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

Direct reflectance measurements from drones: sensor absolute radiometric calibration and system tests for forest reflectance characterization

Drone-based remote sensing has evolved rapidly in recent years. Miniaturized hyperspectral imaging sensors are becoming more common as they provide more abundant information of the object compared to traditional cameras. Reflectance is a physically defined object property and therefore often preferred output of the remote sensing data capture to be used in the further processes. Absolute calibration of the sensor provides a possibility for physical modelling of the imaging process and enables efficient procedures for reflectance correction. Our objective is to develop a method for direct reflectance measurements for drone-based remote sensing. It is based on an imaging spectrometer and irradiance spectrometer. This approach is highly attractive for many practical applications as it does not require in situ reflectance panels for converting the sensor radiance to ground reflectance factors. We performed SI-traceable spectral and radiance calibration of a tuneable Fabry-Pérot Interferometer -based (FPI) hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). The camera represents novel technology by collecting 2D format hyperspectral image cubes using time sequential spectral scanning principle. The radiance accuracy of different channels varied between ±4% when evaluated using independent test data, and linearity of the camera response was on average 0.9994. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI. The drone-based direct reflectance measurement system showed promising results with imagery collected over Wytham Forest (Oxford, UK)

Direct reflectance transformation methodology for drone-based hyperspectral imaging

Multi- and hyperspectral cameras on drones can be valuable tools in environmental monitoring. A significant shortcoming complicating their usage in quantitative remote sensing applications is insufficient robust radiometric calibration methods. In a direct reflectance transformation method, the drone is equipped with a camera and an irradiance sensor, allowing transformation of image pixel values to reflectance factors without ground reference data. This method requires the sensors to be calibrated with higher accuracy than what is usually required by the empirical line method (ELM), but consequently it offers benefits in robustness, ease of operation, and ability to be used on Beyond-Visual Line of Sight flights. The objective of this study was to develop and assess a drone-based workflow for direct reflectance transformation and implement it on our hyperspectral remote sensing system. A novel atmospheric correction method is also introduced, using two reference panels, but, unlike in the ELM, the correction is not directly affected by changes in the illumination. The sensor system consists of a hyperspectral camera (Rikola HSI, by Senop) and an onboard irradiance spectrometer (FGI AIRS), which were both given thorough radiometric calibrations. In laboratory tests and in a flight experiment, the FGI AIRS tilt-corrected irradiances had accuracy better than 1.9% at solar zenith angles up to 70◦. The system’s lowaltitude reflectance factor accuracy was assessed in a flight experiment using reflectance reference panels, where the normalized root mean square errors (NRMSE) were less than ±2% for the light panels (25% and 50%) and less than ±4% for the dark panels (5% and 10%). In the high-altitude images, taken at 100–150 m altitude, the NRMSEs without atmospheric correction were within 1.4%–8.7% for VIS bands and 2.0%–18.5% for NIR bands. Significant atmospheric effects appeared already at 50 m flight altitude. The proposed atmospheric correction was found to be practical and it decreased the high-altitude NRMSEs to 1.3%–2.6% for VIS bands and to 2.3%– 5.3% for NIR bands. Overall, the workflow was found to be efficient and to provide similar accuracies as the ELM, but providing operational advantages in such challenging scenarios as in forest monitoring, large-scale autonomous mapping tasks, and real-time applications. Tests in varying illumination conditions showed that the reflectance factors of the gravel and vegetation targets varied up to 8% between sunny and cloudy conditions due to reflectance anisotropy effects, while the direct reflectance workflow had better accuracy. This suggests that the varying illumination conditions have to be further accounted for in drone-based in quantitative remote sensing applications

