Vliv atmosférické a topografické korekce na přesnost odhadu množství chlorofylu ve smrkových lesních porostech

Odstraňování efektů zemské atmosféry (tzv. atmosférická korekce) je jednou z klíčových součástí předzpracování obrazových dat dálkového průzkumu Země používaných pro kvantitativní nebo semi-kvantitativní analýzu. Přestože v současné době existuje velké množství robustních výpočetních technik kvantitativního odhadu různých parametrů zemského povrchu, vliv atmosférické korekce na výsledky těchto odhadů zpravidla není brán dostatečně v úvahu. Hlavním cílem této práce je zhodnocení vlivu použití různých technik atmosférické korekce na přesnost kvantitativního odhadu množství chlorofylu v lesních porostech smrku ztepilého (Picea abies). Obsah chlorofylu byl určován na podkladě výpočtu vybraných vegetačních indexů, které jsou na obsah chlorofylu citlivé (ANCB650-720, MSR, N718, TCARI/OSAVI a D718/D704). Hodnoty těchto indexů byly simulovány pomocí kombinace modelů radiativního transferu PROSPECT a DART. Výsledné odhady obsahu chlorofylu byly na závěr validovány pomocí výsledků laboratorního stanovení obsahu chlorofylu v odebraných vzorcích smrkových jehlic. Kromě toho byl v rámci práce odvozen nový index pro hodnocení podobnosti dvou srovnávaných spekter nazvaný normalized Area Under Difference Curve (nAUDC). V rámci této práce byla testována potenciální možnost náhrady standardní atmosférické korekce...Removal of atmospheric effects (atmospheric correction) is an essential step in a pre-processing chain of all remotely sensed image data used for any quantitative or semi-quantitative analysis. Although there are many robust computing techniques allowing quantitative estimation of various parameters of the Earth's surface, the influence of atmospheric correction on the accuracy of such estimation is usually not taken into account at all. The main focus of this thesis is to assess the influence of the use of different atmospheric correction techniques on the Norway spruce (Picea abies) canopy chlorophyll content estimation accuracy. Canopy chlorophyll content was estimated using values of chlorophyll sensitive vegetation indices (ANCB650-720, MSR, N718, TCARI/OSAVI and D718/D704) simulated by a coupling of PROSPECT and DART radiative transfer models and validated by a ground-truth dataset. A new spectral similarity index called normalized Area Under Difference Curve (nAUDC) was developed to allow mutual comparison of two spectra originating from hyperspectral datasets corrected by different atmospheric correction methods. Potential substitutability of the standard physically-based ATCOR-4 atmospheric correction by the empirical correction based on the data acquired by the downwelling irradiance...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

A Compact, High Resolution Hyperspectral Imager for Remote Sensing of Soil Moisture

Measurement of soil moisture content is a key challenge across a variety of fields, ranging from civil engineering through to defence and agriculture. While dedicated satellite platforms like SMAP and SMOS provide high spatial coverage, their low spatial resolution limits their application to larger regional studies. The advent of compact, high lift capacity UAVs has enabled small scale surveys of specific farmland cites. This thesis presents work on the development of a compact, high spatial and spectral resolution hyperspectral imager, designed for remote measurement of soil moisture content. The optical design of the system incorporates a bespoke freeform blazed diffraction grating, providing higher optical performance at a similar aperture to conventional Offner-Chrisp designs. The key challenges of UAV-borne hyperspectral imaging relate to using only solar illumination, with both intermittent cloud cover and atmospheric water absorption creating challenges in obtaining accurate reflectance measurements. A hardware based calibration channel for mitigating cloud cover effects is introduced, along with a comparison of methods for recovering soil moisture content from reflectance data under varying illumination conditions. The data processing pipeline required to process the raw pushbroom data into georectified images is also discussed. Finally, preliminary work on applying soil moisture techniques to leaf imaging are presented

Hyperspectral Observations for Atmospheric Remote Sensing: Instrumentation, Atmospheric Correction, and Spectral Unmixing

