Free-Resolution Probability Distributions Map-Based Precise Vehicle Localization in Urban Areas

Light detection and ranging (LiDAR) is an active remote sensing technology that can be used to directly acquire three-dimensional forest information, and it has great potential to replace traditional time-consuming field surveys. We aimed to determine the accuracy with which enhanced airborne LiDAR with high density (mean, 47 points m−2) can replicate detailed vertical foliage arrangement and three forest structural indices, i.e. number of layers (NSTRAT), sum of the percent vegetation cover (PCVC), and foliage height diversity (FHD), all of which are useful for characterizing avian habitats. Thirteen 40-m-long lines were set in an urban forest area, and LiDAR-based and field-based cross-sectional profiles with 1 × 1-m meshes were compared. In addition, we performed random LiDAR point thinning to determine whether the replications were possible with lower point densities. The 1 × 1-m mesh vertical foliage arrangements were replicated with high accuracy by using LiDAR (mean producer's accuracy, 0.756; mean user's accuracy, 0.810) before the point reduction. All the three indices were also replicated with high accuracy (<0.12 of the relative root mean square error for the 1:1 relationship). Even when the mean point density was reduced to 2.4 points m−2, the three indices could be quantitatively estimated with high accuracy by using simple linear regression analyses (>0.8 in R 2). For NSTRAT and FHD, numerical replication was possible with high accuracy (<0.2 and <0.1, respectively, of the relative root mean square error for the 1:1 relationship), even when the mean point density was reduced to 2.4 points m−2. In conclusion, enhanced high-density LiDAR seems to be highly suitable as a substitute for field surveys for delineating detailed vertical forest structure in order to characterize forest avian habitats. Moreover, lower-density LiDAR data could replicate the forest structural indices, suggesting its useful low-cost application in quantitative avian habitat models.

Estimates of forest stand mean height using airborne LiDAR (light detection and ranging) instruments have been reported previously with accuracies comparable to traditional ground‐based measurements. However, the small area covered by a LiDAR sensor in a single aircraft overpass is a significant hindrance for large‐scale forest inventories. In comparison, airborne interferometric synthetic aperture radar (InSAR) systems are also able to make estimates of surface height, but the swath coverage is often far greater, typically five or ten times that of the LiDAR coverage. A set of interferometric data takes was acquired by the ESAR airborne sensor over a managed pine plantation at Thetford Forest, UK. Scattering phase centre height estimates were made from two single‐pass X‐band acquisitions and polarimetric repeat‐pass L‐band acquisitions and compared with height estimates made from a separate LiDAR acquisition. The relationship between the scattering phase centre heights and stand top heights estimated, and the accuracy of stand top height estimates estimated from InSAR and LiDAR is quantified by the root mean square error (rmse). General yield class models by the Forestry Commission (UK) were used to estimate stand top height from a GIS database used for forest management. The longer wavelength L‐band radiation penetrates deeper into the canopy than the X‐band, and the scattering phase centre height is affected by both forest structural parameters (canopy density, understorey, and gaps) and sensor parameters (look‐angle and reduced coherence through temporal and volume decorrelation). Consequently, a simple translation of scattering phase centre height into stand top height gives noisy results for L‐band, with observed rmse values between ±3.1 m in the near‐range and ±6.4 m in the far‐range. The X‐band based top height estimates are more accurate, with rmse between ±2.9 m in the near‐ and ±4.1 m in the far‐range, which can be further reduced by an empirical incidence angle correction. Stand top height estimates from LiDAR achieved an rmse of only ±2.0 m. The X‐band scattering phase centre heights have also been related to mean stand height and are comparable with heights observed from the LiDAR sensor and field measurements. An rmse of ±2.5 m for the mean stand height estimates based on the X‐band dataset was found. Finally, we briefly discuss error propagation from the use of a terrain model, here provided by the Ordnance Survey.

BackgroundThe increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference.ResultsThrough a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m ×18 m map units was found to range between 9 and 447 Mg ·ha−1. The corresponding root mean square errors ranged between 10 and 162 Mg ·ha−1. For the entire study region, the mean aboveground biomass was 55 Mg ·ha−1 and the corresponding relative root mean square error 8%. At this level 75% of the mean square error was due to the uncertainty associated with tree-level models.ConclusionsThrough the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.

