Ormancılıkta makine öğrenmesi kullanımı

Öz

Gelişen teknolojiyle beraber diğer disiplinlerde olduğu gibi ormancılıkta da geleneksel uygulamaların daha ekonomik, etkin, hızlı ve kolay yapılabilmesi için yenilikçi yaklaşımların kullanımına talepler ve ihtiyaçlar artmaktadır. Özellikle son dönemde ortaya çıkan ormancılık bilişimi, hassas ormancılık, akıllı ormancılık, Ormancılık (Forestry) 4.0, iklim-akıllı ormancılık, sayısal ormancılık ve ormancılık büyük verisi gibi terimler ormancılık disiplinin gündeminde yer almaya başlamıştır. Bunların neticesinde de makine öğrenmesi ve son dönemde ortaya çıkan otomatik makine öğrenmesi (AutoML) gibi modern yaklaşımların ormancılıkta karar verme süreçlerine entegre edildiği akademik çalışmaların sayısında önemli artışlar gözlenmektedir. Bu çalışma, makine öğrenmesi algoritmalarının Türkçe dilinde anlaşılırlığını daha da artırmak, yaygınlaştırmak ve ilgilenen araştırmacılar için ormancılıkta kullanımına yönelik bir kaynak olarak değerlendirilmesi amacıyla ortaya konulmuştur. Böylece çeşitli ormancılık faaliyetlerinde makine öğrenmesinin hem geçmişten günümüze nasıl kullanıldığını hem de gelecekte kullanım potansiyelini ortaya koyan bir derleme makalesinin ulusal literatüre kazandırılması amaçlanmıştır.

Anahtar Kelimeler

• AutoML
• Büyük veri
• Makine öğrenmesi
• Ormancılık

Using Machine Learning in Forestry

Öz

Advanced technology has increased demands and needs for innovative approaches to apply traditional methods more economically, effectively, fast and easily in forestry, as in other disciplines. Especially recently emerging terms such as forestry informatics, precision forestry, smart forestry, Forestry 4.0, climate-intelligent forestry, digital forestry and forestry big data have started to take place on the agenda of the forestry discipline. As a result, significant increases are observed in the number of academic studies in which modern approaches such as machine learning and recently emerged automatic machine learning (AutoML) are integrated into decision-making processes in forestry. This study aims to increase further the comprehensibility of machine learning algorithms in the Turkish language, to make them widespread, and be considered a resource for researchers interested in their use in forestry. Thus, it was aimed to bring a review article to the national literature that reveals both how machine learning has been used in various forestry activities from the past to the present and its potential for use in the future.

Anahtar Kelimeler

• AutoML
• Big data
• Machine learning
• Forestry

Kaynakça

1. Achu, A. L., Thomas, J., Aju, C. D., Gopinath, G., Kumar, S., Reghunath, R., 2021. Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India. Ecological Informatics, 64, 101348.
2. Aghalarova, S., Bozkurt Keser, S., 2022. AutoML tekniği uygulayarak öğrencilerin akademik performanslarının tahmin edilmesi. El-Cezerî Fen ve Mühendislik Dergisi, 9(2): 394-412.
3. Ågren, A. M., Larson, J., Paul, S. S., Laudon, H., Lidberg, W., 2021. Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape. Geoderma, 404, 115280.
4. Ahmadi, K., Kalantar, B., Saeidi, V., Harandi, E. K., Janizadeh, S., Ueda, N., 2020. Comparison of machine learning methods for mapping the stand characteristics of temperate forests using multi-spectral sentinel-2 data. Remote Sensing, 12(18), 3019.
5. Akıncı, H.A., Akıncı, H., 2023. Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey. Earth Science Informatics, 16(1):397-414.
6. Akyol, A., Örücü, Ö.K.,2020. Investigation and evaluation of stone pine (Pinus pinea L.) current and future potential distribution under climate change in Turkey. Cerne, 25(4):415-423.
7. Akyüz, T., 2019. Bursa Orman Bölge Müdürlüğü’nde Yangın Tehlikesinin Modellenmesi ve Haritalanması. Doktora tezi, Kastamonu Üniversitesi, Fen Bilimleri Enstitüsü, Kastamonu.
8. Allen, M.J., Grieve, S.W., Owen, H.J., Lines, E. R., 2022. Tree species classification from complex laser scanning data in Mediterranean forests using deep learning. Methods in Ecology and Evolution, DOI: 10.1111/2041-210X.13981 .

9. Almeida, R.O., Munis, R.A., Camargo, D.A., da Silva, T., Sasso Júnior, V.A., Simões, D., 2022. Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning. Forests, 13(10), 1737.
10. Arjasakusuma, S., Swahyu Kusuma, S., Phinn, S., 2020. Evaluating variable selection and machine learning algorithms for estimating forest heights by combining lidar and hyperspectral data. ISPRS International Journal of Geo-Information, 9(9), 507.
11. Arslan, E.S., Akyol, A., Örücü, Ö.K., Sarıkaya, A.G., 2020. Distribution of rose hip (Rosa canina L.) under current and future climate conditions. Regional Environmental Change, 20(3), 107.
12. Asadi, H., Dowling, R., Yan, B., Mitchell, P., 2014. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PLoS ONE, 9, DOI: 10.1371/journal.pone.88225 .
13. Ataş, M., &Talay, A., 2022. Development of Automatic Tree Counting Software from UAV Based Aerial Images With Machine Learning. arXiv preprint, DOI: 10.48550/arXiv.2201.02698 .
14. Atkins, J.W., Bond‐Lamberty, B., Fahey, R.T., Haber, L.T., Stuart‐Haëntjens, E., Hardiman, B.S., LaRue, E., McNeil, B.E., Orwig, D.A., Stovall, A.E.L., Tallant, J.M., Walter, J.A., Gough, C. M., 2020. Application of multidimensional structural characterization to detect and describe moderate forest disturbance. Ecosphere, 11(6), DOI: 10.1002/ecs2.3156 .
15. Attarchi, S., Gloaguen, R., 2014. Classifying complex mountainous forests with L-Band SAR and landsat data integration: a comparison among different machine learning methods in the hyrcanian forest. Remote Sensing, 6(5):3624-3647.
16. Ayan, S., Bugday, E., Varol, T., Özel, H.B.,Thurm, E.A., 2022. Effect of climate change on potential distribution of oriental beech (Fagus orientalis Lipsky.) in the twenty-first century in Turkey. Theoretical and Applied Climatology, 148(1-2):165-177.
17. Aybar-Ruiz, A., Jiménez-Fernández, S., Cornejo-Bueno, L., Casanova-Mateo, C., Sanz-Justo, J., Salvador-González, P., Salcedo-Sanz, S., 2016. A novel grouping genetic algorithm-extreme learning machine approach for global solar radiation prediction from numerical weather models inputs. Solar Energy, 132:129–142.
18. Babalik, A.A., Sarikaya, O., Orucu, O.K., 2021. The Current and future compliance areas of Kermes Oak (Quercus coccifera L.) under climate change in Turkey. Fresenius Environmental Bulletin, 30(01):406-413.
19. Balasso, M., Hunt, M., Jacobs, A., O’Reilly-Wapstra, J., 2022. Development of a segregation method to sort fast-grown Eucalyptus nitens (H. Deane & Maiden) Maiden plantation trees and logs for higher quality structural timber products. Annals of Forest Science, 79(1):1-15.
20. Balestra, M., Chiappini, S., Malinverni, E.S., Galli, A., Marcheggiani, E., 2021. A Machine Learning Approach for Mapping Forest Categories: An Application of Google Earth Engine for the Case Study of Monte Sant’Angelo, Central Italy. In Computational Science and Its Applications–ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, Proceedings, Part VII 21 (pp. 155-168). Springer International Publishing.
21. Bar, S., Parida, B.R., Pandey, A.C., 2020. Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment, 18, DOI:10.1016/j.rsase.2020.100324 .
22. Barboza, F., Kimura, H., Altman, E., 2017. Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83: 405–417.
23. Bayraktar, C., 2022. Endüstri 4.0 için bir anomali tespit sistemi çerçeve geliştirilmesi. Doktora Tezi, Gazi Üniversitesi, Bilişim Enstitüsü, Ankara.
24. Becker, R.M., Keefe, R.F., 2022. A novel smartphone-based activity recognition modeling method for tracked equipment in forest operations. Plos one, 17(4), DOI: 10.1371/journal.pone.0266568 .
25. Beker, T., 2019. Big data and machine learning for global evaluation of habitat suitability of European forest species. Msc Dissertation, Politecnico di Milano, Italy.
26. Bera, B., Shit, P.K., Sengupta, N., Saha, S., Bhattacharjee, S., 2022. Forest fire susceptibility prediction using machine learning models with resampling algorithms, Northern part of Eastern Ghat Mountain range (India). Geocarto International, 37(1):1-26, DOI:10.1080/10106049.2022.2060323 .
27. Bettinger, P., Boston, K., Siry, J., Grebner, D.L., 2016. Forest management and planning. Academic Press, USA.
28. Bhatnagar, S., Puliti, S., Talbot, B., Heppelmann, J.B., Breidenbach, J., Astrup, R., 2022. Mapping wheel-ruts from timber harvesting operations using deep learning techniques in drone imagery. Forestry, 95(5):698-710.
29. Blumroeder, J.S., Burova, N., Winter, S., Goroncy, A., Hobson, P.R., Shegolev, A., Dobrynin, D., Amosova, I., Ilina, O., Parinova, T., Volkov, A., Graebener, U.F., Ibisch, P. L., 2019. Ecological effects of clearcutting practices in a boreal forest (Arkhangelsk Region, Russian Federation) both with and without FSC certification. Ecological Indicators, 106, DOI:10.1016/j.ecolind.2019.105461.
