Статьи 2026 г.

Текст статьи

---

СПИСОК ЛИТЕРАТУРЫ (REFERENCES)

Бойцов А. К., Логачев А. А., Мусин Х. Г. Использование искусственных нейронных сетей для определения перспективности использования клонов гибридных пород древесины для плантационного лесовыращивания // Изв. СПбЛТА. 2021. Вып. 237. С. 288–298 [Boytsov A. K., Logachev A. A., Musin Kh. G. Ispol’zovanie iskusstvennykh neyronnykh setey dlya opredeleniya perspektivnosti ispol’zovaniya klonov gibridnykh porod drevesiny dlya plantatsionnogo lesovyrashchivaniya (Using artificial neural networks to determine the prospects for using clones of hybrid wood species for plantation forestry) // Izv. SPbLTA (Proc. St. Petersburg For. Engineer. Acad.) 2021. Iss. 237. P. 288–298 (in Russian with English abstract)].

Быков Б. А. Экологический словарь. Алма-Ата: Наука, 1983. 215 с. [Bykov B. A. Ekologicheskiy slovar’ (Ecol. dictionary). Alma-Ata: Nauka, 1983. 215 p. (in Russian)].

Галушкин А. И. Теория нейронных сетей. Кн. 1: учеб. пособ. для вузов. М.: ИПРЖР, 2000. 416 с. [Galushkin A. I. Teoriya neyronnykh setey (Theory of neural networks. Book 1: Textbook for universities). Moscow: IPRZhR, 2000. 416 p. (in Russian)].

Каляев И. А. Искусственный интеллект: камо грядеши? // Экономические стратегии. 2019. Т. 21. № 5 (163). С. 6−15 [Kalyaev I. A. Iskusstvenny intellekt: kamo gryadeshi? (Artificial Intelligence: Where are you going?) // Ekonomicheskie strategii (Economic Strategies). 2019. V. 21. N. 5 (163). P. 6−15 (in Russian with English abstract)].

Кедров А. В., Тарасов А. В. Классификация лесной растительности методом нейронных сетей // Вестн. Перм. нац. иссл. политех. ун-та. Электротехника, информ. технол., сист. управл. 2017. № 22. С. 44–54 [Kedrov A. V., Tarasov A. V. Klassifikatsiya lesnoy rastitel’nosti metodom neyronnykh setey (Classification of forest vegetation using neural networks) // Vestn. Perm. nats. issl. politekh. un-ta. Elektrotekhnika, inform. tekhnol., sist. upravl. (Bull. Perm Nat. Res. Polytech. Univ.. Electr. Engineer., Inform. Technol., and Control Systems). 2017. N. 22. P. 44–54 (in Russian with English abstract)].

Кричевский М. Л. Интеллектуальный анализ в менеджменте. СПб.: СПбГУАП, 2005. 208 с. [Krichevsky M. L. Intellektual’ny analiz v menedzhmente (Intellectual analysis in management). St. Petersburg: SPbGUAP (St. Petersburg St. Univ. Aerospace Instrumentation), 2005. 208 p. (in Russian)].

Михова Е. Д., Данилин И. М. Классификация породного состава леса на аэрофотоснимке с использованием нейронной сети // Актуальные проблемы авиации и космонавтики. Мат-лы V Междунар. науч.-практ. конф. творческой молодежи, посвящ. дню космонавтики. 2016. Т. 12. № 1. С. 632–634 [Mikhova E. D., Danilin I. M. Klassifikatsiya porodnogo sostava lesa na aerofotosnimke s ispol’zovaniem neyronnoy seti (Classification of the forest species composition on an aerial photograph using a neural network) // Aktual’nye problemy aviatsii i kosmonavtiki. Mat-ly V Mezhdunar. nauch.-prakt. konf. tvorcheskoy molodezhi, posvyashch. dnyu kosmonavtiki (Actual problems of aviation and cosmonautics. Proc. V Int. Conf. creative youth, dedicated to the day of cosmonautics). 2016. V. 12. N. 1. P. 632–634 (in Russian)].

Никологорская А. В., Ясинский Ф. Н. Временные ряды, их анализ и прогнозирование. Иваново: Иванов. гос. энерг. ун-т, 2010. 80 с. [Nikologorskaya A. V., Yasinskiy F. N. Vremennye ryady, ikh analiz i prognozirovanie (Time series, their analysis, and forecasting). Ivanovo: Ivanovo gos. energy. univ. (Ivanovo St. Power Engineer. Univ.), 2010. 80 p. (in Russian)].

Полещук О. М., Васильев С. Б. Нейронечеткая модель для прогноза семеношения лесных культур в условиях техногенных ландшафтов // Лесн. вестн. 2018. Т. 22. № 1. С. 31–35 [Poleshchuk O. M., Vasil’ev S. B. Neyronechetkaya model’ dlya prognoza semenosheniya lesnykh kul’tur v usloviyakh tekhnogennykh landshaftov (Neuro-fuzzy model for predicting seed production of forest crops in the conditions of technogenic landscapes) // Lesn. vestn. (For. Bull.). 2018. V. 22. N. 1. P. 31–35 (in Russian with English abstract)].

