Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives

Mapping vegetation density and water inundation in a recovering wetland : the Mesopotamian Marshlands

Once considered the largest wetland in Central Asia, the Mesopotamian Marshlands of Southeastern Iraq have nearly disappeared. Various hydrological projects by the Iraqi government and dam construction in the region have nearly destroyed these once rich freshwater wetlands by over 90%. With the launching of Operation Iraqi Freedom recent attempts at restoration have been undertaken. To assess these changes and determine the success of restoration attempts this study is using 275-m multiangular MISR imagery to measure vegetation densities and water inundation patterns. By implementing a mapping method called spectral linear unmixing, fractional estimates of vegetation and water have been determined for six images from 2000-2005. Three subsets of the MISR imagery are compared: multispectral + multiangular, multispectral bands only, and multiangular bands only. Spectral linear unmixing, from all three subsets show a change in vegetation coverage from an average of 4490 km2 in 2000, to 9584 km2 in 2005 representing an increase of 114%. Results for water inundation also indicate an average increase of 109% from 3814 km2 in 2000 to 7986 km2 in 2005. To validate the MISR estimates, I compared these results with fractional estimates of vegetation and water derived from high spatial resolution 2-m QuickBird imagery based on data from 2002 and 2005. Each MISR image is rescaled to the 2-m spatial resolution of QuickBird so that a pixel-by-pixel comparison can be made. The validation results for 2002 and 2005 shows the multispectral + multiangular MISR approach identifies 28% of the water that QuickBird detects. Separately, multispectral and multiangular subsets were only able to detect 11% each. When examining vegetation coverage the multispectral subset is able to detect 69% whereas the multispectral + multiangular, and multiangular subsets detect 61% and 21%, respectively. Results presented in this paper are encouraging and suggest that the Mesopotamian Marshlands is recovering as vegetation is establishing and water is once again present. Interseasonal and interannual images may improve results and be more representative of the region

Skylab/EREP application to ecological, geological, and oceanographic investigations of Delaware Bay

The author has identified the following significant results. Skylab/EREP S190A and S190B film products were optically enhanced and visually interpreted to extract data suitable for mapping coastal land use; inventorying wetlands vegetation; monitoring tidal conditions; observing suspended sediment patterns; charting surface currents; locating coastal fronts and water mass boundaries; monitoring industrial and municipal waste dumps in the ocean; and determining the size and flow direction of river, bay, and man-made discharge plumes. Film products were visually analyzed to identify and map ten land use and vegetation categories at a scale of 1:125,000. Thematic maps were compared with CARETS land use maps, resulting in classification accuracies of 50 to 98%. Digital tapes from S192 were used to prepare thematic land use maps. The resolutions of the S190A, S190B, and S192 systems were 20-40m, 10-20m, and 70-100m, respectively

Skylab/EREP application to ecological, geological, and oceanographic investigations of Delaware Bay

Skylab/EREP S190A and S190B film products were optically enhanced and visually interpreted to extract data suitable for; (1) mapping coastal land use; (2) inventorying wetlands vegetation; (3) monitoring tidal conditions; (4) observing suspended sediment patterns; (5) charting surface currents; (6) locating coastal fronts and water mass boundaries; (7) monitoring industrial and municipal waste dumps in the ocean; (8) determining the size and flow direction of river, bay and man-made discharge plumes; and (9) observing ship traffic. Film products were visually analyzed to identify and map ten land-use and vegetation categories at a scale of 1:125,000. Digital tapes from the multispectral scanner were used to prepare thematic maps of land use. Classification accuracies obtained by comparison of derived thematic maps of land-use with USGS-CARETS land-use maps in southern Delaware ranged from 44 percent to 100 percent

NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation

EXECUTIVE SUMMARY: The Coastal Change Analysis Programl (C-CAP) is developing a nationally standardized database on landcover and habitat change in the coastal regions of the United States. C-CAP is part of the Estuarine Habitat Program (EHP) of NOAA's Coastal Ocean Program (COP). C-CAP inventories coastal submersed habitats, wetland habitats, and adjacent uplands and monitors changes in these habitats on a one- to five-year cycle. This type of information and frequency of detection are required to improve scientific understanding of the linkages of coastal and submersed wetland habitats with adjacent uplands and with the distribution, abundance, and health of living marine resources. The monitoring cycle will vary according to the rate and magnitude of change in each geographic region. Satellite imagery (primarily Landsat Thematic Mapper), aerial photography, and field data are interpreted, classified, analyzed, and integrated with other digital data in a geographic information system (GIS). The resulting landcover change databases are disseminated in digital form for use by anyone wishing to conduct geographic analysis in the completed regions. C-CAP spatial information on coastal change will be input to EHP conceptual and predictive models to support coastal resource policy planning and analysis. CCAP products will include 1) spatially registered digital databases and images, 2) tabular summaries by state, county, and hydrologic unit, and 3) documentation. Aggregations to larger areas (representing habitats, wildlife refuges, or management districts) will be provided on a case-by-case basis. Ongoing C-CAP research will continue to explore techniques for remote determination of biomass, productivity, and functional status of wetlands and will evaluate new technologies (e.g. remote sensor systems, global positioning systems, image processing algorithms) as they become available. Selected hardcopy land-cover change maps will be produced at local (1:24,000) to regional scales (1:500,000) for distribution. Digital land-cover change data will be provided to users for the cost of reproduction. Much of the guidance contained in this document was developed through a series of professional workshops and interagency meetings that focused on a) coastal wetlands and uplands; b) coastal submersed habitat including aquatic beds; c) user needs; d) regional issues; e) classification schemes; f) change detection techniques; and g) data quality. Invited participants included technical and regional experts and representatives of key State and Federal organizations. Coastal habitat managers and researchers were given an opportunity for review and comment. This document summarizes C-CAP protocols and procedures that are to be used by scientists throughout the United States to develop consistent and reliable coastal change information for input to the C-CAP nationwide database. It also provides useful guidelines for contributors working on related projects. It is considered a working document subject to periodic review and revision.(PDF file contains 104 pages.

From In Situ to satellite observations of pelagic Sargassum distribution and aggregation in the Tropical North Atlantic Ocean

International audienceThe present study reports on observations carried out in the Tropical North Atlantic in summer and autumn 2017, documenting Sargassum aggregations using both ship-deck observations and satellite sensor observations at three resolutions (MSI-10 m, OLCI-300 m, VIIRS-750 m and MODIS-1 km). Both datasets reported that in summer, Sargassum aggre-gations were mainly observed off Brazil and near the Caribbean Islands, while they accumulated near the African coast in autumn. Based on in situ observations, we propose a five-class typology allowing standardisation of the description of in situ Sargassum raft shapes and sizes. The most commonly observed Sargassum raft type was windrows, but large rafts composed of a quasi-circular patch hundreds of meters wide were also observed. Satellite imagery showed that these rafts formed larger Sargassum aggregations over a wide range of scales, with smaller aggregations (of tens of m 2 area) nested within larger ones (of hundreds of km 2). Match-ups between different satellite sensors and in situ observations were limited for this dataset, mainly because of high cloud cover during the periods of observation. Nevertheless, comparisons between the two datasets showed that satellite sensors successfully detected Sargassum abundance and aggregation patterns consistent with in situ observations. MODIS and VIIRS sensors were better suited to describing the Sargas-sum aggregation distribution and dynamics at Atlantic scale, while the new sensors, OLCI and MSI, proved their ability to detect Sargassum aggregations and to describe their (sub-) mesoscale nested structure. The high variability in raft shape, size, thickness, depth and biomass density observed in situ means that caution is called for when using satellite maps of Sargassum distribution and biomass estimation. Improvements would require additional in situ and airborne observations or very high-resolution satellite imagery

Mapping Regional Inundation with Spaceborne L-Band SAR

Shortly after the launch of ALOS PALSAR L-band SAR by the Japan Space Exploration Agency (JAXA), a program to develop an Earth Science Data Record (ESDR) for inundated wetlands was funded by NASA. Using established methodologies, extensive multi-temporal L-band ALOS ScanSAR data acquired bi-monthly by the PALSAR instrument onboard ALOS were used to classify the inundation state for South America for delivery as a component of this Inundated Wetlands ESDR (IW-ESDR) and in collaboration with JAXA’s ALOS Kyoto and Carbon Initiative science programme. We describe these methodologies and the final classification of the inundation state, then compared this with results derived from dual-season data acquired by the JERS-1 L-band SAR mission in 1995 and 1996, as well as with estimates of surface water extent measured globally every 10 days by coarser resolution sensors. Good correspondence was found when comparing open water extent classified from multi-temporal ALOS ScanSAR data with surface water fraction identified from coarse resolution sensors, except in those regions where there may be differences in sensitivity to widespread and shallow seasonal flooding event, or in areas that could be excluded through use of a continental-scale inundatable mask. It was found that the ALOS ScanSAR classification of inundated vegetation was relatively insensitive to inundated herbaceous vegetation. Inundation dynamics were examined using the multi-temporal ALOS ScanSAR acquisitions over the Pacaya-Samiria and surrounding areas in the Peruvian Amazon

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists

Classifying and Mapping Aquatic Vegetation in Heterogeneous Stream Ecosystems Using Visible and Multispectral UAV Imagery

