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Advanced Scientific Methods and Tools in Sustainable Forest Management: A Synergetic Perspective

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Forest Dynamics and Conservation

Abstract

The field of forest management is characterized by the presence of diverse stakeholder groups which approach the task from different, sometimes conflicting, perspectives. Owners and timber producers are interested in maximized harvesting of forest raw materials while the goal of a broader public is to conserve forests. Forest ecosystems provide a spectrum of unique goods and services, such as food and medicinal plants, support of biodiversity, water and air quality, wildlife accommodation and climate mitigation. There is an obvious necessity to harmonize the needs of the stakeholder groups whereby forest conservation and logging are complementing, not competing, goals which can be achieved by promoting the ideas of sustainability. The problem of sustainability in forest management is approached from the perspective of advanced scientific methods and tools. A variety of theoretical concepts underlying the idea of sustainability in forestry studies is reviewed, and an integrated framework for a synergetic sustainable forest management is proposed. The framework accommodates various contributing concepts, such as sustainable development and its 17 goals, forest ecological-economic-social systems, forest ecosystem services and benefits, forest informatics, precision forestry, adaptive forest management, and data science. A nine-step roadmap for practical implementation of the framework is suggested comprising of: (1) data acquisition; (2) data storage; (3) data access; (4) data extraction; (5) data preprocessing; (6) data analysis; (7) modelling; (8) optimization; and (9) decision-making. Applications of advanced scientific methods and tools at each step of the roadmap are demonstrated. Integration of the multiple technologies and tools is a prominent current trend.

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References

  • Aakash RS, Nishanth M, Rajageethan R, Rao R, Ezhilarasie R (2018) Data mining approach to predict forest fire using fog computing. In Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 14–15 June 2018; p 1582–1587

    Google Scholar 

  • Aber J, Neilson RP, McNulty S, Lenihan DB, Drapek J (2001) Forest processes and global environmental change: predicting the effects of individual and multiple stressors. Bioscience 51(9):735–751

    Article  Google Scholar 

  • Ahmadi K, Kalantar B, Saeidi V, Harandi EKG, 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 Sens 12(3019):24. https://doi.org/10.3390/rs12183019

    Article  Google Scholar 

  • Athanasis N, Themistocleous M, Kalabokidis K (2017) Wildfire prevention in the era of big data. In: European, Mediterranean, and Middle Eastern Conference on Information Systems. Springer, Cham, pp 111–118

    Google Scholar 

  • Athanasis N, Themistocleous M, Kalabokidis K, Chatzitheodorou C (2019) Big data analysis in UAV surveillance for wildfire prevention and management. In: European, Mediterranean, and Middle Eastern Conference on Information Systems, vol 341. Springer, Cham, pp 47–58

    Google Scholar 

  • Bare BB, Briggs DG, Roise JP, Schreuder GF (1984) A survey of systems analysis models in forestry and the forest products industries. Eur J Oper Res 18(1):1–18

    Article  Google Scholar 

  • Barett GW, Odum EP (2000) The twenty-first century. The world at carrying capacity. Bioscience 50:363–368

    Article  Google Scholar 

  • Bauwens S, Bartholomeus H, Calders K, Lejeune P (2016) Forest inventory with terrestrial LiDAR: a comparison of static and hand-held mobile laser scanning. Forests 7(6):127

    Article  Google Scholar 

  • Becker G (2001) Precision Forestry in Central Europe—new perspectives for a classical management concept. In: Proceedings of the First International Precision Forestry Symposium. University of Washington, Seattle, WA, pp 17–20

    Google Scholar 

  • Bell J (2020) Machine learning: hands-on for developers and technical professionals. Willey, Indianapolis, IN, p 400

    Book  Google Scholar 

  • Blanco JA, Zavala MA, Imbert JB, Castillo FJ (2005) Sustainability of forest management practices: evaluation through a simulation model of nutrient cycling. For Ecol Manag 213:209–228

    Article  Google Scholar 

  • Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci 99(suppl 3):7280–7287. https://doi.org/10.1073/pnas.082080899

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Buhne HST, Pettorelli N (2018) Better together: integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science. Methods Ecol Evol 9(4):849–865

    Article  Google Scholar 

  • Cady F (2017) The data science handbook. J. Wiley, Hoboken, NJ, p 396

    Book  Google Scholar 

  • Calza F, Parmentola A, Tutore I (2020) Big data and natural environment. How does different data support different green strategies? Sustainable Futures 2:100029. https://doi.org/10.1016/j.sftr.2020.100029

    Article  Google Scholar 

  • Campo PC, Mendoza GA, Guizol P, Villanueva TR, Bousquet F (2009) Exploring management strategies for community-based forests using multi-agent systems: a case study in Palawan, Philippines. J Environ Manag 90:3607–3615. https://doi.org/10.1016/j.jenvman.2009.06.016

    Article  Google Scholar 

  • CBD (2013) Decision document UNEP/CBD/COP/DEC/X/2; quick guides to the Aichi Biodiversity Targets, Version 2. CBD

    Google Scholar 

  • Chen X, Li T, Ruan L, Xu K, Huang J, Xiong Y (2015) Research and application of fire risk assessment based on satellite remote sensing for transmission line. Proc World Congr Eng Comput Sci 2219:284–287

    Google Scholar 

  • Chowdhary KR (2020) Fundamentals of artificial intelligence. Springer Nature, p 716