30. Bohanec, M., Kljaji´c Borštnar, M., Robnik-Šikonja, M., 2017. Explaining machine learningmodels in sales predictions. Expert Systems with Applications, 71: 416–428, DOI: 10.1016/j.eswa.2016.11.010.
31. Bolat, F., Ercanli, I., & Günlü, A., 2023. Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors. iForest-Biogeosciences and Forestry, 16(1):30-37, DOI: 10.3832/ifor4116-015.
32. Bonannella, C., Hengl, T., Heisig, J., Parente, L., Wright, M. N., Herold, M., De Bruin, S., 2022. Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning. PeerJ, 10, DOI: 10.7717/peerj.13728.
33. Borz, S.A., Cheta, M., Bîrda, M., Proto, A.R., 2022. Classifying operational events in cable yarding by a machine learning application to GNSS-collected data: A case study on gravity-assisted downhill yarding. Bulletin of the Transilvania University of Brasov. Series II: Forestry. Wood Industry. Agricultural Food Engineering, 15(64)(1):13-32, DOI:10.31926/but.fwiafe.2022.15.64.1.2.
34. Brigot, G., Simard, M., Colin-Koeniguer, E., Boulch, A., 2019. Retrieval of forest vertical structure from PolInSAR data by machine learning using LIDAR-derived features. Remote Sensing, 11(4):381, DOI: 10.3390/rs11040381.
35. Brovelli, M. A., Sun, Y., Yordanov, V., 2020. Monitoring forest change in the amazon using multi-temporal remote sensing data and machine learning classification on Google Earth Engine. ISPRS International Journal of Geo-Information, 9(10):580.
36. Bugday, E., 2018. Application of artificial neural network system based on ANFIS using GIS for predicting forest road network suitability mapping. Fresenius Environmental Bulletin, 27(3):1656-1668.
37. Bugday, E., 2022. A GIS based landslide susceptibility mapping using machine learning and alternative forest road routes assessment in protection forests. Šumarski list, 146(3-4):137-147.
38. Bui, D.T., Hoang, N. D., Samui, P., 2019. Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam). Journal of environmental management, 237:476-487.
39. Bui, D.T., Van Le, H., Hoang, N.D., 2018. GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method. Ecological Informatics, 48:104-116.
40. Bulut, S., 2023. Machine learning prediction of above-ground biomass in pure Calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region, Türkiye. Ecological Informatics, 74, DOI: 10.1016/j.ecoinf.2022.101951.
41. Bulut, S., Günlü, A., Çakır, G., 2022. Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Turkey. Geocarto International, (just-accepted), 38:1-19, DOI: 10.1080/10106049.2022.2158238 .
42. Caffaratti, G.D., Marchetta, M.G., Euillades, L.D., Euillades, P.A., Forradellas, R.Q., 2021. Improving forest detection with machine learning in remote sensing data. Remote Sensing Applications: Society and Environment, 24, 100654.
43. Campos-Vargas, C., Sanchez-Azofeifa, A., Laakso, K., Marzahn, P., 2020. Unmanned aerial system and machine learning techniques help to detect dead woody components in a tropical dry forest. Forests, 11(8):827.
44. Chaubey, P., Yadav, N. J., Chaurasiya, A., Ranbhise, S., 2020. Forest Fire Prediction System using Machine Learning. International Journal for Research in Applied Science & Engineering Technology, 8(12):539-546.
45. Chen, G., Hay, G. J., St-Onge, B., 2012. A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: A case study in Quebec, Canada. International Journal of Applied Earth Observation and Geoinformation, 15:28-37.
46. Chen, L., Ren, C., Zhang, B., Wang, Z., Xi, Y., 2018. Estimation of forest above-ground biomass by geographically weighted regression and machine learning with sentinel imagery. Forests, 9(10):582.
47. Cramer, S., Kampouridis, M., Freitas, A.A., Alexandridis, A.K., 2017. An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Systems with Applications, 85:169–181.
48. Crisigiovanni, E. L., Filho, A. F., Pesck, V. A., de Lima, V.A., 2021. Potential of machine learning and WorldView-2 images for recognizing endangered and invasive species in the Atlantic Rainforest. Annals of Forest Science, 78(2):54.
49. Csillik, O., Kumar, P., Mascaro, J., O’Shea, T., Asner, G. P., 2019. Monitoring tropical forest carbon stocks and emissions using Planet satellite data. Scientific reports, 9(1): 1-12.
50. Çalışkan, E., & Sevim, Y., 2022. Forest road extraction from orthophoto images by convolutional neural networks. Geocarto International, 1-15.
51. Çoban, H. O., Örücü, Ö. K., Arslan, E. S., 2020. MaxEnt modeling for predicting the current and future potential geographical distribution of Quercus libani Olivier. Sustainability, 12(7), 2671.
52. D’Amico, G., Francini, S., Giannetti, F., Vangi, E., Travaglini, D., Chianucci, F., Mattioli, W., Grotti, M., Puletti, N., Corona, P. & Chirici, G., 2021 A deep learning approach for automatic mapping of poplar plantations using Sentinel-2 imagery, GIScience & Remote Sensing, 58:8, 1352-1368, DOI: 10.1080/15481603.2021.1988427
53. Dai, S., Zheng, X., Gao, L., Xu, C., Zuo, S., Chen, Q., Wei, X., Ren, Y., 2021. Improving plot-level model of forest biomass: A combined approach using machine learning with spatial statistics. Forests, 12(12), 1663, DOI: 10.3390/f12121663
54. Dalir, P., Naghdi, R., Gholami, V., Tavankar, F., Latterini, F., Venanzi, R., Picchio, R., 2022. Risk assessment of runoff generation using an artificial neural network and field plots in road and forest land areas. Natural Hazards, 113(3):1451-1469.
55. Dalla Corte, A.P., Souza, D.V., Rex, F.E., Sanquetta, C.R., Mohan, M., Silva, C.A., ... & Broadbent, E.N., 2020. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, 179, DOI: 10.1016/j.compag.2020.105815
56. Dampage, U., Bandaranayake, L., Wanasinghe, R., Kottahachchi, K., Jayasanka, B., 2022. Forest fire detection system using wireless sensor networks and machine learning. Scientific reports, 12(1):46.
57. Dang, A.T.N., Nandy, S., Srinet, R., Luong, N.V., Ghosh, S., Kumar, A.S., 2019. Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam. Ecological Informatics, 50:24-32.
58. de Oliveira, V.A., Rodrigues, A.F., Morais, M.A.V., Terra, M.D.C.N.S., Guo, L., & de Mello, C.R., 2021. Spatiotemporal modelling of soil moisture in an A tlantic forest through machine learning algorithms. european Journal of soil science, 72(5), 1969-1987.
59. Dimou, V., Demertzis, K., Kantartzis, A., 2023. Harvesting wind damaged trees: a study of prediction of windthrow damage in mixed-broadleaf stands via a machine learning model. International Journal of Forest Engineering, 1-15.
60. Doody, T.M., Benyon, R.G., Gao, S., 2023. Fine scale 20‐year timeseries of plantation forest evapotranspiration for the Lower Limestone Coast. Hydrological Processes, e14836.
61. dos Reis, A.A., Carvalho, M.C., de Mello, J.M., Gomide, L.R., Ferraz Filho, A.C., & Acerbi Junior, F.W., 2018. Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods. New Zealand Journal of Forestry Science, 48(1):1-17, DOI: 10.1186/s40490-017-0108-0 .
62. Dou, X., Yang, Y., Luo, J., 2018. Estimating forest carbon fluxes using machine learning techniques based on eddy covariance measurements. Sustainability, 10(1):203.
63. Doyle, C., Beach, T., Luzzadder-Beach, S., 2021. Tropical forest and wetland losses and the role of protected areas in Northwestern Belize, revealed from landsat and machine learning. Remote Sensing, 13(3):379.
64. Duan, X., Li, J., & Wu, S., 2022. MaxEnt Modeling to Estimate the Impact of Climate Factors on Distribution of Pinus densiflora. Forests, 13(3):402. DOI: 10.3390/f13030402.
65. Dube, T., Mutanga, O., Adam, E., Ismail, R., 2014. Intra-and-inter species biomass prediction in a plantation forest: testing the utility of high spatial resolution spaceborne multispectral rapideye sensor and advanced machine learning algorithms. Sensors, 14(8):15348-15370.
66. Dwiasnati, S., Devianto, Y., 2021. Classification of forest fire areas using machine learning algorithm. World Journal of Advanced Engineering Technology and Sciences, 3(1):008-015.
67. Eckhart, T., Pötzelsberger, E., Koeck, R., Thom, D., Lair, G. J., van Loo, M., Hasenauer, H., 2019. Forest stand productivity derived from site conditions: an assessment of old Douglas-fir stands (Pseudotsuga menziesii (Mirb.) Franco var. menziesii) in Central Europe. Annals of forest science, 76:1-11.
68. Eker, R., Aydin, A., 2014. Assessment of forest road conditions in terms of landslide susceptibility: a case study in Yığılca Forest Directorate (Turkey). Turkish Journal of Agriculture and Forestry, 38(2):281-290.
69. Elmas, B., 2021. Identifying species of trees through bark images by convolutional neural networks with transfer learning method. Journal of the Faculty of Engineering and Architecture of Gazi University, 36(3):1254-1269.
70. Elshewey, A.M., Elsonbaty, A.A., 2020. Forest Fires Detection Using Machine Learning Techniques. Journal of Xi'an University of Architecture & Technology, 12(IX).
