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The document lists various references related to forestry, remote sensing, and machine learning applications in environmental monitoring. It includes studies on forest biodiversity, tree health assessment, and the use of satellite data for forest management. Additionally, it mentions tools and platforms for data analysis and image processing relevant to these fields.
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0% found this document useful (0 votes)
8 views2 pages

9 No.

The document lists various references related to forestry, remote sensing, and machine learning applications in environmental monitoring. It includes studies on forest biodiversity, tree health assessment, and the use of satellite data for forest management. Additionally, it mentions tools and platforms for data analysis and image processing relevant to these fields.
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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Bibliography / References:

o FAO; UNEP. The State of the World’s Forests 2020: Forests, Biodiversity, and
People. The State of the World’s Forests (SOFO); FAO: Rome, Italy; UNEP:
Rome, Italy, 2020; ISBN 978-92-5-132419-6.
o Lopatin, J.; Dolos, K.; Kattenborn, T.; Fassnacht, F. How Canopy Shadow Affects
Invasive Plant Species Classification in High Spatial Resolution Remote Sensing.
Remote Sens. Ecol. Conserv. 2019, 5, 302–317. [CrossRef]
o Shang, X.; Chisholm, L.A. Classification of Australian Native Forest Species
Using Hyperspectral Remote Sensing and Machine Learning Classification
Algorithms. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 2013, 7, 2481–
2489. [CrossRef]
o Neuner, S.; Albrecht, A.; Cullmann, D.; Engels, F.; Griess, V.C.; Hahn, W.A.;
Hanewinkel, M.; Härtl, F.; Kölling, C.; Staupendahl, K.; et al. Survival of Norway
Spruce Remains Higher in Mixed Stands Under a Dryer and Warmer Climate.
Glob. Chang. Biol. 2015, 21, 935–946. [CrossRef] [PubMed]
o Härtl, F.H.; Barka, I.; Hahn, W.A.; Hlásny, T.; Irauschek, F.; Knoke, T.; Lexer,
M.J.; Griess, V. Multifunctionality in European Mountain Forests—An
Optimization Under Changing Climatic Conditions. Can. J. For. Res. 2016, 46,
163–171. [CrossRef]
o Sylvain, J.-D.; Drolet, G.; Brown, N. Mapping Dead Forest Cover Using a Deep
Convolutional Neural Network and Digital Aerial Photography. ISPRS J.
Photogramm. Remote Sens. 2019, 156, 14–26. [CrossRef]
o S., Andric; Irimia, R.; Petropoulos, G.P.; Anand, A.; Srivastava, P.K.; Plesoianu,
A.; Faraslis, I.; Stateras, D.; Kalivas, D. Tree Detection and Health Assessment
From Ultra-High Resolution UAV Imagery and Deep Learning. Geocarto Int.
2022, 37, 10459–10479. [CrossRef]
o de Lima, R.A.F.; Phillips, O.L.; Duque, A.; Tello, J.S.; Davies, S.J.; de Oliveira,
A.A.; Muller, S.; Honorio Coronado, E.N.H.; Vilanova, E.; Cuni-Sanchez, A.; et
al. Making Forest Data FAIR and Open. Nat. Ecol. Evol. 2022, 6, 656–658.
[CrossRef]
o Kalantar, B.; Javadpour, N.; Khosroshahi, M.; Saeedi, P. Deep Learning-Based
Approaches for Tree Cover and Health Monitoring Using Remote Sensing Data.
Remote Sens. Environ. 2021, 267, 112748. [CrossRef]
o Indian Forest Survey Report 2021. Forest Cover Analysis and Environmental
Data for Indian Forest Management. Forest Survey of India (FSI), Ministry of
Environment, Forest and Climate Change, Government of India.
o National Remote Sensing Centre (NRSC), ISRO. Indian High-Resolution
Satellite Data for Forestry and Environmental Monitoring. Hyderabad, India.
o India Biodiversity Portal (IBP). Citizen Science Contributions and Biodiversity
Records in India. Accessed 2022.
o Google Colab. Cloud-Based Jupyter Notebook Environment for Machine
Learning and Deep Learning Applications. Google LLC. Accessed 2022.
o Google Earth Pro. Google Earth Satellite Imagery for Urban and Forested Area
Mapping. Accessed 2022.
o Sentinel Hub. Sentinel-2 Data Products. European Space Agency (ESA), Sentinel
Missions Overview. Accessed 2022.
o Kaggle. Open Datasets for Machine Learning Applications. Kaggle.com.
Accessed 2022.
o NASA Earth Science Division. Satellite Data for Urban Forestry and
Biodiversity Research. NASA, Washington, DC, 2021.
o LabelImg. Open-Source Image Annotation Tool for Bounding Boxes. GitHub
Repository, 2022.
o MakeSense. Web-Based Annotation Tool for Object Detection and Segmentation.
Accessed 2022.
o He, K.; Gkioxari, G.; Dollár, P.; Girshick, R. Mask R-CNN. IEEE Trans. Pattern
Anal. Mach. Intell. 2020, 42, 386–397.
o Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You Only Look Once: Unified,
Real-Time Object Detection. Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 2016.
o OpenCV. Open Source Computer Vision Library for Image Processing and
Computer Vision Tasks. Accessed 2022.
o Pandas Development Team. Pandas: Python Data Analysis Library. Accessed
2022.

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