    Book  Google Scholar 

  • Crisci C, Ghattas B, Perera G (2012) A review of supervised machine learning algorithms and their applications to ecological data. Ecol Model 240:113–122

    Article  Google Scholar 

  • Czimbera K, Gálos B (2016) A new decision support system to analyse the impacts of climate change on the Hungarian forestry and agricultural sectors. Scand J For Res 31(7):664–673. https://doi.org/10.1080/02827581.2016.1212088

    Article  Google Scholar 

  • Dale VH, Joyce LA, McNulty S, Ronald P, Neilson RP (2000) The interplay between climate change, forests, and disturbances. Sci Total Environ 262(3):201–204. https://doi.org/10.1016/S0048-9697(00)00522-2

    Article  CAS  PubMed  Google Scholar 

  • Dash J, Pont D, Brownlie R, Dunningham A, Watt M, Pearse G (2016) Remote sensing for precision forestry. NZ J For 60(4):15–24

    Google Scholar 

  • Data Science and Big Data Analytics: discovering, analyzing, visualizing and presenting data (2015) J. Wiley, Indianapolis, IN, USA, p 410

    Google Scholar 

  • de Almeida RV, Crivellaro F, Narciso M, Sousa AI, Vieira P (2020) Bee2Fire: a deep learning powered forest fire detection system. In Proceedings of the ICAART 2020—12th International Conference on Agents and Artificial Intelligence, Valletta, Malta, 22–24 February 2020; SciTePress: Setúbal, Portugal, vol 2, p 603–609

    Google Scholar 

  • Dhar V (2013) Data science and prediction. Commun ACM 56:64–73

    Article  Google Scholar 

  • Erechtchoukova MG, Khaiter PA (2004) Data organization for efficient water quality assessment based on information collected from stationary monitoring system. In: Liong S-Y, Phoon K-K, Babovic V (eds) Proc. 6th Int. Conf. on Hydroinformatics’2004. Singapore, vol 1. World Scientific Publishing, Singapore, pp 684–691. (ISBN 981-238-787-0)

    Google Scholar 

  • FAO and UNEP (2020) The state of the world’s forests 2020. Forests, biodiversity and people. Rome. https://doi.org/10.4060/ca8642en

  • Fardusi MJ, Chianucci F, Barbati A (2017) Concept to practice of Geospatial tools to assist forest management and planning under precision forestry framework: a review. Ann Silvicultural Res 41(1):3–14

    Google Scholar 

  • Fontes L, Bontemps JD, Bugmann H, Van Oijen M, Gracia C, Kramer K, Lindner M, Rötzer T, Skovsgaard JP (2010) Models for supporting forest management in a changing environment. For Syst 19:8–29

    Google Scholar 

  • Forest Informatics (I. K. Morpheus – Ed.). (2013). UtilPublishing. p 56

    Google Scholar 

  • Geijzendorffer IR, Cohen-Shacham E, Cord AF, Cramer W, Guerra C, Martín-López B (2017) Ecosystem services in global sustainability policies. Environ Sci Pol 74:40–48

    Article  Google Scholar 

  • Ghani KR, Zheng K, Wei JT, Friedman CP (2014) Harnessing big data for health care and research: are urologists ready? Eur Urol 66(6):975–977

    Article  PubMed  Google Scholar 

  • Goodland R (1995) The concept of environmental sustainability. Annu Rev Ecol Syst 26:1–24

    Article  Google Scholar 

  • Gorstko AB, Khaiter PA (1991) On a question of economic assessment of forest resources. Econ Math Meth 27(3):522–526

    Google Scholar 

  • Granell C, Havlik D, Schade S, Sabeur Z, Delaney C, Pielorz J, Usländer T, Mazzetti P, Schleidt K, Kobernus M, Havlik F, Bodsberg NR, Berre A, Mon JL (2016) Future internet technologies for environmental applications. Environ Model Softw 78:1–15. https://doi.org/10.1016/j.envsoft.2015.12.015

    Article  Google Scholar 

  • Griggs D, Stafford-Smith M, Gaffney O, Rockström J, Öhman MC, Shyamsundar P, Steffen W, Glaser G, Kanie N, Noble I (2013) Policy: sustainable development goals for people and planet. Nature 495:305–307. https://doi.org/10.1038/495305a

    Article  CAS  PubMed  Google Scholar 

  • Grigolato S, Mologni O, Cavalli R (2017) GIS applications in Forest operations and road network planning: an overview over the last two decades. Croatian J For Eng 38:175–186

    Google Scholar 

  • Hart E, Sim K, Gardiner B, Kamimura K (2017) A hybrid method for feature construction and selection to improve wind-damage prediction in the forestry sector. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '17), 1121–1128. https://doi.org/10.1145/3071178.3071217

  • Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Ullah Khan S (2015) The rise of “big data” on cloud computing: review and open research issues. Inf Syst 47:98–115

    Article  Google Scholar 

  • Heinimann HR (2010) A concept in adaptive ecosystem management—an engineering perspective. For Ecol Manag 259:848–856

    Article  Google Scholar 

  • Hey T (2009) The fourth paradigm: data-intensive scientific discovery. Microsoft Research, California

    Google Scholar 

  • Hoganson HM, Borges JG (1998) Using dynamic programming and overlapping subproblems to address adjacency in large harvest scheduling problems. For Sci 44(4):526–538