71. Ercanlı, İ., 2020. Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height. Forest Ecosystems, 7(1):1-18.
72. Ercanlı, İ., Bolat, M.Ş.F., 2022. A major challenge to machine learning models: Compatible predictions with biological realism in forestry: A case study of individual tree volume.
73. Eslami, R., Azarnoush, M., Kialashki, A., Kazemzadeh, F., 2021. GIS-based forest fire susceptibility assessment by random forest, artificial neural network and logistic regression methods. Journal of Tropical Forest Science, 33(2):173-184.
74. Esmkhani, A., Erfanifard, Y., Darvishi Boloorani, A., Neysani Samany, N., 2022. Species recognition of Pistacia and Amygdalus individuals using combination of UAV-based RGB imagery and digital surface model. Journal of Wood and Forest Science and Technology, 29(3):93-111.
75. Fajardo, A., Llancabure, J.C., & Moreno, P.C., 2022. Assessing forest degradation using multivariate and machine‐learning methods in the Patagonian temperate rain forest. Ecological Applications, 32(2), e2495. DOI: 10.1002/eap.2495. Fan, J., Han, F., Liu, H., 2014. Challenges of big data analysis. National Science Review, 1(2): 293-314.
76. Fang, K., Shen, C., Kifer, D., Yang, X., 2017, Prolongation of SMAP to spatiotemporally seamless coverage of continental U.S. using a deep learning neural network. Geophysical Research Letters, 44(21): 11-030.
77. FAO & ITPS, 2015. Status of the World’s Soil Resources (SWSR) – Main Report. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, Rome, Italy 648p.
78. Fararoda, R., Reddy, R.S., Rajashekar, G., Chand, T.K., Jha, C.S., Dadhwal, V.K., 2021. Improving forest above ground biomass estimates over Indian forests using multi source data sets with machine learning algorithm. Ecological Informatics, 65, 101392.
79. Feng, Y., Audy, J.F.,2020. Forestry 4.0: a framework for the forest supply chain toward Industry 4.0. Gestão & Produção, 27.
80. Fidanboy, M., Okyay, S., 2022. Derin öğrenmeye dayalı orman yangını tahmin modeli geliştirilmesi ve Türkiye yangın risk haritasının oluşturulması. Ormancılık Araştırma Dergisi, 9(2):206-218.
81. Firebanks-Quevedo, D., Planas, J., Buckingham, K., Taylor, C., Silva, D., Naydenova, G., Zamora-Cristales, R., 2022. Using machine learning to identify incentives in forestry policy: Towards a new paradigm in policy analysis. Forest Policy and Economics, 134, 102624.
82. Firebanks-Quevedo, Daniel, Planas, J., Buckingham, K., Taylor, C., Silva, D., Naydenova, G., Zamora-Cristales, R. 2022. Using machine learning to identify incentives in forestry policy: Towards a new paradigm in policy analysis. Forest Policy and Economics, 134, DOI: 10.1016/j.forpol.2021.102624.
83. Fragni, R., Trifirò, A., Nucci, A., Seno, A., Allodi, A., Di Rocco, M., 2018. Italian tomato-based products authentication by multi-element approach: A mineral elements database to distinguish the domestic provenance. Food Control, 93:211–218.
84. Furuya, D.E.G., Aguiar, J.A.F., Estrabis, N.V., Pinheiro, M.M.F., Furuya, M.T.G., Pereira, D.R., ... & Ramos, A.P.M., 2020. A machine learning approach for mapping forest vegetation in riparian zones in an Atlantic Biome Environment using Sentinel-2 imagery. Remote Sensing, 12(24), DOI: 10.3390/rs12244086.
85. G. Selvi Et Al., 2021. Automated Machine Learning Platform Otomatik Makine Öğrenmesi Platformu. 6th International Conference on Computer Science and Engineering, UBMK 2021, pp.769-774, Ankara, Turkey.
86. Gao, W., Qiu, Q., Yuan, C., Shen, X., Cao, F., Wang, G., Wang, G., 2022. Forestry Big Data: A Review and Bibliometric Analysis. Forests, 13(10), 1549.
87. García-Gutiérrez, J., Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C., 2015. A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables. Neurocomputing, 167, 24-31.
88. Garzon, M.B., Blazek, R., Neteler, M., De Dios, R.S., Ollero, H.S., Furlanello, C., 2006. Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian Peninsula. Ecological modelling, 197(3-4):383-393.
89. Gastaldo, P., Pinna, L., Seminara, L., Valle, M., Zunino, R., 2015. A tensor-based approach to touch modality classification by using machine learning. Rob. Auton. Syst., 63:268–278.
90. Ge, S., Gu, H., Su, W., Praks, J., Antropov, O., 2022. Improved semisupervised unet deep learning model for forest height mapping with satellite sar and optical data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5776-5787.
91. Ghosh, S.M., Behera, M.D., 2018. Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Applied Geography, 96, 29-40.
92. Ghosh, S.M., Behera, M.D., Paramanik, S., 2020. Canopy height estimation using sentinel series images through machine learning models in a mangrove forest. Remote Sensing, 12(9), 1519.
93. Gleason, C.J., Im, J., 2012. Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sensing of Environment, 125, 80-91.
94. Gonçalves, S.B., Fiedler, N.C., Silva, J.P.M., da Silva, G.F., da Silva, M.L.M., Minette, L.J., ... & Filho, R.N.D.A., 2021. Machine learning techniques to estimate mechanised forest cutting productivity. Southern Forests: a Journal of Forest Science, 83(4):276-283, DOI:10.2989/20702620.2021.1994342 . Görgens, E.B., Montaghi, A., Rodriguez, L.C.E., 2015. A performance comparison of machine learning methods to estimate the fast-growing forest plantation yield based on laser scanning metrics. Computers and Electronics in Agriculture, 116:221-227.
95. Grabska, E., Frantz, D., Ostapowicz, K., 2020. Evaluation of machine learning algorithms for forest stand species mapping using Sentinel-2 imagery and environmental data in the Polish Carpathians. Remote Sensing of Environment, 251, 112103.
96. Grebner, D.L., Bettinger, P., Siry, J., Boston, K., 2021. Introduction to forestry and natural resources. Academic press.
97. Grondin, V., Fortin, J. M., Pomerleau, F., Giguère, P., 2023. Tree detection and diameter estimation based on deep learning. Forestry, 96(2):264-276.
98. Günlü, A., Ercanlı, İ., 2020. Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey. Geocarto International, 35(1):17-28.
99. Hamdi, Z.M., Brandmeier, M., Straub, C., 2019. Forest damage assessment using deep learning on high resolution remote sensing data. Remote Sensing, 11(17), 1976.
100. Hamidi, S.K., Zenner, E.K., Bayat, M., Fallah, A., 2021. Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest. Annals of Forest Science, 78:1-16.
101. Hamilton, D., Brothers, K., McCall, C., Gautier, B., Shea, T., 2021. Mapping forest burn extent from hyperspatial imagery using machine learning. Remote Sensing, 13(19), 3843.
102. Han, H., Wan, R., Li, B., 2021. Estimating forest aboveground biomass using Gaofen-1 images, Sentinel-1 images, and machine learning algorithms: A case study of the Dabie Mountain Region, China. Remote Sensing, 14(1):176.
103. Han, L., Yang, G., Dai, H., Xu, B., Yang, H., Feng, H., ... & Yang, X., 2019. Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant methods, 15(1):1-19, DOI:10.1186/s13007-019-0394-z .
104. Haq, M.A., Rahaman, G., Baral, P., Ghosh, A., 2021. Deep learning based supervised image classification using UAV images for forest areas classification. Journal of the Indian Society of Remote Sensing, 49:601-606.
105. Hart, E., Sim, K., Kamimura, K., Meredieu, C., Guyon, D., Gardiner, B., 2019. Use of machine learning techniques to model wind damage to forests. Agricultural and forest meteorology, 265:16-29.
106. Hartley, F. M., Maxwell, A. E., Landenberger, R. E., Bortolot, Z. J., 2022. Forest Type Differentiation Using GLAD Phenology Metrics, Land Surface Parameters, and Machine Learning. Geographies, 2(3):491-515.
107. He, J., Fan, C., Geng, Y., Zhang, C., Zhao, X., & Gadow, K. V., 2022. Assessing scale‐dependent effects on Forest biomass productivity based on machine learning. Ecology and Evolution, 12(7), DOI: 10.1002/ece3.9110 .
108. Heidari, M. J., Najafi, A., & Borges, J. G., 2022. Forest roads damage detection based on deep learning algorithms. Scandinavian Journal of Forest Research, 37(5-8):366-375.
109. Helms, J.A. (Ed.), 1998. The Dictionary of Forestry. Society of American Foresters, Bethesda, MD.
110. Hirigoyen, A., Acosta-Muñoz, C., Salamanca, A. J. A., Varo-Martinez, M. Á., Rachid-Casnati, C., Franco, J., Navarro-Cerrillo, R., 2021. A machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data. Annals of Forest Research, 64(2):165-183.
111. Holmström, E., Raatevaara, A., Pohjankukka, J., Korpunen, H., Uusitalo, J., 2023. Tree log identification using convolutional neural networks. Smart Agricultural Technology, 4, 100201.
112. Hossain, J., & Halder, T., 2022. Quantifying forest cover changes in response to climate change using a machine learning model.Journal of Research in Environmental and Earth Sciences,8(9) pp: 118-131.
113. Hu, T., Sun, Y., Jia, W., Li, D., Zou, M., Zhang, M., 2021. Study on the estimation of forest volume based on multi-source data. Sensors, 21(23), 7796.