Романов А. А., Рубанов К. А. Сравнение методов объектно-ориентированной и нейросетевой классификации данных дистанционного зондирования Земли на основе материалов систем Landsat-5 и Orbview-3 // Современные проблемы дистанционного зондирования Земли из космоса. 2012. Т. 9. № 4. С. 29–36 [Romanov A. A., Rubanov K. A. Sravnenie metodov ob’ektno-orientirovannoy i neyrosetevoy klassifikatsii dannykh distantsionnogo zondirovaniya Zemli na osnove materialov sistem Landsat-5 i Orbview-3 (Comparison of object-oriented and neural network classification methods for remote sensing data based on Landsat-5 and Orbview-3 systems) // Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa (Modern Problems of Earth Remote Sensing from Space). 2012. V. 9. N. 4. P. 29–36 (in Russian with English abstract)].

Станкевич Т. С. Разработка метода оперативного прогнозирования динамики развития лесного пожара посредством искусственного интеллекта и глубокого машинного обучения // Вестн. Иркут. гос. техн. ун-та. 2018. Т. 22. № 9 (140). С. 111–120 [Stankevich T. S. Razrabotka metoda operativnogo prognozirovaniya dinamiki razvitiya lesnogo pozhara posredstvom iskusstvennogo intellekta i glubokogo mashinnogo obucheniya (Development of a method for operational forecasting of the dynamics of forest fire development using artificial intelligence and deep machine learning) // Vestn. Irkut. gos. un-ta (Bull. Irkutsk St. Tech. Univ.). 2018. V. 22. N. 9 (140). P. 111–120 (in Russian with English abstract)].

Станкевич Т. С. Моделирование распространения лесного пожара при нестационарности и неопределенности посредством искусственного интеллекта и глубокого машинного обучения // Вестн. Астрахан. гос. тех. ун-та. Сер.: Управл., вычисл. техника и информатика. 2019. № 3. С. 97–107 [Stankevich T. S. Modelirovanie rasprostraneniya lesnogo pozhara pri nestatsionarnosti i neopredelennosti posredstvom iskusstvennogo intellekta i glubokogo mashinnogo obucheniya (Modeling the spread of a forest fire in the presence of non-stationarity and uncertainty using artificial intelligence and deep machine learning) // Vestn. Astrakhan. gos. tekh. un-ta. Ser.: Upravl., vychisl. tekhnika i informatika (Bull. Astrakhan St. Tech. Univ. Ser.: Control, Comp. Engineer., and Informatics). 2019. N. 3. P. 97–107 (in Russian with English abstract)].

Усольцев В. А. Моделирование биомассы и морфологии корней растений. Экологические аспекты. Екатеринбург: УГЛТУ, 2025. 195 с. [Usoltsev V. A. Modelirovanie biomassy i morfologii korney rasteniy. Ekologicheskie aspekty (Modeling of biomass and morphology of plant roots. Environmental aspects). Yekaterinburg: UGLTU (Ural. St. For. Engineer. Univ.), 2025. 195 p. (in Russian)].

Хайкин С. Нейронные сети. Полный курс. 2-е изд. Пер. с англ. М.: ООО «И. Д. Вильямс», 2016. 1104 с. [Khaykin S. Neyronnye seti. Polny kurs (Neural networks. A comprehensive course). 2^nd ed. Translation from English. Moscow: LLC «I. D. Williams», 2016. 1104 p. (in Russian)].

Часовских В. П., Стариков Е. Н., Акчурина Г. А., Кох Е. В. Системы искусственного интеллекта: учеб. пособ. Екатеринбург: УрГЭУ, 2024. 194 с. [Chasovskikh V. P., Starikov E. N., Akchurina G. A., Kokh E. V. Sistemy iskusstvennogo intellekta: ucheb. posob. (Artificial intelligence systems: tutorial). Yekaterinburg: UrGEU (Ural St. Econ. Univ.), 2024. 194 p. (in Russian)].

Часовских В. П., Усольцев В. А., Кох Е. В. Естественный и искусственный интеллект как инструмент преобразования данных: моногр. Электрон. текст. Киров: МЦИТО, 2025. 253 с. [Chasovskikh V. P., Usoltsev V. A., Kokh E. V. Estestvenny i iskusstvenny intellekt kak instrument preobrazovaniya dannykh (Natural and artificial intelligence as a tool for data transformation: monogr. (monograph. Electron. text.). Kirov: MTSITO (Interregional Center for Innovative Technologies in Education), 2025. 253 p. (in Russian)].

Ясинский Ф. Н., Потемкина О. В., Сидоров С. Г., Евсеева А. В. Прогнозирование вероятности возникновения лесных пожаров с помощью нейросетевого алгоритма на многопроцессорной вычислительной технике // Вестн. Иванов. гос. энерг. ун-та. 2011. № 2. С. 82–84 [Yasinsky F. N., Potemkina O. V., Sidorov S. G., Evseeva A. V. Pognozirovanie veroyatnosti vozniknoveniya lesnykh pozharov s pomoshch’yu neyrosetevogo algoritma na mnogoprotsessornoy vychislitel’noy tekhnike (Forecasting the probability of forest fires using a neural network algorithm on multiprocessor computing equipment) // Vestn. Ivanov. gos. energ. un-ta (Bull. Ivanovo St. Power Engineer. Univ.) 2011. N. 2. P. 82–84 (in Russian with English abstract)].

