Accurate, high-resolution land cover maps are essential to a wide range of landscape analysis and monitoring efforts. The data produced in these assessments provide quantitative evidence that decision-makers and researchers need to understand and materially affect positive solutions to climate change, environmental degradation, and socioeconomic inequality. The SAL has a proven track record of generating quality, high-resolution land cover from complex datasets, uniquely combining the best of automated feature extraction, high-performance computing, and a team of technicians dedicated to comprehensive quality control.

Trees provide a plethora of ecosystem services. A healthy and robust tree canopy is crucial to the sustainability and livability of our communities. The Tree Canopy Assessment protocols were developed by the USDA Forest Service to help communities develop a better understanding of their green infrastructure through tree canopy mapping and data analytics. When integrated with other data, such as property, land use, or demographic variables, a Tree Canopy Assessment can provide vital information to help governments and their citizens chart a greener future.

Burlington, VT Tree Canopy Assessment Report (PDF)

UAS technologies are capable of overcoming the limitations of standard remotely sensed data, especially during weather events where clouds impede visualization. UAS are rapidly deployed systems that collect high-resolution data in real time. The quick turn around time, high spatial accuracy, and low cost of use makes these systems a viable mapping and monitoring option for many organizations. With proper training and protocols, first responders can deploy UAS to assess damage to property, infrastructure, and investigate potential hazards such as landslides. The SAL team have has trained almost 200 first responders to safely deploy drones in disaster events.

Spatial Analysis Lab Helps First Responders Take Flight

Maintaining optimal efficiency of solar panels is critical and costly. Previously, thin film panels had to be manually digitized due to lack of clear borders at panel edges. Drones offer an efficient way to inspect solar panels, yet mapping can still be slow if panels must be manually delineated. The SAL devised a semi-automated, post-processing pipeline to create highly accurate vector delineation of individual solar panels. This solar inspection innovation was developed through a partner project on a 328 megawatt, 1900 acre solar farm with thin film panels. The scalability and efficiency of the lab's feature extraction workflow dramatically reduces maintenance costs by improving solar panel damage assessment.

Unmapped logging roads and skid paths may be a relatively minor contributor to phosphorus pollution in Lake Champlain, but the extent of their inputs is unknown. The SAL mapped forest roads using a combination of LiDAR-derived surface models and automated feature extraction. Eroded sites were identified by examining gully depth and a stream power index derived from flow potential and slope. Field data verified that the most heavily-eroded sites were captured by automated modeling, even as some topographically indistinct road segments were missed, suggesting that a LiDAR-based approach can be useful for pollution estimation even when comprehensive networks cannot be effectively delineated.

In collaboration with Joshua Tree National Park, the UAS Team collected aerial images of the Lost Horse Mine within the park. The mine was about to undergo a restoration project and due to its cultural importance the park requested to document the mine prior to the project starting. The UAS Team not only collected aerial images of the site, but also produced a 3D point cloud of the mine and surrounding area.

Since 2016, the UAS Team has worked with Burlington Electric to generate bi-annual volumetric estimations of the wood chip piles at the McNeil Plant using 3D models generated from overhead UAS imagery. These datasets are produced from a short UAS flight to generate imagery and a digital surface model of the site. The digital surface model is then used to accurately estimate volumes of stockpiles.