Accurate and robust localization is essential for autonomous driving in complex, dynamic environments and across various motion scenarios. Due to signal blockage, Global Navigation Satellite Systems (GNSS) positioning is not reliable in urban complex environments, and inertial navigation systems (INS) is widely integrated using the Kalman filtering for continuous and robust vehicle localization. On the one hand, INS suffers from accumulated errors and it poses a challenge for precise vehicle localization in complex environments, where GNSS has degraded performance. On the other hand, the Kalman filtering takes certain empirical assumptions regarding the probability models of process noise and measurements, which may be not matched with the real-world conditions. To address these issues, we propose a data and model jointly driven neural-Kalman solution for vehicle inertial localization to enhance positioning accuracy and robustness in various motion states under GNSS-denied environments. The proposed solution exploits the data-driven neural networks to real-time predict the covariance of process noise and three-dimensional (3D) vehicle velocity. Within the neural networks, the proposed loss function dynamically adjusts the error penalty weight and establish a connection between the uncertainty and estimation to improve the localization robustness. Finally, the predicted covariance of process noise and 3D velocity are incorporated with the adaptive Kalman filtering to improve the vehicle localization accuracy. We evaluate the proposed method using two datasets, and the results indicate that the average RMSE of 3D velocity regression is reduced by 56.33% and 50.08%, respectively, compared to two deep learning methods. In the positioning accuracy evaluation experiment, the proposed neural Kalman filter method reduces the average ATE and RTE by 21.10% and 23.96% compared with the state-of-the-art baseline methods in two datasets.

Abstract Slippery road weather conditions are prevalent in many regions and cause a regular risk for traffic. Still, there has been less research on how autonomous vehicles could detect slippery driving conditions on the road to drive safely. In this work, we propose a method to predict a dense grip map from the area in front of the car, based on postprocessed multimodal sensor data. We trained a convolutional neural network to predict pixelwise grip values from fused RGB camera, thermal camera, and LiDAR reflectance images, based on weakly supervised ground truth from an optical road weather sensor. The experiments show that it is possible to predict dense grip values with good accuracy from the used data modalities as the produced grip map follows both ground truth measurements and local weather conditions, such as snowy areas on the road. The model using only the RGB camera or LiDAR reflectance modality provided good baseline results for grip prediction accuracy while using models fusing the RGB camera, thermal camera, and LiDAR modalities improved the grip predictions significantly.

Ongoing advancements in Earth observation technologies have led to an increasing demand for fine-grained 3D maps, particularly in urban areas rich of diverse objects. Unlike traditional monochromatic LiDAR (ML), modern multispectral LiDAR (MSL) systems simultaneously capture high resolution geometric and spectral data, especially beneficial for accurate 3D urban mapping. At the same time, deep learning (DL) models have shown promising results in urban mapping, despite their need for large amount of labeled data. This study presents a new method based on zero-shot and K-means unsupervised learning to automatically label 3D MSL data. The benefits of MSL's spatial-spectral information and autoannotated training data have been explored by using KPConv point-wise DL model. Achieved results indicate that the proposed auto-annotation pipeline, with an overall accuracy (OA) of ca 85% and a mean Intersection over Union (mIoU) of ca 70%, could ease laborious annotation task and facilitate the development of new unsupervised point-based semantic segmentation algorithms for 3D land cover classification.

Pedestrian dead reckoning (PDR) and global navigation satellite system are two popular solutions for pedestrian navigation with a smartphone. pedestrian dead reckoning (PDR) estimates the user’s position by analyzing their walking gaits, including step length and heading angle. However, PDR position errors can accumulate over time due to measurement noise. In contrast, GNSS generates position information by processing radio signals. However, these signals can be affected by blockage and interference. GNSS and PDR are often integrated using a Kalman filter (KF) to provide a more reliable solution. While current integration methods rely on position and velocity measurements, pseudo-range measurements for PDR and GNSS integration still need to be explored. To improve the accuracy of pedestrian position estimation in urban areas, we propose a walking-gaits-aided smartphone GNSS approach. This approach involves employing a factor graph optimization (FGO)-based GNSS/ PDR tight integration method. The FGO- GNSS/ PDR tight integration considers the pseudo-range measurements from each satellite, pedestrian position, and step length to optimize the position estimation. We introduce a fuzzy adaptive FGO (A-FGO) to enhance the accuracy further to suppress pseudo-range outliers. We conducted two experiments using a Samsung Galaxy A40 and Huawei Mate 40 Pro smartphones to evaluate the accuracy of the proposed methods. Our experimental results demonstrate that the proposed methods effectively improve the PDR/ GNSS position accuracy.

Hyperspectral LIDAR (HSL) is an innovative active remote sensing technology that allows for the simultaneous collection of spectral and spatial information. In this study, we primarily focus on the radiation correction method of the incident angle and distance effects for the backscatter intensity of HSL. We have developed a comprehensive radiometric correction model that addresses these effects. Additionally, we have applied the correction model to point cloud classification using the random forest method. Comparing the accuracy of point cloud classification before and after correction, we observed a 9.6% improvement in overall accuracy (OA) and a 10.8% improvement in the kappa coefficient. These results indicate that the radiometric correction model significantly enhances the classification accuracy.

Convolutional neural networks (CNN) are capable of extracting features with high accuracy, which is dominant in visual-based classification. Previous researches demonstrate that CNN can extract essential features of the target in the plant feature extraction and classification. Hyperspectral LIDAR (HSL) is a novel active remote sensing technology that can simultaneously collect spectral and spatial information. This paper proposed a novel classification method named VI-CNN for hyperspectral LiDAR, which combines the spectral features with the vegetable index(VI). As far as we know, we are the first to apply CNN to HSL data classification. The VI -CNN is divided into two parts. Firstly, spectral CNN focuses on intra-spectral correlations; secondly, the vegetation indices supplement the biological parameters. The evaluation shows that the concatenation has stronger identification and robustness than standalone methods. The experimental results demonstrate that the VI-CNN significantly improves the classification accuracy against other traditional machine-learning methods.

