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        <title>Frontiers in Remote Sensing | Lidar Sensing section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/remote-sensing/sections/lidar-sensing</link>
        <description>RSS Feed for Lidar Sensing section in the Frontiers in Remote Sensing journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-13T09:19:42.874+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1774149</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1774149</link>
        <title><![CDATA[Low-cost mobile laser scanning for urban tree assessment: accuracy evaluation and application potential]]></title>
        <pubdate>2026-03-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jozef Výbošťok</author><author>Juliána Chudá</author><author>Daniel Tomčík</author><author>Michal Skladan</author><author>Arunima Singh</author><author>Roman Kadlečík</author><author>František Chudý</author><author>Daniel Kükenbrink</author><author>Martin Mokroš</author><author>Janusz Bedkowski</author>
        <description><![CDATA[Recent advances in mobile laser scanning (MLS) have enabled rapid three-dimensional data acquisition for urban tree monitoring, providing an alternative to traditional terrestrial laser scanning (TLS) and photogrammetric approaches. However, the high cost of commercial handheld mobile laser scanning (HMLS) systems limits their routine use in urban green-space inventories. This study evaluates the performance of a low-cost wearable MLS prototype based on a Livox MID-360 sensor and compares it with two commercial HMLS systems (Stonex X120GO and Stonex X200GO) for urban tree assessment. The analysis was conducted in an urban park environment and included 80 individual trees. Tree detection rate (TDR), diameter at breast height (DBH), tree height (TH), crown projection, and point cloud quality were evaluated using commonly applied processing workflows (RayCloud with ITSMe, FSCT, and 3DFin). Using the best-performing workflow, the prototype achieved a DBH RMSE of 2.47 cm and a TH RMSE of 0.43 m, compared to 1.25–2.08 cm (DBH RMSE) and 0.31–0.40 m (TH RMSE) for the commercial systems. Mean cross-section quality metrics further supported data reliability, with Cross Section Quality Index (CSQI) values of 0.78 for the prototype and up to 0.83 for the high-end system, and corresponding Standard Deviation of Radial Distances (SDRD) values of 0.034 m and 0.018 m, respectively. Despite lower point density and increased noise, the low-cost wearable MLS prototype provided comparable TDR, DBH, and TH estimates. Differences in processing time were mainly driven by the selected workflow rather than by the scanning device. Overall, the results demonstrate that low-cost wearable MLS systems can deliver reliable urban tree metrics when combined with suitable processing methods, offering a cost-effective alternative for urban tree inventories and operational monitoring.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1725509</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1725509</link>
        <title><![CDATA[Species level mapping of forest canopy height in Nepal using GEDI with Sentinel-1 and Sentinel-2]]></title>
        <pubdate>2026-01-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abid Nazir</author><author>Niall P. Hanan</author><author>Hammad Gilani</author><author>Him Lal Shrestha</author>
        <description><![CDATA[Forest canopy height mapping is critical for mapping and modeling bio-geophysical and ecological factors, including forest aboveground biomass, carbon reserves, forest carbon emissions, habitat diversity, forest degradation, and restoration success. The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne Light Detection and Ranging (LiDAR) sensor designed specifically to collect data on forest ecosystems worldwide. However, the information obtained by GEDI is not wall-to-wall, requiring data fusion approaches to map spatially continuous canopy heights. This study, for the first time, presents canopy height models for the entire country of Nepal based on interpolated GEDI tree heights fusing Sentinel-2 multispectral imagery with Sentinel-1 synthetic aperture radar (SAR), creating species-specific continuous canopy height models for Nepal at 10 m resolution. Forest plot field data, collected from a nationwide campaign, provided data on species identity, which was used for species mapping and accuracy evaluation. Differences in canopy-architecture and leaf-level traits mean that species-specific models are needed to interpolate GEDI tree heights using the Sentinel optical and SAR data. The national forest height map was compared with an independent set of GEDI data (RMSE = 2.4 m, R2 = 0.92, intercept (c) = 0.53 m and slope (m) = 0.98) and fully independent field data (RMSE = 3.7 m, R2 = 0.74, c = 4.1 m, and m = 0.89). The developed forest type map and canopy height models have the potential to aid in both operational monitoring and hindcasting of historical forest height and its dynamics. Local and national forest management initiatives and international climate and sustainable development projects require this kind of capacity.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1637802</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1637802</link>
        <title><![CDATA[Real-time modulation of a global inland water body reference mask using ICESat-2 for identification of dynamic water surface extents]]></title>