Hyperspectral instruments expand the spectral dimension of remote sensing measurements by collecting data in hundreds of contiguous wavelength channels. Spectrally resolved measurements can be used to derive science products for a diverse range of fields such as atmospheric science, geology, oceanography, ecology, climate monitoring, and agricultural science, to name a few. The spectral information collected by hyperspectral instruments enables more accurate retrievals of physical properties and detection of temporal changes. These advantages have led to an increasing number of active and planned hyperspectral instruments. This thesis describes methods for attributing hyperspectral radiation observations to physical sources.We developed, validated and characterized improvements to a hyperspectral instrument, the Solar Spectral Irradiance Monitor (SSIM), built at the University of Colorado Boulder’s Laboratory for Atmospheric and Space Physics. Contributions include the characterization of the optics’ angular response, testing of an optics stabilizing platform and the development and testing of a spectrometer thermal control system. This instrument was then deployed on an aircraft for a field study with the National Ecological Observatory Network (NEON). SSIM measurements of upwelling and downwelling irradiance were used in conjunction with NEON’s Imaging Spectrometer to enable atmospheric correction of imagery collected below cloud layers.We developed a numerical spectral unmixing algorithm, Informed Non-Negative Matrix Factorization (INMF), to separate contributions to hyperspectral imagery from distinct physical sources such as surface reflectance, atmospheric absorption, molecular scattering, and aerosol scattering. INMF was tailored for hyperspectral applications by introducing algorithmic constraints based on the physics of radiative transfer. INMF was tested using imagery collected by the Hyperspectral Imager for the Coastal Ocean (HICO). To validate the method INMF results were compared to model-based atmospheric correction results. We demonstrate possible applications of INMF by presenting the retrieval of two physical properties, aerosol attributed radiance and seafloor depth. The retrievals were evaluated by comparing INMF output to independent retrievals of aerosol properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ seafloor depth measurements from the U.S. Coastal Relief Model. In these comparisons INMF shows promise for retrieving both physical properties, and may be improved with physics-based constraints on the seafloor and aerosol source spectra

Classification of agricultural crops of the Taita Hills, Kenya using airborne AisaEAGLE imaging spectroscopy data

Land use practices are changing at a fast pace in the tropics. In sub-Saharan Africa forests, woodlands and bushlands are being transformed for agricultural use to produce food for the rapidly growing population. Although food production is crucial for the survivability of the people the uncontrolled expansion of agricultural land at the expanse of natural habitats may in the longer term decrease food production due to disturbances in water balance, increased land erosion and eradication of natural habitats for pollinators. Before the impacts of land use/land cover changes on the ecosystem can be studied the study area needs to be mapped. The study area of this thesis is located in the Taita Hills, Kenya. In previous studies the land use/land cover was mapped on higher hierarchical level in classes such as agricultural land, forest and bushland. In this thesis high spatial and spectral resolution AisaEAGLE imaging spectroscopy data was used to map the common agricultural crops found in the study area. Ground reference data was collected from 5 study plots located in the study area. Over 50 plant species were mapped but only 7 of these were used in the classification. The AisaEAGLE data was acquired in January–February of 2012 and was radiometrically, geometrically and atmospherically corrected. Minimum noise fraction (MNF) transformation was applied to the data to reduce the noise and the dimensionality. Optimal number of MNF bands was defined based on analysis of the information content of the bands. The classification was done with support vector machine (SVM) algorithm using radial basis function (RBF) kernel. Gamma, penalty and probability threshold parameters for the classifier were defined based on analysis of different combinations of these values. The analysis showed that gamma and penalty values had only minor impacts on the classification result. Based on the analysis an optimal threshold level was defined where pixels that were not likely to belong to any of the classes were left unclassified while maximum number of the known targets were correctly classified. Study area was classified with the optimal threshold value 0.90. Classification with threshold value 0.00 was done for reference. The overall accuracies for the classified pixels were 91.52% and 99.70% for the classifications done with probability threshold values 0.00 and 0.90. As the threshold was increased to 0.90 61% of the pixels were left unclassified. At the optimal threshold level between classes misclassifications were almost completely removed whereas the total number of correctly classified testing samples decreased. Applying MNF transformation to the data before the classification increased the overall accuracy from 80.58% to 91.52% while other parameters stayed the same. Results of this thesis showed that SVM classifier used with MNF transformation yielded high overall accuracies for the crop classifications. Adjusting the probability threshold to an optimal level was important since the study area was heterogeneous and only fraction the species were classified. For further applications the possibilities of object-based classification should be considered. The results of this thesis will be shared with the Climate Change Impacts on Ecosystem Services and Food Security in Eastern Africa (CHIESA) –project