114. Hu, Y., Xu, X., Wu, F., Sun, Z., Xia, H., Meng, Q., ... Xiao, X., 2020. Estimating forest stock volume in Hunan Province, China, by integrating in situ plot data, Sentinel-2 images, and linear and machine learning regression models. Remote Sensing, 12(1):186, DOI: 10.3390/rs12010186. Huang, B., Li, Y., Liu, Y., Hu, X., Zhao, W., Cherubini, F., 2023. A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia. Agricultural and Forest Meteorology, 332, DOI:10.1016/j.agrformet.2023.109362 .
115. Huang, H., Wu, D., Fang, L., & Zheng, X., 2022. Comparison of Multiple Machine Learning Models for Estimating the Forest Growing Stock in Large-Scale Forests Using Multi-Source Data. Forests, 13(9), DOI: 10.3390/f13091471 . Huang, S., Dou, H., Jian, W., Guo, C., Sun, Y., 2023. Spatial prediction of the geological hazard vulnerability of mountain road network using machine learning algorithms. Geomatics, Natural Hazards and Risk, 14(1), 2170832.
116. Iban, M. C., Sekertekin, A., 2022. Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey. Ecological Informatics, 69, 101647.
117. Iverson, L. R., Prasad, A. M., Liaw, A., 2004. New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than regression tree analysis. In Proceedings, UK-International Association for Landscape Ecology, pp. 317-320, Cirencester, UK.
118. İlkuçar, M., Kaya, A.İ., Çifci, A., 2018. Mekanik Özelliklere Göre Ağaç Türlerinin Yapay Sinir Ağları ile Tahmini. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 8(1):75-83.
119. Jaafari, A., Pazhouhan, I., & Bettinger, P., 2021. Machine learning modeling of forest road construction costs. Forests, 12(9), DOI:10.3390/f12091169
120. Jackson, P., 1986. Introduction to expert systems. United States: N. p.,Web.
121. Jahani, A., Saffariha, M., 2021. Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques. Scientific Reports, 11(1):1-13.
122. Baker, J., 1975. The DRAGON System—An Overview. In: IEEE Transactions on Acoustics, Speech, and Signal Processing 23.1, pp. 24–29.
123. Janiec, P., Gadal, S., 2020. A comparison of two machine learning classification methods for remote sensing predictive modeling of the forest fire in the North-Eastern Siberia. Remote Sensing, 12(24), 4157.
124. Jelinek, F., Bahl, L., Mercer, R., 1975. Design of a linguistic statistical decoder for the recognition of continuous speech. IEEE Transactions on Information Theory, 21(3):250-256.
125. Jiang, H., 2021. Machine learning fundamentals: A concise introduction. Cambridge University Press.
126. Johnson, P., Abdelfattah, E., 2018. Applying machine learning models to identify forest cover. In 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 471-474). IEEE.
127. Joshi, R.C., Ryu, D., Lane, P.N., Sheridan, G.J., 2023). Seasonal forecast of soil moisture over Mediterranean-climate forest catchments using a machine learning approach. Journal of Hydrology, 619, 129307.
128. Júnior, I. D. S. T., Torres, C. M. M. E., Leite, H. G., de Castro, N. L. M., Soares, C. P. B., Castro, R. V. O., & Farias, A. A., 2020. Machine learning: Modeling increment in diameter of individual trees on Atlantic Forest fragments. Ecological Indicators, 117, DOI:10.3390/f13081295
129. Kalantar, B., Ueda, N., Idrees, M. O., Janizadeh, S., Ahmadi, K., & Shabani, F., 2020. Forest fire susceptibility prediction based on machine learning models with resampling algorithms on remote sensing data. Remote Sensing, 12(22), 3682.
130. Kamarulzaman, A. M. M., Wan Mohd Jaafar, W. S., Abdul Maulud, K. N., Saad, S. N. M., Omar, H., Mohan, M., 2022. Integrated segmentation approach with machine learning classifier in detecting and mapping post selective logging impacts using UAV imagery. Forests, 13(1):48.
131. Kang, J., Schwartz, R., Flickinger, J., Beriwal, S., 2015. Machine learning approaches for predicting radiation therapy outcomes: A clinician’s perspective. Int. J. Radiat. Oncol. Biol. Phys., 93, 1127–1135.
132. Kansal, A., Singh, Y., Kumar, N., Mohindru, V., 2015. Detection of forest fires using machine learning technique: A perspective. In 2015 third international conference on image information processing (ICIIP) (pp. 241-245). IEEE.
133. Kantarcioglu, O., Kocaman, S., Schindler, K., 2023. Artificial neural networks for assessing forest fire susceptibility in Türkiye. Ecological Informatics, 75, 102034.
134. Kauffman, J. S., Prisley, S. P., 2016. Automated estimation of forest stand age using Vegetation Change Tracker and machine learning. Mathematical & Computational Forestry & Natural Resource Sciences, 8(1).
135. Kaya, H., Keklık, İ., Ensarı, T., Alkan, F., & Bırıcık, Y., 2019. Oak leaf classification: an analysis of features and classifiers. In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT) (pp. 1-4). Ieee.
136. Keleş, S., Günlü, A., Ercanli, İ., 2021. Estimating aboveground stand carbon by combining sentinel-1 and sentinel-2 satellite data: A case study from turkey. In Forest Resources Resilience and Conflicts, pp. 117-126,Elsevier.
137. Kim, B., Woo, H., Park, J., 2020. A Study on the Performance of Deep learning-based Automatic Classification of Forest Plants: A Comparison of Data Collection Methods. Journal of Korean Society of Forest Science, 109(1):23-30.
138. Kim, E. S., Lee, B., Kim, J., Cho, N., & Lim, J. H., 2020. Risk assessment of pine tree dieback in Sogwang-Ri, Uljin. Journal of Korean Society of Forest Science, 109(3):259-270.
139. Kim, S. J., Lim, C. H., Kim, G. S., Lee, J., Geiger, T., Rahmati, O., ... & Lee, W. K., 2019. Multi-temporal analysis of forest fire probability using socio-economic and environmental variables. Remote Sensing, 11(1):86.
140. Kislov, D. E., Korznikov, K. A., 2020. Automatic windthrow detection using very-high-resolution satellite imagery and deep learning. Remote Sensing, 12(7), 1145.
141. Kitchin, R., McArdle, G., 2016. What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1), 2053951716631130.
142. Knopp, L., Wieland, M., Rättich, M., Martinis, S., 2020. A deep learning approach for burned area segmentation with Sentinel-2 data. Remote Sensing, 12(15), 2422.
143. Kong, L.,Zhang, Y., Ye, Z.Q.,Liu, X.Q., Zhao, S.Q., Wei, L., Gao, G., 2007. CPC: Assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res., 35:345–349.
144. Kriese, J., Hoeser, T., Asam, S., Kacic, P., Da Ponte, E., Gessner, U., 2022. Deep Learning on Synthetic Data Enables the Automatic Identification of Deficient Forested Windbreaks in the Paraguayan Chaco. Remote Sensing, 14(17):4327.
145. Kuck, T.N., Sano, E.E., Bispo, P.D.C., Shiguemori, E.H., Silva Filho, P.F.F., Matricardi, E.A.T.,2021. A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images. Remote Sensing, 13(17):3341.
146. Kukuk, S. B., & Kilimci, Z. H., 2021. Comprehensive analysis of forest fire detection using deep learning models and conventional machine learning algorithms. International Journal of Computational and Experimental Science and Engineering, 7(2):84-94.
147. Kuruca, M., Matcı, D. K., & Avdan, U., 2021. The potential of Göktürk 2 satellite images for mapping burnt forest areas. Turkish Journal of Agriculture and Forestry, 45(1):91-101.
148. Kuruca, M., Matcı, D. K., & Avdan, U., 2018. Yanmış Orman Alanlarının Destek Vektör Makinaları ve Rotasyon Orman İleri Sınıflandırma Yöntemleri Kullanarak Nesne-Tabanlı Tespiti: Worldvıew-2 Uydu Görüntüsü Örneği. VII. Uzaktan Algılama-CBS Sempozyumu (UZAL-CBS 2018), 18-21 Eylül, Eskişehir.
149. Lapini, A., Pettinato, S., Santi, E., Paloscia, S., Fontanelli, G., Garzelli, A., 2020. Comparison of machine learning methods applied to SAR images for forest classification in mediterranean areas. Remote Sensing, 12(3):369.
150. Lee, B., Jang, K., Kim, E., Kang, M., Chun, J. H., & Lim, J. H., 2019. Predicting forest gross primary production using machine learning algorithms. Korean Journal of Agricultural and Forest Meteorology, 21(1):29-41.
151. Lee, J., Im, J., Kim, K., & Quackenbush, L. J., 2018. Machine learning approaches for estimating forest stand height using plot-based observations and airborne LiDAR data. Forests, 9(5):268.
152. Levers, C., Verkerk, P. J., Müller, D., Verburg, P. H., Butsic, V., Leitão, P. J., ... & Kuemmerle, T., 2014. Drivers of forest harvesting intensity patterns in Europe. Forest ecology and management, 315:160-172, DOI:10.1016/j.foreco.2013.12.030 .
153. Li, M., Im, J., & Beier, C., 2013. Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest. GIScience & Remote Sensing, 50(4):361-384, DOI:10.1080/15481603.2013.819161.
154. Li, S., Lideskog, H., 2021. Implementation of a system for real-time detection and localization of terrain objects on harvested forest land. Forests, 12(9), DOI:10.3390/f12091142
155. Li, W., Niu, Z., Shang, R., Qin, Y., Wang, L., Chen, H., 2020. High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data. International Journal of Applied Earth Observation and Geoinformation, 92, 102163.
