Search

Forest Stock Volume (FSV) is one of the key indicators in forestry resource investigation and management on local, regional, and national scales. Limited by the saturation problems of optical satellite remote-sensing imagery in the retrieving of stock volume, and the high cost of Light Detection And Ranging (LiDAR) data, it is still challenging to estimate FSV in a large area using single-sensor remote-sensing data. In this paper, a method integrated multispectral satellite imagery and LiDAR data was developed to map stock volume in a large area. A random forest model was adopted to estimate the stock volume of larch forest in China based on the training samples from the Airborne Laser Scanning (ALS)-derived stock volume and corresponding Sentinel-2 imagery. Validation using National Forest Inventory (NFI) data, ALS-derived stock volume and ground investigation data demonstrated that the estimated stock volume had a high accuracy (R2 = 0.59, RMSE = 59.69 m3/ha, MD = 39.96 m3/ha when validated with NFI data; R2 ranged from 0.77 to 0.85, RMSE ranged from 38.68 m3/ha to 67.38 m3/ha, MD ranged from 24.90 m3/ha to 37.27 m3/ha when validated with ALS stock volume; R2 = 0.42, RMSE = 79.10 m3/ha, MD = 62.06 m3/ha when validated with field investigation data). Results of this paper indicated the applicability of estimating stock volume of larch forest in a large area by combining Sentinel-2 data and airborne LiDAR data. [ABSTRACT FROM AUTHOR]

The article focuses on a study which aims at comparing differences in aerosol optical depth (AOD) measured by Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). In the study, AOD levels are evaluated in the dust regions of the Sahara and northwest China and biomass burning regions of South America and South Africa. It reveals higher MODIS AOD over dust regions as compared to CALIPSO AOD.

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the Genie slope on the Tibetan Plateau during a field investigation. To qualitatively determine the current status of the surface deformation of this slope, this study used high-resolution optical remote sensing, airborne light detection and ranging (LiDAR), and interferometric synthetic aperture radar (InSAR) technologies for comprehensive analysis. The interpretation of high-resolution optical and airborne LiDAR data revealed that the rear edge of the slope exhibits three levels of scarps. However, no deformation was detected with differential InSAR (D-InSAR) analysis of ALOS-1 radar images from 2007 to 2008 or with Stacking-InSAR and small baseline subset InSAR (SBAS-InSAR) processing of Sentinel-1A radar images from 2017 to 2020. This study verified the credibility of the InSAR results using the standard deviation of the phase residuals, as well as in-borehole displacement monitoring data. A conceptual model of the slope was developed by combining field investigation, borehole coring, and horizontal exploratory tunnel data, and the results indicated that the slope is composed of steep anti-dip layered dolomite limestone and that the scarps at the trailing edges of the slope were caused by historical shallow toppling. Unlike previous remote sensing studies of deformed landslides, this paper argues that remote sensing results with reliable accuracy are also applicable to the study of undeformed slopes and can help make preliminary judgments about the stability of unexplored slopes. The study demonstrates that the long-term consistency of InSAR results in integrated remote sensing can serve as an indicator for assessing slope stability. [ABSTRACT FROM AUTHOR]

Extracting street trees from mobile Light Detection and Ranging (LiDAR) point clouds is still encountering challenges, such as low extraction accuracy and poor robustness in complex urban environment, and difficulty in the segmentation of overlapping trees. To solve these problems, this paper proposed a street tree extraction and segmentation method based on spatial geometric features of object primitives. In this paper, mobile LiDAR point clouds were first segmented into object primitives based on the proposed graph segmentation method, which can release the computation burden effectively. According to the spatial geometric features of the segmented object primitives, stem points were extracted. In doing so, the robustness and accuracy for stem detecting can be improved. Furthermore, voxel connectivity analysis and individual tree optimization were combined successively. In doing so, the neighboring trees could be separated successfully. Four datasets located in Henan Polytechnic University, China, were used for validating the performance of the proposed method. The four mobile LiDAR point clouds contained 106, 45, 76, and 46 trees, respectively. The experimental results showed that the proposed method can achieve the performance of individual tree separation in all the four testing plots. Compared to the other three methods, the proposed method can make a good balance between the commission and omission errors and achieved the highest average F1 scores. [ABSTRACT FROM AUTHOR]

Over the last two decades, airborne light detection and ranging (LiDAR) has been developed into an advanced tool for practical forest resource inventory monitoring over large areas. Nonetheless, improving the accuracy of forest inventory attribute estimations remains an ongoing challenge. This paper introduces a novel framework for estimating forest inventory attributes based on the stratification of vertical forest structures (VFS). According to the composition and spatial arrangement of the superior, middle, and inferior strata in the tree layer, the forest stand was classified into six distinct VFS classes. Subsequently, the multiplicative power models were established for the stratification-based estimations of the forest inventory attributes, including stand volume (VOL), basal area (BA), and above-ground biomass (AGB), by using a rule-based exhaustive combination approach, and their performances were comparatively analyzed. The result indicated that: compared to the accuracy (relative root mean squared error, rRMSE) of the species-based estimation, the weighted average rRMSE of stratumbased VOL, BA, and AGB estimations of four forest types (Chinese fir, Masson pine, eucalyptus, and broadleaf forests) decreased by 0.3%–7.3%, +3.6%–9.4%, and 0.7%–8.7%, respectively, and the accuracy was significantly improved after stratification. Even after clustering the VFS into two or three classes using cluster analysis, the accuracy of forest attribute estimations remained superior to that of the species-based estimations. Notably, the coefficients of variation for both forest attributes and LiDAR metrics experienced a substantial decrease, and their statistical relationship considerably strengthened within most strata post-VFS classification, which led to an improvement in the accuracy of the forest attribute estimations. The methodology presented in this paper provides a significant advance in improving the accuracy of forest inventory attributes for large areas using airborne LiDAR data. [ABSTRACT FROM AUTHOR]

On June 17, 2020, Aniangzhai landslide, an ancient landslide located in Danba County, southwest China, was reactivated by Meilonggou debris flow. The front edge of the slope collapsed, mobilizing a soil mass of about 2.35 × 106 m3. Evaluating the stability of the whole slope is of great importance to avoid further landslides and mitigate the damage for Aniangzhai villagers living on this slope. This paper focuses on the inaccessible upper slope of Aniangzhai landslide (no attention paid before) that exhibits a relative elevation difference of more than 1000 m. Multi-source remote sensing, including unmanned aerial vehicle (UAV) photogrammetry, light detection and ranging (LiDAR), and satellite-based interferometric synthetic aperture radar (InSAR) techniques, was used in this research to identify and evaluate this high mountain upper slope potential hazard in Aniangzhai landslide. Considering the huge height difference and the steep slope of Aniangzhai landslide, an iterative route planning method was proposed and adopted to obtain a 3D model with 0.02 m resolution and a DEM with 0.25 m resolution by using UAV and LiDAR close-in flight method, respectively. Meter-level huge cracks were clearly identified by the high-resolution UAV 3D model and LiDAR data, which confirm that the location of these cracks is related to the morphological structure of this ancient landslide. Time series InSAR analysis reveals the activity of this high-altitude area, with a maximum LOS displacement rate of 15 cm/a. The combination of the above remote sensing technologies confirms and reveals the high potential risk and the reactivated condition of the upper slope of Aniangzhai landslide. Through this finding, we show that the evolution of Aniangzhai landslide happened through four stages with a cascading effect. This paper proves the usefulness of an integrated method to successfully identify and evaluate the high-altitude upper slope potential hazard and compares the technical features of them, providing a reference for future works that aimed at mitigating the potential damage of the upper slope. [ABSTRACT FROM AUTHOR]

