DOI: 10.57760/sciencedb.24172
title: A dataset of UAV_RGB for Kilosa and Kitulang'halo Forest, Morogoro, Tanzania
title: A dataset of UAV_RGB for Kilosa and Kitulang'halo Forest, Morogoro, Tanzania

description: Data DescriptionThis dataset comprises analysis-ready data (ARD) designed to support the estimation of Above-Ground Biomass (AGB) in Miombo woodlands using UAV-derived structural and spectral metrics in combination with machine learning models. The data were collected from 98 georeferenced forest plots located within Kilosa and Kitulangalo forests, situated in Morogoro Region, Tanzania. The UAV flights and corresponding ground forest inventory were conducted during the same temporal window, between February and March 4, 2024.Each plot record includes 23 predictor variables and 1 response variable (AGB). The predictor variables are grouped as follows:Height percentiles (P10 to P100): UAV-derived canopy height percentiles in meters, capturing vertical forest structure.Statistical height metrics: Height standard deviation (Hsd), skewness (Hsk), and kurtosis (Hkurt).Spectral variables: Mean and standard deviation of Red (R), Green (G), and Blue (B) bandscaptured from UAV RGB imagery.Band ratios: R/G, R/B, G/B; used to enhance spectral feature differentiation.Structural complexity: Point Cloud Density (PCD), indicating vegetation density and texture.The response variable, AGB (in Mg/ha), was calculated from field inventory data and aligned with UAV measurements per plot to facilitate supervised learning.The dataset includes 98 rows and 24 columns, saved in CSV format with a file size of approximately 0.02 MB. All variables are fully populated, with no missing data. Column headers are descriptive of the contents, and units are included or implied based on standard practice (e.g., meters for height metrics, Mg/ha for AGB).This dataset is ready for direct import into statistical or geospatial analysis environments (e.g., R, Python, QGIS) for biomass modelling and validation. It provides a high-quality, spatially-explicit dataset for researchers exploring the integration of UAV-based remote sensing and machine learning in tropical dry forest biomass estimation
description: Data DescriptionThis dataset comprises analysis-ready data (ARD) designed to support the estimation of Above-Ground Biomass (AGB) in Miombo woodlands using UAV-derived structural and spectral metrics in combination with machine learning models. The data were collected from 98 georeferenced forest plots located within Kilosa and Kitulangalo forests, situated in Morogoro Region, Tanzania. The UAV flights and corresponding ground forest inventory were conducted during the same temporal window, between February and March 4, 2024.Each plot record includes 23 predictor variables and 1 response variable (AGB). The predictor variables are grouped as follows:Height percentiles (P10 to P100): UAV-derived canopy height percentiles in meters, capturing vertical forest structure.Statistical height metrics: Height standard deviation (Hsd), skewness (Hsk), and kurtosis (Hkurt).Spectral variables: Mean and standard deviation of Red (R), Green (G), and Blue (B) bandscaptured from UAV RGB imagery.Band ratios: R/G, R/B, G/B; used to enhance spectral feature differentiation.Structural complexity: Point Cloud Density (PCD), indicating vegetation density and texture.The response variable, AGB (in Mg/ha), was calculated from field inventory data and aligned with UAV measurements per plot to facilitate supervised learning.The dataset includes 98 rows and 24 columns, saved in CSV format with a file size of approximately 0.02 MB. All variables are fully populated, with no missing data. Column headers are descriptive of the contents, and units are included or implied based on standard practice (e.g., meters for height metrics, Mg/ha for AGB).This dataset is ready for direct import into statistical or geospatial analysis environments (e.g., R, Python, QGIS) for biomass modelling and validation. It provides a high-quality, spatially-explicit dataset for researchers exploring the integration of UAV-based remote sensing and machine learning in tropical dry forest biomass estimation