Extraction of Spatial and Temporal Patterns of Concentrations of Chlorophyll-a and Total Suspended Matter in Poyang Lake Using GF-1 Satellite Data

Poyang Lake is the largest freshwater lake in China. Its ecosystem services and functions, such as water conservation and the sustaining of biodiversity, have significant impacts on the security and sustainability of the regional ecology. The lake and wetlands of the Poyang Lake are among protected aquatic ecosystems with global significance. The Poyang Lake region has recently experienced increased urbanization and anthropogenic disturbances, which has greatly impacted the lake environment. The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are important indicators for assessing the water quality of lakes. In this study, we used data from the Gaofen-1 (GF-1) satellite, in situ measurements of the reflectance of the lake water, and the analysis of the Chl-a and TSM concentrations of lake water samples to investigate the spatial and temporal variation and distribution patterns of the concentrations of Chl-a and TSM. We analyzed the measured reflectance spectra and conducted correlation analysis to identify the spectral bands that are sensitive to the concentration of Chl-a and TSM, respectively. The study suggested that the wavelengths corresponding to bands 1, 3, and 4 of the GF-1 images were the most sensitive to changes in the concentration of Chl-a. The results showed that the correlation between the reflectance and TSM concentration was the highest for wavelengths that corresponded to band 3 of the GF-1 satellite images. Based on the analysis, bands 1, 3, and 4 of GF-1 were selected while using the APPEL (APProach by ELimination) model and were used to establish a model for the retrieval of Chl-a concentrations. A single-band model that was based on band 3 of GF-1 was established for the retrieval of TSM concentrations. The modeling results revealed the spatial and temporal variations of water quality in Poyang Lake between 2015 and 2016 and demonstrated the capacities of GF-1 in the monitoring of lake environment

Remote Sensing Applications in Monitoring of Protected Areas

Protected areas (PAs) have been established worldwide for achieving long-term goals in the conservation of nature with the associated ecosystem services and cultural values. Globally, 15% of the world’s terrestrial lands and inland waters, excluding Antarctica, are designated as PAs. About 4.12% of the global ocean and 10.2% of coastal and marine areas under national jurisdiction are set as marine protected areas (MPAs). Protected lands and waters serve as the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. Some of the PAs are the only places that contain undisturbed landscape, seascape and ecosystems on the planet Earth. With intensified impacts from climate and environmental change, PAs have become more important to serve as indicators of ecosystem status and functions. Earth’s remaining wilderness areas are becoming increasingly important buffers against changing conditions. The development of remote sensing platforms and sensors and the improvement in science and technology provide crucial support for the monitoring and management of PAs across the world. In this editorial paper, we reviewed research developments using state-of-the-art remote sensing technologies, discussed the challenges of remote sensing applications in the inventory, monitoring, management and governance of PAs and summarized the highlights of the articles published in this Special Issue

Remote sensing and bio-geo-optical properties of turbid, productive inland waters: a case study of Lake Balaton

Algal blooms plague freshwaters across the globe, as increased nutrient loads lead to eutrophication of inland waters and the presence of potentially harmful cyanobacteria. In this context, remote sensing is a valuable approach to monitor water quality over broad temporal and spatial scales. However, there remain several challenges to the accurate retrieval of water quality parameters, and the research in this thesis investigates these in an optically complex lake (Lake Balaton, Hungary). This study found that bulk and specific inherent optical properties [(S)IOPs] showed significant spatial variability over the trophic gradient in Lake Balaton. The relationships between (S)IOPs and biogeochemical parameters differed from those reported in ocean and coastal waters due to the high proportion of particulate inorganic matter (PIM). Furthermore, wind-driven resuspension of mineral sediments attributed a high proportion of total attenuation to particulate scattering and increased the mean refractive index (n̅p) of the particle assemblage. Phytoplankton pigment concentrations [chlorophyll-a (Chl-a) and phycocyanin (PC)] were also accurately retrieved from a times series of satellite data over Lake Balaton using semi-analytical algorithms. Conincident (S)IOP data allowed for investigation of the errors within these algorithms, indicating overestimation of phytoplankton absorption [aph(665)] and underestimation of the Chl-a specific absorption coefficient [a*ph(665)]. Finally, Chl-a concentrations were accurately retrieved in a multiscale remote sensing study using the Normalized Difference Chlorophyll Index (NDCI), indicating hyperspectral data is not necessary to retrieve accurate pigment concentrations but does capture the subtle heterogeneity of phytoplankton spatial distribution. The results of this thesis provide a positive outlook for the future of inland water remote sensing, particularly in light of contemporary satellite instruments with continued or improved radiometric, spectral, spatial and temporal coverage. Furthermore, the value of coincident (S)IOP data is highlighted and contributes towards the improvement of remote sensing pigment retrieval in optically complex waters

