Terrestrial vegetation dynamics and their impacts on surface climate

Vegetation controls the exchange of heat, mass and momentum between the land surface and the atmosphere, and is also the primary producer that sustains life on Earth. We combine theoretical analyses, satellite and in-situ observations, and Earth system model simulations in this dissertation to illustrate the key role of vegetation in the climate system and human society. Specifically, this is accomplished via three studies, described below. First, we address the problem of how to retrieve Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from a novel satellite Bidirectional Reflectance Factor product derived from the Multi-Angle Implementation of Atmospheric Correction algorithm. The LAI/FPAR retrieval is done via a radiative transfer model using the recently developed theory of spectral invariants. Our analyses show that the LAI/FPAR data sets developed in this study have higher accuracy and better stability relative to the existing products, especially in cloudy conditions and under high aerosol loadings. Second, we analyze the long-term trend in LAI derived from the Moderate Resolution Imaging Spectroradiometer observations and identify its main driver. We find that over a third of the terrestrial vegetation shows statistically significant increasing trends in LAI (i.e., Earth greening) during the 21st century. Both remote sensing and inventory data show that land-use management is the key driver of this greening, arising primarily from large-scale tree planting and intensive agriculture in emerging countries like China and India. This finding highlights the need for a more realistic representation of land-use practices in Earth system models. Third, we use a new method based on the concept of “two-resistances” and the Community Land Model (CLM5) runs with prescribed satellite-derived LAI to quantify the impacts of Earth greening on land surface temperature (LST). We find that over 90% of the Earth greening can lead to a local cooling effect at the annual scale. Further attribution analysis with multiple data sources reveals that aerodynamic resistance is the dominant factor controlling the LST change. The greening produces a decrease in aerodynamic resistance, which favors increased heat dissipation by turbulent fluxes, including the latent heat flux. These studies that span LAI data production, long-term trends and their impacts highlight the importance of vegetation dynamics in the natural and human systems

