UAV LiDAR use in forestry
Point cloud data captured using UAV LiDAR continue to demonstrate their value in this forestry case study. The Digital Terrain Models (DTMs) created illustrated how this technology can improve operational forestry management. From identifying tree classifications to establishing tree heights and growth rates, the data can be used to assess forest health and inform disease spread prevention, prepare carbon capture and biomass estimates, ascertain drainage patterns and fuel load estimates for forest fire prevention.
The UAV LiDAR forestry survey
An area of woodland (44,256m²) was surveyed near Barnsley in Yorkshire, UK. The Routescene UAV LidarPod was flown at an average height of 45m and velocity of 5m/s. A total of 8,912,679 data points were collected to create an incredibly dense 3D georeferenced point cloud.
Additional processing was undertaken by Dr. Chloe Barnes at 2Excel Geo. The point density of the dataset meant trees could be individually identified, with metrics and information extracted on an individual tree crown basis. This type of approach can be particularly useful in variable heterogeneous forest environments such as this site.
The information collected can be utilized for a number of different scales to provide information to support forest management activities. For example, even though small proportions of a complete forest may be scanned using a UAV LiDAR system, describing the characteristics of these parcels can provide sufficient representative information for the majority. The results can be used to develop management objectives, for instance, growth monitoring and harvesting planning.
Processing the results
Identifying individual tree crowns
From the LiDAR point clouds, the identification of individual tree crowns can be achieved through many approaches. For highly detailed point clouds such as the Barnsley dataset, algorithms to identify individual trees can be applied directly to the LiDAR point cloud. This typically provides a more detailed segmentation as the methodology uses information from the point cloud to locate and identify individual trees. Following segmentation structural metrics can be derived using all points associated with the tree crown. The individual tree identification using the entire point cloud for the Barnsley dataset identified a total of 321 trees.
Creating a DTM to assess ground characteristics
Using Routescene’s proprietary LidarViewer Pro software, a Bare Earth Model was created; this was achieved using a series of filters to extract the model. A set of filters was then run, including the Bare Earth Model, based on a cloth simulation. This model virtually drapes a cloth of different thicknesses onto the upturned point cloud to then drape onto the lower surface of the point cloud. A thin cloth (i.e. silk) will sink into the small undulations, but will not go as far as filling a void created by a man-made structure such as a building.
The resultant model was then used as a filter in its own right, the whole point cloud was passed through the model, and any point that is within a certain distance of the model will be retained, the rest of the points discarded. This method allows for a very high-resolution ground model to be created.
DTMs generated from points classified as ground returns can be a useful dataset for forest management. In particular, information about ground characteristics such as slope can inform the planning and execution of timber harvesting and also dealing with drainage.
Standard metrics summarising height can provide a basic overview of the forest structure. This can be calculated for the forest stand or calculated for each individual tree. A forest stand is a contiguous community of trees sufficiently uniform in composition, structure, age, size, class, distribution, quality, or location to distinguish it from adjacent communities. Due to the strong relationship between canopy height and other biophysical parameters, this data can be used to estimate information such as stand volume, biomass, basal area and mean stem diameter.
Standard Height Metrics
Applications in forest management
The above metrics and other structural information that can be derived from point clouds can be useful for a variety of forest management tasks.
Forest fire mitigation
Canopy bulk density (CBD) and canopy base height (CBH) are important parameters for canopy fuel estimation in fire modeling. CBD is used to predict crown fire spread. CBH is the average height from the ground to the underside of the canopy. The lowest height that there is a sufficient amount of forest canopy fuel to propagate the fire vertically into the canopy.
Bicentile metrics representing the percentage of LiDAR returns recorded at different tree height percentiles have been used alongside canopy cover and canopy density metrics to detect canopy defoliation associated with disease.
Carbon stock estimation
LiDAR intensity metrics, which concern the laser pulse return strength, have also been applied across various forestry applications. A few examples of where these have been implemented alongside height metrics include the assessment of coniferous species mixtures and carbon stock estimation.
Measuring forest changes over time
Multiple LiDAR data acquisitions across different periods facilitate the assessment of structural changes through time. This information can be used for applications such as growth rates, windthrow, deforestation and post-harvesting residue quantification.
The resulting survey produced DTMs that will inform future conservation and land management. High resolution LiDAR point clouds provided 3D data for the detailed assessment of forest structure at the individual tree level. This information can be useful for; tree mapping, timber volume, biomass volume and pre-harvesting assessments. Repeating the survey will inform structural changes through the seasons and over time. LiDAR technology can also be fused with other remotely sensed information such as hyperspectral or thermal imagery which could provide an even more accurate picture of the health of our forests. As LiDAR and other remotely sensed technology continue to develop, more information can be acquired to help prevent forest decay and inform conservation.