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Decreased leaf moisture content, typically measured as equivalent water thickness (EWT), is an early signal of tree stress caused by drought, disease, or pest insects. We investigated the use of two terrestrial laser scanners (TLSs) employing different wavelengths for improving the understanding how EWT can be retrieved in a laboratory setting. Two wavelengths were examined for normalizing the effects of varying leaf structure and geometry on the measured intensity. The relationship between laser intensity features, using red (690 nm) and shortwave infrared (1550 nm) wavelengths, and the EWT of individual leaves or groups of needles were determined with and without intensity corrections. To account for wrinkles and curvatures of the leaves and needles, a model describing the relationship between incidence angle and backscattered intensity was applied. Additionally, a reflectance model describing both diffuse and specular reflectance was employed to remove the fraction of specular reflectance from backscattered intensity. A strong correlation (R² = 0.93, RMSE = 0.004 g/cm²) was found between a normalized ratio of the two wavelengths and the measured EWT of samples. The applied intensity correction methods did not significantly improve the results of the study. The backscattered intensity responded to changes in EWT but more investigations are needed to test the suitability of TLSs to retrieve EWT in a forest environment. [ABSTRACT FROM AUTHOR]

Global warming is posing a threat to the health and condition of forests as the amount and length of biotic and abiotic disturbances increase. Most methods for detecting disturbances and measuring forest health are based on multi- and hyperspectral imaging. We conducted a test with spruce and pine trees using a hyperspectral Lidar instrument in a laboratory to determine the capability of combined range and reflectance measurements to investigate forest health. A simple drought treatment was conducted by leaving the harvested trees outdoors without a water supply for 12 days. The results showed statistically significant variation in reflectance after the drought treatment for both species. However, the changes differed between the species, indicating that drought-induced alterations in spectral characteristics may be species-dependent. Based on our results, hyperspectral Lidar has the potential to detect drought in spruce and pine trees. [ABSTRACT FROM AUTHOR]

Laser scanning systems that simultaneously measure multiple wavelength reflectances integrate the strengths of active spectral imaging and accurate range measuring. The Finnish Geodetic Institute hyperspectral lidar system is one of these. The system was tested in an outdoor experiment for detecting man-made targets from natural ones based on their spectral response. The targets were three camouflage nets with different structures and coloring. Their spectral responses were compared against those of a Silver birch (Betula pendula), Scots pine shoots (Pinus sylvestris L.), and a goat willow (Salix caprea). Responses from an aggregate clay block and a plastic chair were used as man-made comparison targets. The novelty component of the experiment was the 26-h-long measurement that covered both day and night times. The targets were classified with 80.9% overall accuracy in a dataset collected during dark. Reflectances of four wavelengths located around the 700 nm, the so-called red edge, were used as classification features. The addition of spatial aggregation within a 5-cm neighborhood improved the accuracy to 92.3%. Similar results were obtained using a set of four vegetation indices (78.9% and 91.0%, respectively). The temporal variation of vegetation classes was detected to differ from those in man-made classes. [ABSTRACT FROM AUTHOR]

Research activities combining lidar and radar remote sensing have increased in recent years. The main focus in combining lidar-radar forest remote sensing has been on the retrieval of the aboveground biomass (AGB), which is a primary variable related to carbon cycle in land ecosystems, and has therefore been identified as an essential climate variable. In this review, we summarize the studies combining lidar and radar in estimating forest AGB. We discuss the complementary use of lidar and radar according to the relevance of the added value. The most promising prospects for combining lidar and radar data are in the use of lidar-derived ground elevations for improving large-area biomass estimates from radar, and in upscaling of lidar-based AGB data across large areas covered by spaceborne radar missions. [ABSTRACT FROM AUTHOR]

Our objective is to evaluate the boreal forest aboveground biomass (AGB) and stem volume (VOL) imputation accuracy when scanning LiDAR or TerraSAR-X stereo radargrammetry-derived point-height metrics are used as predictors in the nearest neighbor imputation approach. Treewise measured field plots are used as reference data in the AGB and VOL imputations and accuracy evaluations. The digital terrain model (DTM) that is produced by the National Land Survey of Finland is used to obtain aboveground elevation values for the TerraSAR-X stereo radargrammetry. The DTM that is used (i.e., grid size 2 m) is derived from LiDAR surveys with an average point density of ~ 0.5 points/m2. The respective DTM and point data are used in LiDAR imputations of AGB and VOL. The relative root mean square errors (RMSEs) for AGB and VOL are 29.9% (41.3 t/ha) and 30.2% (78.1 m3/ha) when using TerraSAR-X stereo radargrammetry metrics. The respective LiDAR estimation accuracy values are 21.9% (32.3 t/ha) and 24.8% (64.2 m3/ha). LiDAR imputations are clearly more accurate than imputations that are made by using TerraSAR-X stereo radargrammetry metrics. However, the difference between imputation accuracies of LiDAR- and TerraSAR X-based features are smaller than in any previous study in which LiDAR and different types of synthetic aperture radar materials are compared in the variable predictions regarding forests. We conclude that TerraSAR X stereo radargrammetry is a promising remote-sensing technique for large forest-area AGB and VOL mapping and monitoring when an accurate LiDAR-based DTM is available. [ABSTRACT FROM AUTHOR]