Adepoju K., Adelabu S., Fashae O. Vegetation response to recent trends in climate and landuse dynamics in a typical humid and dry tropical region under global change // Adv. Meteorol. 2019. V. 2019. N. 1. Article ID 4946127.

Anees S. A., Zhang X., Khan K. A, Abbas M., Ghramh H. A, Ahmad Z. Estimation of fractional FVC dynamics and its drivers based on multi-sensor data in Dera Ismail Khan, Pakistan // J. King Saud. Univ.-Sci. 2022а. V. 34. N. 6. Article 102217.

Anees S. A., Zhang X., Shakeel M., Al-Kahtani M. A., Khan K. A., Akram M., Ghramh H. A. Estimation of fractional FVC dynamics based on satellite remote sensing in Pakistan: a comprehensive study on the FVC and its drivers // J. King Saud. Univ.-Sci. 2022b. V. 34. N. 3. Article 101848.

Anees S. A., Mehmood K., Khan W. R., Shahzad F., Zhran M., Ayub R., Alarfaj A. A., Alharbi S. A., Liu Q. Spatiotemporal dynamics of vegetation cover: integrative machine learning analysis of multispectral imagery and environmental predictors // Earth Sci. Inform. 2025. V. 18. N. 1. Article 152.

Baldocchi D. D. How eddy covariance flux measurements have contributed to our understanding of global change biology // Glob. Change Biol. 2020. V. 26. N. 1. P. 242–260.

Belgiu M., Drăguţ L. Random forest in remote sensing: A review of applications and future directions // ISPRS J. Photogram. Rem. Sens. 2016. V. 114. Preprint. P. 24–31.

Besnard S., Carvalhais N., Arain M. A., Black A., Brede B., Buchmann N., Chen J., Clevers J. G. P. W., Dutrieux L. P., Gans F., Herold M., Jung M., Kosugi Y., Knohl A., Law B. E., Paul-Limoges E., Lohila A., Merbold L., Roupsard O., Valentini R., Wolf S., Zhang X., Reichstein M. Memory effects of climate and vegetation affecting net ecosystem CO₂ fluxes in global forests // PLoS ONE. 2019. V. 14. N. 2. Article e0211510.

Bi D., Dix M., Marsland S., O’farrell S., Sullivan A., Bodman R., Law R., Harman I., Srbinovsky J., Rashid H. A., Dobrohotoff P., Mackallah C., Yan H., Hirst A., Savita A., Dias F. B., Woodhouse M., Fiedler R., Heerdegen A. Configuration and spin-up of ACCESS-CM2, the new generation Australian community climate and earth system simulator coupled model // J. South. Hemisph. Earth. Syst. Sci. 2020. V. 70. N. 1. P. 225–251.

Blume-Werry G. The belowground growing season // Nat. Clim. Change. 2021. V. 12. N. 1. Article 1922.

Boisvenue C., Smiley B. P., White J. C., Kurz W. A., Wulder M. A. Integration of Landsat time series and field plots for forest productivity estimates in decision support models // For. Ecol. Manag. 2016. V. 376. Preprint. P. 284–297.

Borowiec M. L., Dikow R. B., Frandsen P. B., McKeeken A., Valentini G., White A. E. Deep learning as a tool for ecology and evolution // Methods Ecol. Evol. 2022. V. 13. N. 8. P.1640–1660.

Breiman L. Bagging predictors // Mach. Learn. 1996. V. 24. N. 2. P. 123–140.

Brodrick P. G., Davies A. B., Asner G. P. Uncovering ecological patterns with convolutional neural networks // Trends Ecol. Evol. 2019. V. 34. N. 8. P. 734–745.

Brovelli M. A., Sun Y., Yordanov V. Monitoring forest change in the Amazon using multi-temporal remote sensing data and machine learning classification on Google Earth engine // ISPRS Int. J. Geo-Inf. 2020. V. 9. N. 10. Article 580.

Castillo J. A. A., Apan A. A., Maraseni T. N., Salmo S. G. III. Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery // ISPRS J. Photogram. Rem. Sens. 2017. V. 134. Preprint. P. 70–85.

Chen H., Wang Y., Guo T., Xu C., Deng Y., Liu Z., Ma S., Xu C., Xu C., Gao W. Pre-trained image processing transformer // IEEE/CVF Conf. Comp. Vision and Pattern Recognition (CVPR). Los Alamitos, CA, USA: IEEE Comp. Soc., 2021a. P. 12294–12305.

Chen J., Dafflon B., Tran A. P., Falco N., Hubbard S. S. A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration // Hydrol. Earth Syst. Sci. 2021b. V. 25. N. 11. P. 6041–6066.

