We develop a methodology to derive a global medium resolution (250 m) land mask from several existing data sources. In particular, a number of improved land mask data sets have been developed from satellite measurements recently, though some artifacts and omissions still remain. We show how combining global land mask data from multiple independent data sources can decrease the frequency of artifacts, and improve the data consistency and quality. We use the ocean color product imagery derived from measurements of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) to evaluate and validate the new global land mask implemented in the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system, and demonstrate the improvements in the derived global ocean color data coverage. Results show that when using the new proposed land mask the accuracy of global ocean color data coverage is significantly improved over coastal and inland waters. The new land mask more accurately represents the current global land coverage status, providing more complete and consistent global land/water coverage data set for ocean color remote sensing and for various other satellite Earth observing applications.
Remote Sensing and GIS
Due to the occurrence of more frequent and widespread toxic cyanobacteria events, the ability to predict freshwater cyanobacteria harmful algal blooms (cyanoHAB) is of critical importance for the management of drinking and recreational waters. Lake system specific geographic variation of cyanoHABs has been reported, but regional and state level variation is infrequently examined. A spatio-temporal modeling approach can be applied, via the computationally efficient Integrated Nested Laplace Approximation (INLA), to high-risk cyanoHAB exceedance rates to explore spatio-temporal variations across statewide geographic scales. We explore the potential for using satellite-derived data and environmental determinants to develop a short-term forecasting tool for cyanobacteria presence at varying space-time domains for the state of Florida. Weekly cyanobacteria abundance data were obtained using Sentinel-3 Ocean Land Color Imagery (OLCI), for a period of May 2016–June 2019. Time and space varying covariates include surface water temperature, ambient temperature, precipitation, and lake geomorphology. The hierarchical Bayesian spatio-temporal modeling approach in R-INLA represents a potential forecasting tool useful for water managers and associated public health applications for predicting near future high-risk cyanoHAB occurrence given the spatio-temporal characteristics of these events in the recent past. This method is robust to missing data and unbalanced sampling between waterbodies, both common issues in water quality datasets.
Increasing sea surface temperature and extreme heat events pose the greatest threat to coral reefs globally, with trends exceeding previous norms. The resultant mass bleaching events, such as those evidenced on the Great Barrier Reef in 2016, 2017, and 2020 have substantial ecological costs in addition to economic and social costs. Advancing remote (nanosatellites, rapid revisit traditional satellites) and in-field (drones) technological capabilities, cloud data processing, and analysis, coupled with existing infrastructure and in-field monitoring programs, have the potential to provide cost-effective and timely information to managers allowing them to better understand changes on reefs and apply effective remediation. Within a risk management framework for monitoring coral bleaching, we present an overview of how remote sensing can be used throughout the whole risk management cycle and highlight the role technological advancement has in earth observations of coral reefs for bleaching events.
A comprehensive, high resolution, ground truthed benthic habitat map has been completed for Qatar's coastal zone and Halul Island. The objectives of this research were to; 1. Systematically compare and contrast pixel- and object-based classifiers for benthic mapping in a limited focus area and then to, 2. Apply these learnings to develop an accurate high resolution benthic habitat map for the entirety of the Qatari coastal zone. Results indicate object-based methods proved more efficient and accurate when compared to pixel based classifiers. The developed country-wide map covers 4500 km2 and underscores the complex interplay of seagrass, macroalgal, and reefal habitats, as well as areas of expansive mangrove forests and microbial mats. The map developed here is a first of its kind in the region. Many potential applications exist for the datasets collected to provide fundamental information that can be used for ecosystem-based management decision making.
