Propagation of uncertainty in atmospheric parameters to hyperspectral unmixing

Atmospheric correction (AC) is important in pre-processing of airborne hyperspectral imagery. AC requires knowledge on the atmospheric state expressed by atmospheric condition parameters. Their values are affected by uncertainties that propagate to the application level. This study investigates the propagation of uncertainty from column water vapor (CWV) and aerosol optical depth (AOD) towards abundance maps obtained by means of spectral unmixing. Both Fully Constrained Least Squares (FCLS) and FCLS with Total Variation (FCLS-TV) are applied. We use five simulated datasets contaminated by various noise levels. Three datasets cover two spectral scenarios with different endmembers. A univariate and a bivariate analysis are carried out on CWV and AOD. The other two datasets are used to analyze the effect of surface albedo. The analysis identifies trends in performance degradation caused by the gradual shift in parameter values from their true value. The maximum achievable performance depends upon spectral characteristics of the datasets, noise level, and surface albedo. As expected, under noisy conditions FCLS-TV performs better than FCLS. Our research opens new perspectives for applications where estimation of reflectance is so far considered to be deterministic

Airborne Drones for Water Quality Mapping in Inland, Transitional and Coastal Waters—MapEO Water Data Processing and Validation

Using airborne drones to monitor water quality in inland, transitional or coastal surface waters is an emerging research field. Airborne drones can fly under clouds at preferred times, capturing data at cm resolution, filling a significant gap between existing in situ, airborne and satellite remote sensing capabilities. Suitable drones and lightweight cameras are readily available on the market, whereas deriving water quality products from the captured image is not straightforward; vignetting effects, georeferencing, the dynamic nature and high light absorption efficiency of water, sun glint and sky glint effects require careful data processing. This paper presents the data processing workflow behind MapEO water, an end-to-end cloud-based solution that deals with the complexities of observing water surfaces and retrieves water-leaving reflectance and water quality products like turbidity and chlorophyll-a (Chl-a) concentration. MapEO water supports common camera types and performs a geometric and radiometric correction and subsequent conversion to reflectance and water quality products. This study shows validation results of water-leaving reflectance, turbidity and Chl-a maps derived using DJI Phantom 4 pro and MicaSense cameras for several lakes across Europe. Coefficients of determination values of 0.71 and 0.93 are obtained for turbidity and Chl-a, respectively. We conclude that airborne drone data has major potential to be embedded in operational monitoring programmes and can form useful links between satellite and in situ observations

An optimization approach to estimate and calibrate column water vapour for hyperspectral airborne data

The article describes a novel approach to estimate and calibrate column water vapour (CWV), a key parameter for atmospheric correction of remote-sensing data. CWV is spatially and temporally variable, and image-based methods are used for its inference. This inference, however, is affected by methodological and numeric limitations, which likely propagate to reflectance estimates. In this article, a method is proposed to estimate CWV iteratively from target surface reflectances. The method is free from assumptions for at sensor radiance-based CWV estimation methods. We consider two cases: (a) CWV is incorrectly estimated in a processing chain and (b) CWV is not estimated in a processing chain. To solve (a) we use the incorrect estimations as initial values to the proposed method during calibration. In (b), CWV is estimated without initial information. Next, we combined the two scenarios, resulting in a generic method to calibrate and estimate CWV. We utilized the hyperspectral mapper (HyMap) and airborne prism experiment (APEX) instruments for the synthetic and real data experiments, respectively. Noise levels were added to the synthetic data to simulate real imaging conditions. The real data used in this research are cloud-free scenes acquired from the airborne campaigns. For performance assessment, we compared the proposed method with two state-of-the-art methods. Our method performed better as it minimizes the absolute error close to zero, only within 8–10 iterations. It thus suits existing operational chains where the number of iterations is considerable. Finally, the method is simple to implement and can be extended to address other atmospheric trace gases

Land Cover Change and Water Quality: How Remote Sensing Can Help Understand Driver–Impact Relations in the Lake Titicaca Basin

The increase of human interventions and developments are modifying the land use/land cover (LULC) of the global landscape, thus severely affecting the water quality of rivers and lakes. Appropriate management and effective policy developments are required to deal with the problems of surface water contamination around the globe. However, spatiotemporal variations of water quality and its complex relation with land cover (LC) changes, challenge adequate water resources management. In this study, we explored the use of remote sensing to relate LC change in the Katari River Basin (KRB) located in the Bolivian Andes and water quality on the shores of Lake Titicaca, in order to support water management. An unsupervised classification of Landsat 7 satellite images and trajectory analysis was applied to understand the modifications of LC through time. In addition, water-quality indicators at the outlet of the basin were retrieved from remote-sensing images and its temporal behavior was analyzed. The results show that the expansion of urban areas is the predominant environmental driver in the KRB, which has great impact on the water quality of Lake Titicaca. We conclude that there is a strong link between the rapid growth of urban and industrial areas with the detriment of river and lake water quality. This case study shows how remote sensing can help understand driver–impact relations

