphĂ€re mit Sauerstoff gehört (Osmond et al. 2004). Die Spannbreite der relevanten Muster reicht dabei von der subzellulĂ€ren Ebene bis hin zu raum-zeitlichen Prozessen, die sogar aus dem Weltraum beobachtet werden können. Dies verdeutlicht die vielfĂ€ltigen Möglichkeiten, welche Pflanzen fĂŒr einen Wissenschaftler bieten und vielleicht erklĂ€rt sich damit mein Interesse an diesem Themenkomplex. Dabei liegt mir die Einbeziehung der Pflanzenphysiologie in die klassische Vegetationsgeographie besonders am Herzen. Wer sich mit Vegetation beschĂ€ftigt, stĂ¶ĂŸt bald auf Fragestellungen zum Pflanzenbau und zu modernen Methoden des Managements von Pflanzen im Rahmen derer ackerbaulichen Nutzung, die in den letzten Jahren aufgrund der geĂ€nderten Anforderungen des Landbaus an den Umweltschutz vermehrt auftauchten. Insbesondere im teilflĂ€chenspezifischen Anbau (precision farming) spielt die flĂ€chenhafte Untersuchung von Ackerkulturen eine wichtige Rolle, wobei hier eine besondere Rolle der Fernerkundung als Möglichkeit zur Beobachtung raumzeitlicher Prozesse zwischen und innerhalb von PflanzenbestĂ€nden zukommt. Dabei stehen insbesondere hyperspektrale Instrumente im Zentrum des Interesses, da die Vielzahl der engbandigen KanĂ€le die Analyse von Pflanzeninhaltsstoffen, wie z. B. Chlorophyll, ermöglicht. Damit bietet sich eine Vielzahl von Möglichkeiten zur Beobachtung von pflanzenphysiologischen VorgĂ€ngen und deren raum-zeitlichen Mustern. Im Rahmen dieser Arbeit werden dabei C3 und C4 Pflanzen untersucht, welche die gĂ€ngigsten Wege der Kohlenstoffassimilierung darstellen. Als Beispielpflanzen dienen Weizen (Triticum aestivum L.) und Mais (Zea mays L.), welche im Rahmen von GelĂ€ndekampagnen in den Jahren 2004 und 2005 intensiv beprobt wurden und mit Hilfe von Fernerkundungssensoren im Laufe der Vegetationsperioden dieser beiden Jahre ĂŒberflogen wurden, so oft es die örtlichen Wetterbedingungen erlaubten. Die Fernerkundungssensorik bestand aus dem satellitengestĂŒtzten, Abbildenden Spektrometer CHRIS sowie dem flugzeuggetragenen Hyperspektralsensor AVIS. Die Analyse der Frage zur winkelabhĂ€ngigen Beobachtung von Sonnen- und Schattenchlorophyll basiert auf regelmĂ€ĂŸigen CHRIS ÜberflĂŒgen, welche die fernerkundliche Datengrundlage liefern. RĂ€umlich hochaufgelöste, winkelabhĂ€ngige Aufnahmen konnten im Jahr 2004 mit dem lehrstuhleigenen Sensor AVIS erhoben werden, dessen Daten als wertvolle ErgĂ€nzung dienen. Neben der Analyse von PflanzenbestĂ€nden hinsichtlich ihres Chlorophyllgehaltes und dessen raum-zeitlicher Dynamik stellt die modellhafte Abbildung dieser Dynamik einen weiteren Schwerpunkt dieser Arbeit dar. Pflanzen reagieren aufgrund ihrer sessilen Lebensweise auf globale KlimaverĂ€nderungen und auf regionale UmwelteinflĂŒsse sehr sensibel. Dies verdeutlicht das seit Jahren wachsende Interesse an der Abbildung des pflanzlichen Stoffwechsels und der Photosynthese im Rahmen von Modellen (von Caemmerer 2000). DafĂŒr ist ein vertieftes VerstĂ€ndnis des Metabolismus von Pflanzen erforderlich sowie eben die raum-zeitliche Dynamik, welche mit Hilfe von Fernerkundungsdaten abgebildet werden kann. Daher sollen die fernerkundlich abgeleiteten Chlorophyllgehalte von Sonnen- und Schattenbereichen in das physikalisch-basierte SVAT Modell PROMET implementiert werden. In PROMET wird die Photosynthese von PflanzenbestĂ€nden bereits in einen Sonnen- und Schattenbereich unterteilt vorgenommen. Die obere Bestandesschicht unterliegt dabei einem Strahlungsregime, welches hauptsĂ€chlich von direkter Strahlung dominiert wird. Die untere, beschattete Bestandesschicht unterliegt einem Strahlungsregime, das von der diffusen Strahlungskomponente dominiert wird