    Google Scholar 

  • Hoganson HM, Burk TE (1997) Models as tools for forest management planning. The Commonwealth Forestry Review 76(1):11–17. http://www.jstor.org/stable/42610003

    Google Scholar 

  • Holopainen M, Vastaranta M, Hyyppä J (2014) Outlook for the next generation’s precision forestry in Finland. Forest 5:1682–1694

    Google Scholar 

  • IPBES (2019) In: Brondizio ES, Settele J, Díaz S, Ngo HT (eds) Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES secretariat, Bonn, p 1148. https://doi.org/10.5281/zenodo.3831673

    Chapter  Google Scholar 

  • Jang E, Kang Y, Im J, Lee DW, Yoon J, Kim SK (2019) Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea. Remote Sens 11:271

    Article  Google Scholar 

  • Jørgensen SE (2006) Eco-exergy as sustainability. WIT Press, Southampton

    Google Scholar 

  • Jørgensen SE, Svirezhev YM (2004) Towards a thermodynamic theory for ecological systems. Elsevier, Amsterdam

    Google Scholar 

  • Joshi S, Garg JK, Kaur A, Kumar M (2021) Assessment of wildfire landslide risk using spatial analytics and deep learning techniques for Rudraprayag Forest Division, Uttarakhand. Indian Forester 147(9):824–833

    Article  Google Scholar 

  • Kala AK, Kumar M (2022) Role of Geospatial Technologies in natural resource management. In: Climate impacts on sustainable natural resource management. Wiley Blackwell, Chichester

    Google Scholar 

  • Kalra N, Kumar M (2018) Simulating the impact of climate change and its variability on agriculture. In: Sheraz Mahdi S (ed) Climate change and agriculture in India: impact and adaptation. Springer International Publishing, Cham, pp 21–28. https://doi.org/10.1007/978-3-319-90086-5_3

    Chapter  Google Scholar 

  • Kaya A, Bettinger P, Boston K, Akbulut R, Ucar Z, Siry J, Merry K, Cieszewski C (2016) Optimization in forest management. Curr For Rep 2:1–17. https://doi.org/10.1007/s40725-016-0027-y

    Article  Google Scholar 

  • Khaiter PA (1986) Problems of simulating the ecological-economic system of a forestry complex. In: Proceedings of the 10th Conference on mathematical modeling in the problems of rational environment use, Novorossijsk, 1986. RGU Press, Rostov-on-Don, USSR, p 74–75

    Google Scholar 

  • Khaiter PA (1989) Analyses of the anthropogenic impact on the hydrological regime in a 'forest- watershed' system. In: Proceedings of the 12th Conference on mathematical modeling in the problems of rational environment use, Novorossijsk, 1989. RGU Press, Rostov-on-Don, USSR, p 129–130

    Google Scholar 

  • Khaiter PA (1990a) A study of the hydrological regime in a 'forest-watershed' system under anthropogenic impact. In: Proceedings of the Conference on problems in surface hydrology, Leningrad, 1990. GGI Press, Leningrad, USSR, p 41–43

    Google Scholar 

  • Khaiter PA (1990b) Management of a regional environmental system as an EES-system. In: Proceedings of the Conference on socio-cultural and ecological priorities of regional development, Sochii, 1990. RGU Press, Rostov-on-Don, USSR, p 65

    Google Scholar 

  • Khaiter PA (1991) Modeling of the anthropogenic dynamics of forest biogeocenoses. Znaniye, Kiev, Ukraine

    Google Scholar 

  • Khaiter PA (1993a) Mathematical modeling in the study of the hydrological regime in a 'Forest-Watershed' system. In: Proceedings of the 9th International Conference on computational methods in water resources (CMWR), Boulder, Colorado, 1993. CMP, Southampton, UK, p 789–794

    Google Scholar 

  • Khaiter PA (1993b) Decision support system ‘Forest management’. In: Adey RA (ed) Proceedings of the 7th International Conference on artificial intelligence in engineering. CMP, Southampton, pp 581–589

    Google Scholar 

  • Khaiter PA (1996) Optimal control problem on the basis of a simulation system for environmental applications. Numerical Methods in Engineering Simulation, Merido, Venezuela. (M. Cerrolaza, C. Gajardo, C.A. Brebbia – Eds.). CMP, Southampton, UK, p 297–302

    Google Scholar 

  • Khaiter PA (2005a) “Valuing the ecological and socio-economic services in management of headwater ecosystems.” In 6th Int. Conf. Proc. Hydrology, ecology and water resources in headwaters: 1–10, June 2005, Bergen, Norway

    Google Scholar 

  • Khaiter PA (2005b) Simulation modeling in quantifying ecosystem services and sustainable environmental management. 16th Int. Congress on Modeling and Simulation (ModSim’05). Melbourne, Australia (A. Zerger and R.M. Argent – Eds). Modelling and Simulation Society of Australia and New Zealand, December 2005:347–353

    Google Scholar 

  • Khaiter PA, Erechtchoukova MG (2009) Model aggregation and simplification in sustainable environmental management. Int J Environ Cult Econ Social Sustainability 6(1):227–242

    Google Scholar 

  • Khaiter PA, Erechtchoukova MG (2010a) A model-based quantitative assessment of ecosystem services in the scenarios of environmental management. In: Swayne DA, Yang W, Voinov AA, Rizzoli A, Filatova T (eds) Proceedings of the 5th International Congress on Environmental Modelling and Software (iEMSs 2010). International Environmental Modelling and Software Society, Ottawa, ON, pp 272–279