High-Definition (HD) maps are needed for robust navigation of autonomous vehicles, limited by the on-board storage capacity. To solve this, we propose a novel framework, Environment-Aware Normal Distributions Transform (EA-NDT), that significantly improves compression of standard NDT map representation. The compressed representation of EA-NDT is based on semantic-aided clustering of point clouds resulting in more optimal cells compared to grid cells of standard NDT. To evaluate EA-NDT, we present an open-source implementation that extracts planar and cylindrical primitive features from a point cloud and further divides them into smaller cells to represent the data as an EA-NDT HD map. We collected an open suburban environment dataset and evaluated EA-NDT HD map representation against the standard NDT representation. Compared to the standard NDT, EA-NDT achieved consistently at least 1.5× higher map compression while maintaining the same descriptive capability. Moreover, we showed that EA-NDT is capable of producing maps with significantly higher descriptivity score when using the same number of cells than the standard NDT.

Position and location constitute critical context for Internet of Things (IoT) devices. Global navigation satellite systems (GNSSs) are the primary apparatus providing precise position and location information for IoT devices in outdoor environments. However, in dense urban areas, non-line-of-sight (NLOS) signals will induce large errors in GNSS pseudorange measurements due to the additional signal transmission paths. The vector tracking (VT) technique utilizing a Kalman filter (KF) to estimate navigation solutions has been investigated in NLOS detection, and its advantages have been demonstrated. However, the estimation of NLOS-induced bias has not been thoroughly investigated in the VT framework. In this article, we focus on the estimation and correction of NLOS-induced errors within the VT framework. First, graph optimization (GO) instead of a KF is incorporated with VT to optimize the estimation of navigation solutions. The NLOS-induced bias is then added to the VT state vector as the variable for real-time estimation. Compared with the KF-VT method, in GO-VT, the state transformation and the measurement model are regarded as constraints to optimize the state vector estimation. Hence, the GO-VT framework is more flexible than the KF approach in dealing with state vector changes. An iterative process is conducted to solve for the optimization results; a multiple-correlator scheme is employed in GO-VT to provide the initial values of the NLOS-induced bias. Three collected GPS L1 data sets (static and dynamic) are used to evaluate the proposed method. The statistical results support the conclusion that GO-VT with state augmentation achieves superior position estimation in urban areas.

Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their high spectral resolution, spatial resolution, and signal-to-noise ratio (SNR). However, the tradeoffs between the three core parameters of a hyperspectral imager (SNR, spatial resolution, and spectral resolution) should be considered for designing an efficient imaging system. Only a few reported studies on the analysis of the impact of SNR on identification accuracy are available. Further, the tradeoffs and mutual interactions among these parameters are rarely considered. In this empirical study, our aim was to understand the relationship among the core parameters and their effects on crop identification accuracy by analyzing the tradeoffs and mutual interactions among these parameters. We analyzed the hyperspectral images of a typical plain agricultural area in Xiongan, China, acquired by the newly developed sensor airborne multimodular imaging spectrometer (AMMIS). The fundamental images were transformed to form datasets with different ranges of spectral resolution, spatial resolution, and SNR using data reconstruction methods. We adopted the classification and regression tree (CART), random forest (RF), and k-nearest neighbor (kNN) classifiers, and observed the overall accuracy (OA) across the degraded hyperspectral datasets. The experimental results indicated that the OA decreased with a decreasing SNR. As the spectral resolution became coarser, the OA first increased, plateaued, and then decreased. However, the OA increased with decreasing spatial resolution. This study was performed with the goal of bridging the knowledge gap between the back-end hyperspectral sensor designing and its front-end applications.

In this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles and their attached components, and extract the instance information of the components with different features. Most importantly, we classify the components into different categories by combining a classifier and a probabilistic graphic model named DenseCRF, which is the major contribution of this paper. For the classification of the components using DenseCRF, the unary potentials and the pairwise potentials are first obtained. The unary potentials are obtained from the classifier which takes the instance information of components as the input. The pairwise potentials are calculated considering contextual relations between components. By utilising DenseCRF, the contextual consistency of components is preserved, and the performance is significantly improved compared to our previous work. We collect three datasets to test our framework, and compare the classification performances of six different classifiers with and without DenseCRF. The combination of random forest with DenseCRF outperforms the other methods and achieves high overall accuracies of 83.7%, 96.4% and 95.3% in these three datasets. Experimental results demonstrate that our framework reliably assigns both semantic information and instance information for mobile laser scanning point clouds of road furniture.

Abstract Interest point detection plays an important role in many computer vision applications. This work is motivated by the light detection and ranging odometry task in autonomous driving. Existing methods are not capable of detecting enough interest points in unstructured scenarios where there are little constructions or trees around, and correspondingly light detection and ranging odometry will fail to continuous localisation. An interest point detector is proposed for detecting interest points from multi‐beam light detection and ranging point cloud using unsupervised convolutional neural network. The point cloud is projected into a two‐dimensional structured data according to the scanning geometry. Then the convolutional neural network filters trained in an unsupervised manner are used to generate a local feature map with the two‐dimensional structured data as input. Finally, interest points are obtained by extracting the grids that have significant differences with their neighbour grids. Based on an odometry benchmark, the experiments show that the proposed interest point detector can capture more local details, which contributes to more than 16% error decrease in point cloud registration in highway scenes.

Autonomous driving is challenging in adverse road and weather conditions in which there might not be lane lines, the road might be covered in snow and the visibility might be poor. We extend the previous work on end-to-end learning for autonomous steering to operate in these adverse real-life conditions with multimodal data. We collected 28 hours of driving data in several road and weather conditions and trained convolutional neural networks to predict the car steering wheel angle from front-facing color camera images and lidar range and reflectance data. We compared the CNN model performances based on the different modalities and our results show that the lidar modality improves the performances of different multimodal sensor-fusion models. We also performed on-road tests with different models and they support this observation.

