Moving to Automated Tree Inventory: Comparison of UAS-Derived Lidar and Photogrammetric Data with Manual Ground Estimates

Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests

Biomass stored in young forests has enormous potential for the reduction of fossil fuel consumption. However, to ensure long-term sustainability, the measurement accuracy of tree height is crucial for forest biomass and carbon stock monitoring, particularly in young forests. Precise height measurement using traditional field measurements is challenging and time consuming. Remote sensing (RS) methods can, however, replace traditional field-based forest inventory. In our study, we compare individual tree height estimation from Light Detection and Ranging (LiDAR) and Digital Aerial Photogrammetry (DAP) with field measurements. It should be noted, however, that there was a one-year temporal difference between the field measurement and LiDAR/DAP scanning. A total of 130 trees (32 Scots Pine, 29 Norway Spruce, 67 Silver Birch, and 2 Eurasian Aspen) were selected for height measurement in a young private forest in south-east Finland. Statistical correlation based on paired t-tests and analysis of variance (ANOVA, one way) was used to compare the tree height measured with the different methods. Comparative results between the remote sensing methods and field measurements showed that LiDAR measurements had a stronger correlation with the field measurements and higher accuracy for pine (R2 = 0.86, bias = 0.70, RMSE = 1.44) and birch (R2 = 0.81, bias = 0.86, RMSE = 1.56) than DAP, which had correlation values of (R2 = 0.71, bias = 0.82, RMSE = 2.13) for pine and (R2 = 0.69, bias = 1.19, RMSE = 2.08) for birch. The correlation of the two remote sensing methods with the field measurements was very similar for spruce: LiDAR (R2 = 0.83, bias = 0.30, RMSE = 1.17) and DAP (R2 = 0.83, bias = 0.44, RMSE = 1.26). Moreover, the correlation was highly significant, with minimum error and mean difference (R2 = 0.79–0.98, MD = 0.12–0.33, RMSD = 0.45–1.67) between LiDAR and DAP for all species. However, the paired t-test suggested that there is a significant difference (p < 0.05) in height observation between the field measurements and remote sensing for pine and birch. The test showed that LiDAR and DAP output are not significantly different for pine and spruce. Presumably, the time difference in field campaign between the methods was the reason for these significant results. Additionally, the ANOVA test indicated that the overall means of estimated height from LiDAR and DAP were not significantly different from field measurements in all species. We concluded that utilization of LiDAR and DAP for estimating individual tree height in young forests is possible with acceptable error and comparable accuracy to field measurement. Hence, forest inventory in young forests can be carried out using LiDAR or DAP for height estimation at the individual tree level as an alternative to traditional field measurement approaches.

Classifying Forest Structure of Red-Cockaded Woodpecker Habitat Using Structure from Motion Elevation Data De-Rived from sUAS Imagery

Small unmanned aerial systems (sUAS) and relatively new photogrammetry software solutions are creating opportunities for forest managers to perform spatial analysis more efficiently and cost-effectively. This study aims to identify a method for leveraging these technologies to analyze vertical forest structure of red-cockaded woodpecker habitat in Montgomery County, Texas. Traditional sampling methods would require numerous hours of ground surveying and data collection using various measuring techniques. Structure from Motion (SfM), a photogrammetric method for creating 3-D structure from 2-D images, provides an alternative to relatively expensive LIDAR sensing technologies and can accurately model the high level of complexity found within our study area’s vertical structure. DroneDeploy, a photogrammetry processing app service, was used to post-process and create a point cloud, which was later further processed into a Canopy Height Model (CHM). Using supervised, object-based classification and comparing multiple classifier algorithms, classifications maps were generated with a best overall accuracy of 84.8% using Support Vector Machine in ArcGIS Pro software. Appropriately sized training sample datasets, correctly processed elevation data, and proper image segmentation were among the major factors impacting classification accuracy during the numerous classification iterations performed.