AGROHYDROLOGICAL SIMULATION FOR MITIGATION AND MANAGEMENT OF SOIL MOISTURE DEFICIT IN RAINFED AGRICULTURE

Drought is a natural disaster that occurs in all climatic regions, and is responsible for food insecurity in majority of developing countries. Globally, interventions to mitigate drought impacts have focused in semi-arid and arid areas. However, increased climatic variations have become more frequent in the recent past causing sporadic agricultural droughts with the end results being food shortages even in areas that receive relatively high rainfall. Many of such areas are occupied by vulnerable communities lacking strategic coping mechanisms for mitigation of drought impacts. The aim of this study was to develop a simplified approach for computing a Soil Moisture Deficit Index (SMDI) that integrates limited climatic records common in developing countries together with freely available tools for supporting soil water management decisions under rainfed agriculture. Most soil moisture based drought indices are derived from long term records of measured soil moisture time series. However, such long-term soil moisture records are scarcely available in African countries where they could be of greatest benefit in designing techniques for mitigating drought impacts. Therefore, the main objective of this research was to evaluate the performance of simulated soil moisture time series to develop a SMDI with minimal requirements of input data. To this aim, the study was organized in four consecutive objectives, namely: to identify and adapt a suitable drought indicator in relation to the data availability. Secondly, to assess the feasibility of using a calibrated agro-hydrological model for producing long time series of soil water dynamics and derive SMDI for monitoring agricultural droughts. The third objective was to upscale the SMDI through energy balance modeling using a case study in Northern Uganda. And the fourth objective was, to formulate a soil water management decision support scheme for mitigation of agricultural droughts in rain fed farming systems through application of SMDI. The study is based on agro-hydrological data collected in a dairy farm of 10 ha in Northern Uganda equipped with Mateo station and low cost commercial soil sensors to monitor soil water dynamics in the root zone during two seasons under rain fed maize crops in 2015. Because of the importance of Evapotranspiration in agro-hydrological studies and limited reported research on it at the study site, 13 different simplified reference evapotranspiration (ET0) models were compared with FAO-56 Penman-Monteith to select the best performing simplified model for application in the study area. Evaluation of the 13 ET0 models showed that the Makkink radiation model gave the best prediction of ET0 with Root Mean Squared Error (RMSE) = 0.6 mm, Mean Absolute Error (MAE) = 0.4 mm, Nash Sutcliffe Efficiency (NSE) = 0.8, Coefficient of Agreement (d) = 0.90 and Coefficient of Determination (r2) = 0.7. All temperature based models overestimated ET0 with Thornthwaite giving the worst prediction in all the test statistics. To address the first objective, the state of the art on soil moisture based drought indices were reviewed and the information gathered applied to formulate a new approach to define SMDI. The second objective was addressed through application of 1-dimensional water flow model (Hydrus 1D); firstly, in inverse mode to derive the soil hydraulic properties and secondly in direct mode to generate soil moisture time series by using the ET0 model selected in the previous step in conjunction with gridded climatic data combined with limited weather observations. In the calibration phase, Landsat 8 OLI satellite images were used to estimate crop growth variables. In the second objective, published crop coefficients where used to generate continuous boundary conditions for 21-year agro-hydrological simulations. Calibration results showed good agreement between simulations and observations of water storage in the root zone with r2 = 0.73 during calibration and r2 = 0.70 during validation. The results of the long-time series simulations were used to derive threshold parameters for SMDI definition, following the statistical approach suggested by Hunt et al. (2009). Generation of the threshold parameters; i.e.: water content at field capacity (θFC) and water content at wilting point (θWP), through agro-hydrological simulations gave good comparison with the laboratory determined values through a pressure plate apparatus on undisturbed soil core samples; with r2 = 0.95. Comparison between number of times SMDI<0, within a growing season and maize yields between 2007 to 2015, showed a negative linear correlation with r2 = 0.64. Precipitation (P) and precipitation deficit (D) were fitted on theoretical probability distributions to calculate reference drought indices i.e. Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The fitting distributions (a 2-parameter gamma distribution for P, and a 3-parameter log-logistic distribution for D) gave an acceptable Kolmogorov-Smirnov goodness of fit test at 95% level of significance in both cases. All the reference indices [i.e. SPI, SPEI and Atmospheric Water Deficit (AWD)] showed positive correlation with SMDI demonstrating the robustness of SMDI for agricultural drought monitoring in the study area. The third objective involved analysis of Landsat 8 thermal images to generate evaporative fraction (Λ) through energy balance modeling. A SMDI- Λ regression equation was developed to spatialize SMDI. A comparison between SMDI and Λ through linear regression showed good agreement with r2 = 0.84. An independent check with different sets of images were performed between the SMDI calculated using the SMDI-Λ regression equation and SMDI generated through agro-hydrological simulations provided a good agreement with r2 = 0.85. The last objective involved integration of the results obtained from objectives (i) to (iii) to formulate a decision support scheme. SMDI was found useful to delineate dry and the wet season in Northern Uganda; it showed that the dry season begins between November 25 to December 10 and the wet season begins between March 26 to April 5 of each year. A SMDI-based management decision support scheme was proposed, although it would need further investigations to verify its effectiveness. In conclusion, the approaches developed to define SMDI can easily be implemented in any developing country that experiences similar problems in rain fed farming

Assessing uncertainties of in situ FAPAR measurements across different forest ecosystems