Remote Sensing of Floodpath Lakes and Wetlands: A Challenging Frontier in the Monitoring of Changing Environments

Monitoring of changing lake and wetland environments has long been among the primary focus of scientific investigation, technology innovation, management practice, and decision-making analysis. Floodpath lakes and wetlands are the lakes and associated wetlands affected by seasonal variations of water level and water surface area. Floodpath lakes and wetlands are, in particular, sensitive to natural and anthropogenic impacts, such as climate change, human-induced intervention on hydrological regimes, and land use and land cover change. Rapid developments of remote sensing science and technologies, provide immense opportunities and capacities to improve our understanding of the changing lake and wetland environments. This special issue on Remote Sensing of Floodpath Lakes and Wetlands comprise featured articles reporting the latest innovative research and reflects the advancement in remote sensing applications on the theme topic. In this editorial paper, we review research developments using state-of-the-art remote sensing technologies for monitoring dynamics of floodpath lakes and wetlands; discuss challenges of remote sensing in inventory, monitoring, management, and governance of floodpath lakes and wetlands; and summarize the highlights of the articles published in this special issue

Modeling and multi-temporal characterization of total suspended matter by the combined use of sentinel 2-MSI and landsat 8-OLI Data: The Pertusillo lake case study (Italy)

The total suspended matter (TSM) variability plays a crucial role in a lake's ecological functioning and its biogeochemical cycle. Sentinel-2A MultiSpectral Instrument (MSI) and Landsat 8 Operational Land Instrument (OLI) data offer unique opportunities for investigating certain in-water constituents (e.g., TSM and chlorophyll-a) owing to their spatial resolution (10-60 m). In this framework, we assessed the potential of MSI-OLI combined data in characterizing the multi-temporal (2014-2018) TSM variability in Pertusillo Lake (Basilicata region, Southern Italy). We developed and validated a customized MSI-based TSM model (R2 = 0.81) by exploiting ground measurements acquired during specific measurement campaigns. The model was then exported as OLI data through an intercalibration procedure (R2 = 0.87), allowing for the generation of a TSM multi-temporal MSI-OLI merged dataset. The analysis of the derived multi-year TSM monthly maps showed the influence of hydrological factors on the TSM seasonal dynamics over two sub-regions of the lake, the west and east areas. The western side is more influenced by inflowing rivers and water level fluctuations, the effects of which tend to longitudinally decrease, leading to less sediment within the eastern sub-area. The achieved results can be exploited by regional authorities for better management of inland water quality and monitoring systems

Pareamento bacia-lagoa usando modelagem hidrológica-hidrodinâmica e sensoriamento remoto

