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Relevant bibliographies by topics / Multi-accuracy spatial data

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Journal articles

Academic literature on the topic 'Multi-accuracy spatial data'

Author: Grafiati

Published: 11 March 2023

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Journal articles on the topic "Multi-accuracy spatial data"

1

Belussi, Alberto, and Sara Migliorini. "A framework for integrating multi-accuracy spatial data in geographical applications." GeoInformatica 16, no. 3 (2011): 523–61. http://dx.doi.org/10.1007/s10707-011-0140-9.

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2

Jeong, Weonil. "Multi-level Load Shedding Scheme to Increase Spatial Data Stream Query Accuracy." Journal of the Korea Academia-Industrial cooperation Society 16, no. 12 (2015): 8370–77. http://dx.doi.org/10.5762/kais.2015.16.12.8370.

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Järv, Olle, Henrikki Tenkanen, and Tuuli Toivonen. "Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation." International Journal of Geographical Information Science 31, no. 8 (2017): 1630–51. http://dx.doi.org/10.1080/13658816.2017.1287369.

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4

Shimizu, Katsuto, Tetsuji Ota, Nobuya Mizoue, and Hideki Saito. "Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests." Remote Sensing 12, no. 11 (2020): 1876. http://dx.doi.org/10.3390/rs12111876.

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Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using

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5

Tu, Jinsheng, Haohan Wei, Rui Zhang, et al. "GNSS-IR Snow Depth Retrieval from Multi-GNSS and Multi-Frequency Data." Remote Sensing 13, no. 21 (2021): 4311. http://dx.doi.org/10.3390/rs13214311.

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Global navigation satellite system interferometric reflectometry (GNSS-IR) represents an extra method to detect snow depth for climate research and water cycle managing. However, using a single frequency of GNSS-IR for snow depth retrieval is often found to be challenging when attempting to achieve a high spatial and temporal sensitivity. To evaluate both the capability of the GNSS-IR snow depth retrieved by the multi-GNSS system and multi-frequency from signal-to-noise ratio (SNR) data, the accuracy of snow depth retrieval by different frequency signals from the multi-GNSS system is analyzed,

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6

Rignot, Eric, and Mark R. Drinkwater. "Winter Sea-ice mapping from multi-parameter synthetic-aperture radar data." Journal of Glaciology 40, no. 134 (1994): 31–45. http://dx.doi.org/10.1017/s0022143000003774.

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AbstractThe limitations of current and immediate future single-frequency, single-polarization, space-borne SARs for winter sea-ice mapping are quantitatively examined, and improvements are suggested by combining frequencies and polarizations. Ice-type maps are generated using multi-channel, air-borne SAR observations of winter sea ice in the Beaufort Sea to identify six ice conditions: (1) multi-year sea ice; (2) compressed first-year ice; (3) first-year rubble and ridges; (4) first-year rough ice; (5) first-year smooth ice; and (6) first-year thin ice. At a single polarization, C- (λ = 5.6 cm

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7

Rignot, Eric, and Mark R. Drinkwater. "Winter Sea-ice mapping from multi-parameter synthetic-aperture radar data." Journal of Glaciology 40, no. 134 (1994): 31–45. http://dx.doi.org/10.3189/s0022143000003774.

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AbstractThe limitations of current and immediate future single-frequency, single-polarization, space-borne SARs for winter sea-ice mapping are quantitatively examined, and improvements are suggested by combining frequencies and polarizations. Ice-type maps are generated using multi-channel, air-borne SAR observations of winter sea ice in the Beaufort Sea to identify six ice conditions: (1) multi-year sea ice; (2) compressed first-year ice; (3) first-year rubble and ridges; (4) first-year rough ice; (5) first-year smooth ice; and (6) first-year thin ice. At a single polarization, C- (λ = 5.6 cm

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8

Yao, Zhiying, Yuanyuan Zhao, Hengbin Wang, et al. "Comparison and Assessment of Data Sources with Different Spatial and Temporal Resolution for Efficiency Orchard Mapping: Case Studies in Five Grape-Growing Regions." Remote Sensing 15, no. 3 (2023): 655. http://dx.doi.org/10.3390/rs15030655.

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As one of the most important agricultural production types in the world, orchards have high economic, ecological, and cultural value, so the accurate and timely mapping of orchards is highly demanded for many applications. Selecting a remote-sensing (RS) data source is a critical step in efficient orchard mapping, and it is hard to have a RS image with both rich temporal and spatial information. A trade-off between spatial and temporal resolution must be made. Taking grape-growing regions as an example, we tested imagery at different spatial and temporal resolutions as classification inputs (i

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9

Carl, Gudrun, Sam Levin, and Ingolf Kühn. "spind: an R Package to Account for Spatial Autocorrelation in the Analysis of Lattice Data." Biodiversity Data Journal 6 (February 28, 2018): e20760. http://dx.doi.org/10.3897/bdj.6.e20760.

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spind is an R package aiming to provide a useful toolkit to account for spatial dependence in the analysis of lattice data. Grid-based data sets in spatial modelling often exhibit spatial dependence, i.e. values sampled at nearby locations are more similar than those sampled further apart. spind methods, described here, take this kind of two-dimensional dependence into account and are sensitive to its variation across different spatial scales. Methods presented to account for spatial autocorrelation are based on the two fundamentally different approaches of generalised estimating equations as

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10

Kozoderov, V. V., and V. D. Egorov. "Pattern recognition of forest canopy using the airborne hyperspectral data and multi-bands high spatial resolution satellite sensor worldview-2 data. A results comparison and accuracy estimation." Исследования Земли из Космоса, no. 6 (December 21, 2019): 89–102. http://dx.doi.org/10.31857/s0205-96142019689-102.

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Pattern recognition of forest surface from remote sensing data: using the airborne hyperspectral data and using multi-bands high spatial resolution satellite sensor WorldView‑2 data are investigated. The early proposed method and standard QDA method for calculations were used. A comparison of calculations results were conducted. A recognition calculation accuracy range for airborne and satellite remote sensing data for three forest surface fragments for different created data bases for recognition system has been assessed. Some opportunities of automatic data preparing of created system were d

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