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Tree mortality impacts biodiversity, carbon dynamics and the management of forests. Climate change is expected to increase tree mortality, but understanding of tree mortality rates and the underlying processes is limited; thus, more accurate and efficient tree mortality mapping methods are required. In this study, we investigated the feasibility of using airborne light detection and ranging (LiDAR) waveform (WF) features in detecting dead trees to monitor tree mortality and studied how the WF features of dead trees change over time. We used three consecutive LiDAR campaigns using fixed sensor and flight parameters in a boreal forest in Southern Finland (61.5 ° N, 24.2 ° E). The campaigns spanned four years and were carried out in 2011, 2013 and 2015. A Riegl LMS-Q680i LiDAR sensor, which operates at 1550 nm wavelength, provided return WF data to study the geometric-optical properties of living and dead trees and monitor mortality of Norway spruce (Picea abies H. Karst.). Our findings highlight the differences in radiometric and geometric WF features between living and dead trees. The return WFs from dead trees were consistently elongated and contained more backscattering energy. We also found that as a tree died, the canopy and branch structures became less dense and more irregular, leading to more complex return WFs. The WF features were used for binary classification of living and dead trees, resulting in classification accuracies between 94.7 and 98.5%, depending on the campaign. Distinguishing between living and dead trees is challenging for trees that have died recently when there are only minor defects in the crown and discoloration of foliage. Tree decay after death improved the discernability between living and dead trees as the geometric-optical properties of the crown change. The radiometric and geometric WF features and canopy mortality effects on the WF features are consistent across datasets implying intrinsic quality of information in the WF features. The within-class variance of WF features in dead trees is greater than that in living trees, indicating significant variations in the geometric and radiometric properties of trees between stages of decaying and dying. Our results imply that LiDAR WFs can be used for the accurate detection of dead trees to map tree mortality. • Tree mortality mapping with multitemporal waveform (WF) LiDAR. • Geometric-optical traits of dead trees monitored over time. • Tree decay after death alters geometrical and optical properties of the crown. • WF signals from dead trees are elongated and have stronger backscattering. • Radiometric WF features perform well in classifying dead trees. [ABSTRACT FROM AUTHOR]

The use of multitemporal LiDAR data in forest-monitoring applications has been so far largely unexplored. In this work, we aimed to develop and test a simple method for the detection of snow-induced canopy changes by employing bitemporal LiDAR data acquired in 2006-2010. Our study area was located in southern Finland (62°N, 24°E), where snow-induced damage occurred in 10 permanent Scots pine ( Pinus sylvestris)-dominated plots in winter 2009-2010. For the detection of snow-damaged crowns, we developed a ∆CHM method by contrasting bitemporal LiDAR canopy height models (CHMs) and analyzing the resulting difference image, using binary image operations to extract the damaged crowns. Furthermore, we examined the structural and spatial factors that could explain snow damage at the individual tree level. The ∆CHM method developed is based on two threshold parameters, i.e., the required height difference in the contrasted CHMs and the minimum plausible area of damage. When testing the performance of ∆CHM method, we found that the plot-level omission error rates were 19-75%, while the commission error rates were 0-21%. Furthermore, the relative estimation accuracy of the damaged crown projection area (DCPA) ranged from −16.4 to 5.4%. The observed damage could be explained at tree level by stem tapering, relative tree size, and local stand density. To conclude, ∆CHM method developed constitutes a potential tool for the monitoring of structural canopy changes in the dominant tree layer if dense multitemporal LiDAR data are available. [ABSTRACT FROM AUTHOR]

Abstract: Understory trees in multilayered stands are often ignored in forest inventories. Information about them would benefit silviculture, wood procurement, and biodiversity management. Cost-efficient inventory methods are needed and airborne LiDAR is a promising addition to fieldwork. The overstory, however, obstructs wall-to-wall sampling of the understory using LiDAR, because transmission losses affect echo-triggering probabilities and intensity (peak amplitude) observations. We examined the potential of LiDAR in mapping of understory trees in pine (Pinus sylvestris L.) stands (62°N, 24°E), using careful experimentation. We formulated a conceptual model for the transmission losses and illustrated that loss normalization is highly ill-posed, especially for vegetation. The losses skew the population of targets that produce a subsequent echo. Losses up to 10–15% can occur even if an overstory echo is not triggered. In LiDAR sensors, quantized intensity values start from binary zero, but actually should include an offset, the noise level. We estimated these empirically. Constraining to low-loss pulses and ground data, we estimated parameters for compensation models that were based on the radar equation and employed the geometry of the pulse, as well as the overstory intensity observations as predictors. Intensity variation of second-return data was reduced, but, the intensity data were deemed of low value in species discrimination. Our results highlight differences between sensors in near-ground echo-triggering and height data. Area-based LiDAR height metrics from the understory had reasonable correlation with the density and mean height of the understory trees, whereas tree species seemed out of reach even if the transmission losses were compensated for. We conclude that transmission losses are a general impediment for radiometric analysis of multi-echo pulses in discrete-return and waveform LiDAR data. [Copyright &y& Elsevier]

