| Judul | Advances in Remote Sensing of the Inland and Coastal Water Zones / Edited by: Jacek Lubczonek |
| Penerbitan | Basel : MDPI, 2022 |
| Deskripsi Fisik | 278p. :ill. |
| ISBN | 978-3-0365-6675-7 |
| Subjek | ENVIRONMENTAL & EARTH SCIENCES |
| Abstrak | This reprint covers works related to research on the environments of coastal zones and inland waters, solving complex and current scientific problems in these areas. Tasks undertaken by the authors address current research trends using contemporary remote sensing techniques and cover various thematic areas or scientific approaches. Some works propose new possibilities of using geospatial data or methods of remote sensing data processing. They include issues related to the use of augmented and virtual reality, object-oriented image analysis, UAV-based photogrammetry, fusion of hydroacoustic and photogrammetric data, spatial interpolation, and geodata reduction techniques. These methods are being developed to support safe navigation in inland waters; low-cost monitoring of underwater objects; creating bathymetric maps and topobathymetric digital models or mapping submerged aquatic vegetation. The papers also focus on improving existing solutions and demonstrate new research approaches, including atmospheric correction, adjacency effects for remotely sensed images, classification of wetland vegetation, as well as determining the speed of sound in shallow waters for hydrography applications. These papers demonstrate the potential of the information contained in the remote sensing data acquired by the various platforms, as well as techniques for image processing based on the Google Earth Engine (GEE). |
| Bentuk Karya | Tidak ada kode yang sesuai |
| Target Pembaca | Tidak ada kode yang sesuai |
| Lokasi Akses Online |
https://oer.unair.ac.id/files/original/f04608c4c325014d9534401278f2b535.pdf |
| No Barcode | No. Panggil | Akses | Lokasi | Ketersediaan |
|---|---|---|---|---|
| 333625192 | 550.3 Adv | Baca Online | Perpustakaan Pusat - Online Resources Ebook |
Tersedia |
| Tag | Ind1 | Ind2 | Isi |
| 001 | INLIS000000000160609 | ||
| 005 | 20250422084448 | ||
| 007 | ta | ||
| 008 | 250422################|##########|#|## | ||
| 020 | # | # | $a 978-3-0365-6675-7 |
| 035 | # | # | $a 0010-0425000541 |
| 082 | # | # | $a 550.3 |
| 084 | # | # | $a 550.3 Adv |
| 245 | # | # | $a Advances in Remote Sensing of the Inland and Coastal Water Zones /$c Edited by: Jacek Lubczonek |
| 260 | # | # | $a Basel :$b MDPI,$c 2022 |
| 300 | # | # | $a 278p. : $b ill. |
| 520 | # | # | $a This reprint covers works related to research on the environments of coastal zones and inland waters, solving complex and current scientific problems in these areas. Tasks undertaken by the authors address current research trends using contemporary remote sensing techniques and cover various thematic areas or scientific approaches. Some works propose new possibilities of using geospatial data or methods of remote sensing data processing. They include issues related to the use of augmented and virtual reality, object-oriented image analysis, UAV-based photogrammetry, fusion of hydroacoustic and photogrammetric data, spatial interpolation, and geodata reduction techniques. These methods are being developed to support safe navigation in inland waters; low-cost monitoring of underwater objects; creating bathymetric maps and topobathymetric digital models or mapping submerged aquatic vegetation. The papers also focus on improving existing solutions and demonstrate new research approaches, including atmospheric correction, adjacency effects for remotely sensed images, classification of wetland vegetation, as well as determining the speed of sound in shallow waters for hydrography applications. These papers demonstrate the potential of the information contained in the remote sensing data acquired by the various platforms, as well as techniques for image processing based on the Google Earth Engine (GEE). |
| 650 | # | # | $a ENVIRONMENTAL & EARTH SCIENCES |
| 856 | # | # | $a https://oer.unair.ac.id/files/original/f04608c4c325014d9534401278f2b535.pdf |
| 990 | # | # | $a 333625192 |
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