Procedure Composite Process Instance

HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "ob_id": 43559,
    "computationComponent": [
        {
            "ob_id": 43557,
            "uuid": "91b3899a155146a8be7bc528cfe9bc1e",
            "title": "TLS2trees processing pipeline for ForestScan 1ha plots FG5c1, FG6c2 and FG8c4 in FBRMS-01: Paracou, French Guiana",
            "abstract": "Data for each of the four Gabon FBRMS plots is found within plot directories: OKO-01; OKO-02; OKO-03 and LPG-01. Plot directories contain a main project directory (named using the starting date of data collection and the plot ID, e.g. 2022-06-24_LPG-01_PROJ) with nine data sub-directories, a tile_index.dat file and a 2022-06-24_LPG-01.kmz file as shown in the archived ForestScan_example_data_directory_structure.pdf document.\r\n\r\nThe raw project sub-directory contains all registered scans for each FBRMS 1ha plot. The matrix project sub-directory contains each scan’s Sensor's Orientation and Position (SOP) matrix with the GNSS coordinates (geographical coordinate system: WGS84 Cartesian) for all scans saved separately and made available via a .kmz file under the project main directory, e.g. 2022-06-24_LPG-01.kmz.\r\n\r\nIn order to estimate woody volume and above ground biomass (AGB) for each plot, the TLS2trees processing pipeline was used. TLS2trees is an automated processing pipeline and set of Python command line tools that segments individual trees from plot level point clouds. It consists of existing and new methods and is specifically designed to be horizontally scalable. The TLS2trees pipeline includes three preparatory data steps followed by two segmentation steps: semantic & instance segmentation. Quantitative Structure Modelling (QSM) is then used to estimate morphological and topological tree traits via a four-step process: generate TreeQSM inputs, run TreeQSM, generate optQSM commands and run optQSM. Two final processing steps generated 1) a tree attributes .csv file and 2) tree figures of individually segmented trees arranged by tree DBH size. The complete set of TLS2trees processing files is available for each of the four ForestScan FBRMS plots in Gabon, the step-by-step processing summary below provides details for these files.\r\n\r\nThe first of three preparatory data steps segmented the 100m x 100m plot point clouds into 10m x 10m data tiles and converted each tile from the RIEGL proprietary file format .rxp to .ply format. The resulting <0-NNN>.ply files (NNN is the assigned tile ID number) + a subdirectory named bounding_box containing bounding geometry files + a tile_index.dat file were saved into the rxp2ply project subdirectory. The second preparatory data step down-sampled the data tiles with results saved as tileID.downsample.ply files in the downsample project subdirectory, e.g. 000.downsample.ply. The third preparatory data step generated a tile_index.dat file saved under the project directory. Next, a semantic segmentation step classified the tiled data into leaf, wood, ground or coarse woody debris. For each data tile, three different files tileID.downsample.dem.csv, tileID.downsample.params.pickle, tileID.downsample.segmented.ply + a temporary subdirectory tileID.downsample.tmp were generated and saved in the fsct project subdirectory. Instance segmentation was then used to automatically segment the semantically classified tiled data into individual tree files. Two automatically segmented versions of each tree (with and without canopy leaves) were generated and saved in subdirectories arranged by increasing DBH size (i.e. subdirectory 0.0 contains the smallest trees in the plot) under the clouds project subdirectory, e.g. clouds/N.N/tileID_TreeID.leafon.ply and clouds/N.N/tileID_TreeID.leafoff.ply.\r\n\r\nQuantitative Structure Modelling (QSM) was then used to enclose the wood-only file version (i.e. tileID_TreeID.leafoff.ply) of each individually segmented tree in a set of geometric primitives i.e. cylinders. This allowed for the estimation of morphological and topological traits such as volume, length and surface area metrics for each successfully modelled tree. The first QSM processing step generated 125 modelling input files representing 125 different parameter combinations for each individually segmented tree. These files were saved as tileID_TreeID_NNN.m (NNN ranges from 0 to 124) in the models/intermediate/inputs project subdirectory, e.g. models/intermediate/inputs/tileID_TreeID/tileID_TreeID_<0-124>.m. Next, up to 625 different model candidates for each segmented tree were generated from the modelling input files and saved as tileID_TreeID-NNN.mat files (NNN ranges from 0 to 624) in the models/intermediate/results project subdirectory, e.g. models/intermediate/results/tileID_TreeID/tileID_TreeID-NNN.mat. QSM command files to find the optimal QSM for each segmented tree were then generated and saved as tileID_TreeID_opt.m files in the models/optqsm/commands project subdirectory, e.g. models/optqsm/commands/tileID_TreeID_opt.m. During the final QSM step, an optimal model was found for each successfully modelled segmented tree and saved as a tileID_TreeID.mat file in the models/optqsm/results project subdirectory, e.g. models/optqsm/results/tileID_TreeID.mat.\r\n\r\nAfter QSM modelling, a report file named projectID.tree-attributes.csv was generated for each plot and saved in the attributes project sub-directory, e.g. attributes/projectID.tree-attributes.csv. This report contains estimates of morphological and topological traits for all modelled trees. Due to the >300m scanning range of the Riegl VZ-400i scanner, reports contain trees located both inside and outside the plots which can be filtered using the in_plot variable. Each row in these reports represents a tree with both successfully and unsuccessfully (empty attribute variables) modelled trees included in the reports.\r\n\r\nThe last processing step generated tree figures arranged by descending tree DBH size and saved as projectID.nn.png files (nn refers to the order in which the figures were generated with figure projectID.0.png containing the largest trees) in the figures project sub-directory, e.g. figures/lpg_01.0.png.",
            "keywords": "Terrestrial LiDAR Scanning (TLS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, TLS2trees processing pipeline",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        }
    ],
    "acquisitionComponent": [
        {
            "ob_id": 43558,
            "independentInstrument": [
                43569
            ],
            "instrumentplatformpair_set": []
        }
    ],
    "identifier_set": [],
    "responsiblepartyinfo_set": [
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/207987/?format=api"
    ]
}