156. Li, X., Du, H., Mao, F., Zhou, G., Chen, L., Xing, L., ... & Liu, T., 2018. Estimating bamboo forest aboveground biomass using EnKF-assimilated MODIS LAI spatiotemporal data and machine learning algorithms. Agricultural and Forest Meteorology, 256:445-457, DOI:10.1016/j.agrformet.2018.04.002 .
157. Li, Y., Li, C., Li, M., Liu, Z., 2019. Influence of variable selection and forest type on forest aboveground biomass estimation using machine learning algorithms. Forests, 10(12), 1073.
158. Li, Y., Li, M., Li, C., & Liu, Z., 2020. Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Scientific reports, 10(1): 1-12.
159. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., Bochtis, D., 2018. Machine learning in agriculture: A review. Sensors, 18(8), 2674.
160. Lidberg, W., Nilsson, M., & Ågren, A., 2020. Using machine learning to generate high-resolution wet area maps for planning forest management: A study in a boreal forest landscape. Ambio, 49(2): 475-486.
161. Lim, S., Kim, S., Park, S., Kim, D., 2018. Development of application for forest insect classification using CNN. In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1128-1131, IEEE.
162. Lim, W., Choi, K., Cho, W., Chang, B., Ko, D. W., 2022. Efficient dead pine tree detecting method in the Forest damaged by pine wood nematode (Bursaphelenchus xylophilus) through utilizing unmanned aerial vehicles and deep learning-based object detection techniques. Forest Science and Technology, 18(1):36-43.
163. Lippitt, C. D., Rogan, J., Li, Z., Eastman, J. R., Jones, T. G., 2008. Mapping selective logging in mixed deciduous forest: a comparison of machine learning algorithms. Photogrammetric Engineering and Remote Sensing, 74(10):1201-1211.
164. Liu, B., Gao, L., Li, B., Marcos-Martinez, R., Bryan, B. A., 2020. Nonparametric machine learning for mapping forest cover and exploring influential factors. Landscape Ecology, 35:1683-1699.
165. Liu, X., Liang, S., Li, B., Ma, H., He, T., 2021. Mapping 30 m fractional forest cover over China’s Three-North Region from Landsat-8 data using ensemble machine learning methods. Remote Sensing, 13(13), 2592.
166. Liu, Z., Wang, W. J., Ballantyne, A., He, H. S., Wang, X., Liu, S., ... & Zhu, J., 2023. Forest disturbance decreased in China from 1986 to 2020 despite regional variations. Communications Earth & Environment, 4(1):15, DOI: 10.1038/s43247-023-00676-x.
167. López-Cortés, X.A.; Nachtigall, F.M.; Olate, V.R.; Araya, M.; Oyanedel, S.; Diaz, V.; Jakob, E.; Ríos-Momberg, M.; Santos, L.S., 2017. Fast detection of pathogens in salmon farming industry. Aquaculture, 470:17–24.
168. López-Serrano, P. M., Cárdenas Domínguez, J. L., Corral-Rivas, J. J., Jiménez, E., López-Sánchez, C. A., & Vega-Nieva, D. J., 2019. Modeling of aboveground biomass with Landsat 8 OLI and machine learning in temperate forests. Forests, 11(1):11.
169. Lu, R., Zhu, H., Liu, X., Liu, J. K., & Shao, J. 2014. Toward efficient and privacy-preserving computing in big data era. IEEE Network, 28(4): 46-50.
170. Luo, H., Yue, C., Xie, F., Zhu, B., Chen, S., 2022. A Method for Forest Canopy Height Inversion Based on Machine Learning and Feature Mining Using UAVSAR. Remote Sensing, 14(22), 5849.
171. Luo, W., Zhang, C., Zhao, X., Liang, J., 2021. Understanding patterns and potential drivers of forest diversity in northeastern China using machine‐learning algorithms. Journal of Vegetation Science, 32(2), e13022.
172. Mackowiak, S.D.; Zauber, H.; Bielow, C.; Thiel, D.; Kutz, K.; Calviello, L.; Mastrobuoni, G.; Rajewsky, N.; Kempa, S.; Selbach, M.; et al., 2015. Extensive identification and analysis of conserved small ORFs in animals. Genome Biol., 16, 179.
173. MacMillan, R., Sun, L., Taylor, S. W., 2022. Modeling Individual Extended Attack Wildfire Suppression Expenditures in British Columbia. Forest Science, 68(4):376-388.
174. Mahdavi, A., Aziz, J., 2020. Estimation of Semiarid Forest Canopy Cover Using Optimal Field Sampling and Satellite Data with Machine Learning Algorithms. Journal of the Indian Society of Remote Sensing, 48:575-583.
175. Maione, C.; Barbosa, R.M., 2018. Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review. Crit. Rev. Food Sci. Nutr., 1–12.
176. Maniatis, Y., Doganis, A., Chatzigeorgiadis, M., 2022. Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece. Applied Sciences, 12(6), 2938.
177. Marvin, M., & Seymour, A. P., 1969. Perceptrons. Cambridge, MA: MIT Press, 6, 318-362.
178. Mashhadi, N., Alganci, U., 2021. Determination of forest burn scar and burn severity from free satellite images: A comparative evaluation of spectral indices and machine learning classifiers. International Journal of Environment and Geoinformatics, 8(4):488-497.
179. Melander, L., Einola, K., Ritala, R., 2020. Fusion of open forest data and machine fieldbus data for performance analysis of forest machines. European Journal of Forest Research, 139(2):213-227.
180. Miranda, E. N., Barbosa, B. H. G., Silva, S. H. G., Monti, C. A. U., Tng, D. Y. P., Gomide, L. R., 2022. Variable selection for estimating individual tree height using genetic algorithm and random forest. Forest Ecology and Management, 504, 119828.
181. Mitchell., T. 1997. Machine Learning. New York, NY: McGraw-Hill.
182. Mittal, A., Sharma, G., Aggarwal, R., 2016. Forest fire detection through various machine learning techniques using mobile agent in WSN. International Research Journal of Engineering and Technology.
183. Mohajane, M., Costache, R., Karimi, F., Pham, Q. B., Essahlaoui, A., Nguyen, H., ... & Oudija, F., 2021. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators, 129, DOI:10.1016/j.ecolind.2021.107869
184. Moore, J., Lin, Y., 2019. Determining the extent and drivers of attrition losses from wind using long-term datasets and machine learning techniques. Forestry: An International Journal of Forest Research, 92(4):425-435.
185. Moradi, F., Sadeghi, S. M. M., Heidarlou, H. B., Deljouei, A., Boshkar, E., Borz, S. A., 2022. Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data. Annals of Forest Research, 65(1):165-182.
186. Mosin, V., Aguilar, R., Platonov, A., Vasiliev, A., Kedrov, A., & Ivanov, A., 2019. Remote sensing and machine learning for tree detection and classification in forestry applications. In Image and Signal Processing for Remote Sensing XXV,11155, pp. 130-141. SPIE.
187. Munis, R. A., Almeida, R. O., Camargo, D. A., da Silva, R. B. G., Wojciechowski, J., Simões, D., 2022. Machine learning methods to estimate productivity of harvesters: mechanized timber harvesting in Brazil. Forests, 13(7), 1068.
188. Munro, H. L., Montes, C. R., Gandhi, K. J., 2022. A new approach to evaluate the risk of bark beetle outbreaks using multi-step machine learning methods. Forest Ecology and Management, 520, 120347.
189. Murty, M. N., Avinash, M., 2023. Representation in Machine Learning. Springer Nature.
190. Naderi, S., Bundy, K., Whitney, T., Abedi, A., Weiskittel, A., Contosta, A., 2022. Sharing Wireless Spectrum in the Forest Ecosystems Using Artificial Intelligence and Machine Learning. International Journal of Wireless Information Networks, 29(3):257-268.
191. Naik, P., Dalponte, M., Bruzzone, L., 2022. Automated Machine Learning Driven Stacked Ensemble Modelling for Forest Aboveground Biomass Prediction Using Multitemporal Sentinel-2 Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
192. Nandi, A., Pal, A. K., 2022. Interpreting machine learning models: Learn model interpretability and explainability methods. Berkeley, CA: Apress.
193. Narine, L. L., Popescu, S. C., Malambo, L., 2019. Synergy of ICESat-2 and Landsat for mapping forest aboveground biomass with deep learning. Remote Sensing, 11(12), 1503.
194. Nasiri, V., Darvishsefat, A. A., Arefi, H., Griess, V. C., Sadeghi, S. M. M., Borz, S. A., 2022. Modeling forest canopy cover: A synergistic use of Sentinel-2, aerial photogrammetry data, and machine learning. Remote Sensing, 14(6), 1453.
195. Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., Shaalan, K., 2019. Speech recognition using deep neural networks: A systematic review. IEEE access, 7, 19143-19165.
196. Negara, B. S., Kurniawan, R., Nazri, M. Z. A., Abdullah, S. N. H. S., Saputra, R. W., Ismanto, A., 2020. Riau forest fire prediction using supervised machine learning. In Journal of Physics: Conference Series 1566(1), p. 012002. IOP Publishing, DOI:10.1088/1742-6596/1566/1/012002
197. Neuville, R., Bates, J. S., Jonard, F., 2021. Estimating forest structure from UAV-mounted LiDAR point cloud using machine learning. Remote sensing, 13(3), 352.
198. Nguyen, Q. H., Nguyen, H. D., Le, D. T., Bui, Q. T., 2023. Fine-tuning LightGBM using an artificial ecosystem-based optimizer for forest fire analysis. Forest Science, 69(1):73-82.