Carbon balances are important for understanding global climate change. Assessing such balances on a local scale depends on accurate measurements of material flows to calculate the productivity of the ecosystem. The productivity of the Earth's biosphere, in turn, depends on the ability of plants to absorb sunlight and assimilate biomass. Over the past decades, numerous Earth observation missions from satellites have created new opportunities to derive so-called “essential climate variables” (ECVs), including important variables of the terrestrial biosphere, that can be used to assess the productivity of our Earth's system. One of these ECVs is the “fraction of absorbed photosynthetically active radiation” (FAPAR) which is needed to calculate the global carbon balance. FAPAR relates the available photosynthetically active radiation (PAR) in the wavelength range between 400 and 700 nm to the absorption of plants and thus quantifies the status and temporal development of vegetation. In order to ensure accurate datasets of global FAPAR, the UN/WMO institution “Global Climate Observing System” (GCOS) declared an accuracy target of 10% (or 0.05) as acceptable for FAPAR products. Since current satellite derived FAPAR products still fail to meet this accuracy target, especially in forest ecosystems, in situ FAPAR measurements are needed to validate FAPAR products and improve them in the future. However, it is known that in situ FAPAR measurements can be affected by significant systematic as well as statistical errors (i.e., “bias”) depending on the choice of measurement method and prevailing environmental conditions. So far, uncertainties of in situ FAPAR have been reproduced theoretically in simulations with radiation transfer models (RTMs), but the findings have been validated neither in field experiments nor in different forest ecosystems. However, an uncertainty assessment of FAPAR in field experiments is essential to develop practicable measurement protocols. This work investigates the accuracy of in situ FAPAR measurements and sources of uncertainties based on multi-year, 10-minute PAR measurements with wireless sensor networks (WSNs) at three sites on three continents to represent different forest ecosystems: a mixed spruce forest at the site “Graswang” in Southern Germany, a boreal deciduous forest at the site “Peace River” in Northern Alberta, Canada and a tropical dry forest (TDF) at the site “Santa Rosa”, Costa Rica. The main statements of the research results achieved in this thesis are briefly summarized below: Uncertainties of instantaneous FAPAR in forest ecosystems can be assessed with Wireless Sensor Networks and additional meteorological and phenological observations. In this thesis, two methods for a FAPAR bias assessment have been developed. First, for assessing the bias of the so-called two-flux FAPAR estimate, the difference between FAPAR acquired under diffuse light conditions and two-flux FAPAR acquired during clear-sky conditions can be investigated. Therefore, measurements of incoming and transmitted PAR are required to calculate the two-flux FAPAR estimate as well as observations of the ratio of diffuse-to-total incident radiation. Second, to assess the bias of not only the two- but also the three-flux FAPAR estimate, four-flux FAPAR observations must be carried out, i.e. measurements of top-of-canopy (TOC) PAR albedo and PAR albedo of the forest background. Then, to quantify the bias of the two and three-flux estimate, the difference with the four-flux estimate can be calculated. Main sources of uncertainty of in situ FAPAR measurements are high solar zenith angle, occurrence of colored leaves and increased wind speed. At all sites, FAPAR observations exhibited considerable seasonal variability due to the phenological development of the forests (Graswang: 0.89 to 0.99 ±0.02; Peace River: 0.55 to 0.87 ±0.03; Santa Rosa: 0.45 to 0.97 ±0.06). Under certain environmental conditions, FAPAR was affected by systemic errors, i.e. bias that go beyond phenologically explainable fluctuations. The in situ observations confirmed a significant overestimation of FAPAR by up to 0.06 at solar zenith angles above 60° and by up to 0.05 under the occurrence of colored leaves of deciduous trees. The results confirm theoretical findings from radiation transfer