Because wind power is connected to the grid on a large scale, frequency fluctuation in the power grid, which is defined as a system safety risk to the power grid, occurs from time to time. According to the grid code rules of China, wind turbines are required to be equipped with primary frequency modulation or inertia response control capability, which are used to support the safe and stable operation of the power grid. During the traditional frequency modulation process of the wind turbine, power limiting operation or pitch angle reservation is generally adopted to ensure that the reserved energy can be released at any time to support the frequency change in the power grid. However, the frequency support method leads to a large loss of power generation, and does not consider the coordination between mechanical load characteristics control and primary frequency modulation. In this paper, a mechanical load optimization control strategy for a wind turbine during the primary frequency modulation process, based on LIDAR (light detection and ranging) feed forward control technology, is proposed and verified. Through LIDAR feed forward control, the characteristics of incoming wind speed can be sensed in advance, with the consequence that the wind turbine can participate in, and actively control, the primary frequency modulation procedure. According to the characteristics of incoming wind, for instance the amplitude and turbulence, simultaneously, the size of the reserved pitch angle can be adjusted in real time. This kind of method, coordinating with the mechanical load of the wind turbine, can be used to reduce both the ultimate load and fatigue damage as much as possible. Finally, the mechanical load characteristics of the wind turbine with and without the control strategy are compared and studied through simulation. The research results show that the load optimization control strategy based on LIDAR feed-forward control technology can effectively reduce the fatigue and ultimate loads of the wind turbine while supporting the frequency change in the power grid; especially for the fatigue load of tower base tilt and roll bending moments, the reducing proportion will be about 4.3% and 6.3%, respectively. [ABSTRACT FROM AUTHOR]

Soil moisture plays an important role in crop yield estimation, irrigation management, etc. Remote sensing technology has potential for large-scale and high spatial soil moisture mapping. However, offline remote sensing data processing is time-consuming and resource-intensive, and significantly hampers the efficiency and timeliness of soil moisture mapping. Due to the high-speed computing capabilities of remote sensing cloud platforms, a High Spatial Resolution Soil Moisture Estimation Framework (HSRSMEF) based on the Google Earth Engine (GEE) platform was developed in this study. The functions of the HSRSMEF include research area and input datasets customization, radar speckle noise filtering, optical-radar image spatio-temporal matching, soil moisture retrieving, soil moisture visualization and exporting. This paper tested the performance of HSRSMEF by combining Sentinel-1, Sentinel-2 images and in-situ soil moisture data in the central farmland area of Jilin Province, China. Reconstructed Normalized Difference Vegetation Index (NDVI) based on the Savitzky-Golay algorithm conforms to the crop growth cycle, and its correlation with the original NDVI is about 0.99 (P < 0.001). The soil moisture accuracy of the random forest model (R2 = 0.942, RMSE = 0.013 m3/m3) is better than that of the water cloud model (R2 = 0.334, RMSE = 0.091 m3/m3). HSRSMEF transfers time -consuming offline operations to cloud computing platforms, achieving rapid and simplified high spatial resolution soil moisture mapping. [ABSTRACT FROM AUTHOR]

Continuous and extensive monitoring of forest height is essential for estimating forest above-ground biomass and predicting the ability of forests to absorb CO2. In particular, forest height at the national scale is an important indicator reflecting the national forestry economic construction, environmental governance, and ecological balance. However, the lack of inventory data restricts large-scale monitoring of forest height to some extent. Conducting manual surveys of forest height for large-scale areas would be labor-intensive and time-consuming. The successful launch of the new generation of spaceborne light detection and ranging (LiDAR) (The Ice, Cloud, and Land Elevation Satellite-2/the Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS) has brought new opportunities for national-scale forestry resource surveys. This paper explores a method to survey national forest canopy height from the new generation of ICESat-2/ATLAS data. In view of the sparse sampling and little overlap between repeated spaceborne LiDAR data, a strategy for assessing the overall change of canopy height for large scales is provided. Some spatially continuous ancillary data were used to assist ICESat-2/ATLAS data to generate a wall-to-wall (spatially continuous) forest canopy height map in China by using the machine learning approach and then quantifying the analysis of forest canopy height in various provinces. The results show that there is a good correlation between the model forest height and the verification data, with a root mean squared error (RMSE) of 3.30 m and a coefficient of determination (R2) of 0.87. This indicates that the method for retrieving national forest canopy height is reliable. There are some limitations in areas with lower vegetation coverage or complex topography which need additional filtering or terrain correction to achieve higher accuracy in measuring forest canopy height. Our analysis suggests that ICESat-2/ATLAS data can achieve the retrieval of national forest height at an overall level, and it would be feasible to use ICESAT-2/ATLAS products to estimate forest canopy height change for large-scale areas. [ABSTRACT FROM AUTHOR]

The technology of remote sensing-assisted tree species classification is increasingly developing, but the rapid refinement of tree species classification on a large scale is still challenging. As one of the treasures of ecological resources in China, Arxan has 80% forest cover, and tree species classification surveys guarantee ecological environment management and sustainable development. In this study, we identified tree species in three samples within the Arxan Duraer Forestry Zone based on the spectral, textural, and topographic features of unmanned aerial vehicle (UAV) multispectral remote sensing imagery and light detection and ranging (LiDAR) point cloud data as classification variables to distinguish among birch, larch, and nonforest areas. The best extracted classification variables were combined to compare the accuracy of the random forest (RF), support vector machine (SVM), and classification and regression tree (CART) methodologies for classifying species into three sample strips in the Arxan Duraer Forestry Zone. Furthermore, the effect on the overall classification results of adding a canopy height model (CHM) was investigated based on spectral and texture feature classification combined with field measurement data to improve the accuracy. The results showed that the overall accuracy of the RF was 79%, and the kappa coefficient was 0.63. After adding the CHM extracted from the point cloud data, the overall accuracy was improved by 7%, and the kappa coefficient increased to 0.75. The overall accuracy of the CART model was 78%, and the kappa coefficient was 0.63; the overall accuracy of the SVM was 81%, and the kappa coefficient was 0.67; and the overall accuracy of the RF was 86%, and the kappa coefficient was 0.75. To verify whether the above results can be applied to a large area, Google Earth Engine was used to write code to extract the features required for classification from Sentinel-2 multispectral and radar topographic data (create equivalent conditions), and six tree species and one nonforest in the study area were classified using RF, with an overall accuracy of 0.98, and a kappa coefficient of 0.97. In this paper, we mainly integrate active and passive remote sensing data for forest surveying and add vertical data to a two-dimensional image to form a three-dimensional scene. The main goal of the research is not only to find schemes to improve the accuracy of tree species classification, but also to apply the results to large-scale areas. This is necessary to improve the time-consuming and labor-intensive traditional forest survey methods and to ensure the accuracy and reliability of survey data. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Meteor and ionospheric Irregularity Observation System (MIOS), which consists of multi‐station optical subsystem at Ledong (18.4°N, 109°E) and Sanya (18.3°N, 109.6°E), and radar subsystem including a 38.9 MHz all‐sky interferometric radar and a 47.5 MHz coherent phased array radar at Ledong, has been in full operational since December 2021. This paper describes the system design and first results of meteor plasma density irregularities and corresponding meteoroids. The MIOS optical subsystem consists of a few tens of video cameras for observing optical meteor trail and spectrum. The MIOS phased array is composed of 135 Yagi antennas, arranged in sword‐like grid and grouped into 15 identical subarrays, with distances separated by 2–19.5 times the wavelength for unambiguous interferometry measurements. The phased array can form narrow and wide beams, with half power width of 8° and 24° in azimuth, respectively, allowing narrow‐beam pulse‐to‐pulse steering and wide‐beam multi‐baseline imaging observations in the east‐west direction. Observational results show that the MIOS is capable of unambiguously locating various meteor echoes, that is, head, specular and non‐specular echoes, revealing the structural evolution of field‐aligned and non‐field‐aligned irregularity, and of determining the properties of meteoroids producing/not producing irregularities. Cases of bright meteors producing non‐field‐aligned irregularities and not producing field‐aligned irregularities respectively are presented, and possible factors affecting the generation of meteor trail irregularities are discussed based on current understanding. It is expected that the MIOS will provide an important tool to study the generation and evolution of various meteor trail irregularities and the properties of the corresponding meteoroids. Plain Language Summary: Meteoroids entering the Earth's atmosphere significantly disturb the background ionosphere, producing ionized columns and plasma density irregularities, change the ionospheric composition, and even seed large‐scale ionospheric irregularity structures. However, due to the limitations of previous measurements, some physical processes, for example, how efficiently various meteoroids affect the background ionosphere, either producing or not producing some specific meteor trail irregularity phenomena, and the related meteoroid properties and origins, are less well understood. This calls for a facility to measure the properties of both the meteor trail and its corresponding meteoroid, and thus to reveal potential factors affecting the generation of meteor trail irregularities and the disturbance of the background ionosphere. Under the support of national natural science foundation of China, a Meteor and ionospheric Irregularity Observation System (MIOS) was designed to simultaneously capture the radar and optical signatures of meteors and plasma density irregularities. Here we report the initial measurements of the MIOS, which demonstrate its capabilities for observing various meteor and irregularity phenomena. Key Points: An optical and radar facility (MIOS) intended to study how meteoroids change the Earth's background ionosphere has been developedFirst observations of field‐ and non field‐aligned meteor irregularities and the corresponding meteoroids from the facility are presentedThe facility is expected to play an important role in the study of various meteors, their origin and possible processes affecting ionosphere [ABSTRACT FROM AUTHOR]

