Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data

This study presents and compares new methods to describe the 3D canopy structure with Airborne Laser Scanning (ALS) waveform data as well as ALS point data. The ALS waveform data were analyzed in three different ways; by summing the intensity of the waveforms in height intervals (a); by first normalizing the waveforms with an algorithm based on Beer-Lambert law to compensate for the shielding effect of higher vegetation layers on reflection from lower layers and then summing the intensity (b); and by deriving points from the waveforms (c). As a comparison, conventional, discrete return ALS point data from the laser scanning system were also analyzed (d). The study area was located in hemi-boreal, spruce dominated forest in the southwest of Sweden (Lat. 58° N, Long. 13° E). The vegetation volume profile was defined as the volume of all tree crowns and shrubs in 1 dm height intervals in a field plot and the total vegetation volume as the sum of the vegetation volume profile in the field plot. The total vegetation volume was estimated for 68 field plots with 12 m radius from the proportion between the amount of ALS reflections from the vegetation and the total amount of ALS reflections based on Beer-Lambert law. ALS profiles were derived from the distribution of the ALS data above the ground in 1 dm height intervals. The ALS profiles were rescaled using the estimated total vegetation volume to derive the amount of vegetation at different heights above the ground. The root mean square error (RMSE) for cross validated regression estimates of the total vegetation volume was 31.9% for ALS waveform data (a), 27.6% for normalized waveform data (b), 29.1% for point data derived from the ALS waveforms (c), and 36.5% for ALS point data from the laser scanning system (d). The correspondence between the estimated vegetation volume profiles was also best for the normalized waveform data and the point data derived from the ALS waveforms and worst for ALS point data from the laser scanning system as demonstrated by the Reynolds error index. The results suggest that ALS waveform data describe the volumetric aspects of vertical vegetation structure somewhat more accurately than ALS point data from the laser scanning system and that compensation for the shielding effect of higher vegetation layers is useful. The new methods for estimation of vegetation volume profiles from ALS data could be used in the future to derive 3D models of the vegetation structure in large areas

Stem Quality Estimates Using Terrestrial Laser Scanning Voxelized Data and a Voting-Based Branch Detection Algorithm

A new algorithm for detecting branch attachments on stems based on a voxel approach and line object detection by a voting procedure is introduced. This algorithm can be used to evaluate the quality of stems by giving the branch density of each standing tree. The detected branches were evaluated using field-sampled trees. The algorithm detected 63% of the total amount of branch whorls and 90% of the branch whorls attached in the height interval from 0 to 10 m above ground. The suggested method could be used to create maps of forest stand stem quality data

Co-registration of single tree maps and data captured by a moving sensor using stem diameter weighted linking

A new method for the co-registration of single tree data in forest stands and forest plots applicable to static as well as dynamic data capture is presented. This method consists of a stem diameter weighted linking algorithm that improves the linking accuracy when operating on diverse diameter stands with stem position errors in the single tree detectors. A co-registration quality metric threshold, QT, is also introduced which makes it possible to discriminate between correct and incorrect stem map co-registrations with high probability (>99%). These two features are combined to a simultaneous location and mapping-based co-registration method that operates with high linking accuracy and that can handle sensors with drifting errors and signal bias. A test with simulated data shows that the method has an 89.35% detection rate. The statistics of different settings in a simulation study are presented, where the effect of stem density and position errors were investigated. A test case with real sensor data from a forest stand shows that the average nearest neighbor distances decreased from 1.90 m to 0.51 m, which indicates the feasibility of this method

