High resolution imagery is ideal for being able to identify what is on the ground. Our human eyes have a natural ability to interpret what we see into a real world context. But what if we wanted to answer questions like, “How much of this area is forested?”, or “How much of the agricultural land is actually being farmed?” For that, we need maps, not images. Being able to not just see, but measure land cover types is fundamental to making better land use decisions. And it helps us understand how those decisions impact both our natural and man-made environments.
Whether the issues are related to mapping green infrastructure to enhance community resiliency in the face of increasing environmental and developmental pressures, or ensuring correct tax assessments for agricultural lands under active management or impervious surfaces impacting storm water runoff, the path forward begins with good maps.
In collaboration with research partners at the University of Vermont Spatial Analysis Laboratory, GroundPoint has performed a number of large high resolution land cover mapping projects, including extracting impervious surfaces. Our approach leverages the fusion of LiDAR and digital imagery data using an Object Based Image Analysis (OBIA) approach. Formal accuracy assessments have shown the data to be consistently better than 90% accurate when compared with more expensive manual mapping and photo interpretation methods.