A significant wind shear event occurred in the early morning of 27 December 2009 at the Hong Kong International Airport (HKIA), leading to the diversion of three aircraft to the airport of Shenzhen (about 39 km to the north-northwest of HKIA). This paper documents the meteorological observations and predictions for the event. The significant wind shear arose from disruption of the prevailing easterly to southeasterly flow by the complex terrain to the south of the airport. It was well captured by the Doppler Light Detection and Ranging (LIDAR) systems at HKIA and, as such, the aircraft were given sufficient warning about the occurrence of significant wind shear. The headwind changes along the glide paths as measured by the LIDAR were consistent with the aircraft measurements. The observations of the remote-sensing instruments during the event, including the LIDAR, are described in the paper. Moreover, this paper discusses the possibility of including wind shear forecast information for flight planning purpose for this particular case. The forecast wind field near HKIA based on a high-resolution numerical weather prediction (NWP) model is described. For the present wind shear event, it appears that the occurrence of terrain-disrupted airflow near HKIA may be forecast about 6 h ahead. The timing of significant wind shear is, however, not reproduced well in the NWP forecast. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

WTs are in yaw during most of the operating time. The wake effect of the yawed Wind turbine (WT) leads to lower wind speed and increased turbulence in the wake region of the WT, which leads to increased dynamic load and lower output power of the downstream WT and limits the safety and economic efficiency of the wind farm to a certain extent. Inspired by the 3DJGF model and Jiménez model, this paper proposes a Yawed-3D Jensen-Gaussian full wake (Y-3DJGF) model considering wind shear and double Gaussian distribution over the wake region which is computationally inexpensive and efficient with the 3D wake distribution characteristics of the yaw WT can be obtained. Moreover, a Doppler Light Detection and Ranging (LiDAR)-based field experimental is conducted in a wind farm in North China, and the SCADA data are integrated to analyze the wake data under yaw conditions and verify the accuracy of the Y-3DJGF model with the verification of the proposed model. The results showed that the average of the relative error between the Y-3DJGF model and the measured data was 4.974%, and the fit with the measured data was better than other 5 typical models. Works in this paper can provide reference for improving the energy output of WT and dynamic load analysis of downstream WT. • A modified 3D yaw full wake model (the Y-3DJGF mode) is proposed. • The Y-3DJGF model considers the effects of yaw conditions and wind shear. • The model can quickly characterize the spatial distribution with low computational cost. • AcQuisition and analysis the SCADA, LiDAR data of the yawed WT. • The Y-3DJGF model is validated by the measured data. [ABSTRACT FROM AUTHOR]

The accurate real-time detection of turbulent airflow patterns near airports is important for safety and comfort in commercial aviation. In this paper, a method is developed to identify Lagrangian coherent structures (LCS) from horizontal lidar scans at Hong Kong International Airport (HKIA) in China. LCS are distinguished frame-independent material structures that create localized attraction, repulsion, or high shear of nearby trajectories in the flow. As such, they are the fundamental structures behind airflow patterns such as updrafts, downdrafts, and wind shear. Based on a recently developed finite-domain--finite-time Lyapunov exponent (FDFTLE) algorithm from Tang et al. and on new Lagrangian diagnostics presented in this paper that are pertinent to the extracted FDFTLE ridges, the authors differentiate LCS extracted from lidar data. It is found that these LCS derived from horizontal lidar scans compare well to convergence and divergence suggested by vertical slice scans. At HKIA, horizontal scans are predominant: they cover much bigger azimuthal ranges as compared with only two azimuthal angles from the vertical scans. LCS extracted from horizontal scans are thus advantageous in providing organizing turbulence structures over the entire observational domain as compared with a single line along the vertical scan direction. In Part II of this study, the authors will analyze the evolution of LCS and their impacts on landing aircraft based on recorded flight data. [ABSTRACT FROM AUTHOR]

High-quality optical data is difficult to acquire in southwest China, due to the cloudy and rainy weather. The synthetic aperture radar (SAR) can work for all-time and all-weather, but the revisit period is usually long. Therefore, it is a high demand for the synergy of optical and radar data rather than a single remote sensing data source, particularly for the survey and assessment on the land resource, as well as the global change. This study performed an object-oriented guided segmentation on the difference images generated from 2016 and 2019 quad-pol Radarsat-2 data using the 2019 Sentinel-2 optical data of Meishan region. A Fractal Net Evolution Approach (FNEA) was first utilized to segment the 2016 optical image for an initial segmentation. Then, the second segmentation was implemented using the same approach, where the features of the SAR image were provided for the purpose of guided segmentation. As such, the regions of growth were merged from small to large, in order to eventually form a complete geographical object. A final segmentation was obtained to select the changed and unchanged samples. More importantly, an object contained both changed and unchanged pixels, particularly with a single type of pixel inside the samples. Then, the object-level multidimensional features were extracted using the samples. The initial radar parameters included the backward scattering coefficient, Pauli and Freeman-Durden polarization decomposition parameters. The segmentation was utilized to generate the geographic objects. Meanwhile, some parameters were calculated using the initial parameters, including the mean, and standard deviation, whereas, the texture feature parameters using the Gray-level co-occurrence matrix (GLCM) included the mean, standard deviation, homogeneity, contrast, entropy, and the correlation for a total of 72-dimensional features. The redundant feature dataset was filtered to select the extracted feature set. Consequently, an FSO-RF change detection framework was proposed to achieve the feature optimization and the acquisition of the final change detection. The inter-sample distance metric learning was used to optimize the separability of feature space. The optimal features were used to calculate the distance between the changed and unchanged samples in different features. Finally, the change detection task was implemented using an object-oriented random forest classifier. The existing change detection pixel-based methods were selected to verify the improved model, including the change vector analysis (CVA), principal component analysis (PCA), multivariate alteration detection (MAD), Iteration multivariate alteration detection (IR-MAD), support vector machines (SVM), and random forest (RF) change detection using radar difference image segmentation. The change detection framework showed a remarkable improvement, with an overall accuracy of 92.90% in the test area. Two representative areas were selected in the study area for sampling and validation. The experiments demonstrated that better performance was achieved than that of the traditional, although the accuracy indexes were degraded in some areas with the complex features. The overall accuracies of the two sample areas reached 95.08 % and 88.16%. The algorithm in this paper can well meet the needs of detecting changes in different feature categories such as towns and farmlands, and has certain application value in the monitoring of national land resources. [ABSTRACT FROM AUTHOR]

