scholarly journals INVESTIGATING THE USABILITY OF UAV OBTAINED MULTISPECTRAL IMAGERY IN TREE SPECIES SEGMENTATION

2019 ◽  
Vol XLII-2/W18 ◽  
pp. 159-165
Author(s):  
M. Pap ◽  
S. Kiraly ◽  
S. Moljak
Keyword(s):  
Tree Species ◽  
Low Cost ◽  
Forest Biomass ◽  
Bioenergy Crops ◽  
Biomass Estimation ◽  
Multispectral Imagery ◽  
Short Rotation ◽  
Segmentation Algorithms ◽  
Breeding Efficiency

Abstract. A widely used form of renewable energy are bioenergy crops. One form of it is the energy forestry that includes short rotation coppice plantations in which fast growing species of tree or woody shrub are grown (e.g. poplar, willow). The accurate prediction of forest biomass and volume can be used for the evaluation of plant breeding efficiency as well. The automatic tracking of plant development by traditional methods is quite difficult and labor intensive. Since energy forestries often contain different trees for estimating their volume it is essential to find segments containing the same tree species in the image.We investigated the applicability of a low cost UAV and an intermediate cost UAV in the field of agricultural image segmentation that is the first stage of biomass estimation (Gatziolis et al., 2015, Gaulton et al., 2015).This paper is a case study that shows the results of several segmentation algorithms applied on imagery obtained by a low cost UAV with low-cost camera, and imagery gathered by a UAV and camera set that are of higher quality and price. In the case study, we have observed two small forestry areas that contained six different tree species and their hybrids. Our results show that more expensive, better-equipped drone shots do not necessary provide significantly better segmentation.

Forest Biomass Estimation Based on Forest Inventory Data in Middle and Last Scales

2011 ◽  
Vol 183-185 ◽  
pp. 220-224
Author(s):  
Ming Ze Li ◽  
Wen Yi Fan ◽  
Ying Yu
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Inventory Data ◽  
Biomass Equation ◽  
Tree Species Composition ◽  
Forest Inventory Data ◽  
Ecosystem Carbon

The forest biomass (which is referred to the arbor aboveground biomass in this research) is one of the most primary factors to determine the forest ecosystem carbon storages. There are many kinds of estimating methods adapted to various scales. It is a suitable method to estimate forest biomass of the farm or the forestry bureau in middle and last scales. First each subcompartment forest biomass should be estimated, and then the farm or the forestry bureau forest biomass was estimated. In this research, based on maoershan farm region, first the single tree biomass equation of main tree species was established or collected. The biomass of each specie was calculated according to the materials of tally, such as height, diameter and so on in the forest inventory data. Secondly, each specie’s biomass and total biomass in subcompartment were calculated according to the tree species composition in forest management investigation data. Thus the forest biomass spatial distribution was obtained by taking subcompartment as a unit. And last the forest total biomass was estimated.

scholarly journals Removing the Scaling Error Caused by Allometric Modelling in Forest Biomass Estimation at Large Scales

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 602
Author(s):  
Carl Zhou ◽  
Xiaolu Zhou
Keyword(s):  
Large Scale ◽  
Forest Ecosystems ◽  
Forest Biomass ◽  
Field Measurements ◽  
Allometric Equation ◽  
Biomass Estimation ◽  
Allometric Relationships ◽  
Data Errors ◽  
Pinus Yunnanensis

To estimate the responses of forest ecosystems, most relationships in biological systems are described by allometric relationships, the parameters of which are determined based on field measurements. The use of existing observed data errors may occur during the scaling of fine-scale relationships to describe ecosystem properties at a larger ecosystem scale. Here, we analyzed the scaling error in the estimation of forest ecosystem biomass based on the measurement of plots (biomass or volume per hectare) using an improved allometric equation with a scaling error compensator. The efficiency of the compensator on reducing the scaling error was tested by simulating the forest stand populations using pseudo-observation. Our experiments indicate that, on average, approximately 94.8% of the scaling error can be reduced, and for a case study, an overestimation of 3.6% can be removed in practice from a large-scale estimation for the biomass of Pinus yunnanensis Franch.

Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: A case study from a boreal forest area

2011 ◽  
Vol 115 (12) ◽  
pp. 3599-3614 ◽  
Author(s):  
Erik Næsset ◽  
Terje Gobakken ◽  
Svein Solberg ◽  
Timothy G. Gregoire ◽  
Ross Nelson ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Forest Biomass ◽  
Forest Area ◽  
Biomass Estimation ◽  
Auxiliary Data ◽  
Regional Forest

scholarly journals On-Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery

2021 ◽  
Vol 13 (17) ◽  
pp. 3479
Author(s):  
Maria Pia Del Rosso ◽  
Alessandro Sebastianelli ◽  
Dario Spiller ◽  
Pierre Philippe Mathieu ◽  
Silvia Liberata Ullo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Volcanic Eruption ◽  
Low Cost ◽  
Volcanic Eruptions ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Multispectral Imagery

In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study, with a prototype of an on board Artificial Intelligence (AI) model, and realistic testing equipment and scenario are presented in this work. As a case study, the detection of volcanic eruptions has been investigated with the objective to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been designed and realized from scratch, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements. The CNNs are then tested with experimental hardware, by means of a drone with a paylod composed of a generic processing unit (Raspberry PI), an AI processing unit (Movidius stick) and a camera. The hardware employed to build the prototype is low-cost, easy to found and to use. Moreover, the dataset has been published on GitHub, made available to everyone. The results are promising and encouraging toward the employment of the proposed system in future missions, given that ESA has already moved the first steps of AI on board with the Phisat-1 satellite, launched on September 2020.

