Earth observation remote sensing for environmental monitoring, such as drone, satellite and Internet of Things (IoT) data sources, is revolutionizing land management and conservation practices, by providing levels of detailed information about the environment that’s been difficult to gather on a wide scale before. Its a subject that Lavanya Rajan has developed an in-depth understanding of during her Masters degree at Leicester University and placement year with CSX.
At CSX we are using this technology to gather information about land use, vegetation cover, Land Surface Temperature (LST), and other environmental factors to create transformative, trusted Carbon, Natural Capital, and Biodiversity markets.
What is Earth Observation Remote Sensing?
Earth observation remote sensing is the process of collecting information about the earth’s surface using sensors mounted on drones, satellites or from the rapidly developing concept of IoT sensors.
At CSX we use remote sensing as one of the tools to gather information about the land, to support the creation of new markets for carbon, biodiversity and natural capital offsets. This technology allows us to collect data on for example land use, vegetation cover, Land Surface Temperature, hydrology, water levels and flows and other environmental factors, to make informed decisions about land management and conservation.
Using Google Earth engine – more than just pictures
In the realm of Earth observation remote sensing CSC have found Google Earth Engine (GEE) an invaluable resource. It serves as a powerful platform that facilitates the efficient and effective analysis of satellite imagery, including data from Landsat and Sentinel missions.
GEE provides us with the capability to access a vast repository of remotely sensed data, enabling us to obtain and process images more effectively than ever before. Its user-friendly interface and robust computational capabilities allow us to apply complex algorithms and calculations to derive meaningful insights from these images.
One of the most remarkable features of GEE is its ability to work with extensive time series data. This is particularly valuable for longer-term analyses. GEE allows us to access a vast repository of satellite imagery, enabling us to monitor land cover changes, vegetation growth, and environmental trends over extended periods. This is indispensable for tracking the effects of climate change and human activities on our ecosystems.
We utilize various indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), both of which provide valuable information about vegetation health and water content in the soil.
Google Earth Engine provides us with the capability to derive various environmental indices and visualize them as charts and maps.
NDVI
One of the most used indices in remote sensing is the Normalized Difference Vegetation Index (NDVI). NDVI is a mathematical calculation that can use data gathered from satellites and drones to estimate vegetation health. NDVI measures the difference between the red and near-infrared light reflected by vegetation. Healthy vegetation reflects more near-infrared light and less red light, resulting in a high NDVI value. Conversely, unhealthy vegetation reflects less near-infrared light and more red light, resulting in a low NDVI value.
NDWI
Another index that is useful in remote sensing is the Normalized Difference Water Index (NDWI). NDWI is a calculation that can use earth observation data to estimate the water content in soil. NDWI measures the difference between the near-infrared and shortwave infrared light reflected by water. Areas with high NDWI values indicate the presence of water, while areas with low NDWI values indicate dry soil.
NDTI
NDTI (Normalized Difference Turbidity Index) is used to assess water quality, helping us to monitor changes in water turbidity over time. Visualizing NDTI data on maps and charts can be a vital tool in tracking water quality trends, particularly in areas with water bodies of ecological importance.
EVI
EVI (Enhanced Vegetation Index) quantifies vegetation canopy density, highlighting areas of active photosynthesis and providing information on vegetation health. Over time, generating EVI maps and charts can offer insights into changes in land cover and the impacts of conservation efforts.
LST
Land Surface Temperature is another important factor in land management that can be estimated using remote sensing. LST is a measurement of the temperature of the land surface, which can be influenced by various factors such as vegetation cover, moisture, and land use. LST can be used to monitor changes in the environment, identify areas of drought stress, and estimate heat stress on crops.
The numerous benefits of earth observation remote sensing in land management and conservation development are beginning to become clearly understood. It provides valuable insights into the natural environment and improves efficiency in land management. By using these new techniques we can gather valuable information about the land to make informed decisions about land management and conservation practices.
Looking to the future, the increasing availability of more advanced images and technology to gather information about the land, and the ability to feed this back quicker, will help us to identify land use patterns, detect changes in vegetation cover, and monitor carbon sequestration and emissions. Perhaps even in real time. This will deliver to businesses transparent, verified audit trail for Carbon, Natural Capital, and Biodiversity offsets.
By using these advanced technologies and approaches, we can support the creation of new markets for carbon and natural capital while encouraging positive delivery of Nature Based Solutions. At CSX we are committed to using technology to promote sustainable land use practices, preserve and restore biodiversity and the Natural World.
A time series NDVI analysis from Sentinel 2 satellite data on approx. 400ha of land:
