Cross-Comparison of Individual Tree Detection Methods Using Low and High Pulse Density Airborne Laser Scanning Data

Aaron M. Sparks · Mark V Corrao · Alistair M. S. Smith

Abstract: Numerous individual tree detection (ITD) methods have been developed for use with airborne laser scanning (ALS) data to provide tree-scale forest inventories across large spatial extents. Despite the growing number of methods, relatively few have been comparatively assessed using a single benchmark forest inventory validation dataset, limiting their operational application. In this study, we assessed seven ITD methods, representing three common approaches (point-cloud-based, raster-based, hybrid), across coniferous forest stands with diverse structure and composition to understand how ITD and height measurement accuracy vary with method, input parameters and data, and stand density. There was little variability in accuracy between the ITD methods where the average F-score and standard deviation (±SD) were 0.47 ± 0.03 using a lower pulse density ALS dataset with an average of 8 pulses per square meter (ppm2) and 0.50 ± 0.02 using a higher pulse density ALS dataset with an average of 22 ppm2. Using higher ALS pulse density data produced higher ITD accuracies (F-score increase of 10–13%) in some of the methods versus more modest gains in other methods (F-score increase of 1–3%). Omission errors were strongly related with stand density and largely consisted of suppressed trees underneath the dominant canopy. Simple canopy height model (CHM)-based methods that utilized fixed-size local maximum filters had the lowest omission errors for trees across all canopy positions. ITD accuracy had large intra-method variation depending on input parameters; however, the highest accuracies were obtained when parameters such as search window size and spacing thresholds were equal to or less than the average crown diameter of trees in the study area. All ITD methods produced height measurements for the detected trees that had low RMSE (<1.1 m) and bias (<0.5 m). Overall, the results from this study may help guide end-users with ITD method application and highlight future ITD method improvements.

A Conventional Cruise and Felled-Tree Validation of Individual Tree Diameter, Height and Volume Derived from Airborne Laser Scanning Data of a Loblolly Pine (P. taeda) Stand in Eastern Texas

Mark V Corrao · Aaron M. Sparks · Alistair M. S. Smith

Abstract: Globally, remotely sensed data and, in particular, Airborne Laser Scanning (ALS), are being assessed by the forestry industry for their ability to acquire accurate forest inventories at an individual-tree level. This pilot study compares an inventory derived using the ForestView® biometrics analysis system to traditional cruise measurements and felled tree measurements for 139 Pinus taeda sp. (loblolly pine) trees in eastern Texas. The Individual Tree Detection (ITD) accuracy of ForestView® was 97.1%. In terms of tree height accuracy, ForestView® results had an overall lower mean bias and RMSE than the traditional cruise techniques when both datasets were compared to the felled tree data (LiDAR: mean bias = 1.1 cm, RMSE = 41.2 cm; Cruise: mean bias = 13.8 cm, RMSE = 57.5 cm). No significant difference in mean tree height was observed between the felled tree, cruise, and LiDAR measurements (p-value = 0.58). ForestView-derived DBH exhibited a −2.1 cm bias compared to felled-tree measurements. This study demonstrates the utility of this newly emerging ITD software as an approach to characterize forest structure on similar coniferous forests landscapes.

The Evolution of Forest Inventory

James D Arney · Mark V Corrao

   For nearly two centuries’ foresters have worked within the constraints of data and time to build representative ‘inventories’ that describe the spatial and structural variations of a forest to make actionable decisions. At the core of describing these variations are tree size, number, spatial distribution and species which all vary across any landscape and are essential components of a working forest inventory.

  The quantification of forest density and structure comprises assessing forest stands across the landscape to provide a baseline set of functional metrics that describe the standing volume of similarly forested areas. Describing forests as ‘stands’, and including multiple parameters expressed on a per acre basis, has provided a long and very successful foundation for communication throughout the forestry profession for more than a century.

  Stands are defined as forested areas stratified into similar tree sizes by density and species class. These are often the result of judgments made by an experienced field forester with the aid of available maps and aerial imagery. The value of differences in height and/or density tallied by stand, and occurring between stands, is by design and helps to define components of the forest to achieve management objectives. These ‘stand-based’ metrics, when presented on a per acre basis, continue today to constitute the primary means of communicating forest conditions between ownerships and across geographic regions.

A novel smartphone-based activity recognition modeling method for tracked equipment in forest operations

Ryer M. Becker · Robert F. Keefe

Activity recognition modelling using smartphone Inertial Measurement Units (IMUs) is an underutilized resource defining and assessing work efficiency for a wide range of natural resource management tasks. This study focused on the initial development and validation of a smartphone-based activity recognition system for excavator-based mastication equipment working in Ponderosa pine (Pinus ponderosa) plantations in North Idaho, USA. During mas-tication treatments, sensor data from smartphone gyroscopes, accelerometers, and sound pressure meters (decibel meters) were collected at three sampling frequencies (10, 20, and 50 hertz (Hz)). These data were then separated into 9 time domain features using 4 sliding window widths (1, 5, 7.5 and 10 seconds) and two levels of window overlap (50% and 90%). Random forest machine learning algorithms were trained and evaluated for 40 combinations of model parameters to determine the best combination of parameters. 5 work elements (masticate, clear, move, travel, and delay) were classified with the performance metrics for individual elements of the best model (50 Hz, 10 second window, 90% window overlap) fall-ing within the following ranges: area under the curve (AUC) (95.0% – 99.9%); sensitivity

(74.9% – 95.6%); specificity (90.8% – 99.9%); precision (81.1% – 98.3%); F1-score (81.9% -96.9%); balanced accuracy (87.4% – 97.7%). Smartphone sensors effectively characterized individual work elements of mechanical fuel treatments. This study is the first example of developing a smartphone-based activity recognition model for ground-based forest equip-ment. The continued development and dissemination of smartphone-based activity recognition models may assist land managers and operators with ubiquitous, manufacturer-independent systems for continuous and automated time study and production analysis for mechanized forest operations.