Forest biomass estimation using remote sensing and field inventory: a case study of Tripura, India

2019 ◽  
Vol 191 (9) ◽  
Author(s):  
Prem Chandra Pandey ◽  
Prashant K. Srivastava ◽  
Tilok Chetri ◽  
Bal Krishan Choudhary ◽  
Pavan Kumar
Keyword(s):  
Remote Sensing ◽  
Forest Biomass ◽  
Biomass Estimation

scholarly journals Applied aerial spectroscopy: A case study on remote sensing of an ancient and semi-natural woodland

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260056
Author(s):  
Shara Ahmed ◽  
Catherine E. Nicholson ◽  
Paul Muto ◽  
Justin J. Perry ◽  
John R. Dean
Keyword(s):  
Tree Species ◽  
Low Cost ◽  
Principal Component ◽  
Ground Level ◽  
Empty Space ◽  
Silver Birch ◽  
Dead Trees ◽  
Aerial Vehicle ◽  
Woodland Regeneration

An area of ancient and semi-natural woodland (ASNW) has been investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera. A novel normalised difference spectral index (NDSI) algorithm was developed using principal component analysis (PCA). This novel NDSI was then combined with a simple segmentation method of thresholding and applied for the identification of native tree species as well as the overall health of the woodland. Using this new approach allowed the identification of trees at canopy level, across 7.4 hectares (73,934 m2) of ASNW, as oak (53%), silver birch (37%), empty space (9%) and dead trees (1%). This UAV derived data was corroborated, for its accuracy, by a statistically valid ground-level field study that identified oak (47%), silver birch (46%) and dead trees (7.4%). This simple innovative approach, using a low-cost multirotor UAV with MSI camera, is both rapid to deploy, was flown around 100 m above ground level, provides useable high resolution (5.3 cm / pixel) data within 22 mins that can be interrogated using readily available PC-based software to identify tree species. In addition, it provides an overall oversight of woodland health and has the potential to inform a future woodland regeneration strategy.

A Comparison of Three Biomass Estimation Methods: A Case Study of Pinus tabulaeformis Forests in China

2013 ◽  
Vol 726-731 ◽  
pp. 4237-4240
Author(s):  
Fei Li ◽  
Zhong Yu Wang ◽  
Hua Yong Zhang ◽  
Yi Xin Xu ◽  
Lu Han
Keyword(s):  
Linear Model ◽  
Forest Biomass ◽  
Pinus Tabulaeformis ◽  
Estimation Methods ◽  
Biomass Estimation ◽  
Hyperbolic Model ◽  
Power Model ◽  
Regression Equations ◽  
Relative Errors

Power model, linear model and hyperbolic model were commonly used to estimate forest biomass via stand volume, however the relative accuracy is unclear forPinus tabulaeformisforests in China. In order to compare the accuracies of these models, data from 130Pinus tabulaeformisforest stands were compiled from published literatures. Data of 100 stands were randomly selected to establish regression equations, the other 30 data were used to compare the accuracies of equations either established in this study or in previous studies. The results show that biomass ofPinus tabulaeformisforests could be well estimated by power model and linear model, while hyperbolic model is likely to result in enormous overestimation or underestimation. The mean relative errors of the power model and linear model established in this study are-0.3% and 1.8% respectively. In comparison with models established by previous studies, these two models have better prediction accuracies.

Low Temperature Fault Isolation Using Soft Defect Localization

2012 ◽  
Author(s):  
Kristopher D. Staller
Keyword(s):  
Low Temperatures ◽  
Laser Scanning ◽  
Low Cost ◽  
Fault Isolation ◽  
Laser Scanning Microscopy ◽  
Cold Temperature ◽  
Multiple Point ◽  
Defect Localization ◽  
Low Levels

Abstract Cold temperature failures are often difficult to resolve, especially those at extreme low levels (< -40°C). Momentary application of chill spray can confirm the failure mode, but is impractical during photoemission microscopy (PEM), laser scanning microscopy (LSM), and multiple point microprobing. This paper will examine relatively low-cost cold temperature systems that can hold samples at steady state extreme low temperatures and describe a case study where a cold temperature stage was combined with LSM soft defect localization (SDL) to rapidly identify the cause of a complex cold temperature failure mechanism.

The Entering Marketing Strategy of the Low-cost Carrier into Korea-China Route: The Case Study of Spring Airlines, The Largest Chinese LCC

2015 ◽  
Vol 2 (2) ◽  
pp. 5-20
Author(s):  
Ik Hwan Moon ◽  
Se Jin An ◽  
Yong Jae Yoo
Keyword(s):  
Marketing Strategy ◽  
Low Cost ◽  
Low Cost Carrier
Sign in / Sign up

Export Citation Format

Share Document