Fusions of LiDARs (light detection and ranging) and cameras have been effectively and widely employed in the communities of autonomous vehicles, virtual reality and mobile mapping systems (MMS) for different purposes, such as localization, high definition map or simultaneous location and mapping. However, the extrinsic calibration between a camera and a 3D LiDAR is a fundamental prerequisite to guarantee its performance. Some previous methods are inaccurate, have calibration error that is several times the beam divergence, and often require special calibration objects, thereby limiting their ubiquitous use for calibration. To overcome these shortcomings, we propose a novel and high-accuracy method for the extrinsic calibration between a camera and a 3D LiDAR. Our approach relies on the infrared images from a camera with an infrared filter, and the 2D-3D corresponding points in a scene with the corners of a wall can be extracted to calculate the six extrinsic parameters. Experiments using the Velodyne VLP-16 sensor show that the method can achieve an extrinsic accuracy at the level of the beam divergence, which is fully analyzed and validated from two different aspects. Therefore, the calibration method in this paper is highly accurate, effective and does not require special complicated calibration objects; thus, it meets the requirements of practical applications.

During the mining operation, it is a critical task in coal mines to significantly improve the safety by precision coal mining sorting and rock classification from different layers. It implies that a technique for rapidly and accurately classifying coal/rock in-site needs to be investigated and established, which is of significance for improving the coal mining efficiency and safety. In this letter, a 91-channel hyperspectral LiDAR (HSL) using an acousto-optic tunable filter (AOTF) as the spectroscopic device is designed, which operates based on the wide-spectrum emission laser source with a 5-nm spectral resolution to tackle this issue. The spectra of four-type coal/rock specimens collected by HSL are used to classify with three multi-label classifiers: naive Bayes (NB), logistic regression (LR), and support vector machine (SVM). Furthermore, we discuss and explore whether Gaussian fitting (GF) method and calibration with the reference whiteboard (RB) can enhance the classification accuracy. The experimental results show that the GF technique not only improves the accuracy of range measurement but also optimizes the classification performance using the spectra collected by the HSL. In addition, calibration with RB can improve classification accuracy as well. In addition, we also discuss methods to improve the calibration-free classification accuracy preliminarily.

In this letter, a hyperspectral light detection and ranging (HSL) with 10-nm spectral resolution was designed and tested using a supercontinuum laser source. The major difference between the prototyped HSL and similar instruments was that a liquid crystal tunable filter (LCTF) was installed before the avalanche photodiode detector and utilized as a spectroscopic device. The design allowed continuous wavelength selection of the backscattered echoes in the time dimension. Moreover, for general accuracy evaluation of range measurement and spectral measurement, laboratory experiments for vegetation red edge detection were performed using the prototyped HSL to assess its feasibility on agriculture application. Yellow and green leaves from aloe and dracaena plants were measured by the LCTF-HSL for detecting the corresponding “red edge” position. Spectral profiles measured by an SVC-HR-1024 spectrometer which is designed by SVC company were used as a reference to evaluate the measurements of HSL. The comparison results showed that the red edge positions extracted from the two individual measurements were similar, thus indicating that the LCTF-based high-resolution HSL was effective for this application.

We explore a novel paradigm for light detection and ranging (LIDAR) point classification in mobile laser scanning (MLS). In contrast to the traditional scheme of performing classification for a 3-D point cloud after registration, our algorithm operates on the raw data stream classifying the points on-the-fly before registration. Hence, we call it preregistration classification (PRC). Specifically, this technique is based on spatial correlations, i.e., local range measurements supporting each other. The proposed method is general since exact scanner pose information is not required, nor is any radiometric calibration needed. Also, we show that the method can be applied in different environments by adjusting two control parameters, without the results being overly sensitive to this adjustment. As results, we present classification of points from an urban environment where noise, ground, buildings, and vegetation are distinguished from each other, and points from the forest where tree stems and ground are classified from the other points. As computations are efficient and done with a minimal cache, the proposed methods enable new on-chip deployable algorithmic solutions. Broader benefits from the spatial correlations and the computational efficiency of the PRC scheme are likely to be gained in several online and offline applications. These range from single robotic platform operations including simultaneous localization and mapping (SLAM) algorithms to wall-clock time savings in geoinformation industry. Finally, PRC is especially attractive for continuous-beam and solid-state LIDARs that are prone to output noisy data.

A fully polarimetric Airborne Wind Vector Scatterometer (AWVS) is developed to provide independent airborne wind vector measurements for validation of space-borne GNSS-R measurements. The scatterometer is designed to meet 1 m/s wind speed accuracy requirement. In this ad hoc and low-budget project, the instrument development exploited on some already existing subsystems. The development started from scientific system requirement definition and ended to two experiment flights for wind vector retrieval from three areas at the Gulf of Finland, the Baltic Sea. One of the flights was carried out with a simultaneous overpass of the TDS-l satellite, conducting the GNSS-R measurements. The results confirm the measurement capabilities of the GNSS-R technology and the desired 1 m/s wind speed accuracy of the developed scatterometer. Moreover, the fully polarimetric backscattering results support well other recent measurements and models of cross-polarized sea surface scattering.

