An improved area-based approach for estimating plot-level tree DBH from airborne LiDAR data

The diameter at breast height(DBH) of trees and stands is not only a widely used plant functional trait in ecology and biodiversity but also one of the most fundamental measurements in managing forests. However, systematically measuring the DBH of individual trees over large areas using conventional ground-based approaches is labour-intensive and costly. Here, we present an improved area-based approach to estimate plot-level tree DBH from airborne Li DAR data using the relationship between tree height and DBH, which is widely available for most forest types and many individual tree species. We first determined optimal functional forms for modelling heightDBH relationships using field-measured tree height and DBH. Then we estimated plot-level mean DBH by inverting the height-DBH relationships using the tree height predicted by Li DAR. Finally, we compared the predictive performance of our approach with a classical area-based method of DBH. The results showed that our approach significantly improved the prediction accuracy of tree DBH(R^(2)=0.85–0.90, rRMSE=9.57%–11.26%)compared to the classical area-based approach(R^(2)=0.80–0.83, rRMSE=11.98%–14.97%). Our study demonstrates the potential of using height-DBH relationships to improve the estimation of the plot-level DBH from airborne Li DAR data.

Two theorems on cosymplectic hypersurfaces of six-dimensional Hermitian submanifolds of Cayley algebra

Proves that cosymplectic hypersurfaces of six dimensional Hermitian submanifolds of the octave algebra are ruled manifolds and establishes a necessary and sufficient condition for a cosymplectic hypersurface of Hermitian M 6 O  to be a minimal submanifold of M 6.

The U-Kenmotsu Hypersurfaces Axiom and Six-Dimensional Hermitian Submanifolds of Cayley Algebra

Six-dimensional Hermitian submanifolds of Cayley algebra are considered.It is proved that if such a submanifold of the octave algebta complies with the U-Kenmotsu hypersurfaces axiom,then it is Khlerian.

An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

Comparison of the spatial and temporal variability of macroinvertebrate and periphyton-based metrics in a macrophyte-dominated shallow lake

The influence of spatial differences, which are caused by different anthropogenic disturbances, and temporal changes, which are caused by natural conditions, on macroinvertebrates with periphyton communities in Baiyangdian Lake was compared. Periphyton and macrobenthos assemblage samples were simultaneously col- lected on four occasions during 2009 and 2010. Based on the physical and chemical attributes in the water and sediment, the 8 sampling sites can be divided into 5 habitat types by using cluster analysis. According to coefficients variation analysis （CV）, three primary conclusions can be drawn ： （1） the metrics of Hilsenhoff Biotic Index （HBI）, Percent Tolerant Taxa （PTT）, Percent dominant taxon （PDT）, and community loss index （CLI）, based on macroinvertebrates, and the metrics of algal density （AD）, the proportion of chlorophyta （CHL）, and the proportion of cyanophyta （CYA）, based on periphytons, were mostly constant throughout our study; （2） in terms of spatial variation, the CV values in the macroinvertebratebased metrics were lower than the CV values in the periphyton-based metrics, and these findings may be caused by the effects of changes in environmental factors; whereas, the CV values in the macroinvertebrate-based metrics were higher than those in the periphyton-based metrics, and these results may be linked to the influences of phenology and life history patterns of the macroinvertebrate individuals; and （3） the CV values for the functionalbased metrics were higher than those for the structuralbased metrics. Therefore, spatial and temporal variation for metrics should be considered when assessing applying the biometrics.

A combination of learning and non-learning based method for enhancement, compression and reconstruction of underwater images

Complex nature of underwater environment poses biggest challenge towards image acquisition and transmission of underwater images.This paper proposes an integrated approach which consists of a non-learning enhancement method with deep Convolutional Neural Networks(CNN)for compression and reconstruction of the image.The proposed method does color and contrast correction for image enhancement.The enhanced images are down-sampled using 9-layer CNN followed by Discrete Wavelet Transform(DWT).The decompression is done by using Inverse DWT.Further,the sub-pixel up-sampled image is de-blurred using a three-layer CNN.Residual Dense CNN(RD-CNN)is used to improve the quality of the reconstructed image after deblurring.The quality of the reconstructed images is measured using Peak Signal to Noise Ratio(PSNR)and Structural Similarity Index Metric(SSIM).The proposed model provides better image enhancement,compression,and reconstruction quality than the existing state-of-the-art methods and Super Resolution CNN(SRCNN)respectively.