scholarly journals Early Detection of Douglas-Fir Beetle Infestation with Subcanopy Resolution Hyperspectral Imagery

2003 ◽  
Vol 18 (3) ◽  
pp. 202-206 ◽  
Author(s):  
Rick Lawrence ◽  
Mari Labus
Keyword(s):  
Early Detection ◽  
Spatial Resolution ◽  
Hyperspectral Imagery ◽  
Douglas Fir ◽  
Classification And Regression Tree ◽  
Detection Methods ◽  
Lower Percentage ◽  
Canopy Reflectance ◽  
Multispectral Imagery ◽  
Tree Stress

Abstract Early detection of insect or pathogen infestations in forests would be useful to forest managers who want to make decisions that minimize timber losses. Typical methods of forest reconnaissance to detect infestations have included analysis of multispectral imagery. Multispectral imagery, however, often lacks the sensitivity to detect subtle changes in tree canopy reflectance because of physiologic stress from insects or pathogens. Most hyperspectral imaging has the sensitivity to detect subtle changes in canopy reflectance but lacks high spatial resolution to identify affected trees. Our study examined the use of subcanopy spatial resolution hyperspectral imagery for differentiating Douglas-fir trees attacked by the Douglas-fir beetle. Comparison of the accuracies of step-wise discriminant analysis and classification and regression tree analysis (CART) revealed that CART provided the best separability among tree health classes (93% overall) because of CART's ability to use different band combinations for each class. Predictive accuracy of the CART method was estimated through cross-validation of the dataset using a jackknife resampling technique. Overall classification accuracy was promising (69%), as was classification among healthy and attacked, but still living, trees (50–70%). The results of our study provide support that hyperspectral imagery might be used for detecting and mapping tree stress in Douglas-fir stands. Although the rapid progress of beetle infestation somewhat limited the ability to differentiate among tree stress classes, which might limit the utility of this approach for fast moving infestations, the results were well beyond what might be expected from alternative detection methods. Slower moving infestations would benefit from the use of hyperspectral imagery because a lower percentage of infested trees would be asymtomatic. West. J. Appl. For. 18(3):202–206.

scholarly journals The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies

2014 ◽  
Vol XL-2 ◽  
pp. 213-219 ◽  
Author(s):  
B. Hu ◽  
J. Li ◽  
J. Wang ◽  
B. Hall
Keyword(s):  
Early Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Hyperspectral Imagery ◽  
Classification Model ◽  
Individual Tree ◽  
Very High Spatial Resolution ◽  
Ash Trees ◽  
Combine Information ◽  
Very High

The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, individual trees were first extracted and then classified into different species based on their spectral information derived from hyperspectral imagery, spatial information from VHR imagery, and for each ash tree its health state and EAB infestation stage were determined based on hyperspectral imagery. The developed framework and methods were demonstrated to be effective according to the results obtained on two study sites in the city of Toronto, Ontario Canada. The individual tree delineation method provided satisfactory results with an overall accuracy of 78 % and 19 % commission and 23 % omission errors when used on the combined very high-spatial resolution imagery and LiDAR data. In terms of the identification of ash trees, given sufficient representative training data, our classification model was able to predict tree species with above 75 % overall accuracy, and mis-classification occurred mainly between ash and maple trees. The hypothesis that a strong correlation exists between general tree stress and EAB infestation was confirmed. Vegetation indices sensitive to leaf chlorophyll content derived from hyperspectral imagery can be used to predict the EAB infestation levels for each ash tree.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Early Detection Methods for Silicosis in Australia and Internationally: A Review of the Literature

2021 ◽  
Vol 18 (15) ◽  
pp. 8123
Author(s):  
Emma K. Austin ◽  
Carole James ◽  
John Tessier
Keyword(s):  
Early Detection ◽  
Literature Review ◽  
Detection Method ◽  
Detection Methods ◽  
X Rays ◽  
Review Of The Literature ◽  
Occupational Lung Disease ◽  
Work Related ◽  
Growing Body ◽  
Future Work

