Top PDF Extraction of Vegetation Biophysical Structure from Small-Footprint Full-Waveform Lidar Signals

Extraction of Vegetation Biophysical Structure from Small-Footprint Full-Waveform Lidar Signals

Extraction of Vegetation Biophysical Structure from Small-Footprint Full-Waveform Lidar Signals

It is challenging for scientists and foresters to measure forests at the continental and global scales by exclusively using field-based data collects. Instead, many scientists must collaborate by taking measurements across a range of forests as well as other natural land types. Continental- scale and global-scale ecology are most efficiently performed from spaceborne measurements, where it is easy to cover large areas of Earth. There is, however, the gap of linking the space- based measurements with fine scale site- or plot-based field measurements. To help speed this process the scientific community must turn to air- and spaceborne methods of measuring forest parameters. Passive remote sensing techniques, such as hyper- and multi-spectral imaging, are useful for optically-based measurements of these parameters, e.g., leaf chemistry. However, these systems are lacking as far as deriving structural measurements. It is here where active remote sensing techniques, e.g., light detection and ranging (lidar) and synthetic aperture radar (SAR), can make their mark. To help address this need for data, landscape-, regional-, and continental-scale observatories, such as National Ecological Observatory Network (NEON) (Kampe et al., 2010) and Terrestrial Ecosystem Research Network (TERN) (Likens and Lindenmayer, 2011), are being constructed, which help to bridge the spatial and temporal scales.
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Fusing Small-footprint Waveform LiDAR and Hyperspectral Data for Canopy-level Species Classification and Herbaceous Biomass Modeling in Savanna Ecosystems

Fusing Small-footprint Waveform LiDAR and Hyperspectral Data for Canopy-level Species Classification and Herbaceous Biomass Modeling in Savanna Ecosystems

Land degradation is a major concern in Africa’s sub-Saharan environment, where multi- spectral remote sensing has been used extensively for monitoring purposes [Asner et al., 2007, Cho et al., 2009]. However, in order to explain degradation patterns in further de- tail, there is a need to increase the scale and sensitivity of the instruments. For example, coarse resolution satellite data have not been able to capture intricate landscape metrics that would enable the analysis of bare soil fractional cover, while high resolution hyper- spectral data have been proven to do so [Asner et al., 2003]. The variations in three dimensional structure of vegetation could also prove useful for assessing land degradation and how vegetation changes over time. Light detection and ranging (LiDAR) has proven to be a very promising tool in providing large scale spatial information [Lefsky et al., 1999b] and could potentially provide fine scale structural data required to study this en- vironmental issue. The Carnegie Airborne Observatory (CAO) consists of an advanced spectroscopic imaging and waveform laser remote sensing technology and is used to study ecosystems anywhere in the world, given the need [CAO, 2008]. The effort undertaken to build this system was driven by the need to acquire information that could be readily converted to physical and chemical quantities representative of ecosystem processes and properties, achieved by fusing multiple sensor data [Asner et al., 2007]. This could help us understand “how changes in land use, climate, and natural disturbances affect the struc- ture, composition, and functioning of ecosystems, and how these changes alter the services provided by ecosystems to people” [CAO, 2008].
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Complexity and Dynamics of Semi-Arid Vegetation Structure, Function and Diversity Across Spatial Scales from Full Waveform Lidar

Complexity and Dynamics of Semi-Arid Vegetation Structure, Function and Diversity Across Spatial Scales from Full Waveform Lidar

waveform features. Classification models developed with the 10 m waveform features outperformed those at 1 m (Kappa (κ) = 0.81–0.86 vs. 0.60–0.70, respectively). At 1 m resolution, lidar height features improved the PFT classification accuracy by 10% compared to the analysis without these features. However, at 10 m resolution, the inclusion of lidar derived heights with other waveform features decreased the PFT classification performance by 4%. Pulse width, rise time, percent energy, differential target cross section, and radiometrically calibrated backscatter coefficient were the most important waveform features at both spatial scales. A significant finding is that bare ground was clearly differentiated from shrubs using pulse width. Though the overall accuracy ranges between 0.72 – 0.89 across spatial scales, the two shrub PFTs showed 0.45 - 0.87 individual classification success at 1 m, while bare ground and tree PFTs showed high (0.72 – 1.0) classification accuracy at 10 m. We conclude that small- footprint waveform features can be used to characterize the heterogeneous vegetation in this and similar semi-arid ecosystems at high spatial resolution. Furthermore, waveform features such as pulse width can be used to constrain the uncertainty of terrain modeling in environments where vegetation and bare ground lidar returns are close in time and space. The dependency on spatial resolution plays a critical role in classification performance in tree-shrub co-dominant ecosystems.
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From LiDAR waveforms to Hyper Point Clouds: a novel data product to characterize vegetation structure