Chi H., Sun G., Huang J., Guo Z., Ni W., Fu A. National forest aboveground biomass mapping from ICESat/GLAS data and MODIS imagery in China // Rem. Sens. 2015. V. 7. N. 5. P. 5534–5564.

Chiarito E., Cigna F., Cuozzo G.., Fontanelli G, Mejia Aguilar A., Paloscia S., Rossi M., Santi E., Tapete D., Notarnicola C. Biomass retrieval based on genetic algorithm feature selection and support vector regression in alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data // Eur. J. Rem. Sens. 2021. V. 54. N. 1. P. 209–225.

Christin S., Hervet E., Lecomte N. Applications for deep learning in ecology // Methods Ecol. Evol. 2019. V. 10. N. 10. P. 1632–1644.

Chrysafis I., Mallinis G., Tsakiri M., Patias P. Evaluation of single-date and multi-seasonal spatial and spectral information of Sentinel-2 imagery to assess growing stock volume of a Mediterranean forest // Int. J. Appl. Earth. Obs. Geoinf. 2019. V. 77. Preprint. P. 1–14.

Cipriotti P. A., Wiegand T., Pütz S., Bartoloni N. J., Paruelo J. M. Nonparametric upscaling of stochastic simulation models using transition matrices // Methods Ecol. Evol. 2016. V. 7. Preprint. P. 313–322.

Cleverly J., Eamus D., Edwards W., Grant M., Grundy M. J., Held A., Karan M., Lowe A. J., Prober S. M., Sparrow B., Morris B. TERN, Australia’s land observatory: addressing the global challenge of forecasting ecosystem responses to climate variability and change // Environ. Res. Lett. 2019. V. 14. N. 9. Article 095004.

David R. M., Rosser N. J., Donoghue D. N. Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery // Rem. Sens. Environ. 2022. V. 282. N. 2. Article 113232.

Debebe B., Senbeta F., Teferi E., Diriba D., Teketay D. Analysis of forest cover change and its drivers in biodiversity hotspot areas of the Semien Mountains national park, northwest Ethiopia // Sustainability. 2023. V. 15. N. 4. Article 3001.

Dhanda P., Nandy S., Kushwaha S., Ghosh S., Murthy Y. K., Dadhwal V. Optimizing spaceborne lidar and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms // Prog. Phys. Geogr. 2017. V. 41. Preprint. P. 247–267.

Dietze M. C., Fox A., Beck-Johnson L. M., Betancourt J. L., Hooten M. B., Jarnevich C. S., Keitt T. H., Kenney M. A., Laney C. M., Larsen L. G., Loescher H. W., Lunch C. K., Pijanowski B. C., Randerson J. T., Read E. K., Tredennick A. T., Vargas R., Weathers K. C., White E. P. Iterative near-term ecological forecasting: Needs, opportunities, and challenges // PNAS. 2018. V. 115. N. 7. P. 1424–1432.

Dong J., Xiao X., Menarguez M. A., Zhang G., Qin Y., Thau D., Biradar C., Moore III. B. Mapping paddy rice planting area in Northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth engine // Rem. Sens. Environ. 2016. V. 185. Preprint. P. 142–154.

Dubayah R., Blair J. B., Goetz S., Fatoyinbo L., Hansen M., Healey S., Hofton M., Hurtt G., Kellner J., Luthcke S., Armston J., Tang H., Duncanson L., Hancock S., Jantz P., Marselis S., Patterson P. L., Qi W., Silva C. The global ecosystem dynamics investigation: High-resolution laser ranging of the earth’s forests and topography // Sci. Rem. Sens. 2020. V. 1. Preprint. Article 100002.

Fatah K. K., Mustafa Y. T., Hassan I. O. Flood susceptibility mapping using an analytic hierarchy process model based on remote sensing and GIS approaches in Akre district, Kurdistan region, Iraq // Iraqi Geol. J. 2022. V. 55. N. 2C. P. 123–149.

Fernández-Guisuraga J. M., Suárez-Seoane S., Fernandes P. M., Fernández-García V., Fernández-Manso A., Quintano C., Calvo L. Pre-fire aboveground biomass, estimated from lidar, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems // For. Ecosyst. 2022. V. 9. Preprint. Article 100022.

Forest Map, 2000. Europ. Commis., Joint Res. Centre (JRC), 2012 [Dataset].

Georgopoulos N., Gitas I. Z., Korhonen L., Antoniadis K., Stefanidou A. Estimating crown biomass in a multilayered fir forest using airborne LiDAR data // Rem. Sens. 2023. V. 15. N. 11. Article 2919.

Gibbs H. K., Brown S., Niles J. O., Foley J. A. Monitoring and estimating tropical forest carbon stocks: making REDD a reality // Environ. Res. Lett. 2007. V. 2. N. 4. Article 045023.

Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge: MIT Press, 2016. 800 p.

Gorelick N., Hancher M., Dixon M., Ilyushchenko S., Thau D., Moore R. Google Earth engine: Planetary-scale geospatial analysis for everyone // Rem. Sens. Environ. 2017. V. 202. Preprint. P. 18–27.