Pneumoconiosis, or occupational lung disease, is one of the world’s most prevalent work-related diseases. Silicosis, a type of pneumoconiosis, is caused by inhaling respirable crystalline silica (RCS) dust. Although silicosis can be fatal, it is completely preventable. Hundreds of thousands of workers globally are at risk of being exposed to RCS at the workplace from various activities in many industries. Currently, in Australia and internationally, there are a range of methods used for the respiratory surveillance of workers exposed to RCS. These methods include health and exposure questionnaires, spirometry, chest X-rays, and HRCT. However, these methods predominantly do not detect the disease until it has significantly progressed. For this reason, there is a growing body of research investigating early detection methods for silicosis, particularly biomarkers. This literature review summarises the research to date on early detection methods for silicosis and makes recommendations for future work in this area. Findings from this review conclude that there is a critical need for an early detection method for silicosis, however, further laboratory- and field-based research is required.

scholarly journals Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

2021 ◽  
Vol 13 (10) ◽  
pp. 1975
Author(s):  
Lin Wang ◽  
Yuzhen Zhou ◽  
Qiao Hu ◽  
Zhenghong Tang ◽  
Yufeng Ge ◽  
...  
Keyword(s):  
Early Detection ◽  
Early Development ◽  
Spatial Resolution ◽  
Early Stage ◽  
Semantic Segmentation ◽  
Development Stage ◽  
Unmanned Aircraft ◽  
Economic Losses ◽  
Juniperus Virginiana ◽  
Segmentation Algorithms

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management.

scholarly journals Monitoring Skeletal Anomalies in Big-Scale Sand Smelt, Atherina boyeri, as a Potential Complementary Tool for Early Detection of Effects of Anthropic Pressure in Coastal Lagoons

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 159
Author(s):  
Chiara Leone ◽  
Francesca De Luca ◽  
Eleonora Ciccotti ◽  
Arianna Martini ◽  
Clara Boglione
Keyword(s):  
Early Detection ◽  
Habitat Degradation ◽  
Anthropogenic Impacts ◽  
Coastal Lagoons ◽  
Ecological Status ◽  
Detection Methods ◽  
Anthropogenic Pressures ◽  
Morphological Examination ◽  
Skeletal Anomalies

Mediterranean coastal lagoons are increasingly affected by several threats, all concurrently leading to habitat degradation and loss. Methods based on fish for the assessment of the ecological status are under implementation for the Water Framework Directive requirements, to assess the overall quality of coastal lagoons. Complementary tools based on the use of single fish species as biological indicators could be useful as early detection methods of anthropogenic impacts. The analysis of skeletal anomalies in the big-scale sand smelt, Atherina boyeri, from nine Mediterranean coastal lagoons in Italy was carried out. Along with the morphological examination of fish, the environmental status of the nine lagoons was evaluated using a method based on expert judgement, by selecting and quantifying several environmental descriptors of direct and indirect human pressures acting on lagoon ecosystems. The average individual anomaly load and the frequency of individuals with severe anomalies allow to discriminate big-scale sand smelt samples on the basis of the site and of its quality status. Furthermore, a relationship between skeletal anomalies and the environmental quality of specific lagoons, driven by the anthropogenic pressures acting on them, was found. These findings support the potentiality of skeletal anomalies monitoring in big-scale sand smelt as a tool for early detection of anthropogenic impacts in coastal lagoons of the Mediterranean region.

scholarly journals Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 980
Author(s):  
Hang Shu ◽  
Wensheng Wang ◽  
Leifeng Guo ◽  
Jérôme Bindelle
Keyword(s):  
Heat Stress ◽  
Early Detection ◽  
Dairy Cows ◽  
Real Time ◽  
Dairy Farms ◽  
Time Measurement ◽  
Heat Strain ◽  
Detection Methods ◽  
Livestock Farming ◽  
Real Time Measurement