199. Nguyen, T. T., Nguyen, V. P., Nguyen, V. Q., Hoang, T. P. N., 2022. Applied Machine Learning Algorithms and Landsat 8 for Estimating Aboveground Carbon Stock in Evergreen Broadleaf Forest in Binh Phuoc Province. VNU Journal of Science: Earth and Environmental Sciences, 38(4).
200. Nguyen, V. T., Constant, T., Colin, F., 2021. An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data. Annals of Forest Science, 78(2):1-18.
201. Nilsson, J. N., 1996. Introduction to machine learning: An early draft of a proposed textbook.
202. Opelele, O. M., Yu, Y., Fan, W., Chen, C., Kachaka, S. K., 2021. Biomass Estimation Based on Multilinear Regression and Machine Learning Algorithms in the Mayombe Tropical Forest, in the Democratic Republic of Congo. Appl. Ecol. Environ. Res, 19, 359-377.
203. Ostovar, A., Talbot, B., Puliti, S., Rasmus, A., Ringdahl, O., 2019. Using RGB images and machine learning to detect and classify Root and Butt-Rot (RBR) in stumps of Norway spruce. In NB Nord Conference: Forest Operations in Response to Environmental Challenges, Honne, Norway, June 3-5, Norsk institutt for bioøkonomi (NIBIO).
204. Oyarzo, C., Rossit, D. A., Viana-Céspedes, V., Olivera, A., 2022. Discriminant method approach for harvesting forest operations. In 2022 International Conference on Data Analytics for Business and Industry (ICDABI), pp. 736-740, IEEE.
205. Örücü, Ö. K., 2019. Phoenix theophrasti Gr.’nin iklim değişimine bağlı günümüz ve gelecekteki yayılış alanlarının MaxEnt Modeli ile tahmini ve bitkisel tasarımda kullanımı. Turkish Journal of Forestry, 20(3):274-283.
206. Örücü, Ö. K., Akyol, A., 2019. İklim değişikliğinin Türkiye’de Myrtus communis subsp. communis L.’nin potansiyel dağılımına etkilerinin Maxent ile araştırılması. Ziraat, Orman ve Su Ürünleri Alanında Yeni Ufuklar, 31-49.
207. Örücü, Ö. K., Azadi, H., Arslan, E. S., Kamer Aksoy, Ö., Choobchian, S., Nooghabi, S. N., Stefanie, H. I., 2023. Predicting the distribution of European Hop Hornbeam: application of MaxEnt algorithm and climatic suitability models. European Journal of Forest Research, 1-13.
208. Örücü, Ö. K., Gülçin, D., Özçifçi, İ., Arslan, E. S., 2021. Mor Çiçekli Ormangülünün (Rhododendron ponticum L.) günümüz ve gelecekteki iklim koşullarına göre yayılış alanlarının modellenmesi.
209. Özçelik, R., Diamantopoulou, M. J., Brooks, J. R., Wiant Jr, H. V., 2010. Estimating tree bole volume using artificial neural network models for four species in Turkey. Journal of environmental management, 91(3):742-753.
210. Özdemir, Ş., Örslü, S., 2019. Makine öğrenmesinde yeni bir bakış açısı: otomatik makine öğrenmesi (AutoML). Journal of Information Systems and Management Research, 1(1):23-30.
211. Özkan, C., Sunar, F., Berberoğlu, S., Dönmez, C., 2008. Effectiveness of boosting algorithms in forest fire classification. The international archives of the photogrammetry, remote sensing and spatial information sciences, 37.
212. Pang, Y., Li, Y., Feng, Z., Feng, Z., Zhao, Z., Chen, S., Zhang, H., 2022. Forest Fire Occurrence Prediction in China Based on Machine Learning Methods. Remote Sensing, 14(21), 5546.
213. Park, J., Lim, B., Lee, J., 2021. Analysis of Factors Influencing Forest Loss in South Korea: Statistical Models and Machine-Learning Model. Forests, 12(12), 1636.
214. Peng, Y., Wang, Y., 2022. Automatic wildfire monitoring system based on deep learning. European Journal of Remote Sensing, 55(1):551-567.
215. Perera, P. L. M., Jayakody, J. R. K. C., 2015. Forest cover type predicition with machine learning with R and Weka.
216. Brown P., et al., 1988. A Statistical Approach to Language Translation’. In: Proceedings of the 12th Conference on Computational Linguistics. 1, COLING ’88. Budapest, Hungary: Association for Computational Linguistics, pp. 71–76.
217. Jackson., P.,1990. Introduction to Expert Systems. 2nd ed. USA: Addison-Wesley Longman Publishing Co., Inc., USA
218. Petrusevich, D. A., 2021. Models for dominating forest cover type prediction. In IOP Conference Series: Earth and Environmental Science, 677(5), p. 052119. IOP Publishing.
219. Pham, B. T., Jaafari, A., Avand, M., Al-Ansari, N., Dinh Du, T., Yen, H. P. H., ... & Tuyen, T. T., 2020. Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry, 12(6), 1022.
220. Pilaš, I., Gašparović, M., Novkinić, A., Klobučar, D., 2020. Mapping of the canopy openings in mixed beech–fir forest at Sentinel-2 subpixel level using UAV and machine learning approach. Remote Sensing, 12(23), 3925.
221. Piragnolo, M., Grigolato, S., Pirotti, F., 2019. Planning harvesting operations in forest environment: remote sensing for decision support. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4:33-40.
222. Piragnolo, M., Pirotti, F., Zanrosso, C., Lingua, E., Grigolato, S., 2021. Responding to large-scale forest damage in an alpine environment with remote sensing, machine learning, and web-GIS. Remote Sensing, 13(8), 1541.
223. Pohjankukka, J., Riihimäki, H., Nevalainen, P., Pahikkala, T., Ala-Ilomäki, J., Hyvönen, E., ... & Heikkonen, J., 2016. Predictability of boreal forest soil bearing capacity by machine learning. Journal of Terramechanics, 68:1-8.
224. Polowy, K., & Molińska-Glura, M., 2023. Data Mining in the Analysis of Tree Harvester Performance Based on Automatically Collected Data. Forests, 14(1), 165.
225. Pourghasemi, H. R., Gayen, A., Lasaponara, R., & Tiefenbacher, J. P., 2020. Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling. Environmental research, 184, 109321.
226. Pourshamsi, M., Garcia, M., Lavalle, M., & Balzter, H., 2018. A machine-learning approach to PolInSAR and LiDAR data fusion for improved tropical forest canopy height estimation using NASA AfriSAR Campaign data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10):3453-3463.
227. Pourshamsi, M., Xia, J., Yokoya, N., Garcia, M., Lavalle, M., Pottier, E., & Balzter, H., 2021. Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning. ISPRS Journal of Photogrammetry and Remote Sensing, 172: 79-94.
228. Prakash, A. J., Behera, M. D., Ghosh, S. M., Das, A., & Mishra, D. R., 2022. A new synergistic approach for Sentinel-1 and PALSAR-2 in a machine learning framework to predict aboveground biomass of a dense mangrove forest. Ecological Informatics, 72, 101900.
229. Qiu, J., Wang, H., Shen, W., Zhang, Y., Su, H., Li, M., 2021. Quantifying forest fire and post-fire vegetation recovery in the daxin’anling area of northeastern China using landsat time-series data and machine learning. Remote sensing, 13(4):792.
230. Qu, J., & Cui, X., 2020. Automatic machine learning framework for forest fire forecasting. In Journal of Physics: Conference Series 1651(1), p. 012116. IOP Publishing.
231. Rajbhandari, S., Aryal, J., Osborn, J., Lucieer, A., Musk, R., 2019. Leveraging machine learning to extend ontology-driven geographic object-based image analysis (O-GEOBIA): A case study in forest-type mapping. Remote Sensing, 11(5):503.
232. Rana, P., Miller, D. C., 2019. Machine learning to analyze the social-ecological impacts of natural resource policy: insights from community forest management in the Indian Himalaya. Environmental Research Letters, 14(2), 024008.
233. Rana, P., Miller, D. C., 2021. Predicting the long-term social and ecological impacts of tree-planting programs: Evidence from northern India. World Development, 140, 105367.
234. Reddy, R. S., Babu, G. A., Reddy, A. R. M., 2020. Geospatial Approach for the Analysis of Forest Cover Change Detection using Machine Learning. Geosfera Indonesia, 5(3):335-351.
235. Ren, H., Zhang, L., Yan, M., Chen, B., Yang, Z., Ruan, L., 2022. Spatiotemporal Assessment of Forest Fire Vulnerability in China Using Automated Machine Learning. Remote Sensing, 14(23), 5965.
236. Rhee, J., Im, J., 2017. Meteorological drought forecasting for ungauged areas based on machine learning: Using long-range climate forecast and remote sensing data. Agric. For. Meteorol., 237–238, 105–122.
237. Richard O. Duda and Peter E. Hart., 1973 Pattern Classification and Scene Analysis. New York, NY: John Wiley & Sons, USA
238. Richardson, A., Signor, B.M., Lidbury, B.A., Badrick, T., 2016. Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data. Clin. Biochem. 49:1213–1220.
239. Rosenblatt, F., 1958. The Perceptron: A probabilistic model for information storage and organization in the brain. In: Psychological Review, pp. 65–386.
240. Rumelhart, D. E., Hinton, G. E., McClelland, J. L., 1986a. A general framework for parallel distributed processing. Parallel distributed processing: Explorations in the microstructure of cognition, 1:(45-76), 26.
