Background:

Acquisition of detailed forest stand data has been a somewhat elusive goal for remote sensing and digital image analysis technologies. New stand classifications and associated attribute data are needed to address current forest management activities directed at broadened stewardship responsibilities. These activities require new types of information about stand structures, habitat attributes, and other ecological data. On-the-ground mapping and/or data collection are expensive and time consuming, particularly as ecosystem management and other initiatives address larger landscapes. Remote sensing could offer needed solutions to these classification, mapping and data acquisition needs. New sensors have been developed that have better spatial and spectral resolutions than were available in the past. Better computer software/hardware systems used to analyze remote sensing data have opened new avenues for improvement of the accuracy and efficiency of automated stand analysis.

Data Sources:

Three new sensors with excellent potential for providing information on stand height, density, structure, and species composition are multi-spectral frame camera Figure 1 such as the one operated by the Space Remote Sensing Center, Stennis Space Center, Mississippi; the LIDAR (light detection and ranging) system and developed and operated by Photo Science, Inc., Gaithersburg, Maryland; and the new CARTERRA 1 satellite operated by Space Imaging/EOSAT, Thorton, Colorado.

LIDAR systems work similar to RADAR, as shown in Figure 2. An example of the results of a LIDAR pass over an area is shown in Figure 3. Each track of laser-pulses shows the varying heights of the canopy. New generation LIDAR systems acquire data at regular intervals perpendicular to the aircraft flight path. Combination of data from multiple lines will make it possible to construct detailed stand canopy surface maps.

Image classification techniques may then be applied to the high-resolution imagery to delineate forest-cover types (Figure 4). This classification may be performed in a number of different GIS packages.

Expected Results:

This project will demonstrate how stands can be assessed with minimal field effort. It will also demonstrate how the subject remote sensing technologies can be used to develop stand maps and accompanying resource data that may be incorporated into a GIS for spatial analyses and modeling management prescriptions. The integration and application of several new technologies will allow more of the objectives of sustainable forestry to be addressed, such as ecological sustainability and biodiversity. By providing datasets that include ecological parameters, a more holistic management approach may be considered.