LIDAR Analysis in ArcGIS for Forestry Applications

LIDAR stands for light detection and ranging. In its most common form, it is an airborne optical remote-sensing technology that measures scattered light to find range and other information on a distant target. Similar to radar technology, which uses radio waves, the range to an object is determined by measuring the time delay between transmission of a pulse and detection of a reflected signal. Instead of radio waves, lidar uses much shorter wavelengths of the electromagnetic spectrum, typically in the ultraviolet, visible, or nearinfrared range.

This technology allows the direct measurement of three-dimensional structures and the underlying terrain. Depending on the methodology used to capture the data, the resultant data can be very dense, for example, five points per meter. Such high resolution gives higher accuracy for the measurement of the height of features on the ground and above the ground. The ability to capture the height at such high resolution is lidar's principal advantage over conventional optical instruments, such as digital cameras, for elevation model creation.

Also captured by the lidar sensors is the intensity of each return. The intensity value is a measure of the return signal strength. It measures the peak amplitude of return pulses as they are reflected back from the target to the detector of the lidar system. Intensity is often used as an aid in feature detection and, where conventional aerial photography is not available, can be used as a pseudoimage to provide context of the lidar acquisition area.

In forestry, lidar can be used to measure the three-dimensional structure of a forest stand and produce a model of the underlying terrain. The structure of the forest will typically generate a first return from the uppermost limit of the canopy, followed by less intense returns through the canopy, down to the underlying terrain. Returns are classified into ground and aboveground sources. The ground returns can generate a detailed terrain of the area of interest, while the canopy returns can be filtered to provide forest structure at the canopy and middle level of the forest.

Advantages to the Forest Industry

The ability to simultaneously visualize the ground and model the canopy structure provides significant advantages to the forest industry. Traditionally, foresters and land managers have relied on topographic maps for terrain classification and field-based surveys to obtain tree volumes and height information. Lidar data provides significant improvements over both these techniques.

Existing topographic maps depict contours and rivers, which have been, for the most part, captured from aerial photography using stereographic terrain generation techniques. In areas where the tree canopy obscures the underlying terrain, interpretive methods are used to depict where streams and contours occur. Terrains generated from lidar data more accurately represent these geographic features. Lidar penetrates the tree canopy to return a more accurate interpretation of the ground surface. This increases the accuracy of terrain classification and thereby the resultant interpretation and analysis of the geographic features.

Lidar has provided significant benefits for forest development and engineering operations including locating roads, harvest planning, forest regeneration, and more. The ability to identify suitable creek crossings, determine optimal routes, and locate previously unmapped historic roads aids in reducing costs and creating operational efficiencies.

Lidar has also offered an improvement to existing forest inventory methods and procedures. Traditional field-based timber inventory methods are based on measurements derived from systematically sampling plots in forest stands. This statistical sampling method is most often used in forests where measuring every tree is impractical. Tree volumes and heights are calculated in each sample plot, then generalized throughout a forest stand that shares similar characteristics. Estimated results help describe stand characteristics but are inaccurate due to variability in growing conditions throughout the forest, sampling bias, and lack of precision. In addition, the time to collect such measurements is both lengthy and expensive, as many sample plots may be required to provide a reliable representation. Lidar can overcome these limitations.

An increasing number of forestry and land management organizations are using lidar for forest inventory measurements. A wide range of information can be directly obtained from lidar including digital elevation models, tree heights and digital surface models, crown cover, forest structure, crown canopy profile.

Postprocessing of lidar data can reveal: Volume—Canopy geometric volume, Biomass—Canopy cover, Density—Height-scaled crown openness index and counts of delineated crowns, Foliage projected cover—Crown dimensions.

The forest industry is requiring increasingly precise inventories to guide forest
management activities. Using lidar data, forest inventories can be conducted at nearly the single tree level, offering more accurate representations of the true forest stand structure.

For forest inventory activities, lidar has been used primarily to retrieve basic structural tree attributes including height, canopy cover, and vertical profiles. These attributes can be used to derive other critical forestry measurements including basal area and timber volume, as well as biomass for alternative energy and carbon sequestration analysis.

ESRI
has recently published a white paper entitled, “LiDAR Analysis in ArcGIS 9.3.1 for Forestry Applications“. Written by Gordon Sumerling of ESRI Australia the paper seeks to “step through processes to convert lidar data into a format ArcGIS can process, explain methods to interpret the lidar data, and show how ArcGIS can disseminate the data to those who are not geospatial analysts”. This is the papper. download
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