Paula Litkey

Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture

Imaging using lightweight, unmanned airborne vehicles (UAVs) is one of the most rapidly developing fields in remote sensing technology. The new, tunable, Fabry-Perot interferometer-based (FPI) spectral camera, which weighs less than 700 g, makes it possible to collect spectrometric image blocks with stereoscopic overlaps using light-weight UAV platforms. This new technology is highly relevant, because it opens up new possibilities for measuring and monitoring the environment, which is becoming increasingly important for many environmental challenges. Our objectives were to investigate the processing and use of this new type of image data in precision agriculture. We developed the entire processing chain from raw images up to georeferenced reflectance images, digital surface models and biomass estimates. The processing integrates photogrammetric and quantitative remote sensing approaches. We carried out an empirical assessment using FPI spectral imagery collected at an agricultural wheat test site in the summer of 2012. Poor weather conditions during the campaign complicated the data processing, but this is one of the challenges that are faced in operational applications. The

Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning

The demand for detailed ground reference data in quantitative forest inventories is growing rapidly, e.g., to improve the calibration of the developed models of airborne-laser-scanning-based inventories. The application of terrestrial laser scanning (TLS) in the forest has shown great potential for improving the accuracy and efficiency of field data collection. This paper presents a fully automatic stem-mapping algorithm using single-scan TLS data for collecting individual tree information from forest plots. In this method, the stem points are identified by the spatial distribution properties of the laser points, the stem model is built up of a series of cylinders, and the location of the stem is estimated by the model. The experiment was performed on nine plots with 10-m radius. The stem-location maps measured in the field by traditional methods were used as the ground truth. The overall stem-mapping accuracy was 73%. The result shows that, in a relatively dense managed forest, the majority of stems can be located by the automatic algorithm. The proposed method is a general solution for stem locating where particular plot knowledge and data format are not required.

Radiometric Calibration of LIDAR Intensity With Commercially Available Reference Targets

We present a new approach for radiometric calibration of light detection and ranging (LIDAR) intensity data and demonstrate an application of this method to natural targets. The method is based on 1) using commercially available sand and gravel as reference targets and 2) the calibration of these reference targets in the laboratory conditions to know their backscatter properties. We have investigated the target properties crucial for accurate and consistent reflectance calibration and present a set of ideal targets easily available for calibration purposes. The first results from LIDAR-based brightness measurement of grass and sand show that the gravel-based calibration approach works in practice, is cost effective, and produces statistically meaningful results: Comparison of results from two separate airborne laser scanning campaigns shows that the relative calibration produces repeatable reflectance values.

Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level

Low-cost, miniaturized hyperspectral imaging technology is becoming available for small unmanned aerial vehicle (UAV) platforms. This technology can be efficient in carrying out small-area inspections of anomalous reflectance characteristics of trees at a very high level of detail. Increased frequency and intensity of insect induced forest disturbance has established a new demand for effective methods suitable in mapping and monitoring tasks. In this investigation, a novel miniaturized hyperspectral frame imaging sensor operating in the wavelength range of 500–900 nm was used to identify mature Norway spruce (Picea abies L. Karst.) trees suffering from infestation, representing a different outbreak phase, by the European spruce bark beetle (Ips typographus L.). We developed a new processing method for analyzing spectral characteristic for high spatial resolution photogrammetric and hyperspectral images in forested environments, as well as for identifying individual anomalous trees. The dense point clouds, measured using image matching, enabled detection of single trees with an accuracy of 74.7%. We classified the trees into classes of healthy, infested and dead, and the results were promising. The best results for the overall accuracy were 76% (Cohen’s kappa 0.60), when using three color classes (healthy, infested, dead). For two color classes (healthy, dead), the best overall accuracy was 90% (kappa 0.80). The survey methodology based on high-resolution hyperspectral imaging will be of a high practical value for forest health management, indicating a status of bark beetle outbreak in time.

Effect of incidence angle on laser scanner intensity and surface data

We present a comprehensive experimental set of data on the dependence of the laser intensity on the angle of incidence to the target surface. The measurements have been performed in the laboratory for samples with a Nd:YAG laser and terrestrial laser scanner. The brightness scale data were also compared with data acquired by airborne laser scanning (ALS). The incidence angle effect is evident for all the targets. The effect is significant for incidence angles >20 degrees, and stronger for bright targets. However, effects due to some of the other surface properties, such as roughness, were also detected. We also found a set of gravel samples for which the incidence angle effect was minor even up to 40 degrees . The data provide an important reference for the interpretation and applications, e.g., full-waveform data processing of a laser scanner and ALS intensity calibration.

Brightness Measurements and Calibration With Airborne and Terrestrial Laser Scanners

Brightness measurement with an airborne or terrestrial laser scanner is a new concept since the intensity information recorded by the laser scanner detectors has, thus far, not been used or implemented in surface brightness studies. This is partly due to the calibration problems and the lack of information on the behavior of laser light in the observation geometry where laser scanners operate. In addition, the 3-D position information has, thus far, been sufficient for surface modeling. We present a new type of empirical calibration scheme for laser scanner intensity developed with a terrestrial laser scanner in laboratory and field conditions using brightness targets and a calibrated reference panel. We compare the results with those obtained from airborne laser scanner flight campaigns using the same set of brightness targets. It turns out that the relative calibration of laser scanner intensity is possible using a calibrated grayscale but requires background information of the targets and the conditions in which the measurements are carried out. We also discuss the feasibility and uses of a laser-scanner-based intensity measurement in general.

Tree Classification with Fused Mobile Laser Scanning and Hyperspectral Data

Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin.

Individual Tree Species Classification by Illuminated—Shaded Area Separation

A new method, called Illumination Dependent Colour Channels (IDCC), is presented to improve individual tree species classification. The method is based on tree crown division into illuminated and shaded parts on a digital aerial image. Colour values of both sides of the tree crown are then used in species classification. Tree crown division is achieved by comparing the projected location of an aerial image pixel with its neighbours on a Canopy Height Model (CHM), which is calculated from a synchronized LIDAR point cloud. The sun position together with the mapping aircraft position are also utilised in illumination status detection. The new method was tested on a dataset of 295 trees and the classification results were compared with ones measured with two other feature extraction methods. The results of the developed method gave a clear improvement in overall tree species classification accuracy.

Use of Naturally Available Reference Targets to Calibrate Airborne Laser Scanning Intensity Data

We have studied the possibility of calibrating airborne laser scanning (ALS) intensity data, using land targets typically available in urban areas. For this purpose, a test area around Espoonlahti Harbor, Espoo, Finland, for which a long time series of ALS campaigns is available, was selected. Different target samples (beach sand, concrete, asphalt, different types of gravel) were collected and measured in the laboratory. Using tarps, which have certain backscattering properties, the natural samples were calibrated and studied, taking into account the atmospheric effect, incidence angle and flying height. Using data from different flights and altitudes, a time series for the natural samples was generated. Studying the stability of the samples, we could obtain information on the most ideal types of natural targets for ALS radiometric calibration. Using the selected natural samples as reference, the ALS points of typical land targets were calibrated again and examined. Results showed the need for more accurate ground reference data, before using natural samples in ALS intensity data calibration. Also, the NIR camera-based field system was used for collecting ground reference data. This system proved to be a good means for collecting in situ reference data, especially for targets with inhomogeneous surface reflection properties.

Map updating and change detection using vehicle-based laser scanning

The vehicle-based laser scanning (VLS, also known as mobile mapping) is a new technology, which is currently under development for creating 3D models of the surrounding environment. VLS is based on the integration of GPS, IMU, laser scanner and preferably digital cameras mounted on top of a moving platform, i.e. a car in most applications. VLS is a logical development after the first operative Airborne Laser Scanner (ALS) in 1994 and Terrestrial Laser Scanners mounted on top of a tripod. The data/image processing of VLS are mainly based on modifications of the methods created for ALS and TLS taking into account the differences of VLS compared to ALS and TLS. Compared to ALS, the geometry of VLS scanning is different and the pulse density varies as function of range. Two main differences between stationary TLS and constantly moving VLS are the evenness of the data and the perspective. In VLS, the point cloud is evenly distributed along the driving direction, and the viewing direction to the target remains constant. In the stop-and-go mode, the data characteristics of the VLS and conventional TLS are similar. A reasonable amount of research has been done to develop methods for single-time VLS processing, but there have not been any attempts to our knowledge of multitemporal processing of VLS data. In this paper, the high potential of change detection based on multitemporal VLS point clouds was demonstrated. Example cases include the change detection of city models and defoliation of city trees. A method to map biomass and biomass change of (city) trees was developed.