        <pubdate>2026-01-26T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Jeremy Stoll</author><author>Michael Jasinski</author><author>John Robbins</author><author>David Hancock</author><author>Jyothi Nattala</author>
        <description><![CDATA[In order to take advantage of the unprecedented opportunity to measure global inland surface water heights using new-generation spaceborne instruments like ICESat-2’s Advanced Topographic Laser Altimeter System (ATLAS), robust water body shape information is necessary to group photon returns into body transits for along-track processing and to define water edges. A code environment that is able to combine known water body extent from past studies with real-time satellite observations can allow for such results. Even a static water body mask representing the state of a previous time, when working in conjunction with dynamic width-finding software algorithms that take advantage of contemporary return analysis, offers the opportunity for analysis of water surface profiles that change between satellite overpasses. However, previously existing masks do not include all water body types, are of insufficient resolution, or are not native to a format that allows for precise buffering, scalability, and efficient file size. By merging diverse buffered data sets in a modular, updatable fashion, a new global inland water body masking approach is created that can work in conjunction with an operational retrieval algorithm to identify water bodies globally and process to their edges for ICESat-2 analysis or other scientific investigations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1622210</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1622210</link>
        <title><![CDATA[3D reconstruction and morphological characteristic study of Abdullahpuram palace building in Vellore-Bangalore NH using terrestrial LiDAR data]]></title>
        <pubdate>2025-12-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Apoorva Mahendra Rekha</author><author>Vaani Nagarajan</author>
        <description><![CDATA[Effective preservation of cultural heritage structures requires precise, non-destructive, and scalable documentation techniques. However, conventional survey methods often fail to capture intricate geometric features and to quantify localized surface deterioration such as spalling and plaster loss. Terrestrial LiDAR scanning provides high-resolution point cloud data well-suited for such applications, though challenges persist in data registration, segmentation, and deterioration quantification. This study applies terrestrial LiDAR technology to the documentation of the Abdullahpuram Palace, a 19th-century heritage building located in Vellore, Tamil Nadu, India, which exhibits Indo-Saracenic architectural influences (as reported by the Tamil Nadu Heritage Commission, 2019). Multiple scans were registered using Cyclone 360, and the data were pre- and post-processed in CloudCompare for noise filtering, segmentation, and geometric refinement. Surface deterioration was assessed by extracting 3D surface profiles and quantifying volume of material loss using convex hull and raster-based analyses in MeshLab and ArcGIS, respectively. It is to be noted that material loss represents the surface-level deterioration rather than direct evidence of structural failure. Additionally, an octree-based downscaling approach was also implemented to facilitate multi-scale visualization and improve computational efficiency for large datasets. The methodology enhances heritage documentation, supports objective condition assessment, and aligns with sustainable conservation principles articulated in SDG 9 and 11.4. The findings highlight the potential of terrestrial LiDAR and advanced point cloud processing to develop accurate, scalable, and non-invasive documentation strategies for heritage conservation globally.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1697927</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1697927</link>
        <title><![CDATA[Estimation of individual tree biomass for three tree species using LiDAR and multispectral data in megacity Shanghai]]></title>
        <pubdate>2025-12-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Peiwen Luo</author><author>Yanwen Zhang</author><author>Junjie Ruan</author><author>Guowei Zhang</author><author>Juan Tan</author><author>Qing Wang</author><author>Kankan Shang</author>
        <description><![CDATA[Urban forest parks are vital ecological barriers that safeguard urban ecological security and provide essential ecosystem services. Aboveground biomass (AGB) is a key indicator for evaluating these services. This study targeted three tree species—Ligustrum lucidum, Camphora officinarum and Koelreuteria paniculata—in Haiwan National Forest Park of Shanghai, China. Based on field-measured individual tree AGB, high-density point clouds from terrestrial laser scanning (TLS), and features from UAV multispectral imagery, four machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Support Vector Regression (SVR)—were developed. SHapley Additive exPlanations (SHAP) analysis was conducted to identify key predictors and quantify their importance. The results show that: (1) Data fusion of TLS and multispectral imagery significantly, improves estimation accuracy compared with single data