simulations, which could now for the first time be quantified under field conditions. As a new finding, the influence of wind speed could be shown, which was particularly evident at the boreal location with a significant bias of FAPAR values at wind speeds above 5 ms-1. The uncertainties of the two-flux FAPAR estimate are acceptable under typical summer conditions. Three-flux or four-flux FAPAR measurements do not necessarily increase the accuracy of the estimate. The highest average relative bias of different FAPAR estimates were 2.1% in Graswang, 8.4% in Peace River and -4.5% in Santa Rosa. Thus, the GCOS accuracy threshold of 10% set by the GCOS was generally not exceeded. The two-flux FAPAR estimate was only found to be biased during high wind speeds, as changes in the TOC PAR albedo are not considered in two-flux FAPAR measurements. Under typical summer conditions, i.e. low wind speed, small solar zenith angle and green leaves, two-flux FAPAR measurements can be recommended for the validation of satellite-based FAPAR products. Based on the results obtained, it must be emphasized that the three-flux FAPAR estimate, which has often been preferred in previous studies, is not necessarily more accurate, which was particularly evident in the tropical location. The discrepancies between ground measurements and the current Sentinel-2 FAPAR product still largely exceed the GCOS target accuracy at the respective study sites, even when considering uncertainties of FAPAR ground measurements. It was found that the Sentinel-2 (S2) FAPAR product systematically underestimated the ground observations at all three study sites (i.e. negative values for the mean relative bias in percent). The highest agreement was observed at the boreal site Peace River with a mean relative deviation of -13% (R²=0.67). At Graswang and Santa Rosa, the mean relative deviations were -20% (R²=0.68) and -25% (R²=0.26), respectively. It was argued that these high discrepancies resulted from both the generic nature of the algorithm and the higher ecosystem complexity of the sites Graswang and Santa Rosa. It was also found that the temporal aggregation method of FAPAR ground data should be well considered for comparison with the S2 FAPAR product, which refers to daily averages, as overestimation of FAPAR during high solar zenith angles could distort validation results. However, considering uncertainties of ground measurements, the S2 FAPAR product met the GCOS accuracy requirements only at the boreal study site. Overall, it has been shown that the S2 FAPAR product is already well suited to assess the temporal variability of FAPAR, but due to the low accuracy of the absolute values, the possibilities to feed global production efficiency models and evaluate global carbon balances are currently limited. The accuracy of satellite derived FAPAR depends on the complexity of the observed forest ecosystem. The highest agreement between satellite derived FAPAR product and ground measurements, both in terms of absolute values and spatial variability, was achieved at the boreal site, where the complexity of the ecosystem is lowest considering forest structure variables and species richness. These results have been elaborated and presented in three publications that are at the center of this cumulative thesis. In sum, this work closes a knowledge gap by displaying the interplay of different environmental conditions on the accuracy of situ FAPAR measurements. Since the uncertainties of FAPAR are now quantifiable under field conditions, they should also be considered in future validation studies. In this context, the practical recommendations for the implementation of ground observations given in this thesis can be used to prepare sampling protocols, which are urgently needed to validate and improve global satellite derived FAPAR observations in the future.Projektionen zukünftiger Kohlenstoffbilanzen sind wichtig für das Verständnis des globalen Klimawandels und sind auf genaue Messungen von Stoffflüssen zur Berechnung der Produktivität des Erdökosystems angewiesen. Die Produktivität der Biosphäre unserer Erde wiederum ist abhängig von der Eigenschaft von Pflanzen, Sonnenlicht zu absorbieren und Biomasse zu assimilieren. Über die letzten Jahrzehnte haben zahlreiche Erdbeobachtungsmissionen von Satelliten neue Möglichkeiten