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21 results for “Forest cover change”
Global forest cover loss tipping points leading to changing hydrologic responses
<p>This dataset describes the methods used to develop the results for study entitled: Global forest cover loss tipping points leading to changing hydrologic responses.</p> <p>EVENTS_List_45.docx is a table describing each deforestation event used for the study</p> <p>MATLAB Script 1: Plotting Hydrologic Sensitive Area against Tree cover loss every 10 % tree cover loss for all 45 events and adjusting Richard's curve function to obtain the parameters. This script uses EXCEL SHEET: HSiaresults.xlsx</p> <p>MATLAB Script 2: Computing the critical points of acceleration based on the Richards curve parameters. This script uses the parameters or results obtained in Script one.</p> <p>MATLAB Script 3: Plotting the climate and water yield direction against tree cover loss. This script used EXCEL SHEET: direction.xlsx</p>
Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways
<p>This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).</p>
Database for study on Vietnam's forest cover changes 2005-2016
<p>The database provided here forms the basis of a study which is published in the journal World Development (year 2020) under the title ' Vietnam’s forest cover changes 2005-2016: veering from transition to (yet more) transaction?'. The database contains two hundred province-level data variables provided by government agencies. The database includes potentially relevant indicators of the Vietnamese provinces’ terrain and geography (G variables), forestland and tree plantation cover/changes (F), population density/changes and ethnic composition (P), labor and poverty (L), development of infrastructure (S) and hydro-electric capacities (H), agricultural cultivations/changes and productivity (A), industrial wood processing capacities (W), illicit logging (C), forestland tenure/contracts (T), payments for forest ecosystem service (PFES) funding/coverage (E), as well as various indices of governance, institutional and public administration, and socio-economic performance (I). Each variable contains 63 data (i.e. 63 Vietnamese provinces) and is described in detail in the file, including data type and summary statistics. The variables were mostly accessible directly from government sources, or indirectly from NGOs or via international publications (the sources are indicated in the file).</p>
MODIS tree cover change of North American boreal forests 2000-2019
<p>The published files are two maps of North American boreal forest tree cover trends between 2000 and 2019. Pixel values are annual trends in tree cover expressed as % change per year. The trends are based on annual tree cover estimates from the MODIS Vegetation Continuous Field version 6 product. We quantified tree cover trends per pixel through Theil-Sen's slope estimation using the 'zyp' package in R. We followed the Yue-Pilon pre-whitening method to account for temporal autocorrelation. We created a trend map for all data points within the boreal biome boundary following Gauthier et al. 2015, Science (<a href="https://doi.org/10.1126/science.aaa9092">DOI: 10.1126/science.aaa9092</a>) and added a 120km buffer around it (tcchange_all_points_clipped.tif). We also produced a map where we masked out non-significant trends based on a Mann-Kendall-test (tcchange_significant_points.tif). Both maps have a spatial resolution of around 1,000m.</p> <p>The map forms the key results in our manuscript: Rotbarth et al. 2023. 'North American boreal forests: Northern expansion is not compensating for southern declines'. Nature Communications</p>
Satellite_Observed Changes in Forest Cover over Northern China from 1996_2020
<p>This dataset is associated with a research article entitled "Satellite_Observed Changes in Forest Cover over Northern China from 1996_2020".</p> <p>As an important part of the land surface, forest is an important factor affecting global carbon, water cycle and climate change; Fractional Forest Cover (FFC) represents the proportion of forest canopy cover area to the whole pixel observed from the vertical direction. It is an important parameter for monitoring forest resources and is related to forest structure or attributes, such as forest area or forest distribution density. It is a variable representing forest cover on a continuous scale.</p> <p>Based on the remote sensing images of Gaofen-2 and Landsat-8, a model for estimating the FFC in the three-north regions of China is constructed based on the ensemble machine learning method. From 1996 to 2020, the annual FFC products with a resolution of 30 meters covering the three northern regions of China with long time series have been generated. The data values contained in the FFC range from 0 to 100, the "Nodata" value is set to -99, and the data file is provided in Geo-Tiff format.</p>
Fig. 3. Forest cover within 300 in Long-term changes in avian relative abundances in relation to human disturbance in a tropical dry forest in central Myanmar
Fig. 3. Forest cover within 300 meters of bird survey points in 1999 and 2020.
