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Improved LiDAR-derived Biomass for Sonoma County, CA

Submitted by ORNL DAAC Staff on 2020-07-06
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Estimated aboveground biomass (Mg/ha) for Sonoma County at 30 m spatial resolution with the 5th-95th percentile range and the standard deviation (SD) of per-pixel biomass estimates shown in the top left and bottom left, respectively.

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A new dataset used a parametric modeling approach to estimate biomass from airborne LiDAR data and field measurements.

NASA Data Helps Researcher Understand Coastal Mangrove Forests

Submitted by ORNL DAAC Staff on 2020-06-25
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Dr. David Lagomasino uses satellite, airborne, drone, and ground measurements to identify areas of coastal resilience and vulnerability.

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Dr. David Lagomasino uses NASA data to study how the Earth's landscape responds to changes caused by natural events and rapid urban expansion.

Soil Organic Carbon Estimates for Mexico

Submitted by ORNL DAAC Staff on 2020-03-30
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(A) Digital map of soil organic carbon (kg/m2) at 1 m depth and 90 m spatial resolution. The black and blue colored areas are urban areas and water bodies. (B) An area in northern Mexico illustrating the level of detail achieved by mapping across 90 m grids.

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Estimates of soil organic carbon are now available from Mexico at a 90 meter resolution.

Historical and Future Leaching from the Mississippi River Basin

Submitted by ORNL DAAC Staff on 2020-01-20
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Spatial distribution of mean dissolved inorganic carbon (DIC) leaching (g c/m2/yr) in the 2090s estimated by S(ALL) simulation experiment (including future climate, elevated CO2, and land use changes). Climate change scenarios were derived from three climate models named CCSM3 (panels a and b), ECHAM (c and d), and CCCMA (e and f) under high (A2) and low (B1) emission scenarios, respectively.

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New data from the Carbon Monitoring System (CMS) offers insights into how environmental factors affect the dynamics of leaching.

Predicted Annual Soil Respiration from the Carbon Monitoring System

Submitted by ORNL DAAC Staff on 2020-01-13
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A global map of predicted annual soil respiration (Rs) at 1 km spatial resolution created by applying the QRF model to gridded covariates. Right is a plot of the latitudinal mean predicted annual Rs.

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A machine learning approach was used to derive the predicted annual soil respiration and uncertainty at a 1 km spatial resolution.

Amazonia Forest Canopy Structure from LiDAR

Submitted by ORNL DAAC Staff on 2020-01-06
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Bounding boxes for LiDAR tiles from surveys over western Para, Brazil, are depicted in Google Earth from the KMZ companion file. Each feature in the KMZ provides key metadata about the corresponding tile.

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LiDAR surveys over forest research sites across the Amazon rainforest in Brazil are available.