scholarly journals Classifying Forest Structure of Red-Cockaded Woodpecker Habitat Using Structure from Motion Elevation Data De-Rived from sUAS Imagery

Drones ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 26
Author(s):  
Brett Lawrence
Keyword(s):  
Structure From Motion ◽  
Forest Structure ◽  
Training Sample ◽  
Support Vector ◽  
Montgomery County ◽  
Unmanned Aerial Systems ◽  
Post Process ◽  
Elevation Data ◽  
Red Cockaded Woodpecker ◽  
Major Factors

Small unmanned aerial systems (sUAS) and relatively new photogrammetry software solutions are creating opportunities for forest managers to perform spatial analysis more efficiently and cost-effectively. This study aims to identify a method for leveraging these technologies to analyze vertical forest structure of red-cockaded woodpecker habitat in Montgomery County, Texas. Traditional sampling methods would require numerous hours of ground surveying and data collection using various measuring techniques. Structure from Motion (SfM), a photogrammetric method for creating 3-D structure from 2-D images, provides an alternative to relatively expensive LIDAR sensing technologies and can accurately model the high level of complexity found within our study area’s vertical structure. DroneDeploy, a photogrammetry processing app service, was used to post-process and create a point cloud, which was later further processed into a Canopy Height Model (CHM). Using supervised, object-based classification and comparing multiple classifier algorithms, classifications maps were generated with a best overall accuracy of 84.8% using Support Vector Machine in ArcGIS Pro software. Appropriately sized training sample datasets, correctly processed elevation data, and proper image segmentation were among the major factors impacting classification accuracy during the numerous classification iterations performed.

scholarly journals Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion

Forests ◽  
2018 ◽  
Vol 9 (3) ◽  
pp. 119 ◽  
Author(s):  
Michael Alonzo ◽  
Hans-Erik Andersen ◽  
Douglas Morton ◽  
Bruce Cook
Keyword(s):  
Boreal Forest ◽  
Structure From Motion ◽  
Forest Structure

Understory biomass estimation based on Structure from Motion data by Multi-layered forest drone survey

2021 ◽  
Author(s):  
Yupan Zhang ◽  
Yuichi Onda ◽  
Hiroaki Kato ◽  
Xinchao Sun ◽  
Takashi Gomi
Keyword(s):  
Structure From Motion ◽  
Forest Structure ◽  
Large Scale ◽  
Forest Canopy ◽  
Point Clouds ◽  
Understory Vegetation ◽  
Biomass Estimation ◽  
Canopy Openness ◽  
Cubic Model ◽  
Overlap Analysis

<p>Understory vegetation is an important part of evapotranspiration from forest floor. Forest management changes the forest structure and then affects the understory vegetation biomass (UVB). Quantitative measurement and estimation of  UVB is a step cannot be ignored in the study of forest ecology and forest evapotranspiration. However, large-scale biomass measurement and estimation is challenging. In this study, Structure from Motion (SfM) was adopted simultaneously at two different layers in a plantation forest made by Japanese cedar and Japanese cypress to reconstruct forest structure from understory to above canopy: i) understory drone survey in a 1.1h sub-catchment to generate canopy height model (CHM) based on dense point clouds data derived from a manual low-flying drone under the canopy; ii) Above-canopy drone survey in whole catchment (33.2 ha) to compute canopy openness data based on point clouds of canopy derived from an autonomous flying drone above the canopy. Combined with actual biomass data from field harvesting to develop regression models between the CHM and UVB, which was then used to map spatial distribution of  UVB in sub-catchment. The relationship between UVB and canopy openness data was then developed by overlap analysis. This approach yielded high resolution understory over catchment scale with a point cloud density of more than 20 points/cm<sup>2</sup>. Strong coefficients of determination (R-squared = 0.75) of the cubic model supported prediction of UVB from CHM, the average UVB was 0.82kg/m<sup>2</sup> and dominated by low ferns. The corresponding forest canopy openness in this area was 42.48% on average. Overlap analysis show no significant interactions between them in a cubic model with weak predictive power (R-squared < 0.46). Overall, we reconstructed the multi-layered structure of the forest and provided models of UVB. Understory survey has high accuracy for biomass measurement, but it’s inherently difficult to estimate UVB only based on canopy openness result.</p>

scholarly journals Mapping Forest Structure Using UAS inside Flight Capabilities

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2245 ◽  
Author(s):  
Karel Kuželka ◽  
Peter Surový
Keyword(s):  
Least Squares ◽  
Forest Structure ◽  
European Beech ◽  
Point Clouds ◽  
Unmanned Aerial Systems ◽  
Structure Mapping ◽  
Close Range Photogrammetry ◽  
3D Point Clouds ◽  
Ellipse Method ◽  
Close Range

