Deep Learning-Based Models for Classification of Invasive Plant Species from Hyperspectral Remotely Sensed Data

2021 ◽  
Author(s):  
Abdulla A. Omeer ◽  
Ratnadeep R. Deshmukh
2020 ◽  
Vol 250 ◽  
pp. 112037 ◽  
Author(s):  
Jie Dai ◽  
Dar A. Roberts ◽  
Doug A. Stow ◽  
Li An ◽  
Sharon J. Hall ◽  
...  

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Sana Qazi ◽  
Javed Iqbal ◽  
Junaid Aziz Khan

This study focuses on the risk of pollen allergy due to paper mulberry (Broussonetia papyrifera L.), an Asian invasive plant species now common in large parts of the world. Pollen plays a key role in the pathogenesis of respiratory allergic diseases, particularly rhinitis and asthma, and Islamabad, a major metropolitan city, is severely affected by allergy owing to B. papyrifera pollen. Due to its seasonality and other relationships with climatic variables, we used remote sensing to monitor the trend of pollen count. We also mapped the localisation of patients affected by pollen allergy using geographic information systems. The maximum likelihood algorithm was applied to SPOT-5 satellite imagery for land use/land cover classification. Temporal analysis of remotely sensed data revealed an increasing trend of paper mulberry density towards the southern and south-western part of Islamabad. Although not evident during rainfall, a clear positive correlation was found between patient count and pollen count. Field survey data and hotspot spatial analysis of allergy patients revealed that residents of Shakerperiyan and Lok Virsa areas (Sectors H-8, I-8, I-9, G-8, G-7 and G-6 in Islamabad) had more pronounced symptoms compared to residents of other sectors. The methodology adopted used in this study can be used to map the distribution of similar invasive species in other parts of the country.


EDIS ◽  
2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Amr Abd-Elrahman ◽  
Katie Britt ◽  
Tao Liu

Deep learning classification of invasive species using widely-used ArcGIS Pro software and increasingly common drone imagery can aid in identification and management of natural areas. A step-by-step implementation, with associated data for users to access, is presented to make this technology more widely accessible to GIS analysts, researchers, and graduate students working with remotely sensed data in the natural resource field.


2021 ◽  
Author(s):  
Johanna Yletyinen ◽  
George L. W. Perry ◽  
Olivia R. Burge ◽  
Norman W. H. Mason ◽  
Philip Stahlmann‐Brown

2021 ◽  
Vol 167 ◽  
pp. 113476
Author(s):  
Ricardo Almeida ◽  
Fernando Cisneros ◽  
Cátia V.T. Mendes ◽  
Maria Graça V.S. Carvalho ◽  
Maria G. Rasteiro ◽  
...  

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
pp. e76432 ◽  
Author(s):  
Marco A. Molina-Montenegro ◽  
Cristian Salgado-Luarte ◽  
Rómulo Oses ◽  
Cristian Torres-Díaz

2013 ◽  
Vol 101 (3) ◽  
pp. 593-608 ◽  
Author(s):  
Melba M. Crawford ◽  
Devis Tuia ◽  
Hsiuhan Lexie Yang

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