Synchronous heterogeneous optical remote sensing (RS) now is marching from integrated high-dimensional optical RS to single-system hyperdimensional optical RS. The representative case includes light detection and ranging (LiDAR) – from its integration with hyperspectral imager to the cutting-edge hyperspectral LiDAR. In this process, some kinds of pseudo-hyperdimensional optical RS systems are designed in order to achieve the performance comparable to the latter. However, whether such systems can give the satisfactory results for the applications with strict requirements on accuracy is always a concern for the discipline. Aiming at this issue, we attempt to make its data quality assessment, in the case of Leica BLK360. The results show the modes of laser reflectance, RGB reflectance, and laser footprint diameter index changing along with the ranging distance increasing. As regard to the key point of data quality assessment, i.e., how to assign the reflectance amplitudes of the red-green-blue (RGB) spectral bands for each laser footprint, we compare the performance of BLK360 with that of a real-sense hyperspectral LiDAR, the prototype developed by Finnish Geospatial Research Institute. The statistical comparison indicates that the latter works better, since it is not restricted to the problem caused by image matching to laser footprint, which is generally inherent in pseudo-hyperdimensional optical RS. With such challenges in application of pseudo-hyperdimensional optical RS clarified, the necessity of stepping into the stage of hyperdimensional optical RS is highlighted.
Laser scanning technology has enabled to study three-dimensional (3D) structures in forests. For example, airborne laser scanning (ALS) point cloud has been applied to detect individual trees and segment tree crowns. However, the accuracy of such approach remains a challenge because of the intersected crowns and complicated understories. We developed a metabolic theory-based algorithm for individual tree detection and crown segmentation from ALS data. The algorithm is composed of two parts, of which one is an unscaled transporting distance-based top-to-bottom detection approach, and the other is a scaled transporting distance-based segmentation approach. The unscaled transporting distance for detection is the absolute distance from tree root to crown and then to ALS point based on the tree structure models, whereas the scaled transporting distance for segmentation is the unscaled distance that is scaled by an initial tree height obtained during detection. This is based on a basic metabolic theory that vascular plants tend to minimize the material transporting distance from root to leaves. Hence, seven types of materials transporting distance models were built based on monopodial branching structure or crown-centered structure. The performance of the proposed approach was then further examined and compared with two typical canopy height model-based approaches and one typical point cloud-based approach, taking forest in Oxfordshire, UK, as a case study. The results showed that our approach can reach a recall of 1.00, a precision of 0.96, and an F-score of 0.98 and can reach to much higher accuracy for tree height (R2 = 0.8045) than the comparison approaches (R2 < 0.2) in the study plot. One of the main reasons that led to such low accuracy of comparison approaches is much overestimation of understory height with a mean error that is 2.9 times higher than that of our approach on average. Furthermore, ALS point-to-point level accuracy assessment shows 9.7% more ALS points were truly assigned in our approach than that of comparison approaches. It is noticed that the algorithm presented is not sensitive to the two key parameters: p (a percentage determining the threshold of unscaled transporting distances) ranging from 32.0% to 34.0% and λ (the proportion between the assumed crown center height and tree height) ranging from 0.70 to 0.90 based on our data set. Such high accuracy of our approach can greatly improve detections of individual tree and crown segmentation, especially in delineating understories in complex-structured forest.
Light Detection And Ranging (LiDAR) is an important branch of remote sensing (RS) technology, and its hardware and software in practical applications are getting more and more mature. Now, it is time for the community to think about its future, and a potential way of further pushing forward LiDAR RS technical progress, no doubt, is to develop its nextgeneration systems and approaches. Hyperspectral LiDAR is such a representative case, which, theoretically, is designed to synchronously collect the spectral and range information of objects. This advantage can inherently handle the errors caused when fusing those corresponding hypespectral images and point clouds in the traditional routines of 4D mapping, and hence, has attracted numerous attention on developing its prototype systems. With the performance enhancements of such prototype systems, more efforts need to be deployed onto pushing these prototypes to practical applications. In the case of the hyperspectral LiDAR prototype system developed by the Finnish Geospatial Research Institute, this study examined its applicability for investigating the intraday 3D variations of tree biophysics and biochemistry. The collected point clouds proved to be able to characterize the biophysical variation of trees in terms of laser point-represented tree geometrical centre. For the aspect of biochemical characterization, the hyperspectral LiDAR was validated through the retrievals of the 3D distributions of the fractions of photosynthetically active radiation (FAPARs), crown chlorophyll concentrations, and crown nitrogen concentrations, and the intraday biochemical variations were characterized by their day-and-night differences. The tests showed that hyperspectral LiDAR will be a kind of technology of high potentials for mapping biophysics and biochemistry and their dynamics.