In the low-lying, river-rich Pearl River Delta in South China, an extensive network of flood defense levees, spanning over 4400 km, plays a crucial role in urban flood management. These levees are designed to withstand floods and storm surges, yet their failure can lead to significant human and economic losses, highlighting the need for robust urban flood defense strategies. This necessitates the development of a sophisticated geographic information system for the levee network and rapid, accurate assessment methods for levee conditions to support water management and flood mitigation efforts. This study focuses on the levees along the Hengmen waterway in the Pearl River Delta, utilizing airborne Light Detection and Ranging (LiDAR) technology to gather 3D spatial data of the levees. Employing the Cloth Simulation Filter (CSF) algorithm, non-ground point cloud data were extracted. The study improved upon the region-growing algorithm, using a seed point set approach for the automatic extraction of levee point cloud data. The accuracy and completeness of levee extraction were evaluated using the quality index. This method achieved effective extraction of four levee types, showing significant improvements over traditional algorithms, with extraction quality ranging from 72% to 83%. Key research outcomes include the development of a novel method for detecting localized levee depressions based on the computation of the variance of angles between normal vectors in single-phase levee point cloud data. An adaptive optimal neighborhood approach was utilized to accurately determine the normal vectors, effectively representing the local morphology of the levee point clouds. Applied in three levee depression detection experiments, this method proved effective, demonstrating the capability of single-phase data in identifying regions of levee depression deformation. This advancement in levee monitoring technology marks a significant step forward in enhancing urban flood defense capabilities in regions such as the cities of the Pearl River Delta in China. [ABSTRACT FROM AUTHOR]

The infestation of pine shoot beetles (Tomicus spp.) in the forests of Southwestern China has inflicted serious ecological damages to the environment, causing significant economic losses. Therefore, accurate and practical approaches to detect pest infestation have become an urgent necessity to mitigate these harmful consequences. In this study, we explored the efficiency of thermal infrared (TIR) technology in capturing changes in canopy surface temperature (CST) and monitoring forest health at the scale of individual tree crowns. We combined data collected from TIR imagery and light detection and ranging (LiDAR) using unmanned airborne vehicles (UAVs) to estimate the shoot damage ratio (SDR), which is a representative parameter of the damage degree caused by forest infestation. We compared multiple machine learning methods for data analysis, including random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), to determine the optimal regression model for assessing SDR at the crown scale. Our findings showed that a combination of LiDAR metrics and CST presents the highest accuracy in estimating SDR using the RF model (R2 = 0.7914, RMSE = 15.5685). Our method enables the accurate remote monitoring of forest health and is expected to provide a novel approach for controlling pest infestation, minimizing the associated damages caused. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the evening of 11 April 2011, vortex/wave shedding from a mountain to the south of the Hong Kong International Airport (HKIA) appeared to occur following the establishment of fresh to strong easterly airflow in Hong Kong. This paper documents the observations by the various meteorological instruments of the event and discusses the possibility of forecasting the vortex/wave shedding using a high resolution numerical weather prediction (NWP) model. The vortex/wave shedding was well observed by the Light Detection and Ranging (LIDAR) system at HKIA. The vortex/wave appeared as an area of reversed flow against the background east to southeasterly airflow in the airport area. It was detached from the mountain and moved to the northwest with the background southeasterly flow, with a gradual decrease of the spatial extent in the area of reversed flow. The vortex/wave brought about significant wind shear to both arrival flight paths to the west of HKIA, as evidenced from the headwind profile data measured by the LIDARs and the aircraft data. It had rather large vertical extent, reaching the top of the boundary layer, but did not extend downward to the surface, as shown from the vertical scans of the LIDAR and the surface wind observations. The shedding event is successfully captured by high-resolution NWP model with a spatial resolution of 50 m. The model output has a lead time of 6-7 h ahead of the event, indicating the possible occurrence of terrain-induced wind shear in the form of vortex/wave shedding from a mountain. As a result, the numerical simulation could be useful in giving an early alert to the aviation weather forecaster about the significant wind shear event, which could be hazardous to the landing aircraft. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

In the morning of 5 March 2010, an aircraft experienced tail strike when it departed from the south runway of the Hong Kong International Airport to the west. This paper documents the meteorological conditions in the tail strike event, and discusses the alerting and the forecasting aspects of the event. Based on the analysis of the aircraft manufacturer, the tail strike is believed to arise from significant wind shear (headwind loss) experienced by the aircraft. By analysing the surface and the LIDAR observations, it appears that the significant wind shear is related to disruption of the background southwesterly flow by the complex terrain near the airport. From the surface observations, there is a microscale cyclone at the western part of the south runway. The radial velocity image of the LIDAR also depicts an area of reverse flow in that region against the background southerly flow. The alerting of the wind shear based on the two wind shear alerting algorithms developed by the Hong Kong Observatory is discussed, and it turns out that the anemometer-based wind shear algorithm could successfully capture this wind shear occurring close to the ground. On the other hand, due to geometrical constraint, the LIDAR does not pick up the headwind loss experienced by the aircraft. The forecasting of this wind shear event is considered by performing high resolution (down to a horizontal resolution of 50 m) using a mesoscale numerical weather prediction model. The simulated headwind profile at the event time is generally consistent with the aircraft data, though the headwind loss in the simulation is smaller than the reality. Together with the forecast wind gusts, it may be possible to alert the aviation weather forecaster at an earlier time about the occurrence of terrain-induced wind shear over the south runway in this particular event. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Coastal wetlands are considered the most biologically diverse of all ecosystems. Understanding their structures and functions is important for coastal environmental management and development. Airborne LiDAR (Light Detection and Ranging) has emerged as an effective tool for creating high-resolution digital surface models (DSM, highest elevation points) and digital terrain models (DTM, ground only points), which are vital geographic information sources for various applications in coastal areas, and investigating spatial patterns of vegetation in areas that are difficult to access. This paper investigates LiDAR's capability for mapping marine wetlands, extracting vegetation and channel networks, and identifying intertidal zones. The Yellow River Delta, China, was selected as a study site to conduct experiments. With only a 4.5-h flight, an area of more than 670 km2 was surveyed with great detail. High-resolution DSMs and DTMs were generated, the vegetation coverage and heights were extracted using methods based on height and multi-return and the results were compared, and the water and tidal channels, which reflect the complete water transport system in the area, were mapped and measured. By combining LiDAR data and local tidal observations, the intertidal zone, which is a significant part of coastal wetlands, is clearly identified. [ABSTRACT FROM AUTHOR]

Huaneng Rudong 300 MW offshore wind farm project is located in eastern China. The wake effect is one of the major concerns for wind farm operators, as all 70 units are plotted in ranks, and the sea surface roughness is low. This paper investigated the wake intensity by combining a field test and a numerical simulation. To carry out further yaw optimization, a Gaussian wake model was adopted. Firstly, a 3D Light Detection and Ranging device (LiDAR) was used to capture the features in both horizontal and vertical directions of the wake. It indicated that Gaussian wake model can precisely predict the characteristics under time average and steady state in the wind farm. The predicted annual energy production (AEP) of the whole wind farm by the Gaussian model is compared with the calculation result of the actuator line (AL)-based LES method, and the difference between the two methods is mostly under 10%. [ABSTRACT FROM AUTHOR]

This paper reports the use of LIDAR technique, vertical wind profiler (VWP) and the coupled MM5-CMAQ air quality modeling system to investigate a high PM10 concentration episode occurred in Beijing, China during January 8–9, 2004. Through the regression analysis between the observed PM10 concentrations and the observed extinction coefficients, the converting formulas from the LIDAR records to the PM10 mass concentrations in Beijing have been found. Further, a 2-level-nested grid domain with spatial resolutions of 36 and 12 km have been designed and employed for this study, and the coupled MM5-CMAQ modeling system has then been evaluated using both the ground-level PM10 observations and the vertical profiles of PM10 deduced from the measured LIDAR extinction coefficients. Based on the verified modeling system, two emission scenarios were designed to quantitatively assess the trans-boundary PM10 contributions from the surrounding provinces of Beijing. The results illustrated that the particulate matter buildup over the Beijing region was due to a number of factors, including pollutants brought in from its surrounding provinces by the southwest winds and the emission from local sources within Beijing. It indicates that while Beijing needs to take positive steps to reduce its own pollution emissions, much effort should also be placed on demanding more pollution reduction and better environmental performance from its surrounding provinces. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (Rmatch) of tree tops is up to 0.92, the coefficient of determination (R2) of tree height estimation is up to.94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping. [ABSTRACT FROM AUTHOR]