From LiDAR waveforms to Hyper Point Clouds: a novel data product to characterize vegetation structure

that of DR LiDAR data through directly transforming all waveform intensities into points with the aid of georeferenced data. This process will be detailed later in Section 2.3.1; Second, the other type of FW LiDAR data processing directly extracts vegetation’s vertical information from waveforms as waveform signatures or features for possible applications. Their effectiveness has been demonstrated in previous studies (Drake et al., 2002; Hermosilla et al., 2014; Zhou et al., 2017a). This second concept was first used in the large-footprint waveform and it was introduced into the small-footprint waveform recently with the aid of tree crown boundary. The main reason for the necessity of tree crown boundary is that an individual small-footprint waveform only captures a small portion of tree crowns intercepted by the laser beam (Zhou et al., 2017a). More useful and representative vegetation information can be extracted through employing all waveforms within the tree crown boundary rather than only using an individual waveform as the large-footprint waveform. Actually, the demand for additional information such as tree crown boundary gives rises to another concern of directly using small-footprint FW LiDAR data. The waveform decomposition needs to be done to obtain the Canopy Height Model (CHM) as the input to tree crown segmentation, which possibly requires users to have a deep understanding of complicated waveform processing methods and precludes the practitioners’ willingness to explore FW LiDAR data’s potential. Therefore, we introduced the CHM-like products derived from the HPC without the aid of dedicated waveform processing algorithms, namely the HPC-based intensity and percentile height (PH) surfaces, for tree segmentation and further explored their potential applications.
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Towards scale-invariant aboveground biomass estimation in Savanna ecosystems using small-footprint waveform lidar

Towards scale-invariant aboveground biomass estimation in Savanna ecosystems using small-footprint waveform lidar

Land degradation is becoming an issue of increasing concern in the savanna ecosystems of southern Africa. As a result, there is a growing need to map structural changes at the fine scale, while retaining the ability to aggregate up to landscape level for analysis across land use gradients. Aboveground biomass (AGB) is an important indicator of vegetation structure and therefore is the ideal variable for estimation from light detection and ranging (lidar) data. To avoid the effects of scale, this paper takes a tree-delineation approach for segmentation of the structurally heterogeneous savanna environment. Diameter at breast height (DBH) measurements collected in-field are then regressed against lidar-derived statistics to estimate DBH on a per tree basis, from which biomass follows naturally by allometry. The result is a spatially explicit biomass map of the savanna environment, believed to be one of the first of its kind, that can be scaled by aggregation of per-tree biomass distributions.
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Electroreception in G  carapo: detection of changes in waveform
of the electrosensory signals

Electroreception in G carapo: detection of changes in waveform of the electrosensory signals

generating component (using the nomenclature introduced by Aguilera et al., 2001) (Fig. 1A). A plastic cylinder with conducting carbon discs at both ends was used to explore the effect of longitudinal impedance changes on the sLEOD. When the ends of the cylindrical stimulus-object were not connected (open circuit), the current intensity along the longitudinal axis was null and the sLEOD was minimal (peak-to-peak amplitude was 0.6 of the basal value in the absence of the object). When the ends of the cylindrical stimulus-object were short-circuited, the current flowing along the longitudinal axis and the sLEOD were maximal (approximately 2.5–3 times the value obtained with open circuit). Therefore, maximum and minimum effective values (rms values) of the reafferent signal were caused by short circuit and open circuit, respectively (grey and black labels, Fig. 1). Between these boundaries, the rms value decreased monotonically with object resistance. When time was standardised using the head-to-tail EOD as a reference (Fig. 1B), the corresponding sLEOD values between waveforms obtained using different resistive loads were very well correlated (r 2 >0.99), indicating a very similar waveform
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Living on the edge: utilising lidar data to assess the importance of vegetation structure for avian diversity in fragmented woodlands and their edges

Living on the edge: utilising lidar data to assess the importance of vegetation structure for avian diversity in fragmented woodlands and their edges

The Euclidean distance (m) from the centroid of a cell to the nearest hedgerow (calculated for the edge cells only). Assessed as a continuous variable (1) and as a categorical variable (2) divided into 25 m classes, i.e.: 0 – 25 m, > 25 – 50 m, etc.