Abstract: Remote sensing of forest canopy cover has been widely studied recently, but little attention has been paid to the quality of field validation data. Ecological literature has two different coverage metrics. Vertical canopy cover (VCC) is the vertical projection of tree crowns ignoring within-crown gaps. Angular canopy closure (ACC) is the proportion of covered sky at some angular range around the zenith, and can be measured with a field-of-view instrument, such as a camera. We compared field-measured VCC and ACC at 15° and 75° from the zenith to different LiDAR (Light Detection and Ranging) metrics, using several LiDAR data sets and comprehensive field data. The VCC was estimated to a high precision using a simple proportion of canopy points in first-return data. Confining to a maximum 15° scan zenith angle, the absolute root mean squared error (RMSE) was 3.7–7.0%, with an overestimation of 3.1–4.6%. We showed that grid-based methods are capable of reducing the inherent overestimation of VCC. The low scan angles and low power settings that are typically applied in topographic LiDARs are not suitable for ACC estimation as they measure in wrong geometry and cannot easily detect small within-crown gaps. However, ACC at 0–15° zenith angles could be estimated from LiDAR data with sufficient precision, using also the last returns (RMSE 8.1–11.3%, bias –6.1–+4.6%). The dependency of LiDAR metrics and ACC at 0–75° zenith angles was nonlinear and was modeled from laser pulse proportions with nonlinear regression with a best-case standard error of 4.1%. We also estimated leaf area index from the LiDAR metrics with linear regression with a standard error of 0.38. The results show that correlations between airborne laser metrics and different canopy field characteristics are very high if the field measurements are done with equivalent accuracy. [Copyright &y& Elsevier]

Abstract: Recently, the intensity characteristics of discrete-return LiDAR sensors were studied for vegetation classification. We examined two normalization procedures affecting LiDAR intensity through the scanning geometry and the system settings, namely, range normalization and the effects of the automatic gain control (AGC) in the Optech ALTM3100 and Leica ALS50-II sensors. Range normalization corresponds to weighting of the observed intensities with the term , where is the range, is a mean reference range, and is the exponent that is, according to theory, dependent on the target geometry. LiDAR points belonging to individual tree crowns were extracted for 13 887 trees in southern Finland. The coefficient of variation (CV) of the intensity was analyzed for a range of values of exponent . The tree species classification performance using 13 intensity variables was also used for sensitivity analysis of the effect of . The results were in line with the established theory, since the optimal level of was lower for trees with large or clumped leaves and higher for diffuse coniferous crowns. Different echo groups also showed varying responses. Single-return pulses that represented strong reflections had a lower optimal value of than the first and all echoes in a pulse. The gain in classification accuracy from the optimal selection of the exponent was 2%–3%, and the optimum for classification was different from that obtained using the CV analysis. In the ALS50-II sensor, the combined and optimized AGC and normalizations had a notably larger effect (6%–9%) on classification accuracy. Our study demonstrates the ambiguity of normalization in vegetation canopies. [Copyright &y& Elsevier]

Abstract: High backscatter reflectance at NIR wavelengths has been observed for reindeer lichens (Cladina sp.) in the laboratory. The results suggested that lichens could be separated from soil and other parts of forest understory using this property. An experiment was carried out to test this hypothesis in situ. The lichen vegetation of a 960-m2 plot in a barren pine stand in Juupajoki, Finland was mapped in 3D, using methods of close-range photogrammetry. The data of two airborne discrete-return sensors were compared for their ability to classify understory lichen vegetation. Normalization of the LiDAR intensities was carried out, using natural targets. The results showed that lichen surfaces had a higher intensity than on average. Normalization of the intensities improved separability of lichens from other surfaces, and the best-case classification accuracy was 75%. Detailed analysis of geometric errors revealed small, decimeter-level planimetric offsets in the LiDAR datasets that affected the results notably. [Copyright &y& Elsevier]