Dams and reservoirs are human-made infrastructures that have attracted increasing attention because of their societal and environmental significance. Towards better management and conservation of reservoirs, a dataset of reservoir-catchment characteristics is needed, considering that the amount of water and material flowing into and out of reservoirs depends on their locations on the river network and the properties of the upstream catchment. To date, no dataset exists for reservoir-catchment characteristics. The aim of this study is to develop the first database featuring reservoir-catchment characteristics for 3254 reservoirs with storage capacity totaling 682 595 km3 (73.2 % of reservoir water storage capacity in China) to support the management and conservation of reservoirs in the context of catchment level. To ensure a more representative and accurate mapping of local variables of large reservoirs, reservoir catchments are delineated into full catchments (their full upstream contributing areas) and intermediate catchments (subtracting the area contributed by upstream reservoirs from the full upstream part of the current reservoir). Using both full catchments and intermediate catchments, characteristics of reservoir catchments were extracted, with a total of 512 attributes in six categories (i.e., reservoir and catchment body characteristics, topography, climate, soil and geology, land cover and use, and anthropogenic activity characteristics). Besides these static attributes, time series of 15 meteorological variables of catchments were extracted to support hydrological simulations for a better understanding of drivers of reservoir environment change. Moreover, we provide a comprehensive and extensive reservoir dataset on water level (data available for 20 % of 3254 reservoirs), water surface area (99 %), storage anomaly (92 %), and evaporation (98 %) from multisource satellites such as radar and laser altimeters and images from Landsat and Sentinel satellites. These products significantly enhance spatial and temporal coverage in comparison to existing similar products (e.g., 67 % increase in spatial resolution of water level and 225 % increase in storage anomaly) and contribute to our understanding of reservoir properties and functions within the Earth system by incorporated national or global hydrological modeling. In situ data of 138 reservoirs are employed in this study as a valuable reference for evaluation, thus enhancing our confidence in the data quality and enhancing our understanding of the accuracy of current satellite datasets. Along with its extensive attributes, the Reservoir dataset in China (Res-CN) can support a broad range of applications such as water resources, hydrologic/hydrodynamic modeling, and energy planning. Res-CN is on Zenodo through 10.5281/zenodo.7664489 (Shen et al., 2022c). [ABSTRACT FROM AUTHOR]

In general, low density airborne LiDAR (Light Detection and Ranging) data are typically used to obtain the average height of forest trees. If the data could be used to obtain the tree height at the single tree level, it would greatly extend the usage of the data. Since the tree top position is often missed by the low density LiDAR pulse point, the estimated forest tree height at the single tree level is generally lower than the actual tree height when low density LiDAR data are used for the estimation. To resolve this problem, in this paper, a modified approach based on three-dimensional (3D) parameter tree model was adopted to reconstruct the tree height at the single tree level by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data. The approach was applied to two coniferous forest plots in the subtropical forest region, Fujian Province, China. The following conclusions were reached after analyzing the results: The marker-controlled watershed segmentation method is able to effectively extract the crown profile from sub meter-level resolution images without the aid of the height information of LiDAR data. The adaptive local maximum method satisfies the need for detecting the vertex of a single tree crown. The improved following-valley approach is available for estimating the tree crown diameter. The 3D parameter tree model, which can take advantage of low-density airborne LiDAR data and high resolution images, is feasible for improving the estimation accuracy of the tree height. Compared to the tree height results from only using the low density LiDAR data, this approach can achieve higher estimation accuracy. The accuracy of the tree height estimation at the single tree level for two test areas was more than 80%, and the average estimation error of the tree height was 0.7 m. The modified approach based on the three-dimensional parameter tree model can effectively increase the estimation accuracy of individual tree height by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and light detection and ranging (LiDAR) can be used to analyze the geomorphic features in complex plateau mountains. Accordingly, a UAV–LiDAR system was adopted in this study to acquire images and lidar point-cloud dataset in the annular structure of Lufeng, Yunnan. A three-dimensional (3D) model was constructed based on structure from motion and multi-view stereo (SfM–MVS) in combination with a high-resolution digital elevation model (DEM). Geomorphic identification, measurement, and analysis were conducted using integrated visual interpretation, DEM visualization, and geographic information system (GIS) topographic feature extraction. The results indicated that the 3D geomorphological visualization and mapping were based on DEM, which was employed to identify the dividing lines and ridges that were delineated of the pit rim structure. The high-resolution DEM retained more geomorphic detail information, and the topography and the variation between ridges were analyzed in depth. The catchment and ponding areas were analyzed using accurate morphological parameters through a multi-angle 3D visualization. The slope, aspect, and topographic wetness index (TWI) parameters were analyzed through mathematical statistics to qualitatively and accurately analyze the differences between different ridges. This study highlighted the significance of the UAV–LiDAR high-resolution topographic measurements and the SfM–MVS 3D scene modelling in accurately identifying geomorphological features and conducting refined analysis. An effective framework was established to acquire high-precision topographic datasets and to analyze geomorphological features in complex mountain areas, which was beneficial in deepening the research on numerical simulation analysis of geomorphological features and reveal the process evolution mechanism. [ABSTRACT FROM AUTHOR]

The accurate classification of tree species is essential for the sustainable management of forest resources and the effective monitoring of biodiversity. However, a literature review shows that most of the previous unmanned aerial vehicle (UAV) light detection and ranging (LiDAR)-based studies on fine tree species classification have used only limited intensity features, accurately identifying relatively few tree species. To address this gap, this study proposes developing a new intensity feature—intensity frequency—for the LiDAR-based fine classification of eight tree species. Intensity frequency is defined as the number of times a certain intensity value appears in the individual tree crown (ITC) point cloud. In this study, we use UAV laser scanning to obtain LiDAR data from urban forests. Intensity frequency features are constructed based on the extracted intensity information, and a random forest (RF) model is used to classify eight subtropical forest tree species in southeast China. Based on four-point cloud density sampling schemes of 100%, 80%, 50% and 30%, densities of 230 points/m2, 184 points/m2, 115 points/m2 and 69 points/m2 are obtained. These are used to analyze the effect of intensity frequency on tree species classification accuracy under four different point cloud densities. The results are shown as follows. (1) Intensity frequencies of trees are not significantly different for intraspecies (p > 0.05) values and are significantly different for interspecies (p < 0.01) values. (2) The intensity frequency features of LiDAR can be used to classify different tree species with an overall accuracy (OA) of 86.7%. Acer Buergerianum achieves a user accuracy (UA) of over 95% and a producer accuracy (PA) of over 90% for four density conditions. (3) The OA varies slightly under different point cloud densities, but the sum of correct classification trees (SCI) and PA decreases rapidly as the point cloud density decreases, while UA is less affected by density with some stability. (4) The priori feature selected by mean rank (MR) covers the top 10 posterior features selected by RF. These results show that the new intensity frequency feature proposed in this study can be used as a comprehensive and effective intensity feature for the fine classification of tree species. [ABSTRACT FROM AUTHOR]

This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface. [ABSTRACT FROM AUTHOR]

Forests are the largest terrestrial ecosystem carbon pool and provide the most important nature-based climate mitigation pathway. Compared with belowground biomass (BGB) and soil carbon, aboveground biomass (AGB) is more sensitive to human disturbance and climate change. Therefore, accurate forest AGB mapping will help us better assess the mitigation potential of forests against climate change. Here, we developed six models to estimate national forest AGB using six machine learning algorithms based on 52,415 spaceborne Light Detection and Ranging (LiDAR) footprints and 22 environmental features for China in 2007. The results showed that the ensemble model generated by the stacking algorithm performed best with a determination coefficient (R2) of 0.76 and a root mean square error (RMSE) of 22.40 Mg/ha. The verifications at pixel level (R2 = 0.78, RMSE = 16.08 Mg/ha) and provincial level (R2 = 0.53, RMSE = 14.05 Mg/ha) indicated the accuracy of the estimated forest AGB map is satisfactory. The forest AGB density of China was estimated to be 53.16 ± 1.63 Mg/ha, with a total of 11.00 ± 0.34 Pg. Net primary productivity (NPP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), average annual rainfall, and annual temperature anomaly are the five most important environmental factors for forest AGB estimation. The forest AGB map we produced is expected to reduce the uncertainty of forest carbon source and sink estimations. [ABSTRACT FROM AUTHOR]

