Remote Sensing
Remote sensing involves acquiring information about a certain case without direct contact, typically through the measurement of reflected or emitted radiation (light). In this case, satellites collect data with specialized sensors and with this data we assess vegetation health, land use changes and other ecological changes on a project wide level. We use dozens of technical tools for various analyses and monitoring, here are some examples.
True-Color Satellite Image
This image was created with information from the Sentinel-2 satellite which was launched into orbit in 2015. It carries a high-resolution multispectral imager with 13 spectral bands meaning it can detect radiation beyond the visible spectrum (near infrared and short wave infra red) with a spatial resolution of 10m. Through the analysis of this information, one can obtain different ‘composites’ that highlight different land characteristics and monitor environmental changes. The following images show some of those composites that are being used to monitor.
False Color Composite
This image uses a combination of visible and infrared light to highlight features that are otherwise invisible to the human eye. Vegetation appears red due to its strong reflection in the near-infrared spectrum, while water and urban areas show up in varying shades of blue and gray. It’s commonly used for vegetation health monitoring and land cover classification.
Moisture Index
This image shows the moisture content in vegetation and soil by analyzing the reflectance of near-infrared and shortwave infrared wavelengths. Blue areas indicate higher moisture levels, while warmer/ orange regions suggest dryness. It is crucial for assessing drought conditions, soil health, and crop water stress.
NDVI (Normalized Difference Vegetation Index)
NDVI measures vegetation health and density by comparing the reflectance of red and near-infrared light. Healthy, dense vegetation has high NDVI values, appearing green, while bare soil or sparse vegetation shows lower values, often brown or yellow. This index is widely used in agriculture, forestry, and ecological studies.
SWIR (Shortwave Infrared)
SWIR imagery penetrates haze, smoke, and clouds, revealing surface features such as vegetation, soil moisture, and mineral composition. It is especially useful for monitoring wildfires, detecting water bodies, and analyzing soil properties. SWIR wavelengths can also highlight vegetation stress.
Canopy Height
This image shows the canopy height, derived from a combination of aerial and GEDI lidar (Tolan etal. (2023)) data such as LiDAR or radar. Taller areas, typically forested regions, are represented in warm colors, while lower heights like grasslands or bare soil are shown in cooler tones. It helps in assessing forest biomass, carbon storage, and habitat quality.