Bibliografías: "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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3

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 Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

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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, and a joint retrieval is carried out by combining the multi-GNSS system retrieval results. The SNR data of the global positioning system (GPS), global orbit navigation satellite system (GLONASS), Galileo satellite navigation system (Galileo), and BeiDou navigation satellite system (BDS) from the P387 station of the U.S. Plate Boundary Observatory (PBO) are analyzed. A Lomb–Scargle periodogram (LSP) spectrum analysis is used to compare the difference in reflector height between the snow-free and snow surfaces in order to retrieve the snow depth, which is compared with the PBO snow depth. First, the different frequency retrieval results of the multi-GNSS system are analyzed. Then, the retrieval accuracy of the different GNSS systems is analyzed through multi-frequency mean fusion. Finally, the joint retrieval accuracy of the multi-GNSS system is analyzed through mean fusion. The experimental shows that the retrieval results of different frequencies of the multi-GNSS system have a strong correlation with the PBO snow depth, and that the accuracy is better than 10 cm. The multi-frequency mean fusion of different GNSS systems can effectively improve the retrieval accuracy, which is better than 7 cm. The joint retrieval accuracy of the multi-GNSS system is further improved, with a correlation coefficient (R) between the retrieval snow depth and the PBO snow depth of 0.99, and the accuracy is better than 3 cm. Therefore, using multi-GNSS and multi-frequency data to retrieve the snow depth has a good accuracy and feasibility.

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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) and L- (λ = 24 cm) band frequencies yield a classification accuracy of 67 and 71%, because C-band confuses multi-year ice and compressed, rough, thick first-year ice surrounding multi-year ice floes, and L-band confuses multi-year ice and deformed first-year ice. Combining C- and L-band improves classification accuracy by 20%. Adding a second polarization at one frequency only improves classification accuracy by 10–14% and separates thin ice and calm open water. Under similar winter-ice conditions, ERS-1 (Cvv) and Radarsat (CHH) would overestimate the multi-year ice fraction by 15% but correctly map the spatial variability of ice thickness; J-ERS-1 (LHH) would perform poorly;and J-ERS-1 combined with ERS-1 or Radarsat would yield reliable estimates of the old, thick, first-year and thin-ice fractions, and of the spatial distribution of ridges. With two polarizations, future single-frequency space-borne SARs could improve our current capability to discriminate thinner ice types.

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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) and L- (λ = 24 cm) band frequencies yield a classification accuracy of 67 and 71%, because C-band confuses multi-year ice and compressed, rough, thick first-year ice surrounding multi-year ice floes, and L-band confuses multi-year ice and deformed first-year ice. Combining C- and L-band improves classification accuracy by 20%. Adding a second polarization at one frequency only improves classification accuracy by 10–14% and separates thin ice and calm open water. Under similar winter-ice conditions, ERS-1 (Cvv) and Radarsat (CHH) would overestimate the multi-year ice fraction by 15% but correctly map the spatial variability of ice thickness; J-ERS-1 (LHH) would perform poorly;and J-ERS-1 combined with ERS-1 or Radarsat would yield reliable estimates of the old, thick, first-year and thin-ice fractions, and of the spatial distribution of ridges. With two polarizations, future single-frequency space-borne SARs could improve our current capability to discriminate thinner ice types.

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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 (including from Worldview-2, Landsat-8, and Sentinel-2) and compared and assessed their orchard-mapping performance using the same classifier of random forest. Our results showed that the overall accuracies improved from 0.6 to 0.8 as the spatial resolution of the input images increased from 58.86 m to 0.46 m (simulated from Worldview-2 imagery). The overall accuracy improved from 0.7 to 0.86 when the number of images used for classification was increased from 2 to 20 (Landsat-8) or approximately 60 (Sentinel-2) in one year. The marginal benefit of increasing the level of details (LoD) of temporal features on accuracy is higher than that of spatial features, indicating that the classification ability of temporal information is higher than that of spatial information. The highest accuracy of using a very high-resolution (VHR) image can be exceeded only by using four to five medium-resolution multi-temporal images, or even two to three growing season images with the same classifier. Combining the spatial and temporal features from multi-source data can improve the overall accuracies by 5% to 7% compared to using only temporal features. It can also compensate for the accuracy loss caused by missing data or low-quality images in single-source input. Although selecting multi-source data can obtain the best accuracy, selecting single-source data can improve computational efficiency and at the same time obtain an acceptable accuracy. This study provides practical guidance on selecting data at various spatial and temporal resolutions for the efficient mapping of other types of annual crops or orchards.

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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 well as wavelet-revised methods. Both methods are extensions to generalised linear models. spind also provides functions for multi-model inference and scaling by wavelet multiresolution regression. Since model evaluation is essential for assessing prediction accuracy in species distribution modelling, spind additionally supplies users with spatial accuracy measures, i.e. measures that are sensitive to the spatial arrangement of the predictions.

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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 displayed. Some special features of pattern recognition of forest surfaces from hyperspectral airborne data and from multi-bands high spatial resolution satellite data were discussed.

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