Based on long-term lidar (light detection and ranging) observations at Yanqing (40.5°N, 116°E) and Pingquan (41°N, 118.7°E), cloud events occurred in the vicinity of the stratopause above Beijing were reported for the first time. These events occurred with tenuous and sparse layers within the altitude range of 33–65 km, and the maximum VBSC value ranged from 1 × 10 − 10 m − 1 sr − 1 to 5.5 × 10 − 9 m − 1 sr − 1 . Considering temperature and water vapor measurements from SABER/TIMED, the occurrence mechanism of these lidar-observed cloud events was examined. It was found that some cloud layers resulted from the nucleation of water vapor due to the local meteorological changes in the middle atmosphere, while other lidar-observed clouds could comprise floating clusters of cosmic dust, hydrate droplets, volcanic ash, space traffic exhaust, etc. These cloud events are rare cloud-like phenomena in the middle atmosphere observed by lidars at midlatitudes in China; they differ from NLCs and PSCs in terms of altitude distribution and seasonal variation, and the relevant microphysics processes behind their occurrence are likely meaningful to meteorology at midlatitudes. [ABSTRACT FROM AUTHOR]

Atmospheric carbon dioxide (CO2) is recognized as the most important component of the greenhouse gases, the concentration of which has increased rapidly since the pre-industrial era due to anthropogenic emissions of greenhouse gases (GHG). The accurate monitoring of carbon dioxide is essential to study the global carbon cycle and radiation budget on Earth. The Aerosol and Carbon Detection Lidar (ACDL) instrument onboard the Atmospheric Environmental Monitoring Satellite (AEMS) was successfully launched in April 2022, which allows a new perspective to quantify the global spatial distribution of atmospheric CO2 with high accuracy. In this work, the impact of the Doppler shift on CO2 measurements for an integrated-path differential absorption (IPDA) light detection and ranging (lidar) system was evaluated to meet the weighted column-averaged mixing ratio of carbon dioxide (XCO2) measurement requirements of less than one part per million (ppm). The measurement uncertainties due to the Doppler shift were first evaluated in airborne IPDA observations. The result shows that most of the Doppler shift is in the range of 6–8 MHz, resulting in 0.26-0.39 ppm deviations in the XCO2 results. The deviations between the XCO2 retrievals and in situ measurements decreased to 0.16 ppm after the correction of the Doppler shift from 11:28:29 to 11:28:49 in the flight campaign. In addition, the online Doppler shift accounts for 98% of the deviations between XCO2 retrievals and in situ measurements. Furthermore, the impact of the Doppler shift on ACDL measurements is also assessed. The differences between the XCO2 retrievals with and without Doppler shift are used to quantify measurement uncertainties due to the Doppler effect. The simulations reveal that a pointing misalignment of 0.067 mrad can lead to a mean bias of about 0.30 ppm (0.072%) in the CO2 column. In addition, CO2 measurements are more sensitive to the Doppler shift at high altitudes for IPDA lidar, so the largest differences in the CO2 columns are found on the Qinghai–Tibet Plateau in China. [ABSTRACT FROM AUTHOR]

The vertical distribution of winter ozone in the Pearl River Delta in 2021 was analyzed using electrochemical concentration cell (ECC) ozonesonde and compared with the ozone Light detection and ranging (LiDAR) data. The transport of stratospheric ozone to the troposphere was also explored. The results show that (1) the maximum value of the stratospheric ozone layer was about 25 km. The stratospheric ozone profile is mainly unimodal, and the stratospheric ozone content was much higher than that of the troposphere. In the troposphere, the ozone content near the ground (0-2 km) was slightly higher than that between 5 and 15 km. The ozone profile in the upper and middle troposphere showed a peak structure, which was mainly divided into single peak (22%) and multipeak (77%) profiles. (2) In the middle and lower troposphere, the vertical profile of ozone detected by LiDAR is significantly different from that detected by sounding system. The difference between the two ranged from 10 ppb to 60 ppb at the same height. Ozone sounding can supplement the blind area of the near-ground layer by LiDAR ozone detection and can calibrate the inversion results of LiDAR. (3) On January 5, 2022, stratospheric ozone was transported to the troposphere. The stratospheric intrusion occurred due the high wind speed of the subtropical jet centre over Qingyuan and due to strong subsidence movement. When the stratospheric air mass moved to the troposphere, and subsidence airflow encountered, the ozone concentration near the ground was rapidly increased in a short time. It was identified as an "exceptional event." [ABSTRACT FROM AUTHOR]

Artificial impervious surface area (ISA) documents the human footprint. Accurate, timely, and detailed ISA datasets are therefore essential for global climate change studies and urban planning. However, due to the lack of sufficient training samples and operational mapping methods, global ISA datasets at a 10 m resolution are still lacking. To this end, we proposed a global ISA mapping method leveraging multi-source geospatial data. Based on the existing satellite-derived ISA maps and crowdsourced OpenStreetMap (OSM) data, 58 million training samples were extracted via a series of temporal, spatial, spectral, and geometric rules. We then produced a 10 m resolution global ISA dataset (GISA-10m) from over 2.7 million Sentinel optical and radar images on the Google Earth Engine platform. Based on test samples that are independent of the training set, GISA-10m achieves an overall accuracy of greater than 86 %. In addition, the GISA-10m dataset was comprehensively compared with the existing global ISA datasets, and the superiority of GISA-10m was confirmed. The global road area was further investigated, courtesy of this 10 m dataset. It was found that China and the US have the largest areas of ISA and road. The global rural ISA was found to be 2.2 times that of urban while the rural road area was found to be 1.5 times larger than that of the urban regions. The global road area accounts for 14.2 % of the global ISA, 57.9 % of which is located in the top 10 countries. Generally speaking, the produced GISA-10m dataset and the proposed sampling and mapping method are able to achieve rapid and efficient global mapping, and have the potential for detecting other land covers. It is also shown that global ISA mapping can be improved by incorporating OSM data. The GISA-10m dataset could be used as a fundamental parameter for Earth system science, and will provide valuable support for urban planning and water cycle study. The GISA-10m can be freely downloaded from 10.5281/zenodo.5791855 (Huang et al., 2021a). [ABSTRACT FROM AUTHOR]

Accurate and automatic identification of tree species information at the individual tree scale is of great significance for fine-scale investigation and management of forest resources and scientific assessment of forest ecosystems. Despite the fact that numerous studies have been conducted on the delineation of individual tree crown and species classification using drone high-resolution red, green and blue (RGB) images, and Light Detection and Ranging (LiDAR) data, performing the above tasks simultaneously has rarely been explored, especially in complex forest environments. In this study, we improve upon the state of the Mask region-based convolution neural network (Mask R-CNN) with our proposed attention complementary network (ACNet) and edge detection R-CNN (ACE R-CNN) for individual tree species identification in high-density and complex forest environments. First, we propose ACNet as the feature extraction backbone network to fuse the weighted features extracted from RGB images and canopy height model (CHM) data through an attention complementary module, which is able to selectively fuse weighted features extracted from RGB and CHM data at different scales, and enables the network to focus on more effective information. Second, edge loss is added to the loss function to improve the edge accuracy of the segmentation, which is calculated through the edge detection filter introduced in the Mask branch of Mask R-CNN. We demonstrate the performance of ACE R-CNN for individual tree species identification in three experimental areas of different tree species in southern China with precision (P), recall (R), F1-score, and average precision (AP) above 0.9. Our proposed ACNet–the backbone network for feature extraction–has better performance in individual tree species identification compared with the ResNet50-FPN (feature pyramid network). The addition of the edge loss obtained by the Sobel filter further improves the identification accuracy of individual tree species and accelerates the convergence speed of the model training. This work demonstrates the improved performance of ACE R-CNN for individual tree species identification and provides a new solution for tree-level species identification in complex forest environments, which can support carbon stock estimation and biodiversity assessment. [ABSTRACT FROM AUTHOR]

Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data. [ABSTRACT FROM AUTHOR]

Forest dynamics, including forest loss and gain, are long-term complex ecological processes affected by nature and human activities. It is particularly important to understand the long-term forest dynamics of protected areas to evaluate their conservation efforts. This study adopted the Landsat tree-canopy cover (TCC) method to derive annual TCC data for the period 1984–2020 for the protected areas of northeast China, where protection policies have been carried out since the end of the 20th century, e.g., the Natural Forest Conversion Program (NFCP). A strong correlation was found between the TCC estimates derived from Landsat and LiDAR observations, suggesting the high accuracy of TCC. Forest loss and gain events were also identified from the time series of TCC estimates. High correlations were reported for both forest loss (Producer's accuracy = 85.21%; User's accuracy = 84.26%) and gain (Producer's accuracy = 87.74%; User's accuracy = 88.31%), suggesting that the approach can be used for monitoring and evaluating the effectiveness of the NFCP and other forest conservation efforts. The results revealed a fluctuating upward trend of the TCC of the protected area from 1986 to 2018. The increased area of TCC was much larger than the decreased area, accounting for 59.68% and 40.34%, respectively, suggesting the effectiveness of protection policies. Since the NFCP was officially implemented in 1998, deforestation was effectively curbed, the area of forest loss was significantly reduced (slope: −14.24%/year), and the area of forest gain significantly increased (slope: 4.13%/year). We found that regional forest changes were mainly manifested in "forest gain after loss (forest recovery)" and "forest gain with no loss (forest newborn)", accounting for 40.29% and 37.28% of the total area of forest change, respectively. Moreover, the forest gain area far exceeds the forest loss area, reaching 11.24 million hectares, suggesting a successful forest recovery due to forest protection. [ABSTRACT FROM AUTHOR]

Water is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources is becoming increasingly serious worldwide. Therefore, the monitoring of water bodies is indispensable. Remote sensing has the advantages of real time, wide coverage, and rich information and has become a brand-new technical means to quickly obtain water information. This study summarizes the current common methods of water extraction based on optical and radar images, including the threshold method, support vector machine, decision tree, object-oriented extraction, and deep learning, as well as the advantages and disadvantages of each method. These methods were applied to the Huai River Basin in China and Nam Co on the Qinghai-Tibet Plateau. The extraction results show that all the aforementioned approaches can obtain reliable results. Among them, the threshold segmentation method based on normalized difference water index is more robust than others. In the water extraction process, there are still many problems that restrict the accuracy of the results. In the future, researchers will continue to search for more automatic, extensive, and high-precision water extraction methods. [ABSTRACT FROM AUTHOR]

Airborne LiDAR (Light Detection And Ranging) remote sensing for individual tree-level forest inventory necessitates proper extraction of individual trees and accurate measurement of tree structural parameters. Due to the inadequate tree finding capability offered by LiDAR technology and the complex patterns of forest canopies, significant omission and commission errors occur frequently in the segmentation results. Aimed at error reduction and accuracy refinement, this paper presents a novel adaptive mean shift-based clustering scheme aided by a tree trunk detection technique to segment individual trees and estimate tree structural parameters based solely on the airborne LiDAR data. Tree trunks are detected by analyzing points' vertical histogram to detach all potential crown points and then clustering the separated trunk points according to their horizontal mutual distances. The detected trunk information is used to adaptively calibrate the kernel bandwidth of the mean shift procedure in the fine segmentation stage by applying an original 2D (two-dimensional) estimation of individual crown diameters. Trunk detection results and LiDAR point clusters generated by the adaptive mean shift procedures serve as mutual references for final detection of individual trees. Experimental results show that a combination of adaptive mean shift clustering and detected tree trunk can provide a significant performance improvement in individual tree-level forest measurement. Compared with conventional clustering techniques, the trunk detection-aided mean shift clustering approach can detect 91.1% of the trees ("recall") with a higher tree positioning accuracy (the mean positioning error is reduced by 33%) in a multi-layered coniferous and broad-leaved mixed forest in South China, and 93.5% of the identified trees are correct ("precision"). The tree detection brings the estimation of structural parameters for individual trees up to an accuracy level: −2.2% mean relative error and 5.8% relative RMSE (Root Mean Square Error) for tree height and 0.6% mean relative error and 21.9% relative RMSE for crown diameter, respectively. [ABSTRACT FROM AUTHOR]

• Three terrain-complexities wind farm areas are selected according to RIX. • LiDAR experiments were conducted on three different terrain wind farm areas. • Wake expansions under different terrains are studied and compared with each other. • The wake characteristics under nine cases are analyzed quantitatively. Large-scale turbines' wake expansion is influenced both by atmospheric conditions and terrain effects. This paper conducted comparative experimental measurements on the wind turbines' wake in three wind farms areas of different terrain complexities using Doppler Light Detection and Ranging (LiDARs). Three wind farm areas of different terrain complexities in North China were selected and wake expansions were detected by three LiDARs over a six-month measurement period. Three wake interaction conditions of separate, full, and half wakes were discussed separately in the aforementioned three wind farm areas of different terrain complexities with a total of nine different cases. The velocity deficit (VD) exhibited a complex expression with the terrain complexity increases. In separate wake condition, the VD trend of flat terrain was gentle, and the decline slopes in moderate and complex areas were 0.1243 and 0.0082 respectively. Results also showed that the wake width (WW) became wider as the terrain complexity increases, and the more complex the terrain was, the faster the WW changes. In separate wake, upstream of full and half wake, WWs in moderate and complex areas showed the same growth trend with an increase rate of 87.5%, which was twice as much as that in flat terrain. Results of this study can provide guidance for the micro-siting arrangement and control strategies of wind turbines in wind farms with different terrain complexities. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Estimate soil moisture content on the spatial scale of the catchment region. • The synergic application of Sentinel-1 radar backscattering coefficients and Sentinel-2 optical images to monitor soil moisture content. • The revision of the water cloud model localization. • Inversion of vegetation moisture content by combining NDVI from Sentinel-2 and on-site samplings. Remote sensing techniques, which provide timely and accurate soil moisture information to improve food production, are becoming increasingly popular among agricultural production in China and many other regions. It also shows its importance in monitoring agricultural basins where the meteorological and topographic conditions vary greatly. In this case, the reliability of soil moisture content (SMC) monitoring for theses basins could be promised. The main objective of this paper is to develop a synergic method of radar and optical data, as well as an on-site sampling scheme, to accurately estimate SMC in the basin. The reason is that in the winter wheat production area of Dawen River basin in China, there are few conventional SMC testing equipment. Considering the characteristics of different crops and the conditions of crops in different growth periods, the VV polarization mode of Sentinel-1 C-band Synthetic Aperture Radar (SAR) images was used to retrieve SMC. Combined with the Normalized difference vegetation index (NDVI) extracted from Sentinel-2 MSI optical images and the optimized Water Cloud Model (WCM), the surface backscattering coefficient was obtained by eliminating vegetation moisture content (VMC), and the SMC model was established. The results showed that from March to May in 2020, the coefficient of determination (R2) between the estimated VMC and the field observation was 0.655, and the Root Mean Square Error (RMSE) between the two was 0.334. In the inversion algorithm model, the R2 between the retrieved SMC and the field measurement was 0.506, and the RMSE was 3.725. [ABSTRACT FROM AUTHOR]

