Remote sensing of the coastal zone of the European seas

Marullo, Salvatore; Patsaeva, Svetlana V.; Fiorani, Luca

doi:10.1080/01431161.2018.1535932

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…These days, enhanced capabilities in updated sensor options (LiDAR, hyperspectral, multispectral, natural color) have increased application of modeling studies to bays, estuaries, and other coastal regions. Entire books and special issues such as Wang's [61] "Remote Sensing of Coastal Environments: An Overview" and Marullo et al's [62] "Remote sensing of the coastal zone of the European seas" begin to address recent integration of remote sensing technology with in situ verifications and modeling. Combining LiDAR with high-resolution multispectral satellite or drone technology imagery greatly enhances coastal insights [63][64][65].…”

Section: How Remote Sensing Has Aided Studies Of Tailings Impacts Across Grand (Big) Traverse Baymentioning

confidence: 99%

Coastal Remote Sensing: Merging Physical, Chemical, and Biological Data as Tailings Drift onto Buffalo Reef, Lake Superior

et al. 2021

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On the Keweenaw Peninsula of Lake Superior, two stamp mills (Mohawk and Wolverine) discharged 22.7 million metric tonnes (MMT) of tailings (1901–1932) into the coastal zone off the town of Gay. Migrating along the shoreline, ca. 10 MMT of the tailings dammed stream and river outlets, encroached upon wetlands, and contaminated recreational beaches. A nearly equal amount of tailings moved across bay benthic environments into critical commercial fish spawning and rearing grounds. In the middle of the bay, Buffalo Reef is important for commercial and recreational lake trout and lake whitefish production (ca. 32% of the commercial catch in Keweenaw Bay, 22% along southern Lake Superior). Aerial photographs (1938–2016) and five LiDAR and multispectral over-flights (2008–2016) emphasize: (1) the enormous amounts of tailings moving along the beach; and (2) the bathymetric complexities of an equal amount migrating underwater across the shelf. However, remote sensing studies encounter numerous specific challenges in coastal environments. Here, we utilize a combination of elevation data (LiDAR digital elevation/bathymetry models) and in situ studies to generate a series of physical, chemical, and biological geospatial maps. The maps are designed to help assess the impacts of historical mining on Buffalo Reef. Underwater, sand mixtures have complicated multispectral bottom reflectance substrate classifications. An alternative approach, in situ simple particle classification, keying off distinct sand end members: (1) allows calculation of tailings (stamp sand) percentages; (2) aids indirect and direct assays of copper concentrations; and (3) permits determinations of density effects on benthic macro-invertebrates. The geospatial mapping shows how tailings are moving onto Buffalo Reef, the copper concentrations associated with the tailings, and how both strongly influence the density of benthic communities, providing an excellent example for the International Maritime Organization on how mining may influence coastal reefs. We demonstrate that when large amounts of mine tailings are discharged into coastal environments, temporal and spatial impacts are progressive, and strongly influence resident organisms. Next steps are to utilize a combination of hi-resolution LiDAR and sonar surveys, a fish-monitoring array, and neural network analysis to characterize the geometry of cobble fields where fish are successful or unsuccessful at producing young.

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Section: How Remote Sensing Has Aided Studies Of Tailings Impacts Across Grand (Big) Traverse Baymentioning

confidence: 99%

Coastal Remote Sensing: Merging Physical, Chemical, and Biological Data as Tailings Drift onto Buffalo Reef, Lake Superior

et al. 2021

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“…Remote sensing is the acquisition of information about objects or phenomena without physical contact [1]. A large amount of remote sensing data has been generated and applied, and improvements in the spatial and temporal resolution of remote sensing images have made them become the main data source for object extraction [2], such as tree crown extraction [3], coastal zone detection [4], road recognition [5], etc. Buildings constitute the main component of urban areas, and building extraction using remote sensing images has become a hot research topic as remote sensing technology has the advantage of being fast, large-scale, and economical.…”

Section: Introductionmentioning

confidence: 99%

A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

Lai

Yang

et al. 2019

Remote Sensing

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Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.

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Görüntü Eşleştirme Kaynaklı Nokta Bulutu Üzerinden Otomatik Bina Çıkarımı ve Düzgünleştirme

KARSLI¹,

YILMAZTÜRK²

2022

Turkish Journal of Remote Sensing

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Harita bileşeni bina katmanı sınırlarının klasik veya uzaktan algılama verilerinden manuel olarak belirlenmesi zaman alıcı ve çaba gerektiren önemli bir işlemdir. Görüntülerden eşleştirme yöntemiyle üretilen nokta bulutları, yoğun ve doğruluğu yüksek üç boyutlu (3B) konum bilgisi içermektedir. Binaların 3B nokta bulutlarından otomatik olarak çıkarılması geometrik düzensizlikleri, çıkarılacakları nokta bulutu yoğunluğu ve hassasiyeti açısından zor bir problemdir. Bu çalışmada, voksel temelli sekizdal (Octree) veri organizasyon metodu otomatikleştirilerek iyileştirilmiş Octree (I-Octree) yaklaşımı geliştirilmiş ve görüntülerden üretilen nokta bulutları üzerinde bina detaylarının otomatik çıkarımı ve düzgünleştirilmesi amaçlanmıştır. Elazığ bölgesinde seçilen çalışma alanında 3B nokta bulutu görüntülerden üretilmiş, zemin ve zemin üstü objeler SMRF metodu ile ayıklanmış, DBSCAN algoritması ile bina objeleri gürültülerden ayıklanarak sınıflandırılmış ve sekizdal ile I-Octree yöntemlerinin sınıflandırılan objelere uygulanması ile ortaya çıkarılan bina detaylarına ABORE metodu ile kenar düzgünleştirmesi işlemi uygulanmıştır. Otomatik olarak çıkarılan bina verileri çalışma alanını içeren 1/1000 ölçekli hâlihazır harita referans verisi desteğiyle piksel tabanlı tamlık (Cp), doğruluk (Cr), kalite (Q) ve F-skor (F-1) metrikleri ile doğrulanmıştır. Doğrulama sonuçları her bir metrik için maksimum değer olarak %94 üzerinde elde edilmiştir. Görüntülerden üretilmiş nokta bulutları üzerinden, geliştirilen I-Octree yaklaşımı ile bina detayı çıkarılması noktasında hızlı ve ucuz bir harita üretimi sürecine katkıda bulunabileceği sonucuna varılmıştır.

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Remote sensing of the coastal zone of the European seas

Cited by 3 publications

References 8 publications

Coastal Remote Sensing: Merging Physical, Chemical, and Biological Data as Tailings Drift onto Buffalo Reef, Lake Superior

Coastal Remote Sensing: Merging Physical, Chemical, and Biological Data as Tailings Drift onto Buffalo Reef, Lake Superior

A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

Görüntü Eşleştirme Kaynaklı Nokta Bulutu Üzerinden Otomatik Bina Çıkarımı ve Düzgünleştirme

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