The impact of the spectral dimension of hyperspectral datasets on plant disease detection

Precision Agriculture as an information based approach requires explicit spatial information about the within field heterogeneities for site-specific applications. Thus, the usage of cost-intensive agrochemicals and the impact on the environment can be significantly reduced. Spectroscopic approaches are thereby a promising tool for providing fast and precise information on a local to regional level. In this thesis, the impact of hyperspectral near-range and remote sensing data for crop stress detection will be observed since spectroscopic approaches are of great interest for Precision Agriculture. Two greenhouse experiments and three field experiments were conducted with spectroscopic measurements to examine possibilities and limitations of hyperspectral data. The data were acquired using a near-range non-imaging spectrometer (ASD Fieldspec 3) and a near-range imaging spectrometer (ImSpec V10E) in the greenhouse, or were acquired by the airborne sensor systems HyMapTM, ROSIS or AISA for the field experiments. The methodical foci thereby are the improvement of binary detection approaches, discriminating 'vital' and 'infected' wheat stands or parts of wheat stands, and quantification approaches to estimate disease severities at canopy level. This thesis examines the spectral dimension of hyperspectral data for crop stress detection by assessing data redundancy and defining spectral necessities. Different feature selection methods were tested for their suitability in reducing the high amount of spectral data without losing significant information. Conventional classification approaches and recent developments, such as support vector machines for classification (SVM), were thereby tested based on the identified spectral subsets to assess the status of different wheat stands. By focusing on phenomenon-specific spectral bands, stressed wheat stands could successfully be identified with high accuracies. Using optimal band combinations could even increase classification accuracies. The results showed that not the entire spectrum of hyperspectral data is necessary for the detection of fungal infections in wheat. These findings are particularly interesting for future spectral sensor design and remote sensing missions that are aiming at the provision of spatial information for agricultural practice. The ability of hyperspectral data in quantifying the severity of fungal diseases was observed. Site-specific fungicide treatments based on application maps are technically possible and doses can be adjusted if the maps provide information about the health status of the crops. Crop growth anomalies caused by fungal infections were observed, which differed significantly within one field. The derivation of disease severities based on hyperspectral near-range and remote sensing data were examined using classification approaches and support vector machines for regression (SVR). Fungal infections of wheat stands in the greenhouse and wheat stands in the field could be quantified with a high level of certainty. The results are very promising and the findings may be of great interest for agricultural questionnaires and automatic phenotyping approaches, since the presented approaches are fast and non-destructive. Spatial maps with continual disease severity data could be derived, which can be used to generate application maps for agricultural practice. Since the study shows that a reduction of hyperspectral data to a few but specifically selected spectral bands can improve the classification accuracies or regression analyses, a preliminary feature selection should be considered when working with hyperspectral remote sensing data. Agricultural and geographical approaches that are based on spatial-spectral information may thus profit from a faster and more reliable extraction of information. The study shows great advantages of the usage of hyperspectral imaging data but also the necessity of advanced and innovative analyzing methods

Sensitivity of the ground-based downwelling irradiance recorded by the FODIS sensor in respect of different angular positions

Some airborne hyperspectral sensors (e.g. AISA) can measure spectral downwelling irradiance using an additional cosine sensor mounted on a roof of an aircraft. The downwelling irradiance data, however, are rarely used for any atmospheric correction or compensation of different sun-sensor geometry, partly because they are sensitive towards continuous motion of the airborne platform. The airborne hyperspectral system AISA Eagle (Specim, Ltd., Finland), combined with the Fiber Optic Downwelling Irradiance Sensor (FODIS), were used for ground-based outdoor static measurements. The FODIS sensor was tilted into various zenith and azimuth angles. The data analysis revealed high sensitivity of the raw recorded FODIS signal towards different angular position. Simple cosine corrections reduced variation in the recorded FODIS signal. The variability (standard deviation of all measurements) decreased by 88% after the cosine correction was applied