Grünig M., Razavi E., Calanca P., Mazzi D., Wegner J. D., Pellissier L. Applying deep neural networks to predict incidence and phenology of plant pests and diseases // Ecosphere. 2021. V. 12. N. 10. Article e03791.

Guo Q., Du S., Jiang J., Guo W., Zhao H., Yan X., Zhao Y., Xiao W. Combining GEDI and Sentinel data to estimate forest canopy mean height and aboveground biomass // Ecol. Inform. 2023. V. 78. Preprint. Article 102348.

Habeeb H. N., Mustafa Y. T. Deep learning approaches for estimating forest vegetation cover and exploring influential ecosystem factors // Earth Sci. Inform. 2024. V. 17. N. 4. P. 3379–3396.

Habeeb H. N., Mustafa Y. T. Deep learning-based prediction of forest cover change in Duhok, Iraq: Past and future // Forestist. 2025. V. 75. Preprint. Article 0068.

Hansen W. D., Schwartz N. B., Williams A. P., Albrich K., Kueppers L. M., Rammig A., Reyer C. P. O., Staver A. C., Seidl R. Global forests are influenced by the legacies of past inter-annual temperature variability // Environ. Res. Ecol. 2022. V. 1. N. 1. Article 011001.

Hansson A. C., Steen E., Andren O. Root-growth of daily irrigated and fertilized barley investigation with ingrowth cores, soil cores and minirhizotrons // Swed. J. Agr. Res. 1992. V. 22. N. 4. P. 141–152.

Hauglin M., Gobakken T., Astrup R., Ene L. Estimating single-tree crown biomass of Norway spruce by airborne laser scanning: A comparison of methods with and without the use of terrestrial laser scanning to obtain the ground reference data // Forests. 2014. V. 5. N. 3. P. 384–403.

Herold M., Carter S., Avitabile V., Espejo A. B., Jonckheere I., Lucas R., McRoberts R. E., Næsset E., Nightingale J., Petersen R. The role and need for space-based forest biomass-related measurements in environmental management and policy // Surv. Geophys. 2019. V. 40. N. 4. P. 757–778.

Hojo A., Avtar R., Nakaji T., Tadono T., Takagi K. Modeling forest above-ground biomass using freely available satellite and multisource datasets // Ecol. Inform. 2023. V. 74. N. 12. Article 101973.

Huang Y., Yan J., Zhang Y., Ye W., Zhang C., Gao P., Lv X. Automatic segmentation of cotton roots in high resolution minirhizotron images based on improved OCRNet // Front. Plant Sci. 2023. V. 14. Preprint. Article 1147034.

Hyyppä J., Kelle O., Lehikoinen M., Inkinen M. A segmentation based method to retrieve stem volume estimates from 3D tree height models produced by laser scanners // IEEE Trans. Geosci. Remote Sens. 2001. V. 39. N. 5. P. 969–975.

Irrgang C., Boers N., Sonnewald M., Barnes E. A., Kadow C., Staneva J., Saynisch-Wagner J. Towards neural Earth system modelling by integrating artificial intelligence in Earth system science // Nat. Mach. Intel. 2021. V. 3. N. 8. P. 667–674.

Jia X., Willard J., Karpatne A., Read J., Zwart J., Steinbach M., Kumar V. Physics guided RNNs for modeling dynamical systems: a case study in simulating lake temperature profiles // Proc. 2019 Soc. Industr. Appl. Mathem. Int. Conf. Data Mining (SDM), 2019. P. 558–566.

Jiang F., Deng M., Tang J., Fu L., Sun H. Integrating spaceborne lidar and Sentinel-2 images to estimate forest aboveground biomass in Northern China // Carbon Balance Manag. 2022. V. 17. Preprint. Article 12.

Johnson M. G., Tingey D. T., Phillips D. L., Storm M. J. Advancing fine root research with minirhizotrons // Environ. Exp. Bot. 2001. V. 45. N. 3. P. 263–289.

Karpatne A., Atluri G., Faghmous J. H., Steinbach M., Banerjee A., Ganguly A., Shekhar S., Samatova N., Kumar V. Theory guided data science: a new paradigm for scientific discovery from data // IEEE Trans. Knowl. Data Eng. 2017. V. 29. N. 10. P. 2318–2331.

Karpatne A., Ebert-Uphoff I., Ravela S., Babaie H. A., Kumar V. Machine learning for the geosciences: challenges and opportunities // IEEE Trans. Knowl. Data Eng. 2019. V. 31. N. 8. P. 1544–1554.

Kattenborn T., Eichel J., Wiser S., Burrows L., Fassnacht F. E., Schmidtlein S. Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery // Rem. Sens. Ecol. Conserv. 2020. V. 6. N. 4. P. 472–486.

Kattge J., Bönisch G., Dıaz S., Lavorel S., Prentice I. C., Leadley P., Tautenhahn S., Werner G. D. A., Aakala T., Abedi M., Acosta A. T. R., Wirth C. TRY plant trait database – enhanced coverage and open access // GCB. 2020. V. 26. N. 1. P. 119–188.

Kays R., McShea W. J., Wikelski M. Born-digital biodiversity data: Millions and billions // Divers. Distrib. 2020. V. 26. N. 5. P. 644–648.

