Using Multi-indices Approach to Quantify Mangrove Changes Over the Western Arabian Gulf along Saudi Arabia Coast

Mangroves habitat present an important resource for large coastal communities benefiting from activities such as fisheries, forest products and clean water as well as protection against coastal erosion and climate related extreme events. Yet they are increasingly threatened by natural pressure and anthropogenic activities. We observed an inaccurate distribution of mangroves over the Western Arabian Gulf (WAG) which is a vital habitat and resource for the local ecosystem, according to the United Stated Geological Survey (USGS) mangrove database through spectral analysis. Change detection analysis is conducted on mangrove forests along the Saudi Arabian coast of the WAG for the years 2000, 2010 and 2018 using Landsat 7 & 8 data. Three supervised classification methodologies are employed for mangrove mapping, including Supported Vector Machine (SVM), Decision Tree (DT), referred to as Classification and Regression Trees (CART) and Random Forest (RF). CART’s accuracy was recorded to be \u3e95% while other classifiers were \u3e90%. The CART supervised learning classifier, mapping mangroves’ distribution and biomass using Google Earth Engine (GEE) online platform, indicates an overall increase in the northern Tarut Bay and Tarut Island, by 0.21 km2 from 2000 to 2010 and by 1.4 km2 from 2010 to 2018. The increase might be due to mitigation strategies such as mangrove breeding and plantation. It can be challenging to detect changes in certain regions due to the inadequate resolution of Landsat where submerged mangroves can be confused with salt marshes and macro algae. We employed a new method to identify and analyze submerged mangrove forests distribution via a submerged mangrove recognition index (SMRI) and Normalized Difference Vegetation Index (NDVI) in Abu Ali Island. Our results show the robustness of SMRI as an effective indicator to detect submerged mangroves in both high and medium spatial resolution satellite images. NDVI values differentiated submerged mangroves from tidal flats between Landsat 7 & 8 as well as during conditions of low and high tides. High resolution WorldView-2 image showed agreement of mangroves distribution with the SMRI and NDVI results

Optical Satellite Remote Sensing of the Coastal Zone Environment — An Overview

Optical remote-sensing data are a powerful source of information for monitoring the coastal environment. Due to the high complexity of coastal environments, where different natural and anthropogenic phenomenon interact, the selection of the most appropriate sensor(s) is related to the applications required, and the different types of resolutions available (spatial, spectral, radiometric, and temporal) need to be considered. The development of specific techniques and tools based on the processing of optical satellite images makes possible the production of information useful for coastal environment management, without any destructive impacts. This chapter will highlight different subjects related to coastal environments: shoreline change detection, ocean color, water quality, river plumes, coral reef, alga bloom, bathymetry, wetland mapping, and coastal hazards/vulnerability. The main objective of this chapter is not an exhaustive description of the image processing methods/algorithms employed in coastal environmental studies, but focus in the range of applications available. Several limitations were identified. The major challenge still is to have remote-sensing techniques adopted as a routine tool in assessment of change in the coastal zone. Continuing research is required into the techniques employed for assessing change in the coastal environment

Contribution of remote sensing technologies to a holistic coastal and marine environmental management framework: a review

Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-e ective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as theWater Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.Murray Foundation: 25.26022020info:eu-repo/semantics/publishedVersio

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

Seasonality and nutrient-uptake capacity of Sargassum spp. in Western Australia

The eight-band high resolution multispectral WorldView-2 satellite imagery demonstrated potential for mapping and monitoring Sargassum spp. beds and other associated coastal marine habitats around Rottnest Island and Point Peron. Sargassum spp. in Western Australian coast showed seasonal changes in canopy cover and mean thallus length which are also significantly influenced by the nutrient concentrations. This study documented the life cycle of Sargassum spinuligerum and successfully cultivated the species for the first time in Western Australia

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

Opportunities for seagrass research derived from remote sensing : a review of current methods

Seagrass communities provide critical ecosystem and provisioning services for both human populations and a wide range of associated species globally. However, it has been reported that seagrass area is decreasing at a rapid rate in many parts of the world, mostly due to anthropogenic activities including global change (pollution and climate change). The aim of this review article is to highlight the range of current tools for studying seagrasses as well as identify the benefits and limitations of a range of remote sensing and traditional methodologies. This paper provides a discussion of the ecological importance of seagrass meadows, and recent trends and developments in seagrass research methods are discussed including the use of satellite images and aerial photographs for seagrass monitoring and various image processing steps that are frequently utilised for seagrass mapping. The extensive use of various optical, Radar and LiDAR data for seagrass research in recent years has also been described in detail. The review concludes that the recent explosion of new methods and tools available from a wide range of platforms combined with the recent recognition of the importance of seagrasses provides the research community with an excellent opportunity to undertake a range of timely research. This research should include mapping the extent and distribution of seagrasses, identifying the drivers of change and factors that confer resilience, as well as quantification of the ecosystem services provided. Whilst remotely sensed data provides an important new tool it should be used in conjunction with traditional methods for validation and with a knowledge of the limitations of results and careful interpretation