Globally, shrimp aquaculture has undergone a rapid development in the last decades, as it can help to satisfy the increasing food demand of a growing population. However, shrimp production can be accompanied by environmental impacts, such as land cover changes associated with pond construction, or the degradation of coastal areas through pollution. Environmental footprinting, has proven to be a valuable tool for tracing environmental impacts from human consumption back to their location and sector of origin. Here, we focus on the land footprint, which quantifies the area of required land resources to satisfy human consumption (of shrimp production). However, today’s footprinting tools often lack spatially explicit land cover information for land footprint assessments. In this study we developed a new method, which allows us to identify the land cover change caused by shrimp pond construction in Thailand without using sample shrimp pond shape polygons as input data. We use the global water surface explorer (using globally 3 million Landsat 5 TM, Landsat 7 ETM and Landsat 8 OLI images, acquired between 1984 and 2015), aerial photographs and land cover maps in combination with known aquaculture locations, to identify water areas in Thailand that have a high likelihood to be a shrimp pond and to assess the corresponding land cover change. We estimated that in 2015 an area of 377 km2 had a high likelihood of being shrimp pond water area. Further, we show that the construction of shrimp ponds in Thailand was responsible for the transformation of 552 km2 primary habitat, such as mangrove areas. Our results support the environmental footprint assessment of shrimp ponds in Thailand, while our proposed method allows identifying possible shrimp pond areas on a global scale.
Fisheries bycatch has been identified as the greatest threat to marine mammals worldwide. Characterizing the impacts of bycatch on marine mammals is challenging because it is difficult to both observe and quantify, particularly in small-scale fisheries where data on fishing effort and marine mammal abundance and distribution are often limited. The lack of risk frameworks that can integrate and visualize existing data have hindered the ability to describe and quantify bycatch risk. Here, we describe the design of a new geographic information systems tool built specifically for the analysis of bycatch in small-scale fisheries, called Bycatch Risk Assessment (ByRA). Using marine mammals in Malaysia and Vietnam as a test case, we applied ByRA to assess the risks posed to Irrawaddy dolphins (Orcaella brevirostris) and dugongs (Dugong dugon) by five small-scale fishing gear types (hook and line, nets, longlines, pots and traps, and trawls). ByRA leverages existing data on animal distributions, fisheries effort, and estimates of interaction rates by combining expert knowledge and spatial analyses of existing data to visualize and characterize bycatch risk. By identifying areas of bycatch concern while accounting for uncertainty using graphics, maps and summary tables, we demonstrate the importance of integrating available geospatial data in an accessible format that taps into local knowledge and can be corroborated by and communicated to stakeholders of data-limited fisheries. Our methodological approach aims to meet a critical need of fisheries managers: to identify emergent interaction patterns between fishing gears and marine mammals and support the development of management actions that can lead to sustainable fisheries and mitigate bycatch risk for species of conservation concern.
Harmful algal bloom (HAB) species in the Chesapeake Bay can negatively impact fish, shellfish, and human health via the production of toxins and the degradation of water quality. Due to the deleterious effects of HAB species on economically and environmentally important resources, such as oyster reef systems, Bay area resource managers are seeking ways to monitor HABs and water quality at large spatial and fine temporal scales. The use of satellite ocean color imagery has proven to be a beneficial tool for resource management in other locations around the world where high-biomass, nearly monospecific HABs occur. However, remotely monitoring HABs in the Chesapeake Bay is complicated by the presence of multiple, often co-occurring, species and optically complex waters. Here we present a summary of common marine and estuarine HAB species found in the Chesapeake Bay, Alexandrium monilatum, Karlodinium veneficum, Margalefidinium polykrikoides, and Prorocentrum minimum, that have been detected from space using multispectral data products from the Ocean and Land Colour Imager (OLCI) sensor on the Sentinel-3 satellites and identified based on in situ phytoplankton data and ecological associations. We review how future hyperspectral instruments will improve discrimination of potentially harmful species from other phytoplankton communities and present a framework in which satellite data products could aid Chesapeake Bay resource managers with monitoring water quality and protecting shellfish resources.