The world is witnessing massive demographic residents transitions from rural to urban, resulting in alarming consequences. These shifts highlight the necessity of comprehensive urban land-use mapping for current assessment and future city planning and management. The integration of Light Detection and Ranging (LiDAR) and imagery data allows using their complementary geometric and radiometric characteristics, respectively, with a potential increase in urban mapping accuracies. However, airborne LiDAR data may not always come with onboard optical images collected during the same flight mission. Indirect geo-referencing can be adopted if ancillary imagery data are available. Nevertheless, automatic recognition of control primitives in LiDAR and imagery datasets becomes challenging when collected on different dates. This dissertation proposes a generic geo-registration mechanism using the Phase Congruency (PC) model and scene abstraction to overcome the stated challenges. The Root Mean Square Error (RMSE) is between one to two pixels. The proposed workflow is computationally efficient, especially with small datasets, and generic enough to apply to various urban morphologies. A point-based classification was then performed on a point cloud covering a residential area using machine learning. The data was geo-registered to an aerial image. The classification tried multiple combinations of the inherited image spectra and the height as the only geometric feature in the classifying spaces. The acquired radiometric features added to the height gave an overall accuracy above 97% using Multi-Layer Perceptron (MLP) neural networks, surpassing those achieved with height-based geometric-oriented feature spaces. However, misclassifications occurred among different classes due to the independent acquisition of the aerial and LiDAR data and shadow and orthorectification problems from the aerial images. The dissertation then moves on to modify the color-based segmentation algorithm for the object-based classification of the multispectral LiDAR version of the same point cloud. The dissertation proposes a multilevel classification through vectors of inherited and calculated height-based geometric and spectral features structured in different classification scenarios (combinations). An overall mapping accuracy higher than 98% was achieved using MLP neural networks, combining the entire feature vectors and their optimized output space projected by the Principal Component Analysis (PCA). This combination also eliminates some of the abovementioned misclassifications.

Reliable knowledge of the ego position for vehicles is a crucial requirement for many automotive applications. In order to solve this problem for satellite-based localization in dense urban areas, multipath situations need to be handled carefully. This paper proposes a lightweight multipath detection algorithm which is based on dynamically built 3D environmental maps. The algorithm is evaluated with simulated and real-world data. Furthermore, it is applied to a combined GPS and GLONASS system in combination with a loosely coupled integration of odometry measurements from the vehicle.

The growing focus on the role of forests in carbon sequestration highlights the importance of accurately and efficiently measuring biophysical traits, such as diameter at breast height (DBH) and tree height. Understanding genetic contributions to trait variation is crucial for enhancing carbon storage through the genetic improvement of forest trees. Light detection and ranging (LiDAR) has been used to estimate DBH and tree height; however, few studies have explored the heritability of these traits or assessed the accuracy of biomass increment selection based on them. Therefore, this study aimed to leverage LiDAR to measure DBH and tree height, estimate tree heritability, and evaluate the accuracy of timber volume selection based on these traits, using 60-year-old larch as the study material. Unmanned aerial vehicle laser scanning (ULS) and backpack laser scanning (BLS) were compared against hand-measured values. The accuracy of DBH estimations using BLS resulted in a root mean square error (RMSE) of 2.7 cm and a coefficient of determination of 0.67. Conversely, the accuracy achieved with ULS was 4.0 cm in RMSE and a 0.24 coefficient of determination. The heritability of DBH was higher with BLS than with ULS and even exceeded that of hand measurements. Comparisons of timber volume selection accuracy based on the measured traits demonstrated comparable performance between BLS and ULS. These findings underscore the potential of using LiDAR remote sensing to quantitatively measure forest tree biomass and facilitate their genetic improvement of carbon-sequestration ability based on these measurements.

Increasing use of airborne light detection and ranging (LiDAR) in forest inventories offers new possibilities to apply process-based forest models (PBM) in practice. We present a new approach, where a simplified PBM is run using inputs derived from the LiDAR data. The PBM was built by combining several existing models together, and it was tested with 52 Scots pine (Pinus sylvestris L.) dominated sample plots in Finland with the LiDAR (PBM_LIDAR) and field (PMB_FIELD) inputs. The results were compared with empirical growth predictions (EM_FIELD) and field reference growth. LiDAR-based stand variables (mean height of tree and crown base and leaf area index) were, on average, well in line with the field measurements. Basal area growth was slightly underestimated with the PBM_LIDAR (bias 4.1%, root mean square prediction error (RMSPE, 26.7%) and overestimated with the PBM_FIELD (bias –10.2%, RMSPE 33.3%), the EM_FIELD being the least biased (bias –1.9%, RMSPE of 24.6%). The bias varied with stand age and fertility. The dependence on field reference growth was highest with EM_FIELD and PBM_LIDAR (R2 = 0.47 and 0.34, respectively), and lowest with PBM_FIELD (R2 = 0.18). Despite several development needs, the approach is promising for easy incorporation of canopy and weather data into forest growth predictions.

