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.
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 — from plot boundaries to multispectral rasters.
Explore the section →Canopy Height Modeling & Terrain Extraction
Translate raw LiDAR into canopy height models and terrain surfaces with PDAL, rasterio, and reproducible Python workflows — strict CRS, validated DTM, defensible CHM.
Explore the section →Species Distribution Modeling with MaxEnt
From presence-only data preparation through environmental covariate stacking, MaxEnt training, and spatially explicit validation — production-grade habitat suitability mapping in Python.
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, and rate-of-spread estimates — all in reproducible Python.
Explore the section →Start here
Six 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 →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 →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 →Burn Severity Mapping with Python
Map post-fire burn severity in Python: compute NBR, dNBR, and RdNBR from Sentinel-2 with rasterio and numpy, then threshold to Key & Benson classes.
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 →Browse the full library
Every topic and guide on the site, organised by pipeline.