Detection of the forest disturbance using UAV multispectral photogrammetry

Epidemic (calamitous) overpopulations of bark beetles (Scolytinae Latreille, 1804) caused by climate change and inappropriate tree species composition currently have the most negative impacts on the development of Europe's mixed and boreal forests. Epidemic overpopulations can significantly undermine forest health and cause economic losses. It is therefore essential to use appropriate methods for early detection of bark beetle disturbance. Multispectral remote sensing (RS) methods using unmanned aerial systems (UAS) represent a new option for contactless landscape monitoring providing quantitative information on vegetation health with high spatiotemporal resolution and therefore appear to be suitable for early detection of disturbance. The thesis focused on the validation of the use of UAS multispectral photogrammetry and image classification methods for the detection of individual forest disturbance stages caused by the spruce bark beetle (Ips typographus Linnaeus, 1758) at the level of individual trees for the study of disturbance dynamics. In this dissertation, all important aspects of detection were elaborated: analysis of the suitability of spectral bands for disturbance detection, radiometric calibration of multispectral cameras, automated segmentation of individual canopies from...V současné době nejvíce negativně ovlivňují vývoj smíšených a boreálních lesů Evropy epidemické (kalamitní) přemnožení kůrovců (Scolytinae Latreille, 1804) vlivem klimatické změny a nevhodné skladby dřevin, která mohou výrazně narušit zdravotní stav lesů a způsobit ekonomické ztráty. Proto je nezbytné použít vhodné metody pro včasnou detekci kůrovcové disturbance. Metody multispektrálního dálkového průzkumu země (DPZ) pomocí bezpilotních leteckých systémů (UAS) představují novou možnost bezkontaktního monitoringu krajiny poskytující kvantitativní informaci o zdravotním stavu vegetace s vysokým časoprostorovým rozlišením, proto se jeví jako vhodné i pro včasnou detekci disturbance. Disertační práce se zaměřila na ověření využití metod UAS multispektrální fotogrammetrie a klasifikace obrazu pro detekci disturbance lesa způsobené lýkožroutem smrkovým (Ips typographus Linnaeus, 1758) na úrovni jednotlivých stromů s rozlišením jednotlivých fází napadení pro studium dynamiky disturbance. V disertační práci byly rozpracovány všechny důležité aspekty detekce: analýza vhodnosti spektrálních pásem pro detekci disturbance, radiometrická kalibrace multispektrálních kamer, automatizovaná segmentace jednotlivých korun z fotogrammetrického mračna bodů (PPC) a klasifikace fází disturbance na úrovni jednotlivých...Department of Physical Geography and GeoecologyKatedra fyzické geografie a geoekologiePřírodovědecká fakultaFaculty of Scienc

Service robotics and machine learning for close-range remote sensing

L'abstract è presente nell'allegato / the abstract is in the attachmen

Computational approaches for sub-meter ocean color remote sensing

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mechanical and Oceanographic Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2021.The satellite ocean color remote sensing paradigm developed by government space agencies enables the assessment of ocean color products on global scales at kilometer resolutions. A similar paradigm has not yet been developed for regional scales at sub-meter resolutions, but it is essential for specific ocean color applications (e.g., mapping algal biomass in the marginal ice zone). While many aspects of the satellite ocean color remote sensing paradigm are applicable to sub-meter scales, steps within the paradigm must be adapted to the optical character of the ocean at these scales and the opto-electronics of the available sensing instruments. This dissertation adapts the three steps of the satellite ocean color remote sensing paradigm that benefit the most from reassessment at sub-meter scales, namely the correction for surface-reflected light, the design and selection of the opto-electronics and the post-processing of over-sampled regions. First, I identify which surface-reflected light removal algorithm and view angle combination are optimal at sub-meter scales, using data collected during a field deployment to the Martha’s Vineyard Coastal Observatory. I find that of the three most widely used glint correction algorithms, a spectral optimization based approach applied to measurements with a 40∘ view angle best recovers the remotesensing reflectance and chlorophyll concentration despite centimeter scale variability in the surface-reflected light. Second, I develop a simulation framework to assess the impact of higher optical and electronics noise on ocean color product retrieval from unique ocean color scenarios. I demonstrate the framework’s power as a design tool by identifying hardware limitations, and developing potential solutions, for estimating algal biomass from high dynamic range sensing in the marginal ice zone. Third, I investigate a spectral super-resolution technique for application to spatially over-sampled oceanic regions. I determine that this technique more accurately represents spectral frequencies beyond the Nyquist and that it can be trained to be invariant to noise sources characteristic of ocean color remote sensing on images with similar statistics as the training dataset. Overall, the developed and critically assessed sub-meter ocean color remote sensing paradigm enables researchers to collect high fidelity sub-meter data from imaging spectrometers in unique ocean color scenarios.Ryan O’Shea was supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. This research was funded by Woods Hole Oceanographic Institution’s Edwin W. Hiam Ocean Science and Technology Award Fund, its Ocean Venture Funds, its Academic Programs Office, and the National Aeronautics and Space Administration via grant number CCE NNX17AI72G to Dr. Samuel Laney. The raw data for Figures 3-3 and 3-4 were provided through Australian Antarctic Science grants 2678 and 4390

