Trees In The Tropical Forest

Working With Trees In The Tropical Forest Using Open Source Geospatial Tools

Most people who end up doing this kind of work start by trying to map everything at once. It doesn't work. Tropical forest canopy is dense, the understory is chaotic, and satellite data alone will only get you so far before you need to ground-truth or bring in lidar. I spent about three months last year trying to map canopy cover across a patch of lowland rainforest using just Sentinel-2 imagery. Ended up pulling a quick lidar point cloud from GEDI and merging it into QGIS. The Sentinel data alone was flagging a lot of false positives where the canopy had gaps and the soil was reflecting back through. Combining the two data sources brought the accuracy up to around 87% versus the field plots I had. If you're just starting out and want to do this yourself, here's the practical path that actually works instead of the idealized version most tutorials push.

Setting Up Your First Trees In The Tropical Forest Project

Start by grabbing Sentinel-2 L2A data from Copernicus Browser or the USGS EarthExplorer. L2A means it's already atmospherically corrected, which saves you a step. You also want the S2 MSI surface reflectance bands — specifically B4 (red), B8 (nIR), and B11 (SWIR) for vegetation indices. For the actual mapping, install QGIS if you don't have it, then grab the Semi-Automatic Classification Plugin. It's clunky but it handles batch processing of multi-temporal imagery better than most alternatives. Before you run anything, check your scene for cloud contamination. Tropical forests are almost never cloud-free in any given month. You'll want at least three to five cloud-free composites from the same season across two or more years. I used a median composite approach — basically stacking all the images and taking the median pixel value across the stack. This smooths out seasonal variation and reduces noise from clouds that slipped through the mask. The whole stacking and masking process took about forty minutes on my machine for a 50-kilometer square area.

Building a Classification That Actually Holds Up

The most common mistake is using NDVI alone. NDVI saturates in dense tropical canopy, meaning once you hit a certain leaf area index, it stops responding. You get a flat signal and can't distinguish between healthy dense forest and degraded forest with high canopy cover. I learned this the hard way when my classifier was telling me two completely different forest patches were identical because their NDVI values converged. Instead, combine NDVI with a canopy height model from GEDI or ALOS World 3D. GEDI data is free through LAADS DAAC and gives you actual forest structure metrics — fractional canopy cover, mean canopy height, and relative height distribution. Download the Level 2A product and extract the shots that overlap your area of interest. In QGIS you can rasterize the point cloud into a 30-meter grid to match Sentinel resolution. This structural data adds a completely different dimension to the classification and makes it much harder for the algorithm to confuse forest types. For the classification itself, I use the Random Forest algorithm built into the SAGA GIS toolbox inside QGIS. It handles high-dimensional data well and gives you variable importance scores so you can see which bands and indices are actually driving your classification. In my project, B8 (nIR) and the GEDI fractional cover metric were by far the most important variables. B4 (red) and B11 (SWIR) provided supporting information but weren't decisive on their own.

Validation and Where Things Fall Apart

Accuracy assessment is where most projects quietly fail. A 90% overall accuracy number sounds fine until you look at the confusion matrix and see that the model is misclassifying primary forest as secondary growth at a 15% rate. For tropical forest work, you need class-specific recall and precision, not just overall accuracy. I pulled validation points from existing forest inventory plots and from higher-resolution basemaps in Google Earth Studio where I could visually inspect the canopy structure. One specific edge case I ran into: riparian forests. Trees along river channels in the tropics have different spectral signatures because the water table is near the surface and the species composition shifts. My classifier was treating these as a separate category or mislabeling them entirely. The workaround was to buffer the river networks from a hydrology dataset and manually reclassify those zones after the main classification pass. Takes an extra hour or two but it matters if you're publishing or presenting this data.

Exporting and Sharing Your Results

When you're ready to output, export as GeoTIFF with the appropriate projection. WGS84 Web Mercator is fine for visualization but terrible for area calculations. Use a local UTM zone if you're doing any kind of area-based analysis. Also make sure to save your classification attributes table — the land cover categories, confidence values, and variable importance scores. Most people skip this and then can't reproduce their work six months later. The whole pipeline from raw data to final map usually takes a few days for a first-timer on a moderate-sized area. If you've done it before, the same workflow runs in about six to eight hours including validation. There's no single download link or installer that handles all of this because the data sources are separate, but the tools are all free and open source. QGIS, SAGA, and the GEDI data portal cover everything you need.