Hyperspectral LiDAR (HSL) possesses the advantages of the LiDAR and the hyperspectral detection, and detects ranging and spectrum information synchronously, by one HSL system. The data fusion is also avoided. At present, the spectrum range of reported HSLs usually covers only 500 nm-1000 nm (from visual (VIS) to near infrared (NIR) band). However, there is requirement to extend the spectrum range to short wave infrared (SWIR) band, which often contains more useful spectral information. In this paper, a HSL covering the spectrum from VIS to SWIR is reported. In the HSL, the echoes are divided into two sections and are detected by the different optoelectronic devices, of which the spectral response ranges are respectively compatible to the corresponding echoes. The HSL detection experiment in the laboratory was carried out. The waveforms of the echoes were analyzed, and the spectra of different targets were measured by the HSL. The experiment results demonstrate the capability of the prototyped HSL that obtaining the ranging information and the spectrum information of the targets in VIS-SWIR bands synchronously.

Recently, a major effort has been made to develop methods or tools for rock characterization and mineral content mapping. Light detection and ranging (LiDAR) is an efficient active remote sensing technique for collecting geometry information about rock surfaces. However, traditional LiDAR sensors work with a single-wavelength laser source, and it is unfeasible to obtain spectral information using one LiDAR sensor. The combination of hyperspectral imaging and LiDAR techniques is an emerging method for acquiring spatial and spectral information simultaneously that allows remote mapping of high-resolution mineral content and distributions and identifies subtle chemical variations. Unfortunately, spatial and spectral data registration, which introduces additional complicated data processing, is an inevitable and essential issue for this method. In this letter, first, we investigate the feasibility of ore classification applications with hyperspectral LiDAR (HSL). HSL consists of 17 spectral channels covering the visible–shortwave infrared (SWIR) spectral range. Spatial and spectral information about seven different ore samples is obtained under a controlled laboratory environment using HSL. The standard deviation of the distance measurements is less than 1.1 cm for different spectral channels, and the classification accuracy can reach 100% if all 17 spectral measurements are used. To optimize the system design with lower cost and system complexity, a spectral band selection criterion is built based on the feature contribution degree (FCD), which is calculated using the normalized variance of the reflectance values for different ore samples at each wavelength. Two different strategies of FCD selection are tested to generate vectors: ascending sequences and descending sequences. Feature vectors with descending sequences have better classification accuracy. In addition, the results show that the classification accuracy can reach 100% with the feature vector of the seven largest FCD values compared to 59.57% for the feature vector with the seven smallest FCD values. Moreover, we find that the channels with high FCD values are primarily centered in SWIR bands. This result could be a reference for optimizing the hardware design of HSL for ore classification or mineral identification.

This paper presents the estimation of the ground elevation and canopy top elevation from the data collected by an airborne frequency-modulated continuous waveform profiling radar, Tomoradar. The estimated ground and canopy top elevations are critical for the derivation of reference information for the satellite-borne microwave radar data and the modeling of interaction between microwave radar signal and foliage. The methods of estimating the ground elevation and canopy top elevation from profiling radar are introduced, and the accuracy was evaluated via digital terrain model and Velodyne VLP-16 LiDAR integrated with the Tomoradar. To our knowledge, the ranging radar and the LiDAR data were simultaneously collected for the first time. The evaluation proved that the root-mean-square error (RMSE) of ground level estimation of the developed profiling radar can reach up to 0.33 m. When comparing the estimated canopy top peak elevation between the profile radar data and the LiDAR data, it was found that the side lobes of Tomoradar antenna system may produce undesired canopy backscatters when the size of the canopy gap is comparable to the footprint size of the main lobe, resulting in a higher canopy top elevation measurement from Tomoradar than that from LiDAR. The RMSE of the estimated canopy top peak elevation between two data sets was 0.32 m in the best case and 0.852 m on average. Moreover, the RMSE of point-to-point comparing the entire canopy tops elevation estimated from the data of two active remote sensing systems is 0.799 m after excluding the outliers.

Multispectral airborne laser scanning (ALS) data have recently become available. The objective of this letter is to study the feasibility of these data for road mapping-for road detection and road surface classification. The results are compared with the results of traditional aerial ortho images using object-based image analysis and Random Forest classification. The results demonstrate that the multispectral ALS data are feasible for automatic road detection and a significant improvement compared to the use of optical aerial imagery is obtained. In a test using ALS data, 80.5% points representing roads were classified correctly. When aerial images were used, the percentage decreased to 71.6%.

One of the biggest challenges in forestry research is the effective and accurate measuring and monitoring of forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. This paper presented a novel computational method of low-cost forest inventory using global navigation satellite system (GNSS) signals in a crowdsourcing approach. Statistical features of GNSS signals were extracted from widely available GNSS devices and were used for predicting forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions. The basic evidence of the predictions is the physical correlations between forest variables and the responses of GNSS signals penetrating through the forest. The random forest algorithm was applied to the predictions. GNSS-derived prediction accuracies were comparable with those of the most accurate 2-D remote sensing techniques, and the predictions can be improved further by integration with other publicly available data sources without additional cost. This type of crowdsourcing technique enables the collection of up-to-date forest data at low cost, and it significantly contributes to the development of new reference data collection techniques for forest inventory. Currently, field reference can account for half of the total costs of forest inventory.

In this paper, a range calibration and modulation sweep linearity verification method of a helicopter-borne FM-CW (Frequency-Modulated Continuous Waveform) profiling radar are presented. The radar is designed to collect backscatter signal waveforms to build target stand profiles. Various forest elements e.g. tree height; density; species, etc. can be evaluated through this profiling radar. This paper investigates the relation between target range and the received corresponding backscatter signal frequency and verifies the linearity of modulated RF (radio frequency) signal. The calibration result can be achieved based on a ground calibration test with restricted distance rather than the full measurement range. And the calibrated output can be directly adopted for airborne forestry inventory. The field test proves that the proposed calibration method is applicable for FMCW radar calibration using a centimeter level accurate fly trajectory with the help of Global Navigation Satellite System (GNSS) and IMU (Inertial Measurement Units) and a DEM (Digital Terrain Model) of the test field.