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Laboratory for Atmospheres 2008 Technical Highlights

The 2008 Technical Highlights describes the efforts of all members of the Laboratory for Atmospheres. Their dedication to advancing Earth Science through conducting research, developing and running models, designing instruments, managing projects, running field campaigns, and numerous other activities, is highlighted in this report. The Laboratory for Atmospheres (Code 613) is part of the Earth Sciences Division (Code 610), formerly the Earth Sun Exploration Division, under the Sciences and Exploration Directorate (Code 600) based at NASA s Goddard Space Flight Center in Greenbelt, Maryland. In line with NASA s Exploration Initiative, the Laboratory executes a comprehensive research and technology development program dedicated to advancing knowledge and understanding of the atmospheres of Earth and other planets. The research program is aimed at understanding the influence of solar variability on the Earth s climate; predicting the weather and climate of Earth; understanding the structure, dynamics, and radiative properties of precipitation, clouds, and aerosols; understanding atmospheric chemistry, especially the role of natural and anthropogenic trace species on the ozone balance in the stratosphere and the troposphere; and advancing our understanding of physical properties of Earth s atmosphere. The research program identifies problems and requirements for atmospheric observations via satellite missions. Laboratory scientists conceive, design, develop, and implement ultraviolet, infrared, optical, radar, laser, and lidar technology for remote sensing of the atmosphere. Laboratory members conduct field measurements for satellite data calibration and validation, and carry out numerous modeling activities. These modeling activities include climate model simulations, modeling the chemistry and transport of trace species on regional-to-global scales, cloud-resolving models, and development of next-generation Earth system models. Interdisciplinary research is carried out in collaboration with other laboratories and research groups within the Earth Sciences Division, across the Sciences and Exploration Directorate, and with partners in universities and other Government agencies. The Laboratory for Atmospheres is a vital participant in NASA s research agenda. Our Laboratory often has relatively large programs, sizable satellite missions, and observational campaigns that require the cooperative and collaborative efforts of many scientists. We ensure an appropriate balance between our scientists responsibility for these large collaborative projects and their need for an active individual research agenda. This balance allows members of the Laboratory to continuously improve their scientific credentials. Members of the Laboratory interact with the general public to support a wide range of interests in the atmospheric sciences. Among other activities, the Laboratory raises the public s awareness of atmospheric science by presenting public lectures and demonstrations, by making scientific data available to wide audiences, by teaching, and by mentoring students and teachers. The Laboratory makes substantial efforts to attract new scientists to the various areas of atmospheric research. We strongly encourage the establishment of partnerships with Federal and state agencies that have operational responsibilities to promote the societal application of our science products. This report describes our role in NASA s mission, gives a broad description of our research, and summarizes our scientists major accomplishments during calendar year 2008. The report also contains useful information on human resources, scientific interactions, and outreach activities