Reduced visibility and adverse cloud cover is a major issue for aviation, road traffic, and military activities. Synoptic meteorological stations and LIDAR measurements are common tools to detect meteorological conditions. However, a low density of meteorological stations and LIDAR measurements may limit a detailed spatial analysis. While geostationary satellite data is a valuable source of information for analyzing the spatio-temporal variability of fog and clouds on a global scale, considerable effort is still required to improve the detection of atmospheric variables on a local scale, especially during the night.
In this study we propose to use thermal camera images to (1) improve cloud detection and (2) to study visibility conditions during nighttime. For this purpose, we leverage FLIR A320 and FLIR A655sc Stationary Thermal Imagers installed in the city of Bern, Switzerland. We find that the proposed data provides detailed information about low clouds and the cloud base height that is usually not seen by satellites. However, clouds with a small optical depth such as thin cirrus clouds are difficult to detect as the noise level of the captured thermal images is high.
The second part of this study focuses on the detection of structural features. Predefined targets such as roof windows, an antenna, or a small church tower are selected at distances of 140m to 1210m from the camera. We distinguish between active targets (heated targets or targets with insufficient thermal insulation) and passive structural features to analyze the sensor's visibility range. We have found that a successful detection of some passive structural features highly depends on incident solar radiation. Therefore, the detection of such features is often hindered during the night. On the other hand, active targets can be detected without difficulty during the night due to major differences in temperature between the heated target and its surrounding non-heated objects. We retrieve response values by the cross-correlation of master edge signatures of the targets and the actual edge-detected thermal camera image. These response values are a precise indicator of the atmospheric conditions and allows us to detect restricted visibility conditions.
This study presents an automatic visibility retrieval of a FLIR A320 Stationary Thermal Imager installed on a measurement tower on the mountain Lagern located in the Swiss Jura Mountains. Our visibility retrieval makes use of edges that are automatically detected from thermal camera images. Predefined target regions, such as mountain silhouettes or buildings with high thermal differences to the surroundings, are used to derive the maximum visibility distance that is detectable in the image. To allow a stable, automatic processing, our procedure additionally removes noise in the image and includes automatic image alignment to correct small shifts of the camera. We present a detailed analysis of visibility derived from more than 24000 thermal images of the years 2015 and 2016 by comparing them to (1) visibility derived from a panoramic camera image (VISrange), (2) measurements of a forward-scatter visibility meter (Vaisala FD12 working in the NIR spectra), and (3) modeled visibility values using the Thermal Range Model TRM4. Atmospheric conditions, mainly water vapor from European Center for Medium Weather Forecast (ECMWF), were considered to calculate the extinction coefficients using MODTRAN. The automatic visibility retrieval based on FLIR A320 images is often in good agreement with the retrieval from the systems working in different spectral ranges. However, some significant differences were detected as well, depending on weather conditions, thermal differences of the monitored landscape, and defined target size.
KEYWORDS: Calibration, Hyperspectral imaging, Chemical analysis, Spectroscopy, Absorption, Imaging spectroscopy, Data modeling, RGB color model, Signal to noise ratio, Cameras
We investigate the potential of hyperspectral imaging spectrometry for the analysis of fresh sediment cores. A sediment-core-scanning system equipped with a camera working in the visual to near-infrared range (400 to 1000 nm) is described and a general methodology for processing and calibrating spectral data from sediments is proposed. We present an application from organic sediments of Lake Jaczno, a freshwater lake with biochemical varves in northern Poland. The sedimentary pigment bacteriopheophytin a (BPhe a) is diagnostic for anoxia in lakes and, therefore, an important ecological indicator. Calibration of the spectral data (BPhe a absorption ∼800 to 900 nm) to absolute BPhe a concentrations, as measured by high-performance-liquid-chromatography, reveals that sedimentary BPhe a concentrations can be estimated from spectral data with a model uncertainty of ∼10%. Based on this calibration model, we use the hyperspectral data from the sediment core to produce high-resolution intensity maps and time series of relative BPhe a concentrations (∼10 to 20 data points per year, pixel resolution 70×70 μm2). We conclude that hyperspectral imaging is a very cost- and time-efficient method for the analysis of lake sediments and provides insight into the spatiotemporal structures of biogeochemical species at a degree of detail that is not possible with wet chemical analyses.