In pursuit of precision livestock farming, the real-time measurement for heat strain-related data has been more and more valued. Efforts have been made recently to use more sensitive physiological indicators with the hope to better inform decision-making in heat abatement in dairy farms. To get an insight into the early detection of heat strain in dairy cows, the present review focuses on the recent efforts developing early detection methods of heat strain in dairy cows based on body temperatures and respiratory dynamics. For every candidate animal-based indicator, state-of-the-art measurement methods and existing thresholds were summarized. Body surface temperature and respiration rate were concluded to be the best early indicators of heat strain due to their high feasibility of measurement and sensitivity to heat stress. Future studies should customize heat strain thresholds according to different internal and external factors that have an impact on the sensitivity to heat stress. Wearable devices are most promising to achieve real-time measurement in practical dairy farms. Combined with internet of things technologies, a comprehensive strategy based on both animal- and environment-based indicators is expected to increase the precision of early detection of heat strain in dairy cows.

Highly sensitive and rapid detection of citrus Huanglongbing pathogen (Candidatus Liberibacter asiaticus) using Cas12a-based methods

2021 ◽  
Author(s):  
Matthew Wheatley ◽  
Yong-Ping Duan ◽  
Yinong Yang
Keyword(s):  
Early Detection ◽  
Nucleic Acids ◽  
Detection Sensitivity ◽  
Sensitive Detection ◽  
Detection Methods ◽  
Candidatus Liberibacter Asiaticus ◽  
Fluorescent Reporter ◽  
Candidatus Liberibacter ◽  
Citrus Huanglongbing ◽  
Liberibacter Asiaticus

Citrus Huanglongbing (HLB) or greening is one of the most devastating diseases of citrus worldwide. Sensitive detection of its causal agent, Candidatus Liberibacter asiaticus (CLas), is critical for early diagnosis and successful management of HLB. However, current nucleic acid-based detection methods are often insufficient for the early detection of CLas from asymptomatic tissue, and unsuitable for high-throughput and field-deployable diagnosis of HLB. Here we report the development of the Cas12a-based DETECTR (DNA endonuclease-targeted CRISPR trans reporter) assay for highly specific and sensitive detection of CLas nucleic acids from infected samples. The DETECTR assay, which targets the five-copy nrdB gene specific to CLas, couples isothermal amplification with Cas12a trans-cleavage of fluorescent reporter oligos and enables detection of CLas nucleic acids at the attomolar level. The DETECTR assay was capable of specifically detecting the presence of CLas across different infected citrus, periwinkle and psyllid samples, and shown to be compatible with lateral flow assay technology for potential field-deployable diagnosis. The improvements in detection sensitivity and flexibility of the DETECTR technology position the assay as a potentially suitable tool for early detection of CLas in infected regions.

Use of RNA isolated from feces as a promising tool for the early detection of colorectal cancer

2012 ◽  
Vol 27 (2) ◽  
pp. 82-89 ◽  
Author(s):  
Giuliano Bernal
Keyword(s):  
Colorectal Cancer ◽  
Early Detection ◽  
Low Cost ◽  
Occult Blood ◽  
Detection Methods ◽  
Fecal Occult Blood ◽  
Public Health Services ◽  
Promising Alternative ◽  
Altered Expression ◽  
Fecal Occult

Colorectal cancer is one of the most common forms of cancer worldwide. Early detection would allow patients to be treated surgically and halt the progression of the disease; however, the current methods of early detection are invasive (colonoscopy and sigmoidoscopy) or have low sensitivity (fecal occult blood test). The altered expression of genes in stool samples of patients with colorectal cancer can be determined by RT-PCR. This is a noninvasive and highly sensitive technique for colorectal cancer screening. According to information gathered in this review and our own experience, the use of fecal RNA to determine early alterations in gene expression due to malignancy appears to be a promising alternative to the current detection methods and owing to its low cost could be implemented in public health services.

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