Estimating the water quality condition of river and lake water in the Midwestern United States from its spectral characteristics

This study focuses on developing/calibrating remote sensing algorithms for water quality retrieval in Midwestern rivers and lakes. In the first part of this study, the spectral measurements collected using a hand-held spectrometer as well as water quality observations for the Wabash River and its tributary the Tippecanoe River in Indiana were used to develop empirical models for the retrieval of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using a subset of the field measurements with the rest for model validation. Spectral characteristics indicative of waters dominated by different inherent optical properties (IOPs) were identified and used as the basis of selecting bands for empirical model development. The second part of this study focuses on the calibration of an existing bio-geo-optical model for studying the spatial variability of chl, non-algal particles (NAP), and colored dissolved organic matter (CDOM) in episodic St. Joseph River plumes in southern Lake Michigan. One set of EO-1 Hyperion imagery and one set of boat-based spectrometer measurements were successfully acquired to capture episodic plume events. Coincident water quality measurements were also collected during these plume events. A database of inherent optical properties (IOPs) measurements and spectral signatures was generated and used to calibrate a bio-geo-optical model. Finally, a comprehensive spectral-biogeochemical database was developed for the Wabash River and its tributaries in Indiana by conducting field sampling of the rivers using a boat platform over different hydrologic conditions during summer 2014. In addition to the various spectral measurements taken by a handheld field spectrometer, this database includes corresponding in situ measurements of water quality parameters (chl, NAP, and CDOM), nutrients (TN, TP, dissolved organic carbon (DOC)), water-column IOPs, water depths, substrate types and bottom reflectance spectra. The temporal variability of water quality parameters and nutrients in the rivers was analyzed and studied. A look-up table (LUT) based spectrum matching methodology was applied to the collected observations in the database to simplify the retrieval of water quality parameters and make the data accessible to a wider range of end users. It was found that the ratio of the reflectance peak at the red edge (704 nm) with the local minimum caused by chlorophyll absorption at 677 nm was a strong predictor of chl concentrations (coefficient of determination ( R2) = 0.95). The reflectance peak at 704 nm was also a good predictor for TSS estimation (R2 = 0.75). In addition, we also found that reflectance within the NIR wavelengths (700–890 nm) all showed strong correlation (0.85–0.91) with TSS concentrations and generated robust models. Field measured concentrations of NAP and CDOM at 67% of the sampled sites in the St Joseph River plume fall within one standard deviation of the retrieved means using the spectrometer measurements and the calibrated bio-geo-optical model. The percentage of sites within one standard deviation (88%) is higher for the estimation of chl concentrations. Despite the dynamic nature of the observed plume and the time lag during field sampling, 77% of sampled sites were found to have field measured chl and NAP concentrations falling within one standard deviation of the Hyperion derived values. The spatial maps of water quality parameters generated from the Hyperion image provided a synoptic view of water quality conditions. Analysis highlights that concentrations of NAP, chl, and CDOM were more than three times higher in conjunction with river outflow and inside the river plumes than in ambient water. It is concluded that the storm-initiated plume is a significant source of sediments, carbon and chl to Lake Michigan. The temporal variability of water quality parameters and nutrients in the Wabash River was closely associated with hydrologic conditions, while no significant correlations existed between these parameters and streamflow for the Tippecanoe River, probably due to the two upstream reservoirs. The poor relationship between CDOM and DOC indicates that most DOC in the rivers was from human sources such as wastewater. It was also found that the source of water (surface runoff or combined sewer overflows (CSO)) to a river, water temperature, and nutrients are important factors controlling instream concentrations of phytoplankton. The LUT retrieved chl and NAP concentrations were in good agreement with field measurements with slopes close to 1.0. The average estimation errors for NAP and chl were within 4.1% and 37.7%, respectively, of independently obtained lab measurements. The CDOM levels were not well estimated and the LUT retrievals for CDOM showed large variability, probably due to the small data range collected in this study and the insensitivity of remote sensing reflectance, Rrs, to CDOM change. (Abstract shortened by ProQuest.

