scholarly journals Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries

2019 ◽  
Vol 11 (7) ◽  
pp. 800 ◽  
Author(s):  
Xin Shen ◽  
Lin Cao ◽  
Bisheng Yang ◽  
Zhong Xu ◽  
Guibin Wang
Keyword(s):  
Point Cloud ◽  
Growth Models ◽  
Wide Band ◽  
Point Clouds ◽  
Forest Growth ◽  
Stem Density ◽  
Spectral Indices ◽  
Aerial Photogrammetry ◽  
Highly Correlated ◽  
Structural Metrics

Forest structural attributes are key indicators for parameterization of forest growth models, which play key roles in understanding the biophysical processes and function of the forest ecosystem. In this study, UAS-based multispectral and RGB imageries were used to estimate forest structural attributes in planted subtropical forests. The point clouds were generated from multispectral and RGB imageries using the digital aerial photogrammetry (DAP) approach. Different suits of spectral and structural metrics (i.e., wide-band spectral indices and point cloud metrics) derived from multispectral and RGB imageries were compared and assessed. The selected spectral and structural metrics were used to fit partial least squares (PLS) regression models individually and in combination to estimate forest structural attributes (i.e., Lorey’s mean height (HL) and volume(V)), and the capabilities of multispectral- and RGB-derived spectral and structural metrics in predicting forest structural attributes in various stem density forests were assessed and compared. The results indicated that the derived DAP point clouds had perfect visual effects and that most of the structural metrics extracted from the multispectral DAP point cloud were highly correlated with the metrics derived from the RGB DAP point cloud (R2 > 0.75). Although the models including only spectral indices had the capability to predict forest structural attributes with relatively high accuracies (R2 = 0.56–0.69, relative Root-Mean-Square-Error (RMSE) = 10.88–21.92%), the models with spectral and structural metrics had higher accuracies (R2 = 0.82–0.93, relative RMSE = 4.60–14.17%). Moreover, the models fitted using multispectral- and RGB-derived metrics had similar accuracies (∆R2 = 0–0.02, ∆ relative RMSE = 0.18–0.44%). In addition, the combo models fitted with stratified sample plots had relatively higher accuracies than those fitted with all of the sample plots (∆R2 = 0–0.07, ∆ relative RMSE = 0.49–3.08%), and the accuracies increased with increasing stem density.

scholarly journals Estimating Changes in Forest Attributes and Enhancing Growth Projections: a Review of Existing Approaches and Future Directions Using Airborne 3D Point Cloud Data

2021 ◽  
Vol 7 (1) ◽  
pp. 1-24
Author(s):  
Piotr Tompalski ◽  
Nicholas C. Coops ◽  
Joanne C. White ◽  
Tristan R.H. Goodbody ◽  
Chris R. Hennigar ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Point Cloud ◽  
Laser Scanning ◽  
Growth Models ◽  
Three Dimensional ◽  
Point Clouds ◽  
Forest Growth ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Aerial Photogrammetry

Abstract Purpose of Review The increasing availability of three-dimensional point clouds, including both airborne laser scanning and digital aerial photogrammetry, allow for the derivation of forest inventory information with a high level of attribute accuracy and spatial detail. When available at two points in time, point cloud datasets offer a rich source of information for detailed analysis of change in forest structure. Recent Findings Existing research across a broad range of forest types has demonstrated that those analyses can be performed using different approaches, levels of detail, or source data. By reviewing the relevant findings, we highlight the potential that bi- and multi-temporal point clouds have for enhanced analysis of forest growth. We divide the existing approaches into two broad categories— – approaches that focus on estimating change based on predictions of two or more forest inventory attributes over time, and approaches for forecasting forest inventory attributes. We describe how point clouds acquired at two or more points in time can be used for both categories of analysis by comparing input airborne datasets, before discussing the methods that were used, and resulting accuracies. Summary To conclude, we outline outstanding research gaps that require further investigation, including the need for an improved understanding of which three-dimensional datasets can be applied using certain methods. We also discuss the likely implications of these datasets on the expected outcomes, improvements in tree-to-tree matching and analysis, integration with growth simulators, and ultimately, the development of growth models driven entirely with point cloud data.

scholarly journals Classification of Photogrammetric and Airborne LiDAR Point Clouds Using Machine Learning Algorithms