    SEA ICE AND ICE SHEET SURFACE ROUGHNESS CHARACTERIZATION AND ITS EFFECTS ON BI-DIRECTIONAL REFLECTANCE

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    The roughness of sea ice can affect its bidirectional reflectance distribution function (BRDF) thus influencing retrieval of its physical properties from satellite. We leverage WorldView-1 and Multi-angle Imaging SpectroRadiometer (MISR) remote sensing data sets collected over sea ice and the adjacent McMurdo Ice Shelf in proximity to McMurdo station to first characterize the roughness of sea ice and other snow surfaces and then examine the effects of surface roughness on satellites images of varying spatial resolutions. First, high resolution DEMs were created from stereographic WorldView-1 image pairs with NASA stereo imagery processing tool Ames Stereo Pipeline (ASP). A variety of geomorphometric measures of roughness, including roughness index, were characterized from the high resolution DEMs. Following adequate characterization of sea ice roughness, its impact on surface reflectance derived from optical satellites spanning a range of spatial resolutions was assessed

    Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

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    Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today.JRC.H.4-Monitoring Agricultural Resource

    Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure

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    Abrupt forest disturbances generating gaps \u3e0.001 km2 impact roughly 0.4–0.7 million km2a−1. Fire, windstorms, logging, and shifting cultivation are dominant disturbances; minor contributors are land conversion, flooding, landslides, and avalanches. All can have substantial impacts on canopy biomass and structure. Quantifying disturbance location, extent, severity, and the fate of disturbed biomass will improve carbon budget estimates and lead to better initialization, parameterization, and/or testing of forest carbon cycle models. Spaceborne remote sensing maps large-scale forest disturbance occurrence, location, and extent, particularly with moderate- and fine-scale resolution passive optical/near-infrared (NIR) instruments. High-resolution remote sensing (e.g., ∌1 m passive optical/NIR, or small footprint lidar) can map crown geometry and gaps, but has rarely been systematically applied to study small-scale disturbance and natural mortality gap dynamics over large regions. Reducing uncertainty in disturbance and recovery impacts on global forest carbon balance requires quantification of (1) predisturbance forest biomass; (2) disturbance impact on standing biomass and its fate; and (3) rate of biomass accumulation during recovery. Active remote sensing data (e.g., lidar, radar) are more directly indicative of canopy biomass and many structural properties than passive instrument data; a new generation of instruments designed to generate global coverage/sampling of canopy biomass and structure can improve our ability to quantify the carbon balance of Earth\u27s forests. Generating a high-quality quantitative assessment of disturbance impacts on canopy biomass and structure with spaceborne remote sensing requires comprehensive, well designed, and well coordinated field programs collecting high-quality ground-based data and linkages to dynamical models that can use this information

    MANAGEMENT MATTER? EFFECTS OF CHARCOAL PRODUCTION MANAGEMENT ON WOODLAND REGENERATION IN SENEGAL

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    In Senegal, as in many parts of Africa, nearly 95% of its growing urban population depends on charcoal as their primary cooking energy. Extraction of wood for charcoal production is perceived to drive forest degradation. The Senegalese government and international donor agencies have created different forest management types with the ultimate goal of sustainably managing forests. This research combines local ecological knowledge, ecological surveys and remote sensing analysis to better understand questions related to how extraction for charcoal production and forest management affect Senegalese forests. Information derived from 36 semi-structured interviews suggests that the forests are degrading, but are depended on for income, grazing and energy. Interviewees understand the rules governing forest management types, but felt they had limited power or responsibility to enforce forest regulations. Ecological survey results confirmed that plots harvested for charcoal production are significantly different in forest structure and tree species composition than undisturbed sites. Across harvested and undisturbed and within forest management types the Combretum glutinosum species dominated (53% of all individuals and the primary species used for charcoal production) and demonstrated robust regenerative capacity. Few large, hardwood or fruiting trees were observed and had insufficient regenerative capacity to replace current populations. Species diversity was higher in co-managed areas, but declined after wood was harvested for charcoal production. Proximity to villages, roads and park edges in harvested and undisturbed plots and within forest management types had little impact on forest structure and tree diversity patterns with the harvesting of trees for charcoal spread consistently throughout the landscape. Remote sensing analysis with the MISR derived k(red) parameter demonstrated its ability to accurately classify broad land classes and showed potential when differentiating between pre- and post-harvest conditions over a three year time period, but could not accurately detect subtle changes in forest cover of known harvest time since last harvest in a single MISR scene. This research demonstrated the utility of multidisciplinary research in assessing the effects of charcoal production and forest management types on Senegalese forests; concluding that the effects of charcoal production on forest characteristics and regenerative capacity are consistent throughout all forest management types

