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45 results for “geographic area”
MUSES Leaf Area Index (LAI) Derived from MODIS Data Monthly Global 0.05º Geographic Grid Since 2000
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES LAI product was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). It is provided on Geographic grid and spans from 2000 to 2019 (continuously updated). The MUSES LAI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 2000 – 2019</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Leaf Area Index (LAI) Derived from MODIS Data 8-Day Global 0.05º Geographic Grid Since 2000
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>The MUSES LAI product at 0.05º spatial resolution and 8-day temporal resolution was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). It is provided on Geographic grid and spans from 2000 to 2023 (continuously updated). The MUSES LAI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 2000 – 2023</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Geographic Information System of structural elements in the Niobe-Aphrodite Map Area of Venus: a tool for structural and geologic analysis.
<p>The Niobe Aphrodite Map Area covers over 25% of the surface of Venus and extends from 57N to 57S and 60E to 180E. The structural-element map presented here is derived from the1:10 M-scale geologic maps of Niobe Planitia, U.S. Geological Survey I-2467 and Aphrodite Terra, U.S. Geological Survey I-2476. Both maps are in various stages of review and revision overseen by the U.S. Geological Survey on behalf of NASA.</p> <p>Here we present a Geographic Information System (GIS) that contain the different structural elements of the area (deformation structures and lithodemic units), that can be used to analyze relationships between and among suites of structural elements across this large portion of Venus’ surface.</p> <p>Base images and data on which determination of the structural element determination is based can be accessed and downloaded directly in GIS-ready formats through the USGS Map a Planet website (https://astrogeology.usgs.gov/tools/map-a-planet-2).</p>
FIGURE 1 in The orchid-bee fauna (Hymenoptera: Apidae) of a forest remnant in southern Bahia, Brazil, with new geographic records and an identification key to the known species of the area
FIGURE 1. Map showing the exact location of Parque Estadual da Serra do Conduru, state of Bahia, Brazil. Approximate location of Estação Veracel in southern Bahia is indicated by the orange square.
Fig. 2. Occupied and extirpated sites for Cicindela dorsalis dorsalis within Geographic Recovery Area 5 in Population Trends of the Northeastern Beach Tiger Beetle,Cicindela dorsalis dorsalisSay (Coleoptera: Carabidae: Cicindelinae) in Virginia and Maryland, 1980s Through 2014
Fig. 2. Occupied and extirpated sites for Cicindela dorsalis dorsalis within Geographic Recovery Area 5 (Calvert County, Maryland), Geographic Recovery Area 6 (Tangier Sound, Maryland), and Geographic Recovery Area 7 (Eastern Shore of Chesapeake Bay, Virginia) as of July 2014.
Fig. 1. Occupied and extirpated sites for Cicindela dorsalis dorsalis within Geographic Recovery Areas 5–9 in Population Trends of the Northeastern Beach Tiger Beetle,Cicindela dorsalis dorsalisSay (Coleoptera: Carabidae: Cicindelinae) in Virginia and Maryland, 1980s Through 2014
Fig. 1. Occupied and extirpated sites for Cicindela dorsalis dorsalis within Geographic Recovery Areas 5–9, Maryland and Virginia as of July 2014.
Fig. 3. Occupied and extirpated sites for Cicindela dorsalis dorsalis within Geographic Recovery Area 8 in Population Trends of the Northeastern Beach Tiger Beetle,Cicindela dorsalis dorsalisSay (Coleoptera: Carabidae: Cicindelinae) in Virginia and Maryland, 1980s Through 2014
Fig. 3. Occupied and extirpated sites for Cicindela dorsalis dorsalis within Geographic Recovery Area 8 (Western Shore of Chesapeake Bay, north of Rappahannock River, Virginia) and Geographic Recovery Area 9 (Western Shore of Chesapeake Bay, south of Rappahannock River) as of July 2014.
FIGURE 1. A–E. Study area. A in The family Opilioacaridae (Parasitiformes: Opilioacarida) in Mexico, description of two new species, new records, and geographical distribution
FIGURE 1. A–E. Study area. A. State of Querétaro in Mexico, B. Jalpan de Serra municipality in the state of Querétaro, C1. Collecting area of Neoacarus queretanus in San Juan de los Durán (21° 27' 40.799''N, 99° 10' 45''W; 1311 m asl), C2. Collecting area of Neoacarus haicolous Ojo de Agua de San Francisco (21° 33' 10.289''N, 99° 11' 43.49'' W, 1145 m asl), D. Mesophyll forest, Ojo de Agua de San Francisco village, E. Pine-oak forest, San Juan de los Durán, State of Queretaro.