We evaluated two unmanned aerial systems (UASs), namely the DJI Phantom 4 Pro and DJI Mavic Pro, for 3D forest structure mapping of the forest stand interior with the use of close-range photogrammetry techniques. Assisted flights were performed within two research plots established in mature pure Norway spruce (Picea abies (L.) H. Karst.) and European beech (Fagus sylvatica L.) forest stands. Geotagged images were used to produce georeferenced 3D point clouds representing tree stem surfaces. With a flight height of 8 m above the ground, the stems were precisely modeled up to a height of 10 m, which represents a considerably larger portion of the stem when compared with terrestrial close-range photogrammetry. Accuracy of the point clouds was evaluated by comparing field-measured tree diameters at breast height (DBH) with diameter estimates derived from the point cloud using four different fitting methods, including the bounding circle, convex hull, least squares circle, and least squares ellipse methods. The accuracy of DBH estimation varied with the UAS model and the diameter fitting method utilized. With the Phantom 4 Pro and the least squares ellipse method to estimate diameter, the mean error of diameter estimates was −1.17 cm (−3.14%) and 0.27 cm (0.69%) for spruce and beech stands, respectively.

scholarly journals Searching for Prosociality in Qualitative Data: Comparing Manual, Closed–Vocabulary, and Open–Vocabulary Methods

2020 ◽  
Vol 34 (5) ◽  
pp. 903-916
Author(s):  
William H.B. McAuliffe ◽  
Hannah Moshontz ◽  
Thomas G. McCauley ◽  
Michael E. McCullough
Keyword(s):  
Support Vector Machine ◽  
European Association ◽  
Qualitative Data ◽  
Support Vector Machine Classifier ◽  
Goal Pursuit ◽  
Training Sample ◽  
Support Vector ◽  
Personality Psychology ◽  
Self Presentation ◽  
Personal Strivings

Although most people present themselves as possessing prosocial traits, people differ in the extent to which they actually act prosocially in everyday life. Qualitative data that were not ostensibly collected to measure prosociality might contain information about prosocial dispositions that is not distorted by self–presentation concerns. This paper seeks to characterise charitable donors from qualitative data. We compared a manual approach of extracting predictors from participants’ self–described personal strivings to two automated approaches: A summation of words predefined as prosocial and a support vector machine classifier. Although variables extracted by the support vector machine predicted donation behaviour well in the training sample ( N = 984), virtually, no variables from any method significantly predicted donations in a holdout sample ( N = 496). Raters’ attempts to predict donations to charity based on reading participants’ personal strivings were also unsuccessful. However, raters’ predictions were associated with past charitable involvement. In sum, predictors derived from personal strivings did not robustly explain variation in charitable behaviour, but personal strivings may nevertheless contain some information about trait prosociality. The sparseness of personal strivings data, rather than the irrelevance of open–ended text or individual differences in goal pursuit, likely explains their limited value in predicting prosocial behaviour. © 2020 European Association of Personality Psychology

scholarly journals An Estimation Model on Electricity Consumption of New Metro Stations

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhao Yu ◽  
Yun Bai ◽  
Qian Fu ◽  
Yao Chen ◽  
Baohua Mao
Keyword(s):  
Interior Design ◽  
Back Propagation ◽  
Electricity Consumption ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Estimation Model ◽  
Metro Station ◽  
New Energy ◽  
Passenger Volume ◽  
Major Factors

Electricity consumption of metro stations increases sharply with expansion of a metro network and this has been a growing cause for concern. Based on relevant historical data from existing metro stations, this paper proposes a support vector regression (SVR) model to estimate daily electricity consumption of a newly constructed metro station. The model considers some major factors influencing the electricity consumption of metro station in terms of both the interior design scheme of a station (e.g., layout of the station and allocation of facilities) and external factors (e.g., passenger volume, air temperature and relative humidity). A genetic algorithm with five-fold cross-validation is used to optimize the hyper-parameters of the SVR model in order to improve its accuracy in estimating the electricity consumption of a metro station (ECMS). With the optimized hyper-parameters, results from case studies on the Beijing Subway showed that the estimating accuracy of the proposed SVR model could reach up to 95% and the correlation coefficient was 0.89. It was demonstrated that the proposed model could outperform the traditional methods which use a back-propagation neural network or multivariate linear regression. The method presented in this paper can be an adequate tool for estimating the ECMS and should further assist in the delivery of new, energy-efficient metro stations.