A gestão de recursos hídricos tornou-se cada vez mais complexa devido ao rápido crescimento sócio-econômico e as mudanças ambientais nas bacias hidrográficas nas últimas décadas. Modelos computacionais são importantes ferramentas de suporte na gestão de recursos hídricos e tomada de decisões devido a sua funcionalidade, provendo informações importantes sobre os principais processos físicos, químicos e biológicos, e permitindo melhorar o entendimento desses processos, os quais ocorrem em diferentes escalas espaciais e temporais. Na presente tese, o objetivo foi compreender o funcionamento hidrológico do sistema integrado bacia hidrográfica - lagoa, e os efeitos na hidrodinâmica do lago, utilizando como suporte o acoplamento da modelagem hidrológica - hidrodinâmica, e o uso de técnicas de sensoriamento remoto para o monitoramento de parâmetros de qualidade da água (e.g., clorofilaa, temperatura da superfície d’água e níveis da água). A área de estudo é a bacia hidrográfica da Lagoa Mirim, localizada no sul do Brasil, possuindo uma área total de 58.000 km2 (56% no Uruguai e o restante no Brasil). Foram propostos e testados modelos empíricos para estimativa de clorofila-a emumlago raso subtropical, baseados em imagens do sensor MODIS e técnicas estatísticas. Além disso, foi desenvolvido e avaliado o acoplamento da modelagem hidrológica-hidrodinâmica de grande escala e o sensoriamento remoto. O modelo hidrológico distribuído de grande escala MGBIPH acoplado com o modelo hidrodinâmico IPH-ECO foi utilizado para simular a bacia hidrográfica e os principais componentes hidrodinâmicos da Lagoa Mirim. O modelo mostrou bom desempenho quando comparado com observações de vazões, além de dados provenientes de sensoriamento remoto, através de altimetria espacial. As simulações mostraram importantes aspectos sobre a estrutura de fluxo, campos de velocidade e níveis d’água na lagoa, assim como a influência de grandes rios, forçantes externas como o vento (intensidade e direção) e o impacto do estressor antrópico (retiradas para irrigação) no sistema. As simulações permitiram avaliar aspectos relacionados com as variações espaciais e temporais (diurna, mensal, sazonal e inter-anual) da temperatura da superfície da água, a dinâmica dos fluxos de calor (sensível e latente) e os efeitos de eventos meteorológicos de pequena escala como frentes frias, os quais têm um impacto significativo sobre a temperatura superficial da água e os fluxos de calor na lagoa. Quanto aos modelos empíricos para estimativa de clorofila-a a partir do MODIS, os resultados mostram que um simples e eficiente modelo desenvolvido a partir de análise de regressão múltipla, apresentou ligeiras vantagens sobre os modelos de redes neurais artificiais, modelos multiplicativos não paramétricos e modelos empíricos (e.g., Appel, Kahru, FAI e O14a) usualmente utilizados na estimativa de Chl-a em ambientes aquáticos. Resultados também indicam que é inapropriado generalizar um único modelo desenvolvido a partir do conjunto total de dados, para estimar concentrações de Chl-a na lagoa, o que corrobora a heterogeneidade espacial na distribuição de Chl-a e as diferenças entre regiões (litoral e pelágica). A modelagem hidrológica-hidrodinâmica de grande escala apoiada por