This section depicts a list of resources suitable for visualizing, processing and modelling data for land processes. This collection includes tools for ecosystem and radiative transfer modelling; visualization and processing tools for optical and radar remote sensing data.
This code implements a Monte Carlo sampler for drawing posterior estimates of land cover from an existing land cover map and its corresponding confusion matrix.
Contact: Tristan Quaife (Reading)
Tools for processing and analysing TLS point cloud data, in particular to extract individual trees from point clouds containing many trees. A series of iterative clustering, filtering and pruning operations carried out to assess the contents of a point cloud for tree objects and then to extract these, automatically. The result of this can then be used in quantitative reconstruction modelling tools.Contact: Andy Burt (UCL)
Three dimensional Radiative Transfer model, developed to simulate vegetation canopy BRDF, albedo, absorption, fAPAR, lidar waveform and photon counting. Associated tools: scripts for coupling with atmospheric model 6s, LUT generation and model inversion, and simulation of sensors (e.g. Sentinel 2/3, MODIS).
Contact: Peter North (Swansea University)
3D EO simulation framework developed via ESA funding as part of the Support to Science Element (STSE). 3DVeglab provides an online toolbox of RT modelling tools to enable simulation of (optical) Sentinel 2, 3 and other sensors, for selected highly-detailed 3D modelled scenes.
The scenes were developed from RAMI simulated (synthetic) model scenes, and from detailed field measurements made at forest sites in Switzerland (Laegeren) and Germany (Tharandt). These measurements included very detailed TLS scans of branches and needles, enabling development of very detailed 3D scene models.
Contact: Mat Disney (UCL)
Online RT model checking tool, developed as part of Radiative Transfer Model Intercomparison (RAMI) exercise. Provides online interface to standard set of RT model simulations, to allow testing of new RT models against set of ‘most credible’ surrogate truth RT model simulations (e.g. librat).
Users can set up simulation scenarios, and upload model results, and then receive automated checking and certification against ROMC model ‘truth’. Allows a new model to be ‘validated’ in some sense, at least for a limited set of modelling scenarios where the result is known a priori. Developed by team at EU Joint Research Centre (JRC), Ispra, Italy in conjunction with the RAMI team.
Contact: Mat Disney (UCL)
Python modules for the creation of 3D scenes, running of simulations, and processing of results using librat. Primarily used with wavefront obj files created by the commercial software OnyxTree and developed for waveform lidar simulations of savanna tree/grass scenes.
Contact: John Armston (Queensland Government and NASA GEDI)
3D Monte Carlo Ray Tracing (MCRT) model, for the optical domain. Widely-used in published work simulating EO reflectance data at a range of scales, as well as time-resolved lidar. Also used as ‘most credible’ surrogate RT models in successive phases of the RAMI exercise.
Code is developed as a stand-alone library of function calls, which can be included in user-developed C code (default) and Python.
This tool is owned and maintained by a third party.
NCEO Contact: Shaun Quegan (University of Sheffield)
This tool is owned and maintained by a third party