scholarly journals Low-Cost and Efficient Indoor 3D Reconstruction Through Annotated Hierarchical Structure-from-Motion

2018 ◽  
Vol 11 (1) ◽  
pp. 58 ◽  
Author(s):  
Youli Ding ◽  
Xianwei Zheng ◽  
Yan Zhou ◽  
Hanjiang Xiong ◽  
and Jianya Gong
Keyword(s):  
3D Reconstruction ◽  
Hierarchical Structure ◽  
Structure From Motion ◽  
Low Cost ◽  
Semantic Annotation ◽  
Location Based Services ◽  
Procrustes Analysis ◽  
Support Vector ◽  
Widespread Application ◽  
Indoor Spaces

With the widespread application of location-based services, the appropriate representation of indoor spaces and efficient indoor 3D reconstruction have become essential tasks. Due to the complexity and closeness of indoor spaces, it is difficult to develop a versatile solution for large-scale indoor 3D scene reconstruction. In this paper, an annotated hierarchical Structure-from-Motion (SfM) method is proposed for low-cost and efficient indoor 3D reconstruction using unordered images collected with widely available smartphone or consumer-level cameras. Although the reconstruction of indoor models is often compromised by the indoor complexity, we make use of the availability of complex semantic objects to classify the scenes and construct a hierarchical scene tree to recover the indoor space. Starting with the semantic annotation of the images, images that share the same object were detected and classified utilizing visual words and the support vector machine (SVM) algorithm. The SfM method was then applied to hierarchically recover the atomic 3D point cloud model of each object, with the semantic information from the images attached. Finally, an improved random sample consensus (RANSAC) generalized Procrustes analysis (RGPA) method was employed to register and optimize the partial models into a complete indoor scene. The proposed approach incorporates image classification in the hierarchical SfM based indoor reconstruction task, which explores the semantic propagation from images to points. It also reduces the computational complexity of the traditional SfM by avoiding exhausting pair-wise image matching. The applicability and accuracy of the proposed method was verified on two different image datasets collected with smartphone and consumer cameras. The results demonstrate that the proposed method is able to efficiently and robustly produce semantically and geometrically correct indoor 3D point models.

STRUCTURE-EMBEDDED AUC-SVM

2010 ◽  
Vol 24 (05) ◽  
pp. 667-690 ◽  
Author(s):  
YUNYUN WANG ◽  
SONGCAN CHEN ◽  
HUI XUE
Keyword(s):  
Computational Complexity ◽  
Training Sample ◽  
Decision Function ◽  
Information Loss ◽  
Global Structure ◽  
Support Vector ◽  
Structure Information ◽  
Hinge Loss ◽  
Novel Structure ◽  
High Computational Complexity

AUC-SVM directly maximizes the area under the ROC curve (AUC) through minimizing its hinge loss relaxation, and the decision function is determined by those support vector sample pairs playing the same roles as the support vector samples in SVM. Such a learning paradigm generally emphasizes more on the local discriminative information just associated with these support vectors whereas hardly takes the overall view of data into account, thereby it may incur loss of the global distribution information in data favorable for classification. Moreover, due to the high computational complexity of AUC-SVM induced by the large number of training sample pairs quadratic in the number of samples, sampling is usually adopted, incurring a further loss of the distribution information in data. In order to compensate the distribution information loss and simultaneously boost the AUC-SVM performance, in this paper, we develop a novel structure-embedded AUC-SVM (SAUC-SVM for short) through embedding the global structure information in the whole data into AUC-SVM. With such an embedding, the proposed SAUC-SVM incorporates the local discriminative information and global structure information in data into a uniform formulation and consequently guarantees better generalization performance. Comparative experiments on both synthetic and real datasets confirm its effectiveness.

Least Squares Support Vector Machine for Fault Diagnosis Optimization

2013 ◽  
Vol 347-350 ◽  
pp. 505-508
Author(s):  
Si Yang Liang ◽  
Jian Hong Lv
Keyword(s):  
Fault Diagnosis ◽  
Least Squares ◽  
Learning Algorithm ◽  
Test Sample ◽  
Digital Circuit ◽  
Training Sample ◽  
Support Vector ◽  
Vector Machines ◽  
Sample Data ◽  
Diagnosis Method

In order to improve the diagnostic accuracy of digital circuit, the fault diagnosis method based on support vector machines (SVM) is proposed. The input is fault characteristics of digital circuit; the output is the fault style. The connection of fault characteristics and style was established. Network learning algorithm using least squares, the training sample data is formed by the simulation, the test sample data is formed by the untrained simulation. The method achieved the classification of faulted digital circuits, and the results show that the method has the features of fast and high accuracy.

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