informação de sensoriamento remoto, mostrou ser uma abordagem promissora para o entendimento da estrutura e funcionamento de lagoas rasas de grande porte e longo prazo, úteis para a gestão integrada dos recursos hídricos.The last decade, the water resource management is being complex due to the rapid socioeconomic development and environmental changes in river basins. Computations models are important support tools in water resource management and make decision providing important information and allowing a better comprehension of the physical, chemical and biologic processes, which occur in di erent temporal/spatial scales. In this thesis, the objective was to understand the hydrological functioning of the integrated basin- lake system and its e ects on hydrodynamics, using hydrodynamic - hydrodynamic modeling and water quality monitoring (e.g., chlorophyll-a, water surface temperature and water levels) from remote sensing techniques. The study area is the Lake Mirim basin, located between Brazil and Uruguay (basin total area 58.000 km2). Empirical models were proposed and tested to chlorophyll-a estimation in a shallow subtropical lake, based on MODIS imagery and statistics techniques. In addition, we developed and assessed the coupling of large scale hydrological/hydrodynamic modeling and remote sensing techniques. The large-scale distributed hydrological model MGB-IPH coupled with the hydrodynamic model IPHECO were used to simulate the river basin and the hydrodynamic components of the Lake Mirim. The coupled model showed good performance when compared to in-situ measurements and satellite altimetry data. The simulations showed important aspects relate to flow structure, velocity fields and lake water levels, as well as the influence of large rivers, external forcing as such the wind (intensity and direction), and the impact of anthropogenic stressors (irrigation withdrawals) in the system. The simulations allowed assessing the spatial and temporal variations (diurnal, monthly, seasonal and inter-annual) in the water surface temperature, heat fluxes dynamics (sensible and latent) and the e ects of short time-scale events, as such cold fronts passages over the lake, which cause strong impacts on the water surface temperature and heat fluxes in the lake. Regarding the empirical models developed to chlorophyll-a estimation from MODIS imagery, the results showed that a simple and e cient model developed from multiple regression analysis, performed best in comparison with artificial neural network models, non-parametric multiplicative models, and empirical models (e.g., Appel, Kahru, FAI and O14a) common used in the Chl-a estimation in aquatics environments. Results also indicated that is inappropriate to generalize a single model developed from the total datasets to estimates Chl-a in the lake, which corroborates the spatial heterogeneity (Chl-a distribution) and the di erences among regions (littoral and pelagic). The synergy between large-scale hydrological-hydrodynamic modeling, in situ measurements and remote sensing techniques provided a promising approach to improve the comprehension of the structure and ecosystem functioning of large shallow lakes in long-term time scale, useful to water resources management