geschaffen, sogenannte „essentielle Klimavariablen“ (ECVs), darunter auch wichtige Variablen der terrestrischen Biosphäre, aus Satellitendaten abzuleiten, mit deren Hilfe man die Produktivität unseres Erdsystems computergestützt berechnen kann. Eine dieser „essenziellen Klimavariablen“ ist der Anteil der absorbierten photosynthetisch aktiven Strahlung (FAPAR) die man zur Berechnung der globalen Kohlenstoffbilanz benötigt. FAPAR bezieht die verfügbare photosynthetisch aktive Strahlung (PAR) im Wellenlängenbereich zwischen 400 und 700 nm auf die Absorption von Pflanzen und quantifiziert somit Status und die zeitliche Entwicklung von Vegetation. Um möglichst präzise Informationen aus dem globalen FAPAR zu gewährleisten, erklärte die UN/WMO-Institution zur globalen Klimabeobachtung, das “Global Climate Observing System“ (GCOS), ein Genauigkeitsziel von 10% (bzw. 0.05) FAPAR-Produkte als akzeptabel. Da aktuell satellitengestützte FAPAR-Produkte dieses Genauigkeitsziel besonders in Waldökosystemen immer noch verfehlen, werden dringen in situ FAPAR-Messungen benötigt, um die FAPAR-Produkte validieren und in Zukunft verbessern zu können. Man weiß jedoch, dass je nach Auswahl des Messsystems und vorherrschenden Umweltbedingungen in situ FAPAR-Messungen mit erheblichen sowohl systematischen als auch statistischen Fehlern beeinflusst sein können. Bisher wurden diese Fehler in Simulationen mit Strahlungstransfermodellen zwar theoretisch nachvollzogen, aber die dadurch abgeleiteten Befunde sind bisher weder in Feldversuchen noch in unterschiedlichen Waldökosystemen validiert worden. Eine Unsicherheitsabschätzung von FAPAR im Feldversuch ist allerdings essenziell, um praxistaugliche Messprotokolle entwickeln zu können. Die vorliegende Arbeit untersucht die Genauigkeit von in situ FAPAR-Messungen und Ursachen von Unsicherheit basierend auf mehrjährigen, 10-minütigen PAR-Messungen mit drahtlosen Sensornetzwerken (WSNs) an drei verschiedenen Waldstandorten auf drei Kontinenten: der Standort „Graswang“ in Süddeutschland mit einem Fichten-Mischwald, der Standort „Peace River“ in Nord-Alberta, Kanada mit einem borealen Laubwald und der Standort „Santa Rosa“, Costa Rica mit einem tropischen Trockenwald. Die Hauptaussagen der in dieser Arbeit erzielten Forschungsergebnisse werden im Folgenden kurz zusammengefasst: Unsicherheiten von FAPAR in Waldökosystemen können mit drahtlosen Sensornetzwerken und zusätzlichen meteorologischen und phänologischen Beobachtungen quantifiziert werden. In dieser Arbeit wurden zwei Methoden für die Bewertung von Unsicherheiten entwickelt. Erstens, um den systematischen Fehler der sogenannten „two-flux“ FAPAR-Messung zu beurteilen, kann die Differenz zwischen FAPAR, das unter diffusen Lichtverhältnissen aufgenommen wurde, und FAPAR, das unter klaren Himmelsbedingungen aufgenommen wurde, untersucht werden. Für diese Methode sind Messungen des einfallenden und transmittierten PAR sowie Beobachtungen des Verhältnisses von diffuser zur gesamten einfallenden Strahlung erforderlich. Zweitens, um den systematischen Fehler nicht nur der „two-flux“ FAPAR-Messung, sondern auch der „three-flux“ FAPAR-Messung zu beurteilen, müssen „four-flux“ FAPAR-Messungen durchgeführt werden, d.h. zusätzlich Messungen der PAR Albedo des Blätterdachs sowie des Waldbodens. Zur Quantifizierung des Fehlers der „two-flux“ und „three-flux“ FAPAR-Messung kann die Differenz zur „four-flux“ FAPAR-Messung herangezogen werden. Die Hauptquellen für die Unsicherheit von in situ FAPAR-Messungen sind ein hoher Sonnenzenitwinkel, Blattfärbung und erhöhte Windgeschwindigkeit. An allen drei Untersuchungsstandorten zeigten die FAPAR-Beobachtungen natürliche saisonale Schwankungen aufgrund der phänologischen Entwicklung der Wälder (Graswang: 0,89 bis 0,99 ±0,02; Peace River: 0,55 bis 0,87 ±0,03; Santa Rosa: 0,45 bis 0,97 ±0,06). Unter bestimmten Umweltbedingungen war FAPAR von systematischen Fehlern, d.h. Verzerrungen betroffen, die über phänologisch erklärbare Schwankungen hinausgehen. So bestätigten die in situ Beobachtungen eine signifikante Überschätzung von FAPAR um bis zu 0,06 bei Sonnenzenitwinkeln von über 60° und um bis zu 0,05 bei Vorkommen gefärbter Blätter der Laubbäume. Die Ergebnisse bestätigen theoretische Erkenntnisse aus Strahlungstransfersimulationen, die nun erstmalig unter Feldbedingungen