How Advances in Satellite Technology Made Data Driven Urban Forestry Possible

November 11, 2022  | Alec Sabatini

The first step of an urban tree canopy assessment is to acquire the relevant imagery and other remote sensing data. Though it now seems commonplace, the current state of abundant and accessible spaceborne remote sensing data is an extraordinary feat that has been decades in the making.

So, to fully appreciate the convenience of modern, data-driven urban forestry, we are going to flip back the decades to better understand how we got to this point.

Satellites Take Off

The era of satellites began during the Cold War. The Soviet’s launch of the first satellite, Sputnik 1, in 1957 created a new arena for the two global powers to battle for supremacy. Both countries quickly started pouring resources into developing space programs.

Three years later in 1960, the first weather satellite was launched and provided the first-ever images of cloud patterns in their totality. Over the next two decades, technological advancements and the declassification of military satellites greatly advanced remote sensing capabilities, including the use of multispectral sensors to collect both visible light and near infrared images.

Landsat 1, the first satellite designed specifically to monitor the Earth’s surface, was launched in 1972. Landsat 1 relayed imagery of large sections of the earth’s surface and that data was used in foundational global greenery analyses. The spatial resolution of Landsat 1 was 60 meters, meaning the smallest pixel in its images were 60 meters by 60 meters.

Imagery In Action

Our partner, the Husqvarna Urban Green Space Index (HUGSI), uses Sentinel 2 satellite image dataset to calculate the green metrics they use to rank and compare cities around the world. The Sentinel 2 satellites are operated by the European Space Agency and their datasets are made publicly available by The European Commission’s Copernicus programme. The Sentinel satellites were launched specifically to support land cover monitoring in support of environmental management, climate change analysis, and civil security.

HUGSI uses additional satellites, such as high-resolution satellite imagery from Airbus and Maxar, to complement and validate their results. Once they’ve collected the big picture, they apply deep learning techniques to process the satellite image data and extract relevant metrics from the raw images. These become insights on size, proportion, distribution, and health of green space in urban areas.