Remote Sensing the Unexpected: Tech Approaches for Climate and Justice

For decades, researchers, businesses, and many others have tracked metrics of interest from space using satellite imagery. Although the origins of this technology — known as remote sensing — are military, its applications have diversified over time.

Some of its uses, like identifying objects, are obvious. Others, like the ones described below, are more surprising and are essential tools in the fight against climate change and for the wellbeing of people everywhere.

Poverty

Remote sensing can offer a low-cost approach to quickly mapping poverty in urban areas.

For regions of the world where electricity is an expensive good, nighttime lighting can be a luxury. To identify low-income areas, remote sensing can map the differences in lighting at night. Optical satellites first delineate where residential areas are during the day, separating buildings from infrastructure. Then, light intensity is measured from nighttime images. In the analysis, the strength of light relative to population estimates acts as a proxy of economic resources and thus, income level.

At The Rockefeller Foundation, we have explored correlations between urban infrastructure and economic opportunity in New York City, based on other spectral signatures, here.

Image text: On the left, the full extent of a city is visible in daylight. On the right, the varying nighttime lighting relative to the actual city extent informs us about access to electricity, and by extent, poverty.

Crop Yield

Crop yield estimation begins with the remote identification — using satellite imagery — of crop types, and then includes the measured date of planting, the progression of spectral signatures, and other characteristics to understand plant health. For crops of interest, scientists mimic plant growth using a crop simulator by inputting additional details like the regional soil type, common crop varietal, and irrigation details to generate estimates of crop spectra progression through a season. By comparing the simulated data with the observed data using a machine learning approach, we can then estimate the yield by field.

For example, a field that is greening within a similar magnitude and timeframe of the simulated data will likely have close to the predicted yield. Meanwhile, a field that obtains a duller green relative to the simulated data will likely have a lower yield. This approach allows for the measuring of agricultural statistics across large areas — but still with field resolution — at a significantly lower cost compared to on-the-ground methods, and at much higher accuracy than taking regional harvest statistics. These measures can be used to understand local and global food security for crop insurance quotes, and for many other applications. As part of our work at the intersection between innovation and food security, The Rockefeller Foundation has explored crop type classification and its methods here.

Image text: For four crops grown in California, one wavelength of light measured by an optical satellite is recorded throughout the growing season. These arcs — senescence curves — are used to estimate the health of the crop and estimate the end-of-season yield.

Aquifer Health

Aquifers are large below-ground regions of water accumulation. When water is removed, for example through groundwater pumping, the aquifer can be depleted, causing the naturally porous structures that the aquifer fills to collapse and compress; once collapsed, the aquifer is unlikely to rejuvenate.

One set of satellites, GRACE, is able to measure the shifting levels of underground water by measuring gravity. The GRACE twin satellite system works on the principle that objects in our orbit are essentially free-falling around Earth, pulled by gravity in an orbital trajectory — the greater the gravity, the faster the freefall.

In parts of orbit where there is higher gravity caused by a high-mass object like a large mountain, the front satellite will begin to “fall” faster before the second satellite, creating a wider distance between the two satellites for the duration of the high gravity region. Conversely, in places of low gravity, the front satellite will begin to “fall” slower, allowing the satellite behind to come closer.

As an aquifer depletes, mass decreases and consequently, gravity also decreases. In this way, the distance between the two satellites of GRACE becomes a proxy for measuring gravity and thus, groundwater levels. Learn more here.

Source (public domain): usgs.gov/media/images/land-subsidence-san-joaquin-valley. Image text: Land subsidence in the Central Valley of California due to aquifer compaction, as measured on an electrical post. Between 1925 and 1977, there was an estimated subsidence of 29 feet (9 m).

Image text: On the left, the aquifer is full and because of the increased gravitational force, the GRACE satellite system falls more rapidly and thus the distance between the two satellites is greater. On the right, with the depleted aquifer, there is less gravity and the GRACE satellites fall more slowly, hence they stay closer.

Forest Carbon Storage

Remote sensing can also be used to estimate the potential carbon storage of forest ecosystems. Experts do this by determining the above ground biomass of forest structures using a method called LiDAR (Light Detection and Ranging), which measures forest height and canopy structure via their active sensing method.

LiDAR starts by sending a laser pulse to the ground. The pulse energy “bounces back” off of taller surfaces like tall tree leaves and branches sooner than the ground. By capturing these differences in time of return, the sensor is able to calculate the height of various objects on the surface, and via these combined impressions, estimate biomass. LiDAR can then be used to calculate the above-ground biomass across an entire forest, and use this information to estimate the carbon stored.

Image text: As a LiDAR -bearing airplane flies over and sends energy pulses to the ground, different surfaces will return at different times based on how soon the light energy reaches them. All the returned points can be visualized in a 3-dimensional point-cloud model to visualize the objects captured in the pulses of light.

Live Forest Fire Tracking

Another type of sensor is a Synthetic Aperture Radar (SAR), which works like LiDAR systems by actively sending signals to collect data. In this case, SAR sensors emit energy of a specific wavelength and polarization and record the strength, phase, and polarization of the returned energy; the “echo” of the returned energy can tell us about the far-away surface, somewhat like how bats use echolocation. This data can be used to infer information such as surface texture and structure. Unlike many other sensors, SAR is able to penetrate clouds and haze, and therefore is useful for understanding information in high-cloud or atmospherically contaminated regions.

For example, SAR satellite imagery can be used to track the progression of a wildfire below smoke by comparing forest texture before and during onset of the fire. SAR creates images from before and in near-real-time during a fire to understand where the line of the fire is below the smoke.

(https://www.sciencedirect.com/science/article/pii/S2352409X1630373X)

Image text: On the left, fire and clouds obscure a landscape from observation using optical data sources. On the right, SAR data can be used to understand landscape texture, piercing through the clouds, and comparing before and after SAR images allows us to see the progression of wildfire under smoke, among many other applications.

These are just a few examples of the ways measurements taken remotely from space, airplanes, drones, and other far-off sources can contribute to useful knowledge about the Earth and communities across it. Remote sensing technologies have a wide variety of applications, including surveillance and other exploitations that can breach human rights. For this reason, development should be overseen with care and consideration. Its huge potential will only continue to grow and surprise, and the field will be one to keep an eye on for decades to come. With each progression of these tools, more approaches to understand and fight for climate health and advance global health will be unlocked.