MUSES Leaf Area Index (LAI) Derived from AVHRR Data Monthly Global 0.05º Geographic Grid Since 1981
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from time-series Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1981 – 2019</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Leaf Area Index (LAI) Derived from AVHRR Data 8-Day Global 0.05º Geographic Grid Since 1981
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from time-series Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N</li> <li>Temporal Coverage: 1981 – 2019</li> <li>Spatial Resolution: 0.05º (approximately 5 km)</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Geographic</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
A new method for runoff forecasting in ungauged areas based on the Xinanjiang model incorporating multiple spatial geographical distribution data
<p>This dataset includes two parts:</p> <p>(1) The SHP format layer data of 256 watersheds in North America and 2 watersheds in ChinaThe SHP format layer data of 256 watersheds in North America and 2 watersheds in China.</p> <p>(2) A reference meta-watershed database of spatial data was established, covering a spatial database of 256 watersheds. This provides a data foundation for runoff forecasting in other ungauged watersheds.</p>
Combined Incentive Actions, Focusing on Primary Care, to Improve Cervical Cancer Screening in Women Residing in Socio-economically Disadvantaged and Untracked Geographical Areas: a Hybrid Efficacy and
ClinicalTrials.gov study NCT04312178. IPD Sharing: NO. Countries: 1. Publications: 1.
This Will Be an Observational, Exploratory Study Targeting a Population of Subjects with Diabetes from 4 Different Geographical Areas of Emilia- Romagna.
ClinicalTrials.gov study NCT06639100. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Richness, geographic distribution patterns, and areas of endemism of selected angiosperm groups in Mexico
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Text-fig. 1. The geographic position of the localities mentioned in the text. A – position within the Czech Republic. B – Detailed map of the area. Subsilesian Unit: 1 – Kelč, 2 – Špičky, 3 – Horní Těšice; Silesian Unit: 4 – Loučka, 5 – Osíčko, 6 – Rožnov pod Radhoštěm; Ždánice Unit: 7 – Bohuslavice, 8 – Jestřabice, 9 – Kožušice, 10 – Křepice, 11 – Litenčice, 12 – Mouchnice, 13 – Nikolčice, 14 – Nítkovice, 15 – Nosislav, 16 – Židlochovice. The distribution of the units according to Čtyřoký and Stráník (1995). C – map of the Osíčko vicinity with distribution of the Silesian Unit sediments (gray spots) and collecting points (P1 and P2). The distribution of the Silesian Unit sediments according to Stráník (1999). in An Annotated List Of The Oligocene Fish Fauna From The Osíčko Locality (Menilitic Fm.; Moravia, The Czech Republic)
Text-fig. 1. The geographic position of the localities mentioned in the text. A – position within the Czech Republic. B – Detailed map of the area. Subsilesian Unit: 1 – Kelč, 2 – Špičky, 3 – Horní Těšice; Silesian Unit: 4 – Loučka, 5 – Osíčko, 6 – Rožnov pod Radhoštěm; Ždánice Unit: 7 – Bohuslavice, 8 – Jestřabice, 9 – Kožušice, 10 – Křepice, 11 – Litenčice, 12 – Mouchnice, 13 – Nikolčice, 14 – Nítkovice, 15 – Nosislav, 16 – Židlochovice. The distribution of the units according to Čtyřoký and Stráník (1995). C – map of the Osíčko vicinity with distribution of the Silesian Unit sediments (gray spots) and collecting points (P1 and P2). The distribution of the Silesian Unit sediments according to Stráník (1999).
BOREAS Site and Area Geographic Coordinate Information
In an effort to properly document the sites and areas where data were collected, personnel of the BOReal Ecosystem-Atmosphere Study (BOREAS) Information System (BORIS) obtained and compiled geographic coordinate and other site information from several sources throughout the experiment period. The final set of information is organized into two data sets that provide geographic coordinate and site characteristic information for single sites and corner coordinates for standard geographic areas. The data are stored in two text files as American Standard Code for Information Interchange (ASCII) characters.
Data from: Morbidity rate prediction of dengue hemorrhagic fever (DHF) using the support vector machine and the Aedes aegypti infection rate in similar climates and geographical areas
Background: In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly, and thus, they cannot enhance the capability of the predictive model's learning algorithm. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings: We identified the study areas in three provinces (Nakhon Pathom, Ratchaburi, and Samut Sakhon) of central Thailand that were reported to have a high incidence of dengue outbreaks. Prior to being added to the model, the infection data of the dengue vector, Aedes aegypti, the climate parameters, and the population density were collected from various sources and standardized. This process ensured that the data were not overwhelmed by each other in terms of the distance measures and to enhance the model effectiveness. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Aedes aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. The support vector machine (SVM), a machine learning technique, has been receiving attention in many research areas due to its remarkable generalization performance. In this research, we applied the SVM with the radial basis function (RBF) kernel, referred to as the SVM-R, to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions: The infection rates of the Aedes aegypti female mosq uitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. This approach is more reliable and practical for monitoring dengue outbreaks, particularly in locations with similar climates because it does not rely on only climate factors. In addition, we demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE).
Long-Term Analysis of DImethyl Fumarate, to Slow the Growth of Areas of Geographic Atrophy
ClinicalTrials.gov study NCT04292080. IPD Sharing: NO. Countries: 1. Publications: 0.
Pharmacoinvasive Strategy vs. Primary PCI in STEMI: A Prospective Registry in a Large Geographical Area
ClinicalTrials.gov study NCT03974581. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Morbidity rate prediction of dengue hemorrhagic fever (DHF) using the support vector machine and the Aedes aegypti infection rate in similar climates and geographical areas
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.