Use of Individual Tree and Product Level Data to Improve Operational Forestry

Robert F. Keefe · Eloise G. Zimbelman · Gianni Picchi


Purpose of Review Individual tree detection (ITD) methods and technologies for tracking individual forest products through a smart operational supply chain from stump to mill are now available. The purpose of this paper is to (1) review the related
literature for audiences not familiar with remote sensing and tracking technologies and (2) to identify knowledge gaps in operational forestry and forest operations research now that these new data and systems are becoming more common.

Recent Findings Past research has led to successful development of ITD remote sensing methods for detecting individual
tree information and radio frequency identification (RFID), branding, and other product tracing methods for individual trees
and logs. Blockchain and cryptocurrency that allow independent verifcation of transactions and work activity recognition
based on mobile and wearable sensors can connect the mechanized and motor-manual components of supply chains, bridging gaps in the connectivity of data. However, there is a shortage of research demonstrating use of location-aware tree and product information that spans multiple machines.

Strategic Partnership Announcement

NW Land & Lifestyle Properties and Northwest Management, Inc. Announce Strategic Partnership

Northwest Management, Inc. (NMI) a distinguished forestry consulting firm in the northwest and Northwest Land & Lifestyle Properties, the most experienced timberland and rural real estate firm in the Northwest, announced today a new strategic partnership.

Together, the companies have combined unparalleled knowledge and experience to create a higher level of real estate and land management consulting services for property buyers and sellers in Washington, Idaho and Montana. The partnership will provide a one-stop shop for landowners, real estate investors and people looking to buy or sell timberland, farmland, hunting land and recreational properties. Both NMI and NLLP are dedicated to landowners and buyers and sellers of land with the goal of helping them achieve the success they are looking for.

“We have had a long-standing relationship with NMI, but chose to further develop it into a strategic partnership because it will allow each of us to provide an even higher level of service to our customers and clients in a much larger geographic area,” said Tom Moore, owner and broker of Northwest Land & Lifestyle Properties . “Our marketing services will have a reach unmatched by any other real estate organization in the Northwest.”

“Our vision is to strengthen the real estate services we can provide to our existing client base” said Vincent Corrao, President of NMI. “NMI welcomes the opportunity to work more closely with NLLP.” We regard this as an important step forward in meeting the expectations of our clients and building our real estate brand.”

Learn more about Northwest Land & Lifestyle Properties go to and to learn more about Northwest Management, Inc., visit

About Northwest Land & Lifestyle Properties

Northwest Land & Lifestyle Properties – is the leading, fully integrated network of conventional and auction real estate professionals. The company has been an innovator in lifestyle and country real estate marketing since 1925. Northwest Land & Lifestyle Properties  has a combined network that supports more than 500 offices and 6,000 real estate professionals, with a unique, comprehensive marketing program. The exclusive program includes the highest ranked and largest portfolio of national, state, office, agent and specialty property marketing websites, unequaled national print advertising, a comprehensive internal real estate advertising agency, an extensive buyer database of nearly one million opt-in buyers and additional proprietary programs to advertise properties more broadly.

About Northwest Management, Inc.

Northwest Management, Inc. is a leader in forestland and environmental management, providing expertise in forestry, wildfire, water resources, wildlife, hazard management & planning for over 30 years. Northwest Management, Inc. is a well-seasoned team of resource professionals and a leader in the use of cutting-edge technology to develop natural resource inventories, resource and land management plans.

Lidar-Assisted Forest Inventory

Lidar-Assisted Forest Inventory

LiDAR – Light Detection and Ranging. For all land owners and managers interested in measurement of the location and height of each tree across their landscape, we offer the FOR-D LiDAR- Assisted forest census.

This is an individual tree digital forest that can easily be viewed, downloaded or separated into specific units or areas on a desktop or laptop computer. The FOR-D inventory requires LiDAR acquisition custom specifications and field data using fixed radius plots each with a submeter GPS center and 100% measurement of all trees for increased accuracy and precision of individual tree species, DBH, height, volume comma and many other features these deep digital forests are set up for input into growth and yield models that provide the accuracy needed for valuation an investment calculations, wildfire mitigation and avoid cost calculations, transportation and harvest and management feasibility assessments, as well as forestry wildlife hydrology and recreation planning.

Carbon Programs and Offsets

Carbon Programs and Offsets

For private and tribal land managers interested in a Carbon Offset project we provide fixed radius permanent for test inventory plots. These are often located with a sub meter GPS system and 100% inventoried for increased precision of tree volumes, growth, forest structure and valuation. These plot data are then used as a sample to estimate the inventory across an ownership and calculate the Carbon Offset Credits the forest can provide to efforts addressing Changing Climate conditions.

Continuous Forest Inventory (CFI)

We focus on traditional grid sampling forest measurement methods. Where fixed radius permanent forest inventory plots are monumented and each tree is tagged. These plots are often subsampled comma and the data are modeled across the landscape for a sampled inventory estimate. These plots are revisited on a 10 year frequency to monitor growth, vigor, mortality comma and volume overtime period this information is used primarily for forest wide planning and management.