The need for assessment and management of aquatic vegetation in stream ecosystems is recognized given the importance in impacting water quality, hydrodynamics, and aquatic biota. However, existing approaches to monitor are laborious and its currently not feasible to track spatial and temporal differences at broad scales. The objective of this study was therefore to map and classify aquatic vegetation of a shallow stream with heterogenous mixtures of emergent and submerged aquatic vegetation. Data was collected in the Camden Creek watershed within the Inner Bluegrass Region of central Kentucky. The use of unmanned aerial vehicles (UAVs) was employed and both visible (RGB) and multispectral imagery were collected. Machine learning techniques were applied in an off-the-shelf software (QGIS environment) to develop visible and multispectral classification land-cover maps following an effective object-based image analysis workflow. Visible images were additionally coupled with high frequency water quality data to examine the spatial and temporal behavior of the aquatic vegetation. Results showed high overall classification accuracies (OA=83.5% for the training dataset and OA=83.73% for the validation dataset) for the visible imagery, with excellent user’s and producer’s accuracies for duckweed, both for training and validation. Surprisingly, multispectral overall accuracies were substantial (OA=77.8% for the training dataset and OA=70.2% for the validation dataset) but were inferior to the visible classification results. User’s and producer’s accuracies were lower for almost all classes. However, this approach was unsuccessful in detecting, segmenting and classifying submerged aquatic vegetation (algae) for both datasets. Finally, a change detection algorithm was applied to the visible classified maps and the changes in duckweed areal coverage were successfully estimated

Mapeamento da dinâmica fluvial na Volta Grande do Xingu por meio de imagens multitemporais do satélite Sentinel-1

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2019.As áreas inundáveis na região da Volta Grande do Xingu, Pará, têm sido intensamente pressionadas e degradadas devido às políticas de integração adotadas na região e, mais recentemente, em decorrência da construção da Usina Hidrelétrica de Belo Monte. Para subsidiar o gerenciamento sustentável desses ecossistemas, este estudo visou detectar a dinâmica dos corpos d´água superficiais da Volta Grande do Xingu por meio da análise de imagens multitemporais de radar do satélite Sentinel-1 e o melhor threshold da água. Foram analisados o comportamento do retroespalhamento das amostras de água superficiais com os filtros espaciais Frost, Gamma e Lee, nas janelas 5x5, 7x7 e 9x9. A imagem de 26 de julho de 2017 com o filtro Frost 9x9 exibiu maior acurácia na delimitação de corpos d´água ao adotar um threshold de -20,71 dB usando a imagem Sentinel-2 de 28 de julho de 2017 como verdade (índice Kappa de 0,96 e exatidão global de 98,9%). Esse filtro e threshold foram empregados nas 34 imagens multitemporais Sentinel-1. A amplitude máxima do rio foi mapeada com as imagens da menor e maior vazão registrada pelo satélite durante o período de análise, sendo de 29 de setembro de 2016 (449 cm) e 04 de março de 2017 (848 cm), respectivamente. Além de representar a extensão da inundação, o mapa de ocorrência de água no pixel também representou o tempo que cada pixel ficou coberto por água, fornecendo um panorama do fluxo do rio ao longo do tempo. Isso permite um gerenciamento da vazão remanescente que garanta a manutenção dos ecossistemas aquáticos nos trechos de vazão reduzida, minimizando os impactos sociais, ambientais e econômicos. Os resultados demonstraram um grande potencial metodológico em extrair, de forma rápida, informações sobre a dinâmica de corpos d´água superficiais e em monitorar grandes extensões de áreas inundáveis.The flooded areas of the Volta Grande of Xingu have been intensely pressured and degraded due to the integration policies adopted in region and the construction of the Belo Monte Dam complex more recently. Therefore, the management of these ecosystems is crucial. So, this research aims to detect changes in the surface of the open water bodies from Volta Grande do Xingu, using multitemporal SAR images of Sentinel-1 satellite and best thresholding. The performance of Frost, Gamma and Lee filters in open water delineation was analyzed with windows size 5x5, 7x7 and 9x9. The image from July 26 of 2017 with Frost 9x9 filter exhibited greater accuracy in water bodies delineating. Adopting a -20,71 dB threshold, Kappa index result was 0.96 and overall accuracy 98.9%. This threshold was employed in 34 multitemporal Sentinel-1 images with Frost 9x9. The maximum amplitude of Xingu river was mapped with images demonstrating the smallest and largest flow during the analysis period, being September 29 of 2016 and March 4 of 2017, respectively. The pixel water map representing the time that each pixel was covered by water and show the extent of the flood, providing a panorama of river flow in the 34 images. This enables remnant flow management that ensures the maintenance of aquatic ecosystems in the reduced flow sections, minimizing social, environmental and economic impacts. The results demonstrated a great methodological potential to quickly extract information about the dynamics of surface water bodies and to monitor large areas of flooded areas