Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index

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.     

Photoresist-based integration of enzyme functionality into MEMS

We demonstrate the direct immobilization of glucose oxidase and lactate oxidase onto photoresists commonly used in microfabrication. The method allows for a cost-effective, facile inclusion of enzyme functionality into novel MEMS devices because it does not require any chemical or physical pre-treatment of surfaces, and it is largely compatible with existing fabrication technologies. We used fluorescence imaging and absorbance spectrometry to confirm attachment of the enzymes onto the photoresists and to determine their activity. In addition, the procedure was used to successfully integrate enzyme functionality into a photoresist-based biosensor. This further demonstrates the effectiveness of the approach, and opens a path towards other novel applications in MEMS research.     

Jitter characteristics of an on-chip voltage reference-locked time-to-digital converter

The noise and jitter characteristics of an on-chip voltage reference-locked ring oscillator used in the time-to-digital converter (TDC) of the integrated receiver of a pulsed time-of-flight laser rangefinder are presented. The frequency of the ring oscillator, 683 MHz, was locked to the on-chip voltage reference by means of a frequency-to-voltage converter, resulting in better than 90 ppm/°C stability. The noise and jitter transfer characteristics of the loop were derived, and simulations were performed to see the effects of different noise types (white and 1/f noise) on the cumulative jitter of the locked ring oscillator. Finally, these results were verified by jitter measurements performed using an integrated time-to-digital converter (TDC) fabricated on the same die (0.18 μm CMOS process). The cumulative jitter of the on-chip reference-locked ring oscillator was less than 30 ps (sigma value) over a time range of 70 ns, which made it possible to use this oscillator as the heart of a TDC when aiming at centimetre-level precision (1 cm = 67 ps) in laser ranging.     

BMC via on-the-fly determinization

This paper develops novel bounded model checking (BMC) techniques for asynchronous parallel systems. The aim is to increase the efficiency of BMC by exploiting the inherent concurrency in such systems. This added efficiency is gained by covering more reachable states within a given bound using two techniques. Firstly, a nonstandard execution model, step executions, where multiple actions can take place simultaneously is applied. Secondly, the number of executions the system can have is reduced by modeling the execution of the system components as if they were determinized. This determinization technique also enables the removal of the internal transitions of the components. Step executions can be further restricted to a subclass called process executions without losing any reachable states.The paper presents a translation scheme for BMC of reachability properties. The translation is from an asynchronous system where the components are modeled as labeled transition systems (LTSs) to a propositional formula. The models of the formula correspond to the step executions of the original system where each component is replaced with its determinized counterpart. The formula for step executions can be easily extended in such a way that its models correspond to the process executions of the system. The translation scheme has been implemented and some experimental comparisons performed. The results show that the bound needed to detect a violation of a reachability property is, for step and process executions, in most cases lower than in interleaving executions and that the running time of the model checker using process executions is smaller than of that using steps. Moreover, the performance compares favorably to a state-of-the-art interleaving BMC implementation in the NuSMV system.     

Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics

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.     

Flexible view recognition for indoor navigation based on Gabor filters and support vector machines

Real-world scenes are hard to segment into (relevant) objects and (irrelevant) background. In this paper, we argue for view-based vision, which does not use segmentation, and demonstrate a practical approach for recognizing textured objects and scenes in office environments.

A small set of Gabor filters is used to preprocess texture combinations from input images. The impulse responses of the filters are transformed into feature vectors that are fed to support vector machines. Pairwise feature comparisons decide the classification of views.

We validate the approach on a robot platform using three different types of target objects and indoor scenes: people, doorways, and written signs. The general-purpose system can run in real time, and that recognition accuracies of practical utility are obtained.