Two-phase forest inventory using very-high-resolution laser scanning

In this study, we compared a two-phase laser-scanning-based forest inventory of stands versus a traditional field inventory using sample plots. The two approaches were used to estimate stem volume (VOL), Lorey's mean height (HL), Lorey's stem diameter (DL), and VOL per tree species in a study area in Sweden. The estimates were compared at the stand level with the harvested reference values obtained using a forest harvester. In the first phase, a helicopter acquired airborne laser scanning (ALS) data with >500 points/m2 along 50-m wide strips across the stands. These strips intersected systematic plots in phase two, where terrestrial laser scanning (TLS) was used to model DL for individual trees. In total, phase two included 99 plots across 10 boreal forest stands in Sweden (lat 62.9 degrees N, long 16.9 degrees E). The single trees were segmented in both the ALS and TLS data and linked to each other. The very-high-resolution ALS data enabled us to directly measure tree heights and also classify tree species using a convolutional neural network. Stem volume was predicted from the predicted DBH and the estimated height, using national models, and aggregated at the stand level. The study demonstrates a workflow to derive forest variables and stand-level statistics that has potential to replace many manual field inventories thanks to its time efficiency and improved accuracy. To evaluate the inventories, we estimated bias, RMSE, and precision, expressed as standard error. The laser-scanning-based inventory provided estimates with an accuracy considerably higher than the field inventory. The RMSE was 17 m3/ha (7.24%), 0.9 m (5.63%), and 16 mm (5.99%) for VOL, HL, and DL respectively. The tree species classification was generally successful and improved the three species-specific VOL estimates by 9% to 74%, compared to field estimates. In conclusion, the demonstrated laser-scanning-based inventory shows potential to replace some future forest inventories, thanks to the increased accuracy demonstrated empirically in the Swedish forest study area

Tree crown segmentation in three dimensions using density models derived from airborne laser scanning

This article describes algorithms to extract tree crowns using two-dimensional (2D) and three-dimensional (3D) segmentation. As a first step, a 2D-search detected the tallest trees but was unable to detect trees located below other trees. However, a 3D-search for local maxima of model fits could be used in a second step to detect trees also in lower canopy layers. We compared tree detection results from ALS carried out at 1450 m above ground level (high altitude) and tree detection results from ALS carried out at 150 m above ground level (low altitude). For validation, we used manual measurements of trees in ten large field plots, each with an 80 m diameter, in a hemiboreal forest in Sweden (lat. 58 degrees 28' N, long. 13 degrees 38' E). In order to measure the effect of using algorithms with different computational costs, we validated the tree detection from the 2D segmentation step and compared the results with the 2D segmentation followed by 3D segmentation of the ALS point cloud. When applying 2D segmentation only, the algorithm detected 87% of the trees measured in the field using high-altitude ALS data; the detection rate increased to 91% using low-altitude ALS data. However, when applying 3D segmentation as well, the algorithm detected 92% of the trees measured in the field using high-altitude ALS data; the detection rate increased to 99% using low-altitude ALS data. For all combinations of algorithms and data resolutions, undetected trees accounted for, on average, 0-5% of the total stem volume in the field plots. The 3D tree crown segmentation, which was using crown density models, made it possible to detect a large percentage of trees in multi-layered forests, compared with using only a 2D segmentation method

Individual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads

The collection of field-reference data is a key task in remote sensing-based forest inventories. However, traditional methods of collection demand extensive personnel resources. Thus, field-reference data collection would benefit from more automated methods. In this study, we proposed a method for individual tree detection (ITD) and stem attribute estimation based on a car-mounted mobile laser scanner (MLS) operating along forest roads. We assessed its performance in six ranges with increasing mean distance from the roadside. We used a Riegl VUX1LR sensor operating with high repetition rate, thus providing detailed cross sections of the stems. The algorithm we propose was designed for this sensor configuration, identifying the cross sections (or arcs) in the point cloud and aggregating those into single trees. Furthermore, we estimated diameter at breast height (DBH), stem profiles, and stem volume for each detected tree. The accuracy of ITD, DBH, and stem volume estimates varied with the trees' distance from the road. In general, the proximity to the sensor of branches 0-10 m from the road caused commission errors in ITD and over estimation of stem attributes in this zone. At 50-60 m from roadside, stems were often occluded by branches, causing omissions and underestimation of stem attributes in this area. ITD's precision and sensitivity varied from 82.8% to 100% and 62.7% to 96.7%, respectively. The RMSE of DBH estimates ranged from 1.81 cm (6.38%) to 4.84 cm (16.9%). Stem volume estimates had RMSEs ranging from 0.0800 m(3) (10.1%) to 0.190 m(3) (25.7%), depending on the distance to the sensor. The average proportion of detected reference volume was highly affected by the performance of ITD in the different zones. This proportion was highest from 0 to 10 m (113%), a zone that concentrated most ITD commission errors, and lowest from 50 to 60 m (66.6%), mostly due to the omission errors in this area. In the other zones, the RMSE ranged from 87.5% to 98.5%. These accuracies are in line with those obtained by other state-of-the-art MLS and terrestrial laser scanner (TLS) methods. The car-mounted MLS system used has the potential to collect data efficiently in large-scale inventories, being able to scan approximately 80 ha of forests per day depending on the survey setup. This data collection method could be used to increase the amount of field-reference data available in remote sensing based forest inventories, improve models for area-based estimations, and support precision forestry development