Retrieval of Lake Erie Water Quality Parameters from Satellite Remote Sensing and Impact on Simulations with a 1-D Lake Model

Lake Erie is a freshwater lake, and the most southern of the Laurentian Great Lakes in North America. It is the smallest by volume, the fourth largest in surface area (25,700 km2), and the shallowest of the Laurentian Great Lakes. The lake’s high productivity and warm weather in its watershed has attracted one-third of the total human population of the Great Lake’s basin. The industrial and agricultural activities of this huge population has caused serious environmental problems for Lake Erie namely harmful algal blooms, dissolved organic/inorganic matters from river inputs, and sediment loadings. If these sorts of water contaminations exceed a certain level, it can seriously influence the lake ecosystem. Hence, an effective and continuous water quality monitoring program is of outmost importance for Lake Erie. The use of Earth observation satellites to improve monitoring of environmental changes in water bodies has been receiving increased attention in recent years. Satellite observations can provide long term spatial and temporal trends of water quality indicators which cannot be achieved through discontinuous conventional point-wise in situ sampling. Different regression-based empirical models have been developed in the literature to derive the water optical properties from a single (or band ratio of) remote sensing reflectance (radiance). In situ measurements are used to build these regressions. The repeated in situ measurements in space and/or time causes clustered and correlated data that violates the assumption of regression models. Considering this correlation in developing regression models was one of the topics examined in this thesis. More complicated semi-analytical models are applied in Case II waters, aiming to distinguish several constituents confounding water-leaving signals more effectively. The MERIS neural network (NN) algorithms are the most widely used among semi-analytical models. The applicability of these algorithms to derive chl-a concentration and Secchi Disk Depth (SDD) in Lake Erie was assessed for the first time in this thesis. Satellite-observations of water turbidity were then coupled with a 1-D lake model to improve its performance on Lake Erie, where the common practice is to use a constant value for water turbidity in the model due to insufficient in situ measurements of water turbidity for lakes globally. In the first chapter, four well-established MERIS NN algorithms to derive chl-a concentration as well as two band-ratio chl-a related indices were evaluated against in situ measurements. The investigated products are those produced by NN algorithms, including Case 2 Regional (C2R), Eutrophic (EU), Free University of Berlin WeW WATER processor (FUB/WeW), and CoastColour (CC) processors, as well as from band-ratio algorithms of fluorescence line height (FLH) and maximum chlorophyll index (MCI). Two approaches were taken to compare and evaluate the performance of these algorithms to predict chl-a concentration after lake-specific calibration of the algorithms. First, all available chl-a matchups, which were collected from different locations on the lake, were evaluated at once. In the second approach, a classification of three optical water types was applied, and the algorithms’ performance was assessed for each type, individually. The results of this chapter show that the FUB/WeW processor outperforms other algorithms when the full matchup data of the lake was used (root mean square error (RMSE) = 1.99 mg m-3, index-of-agreement (I_a) = 0.67). However, the best performing algorithm was different when each water optical type was investigated individually. The findings of this study provide practical and valuable information on the effectiveness of the already existing MERIS-based algorithms to derive the trophic state of Lake Erie, an optically complex lake. Unlike the first chapter, where physically-based and already trained algorithms were implemented to evaluate satellite derived chl-a concentration, in the next chapter, two lake-specific, robust semi-empirical algorithms were developed to derive chl-a and SDD using Linear Mixed Effect (LME) models. LME considers the correlation that exists in the field measurements which have been repeatedly performed in space and time. Each developed algorithm was then employed to investigate the monthly-averaged spatial and temporal trends of chl-a concentration and water turbidity during the period of 2005-2011. SDD was used as the indicator of water turbidity. LME models were developed between the logarithmic scale of the parameters and the band ratio of B7:665 nm to B9:708.75 nm for log10chl-a, and the band ratio of B6:620 nm to B4:510 nm for log10SDD. The models resulted in RMSE of 0.30 for log10chl-a and 0.19 for log10SDD. Maps produced with the two LME models revealed distinct monthly patterns for different regions of the lake that are in agreement with the biogeochemical properties of Lake Erie. Lastly the water turbidity (extinction coefficient; Kd) of Lake Erie was estimated using the globally available satellite-based CC product. The CC-derived Kd product was in a good agreement with the SDD field observations (RMSE=0.74 m-1, mean bias error (MBE)=0.53 m-1, I_a=0.53). CC-derived Kd was then used as input for simulations with the 1-D Freshwater Lake (FLake) model. An annual average constant Kd value calculated from the CC product improved simulation results of lake surface water temperature (LSWT) compared to a “generic” constant value (0.2 m-1) used in previous studies (CC lake-specific yearly average Kd value: RMSE=1.54 ºC, MBE= -0.08 ºC; generic constant Kd value: RMSE=1.76 ºC, MBE= -1.26 ºC). Results suggest that a time-independent, lake-specific, and constant Kd value from CC can improve FLake LSWT simulations with sufficient accuracy. A sensitivity analysis was also conducted to assess the performance of FLake to simulate LSWT, mean water column temperature (MWCT) and mixed layer depth (MLD) using different values of Kd. Results showed that the model is very sensitive to the variations of Kd, particularly when Kd value is below 0.5 m-1. The sensitivity of FLake to Kd variations was more pronounced in simulations of MWCT and MLD. This study shows that a global mapping of the extinction coefficient can be created using satellite-based observations of lakes optical properties to improve the 1-D FLake model. Overall, results from this thesis clearly demonstrate the benefits of remote sensing measurements of water quality parameters (such as chl-a concentration and water turbidity) for lake monitoring. Also, this research shows that the integration of space-borne water clarity (extinction coefficient) measurements into the 1-D FLake model improves simulations of LSWT

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

Analyse der Wasserfarbe von Seen mithilfe räumlich hoch und mittel auflösender Satelliten

