Tools tagged with "Biomass"

This resource details the lidar data collection methods employed by NSF NEON to map vegetation height, density, and other surface characteristics over large regions. Using airborne sensors, lidar provides detailed 3D pictures of forest structure, crucial for ecological research. It highlights the advantages of active sensors like lidar for comprehensive ecosystem parameter mapping.

This dataset, hosted by the ETH Zurich Research Collection, provides a global canopy height map for 2020. It was created using data from Sentinel-2 and GEDI, offering valuable insights into forest structures and biomass. The resource is open access and available under a Creative Commons license.

This dataset combines airborne lidar and Landsat satellite data to map vegetation structure in Mexican ecosystems. It includes lidar-derived canopy height values and Landsat-derived vegetation indices for 499,925 sample points across 1,105 aerial images, enabling fine-resolution ecological restoration monitoring.

The 3MSI Lab specializes in 3-D modeling, sensing, and data intelligence, with a particular focus on remote sensing of terrestrial and urban vegetation. It develops advanced algorithms that fuse photogrammetry and LiDAR technologies to retrieve detailed forest canopy structure and monitor vegetation phenology. The lab actively publishes research and releases valuable data products.

This clearinghouse serves as a central repository for the U.S. Forest Service's geospatial data. It offers downloadable datasets, map services, and various raster and vector products relevant to forestry, land management, and environmental monitoring. Users can explore data related to forest conditions, land cover, hazards, and topographic features.

rGEDI is an R package designed for working with NASA's Global Ecosystem Dynamics Investigation (GEDI) data. It provides a suite of functions for downloading, visualizing, clipping, gridding, simulating, and exporting GEDI data. This package facilitates comprehensive analysis and management of GEDI geospatial information within the R environment.

This dataset offers Global Ecosystem Dynamics Investigation (GEDI) Level 4A Version 2 predictions of aboveground biomass density (AGBD) in Mg/ha and associated standard error estimates. It is derived from lidar waveform relative height metrics, calibrated with field plot data. The data covers the global latitude band observed by the International Space Station (ISS), providing crucial information for ecosystem and land cover studies.

lidR is an R package designed for processing airborne LiDAR data. It provides powerful tools for point cloud manipulation, analysis, and visualization, with a strong focus on applications in forestry. The package enables users to perform tasks such as terrain modeling, individual tree segmentation, and forest structure analysis.

This initiative by the Space Research Institute of the Russian Academy of Sciences focuses on satellite monitoring technologies. The project utilizes satellite imagery, such as that from Proba-V, to create detailed maps of forest cover. These maps are a key output of the Proba-V-TerraNorte project, contributing to environmental understanding.

SegmentAnyTree is an open-source GitHub repository dedicated to tree segmentation. It provides code and resources for identifying and delineating individual trees, likely utilizing advanced machine learning techniques on various types of geospatial imagery. This project supports detailed forest analysis and environmental monitoring efforts.