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Building footprint extraction from Digital Surface Models

using Neural Networks

Building footprint extraction from Digital Surface Models using Neural Networks

Two-dimensional (2D) building footprint extraction from imagery or/and DSM data has been a research issue for decades and is of great interest since it plays a key role in three-dimensional (3D) building model generation, map updating, urban planning and reconstruction, infrastructure development, etc. The collection of building footprints often needs a lot of manual work and is both time consuming and costly. Moreover, it is challenging to extract building information from remotely sensed data due to the sophisticated nature of urban environments. Therefore, automatic methods are required for an efficient collection of building footprints from large urban areas containing thousands of buildings.
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Improved measurement of blood pressure by extraction of characteristic features from the cuff oscillometric waveform

Improved measurement of blood pressure by extraction of characteristic features from the cuff oscillometric waveform

Accurate oscillometric blood pressure estimation in an unsupervised environment is challenging in the presence of interference, notably movement artifact which interrupts the air flow in the deflating cuff. While several studies have attempted to detect noise in the blood pressure signals using additional sensing devices such as acceleration and capacitive sensors [29], as well as morphological comparison with good-quality reference pulses [30], none of these studies have investigated the effect of the detected noise on the extraction of accurate blood pressure values from the contaminated signals. In the present study, we integrated an artifact removal block (Figure 1) in our SBP and DBP estimation algorithm which was based solely on the oscillometric signal without using additional sensors or reference signals. Our results demonstrated that the mean and standard deviation of the blood pressure estimation errors between the MAA algorithm and the RS substantially decreased upon artifact removal (Figures 6 and 7, Table 4), which strongly advocates the importance of the artifact removal component proposed in the present study. Furthermore, the MAA algorithm has been well recognized to be susceptible to additive noise as it is derived based on the amplitude of the pulse [31]. The spline interpolation method, commonly used to smooth the envelope of the OMW for eliminating the erroneous peak values generated by artifact, was shown in this study to be ineffective in reducing the interference caused by movement artifact [32].
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Data Fusion for Urban Feature Extraction from LiDAR and Hyperspectral Data

Data Fusion for Urban Feature Extraction from LiDAR and Hyperspectral Data

consider different classes for a few pixels that are adjacent to each other, this leads to a rupture in the results. But by using feature extraction method based on object for each individual pixel class finds the nearest neighbors. Because SVM classification method cannot extract the feature continuously. Therefore, due to the reasons provided, to regulate the construction feature and also the extracted continuous road, morphology close transform used with Hough transform. Then be sure to check whether adding images LiDAR to hyperspectral image data is improving or not. If you use the LiDAR image , overall accuracy and kappa coefficient for the classification of 93.03 and .431 respectively and the use of hyperspectral images to a classification of 98.62 and .899 respectively, were calculated. As a result, an increase from .899 to .958 kappa coefficient of hyperspectral image by adding LiDAR data, show a broadly favorable effect of using LiDAR imagery to improve the results of the classification. A comparison of the proposed method and tournament favorite institution is given in Table 4. a. The selected method of data integration in international competition IEEE.
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Vegetation structure and small-scale pattern in Miombo Woodland, Marondera,  Zimbabwe

Vegetation structure and small-scale pattern in Miombo Woodland, Marondera, Zimbabwe

Spatial heterogeneity is a universal attribute of natural vegetation (Greig-Smith 1979). Patterning exists and can be studied at various levels of biological organization and at widely different spatial and temporal scales (Allen & Starr 1982). For savanna vegetation, most studies have been concerned with regional and community patterns and their determinants and correlates (Walker 1987). Small- scale patterning (within community spatial heterogeneity) has received scant consideration (but see, for example. Macdonald 1978; Belsky 1983). Particularly the occur­ rence, determinants and dynamics of small-scale vegeta­ tion patterns in savannas remain poorly documented and understood. For instance, although Malaisse (1978), Celander (1983) and Chidumayo (1993) have given details of general miombo structure, no information is available on small-scale pattern. In this study, small-scale patterning of the wcxxlv vegetation and correlates between wtxxlland sub- types and various soil properties were investigated for a miombo wtxxiland near Marondera. Zimbabwe.
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Extraction of Signal Waveform Feature Based on Bispectrum

