Innovating Global Vegetation Monitoring with Amazon SageMaker Geospatial Capabilities

In today’s rapidly changing world, monitoring the health of our planet’s vegetation is more critical than ever. Vegetation plays a crucial role in maintaining an ecological balance, providing sustenance, and acting as a carbon sink. Traditionally, monitoring vegetation health has been a daunting task. Methods such as field surveys and manual satellite data analysis are not only time-consuming, but also require significant resources and domain expertise. These traditional approaches are cumbersome. This often leads to delays in data collection and analysis, making it difficult to track and respond swiftly to environmental changes. Furthermore, the high costs associated with these methods limit their accessibility and frequency, hindering comprehensive and ongoing global vegetation monitoring efforts at a planetary scale. In light of these challenges, we have developed an innovative solution to streamline and enhance the efficiency of vegetation monitoring processes on a global scale.

Transitioning from the traditional, labor-intensive methods of monitoring vegetation health, Amazon SageMaker geospatial capabilities offer a streamlined, cost-effective solution. Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. These geospatial capabilities open up a new world of possibilities for environmental monitoring. With SageMaker, users can access a wide array of geospatial datasets, efficiently process and enrich this data, and accelerate their development timelines. Tasks that previously took days or even weeks to accomplish can now be done in a fraction of the time.

Identify Areas of Interest

We begin by illustrating how SageMaker can be applied to analyze geospatial data at a global scale. To get started, we follow the steps outlined in Getting Started with Amazon SageMaker geospatial capabilities. We start with the specification of the geographical coordinates that define a bounding box covering the areas of interest. This bounding box acts as a filter to select only the relevant satellite images that cover the Earth’s land masses.

Data Acquisition

SageMaker geospatial capabilities provide access to a wide range of public geospatial datasets, including Sentinel-2, Landsat 8, Copernicus DEM, and NAIP. For our vegetation mapping project, we’ve selected Sentinel-2 for its global coverage and update frequency. The Sentinel-2 satellite captures images of Earth’s land surface at a resolution of 10 meters every 5 days. We pick the first week of December 2023 in this example. To make sure we cover most of the visible earth surface, we filter for images with less than 10% cloud coverage. This way, our analysis is based on clear and reliable imagery.

SageMaker Geospatial Processing Jobs

When querying data with SageMaker geospatial capabilities, we received comprehensive details about our target images, including the data footprint, properties around spectral bands, and hyperlinks for direct access. With these hyperlinks, we can bypass traditional memory and storage-intensive methods of first downloading and subsequently processing images locally. This index serves as a critical tool for assessing vegetation health and distribution.

Now we have the compute logic defined, we’re ready to start the geospatial SageMaker Processing job. This involves a straightforward three-step process: setting up the compute cluster, defining the computation specifics, and organizing the input and output details.

After launching the job, SageMaker automatically spins up the required instances and configures the cluster to process the images listed in your input manifest. To monitor and manage the processing jobs, you can use the SageMaker console.

Conclusion

The power and efficiency of SageMaker geospatial capabilities have opened new doors for environmental monitoring, particularly in the realm of vegetation mapping.