Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes

The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the ecological status of inland waterbodies and for human and animal health. In this study we examined the performance of four semi-analytical algorithms for the retrieval of chlorophyll a (Chl a) and phycocyanin (C-PC) from data acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) and the Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor. The retrieval accuracies of the semi-analytical models were compared to those returned by optimally calibrated empirical band-ratio algorithms. The best-performing algorithm for the retrieval of Chl a was an empirical band-ratio model based on a quadratic function of the ratio of re!ectance at 710 and 670 nm (R2=0.832; RMSE=29.8%). However, this model only provided a marginally better retrieval than the best semi-analytical algorithm. The best-performing model for the retrieval of C-PC was a semi-analytical nested band-ratio model (R2=0.984; RMSE=3.98 mg m−3). The concentrations of C-PC retrieved using the semi-analytical model were correlated with cyanobacterial cell numbers (R2=0.380) and the particulate and total (particulate plus dissolved) pools of microcystins (R2=0.858 and 0.896 respectively). Importantly, both the empirical and semi-analytical algorithms were able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below current warning thresholds for cyanobacteria in waterbodies. This demonstrates the potential of remote sensing to contribute to early-warning detection and monitoring of cyanobacterial blooms for human health protection at regional and global scales

ANALYSIS OF THE EFFECTS OF ATMOSPHERIC CORRECTION ON ORBITAL IMAGES FOR STUDIES IN INTERIOR WATER BODIES

The water reservoirs, in addition to their significance in electricity generation, serve as vital resources for various other requirements of the population. Images from orbital sensors have been applied to complement the monitoring of these environments and thus overcome the deficiency of spatial and temporal coverage of traditional techniques. However, studies involving water quality are still a great challenge due to the low signal coming from the water body and the interference of external factors (or environmental factors). Image correction/improvement procedures are often proposed, mainly to reduce atmospheric interference. In this study the best available atmospheric correction techniques were evaluated in order to indicate the technique that most closely matches the spectral response of remotely sensed images obtained in the field. During the study six atmospheric correction algorithms were applied (FLAASH, Second simulation of a Satellite Signal in the Solar Spectrum (6S), L8SR, Aquatic Reflectance (NASA/USGS), ACOLITE and Sen2Cor) that, based on the statistical analysis of discriminant analysis and covariance, indicated the 6S for Landsat and Sentinel images and ACOLITE for Landsat images as the most accurate. Although 6S showed a response close to the reference data, low variability in spectral response was observed. For time series, ACOLITE showed better capacity to correct the data. The type of application is also a preponderant factor, since it was evident that the use of time series indicated a different atmospheric correction technique when compared to the analysis of the scenes individually

Surface reflectance characteristics of Langjökull, Iceland

ATM, ETM+ and MODIS radiance data collected for the surface of Langjokull during August 2007. Radiance data from the three different instruments was then converted to surface broadband albedo using a number of different methods. The differences between the methods used to derive the surface albedo were then compared, followed by an analysis of the impact of spatial resolution and sample size on the differences between the derived surface albedo. Analysis of the different datasets combined with transect sample analyses of the derived surface albedos indicated that the most precise methods of deriving albedo from the ATM and ETM+ instruments was through the use of FLAASH for the ATM and dark pixel atmospheric correction or 6S atmospheric correction for the ETM+. These first analyses also indicated that the MODIS data was broadly in agreement with these two datasets. However, analysis of the differences between the different spatial resolution datasets across the three different spatial extents revealed a number of differences between the datasets. At the smallest scale the lower resolution datasets were shown to contain a bias towards representing certain areas in individual pixels. Consequently, the correlation between the datasets was reduced across greater sample sizes. Moreover, this bias was shown to prevent the ETM+ and MODIS imagery from averaging highly variable surfaces. The result of these measurement characteristics was for the low resolution instruments to overestimate the albedo value of snow in the accumulation area, greatly underestimate the albedo in the firn zone and struggle to capture the spatial variability in the ablation zone. The implications of these characteristics for lower resolution data have consequently been outlined, with the extent of the differences between both the low resolution datasets and the ATM dataset, indicating that where possible ETM+ imagery should be used to monitor surface albedo characteristics as it was the most closely correlated with the ATM data

A Semi-analytical Model for Remote Sensing Retrieval of Suspended Sediment Concentration in the Gulf of Bohai, China

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High Resolution Mapping of Soils and Landforms for the Desert Renewable Energy Conservation Plan (DRECP)

The Desert Renewable Energy Conservation Plan (DRECP), a major component of California's renewable energy planning efforts, is intended to provide effective protection and conservation of desert ecosystems, while allowing for the sensible development of renewable energy projects. This NASA mapping report was developed to support the DRECP and the Bureau of Land Management (BLM). We outline in this document remote sensing image processing methods to deliver new maps of biological soils crusts, sand dune movements, desert pavements, and sub-surface water sources across the DRECP area. We focused data processing first on the largely unmapped areas most likely to be used for energy developments, such as those within Renewable Energy Study Areas (RESA) and Solar Energy Zones (SEZs). We used imagery (multispectral and radar) mainly from the years 2009-2011

Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal)

Madeira Island is a biodiversity hotspot due to its high number of endemic/native plant species. In this work we developed and assessed a methodological framework to produce a RapidEye-based vegetation map. Reasonable accuracies were achieved for a 26 categories classification scheme in two different seasons. We tested pixel and object based approaches and the inclusion of a vegetation index band on top of the pre-processed RapidEye bands stack. Object based generally showed to outperform pixel based classification approaches except for linear or highly scattered classes. The addition of a vegetation index to the workflow increased the separability of the Jeffrey-Matusita least separable class pairs, but not necessarily the overall accuracy. The Pontius accuracy assessment highlighted class specific accuracy tradeoffs related to different combinations of the inputs and methods. The approach to be used, in conclusion, should be carefully considered on the basis of the desired result.info:eu-repo/semantics/publishedVersio

Evaluating ACOMP, FLAASH and QUAC on Worldview-3 for satellite derived bathymetry (SDB) in shallow water

Bathymetry map is instrumental for monitoring marine ecosystem and supporting marine transportation. Optical satellite imagery has been widely utilised as an alternative method to derive bathymetry map in shallow water. Nonetheless, interactions between electromagnetic energy and Earth’s atmosphere causing the atmosphere effects pose a significant challenge in satellite-derived bathymetry (SDB) application. In this study, Worldview-3 imagery was used to obtain bathymetry map in shallow water. Three atmospheric correction models (ACOMP, FLAASH and QUAC) were employed to eliminate atmospheric effects on Worldview-3 imagery. Three simple band ratios involving coastal blue, blue, green and yellow band were used to test the performance of atmospheric correction models. ACOMP combined with blue and green band ratio efficaciously provided the best performance where it explained 77% of model values. Bathymetry map obtained from Worldview-3 was also validated using bathymetry data acquired from bathymetric survey over the study area. The estimated depths shared aggregable results with measured depths (depth < 20 m) with accuracy of 2.07 m. This study shows that robust atmospheric correction combined with suitable simple band combinations offered bathymetry map retrieval with relatively high accuracy