sources, with RF consistently achieving the best performance across species (test set R2 = 0.96, 0.92, and 0.91 for L. lucidum, C. officinarum, and K. paniculata, respectively). (2) The effectiveness of data fusion varies by species: for C. officinarum and K. paniculata, fusion models outperformed TLS-only models by 2% and 5% in R2, respectively; for L. lucidum, fusion accuracy (R2 = 0.92) was comparable to TLS alone, both outperforming multispectral-only models. (3) SHAP analysis indicates that structural features from TLS—particularly the interaction between tree height and volume—dominate AGB estimation, contributing over 70% of the total feature importance, while spectral and vegetation index features (e.g., RE, NDVI, OSAVI) contribute about 20%. These findings demonstrate that integrating multi-source remote sensing data enables efficient and precise individual tree AGB estimation tailored to different species, providing a technical basis for intelligent monitoring of urban forests in megacity Shanghai.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1700955</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1700955</link>
        <title><![CDATA[Correction: Application of high-precision terrestrial light detection and ranging to determine the dislocation geomorphology of Yumen Fault, China]]></title>
        <pubdate>2025-10-06T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Shuai Kang</author><author>Qisong Jiao</author><author>Lingyun Ji</author><author>Yaguang Zeng</author><author>Chen Chen</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1664060</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1664060</link>
        <title><![CDATA[Editorial: Advancements in agricultural monitoring with AI enhanced remote sensing techniques]]></title>
        <pubdate>2025-09-15T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Yingying Dong</author><author>Wenjiang Huang</author><author>Hongmei Li</author><author>Liangxiu Han</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1506838</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1506838</link>
        <title><![CDATA[Intra- and inter-rater reliability in log volume estimation based on LiDAR data and shape reconstruction algorithms: a case study on poplar logs]]></title>
        <pubdate>2025-09-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gabriel Osei Forkuo</author><author>Stelian Alexandru Borz</author>
        <description><![CDATA[Producing reliable log volume data is an essential feature in an effective wood supply chain, and LiDAR sensing, supported by portable platforms, is a promising technology for volume measurements. Computer-based algorithms like Poisson interpolation and Random Sampling and Consensus (RANSAC) are commonly used to extract volume data from LiDAR point clouds, and comparative studies have tested these algorithms for accuracy. To extract volume data, point clouds require several post-processing steps, while their outcome may depend largely on human input and operator decision. Despite the increasingly number of studies on accuracy limits, no paper has addressed the reliability of these procedures. This raises at least two questions: (i) Would the same person, working with the same data and using the same procedures get the same results? And (ii) How much would the results deviate when different people process the same data using the same procedures? A set of 432 poplar logs placed on the ground and spaced about 1 m apart, was scanned by a professional mobile LiDAR scanner in groups; the first 418 logs were then individually scanned using an iPhone-compatible app, with the remainder being excluded from this part of the study due to field time constraints and all the logs were manually measured to get the reference biometric data. Three researchers with different experiences processed the datasets produced by scanning twice, following a protocol that included shape reconstruction and volume calculation using Poisson interpolation and RANSAC algorithm for cylinders and cones. The intra- and inter-rater reliability were evaluated using a comprehensive array of statistical metrics. The results show that the most reliable estimates correlate with a greater experience. The Cronbach’s alpha metric at the subject level was high, with values of 0.902–0.965 for the most experienced subject, and generally indicated moderate to excellent intra-rater reliabilities. Moreover, working with Poisson interpolation and RANSAC cylinder shape reconstruction, respectively, indicated a moderate to excellent reliability. For the Poisson interpolation algorithm, the Intraclass Correlation Coefficient (ICC) ranged from 0.770 to 0.980 for multi-log datasets, and from 0.924 to 0.972 for single log datasets. For the same type of input datasets, the ICC varied between 0.761 and 0.855 and from 0.839 to 0.908 for the RANSAC cylinder, and from 0.784 to 0.869 and 0.843 to 0.893 for the RANSAC cone shape reconstruction algorithms, respectively. These values indicate a moderate to excellent inter-rater reliability. Similar to Cronbach’s alpha, the Root Mean Square Error (RMSE) was related in magnitude to the ICC. The results of this study indicate that, for improved reliability and efficiency, it is essential to automate point cloud segmentation using advanced machine learning and computer vision algorithms. This approach would eliminate the subjectivity in segmentation decisions and significantly reduce the time required for the process.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1553026</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1553026</link>