Data from: Patterns and drivers of recent land cover change on two trailing-edge forest landscapes
<p>Climate change is altering the distribution of woody plants by influencing demographic processes and modifying disturbance regimes. Trailing-edge forests may be particularly vulnerable to these effects because they exist at warm, dry margins of tree distributions. To better understand recent climate-driven changes in trailing-edge forests, we used Landsat time series and 1,558 field reference plots to develop annual land cover maps from 1985 to 2020 in two large, biodiverse landscapes in central Arizona, USA. We then combined annual land cover maps with tree ring records and spatial data describing interannual climate, terrain, bark beetle (Curculionidae: Scolytinae) activity, wildfire, and harvest to quantify drivers of forest change. Throughout the two landscapes, forest extent declined by 0.3% and 0.8% from 1985 to 2020. However, considerable variation occurred within the study period, with abrupt (ca. 1–2 years) declines in forest extent followed by gradual (ca. 10 years) recovery on each landscape. Pinyon-juniper (<em>Pinus</em> <em>edulis</em>, <em>Pinus</em> <em>monophylla</em>, and/or <em>Juniperus</em> spp.) cover increased from 1985 to ca. 2000 but declined after 2000, a period of extreme drought and regional tree die-off. In contrast, pine-oak (<em>Pinus</em> <em>ponderosa</em> and <em>Quercus</em> spp.) cover increased from 2000 to 2020, primarily due to declines in ponderosa pine and mixed conifer cover over the same period. Wildfire was a key driver of transitions from forest to non-forest cover in our study area, with the occurrence of multiple compounded drought years playing an important role in unburned areas. By driving transitions to alternative forest types or non-forest cover, disturbance and drought will increasingly shape forest dynamics and ecosystem transformations throughout the southwestern US.</p>
The role of canopy cover dynamics over a decade of changes in the understory of an Atlantic beech-oak forest
<p>This dataset hosts the main data and R codes of the analyses carried out to study the role of canopy cover dynamics over a decade of changes in the understory of an Atlantic Beech-Oak forest. We measured changes in understory taxonomical and functional composition, richness and diversity between 2006 and 2016, and relate those changes to changes in canopy coverage between both years.</p> <p>The data show changes between 2006 and 2016 in the species abundance, richness and diversity of 102 understory communities, and in the functional composition, richness and diversity for five functional traits (Leaf dry matter content, Specific leaf area, Leaf size, Plant height and Seed mass). Changes in canopy coverage between 2006 and 2016 were studied as an explanatory variable.</p> <p>The R codes show the main analyses carried out in the study: 1) The calculation of the functional composition, richness and diversity of the five traits studied (Leaf dry matter content, Specific leaf area, Leaf size, Plant height and Seed mass), 2) a Dunnett's modified Tukey-Kramer test used to test for significant relationships between four plot categories defined according to the presence or absence of canopy gaps (no gaps, gap closure, gap opening and gap persistence) and changes in the taxonomical and functional composition and diversity of the understory from 2006 to 2016. 3) An Indicator Species Analysis used to study the species that were indicators of gap opening or closure in the Atlantic Beech-Oak forest studied. 4) The calculation of the Standardized Effect Size for the functional traits of the indicator species, used to see if the species selected as gap indicators shared similar values of functional traits and had values significantly different from non-indicator species.</p>
The role of canopy cover dynamics over a decade of changes in the understory of an Atlantic beech-oak forest
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Data from: Patterns and drivers of recent land cover change on two trailing-edge forest landscapes
