Search

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: Forest inventories based on single-tree interpretation of airborne laser scanning (ALS) data often rely on an allometric estimation chain in which inaccuracies in the estimates of the diameter at breast height (DBH) propagate to other characteristics of interest such as the stem volume. Our purpose was to test nearest neighbor imputation by the k-Most Similar Neighbor (k-MSN) and the Random Forest (RF) methods for the simultaneous estimation of species, DBH, height and stem volume using ALS data. The predictors included computational alpha shape metrics and variables based on the height and intensity distributions in the ALS data. Separate data sets covering 1898 and 1249 dominant to intermediate trees in a typical Scandinavian stand structure were used for training and validation, respectively. RF proved to be a flexible method with an ability to handle 1846 predictors with no need for their reduction. Classification of Scots pine, Norway spruce and deciduous trees showed an accuracy of 78%, and the estimates of DBH, height and volume had root mean square errors of 13%, 3%, and 31%, respectively, when evaluated against the validation data. The two selection strategies implemented here reduced the number of candidate variables effectively without any substantial effect on the accuracy relative to the use of all predictors. Differences in k-MSN and RF imputations were marginal when the reduced sets of variables were used. Estimation accuracies could be maintained practically unchanged with only 12.5% of the initial reference data (237 trees), provided the distribution of the observations was similar in the reference and target data. Since we used information collected in the field for extracting the ALS point clouds for individual trees, our results represent an optimal case and should nevertheless be validated against automated tree delineation. [Copyright &y& Elsevier]

Imaging geometry, the structure of the forest, and certain tree properties can cause inaccuracy in image measurements of the crown dimensions of individual trees. Measurement error of the crown diameter was studied in relation to various factors to explain this error. A secondary aim was to generate calibration models for improving the accuracy of crown diameter image measurements. The crown diameters of a total of 715 sample trees in southern Finland were measured in the field and from aerial photographs at scales 1:6000, 1 : 12 000, and 1 : 16 000. The photo grammetric image measurement seemed to systematically underestimate the true crown diameter, and the major factor affecting the bias was tree species. The mean underestimation varied from 0.30 to 0.80 m, with root mean square errors of 0.95–1.10 m depending on the tree species. Linear regression analysis was employed to define the factors that had an effect on the image measurements, and calibration models in the form of linear regression models were generated. The calibration models worked reasonably well, and the root mean square error for the calibrated observations decreased by 22% for Scots pine (Pinus sylvestris L.), 53% for Norway spruce (Picea abies (L.) Karst), and 47% for silver birch (Betula pendula Roth). [ABSTRACT FROM AUTHOR]