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 104
Author(s):  
Zaide Duran ◽  
Kubra Ozcan ◽  
Muhammed Enes Atik
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Airborne Lidar ◽  
Aerial Photogrammetry ◽  
Feature Spaces ◽  
Wide Range ◽  
Extract Information

With the development of photogrammetry technologies, point clouds have found a wide range of use in academic and commercial areas. This situation has made it essential to extract information from point clouds. In particular, artificial intelligence applications have been used to extract information from point clouds to complex structures. Point cloud classification is also one of the leading areas where these applications are used. In this study, the classification of point clouds obtained by aerial photogrammetry and Light Detection and Ranging (LiDAR) technology belonging to the same region is performed by using machine learning. For this purpose, nine popular machine learning methods have been used. Geometric features obtained from point clouds were used for the feature spaces created for classification. Color information is also added to these in the photogrammetric point cloud. According to the LiDAR point cloud results, the highest overall accuracies were obtained as 0.96 with the Multilayer Perceptron (MLP) method. The lowest overall accuracies were obtained as 0.50 with the AdaBoost method. The method with the highest overall accuracy was achieved with the MLP (0.90) method. The lowest overall accuracy method is the GNB method with 0.25 overall accuracy.

scholarly journals Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 145 ◽  
Author(s):  
Lin Cao ◽  
Hao Liu ◽  
Xiaoyao Fu ◽  
Zhengnan Zhang ◽  
Xin Shen ◽  
...  
Keyword(s):  
Predictive Models ◽  
Sustainable Forest Management ◽  
Low Cost ◽  
Canopy Structure ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Point Clouds ◽  
Stem Density ◽  
Planted Forests ◽  
Aerial Photogrammetry

Estimating forest structural attributes of planted forests plays a key role in managing forest resources, monitoring carbon stocks, and mitigating climate change. High-resolution and low-cost remote-sensing data are increasingly available to measure three-dimensional (3D) canopy structure and model forest structural attributes. In this study, we compared two suites of point cloud metrics and the accuracies of predictive models of forest structural attributes using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) and digital aerial photogrammetry (DAP) data, in a subtropical coastal planted forest of East China. A comparison between UAV-LiDAR and UAV-DAP metrics was performed across plots among different tree species, heights, and stem densities. The results showed that a higher similarity between the UAV-LiDAR and UAV-DAP metrics appeared in the dawn redwood plots with greater height and lower stem density. The comparison between the UAV-LiDAR and DAP metrics showed that the metrics of the upper percentiles (r for dawn redwood = 0.95–0.96, poplar = 0.94–0.95) showed a stronger correlation than the lower percentiles (r = 0.92–0.93, 0.90–0.92), whereas the metrics of upper canopy return density (r = 0.21–0.24, 0.14–0.15) showed a weaker correlation than those of lower canopy return density (r = 0.32–0.68, 0.31–0.52). The Weibull α parameter indicated a higher correlation (r = 0.70–0.72) than that of the Weibull β parameter (r = 0.07–0.60) for both dawn redwood and poplar plots. The accuracies of UAV-LiDAR (adjusted (Adj)R2 = 0.58–0.91, relative root-mean-square error (rRMSE) = 9.03%–24.29%) predicted forest structural attributes were higher than UAV-DAP (Adj-R2 = 0.52–0.83, rRMSE = 12.20%–25.84%). In addition, by comparing the forest structural attributes between UAV-LiDAR and UAV-DAP predictive models, the greatest difference was found for volume (△Adj-R2 = 0.09, △rRMSE = 4.20%), whereas the lowest difference was for basal area (△Adj-R2 = 0.03, △rRMSE = 0.86%). This study proved that the UAV-DAP data are useful and comparable to LiDAR for forest inventory and sustainable forest management in planted forests, by providing accurate estimations of forest structural attributes.

