Eucalyptus carbon stock estimation in subtropical regions with the modeling strategy of sample plots–airborne LiDAR–Landsat time series data

Updating eucalyptus carbon stock data in a timely manner is essential for better understanding and quantifying its effects on ecological and hydrological processes.At present,there are no suitable methods to accurately estimate the eucalyptus carbon stock in a large area.This research aimed to explore the transferability of the eucalyptus carbon stock estimation model at temporal and spatial scales and assess modeling performance through the strategy of combining sample plots,airborne LiDAR and Landsat time series data in subtropical regions of China.Specifically,eucalyptus carbon stock estimates in typical sites were obtained by applying the developed models with the combination of airborne LiDAR and field measurement data;the eucalyptus plantation ages were estimated using the random localization segmentation approach from Landsat time series data;and regional models were developed by linking LiDAR-derived eucalyptus carbon stock and vegetation age(e.g.,months or years).To examine the models’robustness,the developed models at the regional scale were transferred to estimate carbon stocks at the spatial and temporal scales,and the modeling results were evaluated using validation samples accordingly.The results showed that carbon stock can be successfully estimated using the age-based models(both age variables in months and years as predictor variables),but the month-based models produced better estimates with a root mean square error(RMSE)of 6.51 t⋅ha1 for Yunxiao County,Fujian Province,and 6.33 t⋅ha1 for Gaofeng Forest Farm,Guangxi Zhuang Autonomous Region.Particularly,the month-based models were superior for estimating the carbon stocks of young eucalyptus plantations of less than two years.The model transferability analyses showed that the month-based models had higher transferability than the year-based models at the temporal scale,indicating their possibility for analysis of carbon stock change.However,both the month-based and year-based models expressed relatively poor transferability at a spatial scale.This study provides new insights for cost-effective monitoring of carbon stock change in intensively managed plantation forests.

Influence of voxel size on forest canopy height estimates using full-waveform airborne LiDAR data

Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.

Building Damage Extraction from Post-earthquake Airborne LiDAR Data

Building collapse is a significant cause of earthquake-related casualties; therefore, the rapid assessment of buildings damage is important for emergency management and rescue. Airborne light detection and ranging （LiDAR） can acquire point cloud data in combination with height values, which in turn provides detailed information on building damage. However, the most previous approaches have used optical images and LiDAR data, or pre- and post-earthquake LiDAR data, to derive building damage information. This study applied surface normal algorithms to extract the degree of building damage. In this method, the angle between the surface normal and zenith （0） is used to identify damaged parts of a building, while the ratio of the standard deviation to the mean absolute deviation （σ/δ） of θ is used to obtain the degree of building damage. Quantitative analysis of 85 individual buildings with different roof types （i.e., flat top or pitched roofs） was conducted, and the results confirm that post-earthquake single LiDAR data are not affected by roof shape. Furthermore, the results confirm that θ is correlated to building damage, and that σ/δ represents an effective index to identify the degree of building damage.

A Robust Model Fitting-based Method for Transmission Line Extraction from Airborne LiDAR Point Cloud Data

Airborne Light Detection And Ranging(LiDAR)can provide high-quality three-dimensional information for the safety inspection of electricity corridors.However,the robust extraction of transmission lines from airborne point cloud data is still greatly challenging.Therefore,this paper proposes a robust transmission line extraction method based on model fitting from airborne point cloud data.First,the candidate power line generation method based on height information is used to reduce the computational complexity at the subsequent steps and the false positives in the extracted results.Then,on the basis of the block-and-slice-constraint Euclidean clustering,a linear structure recognition method based on RANdom SAmple Consensus(RANSAC)is proposed to produce the initial individual transmission line components.Finally,a robust nonlinear least square-based fitting method is developed for the individual transmission line to generate the parameters of its mathematical model for further optimizing the extraction.Experiments were performed on LiDAR point cloud data captured from the helicopter and Unmanned Aerial Vehicle(UAV)platform.Results indicate that the proposed method can efficiently extract the different types of transmission lines along electricity corridors,with the average precision of approximately 98.1%,the average recall of approximately 95.9%,and the average quality of approximately 94.2%,respectively.

Nyström-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation

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.