        <title><![CDATA[Elastic and inelastic LiDAR pulse return phenomenology in coastal underwater biological substrates]]></title>
        <pubdate>2025-09-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Matthieu Huot</author><author>Fraser Dalgleish</author><author>Michel Piché</author><author>Philippe Archambault</author>
        <description><![CDATA[In the context of current and future climate-related environmental changes, the development of innovative underwater substrate detection, classification and imaging methods at large spatial scales is key in monitoring and understanding changes from stresses occurring in coastal ocean areas. This development will help understand the spatial distribution and abundance patterns of marine primary producers and ecosystem service providers such as macroalgae, eelgrass and other important ecosystem components such as coral, and can provide insights into future ecosystem response and better management practices. The objective of the current work is to describe an analysis of data acquired by full waveform underwater fluorescence LiDAR, designed for detecting, imaging, and generating 3D point clouds of inert and biological substrates capable of fluorescence. Since the instrument is designed as a small form-factor AUV payload operating at standoff distances of 5–10 m, we chose to implement full-waveform (2.5 Gs/s), pulsed 532 nm laser, capable of generating 1 ns pulses of up to 2.5 uJ at a 200 kHz repetition rate to generate elastic (532 nm) and inelastic (685 nm) 3D point clouds for underwater benthic mapping. Analysis of these acquired waveforms has shown opportunities for improving the point cloud density, by identifying multiple returns within the same waveform, when present. Pulse return processing methods such as Gaussian decomposition and Richardson-Lucy deconvolution are evaluated on data acquired during LiDAR sea-trials over various bottom substrates. As the present LiDAR beam footprint is relatively small to maximize energy density for longer range detection and potential fluorescence response, the number of detected returns per pulse ranges from one in the case of a bare benthic substrate and up to 2 or 3, in areas where for example, macroalgae, kelp, corals and/or other substrates characterized by a vertical structure are present.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1521446</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1521446</link>
        <title><![CDATA[Automated depth correction of bathymetric LiDAR point clouds using PointCNN semantic segmentation]]></title>
        <pubdate>2025-08-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ovi Paul</author><author>Nima Ekhtari</author><author>Craig L. Glennie</author>
        <description><![CDATA[The study explores deep learning to perform direct semantic segmentation of bathymetric lidar points to improve bathymetry mapping. Focusing on river bathymetry, the goal is to accurately and simultaneously classify points on the benthic layer, water surface, and ground near riverbanks. These classifications are then used to apply depth correction to all points within the water column. The study aimed to classify the scene into four classes: river surface, riverbed, ground, and other (for points outside of those three classes), focusing on the river surface and riverbed classes. To achieve this, PointCNN, a convolutional neural network model adept at handling unorganized and unstructured data in 3D space was implemented. The model was trained with airborne bathymetric lidar data from the Swan River in Montana and the Eel River in California. The model was tested on the Snake River in Wyoming to evaluate its performance. These diverse bathymetric datasets across the United States provided a solid foundation for the model’s robust testing. The results were strong for river surface classification, achieving an Intersection over Union of (0.89) and a Kappa coefficient of (0.92), indicating high reliability and minimal errors. The riverbed classification also showed an IoU of (0.7) and a slightly higher Kappa score of (0.76). Depth correction was then performed on riverbed points, proportional to the calculated depth from a surface model formed by Delaunay triangulation of ground and river surface points. The automated process performs significantly faster than traditional manual classification and depth correction processes, saving time and expense. Finally, corrected depths were quantitatively validated by comparing with independent Acoustic Doppler Current Profiler measurements from the Snake River, obtaining a mean depth error of 2 cm and an Root mean square error of 16 cm. These validation results show the reliability and accuracy of the proposed automated bathymetric depth correction workflow.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1599128</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1599128</link>
        <title><![CDATA[A workflow for extracting ungulate trails in wetlands using 3D point clouds obtained from airborne laser scanning]]></title>
        <pubdate>2025-08-04T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Jinhu Wang</author><author>Perry Cornelissen</author><author>W. Daniel Kissling</author>
        <description><![CDATA[Ungulates and other mammalian herbivores can create trails in dense vegetation by trampling and browsing. This can affect vegetation structure and result in the fragmentation of closed, high vegetation, with subsequent impacts on biodiversity. Manually mapping trails in the field or from aerial photographs can be challenging and time-consuming, especially in inaccessible or difficult-to-access habitats such as wetlands and if trails occur beneath the canopy of woody plants (i.e., trees and shrubs) or in other tall vegetation (e.g., reed). Airborne laser scanning (ALS) provides an alternative method because Light Detection and Ranging (LiDAR) can record returns from both the canopy and the ground, as some laser pulses pass through gaps in the vegetation, resulting in highly accurate and dense three-dimensional (3D) point clouds. Here, we present a workflow for extracting ungulate trails using 3D point clouds obtained from country-wide ALS surveys, illustrated by red deer trampling in reedbeds within a 36 km2 marsh area of a Dutch nature reserve. The workflow starts by pre-processing to retile the LiDAR point clouds to designated tiles and removes outliers from the raw data. The (near-)terrain points are then segmented using an iterative filtering algorithm, and digital terrain models are generated with a user-defined resolution. Finally, trail cells are extracted by thresholding the residuals from iterative Laplacian smoothing and then refined by sparse 3D structure tensor voting. The parameters of the workflow were optimized with comprehensive sensitivity analyses. Applying the workflow resulted in a classification of trail and non-trail grid cells at 10 cm resolution across the study area. Compared to manually labeled ground truths, the results showed an overall accuracy of 93% and 90% in regions of red deer grazing only and both geese and red deer grazing, respectively. To test its transferability, the workflow could be applied to other LiDAR data (e.g., ALS surveys from another flight campaign in the same study area or in a different country), to other nature areas (e.g., other rewilding sites or other wetlands), and to other ungulate species (e.g., domesticated livestock or other native large herbivores).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1566077</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1566077</link>
        <title><![CDATA[Application of high-precision terrestrial light detection and ranging to determine the dislocation geomorphology of Yumen Fault, China]]></title>
        <pubdate>2025-05-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shuai Kang</author><author>Qisong Jiao</author><author>Lingyun Ji</author><author>Yaguang Zeng</author><author>Chen Chen</author>
        <description><![CDATA[Ground-based three-dimensional (3D) light detection and ranging (LiDAR) is used to collect high-density point clouds of terrain for high-precision topographic survey, remove information on surface vegetation, and allow for the study of fault rupture. Selected as the study area was the west side of the Yumen Fault in China, characterized by a thrust nappe, and information on this typical fault landform. Fundamental issues such as ground-based 3D LiDAR for field collection, data processing, and 3D fault modeling were then analyzed. Finally, the high-precision topography of the surface rupture in this area was obtained, revealing the typical dextral strike–slip dislocation along the fault zone. In the process of data processing, the iterative closet point (ICP) and the optimal point cloud density were used to improve the high efficiency and precision of data processing. Finally, based on point cloud data processing, a digital elevation model (DEM) with a spatial resolution of 0.1 m was obtained for the study area to classify the geomorphic unit, obtain information on the fault scarp and fault broken gully terrain, and quantitatively study and analyze the horizontal dislocation of gully and displacement distance of the fault scarp. This process revealed several seismic events along the fault zone, accompanied by a typical dextral strike–slip phenomenon.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1397513</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1397513</link>
        <title><![CDATA[The use of kinematic photogrammetry and LiDAR for reconstruction of a unique object with extreme topography: a case study of Dutchman’s Cap, Baltic seacoast, Lithuania]]></title>
        <pubdate>2025-02-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Birutė Ruzgienė</author><author>Lina Kuklienė</author><author>Indrius Kuklys</author><author>Dainora Jankauskienė</author><author>Sérgio Lousada</author>
        <description><![CDATA[Nowadays, the development of Unmanned Aerial Vehicle in conjunction with Photogrammetry and LiDAR technologies, has revolutionized the collection of geospatial data. These technologies enable the acquisition of very high-resolution images and dense point clouds. They also allow the generation of aerial mapping products and simulations of geospatial data for territories that are difficult to access using traditional surveying methods. The paper deals with the use of kinematic remote sensing technique for the study of surface with extreme topography as is a near-vertical cliff named Olandian hat situated in the Seaside regional park on the Baltic sea coast of Lithuania. This area has been significantly eroded by the sea due to the climatic changes of the last few decades, which have caused substantial damage to the coastline. Quantitative measurements show that coastal erosion has led to a retreat of up to [X] meters over the last [Y] years. In order to preserve this unique cultural object, needs to keep an observation on a regular basis (as monitoring) for capture the real situation. Applying an appropriate way for generation of the reliable and accurate spatial models of surface with extreme topography, four data sets were processed: images gained with the camera, oriented horizontally; images gained with the camera tilted at about 50°; combining both image sets collected from camera; LiDAR data. Point clouds and orthophoto maps were generated. The evaluation of aerial mapping products showed that the best accuracy was achieved with products derived from the combined image sets, based on the metric of RMSE, with a mean RMSE of 0.048 m in X, Y, and Z directions. The spatial model generated from LiDAR data is more accurate in Z direction. Correct representation of DEM was not possible using only image data from camera horizontally oriented.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1459524</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1459524</link>
        <title><![CDATA[Monitoring changes of forest height in California]]></title>
        <pubdate>2025-01-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Samuel Favrichon</author><author>Jake Lee</author><author>Yan Yang</author><author>Ricardo Dalagnol</author><author>Fabien Wagner</author><author>Le Bienfaiteur Sagang</author><author>Sassan Saatchi</author>
        <description><![CDATA[Forests of California are undergoing large-scale disturbances from wildfire and tree mortality, caused by frequent droughts, insect infestations, and human activities. Mapping and monitoring the structure of these forests at high spatial resolution provides the necessary data to better manage forest health, mitigate wildfire risks, and improve carbon sequestration. Here, we use LiDAR measurements of top of canopy height metric (RH98) from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission to map vegetation height across the entire California for two different time periods (2019–2020 and 2021–2022) and explore the impact of disturbance. Exploring the reliability of machine learning methods for temporal monitoring of forest is still a developing field. We train a deep neural network to predict forest height metrics at 10-m resolution from radar and optical satellite imagery. Model validation against independent airborne LiDAR data showed a R2≥0.65 for the top of canopy height outperforming existing GEDI-based height maps and with improved sensitivity for mapping tall trees (RH98 ≥ 50 m) across California. Height showed distinct spatial variations across forest types offering quantitative and spatial information to evaluate forest conditions. The model, trained on data from 2019 to 2020, showed a similar accuracy when applied to satellite imagery acquired in 2021–2022 allowing a robust detection of changes caused by natural and man-made disturbances of forest. Changes of height captured impacts of tree mortality and fire intensity, pointing to the influence of wildfire across landscapes. Fires caused more than 60% of the large forest disturbances between the two time periods. This study demonstrates the benefits of using locally trained ML models to rapidly modernize forest management techniques in the age of increasing climate risks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1477503</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1477503</link>
        <title><![CDATA[Spaceborne lidar measurement of global cloud properties through machine learning]]></title>
        <pubdate>2024-10-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Karen Hu</author><author>Xiaomei Lu</author>
        <description><![CDATA[With a large footprint size, multiple scattering measurements of clouds from spaceborne lidar provide useful information about cloud physical properties, such as cloud optical depths and cloud droplet size, both during daytime and nighttime. A neural network algorithm, with a subset of cloud backscatter profiles of dual-polarization and dual-wavelength Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements during daytime as input variables and cloud physical properties derived from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) multi-spectral measurements as output, is developed and evaluated with an independent subset of the collocated CALIPSO and MODIS measurements. The study suggests that with a receiver footprint size of 110 m, CALIPSO lidar measurements are sensitive to liquid-phase cloud optical depth variations from 0 to 25. A larger footprint size, thus more multiple scattering, is required for lidar to have sensitivities to all liquid-phase clouds. The technique can be applied to all 17 years of CALIPSO daytime and nighttime measurements and, thus, provides useful information about global distributions of cloud physical properties both during day and night.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1484122</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1484122</link>