Extraction of Signal Waveform Feature Based on Bispectrum

Abstract: This paper presents a new method of feature extraction for signal waveform. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance. Over the past four decades, considerable work has been done in the area of power spectrum estimation. However, a problem with this method is that it is phase blind. Situations arise in science and engineering whereby signal analysts are required to look beyond second-order statistics and analyze a signal’s Higher-Order Statistics (HOS). In this paper, bispectrum is used to extract the feature of signal. Feature of the signal can be extracted by selecting the eigenvector whose corresponding eigenvalue’s module is the largest as the template of recognition. The experiment being made by our research group suggests that recognition accuracy rate of bispectrum-method can be no less than 90 percent in additive white Gaussian noise channel when SNR (Signal to noise rate) is no less than 8dB .
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Developing a dual wavelength full waveform terrestrial laser scanner to characterise forest canopy structure

Developing a dual wavelength full waveform terrestrial laser scanner to characterise forest canopy structure

The recent development of TLS technologies has provided an opportunity to rapidly capture detailed information on the 3D characteristics of vegetation canopies. TLS combine a laser range-finding measurement and recording system with a two-axis scanning mechanism, and automatically capture 3D point clouds, consisting of many millions of samples, that may be reconstructed to represent the 3D structure of the object scanned. Commercially available TLS provide very precise range measurements over long distances, and are designed to provide 3D models of buildings, ter- rain, rock outcrops and industrial infrastructure. However, they are generally not suitable for measuring forest canopies because their scanning geometry is often sub-optimal, they record at just one laser wavelength and, with a few exceptions, they record only discrete returns, rather than the full-waveform of backscattered laser energy. In addition, the measurement characteristics and data processing algorithms used by commercial TLS manufacturers are often proprietary, and are not made available to the end-user, mak- ing it difficult to work with the radiometric information provided by TLS.
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A full waveform current recorder for electrical prospecting

A full waveform current recorder for electrical prospecting

The current recorder contains the current probe and the data acquisition unit. Figure 2 shows the diagram of the current recorder. The data acquisition unit consists of a GPS an- tenna, an allium box, five printed circuit boards (PCBs), and a built-in battery. The PCB contains a front interface mod- ule, an analog-to-digital converter (ADC) module, a field- programmable gate array (FPGA) module, an Advanced RISC Machine (ARM) module, a clock module, and a power module. The front interface module integrates the protection of the input circuit and anti-aliasing filter, with connectors in- side and outside the allium box. The NET interface is used to communicate with the external user PAD. The ADC module amplifies the voltage signal from the current probe output and converts the signal to a serial digit output. The sample rate is set as either 24 kHz or 2.4 kHz. The bandwidth is set as either 10 kHz or 1 kHz. The FPGA module reads the data stream and adds the GPS time stamp, and the ARM module transfers data from the FPGA to the built-in SD card. The CLK module provides GPS time information and PPS for the ARM and FPGA modules. Each discrete module is in- tegrated using a bus. The power module converts the Li-ion battery voltage to analogue and digital power sources. The capacity of the battery is 20 Ah at 12 V, and the entire power consumption is approximately 6 W. The maximum working time is approximately 40 h, which is sufficient for field work. Figure 3 shows a photograph of the data acquisition unit.
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A hybrid approach to extraction and refinement of building footprints from airborne lidar data

A hybrid approach to extraction and refinement of building footprints from airborne lidar data

Planes are often the basis of building detection and can be obtained employing various algorithms. The mostly used techniques for plane detection from point clouds include Hough transform, Region Growing, and Random Sample Consensus (RANSAC). No matter which technique is chosen, they share a similar scheme: first finding one plane each time, then removing the corresponding points, and going on with the next one in the next iteration. The possible problems are: (1) time consuming search as the remaining points are calculated repeatedly until they have been recognized and removed; (2) points that belong to two adjacent planes may be too early removed with the firstly found plane. To avoid these drawbacks we propose a joint plane detection scheme by enhancing the Hough transform to find multiple planes synchronously. By this means the points are used more efficiently – they are calculated for only one time while being allowed to vote multiple planes.
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Geomorphometric Methods for Burial Mound Recognition and Extraction from High Resolution LiDAR DEMs

Geomorphometric Methods for Burial Mound Recognition and Extraction from High Resolution LiDAR DEMs