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Data from: Wildfire activity and land use drove 20th-century changes in forest cover in the Colorado front range
Recent shifts in global forest area highlight the importance of understanding the causes and consequences of forest change. To examine the influence of several potential drivers of forest cover change, we used supervised classifications of historical (1938–1940) and contemporary (2015) aerial imagery covering a 2932‐km2 study area in the northern Front Range (NFR) of Colorado and we linked observed changes in forest cover with abiotic factors, land use, and fire history. Forest cover in the NFR demonstrated broad‐scale changes 1938–2015 and overall cover increased 7.8%, but there was notable spatial variability and many sites also experienced Forest Loss. Recent (1978–2015) wildfire was the largest single driver of Forest Loss, with fires burning 14.3% of the total study area. Recently burned areas showed net losses of 36.9% forest cover. Reasons for Forest Gain were more complex, with elevation, past mining density, fire history, and topographic heat load index being the strongest predictors of increases in forest cover. Historical mining activity is one of the dominant anthropogenic impacts in ecosystems in the NFR and it had a complex, non‐linear relationship with 20th‐century changes in forest cover. Subalpine stands originating after stand‐replacing fires circa mid‐1800s to early 1900s showed some of the greatest gains in forest cover, indicative of slow and continuous post‐fire recovery through the 20th century. We also investigated factors such as land ownership, road density, forest management activities, and development intensity, which played detectable, but more minor roles in observed change. Twentieth‐century changes in forest cover throughout the NFR are a result of ecological disturbances and anthropogenic influences operating at varying timescales and overlaid upon variability in the abiotic environment.
Data from: Short-term climate change manipulation effects do not scale up to long-term legacies: effects of an absent snow cover on boreal forest plants
1. Despite time lags and non-linearity in ecological processes, the majority of our knowledge about ecosystem responses to long-term changes in climate originates from relatively short-term experiments. 2. We utilized the longest ongoing snow removal experiment in the world and an additional set of new plots at the same location in northern Sweden to simultaneously measure the effects of long-term (11 winters) and short-term (1 winter) absence of snow cover on boreal forest understorey plants, including effects on root growth and phenology. 3. Short-term absence of snow reduced vascular plant cover in the understorey by 42%, reduced fine root biomass by 16%, reduced shoot growth by up to 53%, and induced tissue damage on two common dwarf shrubs. In the long-term manipulation, more substantial effects on understorey plant cover (92% reduced) and standing fine root biomass (39% reduced) were observed, whereas other response parameters, such as tissue damage, were observed less. Fine root growth was generally reduced, and its initiation delayed by c. 3 (short-term) to 6 weeks (long-term manipulation). 4. Synthesis We show that one extreme winter with a reduced snow cover can already induce ecologically significant alterations. We also show that long-term changes were smaller than suggested by an extrapolation of short-term manipulation results (using a constant proportional decline). In addition, some of those negative responses, such as frost damage and shoot growth, were even absolutely stronger in the short-term compared to the long-term manipulation. This suggests adaptation or survival of only those individuals that are able to cope with these extreme winter conditions, and that the short-term manipulation alone would over-predict long-term impacts. These results highlight both the ecological importance of snow cover in this boreal forest, and the value of combining short- and long-term experiments side by side in climate change research.