    Google Scholar 

  • Khaiter PA, Erechtchoukova MG (2010b) Simulating the hydrological Service of Forest for sustainable watershed management. Int J Environ Cult Econ Social Sustainability 6(3):227–240

    Google Scholar 

  • Khaiter PA, Erechtchoukova MG (2012) Quantitative assessment of natural and anthropogenic factors in forest carbon sequestration. In: Seppelt R, Voinov AA, Lange S, Bankamp D (eds) Proceedings of the 6th International Congress on Environmental Modelling and Software (iEMSs 2012). International Environmental Modelling and Software Society, Leipzig, pp 2075–2082

    Google Scholar 

  • Khaiter PA, Erechtchoukova MG (2013) Ecosystem services in environmental sustainability: a formalized approach using UML. In: Piantadosi J, Anderssen RS, Boland J (eds) Proceedings of the 20th International Congress on Modelling and Simulation (ModSim’13). Modelling and Simulation Society of Australia and New Zealand, Adelaide, SA, pp 1805–1811

    Google Scholar 

  • Khaiter PA, Erechtchoukova MG (2014) Environmental software development with UML. In: Ames DP, Quinn NWT, Rizzoli AE (eds) Proceedings of the 7th International Congress on Environmental Modelling and Software (iEMSs 2014). International Environmental Modelling and Software Society, San Diego, CA, pp 1289–1296

    Google Scholar 

  • Khaiter PA, Erechtchoukova MG (2017) Designing a software tool for environmental modelling and decision making in managing of biological invasion cases. In: Denzer R, Schimak G, Hřebíček J (eds) Environmental software systems. Springer-Verlag, Berlin, pp 209–222

    Google Scholar 

  • Khaiter PA, Erechtchoukova MG (2019) Conceptualizing an environmental software modeling framework for sustainable management using UML. J Environ Inf 34(2):123–138. https://doi.org/10.3808/jei.201800400

    Article  Google Scholar 

  • Khaiter PA, Erechtchoukova MG (2020) Perspectives of sustainability: towards design and implementation. In: Sustainability perspectives: science, policy and practice. A global view of theories, policies and practice in sustainable development. Springer, pp 3–17. https://doi.org/10.1007/978-3-030-19550-2_1

    Chapter  Google Scholar 

  • Kirschbaum MUF (1999) CenW, a forest growth model with linked carbon, energy, nutrient and water cycles. Ecol Model 118:17–59

    Article  CAS  Google Scholar 

  • Kovácsová P, Antalová M (2010) Precision forestry—definition and technologies. Pregledničlanci Reviews 11-12:603–611

    Google Scholar 

  • Krawczyk JB, Sissons C, Vincent D (2012) Optimal versus satisfactory decision making: a case study of sales with a target. Comput Manag Sci 9:233–254. https://doi.org/10.1007/s10287-012-0141-7

    Article  Google Scholar 

  • Kumar M (2021) Informatics for the management of forest ecosystem. Manuscript, p 16

    Google Scholar 

  • Kumar M, Kalra N, Khaiter P, Ravindranath NH, Singh V, Singh H, Sharma S, Rahnamayan S (2019c) PhenoPine: a simulation model to trace the phenological changes in Pinus roxhburghii in response to ambient temperature rise. Ecol Model 404:12–20. https://doi.org/10.1016/j.ecolmodel.2019.05.003

    Article  Google Scholar 

  • Kumar M, Kalra N, Ravindranath NH (2020a) Assessing the response of forests to environmental variables using a dynamic global vegetation model: an Indian perspective. Curr Sci 118:700–701

    Google Scholar 

  • Kumar M, Kalra N, Singh H, Sharma S, Rawat PS, Singh RK, Gupta AK, Kumar P, Ravindranath NH (2021b) Indicator-based vulnerability assessment of forest ecosystem in the Indian Western Himalayas: an analytical hierarchy process integrated approach. Ecol Indic 125:107568

    Article  Google Scholar 

  • Kumar M, Phukon AN, Paygude AC, Tyagi K, Singh H (2021c) Mapping phenological functional types (PhFT) in the Indian Eastern Himalayas using machine learning algorithm in Google Earth Engine. Computer and Geosciences 158–104982. https://doi.org/10.1016/j.cageo.2021.104982

  • Kumar M, Phukon SN, Singh H (2021a) The role of communities in sustainable land and forest management. In: Forest resources resilience and conflicts. Elsevier, pp 305–318

    Chapter  Google Scholar 

  • Kumar M, Rawat SPS, Singh H, Ravindranath NH, Kalra N (2018) Dynamic forest vegetation models for predicting impacts of climate change on forests: an Indian perspective. Indian J For 41(1):1–12

    Google Scholar 

  • Kumar M, Savita, Kushwaha SPS (2020b) Managing the forest fringes of India: a national perspective for meeting the sustainable development goals. In: Sustainability perspectives: science, policy and practice, strategies for sustainability. Springer Nature, Switzerland, p 331

    Chapter  Google Scholar 

  • Kumar M, Savita, Singh H, Pandey R, Singh MP, Ravindranath NH, Kalra N (2019b) Assessing vulnerability of forest ecosystem in the Indian Western Himalayan region using trends of net primary productivity. Biodivers Conserv 28(8–9):2163–2182