Determination of shallow substrate from satellite remote sensing data with bio-optic based algorithm

Remote sensing techniques have been widely used for extraction of coastal information including the sea surface, in the water and within beneath of shallow substrates. The coastal water where the substrates are found is classified into ‘case 1’ and ‘case 2’. They are differs based on the water constituents such as phytoplankton, suspended particulate matter (SPM) and coloured dissolved organic materials (CDOM). The interactions of incoming radiance and water-leaving irradiance within the water in both these coastal types have been formulated in the bio-optic algorithm. The bio-optical algorithm has been previously used in mapping ocean-colour, mapping total suspended matter (TSM) and deriving water properties. In this study, the applicability of bio-optical algorithm was examined and analysed over coastal water for detection and mapping of shallow substrates using satellite remote sensing data. Two satellite data sets examined are: (i) the fine resolution Worldview-2, and (ii) medium resolution Landsat-8 OLI, with 0.5m and 30m spatial resolution respectively. The test sites were conducted in Pulau Tinggi and Pulau Merambong, Johor that representing the coastal type 1 and II as well as the shallow (less than 20m) and deep areas (less than 40m). In-situ samples consisted of seagrass, seaweed, coral, mud, sand and ancillary information on water depth were divided into two independent mutual sets and used as input to the algorithm and the respective validations. The results indicated that shallow substrates could be extracted at 91.6 percents overall accuracy with 0.55 of kappa coefficient (k), hence showing good agreement at Pulau Merambong. However, at Pulau Tinggi, the overall accuracy of substrates derived at 52.17 percent (k = 0.33) and 42.22 percent (k=0.26) for Worldview-2 and Landsat OLI, respectively. It is therefore concluded that the bio-optical algorithm has been identified as restricted on deeper water even on the clearer water (type 1) with less TSM. Hence, the potential of bio-optical algorithm for mapping shallow (less than 20m) substrates within Malaysian coastal water is very high with the improvement of water-leaving radiance from deep water model

Feasibility Study for an Aquatic Ecosystem Earth Observing System Version 1.2.

International audienceMany Earth observing sensors have been designed, built and launched with primary objectives of either terrestrial or ocean remote sensing applications. Often the data from these sensors are also used for freshwater, estuarine and coastal water quality observations, bathymetry and benthic mapping. However, such land and ocean specific sensors are not designed for these complex aquatic environments and consequently are not likely to perform as well as a dedicated sensor would. As a CEOS action, CSIRO and DLR have taken the lead on a feasibility assessment to determine the benefits and technological difficulties of designing an Earth observing satellite mission focused on the biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macro-algae, sea grasses and coral reefs. These environments need higher spatial resolution than current and planned ocean colour sensors offer and need higher spectral resolution than current and planned land Earth observing sensors offer (with the exception of several R&D type imaging spectrometry satellite missions). The results indicate that a dedicated sensor of (non-oceanic) aquatic ecosystems could be a multispectral sensor with ~26 bands in the 380-780 nm wavelength range for retrieving the aquatic ecosystem variables as well as another 15 spectral bands between 360-380 nm and 780-1400 nm for removing atmospheric and air-water interface effects. These requirements are very close to defining an imaging spectrometer with spectral bands between 360 and 1000 nm (suitable for Si based detectors), possibly augmented by a SWIR imaging spectrometer. In that case the spectral bands would ideally have 5 nm spacing and Full Width Half Maximum (FWHM), although it may be necessary to go to 8 nm wide spectral bands (between 380 to 780nm where the fine spectral features occur -mainly due to photosynthetic or accessory pigments) to obtain enough signal to noise. The spatial resolution of such a global mapping mission would be between ~17 and ~33 m enabling imaging of the vast majority of water bodies (lakes, reservoirs, lagoons, estuaries etc.) larger than 0.2 ha and ~25% of river reaches globally (at ~17 m resolution) whilst maintaining sufficient radiometric resolution