Forest cover change in China (Version1, from 2000 to 2016)
<p><strong>(1)change class code:</strong></p> <p> 1:"gain"<br> 2:"loss"<br> 3:"gain then loss": afforested areas are eventually destroyed back to non-forest land<br> 4:"loss then gain":forest areas recovered or replanted after disturbances</p> <p><strong>(2)Citation</strong></p> <p><strong> </strong>Please cite the dataset including version number and the following paper when using this forest change result: </p> <p>Jing Guo, Peng Gong, Iryna Dronova & Zhiliang Zhu (2022) Forest cover change in China from 2000 to 2016, International Journal of Remote Sensing, 43:2, 593-606, DOI: <a href="https://doi.org/10.1080/01431161.2021.2022804">10.1080/01431161.2021.2022804</a></p> <p><strong>(3)Notes</strong></p> <p>1. We encourage people to send us feedback if you found some mistakes while using this data via email.</p> <p>2. Welcome discussions around potential collaborations. 【You can email Jing (guoj15@tsinghua.org.cn).】</p> <p> </p>
Data from: Wildfire activity and land use drove 20th-century changes in forest cover in the Colorado front range
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Data from: Short-term climate change manipulation effects do not scale up to long-term legacies: effects of an absent snow cover on boreal forest plants
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Data from: Native forest cover safeguards stream water quality under a changing climate
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Global Forest Cover Change Water Cover 2000 Global 30m V001
The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Program. The GFCC Water Cover 2000 Global dataset provides surface-water information at 30 meter spatial resolution. This dataset was derived from waterbodies in the GFCC Tree Cover ([GFCC30TC](https://doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003)) and Forest Cover Change ([GFCC30FCC](https://doi.org/10.5067/MEaSUREs/GFCC/GFCC30FCC.001)) products based on a classification-tree model. Data are available for selected dates between June 1999 and January 2003. GFCC30WC follows the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD).
Global Forest Cover Change Tree Cover Multi-Year Global 30m V003
The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Program. The GFCC Tree Cover Multi-Year Global dataset is available for four epochs centered on the years 2000, 2005, 2010, and 2015. The dataset is derived from the GFCC Surface Reflectance product ([GFCC30SR](https://doi.org/10.5067/MEaSUREs/GFCC/GFCC30SR.001)), which is based on enhanced Global Land Survey (GLS) datasets. The GLS datasets are composed of high-resolution Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. GFCC30TC provides tree canopy information and can be used to understand forest changes. Each tree cover product features four files associated with it; a tree cover layer with an embedded color map, a tree cover error (uncertainty) file, and an index (provenance) file, plus a list of path/rows that relate to the Surface Reflectance input files. Note that the index file and file list were not generated for the 2015 epoch. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD).
CMS: Mangrove Forest Cover Extent and Change across Major River Deltas, 2000-2016
This dataset provides estimates of mangrove extent for 2016, and mangrove change (gain or loss) from 2000 to 2016, in major river delta regions of eight countries: Bangladesh, Gabon, Jamaica, Mozambique, Peru, Senegal, Tanzania, and Vietnam. For mangrove extent, a combination of Landsat 8 OLI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) elevation data were used to create country-wide maps of mangrove landcover extent at a 30-m resolution. For mangrove change, the global mangrove map for 2000 (Giri et al., 2010) was used as the baseline. Normalized Difference Vegetation Indices (NDVI) were calculated for every cloud- and shadow-free pixel in the Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI collection and used to create an NDVI anomaly from 2000 to 2016. Areas of change (loss or gain) occurred at the extremes of the cumulative anomalies.
Global Forest Cover Change Surface Reflectance Estimates Multi-Year Global 30m V001
The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Program. The GFCC Surface Reflectance Estimates Multi-Year Global dataset is derived from the enhanced Global Land Survey (GLS) datasets for epochs centered on the years 1990, 2000, 2005, and 2010. The GLS datasets are composed of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. Data available for this product represent the best available "leaf-on" date during the peak growing season. The original GLS datasets were enhanced with supplemental Landsat images when data were incomplete for the epoch or inadequate for analysis due to acquisition during "leaf-off" seasons. The enhanced GLS data were acquired June 1984 through August 2011. Atmospheric corrections were applied to seven visible bands to estimate surface reflectance by compensating for the scattering and absorption of radiance by atmospheric conditions. GFCC30SR is a multi-file data product. The surface reflectance data products are used as source data for other datasets in the GFCC collection.For each available date, data files are delivered in a zip folder that consists of six surface reflectance bands, a Top of Atmosphere temperature band, an Atmospheric Opacity layer, and the Landsat Surface Reflectance Quality layer. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
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DANDI Archive for NWB datasets
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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.