The operational processing of NOAA-AVHRR data and the derivation of vegetation index (NDVI), leaf area index (LAI) and vegetation cover fraction for the European Alps is presented. The analysis was done for three elevation zones (<500m, 1000-1500m and >2500m) to show the dynamic characteristic of vegetation in the years 1995 to 1998. The vegetation cover fraction shows a high variability in lower elevations during winter caused by the not persistent snow cover. In elevations above 2500m the high variability could be detected during summer. The exponential approach to derive LAI using NDVI data is only valid for elevations above 2000m or for NDVI less than 0.5. Otherwise the LAI values are saturated because small changes in NDVI result in an increased range of LAI up to 1.5. This prevents an exact
derivation of leaf area index based on the normalized difference vegetation index.
Lake surface water temperature (LSWT) are operationally derived from the National Oceanic and Atmospheric Administration operated Advanced Very High Resolution Radiometer (NOAA - AVHRR) data using a nonlinear sea surface temperature (NLSST) algorithm. The adapted method has been widely examined with the bias of the algorithm around 0.5°C or better. Preliminary analysis shows good agreement between satellite derived LSWT and in - situ measurements at two different lakes. A comparison of LSWT at noon (mean local time) for three lakes is presented. Surface water temperature variations are dominating the annual cycle, however, the varying geospatial attributes of each lake result in specific surface temperature characteristics. Lakes located close to each other can display considerable differences in average surface temperatures by as much as 3°C. Knowledge of this fact gives new insights and possibilities for modeling local scale meteorological phenomena like heat flux, energy budget and evapotranspiration. Using operational satellite-derived lake surface temperature can also improve numerical weather prediction models on local scales.
The aim of this study is the retrieval of aerosol optical depth from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensor over land. The region of interest covers central Europe ranging from 50°N to 40.5°N and from 0°E to 17°E including the European Alps. On the temporal scale, we limit the data set to afternoon NOAA-16 passes of the entire year 2002. In this region, there are sixteen stations from the Aerosol Robotic Network (AERONET) at which we can compare the ground based versus the space borne measurements. The most crucial parameter in the retrieval procedure is the estimate of a correct surface reflectance since inaccuracies of 0.01 can result in AOD variations of ±0.1. Surface reflectance has been estimated by extracting the minimum reflectance within 10° intervals of the satellite zenith angle within two-month intervals. This method eliminates the varying reflectance with varying satellite zenith angle but the extracted surface reflectance still contains an aerosol signal. Most stations show a clear relationship between the AVHRR and the AERONET data. In case of a weak or non-existing relationship, we were able to identify reasons for this behavior. The standard error of estimate is about 0.18. The largest potential for increasing the accuracy of this product posses an improvement of the cloud mask. We can conclude that aerosol retrieval over land using AVHRR is a challenging task but it is possible to extract some valuable results.
Snow and ice play an important role in the earth`s radiation balance because of the high albedo in comparison to other natural surfaces. Furthermore ice and snow is the largest contributor to rivers and ground water over major parts of the middle and high altitudes. These are reasons why hydrological and climatological studies require estimates of snow covered areas. Most of such snow cover maps generated from satellite data include information of snow or not snow for each image pixel. In this study a linear spectral unmixing algorithm is used to calculate snow cover portions within each data cell. We examine the ability of this algorithm for operational and near-real time snow cover estimation at subpixel scale using medium spatial resolution satellite data from NOAA-AVHRR. The automated methodology is presented which produces snow cover fraction maps showing plausible distribution of snow in comparison to TERRA-ASTER data. The qualitative analysis of the results present how suitable the approach implemented in the preliminary processing chain is. Simplifying assumptions are made to the procedure which explains some variation between derived snow cover fraction map and reference data. Further work should include an accurate quantification of areal snow coverage comparison to traditional approaches.
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