quantifiziert werden konnten. Als eine neue Erkenntnis konnte der Einfluss der Windgeschwindigkeit gezeigt werden, der sich besonders am borealen Standort mit einer signifikanten Verzerrung der FAPAR-Werte bei Windgeschwindigkeiten über 5 ms-1 äußerte. Die Unsicherheiten der „two-flux“ FAPAR-Messung sind unter typischen Sommerbedingungen akzeptabel. „Three-flux“ oder „four-flux“ FAPAR-Messungen erhöhen nicht unbedingt die Genauigkeit der Abschätzung. Die höchsten durchschnittlichen relativen systematischen Fehler verschiedener Methoden zur FAPAR-Messung betrugen 2,1% in Graswang, 8,4% in Peace River und -4,5% in Santa Rosa. Damit wurde der durch GCOS festgelegte Genauigkeitsschwellenwert von 10% im Allgemeinen nicht überschritten. Die „two-flux“ FAPAR-Messung wurde nur als fehleranfällig bei hohe Windgeschwindigkeiten befunden, da Änderungen der PAR-Albedo des Blätterdachs bei der „two-flux“ FAPAR-Messung nicht berücksichtigt werden. Unter typischen Sommerbedingungen, also geringe Windgeschwindigkeit, kleiner Sonnenzenitwinkel und grüne Blätter, kann die „two-flux“ FAPAR-Messung für die Validierung von satellitengestützten FAPAR-Produkten empfohlen werden. Auf Basis der gewonnenen Ergebnisse muss betont werden, dass die „three-flux“ FAPAR-Messung, die in bisherigen Studien häufig bevorzugt wurde, nicht unbedingt weniger fehlerbehaftet sind, was sich insbesondere am tropischen Standort zeigte. Die Abweichungen zwischen Bodenmessungen und dem aktuellen Sentinel-2 FAPAR-Produkt überschreiten auch unter Berücksichtigung von Unsicherheiten in der Messmethodik immer noch weitgehend die GCOS-Zielgenauigkeit an den jeweiligen Untersuchungsstandorten. So zeigte sich, dass das S2 FAPAR-Produkt die Bodenbeobachtungen an allen drei Studienstandorten systematisch unterschätzte (d.h. negative Werte für die mittlere relative Abweichung in Prozent). Die höchste Übereinstimmung wurde am borealen Standort Peace River mit einer mittleren relativen Abweichung von -13% (R²=0,67) beobachtet. An den Standorten Graswang und Santa Rosa betrugen die mittleren relativen Abweichungen jeweils -20% (R²=0,68) bzw. -25% (R²=0,26). Es wurde argumentiert, dass diese hohen Abweichungen auf eine Kombination sowohl des generisch ausgerichteten Algorithmus als auch der höheren Komplexität beider Ökosysteme zurückgeführt werden können. Es zeigte sich außerdem, dass die zeitlichen Aggregierung der FAPAR-Bodendaten zum Vergleich mit S2 FAPAR-Produkt, das sich auf Tagesmittelwerte bezieht, gut überlegt sein sollte, da die Überschätzung von FAPAR während eines hohen Sonnenzenitwinkels in den Bodendaten die Validierungsergebnisse verzerren kann. Unter Berücksichtigung der Unsicherheiten der Bodendaten erfüllte das S2 FAPAR Produkt jedoch nur am boreale Untersuchungsstandort die Genauigkeitsanforderungen des GCOS. Insgesamt hat sich gezeigt, dass das S2 FAPAR-Produkt bereits gut zur Beurteilung der zeitlichen Variabilität von FAPAR geeignet ist, aber aufgrund der geringen Genauigkeit der absoluten Werte sind die Möglichkeiten, globale Produktionseffizienzmodelle zu speisen und globale Kohlenstoffbilanzen zu bewerten, derzeit begrenzt. Die Genauigkeit von satellitengestützten FAPAR-Produkten ist abhängig von der Komplexität des beobachteten Waldökosystems. Die höchste Übereinstimmung zwischen satellitengestütztem FAPAR und Bodenmessungen, sowohl hinsichtlich der Darstellung von absolutem Werten als auch der räumlichen Variabilität, wurde am borealen Standort erzielt, für den die Komplexität des Ökosystems unter Berücksichtigung von Waldstrukturvariablen und Artenreichtum am geringsten ausfällt. Die dargestellten Ergebnisse wurden in drei Publikationen dieser kumulativen Arbeit erarbeitet. Insgesamt schließt diese Arbeit eine Wissenslücke in der Darstellung des Zusammenspiels verschiedener Umgebungsbedingungen auf die Genauigkeit von situ FAPAR-Messungen. Da die Unsicherheiten von FAPAR nun unter Feldbedingungen quantifizierbar sind, sollten sie in zukünftigen Validierungsstudien auch berücksichtigt werden. In diesem Zusammenhang können die in dieser Arbeit genannten praktische Empfehlungen für die Durchführung von Bodenbeobachtungen zur Erstellung von Messprotokollen herangezogen werden, die dringend erforderlich sind, um globale satellitengestützte FAPAR-Beobachten validieren und zukünftig verbessern zu können