        <title><![CDATA[Editorial: Lidar and ocean color remote sensing for marine ecology]]></title>
        <pubdate>2024-09-11T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Peng Chen</author><author>Panagiotis Kokkalis</author><author>Yudi Zhou</author><author>Iwona S. Stachlewska</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1404877</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1404877</link>
        <title><![CDATA[Sensitivity analysis of space-based water vapor differential absorption lidar at 823 nm]]></title>
        <pubdate>2024-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rory A. Barton-Grimley</author><author>Amin R. Nehrir</author>
        <description><![CDATA[Measurements of water vapor are important for understanding the hydrological cycle, the thermodynamic structure of the lower troposphere, and broader atmospheric circulation. Subsequently, many scientific communities have emphasized a need for high-accuracy and spatial resolution profiles of water vapor within and above the planetary boundary layer (PBL). Advancements in lidar technologies at the NASA Langley Research Center are ongoing to enable the first space-based water vapor differential absorption lidar (DIAL) that can provide high-accuracy and vertical resolution retrievals of moisture in the PBL and through the mid-troposphere. The performance of this space-based DIAL is assessed here for sensitivity throughout the troposphere and globally with representative canonical cases of water vapor and aerosol loading. The specific humidity retrieval sensitivity to systematic and random errors is assessed, and measurement resolutions and capabilities are provided. We show that tunable operation along the side of the 823-nm absorption line allows for the optimization of the lower-tropospheric water vapor retrievals across different meteorological regimes and latitudes and provides the operational flexibility needed to dynamically optimize random errors for different scientific applications. The analysis presented here suggests that baseline and threshold systematic error requirements of <1.5% and <2.5%, respectively, are achievable. Random error is shown to dominate the retrieval, with errors on the order of 5% within the PBL being achievable with 300-m vertical 50-km horizontal resolutions over open ocean and on the order of 10%–15% over high-albedo surfaces. The flexibility of the DIAL method to trade retrieval precision for spatial resolution is shown, highlighting its strengths over passive techniques to tailor retrievals to different scientific applications. Combined, the total error budget demonstrated here indicates a high impact for space-based DIAL, with technologies being advanced for space missions within the next 5–10 years.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2023.1194580</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2023.1194580</link>
        <title><![CDATA[Validation protocol for the evaluation of space-borne lidar particulate back-scattering coefficient bbp]]></title>
        <pubdate>2023-07-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sayoob Vadakke-Chanat</author><author>Cédric Jamet</author>
        <description><![CDATA[Introduction: Space-borne lidar measurements from sensors such as CALIOP were recently used to retrieve the particulate back-scattering coefficient, bbp, in the upper ocean layers at a global scale and those observations have a strong potential for the future of ocean color with depth-resolved observations thereby complementing the conventional ocean color remote sensed observations as well as overcoming for some of its limitations. It is critical to evaluate and validate the space-borne lidar measurements for ocean applications as CALIOP was not originally designed for ocean applications. Few validation exercises of CALIOP were published and each exercise designed its own validation protocol. We propose here an objective validation protocol that could be applied to any current and future space-borne lidars for ocean applications.Methods: We, first, evaluated published validation protocols for CALIOP bbp product. Two published validation schemes were evaluated in our study, by using in-situ measurements from the BGC-Argo floats. These studies were either limited to day- or nighttime, or by the years used or by the geographical extent. We extended the match-up exercise to day-and nighttime observations and for the period 2010–2017 globally. We studied the impact of the time and distance differences between the in-situ measurements and the CALIOP footprint through a sensitivities study. Twenty combinations of distance (from 9-km to 50-km) and time (from 9 h to 16 days) differences were tested.Results & Discussion: A statistical score was used to objectively selecting the best optimal timedistance windows, leading to the best compromise in term of number of matchups and low errors in the CALIOP product. We propose to use either a 24 h/9 km or 24 h/15 km window for the evaluation of space-borne lidar oceanic products.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2023.1135501</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2023.1135501</link>
        <title><![CDATA[Machine learning for underwater laser detection and differentiation of macroalgae and coral]]></title>
        <pubdate>2023-06-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Matthieu Huot</author><author>Fraser Dalgleish</author><author>David Beauchesne</author><author>Michel Piché</author><author>Philippe Archambault</author>
        <description><![CDATA[A better understanding of how spatial distribution patterns in important primary producers and ecosystem service providers such as macroalgae and coral are affected by climate-change and human activity-related events can guide us in anticipating future community and ecosystem response. In-person underwater field surveys are essential in capturing fine and/or subtle details but are rarely simple to orchestrate over large spatial scale (e.g., hundreds of km). In this work, we develop an automated spectral classifier for detection and classification of various macroalgae and coral species through a spectral response dataset acquired in a controlled setting and via an underwater multispectral laser serial imager. Transferable to underwater lidar detection and imaging methods, laser line scanning is known to perform in various types of water in which normal photography and/or video methods may be affected by water optical properties. Using off the shelf components, we show how reflectance and fluorescence responses can be useful in differentiating algal color groups and certain coral genera. Results indicate that while macroalgae show many different genera and species for which differentiation by their spectral response alone would be difficult, it can be reduced to a three color-type/class spectral response problem. Our results suggest that the three algal color groups may be differentiated by their fluorescence response at 580 nm and 685 nm using common 450 nm, 490 nm and 520 nm laser sources, and potentially a subset of these spectral bands would show similar accuracy. There are however classification errors between green and brown types, as they both depend on Chl-a fluorescence response. Comparatively, corals are also very diverse in genera and species, and reveal possible differentiable spectral responses between genera, form (i.e., soft vs. hard), partly related to their emission in the 685 nm range and other shorter wavelengths. Moreover, overlapping substrates and irregular edges are shown to contribute to classification error. As macroalgae are represented worldwide and share similar photopigment assemblages within respective color classes, inter color-class differentiability would apply irrespective of their provenance. The same principle applies to corals, where excitation-emission characteristics should be unchanged from experimental response when investigated in-situ.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2023.1132208</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2023.1132208</link>
        <title><![CDATA[Using terrestrial laser scanning to evaluate non-destructive aboveground biomass allometries in diverse Northern California forests]]></title>
        <pubdate>2023-05-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Paris Krause</author><author>Brieanne Forbes</author><author>Alexander Barajas-Ritchie</author><author>Matthew Clark</author><author>Mathias Disney</author><author>Phil Wilkes</author><author>Lisa Patrick Bentley</author>
        <description><![CDATA[A crucial part of carbon accounting is quantifying a tree’s aboveground biomass (AGB) using allometric equations, but species-specific equations are limited because data to inform these equations requires destructive harvesting of many trees which is difficult and time-consuming. Here, we used terrestrial laser scanning (TLS) to non-destructively estimate AGB for 282 trees from 5 species at 3 locations in Northern California using stem and branch volume estimates from quantitative structure models (QSMs) and wood density from the literature. We then compared TLS QSM estimates of AGB with published allometric equations and used TLS-based AGB, diameter at breast height (DBH), and height to derive new species-specific allometric AGB equations for our study species. To validate the use of TLS, we used traditional forestry approaches to collect DBH (n = 550) and height (n = 291) data on individual trees. TLS-based DBH and height were not significantly different from field inventory data (R2 = 0.98 for DBH, R2 = 0.95 for height). Across all species, AGB calculated from TLS QSM volumes were approximately 30% greater than AGB estimates using published Forest Service’s Forest Inventory and Analysis Program equations, and TLS QSM AGB estimates were 10% greater than AGB calculated with existing equations, although this variation was species-dependent. In particular, TLS AGB estimates for Quercus agrifolia and Sequoia sempervirens differed the most from AGB estimates calculated using published equations. New allometric equations created using TLS data with DBH and height performed better than equations that only included DBH and matched most closely with AGB estimates generated from QSMs. Our results support the use of TLS as a method to rapidly estimate height, DBH, and AGB of multiple trees at a plot-level when species are identified and wood density is known. In addition, the creation of new TLS-based non-destructive allometric equations for our 5 study species may have important applications and implications for carbon quantification over larger spatial scales, especially since our equations estimated greater AGB than previous approaches.]]></description>
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