Lidar (Light detection and ranging) has special capabilities for remote sensing of many different behaviours of the atmosphere. One of the techniques which show a great deal of promise for several applications is Raman scattering. The detecting capability, including maximum operation range and minimum detectable gas concentration is one of the most significant parameters for lidar remote sensing of pollutants. In this paper, based on the new method for evaluating the capabilities of a Raman lidar system, we present an evaluation of detecting capability of Raman lidar for monitoring atmospheric CO2 in Hefei. Numerical simulations about the influence of atmospheric conditions on lidar detecting capability were carried out, and a conclusion can be drawn that the maximum difference of the operation ranges caused by the weather conditions alone can reach about 0.4 to 0.5km with a measuring precision within 30ppmv. The range of minimum detectable concentration caused by the weather conditions alone can reach about 20 to 35 ppmv in vertical direction for 20000 shots at a distance of 1 km on the assumption that other parameters are kept constant. The other corresponding parameters under different conditions are also given. The capability of Raman lidar operated in vertical direction was found to be superior to that operated in horizontal direction. During practical measurement with the Raman lidar whose hardware components were fixed, aerosol scattering extinction effect would be a significant factor that influenced the capability of Raman lidar. This work may be a valuable reference for lidar system designing, measurement accuracy improving and data processing. [ABSTRACT FROM AUTHOR]

Accurate monitoring of atmospheric carbon dioxide (CO 2) and its distribution is of great significance for studying the carbon cycle and predicting future climate change. Compared to the ground observational sites, the airborne observations cover a wider area and simultaneously observe a variety of surface types, which helps with effectively monitoring the distribution of CO 2 sources and sinks. In this work, an airborne experiment was carried out in March 2019 over the Shanhaiguan area, China (39–41 ∘ N, 119–121 ∘ E). An integrated path differential absorption (IPDA) light detection and ranging (lidar) system and a commercial instrument, the ultraportable greenhouse gas analyser (UGGA), were installed on an aircraft to observe the CO 2 distribution over various surface types. The pulse integration method (PIM) algorithm was used to calculate the differential absorption optical depth (DAOD) from the lidar data. The CO 2 column-averaged dry-air mixing ratio (XCO 2) was calculated over different types of surfaces including mountain, ocean, and urban areas. The concentrations of the XCO 2 calculated from lidar measurements over ocean, mountain, and urban areas were 421.11 ± 1.24, 427.67 ± 0.58, and 432.04 ± 0.74 ppm, respectively. Moreover, through the detailed analysis of the data obtained from the UGGA, the influence of pollution levels on the CO 2 concentration was also studied. During the whole flight campaign, 18 March was the most heavily polluted day with an Air Quality Index (AQI) of 175 and PM 2.5 of 131 µ g m -3. The aerosol optical depth (AOD) reported by a sun photometer installed at the Funing ground station was 1.28. Compared to the other days, the CO 2 concentration measured by UGGA at different heights was the largest on 18 March with an average value of 422.59 ± 6.39 ppm, which was about 10 ppm higher than the measurements recorded on 16 March. Moreover, the vertical profiles of Orbiting Carbon Observatory-2 (OCO-2) and CarbonTracker were also compared with the aircraft measurements. All the datasets showed a similar variation with some differences in their CO 2 concentrations, which showing a good agreement among them. [ABSTRACT FROM AUTHOR]

The spectral clustering method has notable advantages in segmentation. But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging (LiDAR) point cloud data. We proposed the Nyström-based spectral clustering (NSC) algorithm to decrease the computational burden. This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data. The K-nearest neighbour-based sampling (KNNS) was proposed for the Nyström approximation of voxels to improve the efficiency. The NSC algorithm showed good performance for 32 plots in China and Europe. The overall matching rate and extraction rate of proposed algorithm reached 69% and 103%. For all trees located by Global Navigation Satellite System (GNSS) calibrated tape-measures, the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error (RMSE) of 5.97%. For all trees located by GNSS calibrated total-station measures, the values were 0.89 and 4.49%. The method also showed good performance in a benchmark dataset with an improvement of 7% for the average matching rate. The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data. [ABSTRACT FROM AUTHOR]

Mound tombs, popular in the south Yangzi River area in Shang and Zhou Dynasties, are regional cultural remains in China. With the aims of protecting and scientifically analysing cultural relics, laser scanning technology was adopted to study Zhaihuatou mound tombs located in Nonglin Village in Tianwang town, Jiangsu Province. Multiple tombs are held within one mound in good keep and with the typical construction of centripetalism. Accurate tomb LiDAR (Light Detection And Ranging) data were acquired by applying terrestrial laser scanning technology to the field mound excavation. Subsequently, a spatial morphological analysis of the tombs was conducted on the basis of archaeological rules and GIS spatial data processing methods. Using the theory of centripetalism construction of multi-tomb-one-mounds, we proposed an algorithm to determine the concentrated area of the geometric directions of tombs, and centripetalism theory was scientifically validated in comparing results with the excavation data. Spatial data clustering methods were used to analyse and deduce the spatial distribution characteristics of the tombs. We propose and demonstrate that the burial system is in the form of family-clan aggregation, and is useful for developing research on regional burials. Experimental data show that the proposed method is a novel example of how spatial analysis can foster more precise field archaeological excavations on a large scale, and it is significant to study various types of tombs, relics and ruins. [ABSTRACT FROM AUTHOR]

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction. [ABSTRACT FROM AUTHOR]

Multidimensional hydrodynamic modelling becomes tricky when lacking the bathymetric data representing the continuous underwater riverbed surface. Light detection and ranging (LiDAR)-based and radar-based digital elevation models (DEMs) are often used to build the high-accuracy floodplain topography, while in most cases the submerged riverbed could not be detected because both radar and LiDAR operate at wavelengths that cannot penetrate the water. Data from other sources is therefore required to establish the riverbed topography. The inundated river channel is often surveyed with an echo sounder to obtain discrete cross-section data. In this context, an improved algorithm based on the classic flow-oriented coordinates transformation is proposed to generate the riverbed topography using surveyed cross-sections. The dimensionless channel width (DCW) processing method is developed within the algorithm to largely increase the prediction accuracy, especially for the meandering reaches. The generated riverbed topography can be merged with the floodplain DEM to create an integrated DEM for 2D and 3D hydrodynamic simulations. Two case studies are carried out: a benchmark test in the Baxter River, United States, with carefully surveyed channel–floodplain topographic data to validate the algorithm, and a 3D hydrodynamic modelling-based application in Three Gorges Reservoir (TGR) area, China. Results from the benchmark case demonstrate very good consistency between the created topography and the surveyed data with root mean square error (RMSE) = 0.17m and the interpolation accuracy was increased by 55% compared to the traditional method without DCW processing. 3D hydrodynamic modelling results match the observed field data well, indicating that the generated DEM of the TGR area was good enough not only to predict water depths along the tributary, but also to allow the hydrodynamic model to capture the typical features of the complex density currents caused by both the topography of the tributary estuary and the operation rules of TGR. [ABSTRACT FROM AUTHOR]

Forest stand segmentation is a critical process for forest management and inventory. The forest stand segmentation accuracy will determine the forest stand level parameters quality. In this study, we developed an automatic forest stand segmentation algorithm based on ArboLiDAR, a software used to process Light Detection and Ranging (LiDAR) point cloud data. We then optimized the parameters for the algorithm to the Dayekou forest area on Qilian Mountain in China to find the most suitable parameters for automatic stand segmentation. Further, we extracting the forest parameters at the stand level based on Bysh method. Our results showed that the limited region growing method based on the gradient is the most suitable one for analyzing automatic stand segmentation in the studied area. Among our tested parameters groups, the fifth group contains the optimal parameters for the studied area. In addition, for forest parameters, the R2 of mean height (H), average diameter at breast height (D), basal area (G), and Stand volume (V) is 0.744, 0.720, 0.562, 0.696, respectively. The RMSE value is 5.24%, 28.57%, 19.93%, and 17.66%, respectively. Our study serves as a technical basis and reference for future studies that perform more efficient analyses on forest resource inventory in China. [ABSTRACT FROM AUTHOR]