Remote sensing techniques can assist traditional lake monitoring approaches by supplying spatial information on optically active lake ecology indicators, i.e. chlorophyll-a (CHL), total suspended matter (TSM), coloured dissolved organic matter (CDOM), and, especially in optically shallow waters, water depth and substrate composition. The present thesis provides an overview on the current research status concerning lake remote sensing and the benefit of time series analyses for lake ecology. To investigate the suitability of Sentinel-2 and Landsat 8 for lake monitoring and their combination with other sensors this thesis focused on two study areas with highly different optical characteristics, i.e. the oligotrophic Lake Starnberg (southern Germany) and the mesotrophic-eutrophic Lake Kummerow (northern Germany). Using the bio-optical model WASI-2D, Sentinel-2A turned out to be suited for retrieving low TSM and CDOM values. The high spatial resolution enabled the differentiation between bare ground and areas covered by submerged aquatic vegetation. Water depth estimations performed well until half Secchi disk depth. Cross-sensor comparisons demonstrated high correlation of CHL among timely acquired, spatially high and medium resolved sensors. Evaluations with in situ data showed that most of the sensor-in situ match-ups were within an uncertainty range of in situ measurements. Analysing a 9-year MERIS time series with FUB/WeW revealed unprecedented information on temporal trends and seasonal behaviour of CHL, TSM and CDOM at the study area Lake Kummerow. Combining CHL, retrieved with the Modular Inversion and Processing System, from different satellite sensors (MODIS, Landsat 7/ 8, Sentinel-2A) enabled detailed observations of phytoplankton development. Such combinations are a step forward to future lake analyses which may integrate remote sensing data, in situ measurements and environmental modelling.Fernerkundungstechniken können das Seemonitoring mit räumlichen Informationen über optisch aktive Indikatoren der Gewässerökologie liefern, z.B. Chlorophyll-a (CHL), suspendierte Schwebstoffe (TSM), Gelbstoffe (CDOM) und insbesondere in optisch flachen Gewässern, Wassertiefe und Substratbedeckung. Die vorliegende Arbeit gibt einen Überblick über den aktuellen Forschungsstand zur Seefernerkundung und den Nutzen von Zeitreihenanalysen für die Seeökologie. Um die Eignung von Sentinel-2 und Landsat 8 für ein Seenmonitoring und deren Kombination mit anderen Sensoren zu untersuchen, konzentrierte sich diese Arbeit auf zwei Untersuchungsgebiete mit sehr unterschiedlichen optischen Eigenschaften: den oligotrophen Starnberger See (Süddeutschland) und den mesotroph-eutrophen Kummerower See (Norddeutschland). Mit dem bio-optischen Modell WASI-2D erwies sich Sentinel-2A als geeignet, um niedrige TSM- und CDOM-Werte zu bestimmen. Die hohe räumliche Auflösung ermöglichte eine Unterscheidung zwischen unbewachsenem und mit Makrophyten bewachsenem Untergrund. Die Wassertiefenbestimmung verlief bis zur halben Sichttiefe gut. Sensorübergreifende Vergleiche zeigten eine hohe Korrelation von CHL zwischen zeitnah erfassten, räumlich mittel und hoch aufgelösten Sensoren. Auswertungen mit in-situ-Daten zeigten, dass die meisten Sensor-in-situ-Match-ups innerhalb eines Unsicherheitsbereichs von in-situ-Messungen lagen. Die Analyse einer 9-jährigen MERIS-Zeitreihe mit FUB/WeW ergab neue Informationen über zeitliche Trends und saisonales Verhalten von CHL, TSM und CDOM im Untersuchungsgebiet Kummerow See. Die Kombination von CHL aus verschiedenen Satellitensensoren (MODIS, Landsat 7/ 8, Sentinel-2A) mit dem Modular Inversion and Processing System ermöglichte detaillierte Beobachtungen der Phytoplanktonentwicklung. Solche Kombinationen sind ein Schritt für zukünftigen Gewässeranalysen, die Fernerkundungsdaten, in-situ-Messungen und Umweltmodellierung integrieren sollten