Coniferous needle-leaves, shots and canopies : a remote sensing approach

Coniferous forests are important in the regulation of the Earth’s climate and thus continuous monitoring of these ecosystems is crucial to better understand potential responses to climate change. Optical remote sensing (RS) provides powerful methods for the estimation of essential climate variables and for global forest monitoring. However, coniferous forests represent challenging targets for RS methods, mainly due to structural features specific for coniferous trees (e.g. narrow needle leaves, shoot clumping) whose effects on the RS signal are not yet known or not yet fully understood. Recognizing the need for a better adaptation of RS methods to such spatially heterogeneous and structurally complex canopies, this thesis contributes to improving the interpretation of the remotely sensed optical signal reflected from coniferous stands by focusing on specific knowledge gaps identified in the RS methods at different scales of the coniferous canopies. In addition, it explores the application of approaches that simplify the way the structural complexity of such an environment is tackled when using canopy-level radiative transfer approaches. Three main levels based on the identified gaps were defined for the analysis: (needle) leaf level (chapter 2 and 3); shoot level (chapter 4) and canopy level (chapter 5). At leaf levelthis thesis contributes to minimizing the uncertainties and errors related to leaf optical measuring methods adapted for needle leaves. Although optical properties of coniferous leaves are extensively used in RS approaches (i.e. as input or as validation data), there is only a limited number of techniques available for measuring coniferous leaves. The first focus of this thesis was to review the shortcomings and uncertainties of such methods in order to identify application limits and potential improvements (chapter 2). A review showed that a more standardized measuring protocol was needed, for which measurement uncertainties and errors had to be identified, quantified and preferably removed or minimized. Thus, an experimental set-up improving the original method of Mesarch et al. (1999) was presented (chapter 3), which focused on analyzing uncertainties caused by the presence of the sample holder and by the multiple scattering triggered by both the shape of the specific needle cross-section, and the distance between the needles composing a sample. Results showed that both the sample holder and the multiple scattering, triggered specially by the shape of the non-flat cross section of the coniferous needle-leaves, had a non-negligible effect on the optical signal when measured using a standard spectroradiometer coupled to a single-beam integrating sphere and following the method suggested by Mesarch. Thus, approaches designed to measure optical properties of non-flat coniferous needle samples more comprehensively should take into account these effects in their current signal correction algorithms. Needle clumping into shoots quickly transforms the optical signal making the description of the canopy radiative transfer a complex task and encouraging the search for simplified yet robust approaches. Thus, subsequent steps in this thesis focus on one such simplified approach, known as the recollision probability theory (“p-theory”), applied at two hierarchical levels, i.e., shoots (Chapter 4) and the whole canopy (Chapter 5).At shoot level, an empirical verification of the relationship between the photon recollision probability and a structural parameter called STAR was investigated. The approach allows upscaling needle albedo to shoot albedo and was previously theoretically tested only (chapter 4). For this analysis empirical optical measurements of Scots pine needles and shoots were used. Results showed that the approach works well for the VIS and SWIR spectral regions. However, it was less accurate for the NIR and also for sparse shoots (STAR Finally, accurate modelling of the reflectance signal at canopy levelfor coniferous canopies requires realistic representations of the forest stands, which in general implies a large number of input parameters and computationally demanding algorithms. Radiative transfer modelling based on the photon recollision probability offers an alternative for a simplified definition of the forest canopy structure. The performance of such approach for estimation of the leaf chlorophyll content from satellite imaging spectroscopy data acquired by the CHRIS-PROBA sensor was investigated. The approach was compared to a computationally more demanding one based on a detailed 3D structural description of a forest (chapter 5). For this purposes two canopy models, PARAS and DART, representing the first and second approach respectively, were used. Top-of-canopy bidirectional reflectance factors (BRF) were simulated for both models and used to calculate two optical indices, ANCB670–720 and ANMB670–720.Subsequently, the empirical relationships established between the optical indices and the needle-leaf chlorophyll content (Cab) were applied to the CHRIS-PROBA image of a Norway spruce forest stand to retrieve a map of Cab estimates. Results showed that for the spatial resolution of CHRIS-PROBA (17 m), the simpler model PARAS can be applied to retrieve plausible needle-leaf Cab estimates from satellite imaging spectroscopy data with less intensive model parameterization and reduced computational powerthan when using a model like DART. The ANMB670–720 optical indexwas more robust andresulted in a linear relationship between the Cab estimated by both models. This relationship showed, however, a systematic offset that is potentially caused by differences in the implementation of woody elements in each model or by a different parameterization of leaf optical properties. Thus, further investigation on the impact of parameterization differences related to the needle optical properties and the implementation of woody elements in such a model is recommended.</p

Numerical modelling of mesoscale atmospheric dispersion

Fall 1992.Includes bibliographical references

Object recognition in infrared imagery using appearance-based methods

Abstract unavailable please refer to PD

Earth Resources: A continuing bibliography with indexes, issue 15, October 1977

This bibliography lists 387 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1977. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences