Fazenda Classifier
Forest inventory and UAV remote sensing: sampling plots, canopy height, crown morphometry and optional CNN species classification.
Fazenda Classifier integrates forest inventory and UAV remote sensing within QGIS, providing georeferenced measurements of forest structure and an optional framework for tree species classification using convolutional neural networks (CNNs).
The plugin generates metric sampling plot clusters and derives canopy height measurements from digital surface models (DSM) and digital terrain models (DTM). Plot sampling identifies DSM maxima within 1 × 10 m windows and samples terrain elevation at the corresponding physical location. Horizontal coordinate transformations support analyses involving rasters with different coordinate reference systems.
The individual tree workflow combines manual crown delineation, planar crown area, maximum crown span and pointwise DSM–DTM height measurements. Georeferenced CSV exports retain measurements, classification results, methods and coordinate reference information for subsequent analysis.
Optional species classification applies a LeNet CNN to RGB image patches using externally supplied, compatible model weights. The author's trained weights are private and are not distributed. Sampling, height and crown measurement tools remain available without a classification model.
Outputs support research on forest structure, forest carbon, aboveground carbon (AGC) modelling and climate studies through external analytical workflows. This version provides supporting measurements; it does not directly estimate AGC, calculate H5 or simulate climate projections.