Examples

Runnable Jupyter notebooks contributed by the Cecil team. Open one in Colab to run it in the browser — no setup required.

Tutorials

Quickstart

This notebook mirrors the Cecil quickstart end-to-end. By the time you reach the bottom you'll have: a Cecil API key set up in this environment, an Area of Interest (AOI), a subscription to a dataset on that AOI, an…

Introduction to xarray

This notebook will walk through an introduction to the structure of an xarray dataset. The examples below use Impact Observatory 9-Class dataset. If you would like to run this notebook for yourself, just swap out the…

Detect Change

This example will demonstrate how to detect change between two timesteps using Kanop's Screening dataset. The same approach can be used on any subscription you have created. Just use one of your own subscription ids and…

Plot Discrete Land Cover Classes

This notebook will demonstrate how to plot a land cover dataset with discrete, labeled classes. For this example we are using Impact Observatory's Land Cover 9-class dataset, but this same method can be used for any…

Plot from xarray

Xarray provides a wrapper around matplotlib to allow for simple visualisation. This section will explore some basic visualisation options, but the Xarray User Guide on Plotting contains many more examples of interesting…

Subset xarray Datasets

This notebook will explore some of the ways we can subset Xarray Datasets. In particular, we will look at subsetting by variable, by time, and spatial subsetting. First, set up the Cecil client and load the dataset of…

Use cases

Calculate Total Carbon Storage

This example will use Planet's Forest Carbon Monitoring dataset to demonstrate one method of calculating the total carbon stored within an AOI. First, we set up the Cecil client and load the dataset of interest.

Skills

MIT

compute-area-by-threshold

Compute the area within an AOI where a variable from a Cecil dataset crosses a threshold (e.g., "hectares of forest with biomass above 100 Mg/ha"). Reports per-timestep pixel counts, percentage, and an approximate hectare figure.

MIT

land-cover-baseline-and-change

Establish a baseline-year land-cover composition for each AOI in a portfolio and compute year-over-year deltas per class. Feeds CSRD ESRS E4 (biodiversity & ecosystems) ecosystem-extent disclosure and TNFD's Evaluate phase.

MIT

screen-portfolio

Subscribe a chosen Cecil dataset to a portfolio of plots in one batch and return a tidy table mapping each input to its AOI id and subscription id. The foundation for any multi-site analysis (TNFD, CSRD, EUDR, supply chain).

MIT

subscribe-and-load

Subscribe to a Cecil dataset over an Area of Interest and load the result as an xarray.Dataset (raster) or pandas.DataFrame (vector) for analysis in Python.