    Article  Google Scholar 

  • Kumar M, Singh H (2020) Agroforestry as a nature-based solution for reducing community dependence on forests to safeguard forests in rainfed areas of India. In: Nature-based solutions for resilient ecosystems and societies. Springer, pp 289–306

    Chapter  Google Scholar 

  • Kumar M, Singh MP, Singh H, Dhakate PM, Ravindranath NH (2019a) Forest working plan for the sustainable management of forest and biodiversity in India. J Sustain For:1–22. https://doi.org/10.1080/10549811.2019.1632212

  • Kwon SK, Lee YS, Kim DS, Jung HS (2019) Classification of Forest vertical structure using machine learning analysis. Korean J Remote Sens 35(2):229–239. https://doi.org/10.7780/kjrs.2019.35.2.3

    Article  Google Scholar 

  • Larrubia CJ, Kane KR, Wolfslehner B, Guldin R, Rametsteiner E (2017) Using criteria and indicators for sustainable forest management: a way to strengthen results-based management of national forest programmes. Forestry Policy and Institutions Working Paper—Food and Agriculture Organization, 37, Rome, Italy, pp 77

    Google Scholar 

  • Lee J, Im J, Kim K, Quackenbush LJ (2018) Machine learning approaches for estimating forest stand height using plot-based observations and airborne LiDAR data. Forests 9(5):268., 16pp. https://doi.org/10.3390/f9050268

    Article  Google Scholar 

  • Li R, Bettinger P, Danskin S, Hayashi R (2007) A historical perspective on the use of GIS and remote sensing in natural resource management, as viewed through papers published in north American forestry journals from 1976 to 2005. Cartographica 42(2):165–178

    Article  Google Scholar 

  • Liang J, Gamarra JGP (2020) The importance of sharing global forest data in a world of crises. Sci Data 7:424. https://doi.org/10.1038/s41597-020-00766-x

    Article  PubMed  PubMed Central  Google Scholar 

  • Liang X, Wang Y, Jaakkola A, Kukko A, Kaartinen H, Hyyppä J, Honkavaara E, Liu J (2015) Forest data collection using terrestrial image-based point clouds from a handheld camera compared to terrestrial and personal laser scanning. IEEE Trans Geosci Remote Sens 53(9):5117–5132

    Article  Google Scholar 

  • Lin H, Liu X, Wang X, Liu Y (2018) A fuzzy inference and big data analysis algorithm for the prediction of forest fire based on rechargeable wireless sensor networks. Sustain Comput Inform Syst 18:101–111

    Google Scholar 

  • Linser S, Wolfslehner B, Bridge SRJ, Gritten D, Johnson S, Payn T, Prins K, Raši R, Robertson G (2018) 25 years of criteria and indicators for sustainable forest management: how intergovernmental C&I processes have made a difference. Forests 9(9):578. (1-21). https://doi.org/10.3390/f9090578

    Article  Google Scholar 

  • Liu G, Han S, Zhao X, Nelson JD, Wang H, Wang W (2006) Optimisation algorithms for spatially constrained forest planning. Ecol Model 194:421–428. https://doi.org/10.1016/j.ecolmodel.2005.10.028

    Article  Google Scholar 

  • Liu J, Mooney H, Hull V, Davis SJ, Gaskell J, Hertel T, Lubchenco J, Seto KC, Gleick P, Kremen C, Li S (2015) Systems integration for global sustainability. Science 347(6225):1258832-1–1258832-9. https://doi.org/10.1126/science.1258832

    Article  CAS  Google Scholar 

  • Liu L, Shen M, Zhao X, Sun Y, Lu M, Xiong Y (2011) Embedded forest fire monitoring and positioning system based on machine vision. In Proceedings of the 2011 International Conference on Machine Learning and Cybernetics, Guilin, China, 10–13 July 2011, vol 2, pp 631–635

    Google Scholar 

  • Liu S, Duffy AH, Whitfield RI, Boyle IM (2010) Integration of decision support systems to improve decision support performance. Knowl Inf Syst 22:261–286

    Article  Google Scholar 

  • López SAD, Hernández AG, Vigo DD, Caballero R, Molina J (2014) A multi-start algorithm for a balanced real-world open vehicle routing problem. Eur J Oper Res 238:104–113. https://doi.org/10.1016/j.ejor.2014.04.008

    Article  Google Scholar 

  • Loukides M (2020) What is data science? https://www.oreilly.com/radar/what-is-data-science/

  • MacMichael D, Si D (2018) Machine learning classification of tree cover type and application to forest management. Int J Multimedia Data Eng Manage 9(1):1–21. https://doi.org/10.4018/IJMDEM.2018010101

    Article  Google Scholar 

  • Marano G, Langella G, Basile A, Cona F, De Michele C, Manna P, Teobaldelli M, Saracino A, Terribile F (2019) A geospatial decision support system tool for supporting integrated forest knowledge at the landscape scale. Forests bu 10(8):690. https://doi.org/10.3390/f10080690

    Article  Google Scholar 

  • Martin OC, Otto SW (1996) Combining simulated annealing with local search heuristics. Ann Oper Res 63:57–75. https://doi.org/10.1007/BF02601639

    Article  Google Scholar 

  • McDonach K, Yaneske PP (2002) Environmental management systems and sustainable development. Environmentalist 22:217–226