Mounds are landforms created by different processes, but with similar morphology, situation that can be associated with geomorphometric convergence, similar with the morphological convergence from biology [64]. The overall shape of the landform alone cannot always be used to specify exactly the process that created it. That is why all the burial mounds were verified in the field and the classification from remote sensing images was validated (Figures 1 and 2). Only two mistakes (delineated as burial mounds, found to be something else) were revealed by the field validation (IDs 35 and 94, in Figure 1, S4 and S10). The validation was based not on geophysical or archaeological prospection but on the geomorphological observation of the morphology in the field. In general, the burial mound vs. non- burial mound distinction is easy to be done both on remote sensing images and in the field, especially for a trained person (both regarding the local geomorphology and archaeology of the study area). There are not many natural processes that can produce landforms with a shape similar with a burial mound. Humans instead can produce various types of mounds through deposition of materials, and the type of material is an indication of the mound typology. I delineated anthropic burial mounds or natural features (vegetation not filtered from the point cloud and landslide body rough features only when these had similar shape and size with burial mounds). The best field pictures for showing the presence of a burial mound are taken from a neighbor ridge (Figure 4), especially in the case of smoothed burial mounds.
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Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

gle laser pulse, lidar has also been used to construct veg- etation structures as well as observe conditions under the canopy, which helps produce fine-resolution digital elevation models (DEMs), vegetation structures and snow-depth infor- mation. However, the snow depth under canopy can not al- ways be measured because of the signal-intensity attenuation caused by canopy interception (Deems and Painter, 2006; Deems et al., 2006). A recent report applied a univariate- regression model to the snow depth measured in open areas using lidar, with a high-resolution DEM used to accurately quantify the orographic-lift effect on the snow accumulation just prior to melt (Kirchner et al., 2014). From this analysis it could be expected that lidar data might also help explain ad- ditional sources of snow distribution variability in complex, forested terrain.
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Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from Lidar data

Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from Lidar data

snow depth from the linear-regression models (open areas) versus: (a) slope, aspect, and (c) penetration 597  . fraction.[r]

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About the effects of polarising optics on lidar signals and the Δ90 calibration

About the effects of polarising optics on lidar signals and the Δ90 calibration

In this work we describe lidar setups from the laser to the detector by means of the Stokes–Müller formalism (Chipman 2009b) including the transmitter and receiver optics. The Stokes vector describes the flux and the state of polarisation of the light, and the Müller matrices describe how optical elements change the Stokes vector. We develop equations for the two signals of a polarisation-sensitive lidar and for the signals of the polarisation calibration, which are necessary to retrieve the linear depolarisation ratio and the total lidar signal, using different calibration techniques and lidar setups. In order to enable the evaluation of the final errors and to analyse their dependencies on certain optical parameters or misalignments of individual optical elements, we derive first the full equations and then try to find more simple analytical formulations neglecting minor error sources to get an overview of the main critical parameters. For this we neglect the polarisation effects of lenses and of telescope mirrors with small incidence angles of the light beam (Seldomridge et al., 2006; Clark and Breckinridge 2011). Although not considered here, 90° folding mirrors as in Newtonian-type telescopes (Breckinridge et al., 2015; Di et al., 2015) and stress birefringence in windows and lenses or unfavourable coatings might cause severe polarisation effects. This requires further investigation. In general, errors caused by a light beam which is divergent or inclined towards the optical axis are not discussed here; this means the light beams are assumed to be either perfectly parallel before and after polarisation optics, or that an optical element is insensitive to the incident angle regarding polarisation.
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Automated extraction of corn leaf points from unorganized terrestrial LiDAR point clouds

Automated extraction of corn leaf points from unorganized terrestrial LiDAR point clouds

Abstract: Terrestrial LiDAR data can be used to extract accurate structure parameters of corn plant and canopy, such as leaf area, leaf distribution, and 3D model. The first step of these applications is to extract corn leaf points from unorganized LiDAR point clouds. This paper focused on an automated extraction algorithm for identifying the points returning on corn leaf from massive, unorganized LiDAR point clouds. In order to mine the distinct geometry of corn leaves and stalk, the Difference of Normal (DoN) method was proposed to extract corn leaf points. Firstly, the normals of corn leaf surface for all points were estimated on multiple scales. Secondly, the directional ambiguity of the normals was eliminated to obtain the same normal direction for the same leaf distribution. Finally, the DoN was computed and the computed DoN results on the optimal scale were used to extract leave points. The quantitative accuracy assessment showed that the overall accuracy was 94.10%, commission error was 5.89%, and omission error was 18.65%. The results indicate that the proposed method is effective and the corn leaf points can be extracted automatically from massive, unorganized terrestrial LiDAR point clouds using the proposed DoN method. Keywords: corn leaves, terrestrial LiDAR, cloud points, automatic extraction, crop growth monitoring, phenotyping, difference of normal (DoN), directional ambiguity of the normals
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