An Integrated physics-based approach to demonstrate the potential of the Landsat Data Continuity Mission (LDCM) for monitoring coastal/inland waters

Monitoring coastal or inland waters, recognized as case II waters, using the existing Landsat technology is somewhat restricted because of its low Signal-to-Noise ratio (SNR) as well as its relatively poor radiometric resolution. As a primary task, we introduce a novel technique, which integrates the Landsat-7 data as a surrogate for LDCM with a 3D hydrodynamic model to monitor the dynamics of coastal waters near river discharges as well as in a small lake environment. The proposed approach leverages both the thermal and the reflective Landsat-7 imagery to calibrate the model and to retrieve the concentrations of optically active components of the water. To do so, the model is first calibrated by optimizing its thermal outputs with the surface temperature maps derived from the Landsat-7 data. The constituent retrieval is conducted in the second phase where multiple simulated concentration maps are provided to an in-water radiative transfer code (Hydrolight) to generate modeled surface reflectance maps. Prior to any remote sensing task, one has to ensure that a dataset comes from a well-calibrated imaging system. Although the calibration status of Landsat-7 has been regularly monitored over multiple desert sites, it was desired to evaluate its performance over dark waters relative to a well-calibrated instrument designed specifically for water studies. In the light of this, several Landsat- 7 images were cross-calibrated against the Terra-MODIS data over deep, dark waters whose optical properties remain relatively stable. This study is intended to lay the groundwork and provide a reference point for similar studies planned for the new Landsat. In an independent case study, the potential of the new Landsat sensor was examined using an EO-1 dataset and applying a spectral optimization approach over case II waters. The water constituent maps generated from the EO-1 imagery were compared against those derived from Landsat-7 to fully analyze the improvement levels pertaining to the new Landsat\u27s enhanced features in a water constituent retrieval framework

Sensor capability and atmospheric correction in ocean colour remote sensing

© 2015 by the authors; licensee MDPI, Basel, Switzerland. Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges

Mediterranean Forest Species Mapping Using Hyperspectral Imagery

2011/2012Advances in hyperspectral technology provides scientists the opportunity to investigate problems that were difficult if not impossible to approach using multispectral data; among those, species composition which is a very important and dynamic forest parameter, linked with many environmental qualities that we want to map and monitor. This study addresses the problem of Mediterranean forest species mapping using satellite EO-1 Hyperion imagery (30m, 196 bands). Two pixel based techniques were evaluated, namely Spectral Angle Mapper (SAM) and Support Vector Machines (SVM), as well as an object oriented approach (GEOBIA). These techniques were applied in two study areas with different species composition and pattern complexity, namely Thasos and Taksiarchis. Extensive field work provided reference data for the accuracy assessment of the produced maps. Image preprocessing included several steps of data corrections and the Minimum Noise Fraction transformation, as means for data dimensionality reduction. In the case of Thasos, where two conifer species are present, SAM technique resulted in an overall accuracy (OA) of 3.9%, SVM technique yielded OA of 89.0% and GEOBIA achieved an OA of 85.3%. In the case of Taksiarchis, where more species are present – both conifers and broadleaved- the respective OA was 80.0%, 82.6% and 74.1%. All three methodologies implemented to investigate the value of hyperspectral imagery in Mediterranean forest species mapping, achieved very accurate results; in some cases equivalent to forest inventory maps. SAM was the straightest forward to implement, only depending on the training samples. Implementation SVM involved the specification of several parameters as well as the use of custom software and was more successful in the challenging landscape of Taksiarchis. GEOBIA adapted to scale through segmentation and extended the exercise of classification, allowing for knowledge based refinement. Lower accuracies could be attributed to the assessment method, as research on alternative assessment methods better adapted to the nature of object space is ongoing. Two typical Mediterranean forests were studied. In Thasos, two conifer species of the same genus, namely Pinus brutia and Pinus nigra, dominate a big part of the island. Both of them were accurately mapped by all methodologies. In Taksiarchis primarily stands of Quercus frainetto mix with stands of Fagus sylvatica and the aforementioned pines. The two pines were again mapped with high accuracy. However, there was a notable confusion between the two broadleaved species, indicating the need for further research, possibly taking advantage of species phenology. The outcome of the proposed methodologies could confidently meet the current needs for vegetation geographical data in regional to national scale, and demonstrate the value of hyperspectral imagery in Mediterranean forest species mapping.XXIII Ciclo198

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications

Mapping And Monitoring Wetland Environment By Analysis Different Satellite Images And Field Spectroscopy