    Article  Google Scholar 

  • Moore J, Lin Y (2019) Determining the extent and drivers of attrition losses from wind using long-term datasets and machine learning techniques. For Int J For Res 92(4):425–435. https://doi.org/10.1093/forestry/cpy047

    Article  Google Scholar 

  • Moussati AE, Moussaoui O, Benzekri W, El Moussati A, Berrajaa M (2020) Early forest fire detection system using wireless sensor network and deep learning. Artic Int J Adv Comput Sci Appl:11

    Google Scholar 

  • Norde W (1997) Energy and entropy: a thermodynamic approach to sustainability. Environmentalist 17:52–62

    Article  Google Scholar 

  • NRC (National Research Council) (2011) Sustainability and the U.S. EPA. The National Academies Press, Washington, DC. https://doi.org/10.17226/13152

  • Nwanganga F, Chapple M (2020) Practical machine learning in R. Willey, Indianapolis, IN, p 439

    Book  Google Scholar 

  • Olokeogun OS, Kumar M (2020) An indicator based approach for assessing the vulnerability of riparian ecosystem under the influence of urbanization in the Indian Himalayan city. Ecol. Indic, Dehradun. https://doi.org/10.1016/j.ecolind.2020.106796

    Book  Google Scholar 

  • Our Common Future / World Commission on Environment and Development (1987) Oxford University Press, Oxford

    Google Scholar 

  • Pavitra M, Khan S, Jain S, Mn A, Kalyan P (2020) Forest fire detection system using Iot, vol 5. Springer, Singapore

    Google Scholar 

  • Peng J, Zhang H, Wu H, Wei Q (2020) Design of forest fire warning system based on machine vision. In International Conference on Computer Engineering and Networks; Springer Science and Business Media Deutschland GmbH: Berlin, Germany, 1274, pp 352–363

    Google Scholar 

  • Pokhriyal P, Rehman S, Krishna GA, Rajiv P, Kumar M (2020) Assessing forest cover vulnerability in Uttarakhand , India using analytical hierarchy process. Model. Earth Syst Environ. https://doi.org/10.1007/s40808-019-00710-y

  • Porritt J (2006) Capitalism as if the world mattered. Earthscan, London

    Google Scholar 

  • Potter C, Bubier J, Crill P, Lafleur P (2001) Ecosystem modelling of methane and carbon dioxide fluxes for boreal forest sites. Can J For Res 31:208–223

    CAS  Google Scholar 

  • Purnomo H, Guizol P (2006) Simulating forest plantation co-management with a multi-agent system. Math Comput Model 44:535–552. https://doi.org/10.1016/j.mcm.2006.01.009

    Article  Google Scholar 

  • Qu J, Cui X (2020) Automatic machine learning framework for forest fire forecasting. J Phys Conf Ser 1651:012116. https://doi.org/10.1088/1742-6596/1651/1/012116

    Article  Google Scholar 

  • Raum S (2017) The ecosystem approach, ecosystem services and established forestry policy approaches in the United Kingdom. Land Use Policy 64:282–291. https://doi.org/10.1016/j.landusepol.2017.01.030

    Article  Google Scholar 

  • Rawat AS, Kalra N, Singh H, Kumar M (2020) Application of vegetation models in India for understanding the forest ecosystem processes. Indian For 146:99–100

    Google Scholar 

  • Rebain S, McDill M (2003) A mixed-integer formulation of the minimum patch size problem. For Sci 49(4):608–618

    Google Scholar 

  • Reddy CS (2021) Remote sensing of biodiversity: what to measure and monitor from space to species? Biodivers Conserv 30:2617–2631. https://doi.org/10.1007/s10531-021-02216-5

    Article  Google Scholar 

  • Reynolds KM, Twery M, Lexer MJ, Vacik H, Ray D, Shao G, Borges JG (2008) Decision support systems in natural resource management. In: Burstein F, Holsapple C (eds) Handbook on decision support systems. International Handbooks on Information Systems Series, Handbook on Decision Support System 2. Springer, pp 499–534. http://www.springer.com/in/book/9783540487159

    Google Scholar 

  • Robinson AP, Hamann JD (2011) Forest analytics with R: an introduction. Springer New York, New York, NY. https://doi.org/10.1007/978-1-4419-7762-5_1

    Book  Google Scholar 

  • Rönnqvist M (2003) Optimization in forestry. Math Program Ser B 97(1–2):267–284. https://doi.org/10.1007/s10107-003-0444-0

    Article  Google Scholar 

  • Ross K (2015) Measuring sustainable forest management: a report on on-going and emerging global initiatives to develop results frameworks and performance indicators for sustainable development, agriculture and natural resources management. Food and Agriculture Organization of the United Nations (FAO). http://www.fao.org/forestry/42575-0ee3fc1e9d0f9619b8adfbc78f836d604.pdf

  • Sacchelli S (2018) A decision support system for trade-off analysis and dynamic evaluation of forest ecosystem services. iForest 11:171–180. https://doi.org/10.3832/ifor2416-010

    Article  Google Scholar 

  • Saoudi M, Bounceur A, Euler R, Kechadi T (2016) Data mining techniques applied to wireless sensor networks for early forest fire detection. In Proceedings of the International Conference on Internet of things and Cloud Computing, Cambridge, UK, 22–23 March 2016; p 1–7

    Google Scholar 

  • Savvaidis P, Stergioudis A (2012) From desktop GIS to web-based cloud GIS: the globalization of geospatial data management. In: Proceedings Int. Symp. Modern Technologies, Education and Professional Practice in Geodesy and Related Fields Sofia, Bulgaria, 08–09 November

    Google Scholar 

  • Sayad YO, Mousannif H, Al Moatassime H (2019) Predictive modeling of wildfires: a new dataset and machine learning approach. Fire Saf J 104:130–146

    Article  Google Scholar 

  • Scicluna D (2020) An IoT-based forest fire detection system. Bachelor’s Thesis, University of Malta, Msida, Malta

    Google Scholar 

  • Seely B, Welham C, Kimmins H (2002) Carbon sequestration in a boreal forest ecosystem: results from the ecosystem simulation model, FORECAST. For Ecol Manag 169:123–135

    Article  Google Scholar 

  • Segura M, Ray D, Maroto C (2014) Decision support systems for forest management: a comparative analysis and assessment. Comput Electron Agric 101:55–67. https://doi.org/10.1016/j.compag.2013.12.005

    Article  Google Scholar 

  • Šerić L, Stipanicev D, Krstinić D (2018) ML/AI in intelligent forest fire observer network. 3rd EAI International Conference on Management of Manufacturing Systems, November 6–8, Dubrovnik, Croatia, p 10. https://doi.org/10.4108/eai.6-11-2018.2279681

  • Serrano-Ramírez E, Valdez-Lazalde JR, Santos-Posadas HM, Mora-Gutiérrez RA, Gregorio Ángeles-Pérez G (2021) A forest management optimization model based on functional zoning: a comparative analysis of six heuristic techniques. Eco Inform 61(3):101234. https://doi.org/10.1016/j.ecoinf.2021.101234

    Article  Google Scholar 

  • Shanmugavel P (2008) Biodiversity informatics: a virtual access to global resources. In: Muthuchelian K, Kannaiyan S, Gopalam A (eds) Forest biodiversity, vol 1. Associated Publishing Company, pp 40–46

    Google Scholar 

  • Shekhar S, Kang J, Gandhi V (2009) Spatial data mining. In: Liu L, Ozsu T (eds) Encyclopedia of database systems. Springer Publishers, pp 2695–2698

    Chapter  Google Scholar 

  • Simon H (1955) A behavioral model of rational choice. Q J Econ 69:99–118

    Article  Google Scholar 

  • Singh RK, Sinha VSP, Joshi PK, Kumar M (2020a) Mapping of agriculture productivity variability for the SAARC nations in response to climate change scenario for the year 2050. In: Remote sensing and GIScience. Springer, Cham, pp 249–262

    Google Scholar 

  • Singh RK, Sinha VSP, Joshi PK, Kumar M (2020b) A multinomial logistic model-based land use and land cover classification for the South Asian Association for Regional Cooperation nations using Moderate Resolution Imaging Spectroradiometer product. Environ Dev Sustain:1–22

    Google Scholar 

  • Singh RK, Sinha VSP, Joshi PK, Kumar M (2020c) Modelling Agriculture, Forestry and Other Land Use (AFOLU) in response to climate change scenarios for the SAARC nations. Environ Monit Assess 192:1–18

    Article  Google Scholar 

  • Sotnik G, Cassell BA, Duveneck MJ, Scheller RM (2021) A new agent-based model provides insight into deep uncertainty faced in simulated forest management. Landsc Ecol. https://doi.org/10.1007/s10980-021-01324-5

  • Spies TA, White E, Ager A, Kline JD, Bolte JP, Platt EK, Olsen KA, Pabst RJ, Barros AMG, Bailey JD, Charnley S, Morzillo AT, Koch J, Steen-Adams MM, Singleton PH, Sulzman J, Schwartz C, Csuti B (2017) Using an agent-based model to examine forest management outcomes in a fire-prone landscape in Oregon, USA. Ecol Soc 22(1):25. https://doi.org/10.5751/ES-08841-220125

    Article  Google Scholar 

  • Srividhya S, Sankaranarayanan S (2020) IoT-fog enabled framework for forest fire management system. In Proceedings of the World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2020, London, UK, 27–28 July 2020, p 273–276

    Google Scholar 

  • Stergioudis A (2016) Forest management with cloud GIS. In: Proc. 16th International Multidisciplinary Scientific GeoConference (SGEM 2016), 28 June-7 July, Book 2, vol 1, pp 651–656

    Google Scholar 

  • Talbot B, Pierzchała M, Astrup R (2017) Applications of remote and proximal sensing for improved precision in forest operations. Croatian J For Eng 38(2):327–336. https://doi.org/10.5281/zenodo.890539

    Article  Google Scholar 

  • Taylor S, Veal M, Grift T, McDonald T, Corley F (2002) Precision forestry: operational tactics for today and tomorrow. 25th annual Meeting of the council of Forest Engineers

    Google Scholar 

  • Tien Bui D, Hoang ND, 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). J Environ Manag 237:476–487

    Article  Google Scholar 

  • Tuček J (2013) The place of geographic information and geoinformation technology in precision forestry and its complementary relation to adaptive forest management. In: Proceedings of the Conference Implementation of DSS tools into the forestry practice, 19–34

    Google Scholar 

  • Twery MJ (2004) Modelling in forest management. In: Wainwright J, Mulligan M (eds) Environmental modelling: finding simplicity in complexity. Chapter 17. John Wiley & Sons, Ltd, London, pp 295–305