Automatic methods are needed to efficiently process the large point clouds collected using a mobile laser scanning (MLS) system for surveying applications. Machine-learning-based object recognition from MLS point clouds in a road and street environment was studied in order to create maps from the road environment infrastructure. The developed automatic processing workflow included the following phases: the removal of the ground and buildings, segmentation, segment classification, and object location estimation. Several novel geometry-based features, which were previously applied in autonomous driving and general point cloud processing, were applied for the segment classification of MLS point clouds. The features were divided into three sets, i.e., local descriptor histograms (LDHs), spin images, and general shape and point distribution features, respectively. These were used in the classification of the following roadside objects: trees, lamp posts, traffic signs, cars, pedestrians, and hoardings. The accuracy of the object recognition workflow was evaluated using a data set that contained more than 400 objects. LDHs and spin images were applied for the first time for machine-learning-based object classification in MLS point clouds in the surveying applications of the road and street environment. The use of these features improved the classification accuracy by 9.6% (resulting in 87.9% accuracy) compared with the accuracy obtained using 17 general shape and point distribution features that represent the current state of the art in the field of MLS; therefore, significant improvement in the classification accuracy was achieved. Connected component segmentation and ground extraction were the cause of most of the errors and should be thus improved in the future.

Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data.

Stereo images have long been the main practical data source for the high-accuracy retrieval of 3-D information over large areas. However, stereoscopy has been surpassed by laser scanning (LS) techniques in recent years, particularly in forested areas, because the reflection of laser points from object surfaces directly provides 3-D geometric features and because the laser beam has good penetration capacity through forest canopies. In the last few years, image-based point clouds have become a more widely available data source because of advances in matching algorithms and computer hardware. This paper explores the possibility of using consumer cameras for forest field data collection and presents an application of terrestrial image-based point clouds derived from a handheld camera to forest plot inventories. In the experiment, the sample forest plot was photographed in a stop-and-go mode using different routes and camera settings. Five data sets were generated from photographs taken in the field, representing different photographic conditions. The stem detection accuracy ranged between 60% and 84%, and the root-mean-square errors of the estimated diameters at breast height were between 2.98 and 6.79 cm. The performance of image-based point clouds in forest data collection was compared with that of point clouds derived from two LS techniques, i.e., terrestrial LS (the professional level) and personal LS (an emerging technology). The study indicates that the construction of image-based point clouds of forest field data requires only low-cost, low-weight, and easy-to-use equipment and automated data processing. Photographic measurement is easy and relatively fast. The accuracy of tree attribute estimates is close to an acceptable level for forest field inventory but is lower than that achieved with the tested LS techniques.

In this letter, we demonstrate the potential of a small, robust, and low-cost mobile scanner for snow-depth studies. Snow-surface model and depth data are needed for purposes such as flood forecasting, agriculture, optimal management of water resources, and in formulating global climate-change scenarios. Traditionally, manual snow-depth measurements are laborious, time-consuming, and costly. A mobile mapping system comprised of an Ibeo Lux laser scanner, operating at a wavelength of 905 nm, and a NovAtel SPAN-CPT inertial navigation system was used to produce geo-referenced point clouds. The data were acquired first in the fall when the ground was free of snow and then a second time in winter when there was snow on the ground. Reference values from a total of 94 locations were collected with an RTK GPS pole. The maximum reference snow depth was 80 cm. The obtained snow-depth bias was 0.3 cm, which indicated that the proposed data-processing approach was capable of avoiding errors due to laser penetration into the snow, and the root-mean-squared error was 5.5 cm. Mobile laser scanning appears to be a promising technology for cryospheric studies having potential, e.g., to calibrate gravimetric measurements and earth observation satellite data, especially when reference data are needed for areas that are too large for terrestrial laser scanning.

Terrestrial laser scanning (TLS) has been demonstrated to be an efficient measurement method in plot-level forest inventories. A permanent sample plot in national forest inventories is typically a small area of forest with a radius of approximately 10 m. In practice, whether reference data can be automatically and accurately collected for larger plot sizes is of great interest. It is expensive to collect references in large areas utilizing conventional measurement tools. The application of static TLS is a possible choice but is very challenging due to its lack of mobility. In this letter, a mobile laser scanning (MLS) system was tested and its implications for forest inventories were discussed. The system is composed of a high performance laser scanner, a navigation unit, and a six-wheeled all-terrain vehicle. In this experiment, about 0.4 ha forest area was mapped utilizing the MLS system. The stem mapping accuracy was 87.5%; the root mean square errors of the estimations of the diameter at breast height and the location were 2.36 cm and 0.28 m, respectively. These results indicate that the MLS system has the potential to accurately map large forest plots and further research on mapping accuracy and cost-benefit analyses is needed.

This paper reports on a study of measuring stem curves of standing trees of different species and in different growth stages using terrestrial laser scanning (TLS). Pine and spruce trees are scanned using the multiscan approach in the field, and trees are felled to measure them destructively for the purpose of obtaining reference values. The stem curves are automatically retrieved from laser point clouds, resulting in an accuracy of ~i1 cm. The corresponding manual measurements yield similar accuracy but fewer measurements at the upper parts of tree stems, compared with the automated measurements. The stem volumes based on stem curve data and field measurements and the best Finnish national allometric volume equations (using tree species, height, and diameters at heights of 1.3 and 6 m as predictors) result in similar accuracy. The measurement accuracy of the stem curves and stem volumes is similar for both pine and spruce trees. The results of this paper confirm the feasibility of using TLS to produce stem curve data in an automated, accurate and noninvasive way and indicate that the point cloud provides adequate information to accurately derive stem volumes from standing trees. The stem curves and volumes retrieved from point clouds can be employed in various forest management activities, such as the calibration of national or regional allometric curve functions and the prediction of profits in preharvest inventories.

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.