241. Rumelhart, D. E., Hinton, G. E., Williams, R. J., 1986b. Learning representations by back-propagating errors. nature, 323(6088), 533-536.
242. Russell, J., Norvig, S., P., 2010. Artificial Intelligence A Modern Approach Third Edition.
243. Sabancı, K., Ünlersen, M. F., Polat, K., 2016. Classification of different forest types with machine learning algorithms.
244. Sahin, A., Aylak Ozdemir, G., Oral, O., Aylak, B. L., Ince, M., Ozdemir, E., 2023. Estimation of tree height with machine learning techniques in coppice-originated pure sessile oak (Quercus petraea (Matt.) Liebl.) stands. Scandinavian Journal of Forest Research, 1-10.
245. Sakici, O. E., Ozdemir, G., 2018. Stem taper estimations with artificial neural networks for mixed Oriental beech and Kazdaği fir stands in Karabük region, Turkey. Cerne, 24:439-451.
246. Salmivaara, A., Launiainen, S., Perttunen, J., Nevalainen, P., Pohjankukka, J., Ala-Ilomäki, J., ... & Finér, L., 2020. Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology. Forestry: An International Journal of Forest Research, 93(5):662-674.
247. Sanderman, J., Hengl, T., Fiske, G., Solvik, K., Adame, M. F., Benson, L., ... & Landis, E., 2018. A global map of mangrove forest soil carbon at 30 m spatial resolution. Environmental Research Letters, 13(5), 055002.
248. Sani-Mohammed, A., Yao, W., Heurich, M., 2022. Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning. ISPRS Open Journal of Photogrammetry and Remote Sensing, 6, 100024.
249. Saralioglu, E., Vatandaslar, C., 2022. Land use/land cover classification with Landsat-8 and Landsat-9 satellite images: a comparative analysis between forest-and agriculture-dominated landscapes using different machine learning methods. Acta Geodaetica et Geophysica, 1-22.
250. Sarıkaya, O., Şen, İ., 2020. Estimation to current and future potential distribution areas of Pityogenes calcaratus (Eichhoff) in Turkish Forests. International Journal of Agriculture, Forestry and Fisheries, 8(4):118-122.
251. Sari, F., 2022. Identifying anthropogenic and natural causes of wildfires by maximum entropy method-based ignition susceptibility distribution models. Journal of Forestry Research, 1-17.
252. Sarikaya, A. G., Orucu, O. K., 2021. Maxent modeling for predicting the potential distribution of Arbutus andrachne L. belonging to climate change in Turkey. Kuwait Journal of Science, 48(2).
253. Seddouki, M., Benayad, M., Aamir, Z., Tahiri, M., Maanan, M., Rhinane, H., 2023. Using Machine Learning Coupled with Remote Sensing for Forest Fire Susceptibility Mapping. Case Study Tetouan Province, Northern Morocco. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48:333-342.
254. Senanayake, I. P., Yeo, I. Y., Walker, J. P., Willgoose, G. R., 2021. Estimating catchment scale soil moisture at a high spatial resolution: Integrating remote sensing and machine learning. Science of The Total Environment, 776, 145924.
255. Sevinç, V., 2023. Mapping the forest fire risk zones using artificial intelligence with risk factors data. Environmental Science and Pollution Research, 30(2): 4721-4732.
256. Shabani, S., Pourghasemi, H. R., & Blaschke, T., 2020. Forest stand susceptibility mapping during harvesting using logistic regression and boosted regression tree machine learning models. Global Ecology and Conservation, 22, e00974.
257. Shabani, S., Varamesh, S., Moayedi, H., Le Van, B., 2023. Modeling the susceptibility of an uneven-aged broad-leaved forest to snowstorm damage using spatially explicit machine learning. Environmental Science and Pollution Research, 30(12): 34203-34213.
258. Shang, X., & Chisholm, L. A., 2013. Classification of Australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6):2481-2489.
259. Shao, Y., Feng, Z., Sun, L., Yang, X., Li, Y., Xu, B., Chen, Y., 2022. Mapping China’s Forest Fire Risks with Machine Learning. Forests, 13(6):856.
260. Shataee, S., Kalbi, S., Fallah, A., Pelz, D., 2012. Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms. International journal of remote sensing, 33(19):6254-6280.
261. Shen, J., Chen, G., Hua, J., Huang, S., Ma, J., 2022. Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method. Remote Sensing, 14(13), 3238.
262. Shen, X., Huang, Q., Wang, X., Li, J., Xi, B., 2022. A Deep Learning-Based Method for Extracting Standing Wood Feature Parameters from Terrestrial Laser Scanning Point Clouds of Artificially Planted Forest. Remote Sensing, 14(15), 3842.
263. Silva, C. A., Klauberg, C., Hudak, A. T., Vierling, L. A., Jaafar, W. S. W. M., Mohan, M., ... & Saatchi, S., 2017. Predicting stem total and assortment volumes in an industrial Pinus taeda L. forest plantation using airborne laser scanning data and random forest. Forests, 8(7):254.
264. Singh, C., Karan, S. K., Sardar, P., Samadder, S. R., 2022. Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis. Journal of Environmental Management, 308, 114639.
265. Singh, M., Sharma, C., Agarwal, T., & Pal, M. S., 2022. Forest Fire Prediction for NASA Satellite Dataset Using Machine Learning. In 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp. 1-5, IEEE.
266. Smolyakov, V., 2023. Machine learning algorithms in depth. MEAP Edition, Version 3, Manning Early Access Program, Manning Publications Co.
267. Solórzano, J. V., & Gao, Y., 2022. Forest Disturbance Detection with Seasonal and Trend Model Components and Machine Learning Algorithms. Remote Sensing, 14(3):803.
268. Sonti, S.H., 2015. Application of Geographic Information System (GIS) in Forest Management. J Geogr Nat Disast, 5:145. DOI:10.4172/21670587.1000145
269. Stojanova, D., Panov, P., Gjorgjioski, V., Kobler, A., Džeroski, S., 2010. Estimating vegetation height and canopy cover from remotely sensed data with machine learning. Ecological Informatics, 5(4):256-266.
270. Su, H., Shen, W., Wang, J., Ali, A., & Li, M., 2020. Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests. Forest Ecosystems, 7:1-20.
271. Sun, Z., Qian, W., Huang, Q., Lv, H., Yu, D., Ou, Q., ... & Tang, X., 2022. Use Remote Sensing and Machine Learning to Study the Changes of Broad-Leaved Forest Biomass and Their Climate Driving Forces in Nature Reserves of Northern Subtropics. Remote Sensing, 14(5):1066.
272. Şeker, Ş. E., 2020. OptiScorer: Otomatik Makine Öğrenmesi ile Skorlama.
273. Şen, I., Sarikaya, O., & Örücü, Ö. K., 2020. Current and future potential distribution areas of Carphoborus minimus (Fabricius, 1798) in Turkey. Folia Biologica (Kraków), 68(4):141-148.
274. Takahashi, K., Kim, K., Ogata, T., Sugano, S., 2017. Tool-body assimilation model considering grasping motion through deep learning. Rob. Auton. Syst., 91:115–127.
275. Tang, Z., Xia, X., Huang, Y., Lu, Y., Guo, Z., 2022. Estimation of National Forest Aboveground Biomass from Multi-Source Remotely Sensed Dataset with Machine Learning Algorithms in China. Remote Sensing, 14(21), 5487.
276. Tappayuthpijarn, K., Vindevogel, B. S., 2022. High-accuracy Machine Learning Models to Estimate above Ground Biomass over Tropical Closed Evergreen Forest Areas from Satellite Data. In IOP Conference Series: Earth and Environmental Science 1006(1), p. 012001. IOP Publishing.
277. Tariq, A., Shu, H., Siddiqui, S., Munir, I., Sharifi, A., Li, Q., Lu, L., 2022. Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods. Journal of Forestry Research, 33(1):183-194.
278. Tavares Júnior, I. D. S., de Souza, J. R. M., Lopes, L. S. D. S., Fardin, L. P., Casas, G. G., Oliveira Neto, R. R. D., ... & Leite, H. G., 2021. Machine learning and regression models to predict multiple tree stem volumes for teak. Southern Forests: a Journal of Forest Science, 83(4):294-302.
279. Tavasoli, N., Arefi, H., 2021. Comparison of capability of SAR and optical data in mapping forest above ground biomass based on machine learning. Environmental Sciences Proceedings, 5(1):13.
280. Taylor, S. E., Veal, M. W., Grift, T. E., McDonald, T. P., Corley, F. W., 2002. Precision forestry: operational tactics for today and tomorrow. In 25th annual Meeting of the council of Forest Engineers, 6.
281. Tehrany, M. S., Jones, S., Shabani, F., Martínez-Álvarez, F., Tien Bui, D., 2019. A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data. Theoretical and Applied Climatology, 137:637-653.
282. Tiwari, K., Narine, L. L., 2022. A Comparison of Machine Learning and Geostatistical Approaches for Mapping Forest Canopy Height over the Southeastern US Using ICESat-2. Remote Sensing, 14(22), 5651.
283. Tonbul, H., Colkesen, I., Kavzoglu, T., 2022. Pixel-and Object-Based ensemble learning for forest burn severity using USGS FIREMON and Mediterranean condition dNBRs in Aegean ecosystem (Turkey). Advances in Space Research, 69(10):3609-3632.
284. Torre‐Tojal, L., Lopez‐Guede, J. M., Grana Romay, M. M., 2019. Estimation of forest biomass from light detection and ranging data by using machine learning. Expert Systems, 36(4), e12399.
285. Torun, P., Altunel, A. O., 2020. Effects of environmental factors and forest management on landscape-scale forest storm damage in Turkey. Annals of Forest Science, 77:1-13.