    Google Scholar 

  • Twery MJ, Weiskittel AR (2013) Forest-management modelling. In: Wainwright J, Mulligan M (eds) Environmental modelling: finding simplicity in complexity, 2nd edn. Chapter 13. John Wiley & Sons, Ltd, pp 379–397. https://doi.org/10.1002/9781118351475.ch23

    Chapter  Google Scholar 

  • Uemura A, Ishida A, Matsumoto Y (2005) Simulated seasonal changes of CO2 and H2O exchange at the top canopies of two Fagus trees in a winter-deciduous forest. Japan For Ecol Manage 212:230–242

    Google Scholar 

  • UNDP (United Nations Development Program) (2015) http://www.undp.org/content/undp/en/home/sustainable-development-goals.html

  • UNGA (United Nations General Assembly) (2005) 2005 World Summit Outcome, Resolution A/60/1, adopted by the General Assembly on 15 September 2005. www.un.org/en/development/desa/population/migration/generalassembly/docs/globalcompact/A_RES_60_1.pdf

  • UNGA (United Nations General Assembly) (2015) Transforming our world: the 2030 agenda for sustainable development http://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E

  • Vacik H, Lexer MJ (2014) Past, current and future drivers for the development of decision support systems in forest management. Scand J For Res 29:2–19

    Article  Google Scholar 

  • Van der Salm C, Van der Gon HD, Wieggers R, Bleeker A, Van den Toorn A (2006) The effect of afforestation on water recharge and nitrogen leaching in the Netherlands. For Ecol Manag 221:170–182

    Article  Google Scholar 

  • Vásquez F, Cravero A, Castro M, Acevedo P (2021) Decision support system development of wildland fire: a systematic mapping. Forests 12(7):943. https://doi.org/10.3390/f12070943

    Article  Google Scholar 

  • Vega Isuhuaylas LA, Hirata Y, Ventura Santos LC, SerrudoTorobeo N (2018) Natural forest mapping in the Andes (Peru): a comparison of the performance of machine-learning algorithms. Remote Sens 10(5):782. https://doi.org/10.3390/rs10050782

    Article  Google Scholar 

  • Vitolo C, Elkhatib Y, Reusser D, Macleod CJA, Buytaert W (2015) Web technologies for environmental Big Data. Environ Model Softw 63:185–198. https://doi.org/10.1016/j.envsoft.2014.10.007

    Article  Google Scholar 

  • Von Arnold K, Weslien P, Nilsson M, Svensson BH, Klemedtsson L (2005) Fluxes of CO2, CH4 and H2O from drained coniferous forests on organic soils. For Ecol Manag 210:239–254

    Article  Google Scholar 

  • Wang Y, Zhang W, Gao R, Jin Z, Wang X (2021) Recent advances in the application of deep learning methods to forestry. Wood Sci Technol. https://doi.org/10.1007/s00226-021-01309-2

  • Wimolsakcharoen W, Dumrongrojwatthana P, Le Page C, Bousquet F, Trébuil G (2021) An agent-based model to support community forest management and non-timber forest product harvesting in northern Thailand. Socio-Environ Syst Model 3:17894. https://doi.org/10.18174/sesmo.2021a17894

    Article  Google Scholar 

  • Wu J (2013) Landscape sustainability science: ecosystem services and human well-being in changing landscapes. Landsc Ecol 28:999–1023. https://doi.org/10.1007/s10980-013-9894-9

    Article  Google Scholar 

  • Wyniawskyj NS, Napiorkowska M, Petit D, Podder P, Marti P (2019) Forest monitoring in Guatemala using satellite imagery and deep learning. 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 28 July-2 August, Yokohama, Japan. https://doi.org/10.1109/IGARSS.2019.8899782

  • Xiang P, Hou R (2010) Cache and consistency in NOSQL. In: 3rd International Conference on Computer Science and Information Technology, IEEE (Jul. 2010), p 117–120

    Google Scholar 

  • Zhao P, Gao L, Gao T (2020) Extracting forest parameters based on stand automatic segmentation algorithm. Sci Rep 10:1571. https://doi.org/10.1038/s41598-020-58494-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhu Z, Arp PA, Meng F, Bourque CPA, Foster NW (2003) A forest nutrient cycling and biomass model (ForNBM) based on year-round monthly weather conditions, part II: calibration, verification, and application. Ecol Model 170:13–27

    Article  CAS  Google Scholar 

  • Ziesak M (2006) Precision forestry—an overview on the current status of precision forestry. A literature review. In: “Precision Forestry in plantations, semi-natural and natural forests” IUFRO Precision Forestry Conference 2006 Technical University Munich 5–10 March 2006 – StellenboschUniversity http://academic.sun.ac.za/forestry/pf2006/publications.html

  • Zimmerman T (2012) Wildland fire management decision making. J Agric Sci Technol B 2:169–178

    Google Scholar 

  • Zupko R, Rouleau M (2019) ForestSim: spatially explicit agent-based modeling of non-industrial forest owner policies. SoftwareX 9:117–125

    Article  Google Scholar 

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Khaiter, P.A., Erechtchoukova, M.G. (2022). Advanced Scientific Methods and Tools in Sustainable Forest Management: A Synergetic Perspective. In: Kumar, M., Dhyani, S., Kalra, N. (eds) Forest Dynamics and Conservation. Springer, Singapore. https://doi.org/10.1007/978-981-19-0071-6_14

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