This work was dedicated to validating an innovative scheme of combining ultra high resolution airborne photography and mobile laser scanning (MLS) for biotope mapping, which is a topic now widely highlighted for quantifying and monitoring environmental quality. The specific combination plan was to use the MLS data as the 3D references to enrich and calibrate the retrievals from the imagery, meanwhile use the ultra high resolution imagery to expand the coverage of the MLS. Further, the combination program even derived an enhanced concept, i.e., fine-scale 3D biotopes. The test case primarily verified that the proposed plan can provide a new avenue for mapping the biotopes of the spaces of interest closer to their real situations.

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.

Our objective was to evaluate the accuracy of estimating forest canopy height when using scanning LiDAR and TerraSAR-X stereo radargrammetry. The study area was located in southern Finland. We used SAR radargrammetry and LiDAR to extract 3D point clouds to derive predictors used in the non-parametric prediction of forest canopy height. We used tree-wise measured field plots (n=110) as reference data. Our results showed that with SAR radargrammetry, the relative RMSE for forest canopy height was 12.2% whereas it was 8.1% with LiDAR. We concluded that SAR radargrammetry is a promising remote-sensing method for predicting forest canopy height when an accurate digital terrain model is available.

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.

This paper presents a novel attempt at combining the mobile mapping mode and a multiecho-recording laser scanner, as well as a new methodology based on the resulting single-scan point clouds, for enhancing the integrity of individual tree crown reconstruction. The motive stemmed from the widespread but hard-to-reach demand in precision forestry, i.e., efficiently acquiring the integral 3-D structures of single crowns via single-scan light detection and ranging (LiDAR) surveys. For this task, aerospace and aerial LiDAR is generally subject to low sampling density, and static terrestrial LiDAR is restricted to high relocation cost. As a state-of-the-art mapping technology, mobile laser scanning (MLS) can somehow overcome these limitations owing to its strengths of high sampling density and moving efficiency. However, its laser emissions, even from the incorporated peculiar scanner, still suffer from leaf/branch occlusions. To address this challenge, mirroring the half crowns facing the MLS system to the other sides can be assumed as a solution strategy, in a point of view different from typically strengthening laser transmission penetrability. In the case of no multiscans available, this plan turns out to be unstable due to the shortage of reference data. For this issue, this study further expands the roles of multiechoes beyond penetration, namely, also as the self-indicators for correcting the mirrored half crowns. Quantitative evaluation about the integrity of reconstruction in terms of crown outer surface was conducted based on the sample trees, which were measured by the multiecho-recording MLS and a static terrestrial LiDAR from two opposite sides. The promising results basically validated the proposed technique.

A novel k-segments-based 2-D geometric modeling schematic is proposed for characterizing the scan lines of vehicle-based laser scanning (VLS) with just a few geometric primitives. VLS has been developing quickly as a new research focus recently, but the relevant data processing techniques lag behind the system establishment due to the associated huge standwise point clouds collected in a real 3-D sense. To solve this issue, the often-assumed sampling mode based on scan lines suggests an alternative frame for exploring new efficient methodologies in diverse applications. As we know, principal segments can reflect the morphological signatures of the scatter-point-represented objects, and besides, profiles comprised by various open and close outlines can be geometrically modeled by line segments and ellipses, respectively. By combining these two merits, the k-segments-based geometric modeling algorithm can be constructed, which segments and fits the center-clustered points, e.g., in crowns, into ellipses and the line-arranged points, e.g., on walls, into line segments. Eventually, the experiments based on real VLS data primarily validate this new algorithm.

Light detection and ranging (LIDAR) systems based on unmanned aerial vehicles (UAVs) recently are in rapid advancement, while mini-UAV-borne laser scanning has few reported progress, notwithstanding so extensively required. This study established a pioneered mini-UAV-borne LIDAR system - Sensei, schematically with an Ibeo Lux scanner mounted on a small Align T-Rex 600E helicopter. Furthermore, the associated data processing involved in the coordinate triple, pulse intensity, and multiechoes per pulse was explored to validate its applicability for fine-scale mapping, in terms of, e.g., tree height estimation, pole detection, road extraction, and digital terrain model refinement. The feasibility and advantages of mini-UAV-borne LIDAR have been demonstrated by the promising results based on the real-measured data.

This study advances a new method for fusion of geometric models from the overlapping profiles collected by vehicle-based laser scanning (VLS), which is developed as a novel mapping technique. The schematic starts from line segment extraction based on random sample consensus (RANSAC) for each profile, and the resulted line segments are grouped. Then, the grouped line segments are fitted to local planes, and the local planes involved in different profile indicators are fused based on maximum a posteriori (MAP) estimation. Finally, the achieved 3D models are refined under the trial-and-error strategy. The mean distances from the fused local planes to the related inlier points are all less than 0.3 m, and numerical analysis based on the real-measured VLS data has validated the new method.

This paper proposes a novel approach for leaf area index (LAI) estimation based on vehicle-based laser scanning (VLS), which occurs as a state-of-the-art mapping technique. The method is advanced from the traditional terrestrial laser scanning (TLS), which has been primarily validated capable of predicting LAI. The associated schematic is to explore the correlations between VLS and TLS collections of the same trees. If positive, LAI can be retrieved with the related TLS data as reference. In this study, the consistency between the multi-echoes per pulse received by VLS and the single-echo per pulse recorded by TLS is further tackled, and LAI, thus, can be derived more accurately. The experiments based on the real-measured VLS and TLS data have validated the applicability of VLS for estimating LAI.

