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1,071 results for “Mangroves”
Fig. 4. X in Collacyclumines A-D from the endophytic fungus Colletotrichum salsolae SCSIO 41021 isolated from the mangrove Kandelia candel
Fig. 4. X-ray structure of 1.
Fig. 1 in Collacyclumines A-D from the endophytic fungus Colletotrichum salsolae SCSIO 41021 isolated from the mangrove Kandelia candel
Fig. 1. The structures of the compounds 1–5.
Data from: Warming accelerates mangrove expansion and surface elevation gain in a subtropical wetland
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Data from: Factors influencing organic carbon accumulation in mangrove ecosystems
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Mangrove sinkholes (cenotes) of the Yucatan Peninsula, a global hotspot of carbon sequestration
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Data from: Dissolved iron elution from mangrove ecosystem associated with polyphenols and a herbivorous snail
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Data from: Real or fake? natural and artificial social stimuli elicit divergent behavioral and neural responses in mangrove rivulus, Kryptolebias marmoratus
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Data from: Linking Avicennia germinans (Acanthaceae) architecture to gall richness and abundance in Brazilian Amazon mangroves
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Blueflux: Terrestrial Lidar Scans of Mangrove Forests, Everglades, FL, USA, 2022-2023
This dataset contains point clouds of three-dimensional (3D) mangrove forest structure and volume collected from 10 sites in Everglades National Park, Florida. Data were collected during NASA CMS "Blueflux" campaigns in March 2022, October 2022, and March 2023. Products were acquired using a RIEGL VZ-400i terrestrial laser scanner (TLS). TLS is a non-destructive and quantitative method for in situ 3D forest structure measuring and monitoring. Data are provided in LAS (*.las) format.
Global Mangrove Loss Extent, Land Cover Change, and Loss Drivers, 2000-2016
This dataset provides estimates of the extent of mangrove loss, land cover change, and its anthropogenic or climatic drivers in three time periods: 2000-2005, 2005-2010, and 2010-2016. Landsat-based Normalized Difference Vegetation Index (NDVI) anomalies were used to determine loss extent in each period. The drivers of mangrove loss were determined by examining land cover changes using a random forest machine learning technique that considered change from mangrove to wet soil, dry soil, and water at each loss pixel. A series of decision trees used several global-scale land-use datasets to identify the ultimate driver of the mangrove loss. Loss drivers include commodity production (agriculture, aquaculture), settlement, erosion, extreme climatic events, and non-productive conversion. Maps of loss extent per period, mangrove land cover changes, and loss drivers are provided for each of 39 mangrove holding nations.
CMS: LiDAR Data for Mangrove Forests in the Zambezi River Delta, Mozambique, 2014
This data set provides high-resolution LiDAR point cloud data collected during surveys over mangrove forests in the Zambezi River Delta in Mozambique in May 2014. The data are arranged into 144 1- by 1-km tiles.
CMS: Mangrove Canopy Characteristics and Land Cover Change, Tanzania, 1990-2014
This data set provides canopy height, land cover change, and stand age estimates for mangrove forests in the Rufiji River Delta in Tanzania. The estimates were derived from a canopy height model (CHM) using TanDEM-X imagery and Polarimetric SAR interferometry (Pol-InSAR) techniques. Landsat imagery circa 1990 and circa 2014 was used to estimate stand age between 1994 and 2014 and for forest land cover change modeling.
CMS: Aboveground Biomass for Mangrove Forest, Zambezi River Delta, Mozambique
This dataset provides several estimates of aboveground biomass from various regressions and allometries for mangrove forest in the Zambezi River Delta, Mozambique. Plot level estimates of aboveground biomass are based on extensive tree biophysical measurements from field campaigns conducted in September and October of 2012 and 2013. Aboveground biomass estimates for the larger area of mangrove coverage within the delta are based on (1) the plot level data and (2) canopy structure data derived from airborne LiDAR surveys in 2014. The high-resolution canopy height model for the delta region derived from the airborne LiDAR data is also included.
