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.