Keitt T. H., Abelson E. S. Ecology in the age of automation // Science. 2021. V. 373 (6557). P. 858–859.

Keller M., Schimel D. S., Hargrove W. W., Hoffman F. M. A continental strategy for the National Ecological Observatory Network // Front. Ecol. Environ. 2008. V. 6. N. 5. P. 282–284.

Khwarahm N. R., Najmaddin P. M., Ararat K., Qader S. Past and future prediction of land cover land use change based on earth observation data by the CA–Markov model: A case study from Duhok governorate, Iraq // Arab. J. Geosci. 2021. V. 14. N. 15. Article 1544.

Koppa A., Rains D., Hulsman P., Poyatos R., Miralles D. G. A deep learning-based hybrid model of global terrestrial evaporation // Nat. Comm. 2022. V. 13. N. 1. Article 1912.

Kraft B., Jung M., Körner M., Requena Mesa C., Cortes J., Reichstein M. Identifying dynamic memory effects on vegetation state using recurrent neural networks // Front. Big Data. 2019. V. 2. Preprint. Article 31.

LaDeau S. L., Han B. A., Rosi-Marshall E. J., Weathers K. C. The next decade of big data in ecosystem science // Ecosystems. 2017. V. 20. N. 2. P. 274–283.

Lannelongue L., Grealey J., Bateman A., Inouye M. Ten simple rules to make your computing more environmentally sustainable // PLoS Comp. Biol. 2021. V. 17. N. 9. Article e1009324.

LeCun Y., Bengio Y., Hinton G. Deep learning // Nature. 2015. V. 521 (7553). P. 436–444.

Luo M., Anees S. A., Huang Q., Qin X., Qin Z., Fan J., Han G., Zhang L., Shafri H. Z. M. Improving forest above-ground biomass estimation by integrating individual machine learning models // Forests. 2024. V. 15. N. 6. Article 975.

Mauya E. W., Koskinen J., Tegel K., Hämäläinen J., Kauranne T., Käyhkö N. Modelling and predicting the growing stock volume in small-scale plantation forests of Tanzania using multi-sensor image synergy // Forests. 2019. V. 10. N. 3. Article 279.

Mehmood K., Anees S. A., Rehman A., Rehman N. U., Muhammad S., Shahzad F., Liu Q., Alharbi S. A., Alfarraj S., Ansari M. J., Khan W. R. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions // Trees Forest People. 2024a. V. 18. N. 8. Article 100657.

Mehmood K., Anees S. A., Rehman A., Tariq A., Liu Q., Muhammad S., Rabbi F., Pan S. A., Hatamleh W. A. Assessing forest cover changes and fragmentation in the Himalayan Temperate Region: implications for forest conservation and management // J. For. Res. 2024b. V. 35. N. 1. Article 82.

Mehmood K., Anees S. A., Rehman A., Tariq A., Zubair M., Liu Q., Rabbi F., Khan K. A., Luo M. Exploring spatiotemporal dynamics of NDVI and climate-driven responses in ecosystems: Insights for sustainable management and climate resilience // Ecol. Inf. 2024c. V. 80. Preprint. Article 102532.

Meng P., Wang H., Qin S., Li X., Song Z., Wang Y., Yang Y., Gao J. Health assessment of plantations based on lidar canopy spatial structure parameters // Int. J. Digit. Earth. 2022. V. 15. N. 1. P. 712–729.

Mzuri R. T., Omar A. A., Mustafa Y. T. Spatiotemporal analysis of vegetation cover and its response to terrain and climate factors in Duhok Governorate, Kurdistan Region, Iraq // Iraqi Geol. J. 2021. V. 54. N. 1A. P. 110–126.

Narine L. L., Popescu S. C., Malambo L. Using ICESat-2 to estimate and map forest aboveground biomass: A first example // Rem. Sens. 2020. V. 12. N. 11. Article 1824.

Ngwira S., Watanabe T. An analysis of the causes of deforestation in Malawi: A case of Mwazisi // Land. 2019. V. 8. N. 3. Article 48.

Olden J. D., Lawler J. J., Poff N. L. Machine learning methods without tears: a primer for ecologists // Q. Rev. Biol. 2008. V. 83. N. 2. P. 171–193.

Padalia H., Prakash A., Watham T. Modelling aboveground biomass of a multistage managed forest through synergistic use of Landsat-OLI, ALOS-2 L-band SAR and GEDI metrics // Ecol. Inf. 2023. V. 77. N. 12. Article 102234.

Pal M. Random forest classifier for remote sensing classification // Int. J. Rem. Sens. 2005. V. 26. N. 1. P. 217–222.

Perry G. L. W., Seidl R., Bellve A. M., Rammer W. An outlook for deep learning in ecosystem science // Ecosystems. 2022. V. 25. Preprint. P. 1700–1718.

Prasad A., Pedlar J., Peters M., Matthews S., Iverson L., McKenney D., Adams B. Understanding climate change dynamics of tree species: Implications for future forests In: Future forests / S. G. McNulty (Ed.). Chapter 8. Elsevier, 2024. P. 151–175.