Low-Level Aerial Photogrammetry as a Source of Supplementary Data for ALS Measurements

2017 ◽  
Author(s):  
Katarzyna Bobkowska ◽  
Adam Inglot ◽  
Marek Przyborski ◽  
Jedrzej Sieniakowski ◽  
Paweł Tysiac
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Local System ◽  
Data Format ◽  
State Institutions ◽  
Optical Scanning ◽  
Aerial Photogrammetry ◽  
State Administration ◽  
The Cost

The development of laser scanning technology ALS allows to make high-resolution measurements for large areas result-ing in significant reduction of costs. The main stakeholders at heights data received from the airborne laser scanning is mainly state administration. The state institutions appear among projects such as ISOK. Each point is classified in ac-cordance with the standard LAS 1.2, our research focuses on the class 6 – buildings. In the project ISOK, the buildings are not measured in whole (from every side). A typical way to measure the missing elements is to increase the coverage of the cross and additional raids which unfortunately increases the cost measurements. An alternative solution density point clouds ALS is the use of optical scanning and UAV. The article shows the process of density the point clouds coming from ALS using point cloud obtained through optical scanning. The methods that illustrate the process of compaction data format LAS using the following methods: point cloud having field coordinates in the system compatible with the system of clouds acquired with ALS, point cloud in the local system, point cloud in the local system without the scale. The file size, depending on the density of the point cloud was analyzed.

scholarly journals A Comparison of ALS and Dense Photogrammetric Point Clouds for Individual Tree Detection in Radiata Pine Plantations

2021 ◽  
Vol 13 (17) ◽  
pp. 3536
Author(s):  
Irfan A. Iqbal ◽  
Jon Osborn ◽  
Christine Stone ◽  
Arko Lucieer
Keyword(s):  
Forest Inventory ◽  
Point Cloud ◽  
Point Clouds ◽  
Tree Level ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Detection Rates ◽  
Cloud Data ◽  
Tree Detection ◽  
Structural Metrics

Digital aerial photogrammetry (DAP) has emerged as a potentially cost-effective alternative to airborne laser scanning (ALS) for forest inventory methods that employ point cloud data. Forest inventory derived from DAP using area-based methods has been shown to achieve accuracy similar to that of ALS data. At the tree level, individual tree detection (ITD) algorithms have been developed to detect and/or delineate individual trees either from ALS point cloud data or from ALS- or DAP-based canopy height models. An examination of the application of ITDs to DAP-based point clouds has not yet been reported. In this research, we evaluate the suitability of DAP-based point clouds for individual tree detection in the Pinus radiata plantation. Two ITD algorithms designed to work with point cloud data are applied to dense point clouds generated from small- and medium-format photography and to an ALS point cloud. Performance of the two ITD algorithms, the influence of stand structure on tree detection rates, and the relationship between tree detection rates and canopy structural metrics are investigated. Overall, we show that there is a good agreement between ALS- and DAP-based ITD results (proportion of false negatives for ALS, SFP, and MFP was always lower than 29.6%, 25.3%, and 28.6%, respectively, whereas, the proportion of false positives for ALS, SFP, and MFP was always lower than 39.4%, 30.7%, and 33.7%, respectively). Differences between small- and medium-format DAP results were minor (for SFP and MFP, differences between recall, precision, and F-score were always less than 0.08, 0.03, and 0.05, respectively), suggesting that DAP point cloud data is robust for ITD. Our results show that among all the canopy structural metrics, the number of trees per hectare has the greatest influence on the tree detection rates.

scholarly journals Estimating Ground Elevation and Vegetation Characteristics in Coastal Salt Marshes Using UAV-Based LiDAR and Digital Aerial Photogrammetry

2021 ◽  
Vol 13 (22) ◽  
pp. 4506
Author(s):  
Daniele Pinton ◽  
Alberto Canestrelli ◽  
Benjamin Wilkinson ◽  
Peter Ifju ◽  
Andrew Ortega
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Salt Marshes ◽  
Point Cloud ◽  
Point Clouds ◽  
Vegetation Density ◽  
Marsh Vegetation ◽  
Vegetation Height ◽  
Aerial Photogrammetry ◽  
Vegetation Characteristics

