Resource Inventory & Mapping
screte Classification mapping techniques and the ability to perform highly detailed and accurate plant community/land cover classifications in difficult situations. Image processing/remote sensing services include:GRS is a leader in the use of the line-point transect, in combination with the GRS Densitometer™, to generate accurate and detailed species-specific/landscape feature inventories and assessments. This approach was recognized in 2015 in a research study by the Food and Agriculture Organization of the United Nations (FAO) as being the "most accurate,least expensive,and most easily applied" forest canopy cover sampling technique.
GRS implements field sampling efforts using transects of different shapes and spacing, using larger spacing (12-15 feet) for tree-based plant communities, smaller spacing (6-9 feet) for shrub plant communities, and the smallest spacing (3 feet) for herbaceous plant communities. Transects of a closed nature are preferrable as they will always cross all different slopes present at a sample area going both across and against the slope of the terrain as they are traversed during the sampling efforts.
Ken Stumpf, Director, Resource Management Applications at GRS, who has been implementing this methodology during GRS's inventory and mapping projects since the early 1990's says "we have found this approach to be very easy to teach our staff and implement in the field. We have even been able to present workshops to high school students and then hire some of these students to work alongside our professional foresters and botanists as they perform ecological assessments at field sites. It is extremely beneficial to our inventory and mapping efforts that we can develop very detailed, accurate, and standardized results, regardless of the level of expertise of the field staff.This leads to an overall reduction in the costs of collecting field information while still collecting high quality information." This is potentially a major benefit to organizations that may rely on temporary seasonal staff or volunteers to perform such field data collection and assessment activities.
GRS actually uses this approach to sample and describe all types of ecosystems that represent tree, shrub, and herbaceous lifeforms. GRS's adaptations to this sampling methodology enable the development of species-specific cover estimates by canopy layer, as well as estimates by tree size class. Other adaptations have enabled the development of species-specific estimates of average tree diameter, height, and stems per acre. Other sampling protocols, such as the Brown's Transect methodology, have been easily integrated into the line-point sampling approach that have enabled the development of coarse and fine woody debris estimates necessary to develop fire fuel models, as well as species-specific estimates of tree volume and biomass.
Such accurate and standardized field descriptions have supported GRS in their collection of ground truth and accuracy assessment information as they have mapped millions of acres of wildlands, including four National Parks, in California, Alaska, and the Pacific Northwest since 1989. The end result of GRS's mapping efforts based on the development of species/landscape feature-specific detailed quantitative estimates is the development of natural resource information data sets that satisfy the information needs of foresters, ecologists, natural resource planners, wildlife biologists, fire scientists, and other resource professionals all contained in one map data set.
GRS has developed specialized algorithms and processes to enable the development of plant community and species/landscape feature-specific attributes from digital imagery. Our approach, called Discrete Classification, enables the use, evaluation, and identification of individual species and landscape characteristics found on the ground in the image data set. This methodology enables the mapping of species-specific cover, as well as the assignment of classification system names like the National Vegetation Classification System (NVCS) association levels names. GRS has developed Confusion and Fidelity Reports to identify sources of confusion, in terms of botanical/land cover characteristics and verify the fidelity of classification efforts before any actual image classification maps are ever developed. The ability to deal with individual training site data is a tremendous advantage over the more traditional clustered training data approach that is so often used. Training site data can come from a myriad of sources, old and new. GRS has also performed projects requiring the manual interpretation and extraction of GIS features from digital photographic images as well as high-resolution satellite images. GRS image classification projects have resulted in the creation of complex GIS layers with associated database tables consisting of detailed quantitative descriptions of classified features.
GRS has extensive experience in developing high quality GIS data from digital imagery. Whether collected from aircraft or satellites, digital images can be used as a very cost-effective source material for developing an accurate and complete land base for your GIS. GRS has thousands of hours of experience using aerial and satellite imagery from a wide variety of government and private sources. We have compiled geospatial data for both small and large projects ranging from subdivisions to hundreds of thousands of acres.
