Structural Metrics for Terrestrial LiDAR, Digital Photogrammetry and Field inventory in Forest and Agroforestry Landscapes in Kenya's Afromontane Forest - Onyiriagwu, Magnus; Zemp, Clara (My University, 2025)
The dataset contains the scripts and the datasheet (see the Zenodo link) used for the comparative analysis of vegetation structural characteristics of Terrestrial laser scanning (TLS), UAV-based digital aerial photogrammetry (DAP), and ground-based field inventory (Field) in tropical landscapes.
The study was conducted in the Afromontane ecoregion of southwestern Kenya in a transition area bordering the Nyangores forest and the human-settled agroforestry landscapes. All the analyses were conducted in the R statistical programming software. For each of the 3 data sources, we extracted a set of metrics describing the four vegetation structural dimensions: vertical structure, horizontal structure, vegetation density, and structural heterogeneity, using existing methods from the literature. Details and code for the extraction of structural metrics from digital aerial photogrammetry (DAP) are well documented in Mohan et al. (2021) - see here: 10.1515/geo-2020-0290. For the TLS data, we used the structural indices and workflow developed by Ehbrecht et al. (2017) - code can be assessed on github. Estimates of structural metrics from the field data were generated by computing the statistical distribution of the tree height and diameter measurements collected in the field. Description of metrics and (data sources) plot_id: Assigned plot identification number scan_id: unique ID number of the scans per plot LandUse: Vegetation use classification of the plot CR: Canopy ratio (DAP) zmax (m): Maximum returned tree height within a plot (DAP) zmean (m): Mean estimated tree height within a plot (DAP) zsd (m): Standard deviation of tree heights within a plot (DAP) zskew: Height skewness of all trees within a plot (DAP) zkurt: Height kurtosis of all trees within a plot (DAP) zentropy: Height entropy of all trees within a plot (DAP) zq75 (m): 75th height percentile (DAP) zq50 (m): Median height (DAP) zq25 (m): 25th height percentile (DAP) gapArea (m2): Total area of open canopy above 5 m from the ground surface (DAP) gapFrac(%): percentage total of open canopy area above 5 m from the ground surface (DAP) rumple: Canopy rumple index (DAP) LAI: Leaf area index (DAP) FHD: Foliage height diversity (DAP) nStems: Number of stems above 5 m dbh per plot (Field) BA (m2/ha): Basal area of all selected individuals per plot scaled to a hectare (Field) sdBA (m2/ha): Standard deviation of plot-level basal area (Field) meanBA (m2/ha): Average plot-level basal area (Field) sdH (m): Standard deviation of tree heights (Field) maxH (m): Maximum tree height (Field) mean (m): Mean estimated tree height (Field) meanDBH (m): Mean diameter at breast height of all selected trees per plot (Field) sdDBH (m): Standard deviation of diameter at breast height per plot (Field) TopH (m): Maximum recorded tree height within a plot (TLS) ENL: Effective number of layers (TLS) SSCI: Stand structural complexity index (TLS) can.open (%): percentage total of open canopy area above the scanner within a 60 degree scanning position in the * * azimuthal direction. (TLS) MeanFrac: Mean fractal dimension (TLS) UCI: Understory complexity index (TLS) Script 01_CombineMetrics The derived metrics from the single-scan TLS, DTM-independent DAP, and field inventory are merged into a data table. Script 02_BivariateCorrelation Computes the Spearman correlation of all pairs of the three datasets and generates a correlation heatmap. The percentage of significant correlations was extracted from the correlation matrix Script 03_Procrustes Computes Procrustes correlation for the sets of analogous and equivalent metrics between the DAP-TLS, DAP-Field, and TLS-Field structural variables. Each data source was grouped into four structural aspects and compared with similar aspects from the other two data sources. Script 04_PCA Extrapolates the principal component and variable loading of all the structural variables Scrip 05_Classification Performs random forest classification on the combined set of variables.