Case-Control Study of Lung Function in World Trade Center Health Registry Area Residents and Workers

Rationale: Residents and area workers who inhaled dust and fumes from the World Trade Center disaster reported lower respiratory symptoms in two World Trade Center Health Registry surveys (2003–2004 and 2006–2007), but lung function data were lacking. Objectives: To examine the relationship between persistent respiratory symptoms and pulmonary function in a nested case–control study of exposed adult residents and area workers 7–8 years after September 11, 2001. Methods: Registrants reporting post September 11th onset of a lower respiratory symptom in the first survey and the same symptom in the second survey were solicited as potential cases. Registrants without lower respiratory symptoms in either Registry survey were solicited as potential control subjects. Final case–control status was determined by lower respiratory symptoms at a third interview (the study), when spirometry and impulse oscillometry were also performed. Measurements and Main Results: We identified 180 cases and 473 control subjects. Cases were more likely than control subjects to have abnormal spirometry (19% vs. 11%; P,0.05), and impulse oscillometry measurements of elevated airway resistance (R5; 68% vs. 27%; P,0.0001) and frequency dependence of resistance (R5–20; 36% vs. 7%; P , 0.0001). When spirometry was normal, cases were more likely than control subjects to have elevated R5 and R5–20 (62% vs. 25% and 27% vs. 6%, respectively; both P , 0.0001). Associations between symptoms and oscillometry held when factors significant in bivariate comparisons (body mass index, spirometry, and exposures) were analyzed using logistic regression. Conclusions: This study links persistent respiratory symptoms and oscillometric abnormalities in World Trade Center–exposed residents and area workers. ElevatedR5andR5–20 in cases despite normal spirometry suggested distal airway dysfunction as a mechanism for symptoms

Stimulated monocyte IL-6 secretion predicts survival of patients with head and neck squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>This study was performed in order to determine whether monocyte <it>in vitro </it>function is associated with presence, stage and prognosis of head and neck squamous cell carcinoma (HNSCC) disease.</p> <p>Methods</p> <p>Prospective study describing outcome, after at least five years observation, of patients treated for HNSCC disease in relation to their monocyte function. Sixty-five patients with newly diagnosed HNSCC and eighteen control patients were studied. Monocyte responsiveness was assessed by measuring levels of monocyte <it>in vitro </it>interleukin (IL)-6 and monocyte chemotactic peptide (MCP)-1 secretion after 24 hours of endotoxin stimulation in cultures supplied either with 20% autologous serum (AS) or serum free medium (SFM). Survival, and if relevant, cause of death, was determined at least 5 years following primary diagnosis.</p> <p>Results</p> <p>All patients, as a group, had higher <it>in vitro </it>monocyte responsiveness in terms of IL-6 (AS) (<it>t </it>= 2.03; <it>p </it>< 0.05) and MCP-1 (SFM) (<it>t </it>= 2.49; <it>p </it>< 0.05) compared to controls. Increased <it>in vitro </it>monocyte IL-6 endotoxin responsiveness under the SFM condition was associated with decreased survival rate (Hazard ratio (HR) = 2.27; Confidence interval (CI) = 1.05–4.88; <it>p </it>< 0.05). The predictive value of monocyte responsiveness, as measured by IL-6, was also retained when adjusted for age, gender and disease stage of patients (HR = 2.67; CI = 1.03–6.92; <it>p </it>< 0.05). With respect to MCP-1, low endotoxin-stimulated responsiveness (AS), analysed by Kaplan-Meier method, predicted decreased survival (χ = 4.0; <it>p </it>< 0.05).</p> <p>Conclusion</p> <p>In HNSCC patients, changed monocyte <it>in vitro </it>response to endotoxin, as measured by increased IL-6 (SFM) and decreased MCP-1 (AS) responsiveness, are negative prognostic factors.</p