286. Tutmez, B., Ozdogan, M. G., Boran, A., 2018. Mapping forest fires by nonparametric clustering analysis. Journal of forestry research, 29:177-185.
287. Udali, A., Talbot, B., Puliti, S., Crous, J., Lingua, E., & Grigolato, S., 2022. Assessing the potential for forest residue classification and distribution over clear felled areas using UAVs and Machine Learning: a preliminary case study in South Africa. In 2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 160-163. IEEE.
288. Uniyal, S., Purohit, S., Chaurasia, K., Rao, S. S., Amminedu, E., 2022. Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur, India. Urban Forestry & Urban Greening, 67, 127445.
289. URL, 2023. https://www.automl.org/automl/. Erişim: 1 Nisan 2023.
290. Uzun, A., & Örücü, Ö. K., 2020. Adenocarpus complicatus (L.) Gay türünün iklim değişkenlerine bağlı günümüz ve gelecekteki yayılış alanlarının tahmini. Türkiye Ormancılık Dergisi, 21(4):498-508.
291. Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T. D., Tien Bui, D., 2018. Improving accuracy estimation of Forest Aboveground Biomass based on incorporation of ALOS-2 PALSAR-2 and Sentinel-2A imagery and machine learning: A case study of the Hyrcanian forest area (Iran). Remote Sensing, 10(2):172.
292. Varol, T., Durkaya, B., Okan, E., 2018. Estimating carbon storage through machine learning algorithms.International Journal of Recent Engineering Research and Development (IJRERD), 3(3), March 2018, pp. 114-120
293. Varvia, P., Lähivaara, T., Maltamo, M., Packalen, P., Seppänen, A., 2018. Gaussian process regression for forest attribute estimation from airborne laser scanning data. IEEE Transactions on Geoscience and Remote Sensing, 57(6):3361-3369.
294. Vatandaşlar, C., Zeybek, M., 2021. Extraction of forest inventory parameters using handheld mobile laser scanning: A case study from Trabzon, Turkey. Measurement, 177, 109328.
295. Vega Isuhuaylas, L. A., Hirata, Y., Ventura Santos, L. C., Serrudo Torobeo, N., 2018. Natural forest mapping in the Andes (Peru): A comparison of the performance of machine-learning algorithms. Remote Sensing, 10(5):782.
296. Verkerk, P. J., Costanza, R., Hetemäki, L., Kubiszewski, I., Leskinen, P., Nabuurs, G. J., ... & Palahí, M., 2020. Climate-smart forestry: the missing link. Forest Policy and Economics, 115, 102164.
297. Vicentini, M. E., 2021. Machine learning modeling in temporal variability of soil respiration in planted forest areas.
298. Wai, P., Su, H., Li, M., 2022. Estimating Aboveground Biomass of Two Different Forest Types in Myanmar from Sentinel-2 Data with Machine Learning and Geostatistical Algorithms. Remote Sensing, 14(9), 2146.
299. Wang, K., Pan, J., Jiang, L., Sun, Y., Wang, K., Cao, Y., 2022. Research on Remote Sensing Recognition of Forest Fire Smoke Based on Machine Learning. In 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) (pp. 490-495). IEEE.
300. Wang, X., Liu, C., Lv, G., Xu, J., Cui, G., 2022. Integrating multi-source remote sensing to assess forest aboveground biomass in the Khingan mountains of north-eastern China using machine-learning algorithms. Remote Sensing, 14(4), 1039.
301. Wildenhain, J., Spitzer, M., Dolma, S., Jarvik, N., White, R., Roy, M., Griffiths, E., Bellows, D.S., Wright, G.D., Tyers, M., 2015. Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. Cell Syst., 1:383–395.
302. Wu, C., Pang, L., Jiang, J., An, M., Yang, Y., 2020. Machine learning model for revealing the characteristics of soil nutrients and aboveground biomass of Northeast Forest, China. Nature Environment and Pollution Technology, 19(2):481-492.
303. Xi, Z., Xu, H., Xing, Y., Gong, W., Chen, G., Yang, S., 2022. Forest canopy height mapping by synergizing icesat-2, sentinel-1, sentinel-2 and topographic information based on machine learning methods. Remote Sensing, 14(2):364.
304. Ximenes, A. C., Amaral, S., Monteiro, A. M. V., Almeida, R. M., Valeriano, D. M., 2021. Mapping the terrestrial ecoregions of the Purus-Madeira interfluve in the Amazon Forest using machine learning techniques. Forest Ecology and Management, 488, 118960.
305. Yao, J., Raffuse, S. M., Brauer, M., Williamson, G. J., Bowman, D. M., Johnston, F. H., & Henderson, S. B., 2018. Predicting the minimum height of forest fire smoke within the atmosphere using machine learning and data from the CALIPSO satellite. Remote sensing of environment, 206:98-106.
306. Yazdani, M., Shataee Jouibary, S., Mohammadi, J., & Maghsoudi, Y., 2020. Comparison of different machine learning and regression methods for estimation and mapping of forest stand attributes using ALOS/PALSAR data in complex Hyrcanian forests. Journal of Applied Remote Sensing, 14(2), 024509-024509.
307. Yilmaz, H., Yilmaz, O. Y., Akyüz, Y. F., 2017. Determining the factors affecting the distribution of Muscari latifolium, an endemic plant of Turkey, and a mapping species distribution model. Ecology and Evolution, 7(4), 1112-1124.
308. Yoshii, T., Lin, C., Tatsuhara, S., Suzuki, S., Hiroshima, T., 2022. Tree Species Mapping of a Hemiboreal Mixed Forest Using Mask R-CNN. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 6228-6231). IEEE.
309. Yu, J., Li, F., Wang, Y., Lin, Y., Peng, Z., & Cheng, K., 2020. Spatiotemporal evolution of tropical forest degradation and its impact on ecological sensitivity: A case study in Jinghong, Xishuangbanna, China. Science of The Total Environment, 727, 138678.
310. Yu, M., Song, Y. I., Ku, H., Hong, M., Lee, W. K., 2023. National-scale temporal estimation of South Korean Forest carbon stocks using a machine learning-based meta model. Environmental Impact Assessment Review, 98, 106924.
311. Zeybek, M., Vatandaşlar, C., 2021. An automated approach for extracting forest inventory data from individual trees using a handheld mobile laser scanner. Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering, 42(3):515-528.
312. Zhang, B.; He, X.; Ouyang, F.; Gu, D.; Dong, Y.; Zhang, L.; Mo, X.; Huang,W.; Tian, J.; Zhang, S., 2017. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett., 403:21–27.
313. Zhang, N., Chen, M., Yang, F., Yang, C., Yang, P., Gao, Y., ... & Peng, D., 2022. Forest Height Mapping Using Feature Selection and Machine Learning by Integrating Multi-Source Satellite Data in Baoding City, North China. Remote Sensing, 14(18), 4434.
314. Zhang, W., Hu, B., 2021. Forest roads extraction through a convolution neural network aided method. International Journal of Remote Sensing, 42(7), 2706-2721.
315. Zhang, X., Chen, G., Cai, L., Jiao, H., Hua, J., Luo, X., Wei, X., 2021. Impact assessments of Typhoon Lekima on forest damages in subtropical china using machine learning methods and Landsat 8 OLI imagery. Sustainability, 13(9), 4893.
316. Zhang, X., Jiao, H., Chen, G., Shen, J., Huang, Z., Luo, H., 2022. Forest Damage by Super Typhoon Rammasun and Post-Disturbance Recovery Using Landsat Imagery and the Machine-Learning Method. Remote Sensing, 14(15), 3826.
317. Zhang, Y., & Liu, J., 2022. Estimating forest aboveground biomass using temporal features extracted from multiple satellite data products and ensemble machine learning algorithm. Geocarto International, 2153930.
318. Zhang, Y., Ma, J., Liang, S., Li, X., Li, M., 2020. An evaluation of eight machine learning regression algorithms for forest aboveground biomass estimation from multiple satellite data products. Remote Sensing, 12(24), 4015. Zhao, F., Sun, R., Zhong, L., Meng, R., Huang, C., Zeng, X., ... & Wang, Z., 2022. Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning. Remote Sensing of Environment, 269, 112822.
319. Zhao, K., Popescu, S., Meng, X., Pang, Y., Agca, M., 2011. Characterizing forest canopy structure with lidar composite metrics and machine learning. Remote Sensing of Environment, 115(8):1978-1996.
320. Zhao, Q., Yu, S., Zhao, F., Tian, L., Zhao, Z., 2019. Comparison of machine learning algorithms for forest parameter estimations and application for forest quality assessments. Forest Ecology and Management, 434:224-234.
321. Zhao, X., Zheng, Y., Wang, W., Wang, Z., Zhang, Q., Liu, J., Zhang, C., 2023. Habitat Suitability Evaluation of Different Forest Species in Lvliang Mountain by Combining Prior Knowledge and MaxEnt Model. Forests, 14(2):438.
322. Zhao, Y.; Li, J.; Yu, L., 2017. A deep learning ensemble approach for crude oil price forecasting. Energy Econ, 66:9–16.
323. Zheng, S., Gao, P., Zou, X., Wang, W., 2022. Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm. Frontiers in Plant Science, 13.
324. Zhou, C.; Lin, K.; Xu, D.; Chen, L.; Guo, Q.; Sun, C.; Yang, X., 2018. Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture. Comput. Electron. Agric., 146:114–124.
325. Zou, W., Jing, W., Chen, G., Lu, Y., Song, H., 2019. A survey of big data analytics for smart forestry. IEEE Access, 7, 46621-46636.