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2010Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2010Bu çalışmada farklı spektral ve mekansal çözünürlükte uydu görüntülerinin “Terkos Havzası Sulak Alanı” örneğinde; arazi örtüsünde meydana gelen değişimleri ve sulak alan bitki türlerinin belirlenmesinde kullanılabilirlikleri için uygulanabilecek uzaktan algılama yöntemleri ele alınmıştır. Kullanılan yöntemler ile elde edilen yeni işlenmiş görüntülerin performanslarının yersel yansıtım değerleri kullanılarak desteklenmesi ile doğal alanların sürdürülebilir korunma ve yönetimi için uzaktan algılama verilerine dayalı bir altlık rehberin oluşturulması imkanı araştırılmıştır. Elde edilen sonuçlara göre heterojen arazi örtüsü yapısına sahip olan çalışma bölgesinde değişim tespiti için Ana Bileşen Dönüşümüne dayalı değişim tespit yöntemi en iyi sonucu vermiştir. Ayrıca bu çalışmada, hiperspektral Hyperion EO-1 görüntüsü ile sulak alan bitki örtüsünün diğer bitki türlerinden doğru olarak ayırt edilebildiği ortaya konmuştur. Sulak alan bitki türlerinin kendi içinde ayırt edilebilmesi ancak yersel spektroskopi ile mümkün olduğu sonucuna ulaşılmıştır.In this study, different satellite data that has different spectral and spatial resolution and in-situ spectroradiometer measurements were used to analyze hydrophytic vegetation and surrounded land cover for sustainable development and conservation of Terkos wetlands. By supporting performances of processed images with field collected reflectance values, the feasibility of structuring a basic guide based on remote sensing data for sustainable preservation and management of natural lands was searched. According to result, land cover changes in the complex natural area were determined more accurately by using PCA based change detection method Therefore, the performance of spaceborne Hyperion EO-1 hyperspectral data was analyzed to determine the capability of the data for wetland vegetation discrimination than the other vegetated areas. At the last stage of the study, field collected reflectance values that have different wetland flora types were compared by statistical ANOVA method and reflectance differences between vegetation types were put forward through calculations.DoktoraPh

Phytoplankton dynamics and bio-optical variables associated with Harmful Algal Blooms in aquaculture zones

The surveillance of Harmful Algal Blooms (HABs) in aquaculture zones is a crucial component in monitoring and mitigation of adverse effects caused by accumulation of high biomass of algal cells and/or associated toxins. Integrated findings of this thesis strongly stress the significance of synoptic bio-optical and conventional measures for efficient surveillance of HABs and their environmental triggers over required spatio-temporal scales, here shown for a case study in the Ebro Delta, NW Mediterranean. In particular, the installation of an environmental observatory in the Ebro Delta aquaculture area, and the capability of a radiometric sensor system as key component are highly motivated by study results. Yet it was clearly shown that for the interpretation of bio-optical data, detailed knowledge on bloom characteristics is crucial. By such effective coverage of bloom dynamics, combined with insights on environmental scenarios that promote the proliferation of certain taxa, public and private responses can be optimised. In a future scenario, this knowledge can be transferred to predictive models of HABs. In this sense, these future steps may advance towards preventive measures rather than mitigation actions to deal with this environmental hazard

Enhancing spatial resolution of remotely sensed data for mapping freshwater environments

Freshwater environments are important for ecosystem services and biodiversity. These environments are subject to many natural and anthropogenic changes, which influence their quality; therefore, regular monitoring is required for their effective management. High biotic heterogeneity, elongated land/water interaction zones, and logistic difficulties with access make field based monitoring on a large scale expensive, inconsistent and often impractical. Remote sensing (RS) is an established mapping tool that overcomes these barriers. However, complex and heterogeneous vegetation and spectral variability due to water make freshwater environments challenging to map using remote sensing technology. Satellite images available for New Zealand were reviewed, in terms of cost, and spectral and spatial resolution. Particularly promising image data sets for freshwater mapping include the QuickBird and SPOT-5. However, for mapping freshwater environments a combination of images is required to obtain high spatial, spectral, radiometric, and temporal resolution. Data fusion (DF) is a framework of data processing tools and algorithms that combines images to improve spectral and spatial qualities. A range of DF techniques were reviewed and tested for performance using panchromatic and multispectral QB images of a semi-aquatic environment, on the southern shores of Lake Taupo, New Zealand. In order to discuss the mechanics of different DF techniques a classification consisting of three groups was used - (i) spatially-centric (ii) spectrally-centric and (iii) hybrid. Subtract resolution merge (SRM) is a hybrid technique and this research demonstrated that for a semi aquatic QuickBird image it out performed Brovey transformation (BT), principal component substitution (PCS), local mean and variance matching (LMVM), and optimised high pass filter addition (OHPFA). However some limitations were identified with SRM, which included the requirement for predetermined band weights, and the over-representation of the spatial edges in the NIR bands due to their high spectral variance. This research developed three modifications to the SRM technique that addressed these limitations. These were tested on QuickBird (QB), SPOT-5, and Vexcel aerial digital images, as well as a scanned coloured aerial photograph. A visual qualitative assessment and a range of spectral and spatial quantitative metrics were used to evaluate these modifications. These included spectral correlation and root mean squared error (RMSE), Sobel filter based spatial edges RMSE, and unsupervised classification. The first modification addressed the issue of predetermined spectral weights and explored two alternative regression methods (Least Absolute Deviation, and Ordinary Least Squares) to derive image-specific band weights for use in SRM. Both methods were found equally effective; however, OLS was preferred as it was more efficient in processing band weights compared to LAD. The second modification used a pixel block averaging function on high resolution panchromatic images to derive spatial edges for data fusion. This eliminated the need for spectral band weights, minimised spectral infidelity, and enabled the fusion of multi-platform data. The third modification addressed the issue of over-represented spatial edges by introducing a sophisticated contrast and luminance index to develop a new normalising function. This improved the spatial representation of the NIR band, which is particularly important for mapping vegetation. A combination of the second and third modification of SRM was effective in simultaneously minimising the overall spectral infidelity and undesired spatial errors for the NIR band of the fused image. This new method has been labelled Contrast and Luminance Normalised (CLN) data fusion, and has been demonstrated to make a significant contribution in fusing multi-platform, multi-sensor, multi-resolution, and multi-temporal data. This contributes to improvements in the classification and monitoring of fresh water environments using remote sensing