Effect of DEM Uncertainty on the Positional Accuracy of Airborne Imagery

The geometric and atmospheric processing of airborne imagery is a complex task that involves many correction steps. Geometric correction is particularly challenging because slight movements of the aircraft and small changes in topography can have a great impact on the geographic positioning of the processed imagery. This paper focused on how uncertainty in topography, represented by a digital elevation model (DEM), propagates through the geometric correction process. We used a Monte Carlo analysis, in which, first, a geostatistical uncertainty model of the DEM was developed to simulate a large number of DEM realizations. Next, geometric correction was run for each of the simulated DEMs. The analysis of the corrected images and their variability provided valuable information about the positional accuracy of the corrected image. The method was applied to a hyperspectral image of a mountainous area in Calabria, Italy, by using the Shuttle Radar Topography Mission-DEM as the topographic information source. We found out that the uncertainty varies greatly over the whole terrain and is substantial at large off-nadir viewing angles in the across-track direction. Also, positional uncertainty is larger in rugged terrains. We conclude that Monte Carlo uncertainty propagation analysis is a valuable technique in deriving quality layers that inform end users about the positional accuracy of airborne imagery, and we recommend that it is integrated in the operational processing steps of the Processing and Archiving Facilities

EUFAR goes hyperspectral in FP7

vTwo FP6 initiatives i) HYRESSA, hyperspectral remote sensing in Europe specific support action, and ii) EUFAR, European Facility for Airborne Research in Environmental and Geo-sciences, have joined forces in FP7. The FP7 Integrating Activity EUFAR (including HYRESSA) is now a network of 33 European airborne data providers and experts in airborne measurements. With the support of the European Commission, EUFAR provides European scientists with trans-national access to 6 airborne instruments (including hyperspectral imaging sensors) and 20 instrumented aircraft and early-stage researchers and university lecturers with training courses on airborne measurements. This paper reports on EUFAR activities and opportunities for European researchers with special attention to activities and opportunities related to airborne hyperspectral imagin

Initial trust and intentions to buy: The effect of vendor-specific guarantees, customer reviews and the role of online shopping experience Electronic Commerce Research and Applications

This study experimentally investigates the effects of vendor-specific guarantees and customer reviews (1) on the formation of initial consumers’ trust—separating institutional and competence trust—and (2) on first-time consumers’ intentions to buy. In addition, we examine how differing levels of online shopping experience moderate the relationship between trust and consumers’ intentions to buy. The empirical results of the study reveal the relative effectiveness of the two vendor mechanisms, with vendor-specific guarantees having a more positive effect on institutional trust and customer reviews on competence trust. While our results also show that initial trust is a central concept in explaining consumers’ intentions to buy, we find that this relationship is more pronounced for competence trust in case when consumers are more experienced with online shopping. Meanwhile, institutional trust seems a necessary prerequisite for both experienced and inexperienced online shoppers to actually buy from an unfamiliar vendor. Our study provides important managerial implications that are of interest to online vendors, especially for newly established or unknown web-based businesses.status: publishe

Evaluation of user-oriented attractiveness of imaging spectroscopy data using the value-benefit analysis (VBA)

While there is a strong need for hyperspectral imagery, the user-driven requirements are not well defined in view of defined protocols for calibration, acquisition, processing and in-situ measurements in compliance with existing standards. Therefore an analysis was performed in the frame of the EC project HYRESSA, regarding the question “What are the individual user requirements on hyperspectral imagery and the related data products?”. For this analysis a questionnaire and a subsequent benefit-value analysis helped to retrieve users needs and evaluate open items accordingly. Following the methodology of the Value-Benefit Analysis (VBA), the answer can be described in hierarchical ordered multidimensional objective model. The VBA serves as well-known tool for systematic problem solving process as a possibility of comparing projects or solutions. It enables the evaluation on the basis of a multidimensional objective model and can be extended by expert’s preferences. Therefore the scaling method (Law of Comparative Judgment) was applied for receiving the desired ranking judgments. The result, which is the relative value of projects concerning a well-defined main objective can now be produced analytically. The investigation showed – besides details on user needs – that a VBA is a suitable method to analyse needs of hyperspectral data and to support sensor/data specification-building process. The VBA has the advantage, to be easy and clear to handle, resulting in a comprehensive evaluation. The disadvantage are the necessary efforts and the partly non-availability of all sensor data parameters. The paper summarizes all results of the analysis and gives insight to VBA methodology, statistics and others more