Python for Forestry & Ecological GIS Workflows
A practical, field-ready resource for automating ecological analysis, spatial data processing, and conservation reporting in Python — built for foresters, ecologists, and GIS developers who need reproducible pipelines, not one-off notebooks.
Modern ecological work runs on geospatial data: LiDAR canopies, multispectral time series, plot inventories, species occurrences, and administrative boundaries. Stitching those layers into defensible analyses requires more than a notebook — it requires spatial integrity, strict CRS discipline, and pipelines that survive staff transitions and funding cycles.
Every guide on this site is written for production-grade Python work. You will
find runnable code, tuning rationale, and the kind of edge-case detail that
only shows up after you have processed a few terabytes of .laz
or wrestled a dozen ill-projected shapefiles into agreement. The focus is on
canopy height modeling, species distribution mapping, fire risk and fuel
assessment, forest inventory automation, and batch reporting — the workflows
that drive real conservation and management decisions.
The library now runs from raw returns to reportable numbers: individual tree detection and crown segmentation, cloud-native raster workflows that read only the pixels covering your stands, post-fire recovery monitoring, and species distribution projection and transferability. Every page carries hand-drawn diagrams of the thing it is arguing about, and every claim is given a test it could fail.
Four workflows, one toolchain
Start with whichever pipeline matches the data on your desk today.
Ecological GIS Data Foundations
Build reproducible, projection-safe Python pipelines for ingesting, validating, and aligning ecological geospatial data — plot boundaries, multispectral rasters, equal-area statistics, and cloud-native reads that never download a whole scene.
Explore the section →Canopy Height Modeling & Terrain Extraction
Translate raw LiDAR into terrain surfaces, canopy height models and individual tree crowns with PDAL, rasterio and reproducible Python — strict CRS, validated DTM, defensible CHM, seamless mosaics.
Explore the section →Species Distribution Modeling with MaxEnt
From presence-only data preparation through covariate stacking, MaxEnt training and spatially blocked validation to projection and transferability — habitat suitability mapping that states what it cannot support.
Explore the section →Fire Risk & Fuel Assessment
Turn LiDAR, satellite imagery and fire-weather data into defensible fuel maps, wildfire risk surfaces, burn-severity products, rate-of-spread estimates and post-fire recovery trajectories — all in reproducible Python.
Explore the section →Start here
Nine field-tested walkthroughs that anchor the whole library. Each is a complete, runnable pipeline.
Fixing CRS Mismatches in GeoPandas
Diagnose and repair coordinate reference system mismatches in geopandas — when to use set_crs vs to_crs, how to avoid double-transforms, and how to force datum-grid accuracy for forestry data.
Read the guide →Cloud-Native Raster Workflows
Work with COGs, STAC catalogues and chunked arrays instead of downloading terabytes — windowed reads, lazy stacks with xarray and dask, and provenance for reproducible forest analytics.
Read the guide →Normalizing LiDAR Point Clouds with PDAL
Convert absolute LiDAR elevations to height-above-ground with PDAL — CSF ground classification, filters.hag_nn vs filters.hag_delaunay, parameter tuning, and HAGL validation for canopy work.
Read the guide →Individual Tree Detection & Segmentation
Detect individual trees and delineate crowns from a canopy height model or raw point cloud — variable window local maxima, marker-controlled watershed, and F-score validation against field stems.
Read the guide →Canopy Cover from CHM in Python
Calculating canopy cover from a canopy height model means converting a continuous height raster into a binary vegetation mask and reporting the proportion…
Read the guide →Fuel Load Mapping from LiDAR
Derive canopy fuel load, canopy bulk density, canopy base height, and cover from LiDAR in Python, then map to Scott & Burgan 40 fuel models with laspy and PDAL.
Read the guide →Post-Fire Recovery Monitoring
Track how a burned forest recovers with satellite time series — recovery indicators, reference-based normalisation, trajectory fitting, and classifying recovery from multi-year imagery.
Read the guide →Handling Sampling Bias
Correct observer-effort bias in presence-only occurrence records before MaxEnt training — diagnose road-proximity clustering, build a Gaussian kernel-density bias raster aligned to the predictor stack, and configure the biasfile parameter so background points are drawn by sampling probability rather than access.
Read the guide →SDM Projection & Transferability
Project a MaxEnt model to new climates and new regions without over-claiming — clamping, MESS extrapolation maps, ensemble spread, and range-shift metrics computed in Python.
Read the guide →Browse the full library
All 82 topics and guides on the site, organised by pipeline.