Field and Remote-Sensing Assessment of Mangrove Forests and Seagrass Beds in the Northwestern Part of the United Arab Emirates

Howari, Fares M.; Jordan, Benjamin R.; Bouhouche, Naima; Wyllie‐Echeverria, Sandy

doi:10.2112/07-0867.1

Cited by 52 publications

(25 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Indeed, multiple studies have observed pronounced changes in seagrass area over time, both increases [254,261] and decreases [253,258]. These changes in seagrass extent have consistently been reported as consequences of abrupt external events (e.g., increased pollution, land use change, disturbance, etc.).…”

Section: Seagrass Meadowsmentioning

confidence: 98%

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

et al. 2015

View full text Add to dashboard Cite

Multiple stable states are established in coastal tidal wetlands (marshes, mangroves, deltas, seagrasses) by ecological, hydrological, and geomorphological feedbacks. Catastrophic shifts between states can be induced by gradual environmental change or by disturbance events. These feedbacks and outcomes are key to the sustainability and resilience of vegetated coastlines, especially as modulated by human activity, sea level rise, and climate change. Whereas multiple stable state theory has been invoked to model salt marsh responses to sediment supply and sea level change, there has been comparatively little empirical verification of the theory for salt marshes or other coastal wetlands. Especially lacking is long-term evidence documenting if or how stable states are established and maintained at ecosystem scales. Laboratory and field-plot studies are informative, but of necessarily limited spatial and temporal scope. For the purposes of long-term, coastal-scale monitoring, remote sensing is the best viable option. This review summarizes the above topics and highlights the emerging promise and challenges of using remote sensing-based analyses to validate coastal OPEN ACCESS Remote Sens. 2015, 7 10185 wetland dynamic state theories. This significant opportunity is further framed by a proposed list of scientific advances needed to more thoroughly develop the field.

show abstract

Section: Seagrass Meadowsmentioning

confidence: 98%

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

et al. 2015

View full text Add to dashboard Cite

show abstract

“…While VHR imagery has been used to map mangrove extent, this type of imagery has been used almost exclusively for mapping individual species and characterizing canopy structure (see sections VI and VII). The few studies that have used VHR to map mangrove extent have relied upon visual interpretation over small geographic areas as a form of accuracy assessment of classifications derived from less expensive and coarser resolution imagery applied over a larger area (Giri et al, 2007;Howari et al, 2009).…”

Section: Mapping Extent and Changementioning

confidence: 99%

Satellite remote sensing of mangrove forests: Recent advances and future opportunities

Heumann

2011

Progress in Physical Geography: Earth and Environment

282

151

View full text Add to dashboard Cite

Mangroves are salt tolerant woody plants that form highly productive intertidal ecosystems in tropical and subtropical regions. Despite the established importance of mangroves to the coastal environment, including fisheries, deforestation continues to be a major threat due to pressures for wood and forest products, land conversion to aquaculture, and coastal urban development. Over the past 15 years, remote sensing has played a crucial role in mapping and understanding changes in the areal extent and spatial pattern of mangrove forests related to natural disasters and anthropogenic forces. This paper reviews recent advancements in remote-sensed data and techniques and describes future opportunities for integration or fusion of these data and techniques for large-scale monitoring in mangroves as a consequence of anthropogenic and climatic forces. While traditional pixel-based classification of Landsat, SPOT, and ASTER imagery has been widely applied for mapping mangrove forest, more recent types of imagery such as very high resolution (VHR), Polarmetric Synthetic Aperture Radar (PolSAR), hyperspectral, and LiDAR systems and the development of techniques such as Object Based Image Analysis (OBIA), spatial image analysis (e.g. image texture), Synthetic Aperture Radar Interferometry (InSAR), and machine-learning algorithms have demonstrated the potential for reliable and detailed characterization of mangrove forests including species, leaf area, canopy height, and stand biomass. Future opportunities include the application of existing sensors such as the hyperspectral HYPERION, the application of existing methods from terrestrial forest remote sensing, investigation of new sensors such as ALOS PRISM and PALSAR, and overcoming challenges to the global monitoring of mangrove forests such as wide-scale data availability, robust and consistent methods, and capacity-building with scientists and organizations in developing countries.