Greenness Trends and Carbon Stocks of Mangrove Forests Across Mexico, 2001-2015
This dataset provides estimates of greenness trends, above- and belowground carbon stocks, and climate variables of the persistent mangrove forests on the coasts of Mexico (PMFM) at a 1 km resolution from 2001 through 2015. Data are available as one-time estimates or across the temporal range; typically as monthly summaries. One-time estimates of aboveground carbon and soil organic carbon stocks for the PMFM derived from existing sources are provided. Also included are the monthly mean normalized difference vegetation index (NDVI) from MOD13A3 used to derive greenness trends, monthly mean air temperature, and total monthly precipitation from Daymet for 2001-2015 across the PMFM. Other files include the distribution and coverage of PMFM across Mexico. Distributions are provided as four categories of PMFM: (1) Arid mangroves with Surface Water as main input, along the Gulf of California and Pacific Coast (ARsw); (2) humid mangroves with surface water input along the Pacific Coast (HUsw-Pa); (3) humid mangroves with surface water input along the coast of the Gulf of Mexico (HUsw-Gf); (4) humid mangroves with groundwater input along the Gulf of Mexico and Caribbean Sea (HUgw). These data provide a baseline for national monitoring programs, carbon accounting models, and greenness trends in coastal wetlands.
Global Mangrove Distribution, Aboveground Biomass, and Canopy Height
This dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree), and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models. Also provided are (4) in situ field measurement data for selected sites across a wide variety of forest structures (e.g., scrub, fringe, riverine and basin) in mangrove ecotypes of the global equatorial region. Within designated plots, selected trees were identified to species and diameter at breast height (DBH) and tree height was measured using a laser rangefinder or clinometer. Tree density (the number of stems) can be estimated for each plot and expressed per unit area. These data were used to derive plot-level allometry among AGB, basal area weighted height (Hba), and maximum canopy height (Hmax) and to validate the remotely sensed estimates.
CMS: Mangrove Canopy Height Estimates from Remote Imagery, Zambezi Delta, Mozambique
This data set provides high resolution canopy height estimates for mangrove forests in the Zambezi Delta, Mozambique, Africa. The estimates were derived from three separate canopy height models (CHM) using airborne Lidar data, stereophotogrammetry with WorldView 1 imagery, and Interferometric-Synthetic Aperture Radar (In-SAR) techniques with TanDEM-X imagery. The data cover the period 2011-10-14 to 2014-05-06.
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.
CMS: Global Mangrove Canopy Height Maps Derived from TanDEM-X, 2015
This dataset characterizes canopy heights of mangrove-forested wetlands globally for 2015 at 12-m resolution. Estimates of maximum canopy height (height of the tallest tree) were derived from the German Space Agency's TanDEM-X data that produced global digital surface models. Also provided are Lidar estimates of canopy height based on the GEDI instrument, which were used for training and validation of the TanDEM-X estimates of forest height. The coverage of these data follows Global Mangrove Watch's mangrove extent maps. These spatially explicit maps of mangrove canopy height can be used to assess local-scale geophysical and environmental conditions that may regulate forest structure and carbon cycle dynamics. Maps revealed a wide range of canopy heights, including maximum values (>60 m) that surpass maximum heights of other forest types. Maps are provided in cloud optimized GeoTIFF format, and mangrove heights for individual GEDI tiles are compiled in a comma separated values (CSV) files.
Mangrove Extent Baseline from Landsat MSS, 1972-1977
This dataset includes a regional map of mangrove extent for Myanmar, Thailand, and Cambodia for the period of 1972-1977. The map was developed from Landsat 1-2 MSS Collection 1 Tier 2 imagery. Mangrove extent was generated using a Random Forest machine learning algorithm that effectively mapped a total of 15,420.51 km2 at the nominal Landsat scale of 30 m. This map of mangrove extent served as a baseline to analyze changes in mangrove distribution in Southeast Asia from 1970s through 2020. Southeast Asia is home to some of the planet's most carbon-dense and biodiverse mangrove ecosystems. There is still much uncertainty with regards to the timing and magnitude of changes in mangrove cover over the past 50 years. While there are several regional to global maps of mangrove extent in Southeast Asia for the early 21st century, data prior to the mid-1990s are limited due to the scarcity of Earth Observation (EO) data of sufficient quality and the historical limitations to publicly available EO data. The data are provided in Cloud optimized GeoTIFF format at 60-m resolution. In addition, a shapefile outlines the region of analysis.
CMS: Mangrove Canopy Height from High-resolution Stereo Image Pairs, Mozambique, 2012
This data set provides canopy height estimates for mangrove forests at 0.6 x 0.6 m resolution in three study sites located in southeastern Mozambique, Africa: two sites on Inhaca Island and one in the Maputo Elephant Reserve, located in the southern province of Maputo for September, 2012. The estimates were derived from WorldView1 (WV-1) very high resolution (VHR) stereo images processed using the Ames Stereo Pipeline (ASP) digital surface model (DSM) tool.
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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.