    Coupled canopy-atmosphere modelling for radiance-based estimation of vegetation properties

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    Vegetation is an important component of the Earth’s biosphere and therefore plays a crucial role in the carbon exchange of terrestrial ecosystems. Vegetation variables, such as leaf area index (LAI) and leaf chlorophyll content (Cab), can be monitored at global scale using remote sensing (RS). There are two main categories of approaches for estimating the vegetation variables from RS data: empirical and physically-based approaches. Physically-based approaches are more widely applicable because they rely on radiative transfer (RT) models, which can be adapted to the observation conditions and to the observed vegetation. For estimating the vegetation variables, however, the RT model has to be inverted, and this inversion is usually an ill-posed and under-determined problem. Several regularization methods have been proposed to allow finding stable and unique solutions: model coupling, using multi-angular data, using a priori information, as well as applying spatial or temporal constraints. Traditionally, radiance data measured at top-of the atmosphere (TOA) are pre-processed to top-of-canopy (TOC) reflectances. Corrections for atmospheric effects, and, if needed, for adjacency, directional, or topographic effects are usually applied sequentially and independently. Physically, however, these effects are inter-related, and each correction introduces errors. These errors propagate to the TOC reflectance data, which are used to invert the canopy RT model. The performance of the TOC approach is therefore limited by the errors introduced in the data during the pre-processing steps. This thesis proposes to minimize these errors by directly using measured TOA radiance data. In such a TOA approach, the atmospheric RT model, which is normally inverted to perform the atmospheric correction, is coupled to the canopy RT model. The coupled canopy-atmosphere model is inverted directly using the measured radiance data. Adjacency, directional and topographic effects can then be included in the coupled RT model. The same regularization methods as used for TOC approaches can be applied to obtain stable and unique estimates. The TOA approach was tested using four case studies based on mono-temporal data. A) The performance of the TOA approach was compared to a TOC approach for three Norway spruce stands in the Czech Republic, using near-nadir Compact High Resolution Imaging Spectrometer (CHRIS) data. The coupled model included canopy directional effects and simulated the CHRIS radiance data with similar accuracy as the canopy model simulated the atmospherically-corrected CHRIS data. Local sensitivity analyses showed that the atmospheric parameters had much less influence on the simulations than the vegetation parameters, and that the sensitivity profiles of the latter were very similar for both TOC and TOA approaches. The dimensionality of the estimation problem was evaluated to be 3 for both approaches. Canopy cover (Cv), fraction of bark material (fB), Cab, and leaf dry matter content (Cdm) were estimated using look-up tables (LUT) with similar accuracy with both approaches. B) Regularization using multi-angular data was tested for the TOA approach, using four angular CHRIS datasets, for the same three stands as used in A). The coupled model provided good simulations for all angles. The dimensionality increased from 3 to 6 when using all four angles. Two LUTs were built for each stand: a 4-variable LUT with fB, Cv, Cdm, and Cab, and a 7-variable LUT where leaf brown pigment concentration (Cs), dissociation factor (D), and tree shape factor (Zeta) were added. The results did not fully match the expectation that the more angles used, the more accurate the estimates become. Although their exploitation remains challenging, multi-angular data have higher potential than mono-angular data at TOA level. C) A Bayesian object-based approach was developed and tested on at-sensor Airborne Prism Experiment (APEX) radiance data for an agricultural area in Switzerland. This approach consists of two steps. First, up to six variables were estimated for each crop field object using a Bayesian optimization algorithm, using a priori information. Second, a LUT was built for each object with only LAI and Cab as free variables, thus spatially constraining the values of all other variables to the values obtained in the first step. The Bayesian object-based approach estimated LAI more accurately than a LUT with a Bayesian cost function approach. This case study relied on extensive field data allowing defining the objects and a priori data. D) The Bayesian object-based approach proposed in C) was applied to a simulated TOA Sentinel-2 scene, covering the area around Zurich, Switzerland. The simulated scene was mosaicked using seven APEX flight lines, which allowed including all spatial and spectral characteristics of Sentinel-2. Automatic multi-resolution segmentation and classification of the vegetated objects in four levels of brightness in the visible domain enabled defining the objects and a priori data without field data, allowing successful implementation of the Bayesian object-based approach. The research conducted in this thesis contributes to the improvement of the use of regularization methods in ill-posed RT model inversions. Three major areas were identified for further research: 1) inclusion of adjacency and topography effects in the coupled model, 2) addition of temporal constraints in the inversion, and 3) better inclusion of observation and model uncertainties in the cost function. The TOA approach proposed here will facilitate the exploitation of multi-angular, multi-temporal and multi-sensor data, leading to more accurate RS vegetation products. These higher quality products will support many vegetation-related applications.</p
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