A model-based approach to correcting spectral irradiance data using an upward-looking airborne sensor (CASI ILS)

A number of aircraft sensors have the facility to measure spectral downwelling irradiance using a sensor mounted on the roof of the aircraft, but these data are rarely used for atmospheric correction. Part of the problem is that the attitude of the airborne platform is always changing during flight, even in stable conditions, so that direct use of data from an incident light sensor (ILS) can introduce errors into atmospheric correction methods. The continual motion of the ILS is used here to advantage, as a means to fit a sky radiance distribution model developed by Brunger and Hooper (1993) to data from the Itres Instruments CASI ILS. The inclination of the ILS sensor, due to changing aircraft attitude, is considered as the slope plane in the model. The selected model coefficients correspond to parameterised atmospheric conditions and represent atmospheric transmission and the proportion of direct:diffuse flux. The method was used to correct CASI ILS data acquired over a site in southern England. Comparison with spectral irradiance measured simultaneously on the ground shows that the method reduced the variability of the ILS data and also compensated for the effect of different flight directions. The sky radiance distribution at sensor level is also calculated by the model, and shows the characteristics of the sky conditions at the time of each flight

Calibration, validation and the NERC Airborne Remote Sensing Facility

The application of airborne and satellite remote sensing to terrestrial applications has been dominated by empirically-based, semi-quantitative approaches, in contrast to those developed in the marine and atmospheric sciences which have often developed from rigorous physically-based models. Furthermore, the traceability of EO data and the methodological basis of many applications has often been taken for granted, with the result that the repeatability of analyses and the reliability of many terrestrial EO products can be questioned. ‘NCAVEO’ is a recently established network of Earth Observation experts and data users committed to exchanging knowledge and understanding in the area of remote sensing data calibration and validation. It aims to provide a UK-based forum to collate available knowledge and expertise associated with the calibration and validation of EO-based products from both UK and overseas providers, in different discipline areas including land, ocean and atmosphere. This paper will introduce NCAVEO and highlight some of the contributions it hopes to make to airborne remote sensing in the UK

Airborne Visible/Infrared Imaging spectrometer AVIS: Design, characterization and calibration

The Airborne Visible/Infrared imaging Spectrometer AVIS is a hyperspectral imager designed for environmental monitoring purposes. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several experiments between 1999 and 2007. We describe the instrument design and present the results of laboratory characterization and calibration of the system's second generation, AVIS-2, which is currently being operated. The processing of the data is described and examples of remote sensing reflectance data are presented

Restoration of Oyster (Crassostrea virginica) Habitat for Multiple Estuarine Species Benefits

Increase in nitrogen concentration and declining eelgrass beds in Great Bay Estuary have been observed in the last decades. These two parameters are clear indicators of the impending problems for NH’s estuaries. The NH Department of Environmental Services (DES) in collaboration with the New Hampshire Estuaries Project (NHEP) adopted the assumption that eelgrass survival can be used as the water quality target for nutrient criteria development for NH’s estuaries. One of the hypotheses put forward regarding eelgrass decline is that a possible eutrophication response to nutrient increases in the Great Bay Estuary has been the proliferation of nuisance macroalgae, which has reduced eelgrass area in Great Bay Estuary. To test this hypothesis, mapping of eelgrass and nuisance macroalgae beds using hyperspectral imagery was suggested. A hyperspectral imagery was conducted by SpecTIR in August 2007 using an AISA Eagle sensor. The collected dataset was used to map eelgrass and nuisance macroalgae throughout the Great Bay Estuary. This report outlines the configured procedure for mapping the macroalgae and eelgrass beds using hyperspectral imagery. No ground truth measurements of eelgrass or macroalgae were collected as part of this project, although eelgrass ground truth data was collected as part of a separate project. Guidance from eelgrass and macroalgae experts was used for identifying training sets and evaluating the classification results. The results produced a comprehensive eelgrass and macroalgae map of the estuary. Three recommendations are suggested following the experience gained in this study: conducting ground truth measurements at the time of the HS survey, acquiring the current DEM model of Great Bay Estuary, and examining additional HS datasets with expert eelgrass and macroalgae guidance. These three issues can improve the classification results and allow more advanced applications, such as identification of macroalgae types

A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest

Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way

The absolute radiometric calibration of the advanced very high resolution radiometer

The measurement conditions are described for an intensive field campaign at White Sands Missile Range for the calibration of the AVHRRs on NOAA-9, NOAA-10 and NOAA-11, LANDSAT-4 TM and SPOT. Three different methods for calibration of AVHRRs by reference to a ground surface site are reported, and results from these methods are compared. Significant degradations in NOAA-9 and NOAA-10 AVHRR responsivities occurred since prelaunch calibrations were completed. As of February 1988, degradations in NOAA-9 AVHRR responsivities were on the order of 37 percent in channel and 41 percent in channel 2, and for the NOAA-10 AVHRR these degradations were 42 and 59 percent in channels 1 and 2, respectively

Macroalgae and eelgrass mapping in Great Bay Estuary using AISA hyperspectral imagery

Increase in nitrogen concentration and declining eelgrass beds in Great Bay Estuary have been observed in the last decades. These two parameters are clear indicators of the impending problems for NH’s estuaries. The NH Department of Environmental Services (DES) in collaboration with the New Hampshire Estuaries Project (NHEP) adopted the assumption that eelgrass survival can be used as the water quality target for nutrient criteria development for NH’s estuaries. One of the hypotheses put forward regarding eelgrass decline is that a possible eutrophication response to nutrient increases in the Great Bay Estuary has been the proliferation of nuisance macroalgae, which has reduced eelgrass area in Great Bay Estuary. To test this hypothesis, mapping of eelgrass and nuisance macroalgae beds using hyperspectral imagery was suggested. A hyperspectral imagery was conducted by SpecTIR in August 2007 using an AISA Eagle sensor. The collected dataset was used to map eelgrass and nuisance macroalgae throughout the Great Bay Estuary. This report outlines the configured procedure for mapping the macroalgae and eelgrass beds using hyperspectral imagery. No ground truth measurements of eelgrass or macroalgae were collected as part of this project, although eelgrass ground truth data was collected as part of a separate project. Guidance from eelgrass and macroalgae experts was used for identifying training sets and evaluating the classification results. The results produced a comprehensive eelgrass and macroalgae map of the estuary. Three recommendations are suggested following the experience gained in this study: conducting ground truth measurements at the time of the HS survey, acquiring the current DEM model of Great Bay Estuary, and examining additional HS datasets with expert eelgrass and macroalgae guidance. These three issues can improve the classification results and allow more advanced applications, such as identification of macroalgae types

Radiometric calibration of the Earth observing system's imaging sensors

Philosophy, requirements, and methods of calibration of multispectral space sensor systems as applicable to the Earth Observing System (EOS) are discussed. Vicarious methods for calibration of low spatial resolution systems, with respect to the Advanced Very High Resolution Radiometer (AVHRR), are then summarized. Finally, a theoretical introduction is given to a new vicarious method of calibration using the ratio of diffuse-to-global irradiance at the Earth's surfaces as the key input. This may provide an additional independent method for in-flight calibration