Rocky desertification is one of the most serious problems in environmental deterioration, and its accurate mapping is of many implications for maintaining Earth’s sustainability. Compared to traditional field surveying approaches, remote sensing (RS), particularly hyperspectral RS, has proved to be a more efficient solution plan. Yet, hyperspectral RS also suffers from the problem of spectral mixing, since rocky desertification may correspond to various fractions of vegetation, bare soil, and exposed rock. Although linear spectral unmixing (LSU) is an effective method for resolving their ratios, different constraint conditions involving the selections of pure objects and end-member spectra may influence the result. To better overcome this basic problem, the key point of parameter comparison for LSU in field hyperspectral sampling of rocky desertification was investigated. In the typical rocky desertification areas in southwest China, an experiment of field hyperspectral RS sampling various scenarios of ground object mixing was conducted. Then, LSU was operated, by firstly classifying the digital photos of the sample plots to derive the proportion of each object. The specific LSU operations were classified into four types. The selection of end-member spectra were classified into three kinds of cases. With the 12 combination cases of the above-listed scenarios compared, we found that the results under the conditions of ANC and full constraint were better than the ASC and unconstrained conditions, and the performance for the end-member selection scenarios from case A to case C was dropping but could handle more complex situations. These inferences can supply a more solid theoretical basis for better implementing spectral unmixing in hyperspectral RS of rocky desertification.
Tree species information is essential for forest research and management purposes, which in turn require approaches for accurate and precise classification of tree species. One such remote sensing technology, terrestrial laser scanning (TLS), has proved to be capable of characterizing detailed tree structures, such as tree stem geometry. Can TLS further differentiate between broad- and needle-leaves? If the answer is positive, TLS data can be used for classification of taxonomic tree groups by directly examining their differences in leaf morphology. An analysis was proposed to assess TLS-represented broad- and needle-leaf structures, followed by a Bayes classifier to perform the classification. Tests indicated that the proposed method can basically implement the task, with an overall accuracy of 77.78%. This study indicates a way of implementing the classification of the two major broad- and needle-leaf taxonomies measured by TLS in accordance to their literal definitions, and manifests the potential of extending TLS applications in forestry.
The significance of laser return intensity has been widely verified in airborne light detection and ranging (LiDAR)-based forest canopy mapping, but this does not mean that all of its roles have been played. People still ask such questions as “Is it possible using this optical attribute of lasers to investigate individual tree-crown insides wherein laser intensity data are typically yielded in complicated echo-triggering modes?” To answer this question, this study examined the characteristics of the intensities of the laser points within 10 Quercus robur trees by fitting their peak amplitudes into default Gaussian distributions and then analyzing the resulting asymmetric tails. Exploratory data analyses showed that the laser points lying within the distribution tails can indicate primary tree branches in a sketchy way. This suggests that the question can be positively answered, and the traditional restriction of airborne LiDAR in canopy mapping at the crown level has been broken. Overall, this study found a unique way to detect primary tree branches in airborne LiDAR data and pointed out how to explore more ways this optical intensity attribute of airborne LiDAR data can measure tree organs at fine scales and further learn their properties.
High-speed railway construction will produce a large amount of abandoned dregs, so it is necessary to build enough dreg deposition fields along the railway. The task of the department of soil and water conservation is to monitor the construction and usage of abandoned dreg fields according to the design in the whole process of railway construction. As long linear construction projects, many high-speed railways go through regions of complex terrain, which poses great difficulties to monitoring current status of abandoned dreg fields. With the advantages of low cost, flexible launch and landing, safety, under-cloud-flying, hyperspatial image resolution, Unmanned Aerial Vehicles (UAVs) are very suitable for obtaining remote sensing imagery along the railway. One segment of the high-speed railway from Chongqing to Wanzhou and its neighborhood was chosen as the study area to demonstrate key technologies and specific procedures of monitoring abandoned dreg fields using the UAV system. The UAV system and its components are introduced along with the flight trajectories, acquired UAV imagery, and attitude data. Image preprocessing and generation of DEM and DOM are described in detail followed by image-based measurement accuracy assessment and abandoned dreg field status investigation on the resulting DOM and DEM. Results prove the feasibility and effectiveness of applying the fixed wing UAV system to rapidly monitoring the construction and usage of abandoned dreg fields
Learning the shielding effect of tree crowns with various structures on ultraviolet-B (UV-B) transmission is of great
significance, such as for reducing its damage on human. The cutting-edge remote sensing technique of mobile laser
scanning (MLS) is a potential option for tree structure representation. This work was dedicated to investigating the
correlation between the shielding efficiency of UV-B and tree crown structural parameters. Positive correlations were
achieved between the shielding efficiency of UV-B and the canopy structural parameters, and this is of implications for
selecting appropriate tree species for such as livable environment construction.
KEYWORDS: 3D modeling, Laser scanners, Clouds, Principal component analysis, 3D acquisition, Remote sensing, 3D metrology, Three dimensional sensing, LIDAR, Geographic information systems
This study was to attempt the cutting-edge 3D remote sensing technique of static terrestrial laser scanning (TLS) for
parametric 3D reconstruction of juvenile understory trees. The data for test was collected with a Leica HDS6100 TLS
system in a single-scan way. The geometrical structures of juvenile understory trees are extracted by model fitting. Cones
are used to model trunks and branches. Principal component analysis (PCA) is adopted to calculate their major axes.