show abstract

“…Data penginderaan jauh resolusi menengah dan tinggi sebagian besar telah banyak digunakan dan diaplikasikan untuk menghasilkan informasi spasial tematik mangrove (Kuenzer et al, 2011). Pemetaan mangrove umumnya menggunakan beberapa sensor Landsat Multispectral Scanner (MSS) (Tran et al, 2015;Giri et al, 2007), Landsat Thematic Mapper (TM) atau Enhanced Thematic Mapper Plus (ETM+) (Rakotomavo and Fromard, 2010;Ferreira et al, 2007;Howari et al, 2009;Zhang et al, 2017), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (Jean dan Jensen, 2006), data SPOT; (Gao, 1999;Santos et al, 2014), IKONOS (Chadwick, 2011), QuickBird (Wang et al, 2004a), WorldView-2 (Kamal et al, 2014), radar imagery (Cornforth et al, 2013;Rao et al, 1999).…”

Section: Pendahuluanunclassified

Klasifikasi Mangrove Berbasis Objek Dan Piksel Menggunakan Citra Sentinel-2b Di Sungai Liong, Bengkalis, Provinsi Riau

Rosmasita

Siregar

Agus

2018

J. Ilmu dan Teknologi Kelautan Tropis

View full text Add to dashboard Cite

Research on mangrove mapping at the Liong River Bengkalis Riau Province was very limited, therefore the spatial data availability of mangrove in Liong River is also very limited. The use of satellite remote sensing to map mangrove has become widespread as it can provide accurate, effecient, and repeatable assessments. The purposed of this study was to map mangrove at the community level using sentinel 2B imagery based on object-based classification method (OBIA) and it compared pixel-based classification at Liong River, Bengkalis, Riau Provinc. This study was used support vector machine (SVM) algorithm. The scheme classification use is that land cover and mangrove community. The classification data of land cover was collected using unmanned aerial vehicle (UAV) and community mangrove was using transect data of 2013. The result of land cover classification and community mangrove indicated that object-based classification technique was better than pixel-based classification. The highest an overall accuracy of land cover is 78.7% versus 70.9%, whereas mangrove community is 76.6 versus 75.0%. Approximately 7.8% increase in accuracy can be achieved by object-based method of classification for land cover and 1.6% for mangrove community. ABSTRAKPenelitian pemetaan mangrove di Sungai Liong, Bengkalis Provinsi Riau sangat terbatas, sehingga ketersediaan data spasial di wilayah ini masih sangat terbatas. Pemanfaatan citra satelit dapat dijadikan alternatif dalam menyediakan data spasial secara efektif dan efesien. Penelitian ini bertujuan untuk memetakan mangrove sampai tingkat komunitas menggunakan citra sentinel 2B dengan metode klasifikasi berbasis objek/OBIA dan membandingkannya dengan teknik klasifikasi berbasis piksel. Algoritma yang digunakan pada penelitian ini adalah support vector machine (SVM). Pengembangan skema klasifikasi mangrove pada penelitian ini di bagi menjadi 2 level, yaitu kelas penutup lahan di sekitar mangrove dan kelas komunitas mangrove. Data yang digunakan untuk klasifikasi kelas penutup lahan adalah data foto udara yang diperoleh dengan menggunakan pesawat tanpa awak (unmanned aerial vehicle/UAV) dan untuk klasifikasi komunitas menggunakan data transek tahun 2013. Akurasi keseluruhan (OA) yang diperoleh untuk klafikasi penutup lahan mangrove dengan kedua teknik klasifikasi berbasis objek dan piksel berturut-turut adalah 78,7% dan 70,9%. Sedangkan akurasi keseluruhan (OA) untuk klasifikasi komunitas mangrove berbasis objek dan piksel berutruturut yaitu 76,6% dan 75,0%. Sekitar 7,8% peningkatan akurasi pemetaan penutup lahan dan sekitar 1,6% peningkatan akurasi pemetaan komunitas mangrove yang diperoleh dengan metode klasifikasi berbasis objek.Kata kunci : klasifikasi piksel-objek, mangrove, sentinel-2b, sungai liong, support vector machine, unmanned aerial vehicle Klasifikasi Mangrove Berbasis Objek dan Piksel Menggunakan . . .

show abstract

Field and Remote-Sensing Assessment of Mangrove Forests and Seagrass Beds in the Northwestern Part of the United Arab Emirates

Cited by 52 publications

References 16 publications

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

Satellite remote sensing of mangrove forests: Recent advances and future opportunities

Klasifikasi Mangrove Berbasis Objek Dan Piksel Menggunakan Citra Sentinel-2b Di Sungai Liong, Bengkalis, Provinsi Riau

Contact Info

Product

Resources

About