Rahmani F., Lawson K., Ouyang W., Appling A., Oliver S., Shen C. Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data // Environ. Res. Lett. 2021. V. 16. N. 2. Article 024025.

Rahmanzadeh H., Shojaedini S. V. Novel automated method for minirhizotron image analysis: root detection using curvelet transform // IJE Trans. C Asp. 2016. V. 29. N. 3. P. 337–346.

Rammer W., Seidl R. A scalable model of vegetation transitions using deep neural networks // Methods Ecol. Evol. 2019a. V. 10. N. 6. P. 879–890.

Rammer W., Seidl R. Harnessing deep learning in ecology: an example predicting bark beetle outbreaks // Front. Plant Sci. 2019b. V. 10. Preprint. Article 1327.

Rammer W., Braziunas K. H., Hansen W. D., Ratajczak Z., Westerling A. L., Turner M. G., Seidl R. Widespread regeneration failure in forests of Greater Yellowstone under scenarios of future climate and fire // Glob. Change Biol. 2021. V. 27. N. 18. P. 4339–4351.

Rash A., Mustafa Y., Hamad R. Quantitative assessment of land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq // Heliyon. 2023. V. 9. N. 11. Article e21253.

Rawat W., Wang Z. Deep convolutional neural networks for image classification: a comprehensive review // Neural Comp. 2017. V. 29. N. 9. P. 2352–2449.

Razavi S. Deep learning, explained: Fundamentals, explainability, and bridge ability to process-based modelling // EMS. 2021. V. 144. N. 4. Article 105159.

Read J. S., Jia X., Willard J., Appling A. P., Zwart J. A., Oliver S. K., Karpatne A., Hansen G. J. A., Hanson P. C., Watkins W., Steinbach M., Kumar V. Process-guided deep learning predictions of lake water temperature // WRR. 2019. V. 55. N. 11. P. 9173–9190.

Reichstein M., Camps-Valls G., Stevens B., Jung M., Denzler J., Carvalhais N., Prabhat. Deep learning and process understanding for data-driven Earth system science // Nature. 2019. V. 566 (7743). P. 195–204.

Rocha K. D., Silva C. A., Cosenza D. N., Mohan M., Klauberg C., Schlickmann M. B., Xia J., Leite R. V., Alves de Almeida D. R., Atkins J. W., Cardil A., Rowell E., Parsons R., Sánchez-López N., Prichard S. J., Hudak A. T. Crown-level structure and fuel load characterization from airborne and terrestrial laser scanning in a longleaf pine (Pinus palustris Mill.) forest ecosystem // Rem. Sens. 2023. V. 15. N. 4. Article 1002.

Saim A. A., Aly M. H. Machine learning and multisensor data fusion for forest aboveground biomass estimation in Arkansas // Earth Syst. Environ. 2025. Preprint. P. 1–18.

Schiller C., Schmidtlein S., Boonman C., Moreno-Martınez A., Kattenborn T. Deep learning and citizen science enable automated plant trait predictions from photographs // Sci. Rep. 2021. V. 11. N. 1. Article 16395.

Schwartz R., Dodge J., Smith N. A., Etzioni O. Green AI // Comm. ACM. 2020. V. 63. N. 12. P. 54–63.

Selmy S. A. H., Kucher D. E., Mozgeris G., Moursy A. R. A., Jimenez-Ballesta R., Kucher O. D., Fadl M. E., Mustafa A. A. Detecting, analyzing, and predicting land use/land cover (LULC) changes in arid regions using Landsat images, CA-Markov hybrid model, and GIS techniques // Rem. Sens. 2023. V. 15. N. 23. Article 5522.

Shojaedini S. V., Heidari M. A new method for root detection in minirhizotron images: Hypothesis testing based on entropy-based geometric level set decision // I. J. E. Trans. A Basics. 2013. V. 27. N. 1. P. 91–100.

Singh K. K., Chen G., McCarter J. B., Meentemeyer R. K. Effects of lidar point density and landscape context on estimates of urban forest biomass // ISPRS J. Photogram. Rem. Sens. 2015. V. 101. N. 3–4. P. 310–322.

Smith A. G., Petersen J., Selvan R., Rasmussen C. R. Segmentation of roots in soil with U-Net // Plant Methods. 2020. V. 16. N. 1. Article 13.

Stefanidou A., Dragozi E., Tompoulidou M., Gitas I. Z. Forest/non forest mapping using Landsat thematic mapper imagery and Artificial Neural Networks (ANNs) // Вестн. Поволж. гос. технол. ун-та. Сер.: Лес. Экол. Природопольз. 2015. T. 25. № 1 С. 22–33 [Vestn. Povolzh. gos. un-ta (Vestn. Volga St. Univ. Technol. Ser.: For. Ecol. Nat. Manag.)]. 2015. V. 25. N. 1. P. 22–33.

Strubell E., Ganesh A., McCallum A. Energy and policy considerations for modern deep learning research // Proc. Conf. AAAI Artif. Intel. 2020. V. 34. N. 9. P. 13693–13696.