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models wereR2= 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit ofR2= 0.25 and RMSE 13.11 CV%. The predictive performances for the former wereR2= 0.47 and RMSE 9.34 VWC%, and for the latterR2= 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m2digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Continuous large-scale mapping of forest canopy height is crucial for estimating and reporting forest carbon content, analyzing forest degradation and restoration, or to model ecosystem variables such as aboveground biomass. Over the last years, the spaceborne Light Detection and Ranging (LiDAR) sensor specifically designed to acquire forest structure information, Global Ecosystem Dynamics Investigation (GEDI), has been used to extract forest canopy height information over large areas. Yet, GEDI has no spatial coverage for most forested areas in Canada and other high latitude regions. On the other hand, the spaceborne LiDAR called Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a global coverage but was not specially developed to study forested ecosystems. Nonetheless, both spaceborne LiDAR sensors obtain point-based information, making spatially continuous forest canopy height estimation very challenging. This study compared the performance of both spaceborne LiDAR, GEDI and ICESat-2, combined with ALOS-2/PALSAR-2 and Sentinel-1 and -2 data to produce continuous canopy height maps in Canada for the year 2020. A set-aside dataset and airborne LiDAR (ALS) from a national LiDAR campaign were used for accuracy assessment. Both maps overestimated canopy height in relation to ALS data, but GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI. PALSAR-2 HV polarization was the most important covariate to predict canopy height, showing the great potential of L-band in comparison to C-band from Sentinel-1 or optical data from Sentinel-2. The approach proposed here can be used operationally to produce annual canopy height maps for areas that lack GEDI and ICESat-2 coverage.

Climate change is a major contemporary phenomenon with multiple consequences. In urban areas, it exacerbates the urban heat island phenomenon. It impacts the health of the inhabitants and the sensation of thermal discomfort felt in urban areas. Thus, it is necessary to estimate as well as possible the air temperature at any point of a territory, in particular in view of the ongoing rationalization of the network of fixed meteorological stations of Météo-France. Understanding the air temperature is increasingly in demand to input quantitative models related to a wide range of fields, such as hydrology, ecology, or climate change studies. This study thus proposes to model air temperature, measured during four mobile campaigns carried out during the summer months, between 2016 and 2019, in Lyon (France), in clear sky weather, using regression models based on 33 explanatory variables from traditionally used data, data from remote sensing by LiDAR (Light Detection and Ranging), or Landsat 8 satellite acquisition. Three types of statistical regression were experimented: partial least square regression, multiple linear regression, and a machine learning method, the random forest regression. For example, for the day of 30 August 2016, multiple linear regression explained 89% of the variance for the study days, with a root mean square error (RMSE) of only 0.23 °C. Variables such as surface temperature, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) have a strong impact on the estimation model. This study contributes to the emergence of urban cooling systems. The solutions available vary. For example, they may include increasing the proportion of vegetation on the ground, facades, or roofs, increasing the number of basins and water bodies to promote urban cooling, choosing water-retaining materials, humidifying the pavement, increasing the number of public fountains and foggers, or creating shade with stretched canvas.

Airborne Light Detection and Ranging (LiDAR) topographic data provide highly accurate digital terrain information, which is used widely in applications like creating flood insurance rate maps, forest and tree studies, coastal change mapping, soil and landscape classification, 3D urban modeling, river bank management, agricultural crop studies, etc. In this paper, we focus mainly on the use of LiDAR data in terrain modeling/Digital Elevation Model (DEM) generation. Technological advancements in building LiDAR sensors have enabled highly accurate and highly dense LiDAR point clouds, which have made possible high resolution modeling of terrain surfaces. However, high density data result in massive data volumes, which pose computing issues. Computational time required for dissemination, processing and storage of these data is directly proportional to the volume of the data. We describe a novel technique based on the slope map of the terrain, which addresses the challenging problem in the area of spatial data analysis, of reducing this dense LiDAR data without sacrificing its accuracy. To the best of our knowledge, this is the first ever landscape-driven data reduction algorithm. We also perform an empirical study, which shows that there is no significant loss in accuracy for the DEM generated from a 52% reduced LiDAR dataset generated by our algorithm, compared to the DEM generated from an original, complete LiDAR dataset. For the accuracy of our statistical analysis, we perform Root Mean Square Error (RMSE) comparing all of the grid points of the original DEM to the DEM generated by reduced data, instead of comparing a few random control points. Besides, our multi-core data reduction algorithm is highly scalable. We also describe a modified parallel Inverse Distance Weighted (IDW) spatial interpolation method and show that the DEMs it generates are time-efficient and have better accuracy than the one’s generated by the traditional IDW method.