This study evaluates the skills of two types of drone-based point clouds, derived from LiDAR and photogrammetric techniques, in estimating ground elevation, vegetation height, and vegetation density on a highly vegetated salt marsh. The proposed formulation is calibrated and tested using data measured on a Spartina alterniflora-dominated salt marsh in Little Sapelo Island, USA. The method produces high-resolution (ground sampling distance = 0.40 m) maps of ground elevation and vegetation characteristics and captures the large gradients in the proximity of tidal creeks. Our results show that LiDAR-based techniques provide more accurate reconstructions of marsh vegetation (height: MAEVH = 12.6 cm and RMSEVH = 17.5 cm; density: MAEVD = 6.9 stems m−2 and RMSEVD = 9.4 stems m−2) and morphology (MAEM = 4.2 cm; RMSEM = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAEVH = 31.1 cm; RMSEVH = 38.1 cm; MAEVD = 12.7 stems m−2; RMSEVD = 16.6 stems m−2; MAEM = 11.3 cm; RMSEM = 17.2 cm). The accuracy of the classification procedure for vegetation calculation negligibly improves when RGB images are used as input parameters together with the LiDAR-UAV point cloud (MAEVH = 6.9 cm; RMSEVH = 9.4 cm; MAEVD = 10.0 stems m−2; RMSEVD = 14.0 stems m−2). However, it improves when used together with the DAP-UAV point cloud (MAEVH = 21.7 cm; RMSEVH = 25.8 cm; MAEVD = 15.2 stems m−2; RMSEVD = 18.7 stems m−2). Thus, we discourage using DAP-UAV-derived point clouds for high-resolution vegetation mapping of coastal areas, if not coupled with other data sources.

SFM-Forest-Benchmark project: The benchmarking of image-based point cloud for forest inventory

2020 ◽  
Author(s):  
Martin Mokros ◽  
Markus Hollaus ◽  
Yunsheng Wang ◽  
Xinlian Liang
Keyword(s):  
Forest Inventory ◽  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
State Of The Art ◽  
Point Clouds ◽  
The State ◽  
Image Point ◽  
Silver Fir ◽  
Stem Density

<p>The benchmarking project of image-based point cloud for forest inventory (SFM-Forest-Benchmark) was initiated in 2019 and supported by ISPRS Scientific Initiative 2019. The main goal of the project was the evaluation of the applicability of terrestrial image-based point clouds for forest inventories, the clarification of the potential and limitations of the state-of-the-art techniques, and the exploration of the best practices in practical field inventories. In the project, related tree parameter (i.e. tree position diameter at breast height - DBH) were derived from 14 algorithms and evaluated using field inventory data as a reference. In order to clarify the potential of terrestrial image-based point clouds, the results from the image-based point clouds were also compared to results derived from the best available point clouds obtained by terrestrial laser scanning (TLS).</p><p>The project is consisted of two phases. In the first phase, we established two research plots in each country (Austria, China, Czech, Finland and Slovakia), ten plots in total. The stem density ranged from 272 to 875 stems/ha and plot size ranged approximately from 700 to 2500 m<sup>2</sup>. Dominant tree species across research plots were Norway spruce, European beech, bald cypress, Chinese tulip poplar, Scots pine, European silver fir and sessile oak. TLS, images and reference data acquisition were performed on each study site, where TLS data were acquired through multi-scan approach, images were taken in the stop-and-go mode, and tree positions and the DBHs were measured with a tachymeter and a calliper as field references. Images were processed with structure from motion algorithm within Agisoft Metashape software to final point clouds. The TLS data was pre-processed with RiProcess software. And, the co-registration of all three data sources (TLS, SFM, and reference data) was done with OPALS software.</p><p>In the benchmarking phase, we distributed point clouds to participants of the benchmark. Altogether 14 different research groups processed the data with own algorithms. The individual results are evaluated through the reference to clarify the applicability of the image-point clouds in deriving tree parameters, were compared to each other to reveal the state-of-the-art of technologies, and were benchmarked to the up-to-data the most accurate data from TLS to explore the strength and weakness of the image-based point cloud. In this presentation the first benchmark results will be presented and discussed.</p><p>All images and point clouds collected for this project will be available as open access data for non-commercial uses.</p>

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

scholarly journals 3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

2021 ◽  
Vol 13 (5) ◽  
pp. 957
Author(s):  
Guglielmo Grechi ◽  
Matteo Fiorucci ◽  
Gian Marco Marmoni ◽  
Salvatore Martino
Keyword(s):  
Surface Temperature ◽  
Infrared Thermography ◽  
Point Cloud ◽  
Engineering Geology ◽  
Point Clouds ◽  
Jointed Rock ◽  
Geometric Accuracy ◽  
Rock Masses ◽  
Natural Systems ◽  
Temperature Distributions

The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

Sign in / Sign up

Export Citation Format

Share Document