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212 results for “Land use change”
Supported Datasets for the Research Titled: "Impacts of Climate and Land Use Changes on Streamflow in the Mun-Chi River Basin, the Largest Tributary of the Mekong River"
<p><strong>Supported Datasets for the Research Titled: "Impacts of Climate and Land Use Changes on Streamflow in the Mun-Chi River Basin, the Largest Tributary of the Mekong River"</strong></p> <p>Abstract: </p> <p><span>The impact of climate change and human activities poses significant challenges in the tropical region of Southeast Asia, specifically within the Mun-Chi River Basin, the largest tributary of the Mekong River in Thailand. The bias-corrected MPI-ESM1-2-LR, the most appropriate Global Climate Model (GCM) under the Coupled Model Intercomparison Project Phase 6 (CMIP6) for projecting Mun-Chi River flow, represent future climate variations in this basin. The analysis reveals forthcoming transformations in future land use, with cropland areas transitioning into forests and urban areas. While the projected annual streamflow contributing to the Lower Mekong River is expected to slightly increase by up to 4%, with 67% attributed to climate change and 33% to land-use change, temporal variations in the future flow regime reveal a wetter wet season and a drier dry season in this catchment. During the wet season, streamflow is projected to rise by 5% to 18% in 2023-2035 and 10% to 24% in 2036-2050. In contrast, the dry season is expected to experience a decrease of -3% to -9% in 2023-2035 and -6% to -17% in 2036-2050. Projected streamflow fluctuations are more pronounced in mountainous areas and upstream tributaries. These seasonal contrasts highlight the potential impact of more severe drought during the dry season and more severe flooding during the wet season. These potential increases in extreme hydrological events present challenges for efficient water resource management in this watershed and downstream countries. Consequently, effective water regulation and land-use policies are deemed crucial for sustainable management in the Mun-Chi River Basin.</span></p>
Code and data used for findings and figures in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region"
<p><span>This is the code and data used in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region" to generate all findings and figures.</span></p>
Figure 4 from: Razo-León AE, Vásquez-Bolaños M, Muñoz-Urias A, Huerta-Martínez FM (2018) Changes in bee community structure (Hymenoptera, Apoidea) under three different land-use conditions. Journal of Hymenoptera Research 66: 23-38. https://doi.org/10.3897/jhr.66.27367
Figure 4 Ordination diagram derived from NMDS for the different land-use areas and bee tribal data.
Figure 1 from: Razo-León AE, Vásquez-Bolaños M, Muñoz-Urias A, Huerta-Martínez FM (2018) Changes in bee community structure (Hymenoptera, Apoidea) under three different land-use conditions. Journal of Hymenoptera Research 66: 23-38. https://doi.org/10.3897/jhr.66.27367
Figure 1 Location of the APFFSQ and sampling sites.
Figure 2 from: Razo-León AE, Vásquez-Bolaños M, Muñoz-Urias A, Huerta-Martínez FM (2018) Changes in bee community structure (Hymenoptera, Apoidea) under three different land-use conditions. Journal of Hymenoptera Research 66: 23-38. https://doi.org/10.3897/jhr.66.27367
Figure 2 Rarefaction curve for bee richness among the different land-use conditions.
Data supporting the publication of "Interactive effects of climate change and land-use change on mammal range retraction in Great Britain"
<p><strong>Table S1 (Species records) provided as a separate .xlsx file in Supporting Information. </strong>List of species included in the sample with corresponding attributes, number of records and rates of change over time.</p> <p>Column A (Scientific name): species’ accepted scientific name (n = 43 species).</p> <p>Column B (Common name): species’ common name in Great Britain (n = 43 names).</p> <p>Column C (Order): species’ taxonomical Order (n = 6 Orders).</p> <p>Column D (Family): species’ taxonomical Family (n = 14 Families).</p> <p>Column E (Guild): species’ sampling guild (n = 3 Guilds, either Bats, Midlarge, or Small</p> <p>Column F (Distribution): species’ distribution status in Great Britain (n = 3 Statuses, either Native, Naturalised, or Non-Native).</p> <p>Column G (Habitat): species’ habitat preference (n = 2 Habitats, either Terrestrial or Freshwater).</p> <p>Column H (Records): total number of records per species from 1960 to 2016 (average = 10,931).</p> <p>Column I (1960s): total number of records per species from 1960 to 1969 (average = 420).</p> <p>Column J (1970s): total number of records per species from 1970 to 1979 (average = 457).</p> <p>Column K (1980s): total number of records per species from 1980 to 1989 (average = 423).</p> <p>Column L (1990s): total number of records per species from 1990 to 1999 (average = 641).</p> <p>Column M (2000s): total number of records per species from 2000 to 2010 (average = 943).</p> <p>Column N (2010s): total number of records per species from 2011 to 2016 (average = 870).</p> <p>Column O (Hectads TP1): number of hectads where the species has been recorded in Time Period 1, from 1960 to 1992 (average = 892).</p> <p>Column P (Hectads TP2): number of hectads where the species has been recorded in Time Period 2, from 2000 to 2016 (average = 1,117).</p> <p>Column Q (Hectads Total): number of hectads where the species has been recorder from 1960 to 2016 (average = 1,315).</p> <p>Column R (Extirpation rate): species’ extirpation rate, calculated as the ratio of extirpations over the sum of extirpations and persistences (average = 0.24). The sum of extirpation and persistence rates is always equal to 1.</p> <p>Column S (Persistence rate): species’ persistence rate, calculated as the ratio of persistences over the sum of extirpations and persistences (average = 0.76). The sum of persistence and extirpation rates is always equal to 1.</p> <p>Column T (Occupancy TP1): species’ occupancy estimate in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.395).</p> <p>Column U (Occupancy TP2): species’ occupancy estimate in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.403).</p> <p>Column V (Occupancy change): change in the species’ occupancy estimates between Time Periods 1 and 2, as calculated in Frescalo (average = 0.076).</p> <p>Column W (Occupancy change slope): average yearly change in the species’ occupancy estimates from 1960 to 2016, as calculated in Frescalo (average = -0.001).</p> <p>Column X (Frequency TP1): adjusted frequency of occurrence in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.527).</p> <p>Column Y (Frequency TP2): adjusted frequency of occurrence in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.461).</p> <p>Column Z (Frequency change): change in the adjusted frequency of occurrence between Time Periods 1 and 2, as calculated in Frescalo (average = -0.066).</p>
Data from: Exotic species enhance response diversity to land-use change but modify functional composition
Open the record for dataset details and reuse information.
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm
<p>Using the uploaded code in preparation of the Flood vulnerability maps.</p><p> </p>
Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing
<p>This dataset contains continental (Africa) land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. The dataset contains seven classes, prepared annually from 2000 to 2015, using high‐resolution Landsat 7 images (ETM+) and analyzed by Google Earth Engine cloud computing method. The model that generated the LULC classification was evaluated for predictive accuracy across classes as well as overall accuracy. The model achieved an overall accuracy of 88% with class-specific user’s and producer’s accuracies ranged from 84-94% and 79-96% respectively (Midekisa et al., 2017).</p> <p> </p>
Exploring the lunar regolith's thickness and dielectric properties using band-limited impedance at Chang'E-4 landing site
<p>Exploring the lunar regolith's thickness and dielectric properties using band-limited impedance at Chang'E-4 landing site</p> <p>(Journal of Geophysical Research - Planets #2022JE007540).</p>
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm.
<p>The used data in the paper</p>
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm.
<p>Flood points and Meteorological data</p>
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Allen Brain Atlas
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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.