This article proposes a simple and intuitive classification system by which to define full spectral remote sensing reflectance (Rrs(λ)) data with a quantitative output that enables a more manageable handling of spectral information for aquatic science applications. The weighted harmonic mean of the Rrs(λ) wavelengths outputs an Apparent Visible Wavelength (in units of nanometers), representing a one-dimensional geophysical metric of color that is inherently correlated to spectral shape. This dimensionality reduction of spectral information combined with the output along a continuum of wavelength values offers a robust and user-friendly means to describe and analyze spectral Rrs(λ) in terms of spatial and temporal trends and variability. The uncertainty in the algorithm's estimation of spectral shape is demonstrated on a global scale, in addition to the utility of the algorithm to discern spectral-spatial-temporal trends in the ocean, on a per-pixel basis for the entire 22 year continuous ocean color (SeaWiFS and MODIS-Aqua) time-series. This technique can be applied to datasets of varying multi- and hyper-spectral resolutions, providing continuity between heritage and future satellite sensors, and further enabling an effective means of elucidating similarities or differences in complex spectral signatures within the constraints of two dimensions. This straightforward means of conceptualizing multi-dimensional variability can help maximize the potential of the spectral information embedded in remote sensing data.
Interest and growth in marine aquaculture are increasing around the world, and with it, advanced spatial planning approaches are needed to find suitable locations in an increasingly crowded ocean. Standard spatial planning approaches, such as a Multi-Criteria Decision Analysis (MCDA), may be challenging and time consuming to interpret in heavily utilized ocean spaces. Spatial autocorrelation, a statistical measure of spatial dependence, may be incorporated into the planning framework, which provides objectivity and assistance with the interpretation of spatial analysis results. Here, two case studies highlighting applications of spatial autocorrelation analyses in the northeast region of the United States of America are presented. The first case study demonstrates the use of a local indicator of spatial association analysis within a relative site suitability analysis – a variant of a MCDA – for siting a mussel longline farm. This case study statistically identified 17% of the area as highly suitable for a mussel longline farm, relative to other locations in the area of interest. The use of a clear, objective, and efficient analysis provides improved confidence for industry, coastal managers, and stakeholders planning marine aquaculture. The second case study presents an incremental spatial autocorrelation analysis with Moran’s I that is performed on modeled and remotely sensed oceanographic data sets (e.g., chlorophyll a, sea surface temperature, and current speed). The results are used to establish a maximum area threshold for each oceanographic variable within the online decision support tool, OceanReports, which performs an automated spatial analysis for a user-selected area (i.e., drawn polygon) of ocean space. These thresholds provide users guidance and summary statistics of relevant oceanographic information for aquaculture planning. These two case studies highlight practical uses and the value of spatial autocorrelation analyses to improve the siting process for marine aquaculture.
Mangroves provide many ecosystem services including a considerable capacity to sequester and store large amounts of carbon, both in the sediment and in the above-ground biomass. Assessment of mangrove above-ground carbon stock relies on accurate measurement of tree biomass, which traditionally involves collecting direct measurements from trees and relating these to biomass using allometric relationships. We investigated the potential to predict tree biomass using measurements derived from unmanned aerial vehicle (UAV), or drone, imagery. This approach has the potential to dramatically reduce time-consuming fieldwork, providing greater spatial survey coverage and return for effort, and may enable data to be collected in otherwise hazardous or inaccessible areas. We imaged an Avicennia marina (grey mangrove) stand using an RGB camera mounted on a UAV. The imaged trees were subsequently felled, enabling physical measurements to be taken for traditional biomass estimation techniques, as well as direct measurements of biomass and tissue carbon content. UAV image-based tree height measurements were highly accurate (R2 = 0.98). However, the variables that could be measured from the UAV imagery (tree height and canopy area) were poor predictors of tree biomass. Using the physical measurement data, we identified that trunk diameter is a key predictor of A. marina biomass. Unfortunately, trunk diameter cannot be directly measured from the UAV imagery, but it can be predicted (with some error) using models that incorporate other UAV image-based measurements, such as tree height and canopy area. However, reliance on second-order estimates of trunk diameter leads to increased uncertainty in the subsequent predictions of A. marina biomass, compared to using physical measurements of trunk diameter taken directly from the trees. Our study demonstrates that there is potential to use UAV-based imagery to measure mangrove A. marina tree structural characteristics and biomass. Further refinement of the relationship between UAV image-based measurements and tree diameter is needed to reduce error in biomass predictions. UAV image-based estimates can be made far more quickly and over extensive areas when compared to traditional data collection techniques and, with improved accuracy through further model-calibration, have the potential to be a powerful tool for mangrove biomass and carbon storage estimation.