Evaluating and Developing Methods for Non-Destructive Monitoring of Biomass and Nitrogen in Wheat and Rice Using Hyperspectral Remote Sensing

Aboveground plant biomass and plant nitrogen are two important parameters for plant growth monitoring, which have a decisive influence on the final yield. Mismanagement of fertilizer or pesticide inputs leads to poor plant growth, environmental pollution, and accordingly, yield loss. Biomass development is driven by nutrient supply, temperature, and phenology. Crop biomass reaches its highest weight at the harvest time. In contrast, plant nitrogen is dependent from fertilizer inputs to the soil and from biomass. Destructive measurement of both parameters is time-consuming and labor-intensive. Remote sensing offers remotely non-direct observation methods from outer space, air space, or close-range in the field by sensors. This dissertation focuses on non-destructive monitoring of plant biomass (the primary parameter) and plant nitrogen (the secondary parameter) using hyperspectral data from non-imaging field spectrometers and the imaging EO-1 Hyperion satellite. The study was conducted on two field crops: winter wheat of two growing seasons of the Huimin test site in the North China Plain; and rice of three growing seasons of the Jiansanjiang test site in the Sanjiang Plain of China. Study fields were set up in different spatial scales, from small experimental scale to large farmers' scale. Extensive field measurements were carried out, including both destructive measuring and non-destructive hyperspectral remote sensing of biomass and plant nitrogen. Besides, two years' Hyperion images were acquired at the Huimin test site. Four different approaches were used to develop the estimation models, which include: vegetation indices (VIs), band combinations, Optimum Multiple Narrow Band Reflectance (OMNBR) and stepwise Multiple Linear Regression (MLR), and derivatives of reflectance. Based on these four approaches, models were constructed, compared, and improved step by step. Additionally, a multiscale approach and a new VI, named GnyLi, were developed. Since experimental and farmers' fields were differently managed, several calibration and validation methods were tested and the field datasets were pooled. All tested approaches and band selections were greatly influenced by single growth stages. The broad band VIs saturated for both crops at the booting stage at the latest and were greatly outperformed by the narrow band VIs with optimized band combinations. Model applications from experimental to farmers' scale using the narrow bands measured by field spectrometers mostly failed due to the effects of different management practices and crop cultivars at both spatial scales. In contrast, the multiscale approach was successfully applied in winter wheat monitoring to transfer data and knowledge from field spectrometer measurements from the experimental scale to the farmers' field scale and the scale that is covered by the Hyperion imagery. The GnyLi and the Normalized Ratio Index (NRI) based on the optimized band combinations performed the best in the up-scaling process in the winter wheat study. In the rice study, MLR or OMNBR models based on 4–6 narrow bands better explained biomass variability compared to VIs based on broad bands and optimized band combinations. The models were more robust when data from different scales were pooled and then randomly divided into calibration and validation datasets. Additional model improvements were obtained using derivatives of reflectance. This dissertation evaluates different hyperspectral remote sensing approaches for non-destructive biomass and plant nitrogen monitoring, with the main focus on biomass estimation. The results and comparisons of different approaches revealed their potentials and limits. Development of new VIs, such as GnyLi, is advantageous due to the saturation problem of broad band VIs. However, the developed VIs need to be tested and improved for different crops and sites. Detection of optimized band combinations facilitates the development of new VIs, which are site-specific and crop-specific. MLR-based models may better explain the biomass variability; nevertheless, with more bands, they are prone to the issues of over-fitting and collinearity. Hence, no more than six bands were recommended to select from the hyperspectral data. Derivatives of reflectance were beneficial at the early growing season of rice when the canopy was strongly influenced by background signals from soil and water. However, their benefits were reduced when more bands were used

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale