Juha Suomalainen

Full waveform hyperspectral LiDAR for terrestrial laser scanning

We present the design of a full waveform hyperspectral light detection and ranging (LiDAR) and the first demonstrations of its applications in remote sensing. The novel instrument produces a 3D point cloud with spectral backscattered reflectance data. This concept has a significant impact on remote sensing and other fields where target 3D detection and identification is crucial, such as civil engineering, cultural heritage, material processing, or geomorphological studies. As both the geometry and spectral information on the target are available from a single measurement, this technology will extend the scope of imaging spectroscopy into spectral 3D sensing. To demonstrate the potential of the instrument in the remote sensing of vegetation, 3D point clouds with backscattered reflectance and spectral indices are presented for a specimen of Norway spruce.

Study of surface brightness from backscattered laser intensity: calibration of laser data

Systematic laboratory measurements of laser backscatter intensity are presented for brightness calibration targets, and a calibration scheme for airborne laser scanner intensity data is proposed. Thus far, the use of these data has been partly hampered by the variability of the intensity with time, and no test fields have been available for airborne reflectance calibration. Portable brightness targets (tarps), with nominal reflectances from 5% to 70%, were manufactured, and, based on these measurements, found suitable for lidar reflectance standards. Furthermore, the variability of the recorded intensity from the tarps as a function of incidence angle was low. The measurements also provide new information on the surface albedo dependence of backscattering effects: as the surface brightness increases from 5% to 70%, the hotspot brightness peak amplitudes increase by 20% to 30%, and their apparent widths reduce to a half, which implies that hotspots could be used as an albedo discriminator.

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.

A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles

During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of rather large UAVs. In this article we present a lightweight hyperspectral mapping system (HYMSY) for rotor-based UAVs, the novel processing chain for the system, and its potential for agricultural mapping and monitoring applications. The HYMSY consists of a custom-made pushbroom spectrometer (400–950 nm, 9 nm FWHM, 25 lines/s, 328 px/line), a photogrammetric camera, and a miniature GPS-Inertial Navigation System. The weight of HYMSY in ready-to-fly configuration is only 2.0 kg and it has been constructed mostly from off-the-shelf components. The processing chain uses a photogrammetric algorithm to produce a Digital Surface Model (DSM) and provides high accuracy orientation of the system over the DSM. The pushbroom data is georectified by projecting it onto the DSM with the support of photogrammetric orientations and the GPS-INS data. Since an up-to-date DSM is produced internally, no external data are required and the processing chain is capable to georectify pushbroom data fully automatically. The system has been adopted for several experimental flights related to agricultural and habitat monitoring applications. For a typical flight, an area of 2–10 ha was mapped, producing a RGB orthomosaic at 1–5 cm resolution, a DSM at 5–10 cm resolution, and a hyperspectral datacube at 10–50 cm resolution.

Two-channel Hyperspectral LiDAR with a Supercontinuum Laser Source

Recent advances in nonlinear fiber optics and compact pulsed lasers have resulted in creation of broadband directional light sources. These supercontinuum laser sources produce directional broadband light using cascaded nonlinear optical interactions in an optical fibre framework. This system is used to simultaneously measure distance and reflectance to demonstrate a technique capable of distinguishing between a vegetation target and inorganic material using the Normalized Difference Vegetation Index (NDVI) parameters, while the range can be obtained from the waveform of the echoes. A two-channel, spectral range-finding system based on a supercontinuum laser source was used to determine its potential application of distinguishing the NDVI for Norway spruce, a coniferous tree, and its three-dimensional parameters at 600 nm and 800 nm. A prototype system was built using commercial components.

Polarised multiangular reflectance measurements using the finnish geodetic institute field goniospectrometer.

The design, operation, and properties of the Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO) are presented. FIGIFIGO is a portable instrument for the measurement of surface Bidirectional Reflectance Factor (BRF) for samples with diameters of 10 – 50 cm. A set of polarising optics enable the measurement of linearly polarised BRF over the full solar spectrum (350 – 2,500 nm). FIGIFIGO is designed mainly for field operation using sunlight, but operation in a laboratory environment is also possible. The acquired BRF have an accuracy of 1 – 5% depending on wavelength, sample properties, and measurement conditions. The angles are registered at accuracies better than 2°. During 2004 – 2008, FIGIFIGO has been used in the measurement of over 150 samples, all around northern Europe. The samples concentrate mostly on boreal forest understorey, snow, urban surfaces, and reflectance calibration surfaces.

A Permanent Test Field for Digital Photogrammetric Systems

Comprehensive field-testing and calibration of digital photogrammetric systems are essential to characterize their performance, to improve them, and to be able to use them for optimal results. The radiometric, spectral, spatial, and geometric properties of digital systems require calibration and testing. The Finnish Geodetic Institute has maintained a permanent test field for geometric, radiometric, and spatial resolution calibration and testing of high-resolution airborne and satellite imaging systems in Sjokulla since 1994. The special features of this test field are permanent resolution and reflectance targets made of gravel. The Sjokulla test field with some supplementary targets is a prototype for a future photogrammetric field calibration site. This article describes the Sjokulla test field and its construction and spectral properties. It goes on to discuss targets and methods for system testing and calibration, and highlights the calibration and testing of digital photogrammetric systems.

Classification of Spruce and Pine Trees Using Active Hyperspectral LiDAR

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.

Acquisition of Bidirectional Reflectance Factor Dataset Using a Micro Unmanned Aerial Vehicle and a Consumer Camera

This paper describes a method for retrieving the bidirectional reflectance factor (BRF) of land-surface areas, using a small consumer camera on board an unmanned aerial vehicle (UAV) and introducing an advanced calibration routine. Images with varying view directions were taken of snow cover using the UAV. The vignetting effect was corrected from the images, and reflectance factor images were calculated using a calibrated white target as a reference. After spatial registration of the images using a corresponding point method, the target surface was divided into a grid, and a BRF was generated for each grid element. Lastly a model was fitted to the BRF dataset for data interpretation. The retrieved BRF were compared to parallel ground measurements. Comparison showed similar BRF and reflectance factor characteristics, which suggests that accurate measurements can be taken with cheap consumer cameras, if enough attention is paid to calibration of the images.

Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications

Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral-temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is compared to two earlier published methods for constructing STRS: a direct interpolation of the original data and a direct interpolation along the temporal dimension after imputation along the spectral dimension. The STRS derived through all three methods are compared to field measured reflectance spectra, leaf area index (LAI), and canopy chlorophyll of potato plants. The results indicate that the proposed Bayesian approach has the highest correlation (r = 0.953) and lowest RMSE (0.032) to field spectral reflectance measurements. Although the optimized soil-adjusted vegetation index (OSAVI) obtained from all methods have similar correlations to field data, the modified chlorophyll absorption in reflectance index (MCARI) obtained from the Bayesian STRS outperform the other two methods. A correlation of 0.83 with LAI and 0.77 with canopy chlorophyll measurements are obtained, compared to correlations of 0.27 and 0.09, respectively, for the directly interpolated STRS.