The intensity data recorded by airborne laser scanning (ALS) systems are useful for several applications, e.g., automatic point classification, change detection, and environmental studies. Before the intensity values can be used for any specific application, it has to be calibrated for atmospheric effect, range, energy loss, and incidence angle. Some ALS systems use automatic gain control (AGC). AGC is useful for getting laser returns even from low-reflectance surfaces (e.g., dark roofs), but it also changes the recorded intensity during the data acquisition, even within one surface type. This means that the same asphalt road might have totally different intensity values depending on the surrounding environment, which has affected the state of the AGC level. Therefore, it is important to correct the intensity values to neglect the effect of AGC in order to be able to get a normalized intensity value, which is only affected by the target characteristics. A first approach to correct the intensity values for AGC is reported in this letter. The same area was flown with AGC on and off, which allowed the modeling to take place. The results showed that the model produces values that agreed with anR2of 0.76 to the intensities obtained when AGC was turned off.

In this paper we summarize the results of FINSAR campaign, which was arranged to evaluate X- and L-band SAR interferometric and polarimetric SAR techniques for Boreal forest. The main emphasis of the work was on L-band polarimetric interferometry and forest height estimation. Also X-band interferometry and coherence tomography for X- and L-band, phase center height, extinction coefficient of forest and several other aspects of polarimetric interferometry were studied with help of ancillary measurements. Our results show that L-band polarimetric SAR interferometry can estimate well Boreal forest height. Also X-band interferometry shows good potential in height estimation. When accurate ground model is available, tree height can be estimated even by using one polarization interferometry. SAR appears to be more accurate in forest height measurement than forest inventory database, but not as accurate as laser measurement.

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.

In this paper we evaluate X- and L-band SAR coherence tomography in boreal forest with the help of detailed digital terrain and canopy height models, produced by laser scanning. Polarimetric coherence tomography (PCT) needs accurate estimates of ground phase and tree height. Supplemental accurate elevation models allow us to evaluate the performance of PCT in normal case when initial values are derived from RVoG model inversion and provides opportunity to use PCT for nonpolarimetric data. The work is based on E-SAR L-band and X-band measurements in Finland. Our results show that with accurate elevation and tree height information single polarization X-band coherence tomography is feasible and works well. Accurate ground elevation information improves also the performance of fully polarimetric repeat pass L-band PCT. The laser DEM provides better ground phase estimate than RVoG model inversion in the presence of temporal decorrelation. Our results show that accurate ground phase estimation is more critical for successful coherence tomography than other parameters.

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.

We have tested the use of a supercontinuum laser source in laser-based spectral backscatter measurement. The calibration and first results with the prototype instrument are presented with a discussion of improvements and applications in laser-based hyperspectral remote sensing and laboratory measurements. This technique enables the spectral study of the backscatter effects and the calibration and test measurements for the purpose of airborne laser measurement. We also explore the prospect of using a supercontinuum laser source in a broadband (hyperspectral) lidar

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.

In this study the potential of ERS-1/2 Tandem InSAR data for land-cover classification was investigated at a 2500 km/sup 2/ study area around the Helsinki metropolitan area in Southern Finland. A time-series of 14 ERS-1/2 SAR Tandem image pairs was processed into 28 five-look intensity images, 14 Tandem coherence images and two coherence images with a longer temporal baseline (36 and 246 days). All image data was coregistered and orthorectified into map coordinates using an InSAR DEM. A two-stage hybrid classifier method was employed, where the water-class was classified separately in the first classifier stage, and the remaining classes were classified with an ISODATA classifier. Temporal averaging and Principal Components Transformation (PCT) were used to reduce the number of images fed into ISODATA. Classification accuracy was assessed using high-resolution aerial orthophotos, digital base maps and the Finnish National Forest Inventory (NFI). The overall accuracy for six classes (Field/Open Land, Dense Forest, Sparse Forest, Mixed Urban, Dense Urban, Water) was found to be 90% with kappa coefficient of 0.86. Interferometric coherence carries more land-cover related information than the backscattered intensity. This study confirms that the ERS-1/2 Tandem archives could be exploited for land-cover classification.

The comparison of results of different forest studies is extremely difficult due to differences in test sites and studied stand characteristics, validation procedures, parameters used as an evaluation criteria, selection of stands, and the number of predictors used to name but a few. All these account for a large variation of the obtained accuracy. Additionally, in most reports inadequate information is given to convert statistically results from one study to the other. Since very few studies, such as Hyyppa/spl uml/ et al. (2000), exist where various remote sensing data sources and methods are verified in the same test site, much of the knowledge of the applicability of various data sources and methods for forest inventory has to be obtained by studies carried out in different tests sites. However, there is a single parameter, stand size, affecting strongly comparisons of forestry inventory results. The effect of stand size on the accuracy of remote sensing-based standwise forest inventory has not been reported extensively. The most dramatic changes occur at the level where stands are small. Not surprisingly, stand size has been successfully utilized as an auxiliary parameter in some studies. This paper describes how the accuracy of estimation is influenced by the stand size. Both spaceborne and airborne data are used in order to show that the effect is not just based on large pixel sizes or the effects of border pixels in spaceborne data. The accuracy of the following remote sensing data, SPOT Pan and XS, Landsat TM, ERS-1/2 SAR PRI and SLC, and airborne data from imaging spectrometer (AISA) is verified as a function of stand size in the range 1 to 20 ha. The paper presents curves that assist in converting results from one stand size to another and compares results of some studies in different test sites. Stand size seems to explain most of the variability of the results; however, for detailed comparison, more carefully described results are needed. Recommendations to design future forest studies are given in order to help the statistical conversion of results from one study to another.