The uncertainty of biomass estimates from LiDAR and SAR across a boreal forest structure gradient

In this study, we demonstrate the potential of using high spatial resolution airborne imagery to characterize the structural development stages of forest canopies. Four forest succession stages were adopted: stand initiation, young multistory, understory reinitiation, and old growth. Remote sensing metrics describing the spatial patterns of forest structures were derived and a Random Forest learning algorithm was used to classify forest succession stages. These metrics included texture variables from Gray Level Co-occurrence Measures (GLCM), range and sill from the semi-variogram, and the fraction of shadow and its spatial distribution. Among all the derived variables, shadow fractions and the GLCM variables of contrast, mean, and dissimilarity were the most important for characterizing the forest succession stages (classification accuracy of 89%). In addition, a LiDAR (Light Detection and Ranging) derived forest structural index (predicted Lorey’s height) was employed to validate the classification result. The classification using imagery spatial variables was shown to be consistent with the LiDAR derived variable (R2 = 0.68 and Root Mean Square Error (RMSE) = 2.39). This study demonstrates that high spatial resolution imagery was able to characterize forest succession stages with promising accuracy and may be considered an alternative to LiDAR data for this kind of application. Also, the results of stand development stages build a framework for future wildlife habitat mapping.

As the autonomous driving technology develops, research on related sensors is also being actively conducted. One system that is widely used today uses a light source with a wavelength in the 905 nm band for the pulse Light Detection And Ranging (LiDAR) system. This has the disadvantages of being harmful to the human eye and in making digital signal processing difficult at high sampling rates. The Frequency Modulated Continuous Wave (FMCW) LiDAR system has been proposed as an alternative. However, the FMCW LiDAR is formed with a high beat frequency by a method different from that of the FMCW Radar, which causes a hardware burden on the FFT (Fast Fourier Transform) module for interpreting the beat frequency information. In this paper, the FFT module that may occur in the FMCW LiDAR using Digital Down Convert (DDC) technology is extracted at 256 points, which is times smaller than the existing 8192 points, and the beat frequency is 0 to 50 m at 3 cm intervals. As a result of generating and restoring the distance, the performance of 0.03 m Root Mean Square Error (RMSE) compared to the conventional one was confirmed. In this process, the hardware module was directly mounted and verified on the FPGA. In the case of the Simple Threshold-Constant False Alarm Rate (ST-CFAR) provided, the RMSE was measured by generating beat frequencies from 0 to 50 m at 1 cm intervals, and as a result, the result of 0.019 m was confirmed at 0.03 m in the past.

Until now, there have been only a few studies that have made estimates of the woody aboveground biomass (AGB) in an area of agroforestry using remote sensing technology. The woody AGB density was estimated using individual tree analysis (ITA) that incorporated tree species information using a combination of airborne light detection and ranging (LiDAR) and compact airborne spectrographic imagery acquired over a typical agroforestry in northwestern China. First, a series of improved LiDAR processing algorithms was applied to achieve individual tree segmentation, and accurate plot-level canopy heights and crown diameters were obtained. The individual tree species were then successfully classified using both spectral and shape characteristics with an overall accuracy of 0.97 and a kappa coefficient of 0.85. Finally, the tree-level AGB (kg) was estimated based on the ITA; the AGB density (Mg/ha) was then upscaled based on the tree-level AGB values. It is concluded that, compared with the commonly used area-based method combining LiDAR and spectral metrics [root mean square error (RMSE)=19.58 Mg/ha], the ITA method performs better at estimating AGB density (RMSE=10.56 Mg/ha). The tree species information also improved the accuracy of the AGB estimation even though the species are not well diversified in this study area.