A Hierarchical unsupervised method for power line classification from airborne LiDAR data

The automatic classification of power lines from airborne light detection and ranging(LiDAR)data is a crucial task for power supply management.The methods for power line classification can be either supervised or unsupervised.Supervised methods might achieve high accuracy for small areas,but it is time consuming to collect training data over areas of different conditions and complexity.Therefore,unsupervised methods that can automatically work over different areas without sophisticated parameter tuning are in great demand.In this paper,we presented a hierarchical unsupervised LiDAR-based power line classification method that first screened the power line candidate points(including the power line corridor direction detection based on a layered Hough transform,connectivity analysis,and Douglas–Peucker simplification algorithm),followed by the extraction of contextual linear and angular features for each candidate laser points,and finally by setting the feature threshold values to identify the power line points.We tested the method over both forest and urban areas and found that the precision,recall and quality rates were up to 96.7%,88.8%and 78.3%,respectively,for the test datasets and were higher than the ones from a previously developed supervised classification method.Overall,our approach has the advantages of achieving relatively high accuracy and being relatively fast.

Mt.Etna volcano high-resolution topography:airborne LiDAR modelling validated by GPS data

High-resolution digital topography is essential for land management and planning in any type of territory as well as the reproduction of the Earth surface in a geocoded digital format that allows several Digital Earth applications.In a volcanic environment,Digital Elevation Models are a valid reference for multi-temporal analyses aimed to observe frequent changes of a volcano edifice and for the relative detailed morphological and structural analyses.For the first time,a DTM(Digital Terrain Model)and a DSM(Digital Surface Model)covering the entire Mt.Etna volcano(Italy)derived from the same airborne Light Detection and Ranging(LiDAR)are here presented.More than 250 million 3D LiDAR points have been processed to distinguish ground elements from natural and anthropic features.The end product is the highly accurate representation of Mt.Etna landscape(DSM)and ground topography(DTM)dated 2005.Both models have a high spatial resolution of 2 m and cover an area of 620 km2.The DTM has been validated by GPS ground control points.The vertical accuracy has been evaluated,resulting in a root-mean-square-error of±0.24 m.The DTM is available as electronic supplement and represents a valid support for various scientific studies.

Retrieval of forest canopy height in a mountainous region with ICESat-2 ATLAS

Forest canopy height is one of the important forest parameters for accurately assessing forest biomass or carbon sequestration. ICESat-2 ATLAS provides the potential for retrieval of forest canopy height at global or regional scale, but the current canopy height product (ATL08) has coarse resolution and high uncertainty compared to airborne LiDAR-derived canopy height (hereafter ALCH) in mountainous regions, and is not ready for such applications as biomass modeling at finer scale. The objective of this research was to explore the approach to accurately retrieve canopy height from ATLAS data by incorporating an airborne-derived digital terrain model(DTM) and a data-filtering strategy. By linking ATLAS ATL03 with ATL08 products, the geospatial locations,types, and (absolute) heights of photons were obtained, and canopy heights at different lengths (from 20 to 200 m at 20-m intervals) of segments along a track were computed with the aid of airborne LiDAR DTM. Based on the relationship between the numbers of canopy photons within the segments and accuracy of ATLAS mean canopy height compared to ALCH, a filtering method for excluding a certain portion of unreliable segments was proposed.This method was further applied to different ATLAS ground tracks for retrieval of canopy heights and the results were evaluated using corresponding ALCH. The results show that the incorporation of high-precision DTM and ATLAS products can considerably improve the retrieval accuracy of forest canopy height in mountainous regions.Using the proposed filtering approach, the correlation coefficients (r) between ATLAS canopy height and corresponding ALCH were 0.61–0.91, 0.65–0.92, 0.68–0.94 for segment lengths of 20, 60, and 100 m, respectively;RMSE were 1.90–4.35, 1.55–3.63, and 1.34–3.23 m for the same segment lengths. The results indicate the necessity of using high-precision DTM and using the proposed filtering method to retrieve accurate canopy height from ICESat-2 ATLAS in mountainous regions with dense forest cover and complex terrain conditions.

Point Cloud Classification and Accuracy Analysis Based on Feature Fusion

A method for land-cover classification was proposed based on the fusion of features generated from waveform data and point cloud respectively.It aims to partially overcome the ineffectiveness of many traditional classifiers caused by the fact that point cloud is lacking spectral information.The whole flowchart of the method is as follows:Firstly,Gaussian decomposition was applied to fit an echo full-waveform.The parameters associated with the Gaussian function were optimized by LM(Levenberg-Marquard)algorithm.Six and thirteen features were generated to describe the waveform characteristics and the local geometry of point cloud,respectively.Secondly,a random forest was selected as the classifier to which the generated features were input.Relief-F was used to rank the weights of all the features generated.Finally,features were input to the classifier one by one according to the weights calculated from feature ranking,where classification accuracies were evaluated.The experimental results show that the effectiveness of the fusion of features generated from waveform and point cloud for LiDAR data classification,with 95.4%overall accuracy,0.90 kappa coefficient,which outperform the results obtained by a single class of features,no matter whether they were generated from point cloud or waveform data.