Scots pine stands biomass assessment using 3D data from unmanned aerial vehicle imagery in the Chernobyl Exclusion Zone

Thirty-five years after the accident, large forest areas in the Chernobyl Exclusion Zone still contain huge amounts of radionuclides released from the Chernobyl Nuclear Power Plant Unit 4 in April 1986. An assessment of the radiological and radioecological consequences of persistent radioactive contamination and development of remediation strategies for Chernobyl forests imply acquiring comprehensive data on their contamination levels and dynamics of biomass inventories. The most accurate forest inventory data can be obtained in ground timber cruises. However, such cruises in radioactive contaminated forest ecosystems in the Chernobyl Exclusion Zone result in radiation exposures of the personnel involved, which means the need for development of the remote sensing methods. The purpose of this study is to analyze the applicability and limitations of the photogrammetric method for the remote large-scale monitoring of aboveground biomass inventories. Based on field measurements, we estimated the biomass inventories in 31 Scots pine stands including both artificial plantations and natural populations. The stands differed significantly in age (from a few years in natural populations to 115 years in the oldest plantation), productivity (from 0.4 to 19.8 kg m^-2), mean height (from 4.1 to 36 m), and other parameters. Photogrammetric data were obtained from the same stands using unmanned aerial vehicle (UAV). These data were then processed using two approaches to derive the canopy height model (CHM) parameters which were tested for correlation with the aboveground biomass inventories. In the first approach, we found that the inventories correlated well with the mean value of CHM of the site (R² = 0.79). In the second approach, the total aboveground biomass was approximated by a function of the average height of trees detected at the site and the total crown projection area (R² = 0.78). Among other local parameters, the total crown projection area was identified as the major factor impacting the accuracy of the aboveground biomass inventory estimates from the UAV survey data in both approaches. In the dense stands with the high total crown projections areas (more than 0.90), the average relative deviations of the UAV-based aboveground biomass estimates from the results of the field measurements were close to 0, which means the adequate accuracy of the UAV surveys data for radioecological monitoring purposes. The relative deviations of the UAV-based estimates in both approaches increased in the stands consisting of separated groups of trees, which indicates potential limitation of the approaches and need for their further development.

Estimating Primary Forest Attributes and Rare Community Characteristics Using Unmanned Aerial Systems (UAS): An Enrichment of Conventional Forest Inventories

The techniques for conducting forest inventories have been established over centuries of land management and conservation. In recent decades, however, compelling new tools and methodologies in remote sensing, computer vision, and data science have offered innovative pathways for enhancing the effectiveness and comprehension of these sampling designs. Now with the aid of Unmanned Aerial Systems (UAS) and advanced image processing techniques, we have never been closer to mapping forests at field-based inventory scales. Our research, conducted in New Hampshire on complex mixed-species forests, used natural color UAS imagery for estimating individual tree diameters (diameter at breast height (dbh)) as well as stand level estimates of Basal Area per Hectare (BA/ha), Quadratic Mean Diameter (QMD), Trees per Hectare (TPH), and a Stand Density Index (SDI) using digital photogrammetry. To strengthen our understanding of these forests, we also assessed the proficiency of the UAS to map the presence of large trees (i.e., >40 cm in diameter). We assessed the proficiency of UAS digital photogrammetry for identifying large trees in two ways: (1) using the UAS estimated dbh and the 40 cm size threshold and (2) using a random forest supervised classification and a combination of spectral, textural, and geometric features. Our UAS-based estimates of tree diameter reported an average error of 19.7% to 33.7%. At the stand level, BA/ha and QMD were overestimated by 42.18% and 62.09%, respectively, while TPH and SDI were underestimated by 45.58% and 3.34%. When considering only stands larger than 9 ha however, the overestimation of BA/ha at the stand level dropped to 14.629%. The overall classification of large trees, using the random forest supervised classification achieved an overall accuracy of 85%. The efficiency and effectiveness of these methods offer local land managers the opportunity to better understand their forested ecosystems. Future research into individual tree crown detection and delineation, especially for co-dominant or suppressed trees, will further support these efforts.