In the boreal forest zone and in many forest areas, there exist gaps between the forest crowns. For example, in Finland, more than 30% of the first pulse data of laser scanning reflect directly from the ground without any interaction with the canopy. By increasing the number of pulses, it is possible to have samples from each individual tree and also from the gaps between the trees. Basically, this means that several laser pulses can be recorded per m/sup 2/. This allows detailed investigation of forest areas and the creation of a three-dimensional (3D) tree height model. Tree height model can be calculated from the digital terrain and crown models both obtained with the laser scanner data. By analyzing the 3D tree height model by using image vision methods, e.g., segmentation, it is possible to locate individual trees, estimate individual tree heights, crown area, and, by using that data, to derive the stem diameter, number of stems, basal area, and stem volume. The advantage of the method is the capability to measure directly physical dimensions from the trees and use that information to calculate the needed stand attributes. This paper demonstrates for the first time that it is possible to accurately estimate standwise forest attributes, especially stem volume (biomass), using high-pulse-rate laser scanners to provide data, from which individual trees can be detected and characteristics of trees such as height, location, and crown dimensions can be determined. That information can be applied to provide estimates for larger areas (stands). Using the new method, the following standard errors were demonstrated for mean height, basal area and stem volume: 1.8 m (9.9%), 2.0 m/sup 2//ha (10.2%), and 18.5 m/sup 3//ha (10.5%), respectively.

The capability of SAR interferometry has been previously demonstrated in various applications. In particular, the use of interferometric coherence has shown promising results in forest monitoring, however, mainly in discriminating forested and nonforested areas. The authors have collected ERS-1 and ERS-2 Tandem data from two boreal forest test sites in Finland. The data have been processed into interferometric coherence and intensity images. These images have been used to a) compare the behavior of interferometric coherence and intensity for various land-use and forest classes and b) extensive analysis on the behavior of interferometric coherence in boreal forests as a function of stem volume. Based on the observations and the use of a boreal forest semi-empirical backscattering model, they have developed an empirical model that describes interferometric coherence of boreal forests using backscattering information. The results indicate that coherence is more sensitive to the stem volume than the C-band backscattering intensity. However, the intensity and coherence data contain complementary information and therefore, the use of both data sources is beneficial in the observation of boreal forest.

This communications presents a statistical evaluation of the, calibration accuracy of an airborne scatterometer system. The internal and external calibrations were conducted over a two-year time period. It is shown that the absolute calibration accuracy of the Helsinki University of Technology airborne scatterometer (HUTSCAT) is better than 0.6 dB with 90% confidence. The techniques are generally applicable to stable airborne remote-sensing radars.

The application of remote sensing methods in the estimation of forest stand characteristics, especially biomass and stem volume, has been intensively investigated during the last few years. The new methods, however, have not been accurate enough for operational standwise inventory with a required accuracy typically of 15% for main stand characteristics (stem volume, basal area, and mean height). The present work demonstrates the feasibility of a nonimaging helicopter-borne ranging scatterometer for standwise forest inventory. The radar-derived stand profiles were compared with the standwise field inventory data by applying multivariate data analysis methods. The 1300 ha Teijo test site, locating 130 km west of Helsinki, was divided into 18 parallel radar flight lines with a 150 m spacing. A total of 28 radar variables, including profile information and ground and crown backscatter contributions at 5.4 and 9.8 GHz (polarizations VV, HV, and HH), were used in regression model development. The capability of a ranging radar to classify development class, land use class, bog type and fertility (site) class was demonstrated for the first time. The accuracy of the radar-derived estimates for mean height was 1.6 m (13%) meeting the requirement of operational use. The obtained stem volume accuracy of 31 m/sup 3//ha (26%) was slightly better than has been obtained by aerial photographs. The accuracy of stem volume estimation could be easily improved by decreasing the space between flight lines. However, this leads to considerable increase in flight costs, and, therefore, a scanning ranging radar capable of producing three-dimensional (3D) images of forests would be a better alternative.

The backscattering properties of boreal forests are studied using empirical airborne and spaceborne radar data from Finland. Airborne measurements were carried out in the summer of 1992 by the HUTSCAT scatterometer at the Teijo test area in southern Finland. The HUTSCAT scatterometer is an eight-channel helicopter-borne profiling radar operating at the C- and X-bands. The ranging capability of the HUTSCAT scatterometer was employed in the semiempirical modeling of forest backscatter. The backscatter profile information was used in the analysis of the canopy transmissivity and the canopy backscattering coefficient by distinguishing backscattering contributions from the canopy and the ground. Additionally, ERS-1 C-band satellite SAR measurements were obtained for the Teijo test area and for the reference test area in Sodankyla in northern Finland. The radar results were compared with operational ground-based forest assessment data on forest compartments (stands) of the area. The key parameter investigated was the stem (bole) volume per hectare. The results obtained show the behavior of the canopy transmissivity and the canopy backscatter as a function of stem volume (directly related to the forest biomass). The influence of seasonal and diurnal changes on, and the effects of the changes in soil moisture to the backscattering coefficient were also investigated.>

For pt.II see ibid., vol.31, no.1, p.170-9 (1993). HUTSCAT, a helicopter-borne dual-frequency FM-CW scatterometer, is described. The HUTSCAT measures the backscattering properties of a target with a range resolution of 65 cm. The real-time ranging capability is obtained by performing the fast Fourier transform (FFT) to the received time-domain signal. The measurement is made simultaneously at eight channels (VV, HH, HV, and VH modes of polarization at 5.4 GHz and 9.8 GHz). The scatterometer measures the radar return spectrum for eight channels in 16.6 ms, which corresponds to an along-track distance of 0.33 m for the helicopter speed of 20 m/s. The radar system has been designed for remote sensing of forests, sea ice, and snow.>

For pt.I see ibid., vol.31, no.1, p.161-9 (1993). Forest inventory methods based on data acquired with an airborne ranging radar are discussed. The approach can be used to partly automate present labor-dominated forest inventory methods. Using these methods, the mean and dominant tree height can be measured with a standard deviation of 1 m. The stem volume per hectare can be estimated with a relative accuracy of 15% by effectively counting the height distribution of the trees and by calculating the center of backscattered power from the forest canopy profile for a stand with a diameter of 40 m. The methods have been developed using a helicopter-borne eight-channel ranging scatterometer, HUTSCAT, which can measure a radar forest canopy profile with a range resolution of 65 cm.>