Remote Sensing of Wetlands

SymGEO is proud to announce a new project with the Virginia Department of Transportation (VDOT), designed to explore the use of remote sensing techniques in the identification of wetlands. Traditional wetland delineation requires expert field work, which includes many hours of travel, sampling, and precise survey measurement to delineate what can actually be quite a variable environmental boundary. The best indicators of wetlands tend to be the vegetative species, ground elevation, and hydrology connectivity. Soil sampling and other methods are used to further validate findings, but in today’s data-rich environment, a pretty good indication of wetlands can be gathered through remote sensing.

For this project, LiDAR elevation information was combined with multi-spectral imagery to produce a 6-band composite image using ArcGIS Pro. This composite image was then used in conjunction with National Wetland Inventory (NWI) data to establish training sites for a supervised classification algorithm.

Once a suitable number of training sites were established, the supervised classification algorithm was run on the pilot study area. The results were compared with the National Wetland Inventory data, and a substantial improvement in boundary alignment was noted. This is critical in the accurate measurement of potential impacts to wetlands during road construction or property development, so that an equivalent, mitigating wetland area can be created elsewhere.

It was noted that upland forested areas were sometimes identified as wetland forested areas, indicating that elevation relative to nearest water needs to be included in the classification algorithm. The classification results are currently used as a guide for semi-automated wetland area delineation, but we believe the model could mature to include all required factors and accurately, automatically delineate the wetlands.

If you have supporting data and a need for efficient wetland delineation, SymGEO would love to talk!

Building Imagery and Simulation

There are times when generic building models need to be upgraded to give a better representation of what is actually there. This may be useful for “hero” buildings that are immediately identifiable, or perhaps an area that has planned redevelopment activities taking place. Fortunately, adding custom texture in Esri’s CityEngine is a relatively straight-forward process. In the following example, a building is generated from LiDAR, slightly modified for a complex roof, and then ground photography is mapped as a texture onto the building. Before and after textures are shown below, with the actual building shown in Google Streetview for comparison.

generic building textured building google streetview

Another method of adding realism to a presentation is to use the Google Earth platform to capitalize on all of Google’s ground-based LiDAR information and photo mapping (where available). When combined with new building models and a little Photoshop, compelling before-and-after scenarios can be explored in a very cost-effective manner. This example shows where a Kmart complex may be replaced by a high-density residential building.

before-and-after simulation

Viewsheds and site-lines can also be calculated in GIS, as all building models are constructed from either highly accurate geolocated LiDAR information or detailed architectural specifications. This helps pinpoint which existing structures may have their views impacted (shown in green), and so may required additional targeted public outreach before construction begins.

site-lines impacted views

Are you planning a new development, want to explore digital 3D data, or need to have your own virtual world built? Let us know, SymGEO is here to help!