Coordinate transformation and orthogonal projection are used to estimate the parameters of the cones. Then, AutoCAD is
utilized to simulate the morphological characteristics of the understory trees, and to add secondary branches and leaves in
a random way. Comparison of the reference values and the estimated values gives the regression equation and shows that
the proposed algorithm of extracting parameters is credible. The results have basically verified the applicability of TLS for
field phenotyping of juvenile understory trees.
Accurate tree-level characteristic information is increasingly demanded for forest management and environment
protection. The cutting-edge remote sensing technique of terrestrial laser scanning (TLS) shows the potential of filling
this gap. This study focuses on exploring the methods for deriving various tree stem structural parameters, such as stem
position, diameter at breast height (DBH), the degree of stem shrinkage, and the elevation angle and azimuth angle of
stem inclination.
The data for test was collected with a Leica HDS6100 TLS system in Seurasaari, Southern Finland in September 2010.
In the field, the reference positions and DBHs of 100 trees were measured manually.
The isolation of individual trees is based on interactive segmentation of point clouds. The estimation of stem position and
DBH is based on the schematic of layering and then least-square-based circle fitting in each layer. The slope of robust fit
line between the height of each layer and DBH is used to characterize the stem shrinkage. The elevation angle of stem
inclination is described by the angle between the ground plane and the fitted stem axis. The angle between the north
direction and the fitted stem axis gives the azimuth angle of stem inclination.
The estimation of the DBHs performed with R square (R2) of 0.93 and root mean square error (RMSE) of 0.038m.The
average angle corresponding to stem shrinkage is -1.86°. The elevation angles of stem inclinations are ranged from 31°
to 88.3°. The results have basically validated TLS for deriving multiple structural parameters of stem, which help better
grasp tree specialties.
The state-of-the-art remote sensing technologies, namely Unmanned Aerial Vehicle (UAV) based oblique imaging and
Mobile Laser Scanning (MLS) show great potential for spatial information acquisition. This study investigated the
combination of the two data sources for 4D modelling of roadside pole-like objects. The data for the analysis were
collected by the Microdrone md4-200 UAV imaging system and the Sensei MLS system developed by the Finnish
Geodetic Institute. Pole extraction, 3D structural parameter derivation and texture segmentation were deployed on the
oblique images and point clouds, and their results were fused to yield the 4D models for one example of pole-like objects,
namely lighting poles. The combination techniques proved promising.
The problem about reference grid data's overlarge spacing, which makes deviated estimation of un-surveyed points and
poor accuracy of correlation positioning, has been embarrassing Geophysical Fields of the Earth (GFE) referenced
navigation research. The super-resolution images reconstruction methods in remote sensing field give some inspiration,
and its brief method, Maximum A-Posterior (MAP) based on Bayesian theory, is transplanted on grid data. The proposed
algorithm named MAP-G can implement interpolation of reference data field by reflecting whole distribution trend.
Comparison with traditional interpolation algorithms and simulation experiments on underwater terrain/gravity-aided
navigation platform, indicate that MAP-G algorithm can effectively improve navigation's performance.
Laser Radar (LADAR) achieves more applications on aerial aided-navigation in mountainous areas for its good
performance. But plain areas encounter terrain elevation's slow variation and occasional unavailability of Digital Feature
Analysis Database (DFAD), which as necessary reference. Looking for replaceable map source and extracting common
characters for matching, are the fundamental circles of imaging LADAR aided navigation research. In this paper aerial
high-resolution remote sensing (RS) images are applied as substitute for DFAD, and the edge factor is chosen out by
synthetically analyzing RS images' and imaging LADAR point cloud'scharacters. Then edge extraction algorithm based
on multi-scale wavelet is explored to reflect their common features, and weighted Hausdorff distance method is applied
to match for positioning. At last the high-resolution RS images and imaging LADAR data of the same area are assumed
for simulation experiment, which testifies the validity of the methods proposed above.
TERCOM, ICP and TIEM algorithms, which mathematically all apply correlation matching mode, have been developed
for positioning in underwater Terrain-aided Navigation System (TANS), but how to virtually improve their performance
is still research puzzle now. Analyzing the characters of terrain reference data's distribution and vehicles prowling
underwater, we find that grid spacing and accumulation sequence are two decisional elements of underwater TANS.
Then the modified Maximum a Posteriori (MAP) estimation algorithm (M-MAP) from super-resolution images
reconstruction is creatively explored for implementing interpolation to enhance the accuracy of non-surveyed points'
deep-determination, and basic error mechanism model (EMM) based on Mean Absolute Difference (MAD) algorithm is
deduced which can reflect the relationship of underwater TANS's inner factors. Simulation experiments indicate that
adopting appropriate fundamental factors can effectively boost up underwater TANS's navigation competence based on the algorithms listed above.
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