Torrey L., Shavlik J. Transfer learning // Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques. Hershey: IGI global, 2010. P. 242–264.

Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A. N., Kaiser Ł., Polosukhin I. Attention is all you need // Proc. 31^st Int. Conf. Neural Inform. Proces. Syst. NIPS’17. Red Hook, NY, USA: Curran Ass. Inc., 2017. P. 6000–6010.

Wang D., Barabasi A.-L. The Science of Science. 1^st ed. Cambridge: Cambridge Univ. Press, 2021. 326 p.

Wang P., Tan S., Zhang G., Wang S., Wu X. Remote sensing estimation of forest aboveground biomass based on Lasso-SVR // Forests. 2022. V. 13. N. 10. Article 1597.

Wang T., Rostamza M., Song Z., Wang L., McNickle G., Iyer-Pascuzzi A. S., Qiu Z., Jin J. SegRoot: a high throughput segmentation method for root image analysis // Comp. Electr. Agr. 2019. V. 162. Preprint. P. 845–854.

Wang X., Girshick R., Gupta A., He K. Non-local neural networks. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 2018. P. 7794–7803.

Warren J. M., Hanson P. J., Iversen C., Kumar J., Walker A. P., Wullschleger S. D. Root structural and functional dynamics in terrestrial biosphere models—evaluation and recommendations // New Phytol. 2015. V. 205. N. 1. P. 59–78.

Way D. A., Pearcy R. W. Sunflecks in trees and forests: from photosynthetic physiology to global change biology // Tree Physiol. 2012. V. 32. N. 9. P. 1066–1081.

Xie D., Huang H., Feng L., Sharma R. P., Chen Q., Liu Q., Fu L. Aboveground biomass prediction of arid shrub-dominated community based on airborne LiDAR through parametric and nonparametric methods // Rem. Sens. 2023. V. 15. N. 13. Article 3344.

Xu T., Longyang Q., Tyson C., Zeng R., Neilson B. T. Hybrid physically based and deep learning modeling of a snow dominated, mountainous, karst watershed // Water Res. Res. 2022. V. 58. N. 3. Article 030993.

Xu W., Yu G., Zare A., Zurweller B., Rowland D. L., Reyes-Cabrera J., Fritschi F. B., Matamala R., Juenger T. E. Overcoming small minirhizotron datasets using transfer learning // Comp. Electr. Agr. 2020. V. 175. Preprint. Article 105466.

Yan J., Wang X. Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology // Plant J. 2022. V. 111. N. 6. P. 1527–1538.

Yasrab R., Atkinson J. A., Wells D. M., French A. P., Pridmore T. P., Pound M. P. RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures // GigaScience. 2019. V. 8. N. 11. Article 123.

Yosinski J., Clune J., Bengio Y., Lipson H. How transferable are features in deep neural networks? // Proc. 27^th Int. Conf. Neural Inform. Proces. Syst. V. 2. NIPS’14. Cambridge, MA, USA: MIT Press, 2014. P. 3320–3328.

Yu G., Zare A., Sheng H., Matamala R., Reyes-Cabrera J., Fritschi F. B., Juenger T. E. Root identification in minirhizotron imagery with multiple instance learning // Mach. Vis. Appl. 2020. V. 31. N. 6. Article 43.

Zeng G., Birchfield S. T., Wells C. E. Detecting and measuring fine roots in minirhizotron images using matched filtering and local entropy thresholding // Mach. Vis. Appl. 2006. V. 17. N. 4. P. 265–278.

Zeng G., Birchfield S. T., Wells C. E. Rapid automated detection of roots in minirhizotron images // Mach. Vis. Appl. 2010. V. 21. N. 3. P. 309–317.

Zhang H. K., Roy D. P., Yan L., Li Z., Huang H., Vermote E., Skakun S., Roger J. C. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences // Rem. Sens. Environ. 2019. V. 215. Preprint. P. 1–13.

Zhang Y., Gao Z., Wang X., Liu Q. Image representations of numerical simulations for training neural networks // Comp. Model Eng. Sci. 2023. V. 134. N. 2. P. 1–13.

Zhang Y., Ma J., Liang S., Li X., Li M. An evaluation of eight machine learning regression algorithms for forest aboveground biomass estimation from multiple satellite data products // Rem. Sens. 2020. V. 12. N. 24. Article 4015.

Zheng J., Xin D., Cheng Q., Tian M., Yang L. The random forest model for analyzing and forecasting the US stock market in the context of smart finance // Proc. 3^rd Int. Acad. Conf. Blockchain, Inform. Technol. Smart Finance (ICBIS 2024), Atlantis Highlights in Comp. Sci. 21, 2024. P. 82–90.

Zhi W., Feng D., Tsai W.-P., Sterle G., Harpold A., Shen C., Li L. From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale? // Environ Sci. Technol. 2021. V. 55. N. 4. P. 2357–2368.

Zhou G., Tang Y., Zhang W., Liu W., Jiang Y., Gao E., Bai Y. Shadow detection on high-resolution digital orthophoto map using semantic matching // IEEE Trans. Geosci. Rem. Sens. 2023. V. 61. Preprint. Article 4504420.