Most forest inventories based on the use of remote-sensing data produce the required species-specific information by fusing data from different sources (e.g., Light Detection And Ranging (LiDAR) and spectral data). We tested an active hyperspectral LiDAR instrument in a laboratory measurement of spruce and pine trees to find out whether these species could be separated by means of combined range and reflectance measurements. An analysis focused on those pulses that had penetrated through the foliage improved the classification accuracies of the species with otherwise highly similar reflectance properties. Based on a careful selection of the classification features, 18 spruce and pine trees could be classified with accuracies of 78%–97% using independent training and validation data acquired by separate scans. The results denote the potential of using active hyperspectral measurements for species classification. [ABSTRACT FROM PUBLISHER]

Radiometric calibration of airborne laser scanning (ALS) intensity data aims at retrieving a value related to the target scattering properties, which is independent on the instrument or flight parameters. The aim of a calibration procedure is also to be able to compare results from different flights and instruments, but practical applications are sparsely available, and the performance of calibration methods for this purpose needs to be further assessed. We have studied the radiometric calibration with data from three separate flights and two different instruments using external calibration targets. We find that the intensity data from different flights and instruments can be compared to each other only after a radiometric calibration process using separate calibration targets carefully selected for each flight. The calibration is also necessary for target classification purposes, such as separating vegetation from sand using intensity data from different flights. The classification results are meaningful only for calibrated intensity data. [ABSTRACT FROM AUTHOR]

Abstract: A new terrestrial laser system was tested for tree species classification. A dataset consisting of shape parameters of three boreal tree species was collected with Light Detection and Ranging (LIDAR) and integrated with an actively measured reflectance hyperspectra. Tree species were classified using parameters derived from reflectance spectra and point cloud shape distribution. Classification performance was tested with individual, paired, and mixed combinations of both reflectance and shape parameters. The best classification results were obtained with combined datasets consisting of two reflectance and two shape parameters. Of all tested classification parameter combinations, 67.5% were able to classify all trees with over 90% accuracy. The best reflectance spectrum bands for the examined species were located around 550 and 700nm. The best shape parameters described the upper midsection or the tops of the trees. This study was a successful step in developing classification algorithms for integrated LIDAR and hyperspectral data. [ABSTRACT FROM AUTHOR]

We propose an empirical method for nondestructive estimation of chlorophyll in tree canopies. The first prototype of a full waveform hyperspectral LiDAR instrument has been developed by the Finnish Geodetic Institute (FGI). The instrument efficiently combines the benefits of passive and active remote sensing sensors. It is able to produce 3D point clouds with spectral information included for every point, which offers great potential in the field of environmental remote sensing. The investigation was conducted by using chlorophyll sensitive vegetation indices applied to hyperspectral LiDAR data and testing their performance in chlorophyll estimation. The amount of chlorophyll in vegetation is an important indicator of photosynthetic capacity and stress, and thus important for monitoring of forest condition and carbon sequestration on Earth. Performance of chlorophyll estimation was evaluated for 27 published vegetation indices applied to waveform LiDAR collected from ten Scots pine shoots. Reference data were collected by laboratory chlorophyll concentration analysis. The performance of the indices in chlorophyll estimation was determined by linear regression and leave-one-out cross-validation. The chlorophyll estimates derived from hyperspectral LiDAR linearly correlate with the laboratory analyzed chlorophyll concentrations, and they are able to represent a range of chlorophyll concentrations in Scots pine shoots ( R 2 = 0.88, RMSE = 0.10 mg/g). Furthermore, they are insensitive to measurement scale as nearly the same values of vegetation indices were measured in natural setting while scanning the whole canopy and from clipped shoots re-measured with hyperspectral LiDAR in laboratory. The results indicate that the hyperspectral LiDAR instrument has the potential to estimate vegetation biochemical parameters such as the chlorophyll concentration. The instrument holds much potential in various environmental applications and provides a significant improvement over single wavelength LiDAR or passive optical systems for environmental remote sensing. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a measurement system for the acquisition of a virtual hyperspectral LiDAR dataset is presented. As commercial hyperspectral LiDARs are not yet available, the system provides a novel type of data for the testing and developing of future hyperspectral LiDAR algorithms. The measurement system consists of two parts: first, backscattered reflectance spectra are collected using a spectrometer and a cutting-edge technology, white-light supercontinuum laser source; second, a commercial monochromatic LiDAR system is used for ranging. A virtual hyperspectral LiDAR dataset is produced by data fusion. Such a dataset was collected on a Norway spruce (Picea abies) sample. The performance of classification was tested using an experimental hyperspectral algorithm based on a novel combination of the Spectral Correlation Mapper and a region growing algorithm. The classifier was able to automatically distinguish between needles, branches and background, in other words, perform a difficult task using only traditional TLS data. [Copyright &y& Elsevier]