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Mondi Ltd. is an international pulp and paper company with forestry land holdings totaling 306 000 hectares in the KwaZulu-Natal and SE Mpumalanga Provinces in South Africa. The total planted area is 202 000 hectares and the primary product is pulpwood, supplied to two Mondi processing mills, a 720 000 ton/year pulp mill in Richards Bay, and a newsprint/ paper mill in Durban.
Mondi recently acquired an airborne lidar data set covering 27 000 ha in the Melmoth area close to Richards Bay. Being a new investment and the first of its kind for Mondi, it was important that the maximum value be extracted from this lidar acquisition. Because Mondi uses the Esri software platform, the lidar data would have to be useable to GIS users throughout the organizations, particularly forest managers.
A Lidar Workflow for ArcGIS
For Mondi’s first lidar collection, data was acquired at a minimum point density of 6 points/m2, together with false color infrared imagery at a ground resolution of 10cm. The lidar data was delivered as .las formatted files classified as (ground/non-ground) point clouds.
Once received from the acquisition vendor, the files were ingested into Esri’s ArcGIS 10.1 LAS Datasets. Beginning with ArcGIS 10.1, a new data format was developed specifically for lidar data called a LAS Dataset, having a ".lasd" file extension, which enables ArcGIS to organize and manage lidar files in their native format.
Quality Assurance/Control (QA/ QC) processes were required to ensure that the acquisition specifications were met. These checks included assessing whether 100% ground coverage was achieved during the lidar acquisition; whether any outlier data such as points falling below ground level, or points far above maximum canopy height (usually from returns reflected off flying birds), were present and if the minimum point density was achieved. Outlier points were deleted or reclassified as required. Area coverage as well as point spacing was also calculated to ensure the lidar data received conformed to the acquisition specification.
Using ArcGIS 3D Analyst Extension, different continuous surfaces were created from selecting classes from the point cloud, resulting in a variety of useful products. Selecting only ground returns, a Digital Elevation Model (DEM) was produced. By selecting only the first returns, a Digital Surface Model (DSM) was created. Canopy Height Models, Slope, Slope Class, and Aspect were generated from those derived datasets. For better terrain visualization, hillshade representations of the DEMs and DSMs were also produced. Visualizing those datasets provided very detailed information that gave forest managers an easy and intuitive understanding of the terrain in which they had to operate.
Terrain Visualization the Ground Roughness
An important part of forest harvest planning is to have a good understanding of the terrain where harvesting operations are to be conducted. Terrain characteristics influence machine accessibility, log extraction and storage, and machine-to-terrain matching. Historically, this information has been challenging to acquire without extensive ground surveys, which are often too expensive and impractical to implement since terrain features are often hidden by vegetation or topography so that even when infield, it is difficult to obtain a holistic view of the area to be worked.
The ability of lidar pulses to find their way through very small gaps in the canopy enable a lidar survey to produce an extremely high definition rendering of the bare ground, not only in terms of micro-terrain features such as dips and hollows, terraces and similar uneven features, but it can also provide indications of where obstacles exist such as large boulders or stumps that can affect machine access within a stand. Other features that can be highlighted include drainage channels, small wetlands or poorly drained areas and similar features that would impact road design and maintenance. Apart from typically been hidden under canopy, these features are generally below the scale at which most contour or topographic maps are produced, so they are not mapped when using traditional cartographic methods.
Capturing lidar data at six points per square meter, sufficient ground strikes were obtained to reveal the actual terrain features that lie hidden under the forest canopy. Locations where features need to be verified or additional details obtained can be identified from the lidar data and focused ground visits can be planned, resulting in considerable time and financial savings for these fieldbased activities.
When Understanding Slope Equals Business Benefits
Slope plays a major role in many forest planning and operational activities, influencing such factors as safety, productivity, machine selection, site sustainability and accessibility. Therefore, the ability to derive detailed slope and slope class information is important and can have a significant impact on the forest plan.
Figure 1 illustrates how slope affects the operation of mechanical harvesters. Slopes between 45 and 60% require self-leveling harvesters that have a cost premium over conventional harvesters. Cost implications where self-leveling harvesters are needed require these areas to be mapped accurately. In addition to cost, there can be safety implications, where incorrect machine-to-terrain matching may cause a serious accident or injury.
Figure 2 shows brown lines representing roads in the GIS database. When overlaid on the DEM hillshade, any misalignment can be corrected and missing roads quickly identified. Once corrected, distance measurements are more accurate and other road metrics such as minimum turn radii of sharp bends can be calculated. This is an important measurement when longlength logging trucks are used to haul out timber as these vehicles have specific minimum turn radii requirements to be able to travel along routes. Knowing this factor can improve route planning, road maintenance and upgrade plans and extraction cost calculations.
The Pay Off from Better Stand Management
A core function of lidar is the ability to record height data accurately, which is also a critical measure for foresters. Subtracting a DEM from a DSM results in a CHM and provides tree height data and variability within a stand. Summary height statistics such as the mean height and range, at a stand level, can be derived using the statistical functions within ArcGIS Spatial Analyst.
Where additional statistical relationships such as Height/Diameter-at-BreastHeight (DBH) curves are available, further analyses can be done to derive stand tree size and volume estimates. Figure 3 illustrates a CHM, where different aged stands can be identified, as can height variation within a single stand. In mature stands the brown shades indicate higher trees than the yellow and green shades, while the purple colors represent young stands 1 to 3m high.
The impact of incorrect stand boundaries can be significant, particularly where payment for work completed is based on area worked. These area values are usually derived from the GIS database, and if incorrect, can lead to under or overpayments being incurred. There can also be a multiplier effect where several different operations are conducted on a stand. Stand boundaries often change across rotations (and even within rotations, where damage from such events as fire; insect attack or frost occurs), and it is vital to monitor these changes
Stand boundary delineation can be improved using either the Canopy Height Model data or the DSM Hillshade data. When the GIS forest stand boundary vector data is overlaid on either of these datasets, any misalignment is quickly highlighted. Figure 4 displays the GIS forest stand boundary data as a blue line, and deviations from the actual standing trees are clearly seen. Correcting the stand boundary delineation can be done by tracing the edge of the trees highlighted in the CHM or DSM data.
Another use of the CHM is to highlight stands where harvesting has not been completed. The area of these unfelled patches can be extracted and calculated. Figure 5 illustrates this aspect, where the brown, yellow or green colors indicated standing trees within a felled area.
The improvement in slope class definition and its impact on the Mondi’s ability to improve harvest planning justified the cost of obtaining lidar data. The success of this initial project has led Mondi to expand its lidar mapping program. In addition, planning is underway to develop more sophisticated products that will assist in deriving quantitative forest stand structure data that can be used to produce timber volume estimates from the lidar data.
For more information on using lidar with ArcGIS, visit www.esri.com/3d.
Mark Norris-Rodgers is a GIS/remote sensing specialist for Mondi SA. He has 15 years of plantation forest management experience and 15 years of GIS and Remote Sensing experience in forest management.
Ron Behrendt is a consultant for Behron LLC, which provides geospatial consulting services to many industries. He was president of Positive Systems Inc., an IT consulting firm, from 1990 to 2005 and has more than 20 years of IT integration experience.
A 1.866Mb PDF of this article as it appeared in the magazine complete with images is available by clicking HERE