ANALYSIS OF KEY CHALLENGES IN DEVELOPING ARTIFICIAL INTELLIGENCE -DRIVEN SOFTWARE SOLUTIONS FOR FOREST MANAGEMENT IN CHINA

Koleda D. V.

doi:doi:10.58168/FECC2025_68-74

Home / Conferences / FOREST ECOSYSTEMS IN A CHANGING CLIMATE: PROBLEMS AND PROSPECTS / Forest Ecosystems in a Changing Climate: Problems and Prospects : Proceedings of the International Scientific and Practical Conference Dedicated to the 110th Anniversary of the Department of Forestry, Forest Taxation, and Forestry Management and the 95th Anniversary of VGLTU

ANALYSIS OF KEY CHALLENGES IN DEVELOPING ARTIFICIAL INTELLIGENCE -DRIVEN SOFTWARE SOLUTIONS FOR FOREST MANAGEMENT IN CHINA

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ANALYSIS OF KEY CHALLENGES IN DEVELOPING ARTIFICIAL INTELLIGENCE -DRIVEN SOFTWARE SOLUTIONS FOR FOREST MANAGEMENT IN CHINA

Section: FOREST MANAGEMENT, FOREST MANAGEMENT, INFORMATION TECHNOLOGY AND GIS IN THE FORESTRY INDUSTRY

Proceedings: FOREST ECOSYSTEMS IN A CHANGING CLIMATE: PROBLEMS AND PROSPECTS : PROCEEDINGS OF THE INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE DEDICATED TO THE 110TH ANNIVERSARY OF THE DEPARTMENT OF FORESTRY, FOREST TAXATION, AND FORESTRY MANAGEMENT AND THE 95TH ANNIVERSARY OF VGLTU

UDC 630 UDC 004.8

Koleda D. V. ¹

Author and publication information

Authors:

1. Universitet MGU-PPI v Shen'chzhene
Moskovskiy gosudarstvennyy universitet imeni M.V.Lomonosova

Type:

Сonference article

DOI:

https://doi.org/10.58168/FECC2025_68-74

Pages:

from 68 to 74

Published:

15.11.2025

Subject area:

UDC 630
UDC 004.8

Language:

Russian

Keywords:

forest management; Heilongjiang Province; Fujian Province; artificial intelligence (AI); climate and environmental challenges; wildfire detection; biodiversity monitoring

Abstract and keywords

Abstract (English):
This study explores the main challenges in developing artificial intelligence (AI)-driven software solutions for forest management in China, focusing on two ecologically and economically distinct provinces: Heilongjiang and Fujian. Findings show that limited data, environmental complexity, and regional differences in climate and forest types hinder AI implementation. Comparing the provinces shows how local factors affect AI performance and design.

Keywords:
forest management; Heilongjiang Province; Fujian Province; artificial intelligence (AI); climate and environmental challenges; wildfire detection; biodiversity monitoring

References

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25. Fujian // Encyclopaedia Britannica. – URL: https://www.britannica.com/place/Fujian (accessed: 31.05.2025).

26. Ting Y., Li J., Ma L., Zhou J., Wang R., Eichhorn M. P., Zhang H. Status, advancements and prospects of deep learning methods applied in forest studies // International Journal of Applied Earth Observation and Geoinformation. – 2024. – Vol. 131. – Article 103938. – URL: https://doi.org/10.1016/j.jag.2024.103938 (accessed: 31.05.2025).

27. Jiao Q., Fan M., Tao J., Wang W., Liu D., Wang P. Forest fire patterns and lightning-caused forest fire detection in Heilongjiang Province of China using satellite data // Fire. – 2023. – Vol. 6. – Article 166. – URL: https://doi.org/10.3390/fire6040166 (accessed: 31.05.2025).

28. Cheng G., Chen X., Wang C., Li X., Xian B., Yu H. Visual fire detection using deep learning: A survey // Neurocomputing. – 2024. – Vol. 596. – Article 127975. – URL: https://doi.org/10.1016/j.neucom.2024.127975 (accessed: 31.05.2025).

29. Wu Z., Li M., Wang B., Quan Y., Liu J. Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China // Remote Sensing. – 2021. – Vol. 13, Article 1813. – URL: https://doi.org/10.3390/rs13091813 (accessed: 31.05.2025).

30. Yu Z., Zhang M., Zhan Y., Guo Y., Zhang Y., Liang X., Wang C., Fan Y., Shan M., Guo H., Zhou W. Analysis of Temporal and Spatial Evolution Characteristics and Peak Predic tion of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang Province // Agriculture. – 2025. – Vol. 15, Article 1126. – URL: https://doi.org/10.3390/agriculture15111126 (accessed: 31.05.2025).

31. AI-powered biodiversity monitoring in forest ecosystems // PRISM – Sustainability Directory. – URL: https://prism.sustainability-directory.com/scenario/ai-powered-biodiversity-monitoring-in-forest-ecosystems/ (accessed: 31.05.2025).

32. Wang W., Zhai D., Huang B. Implementation gaps affecting the quality of biodiversity conservation management: An ethnographic study of protected areas in Fujian Province, China // Forest Policy and Economics. – 2023. – Vol. 152. – Article 102933. – URL: https://doi.org/10.1016/j.forpol.2023.102933 (accessed: 31.05.2025).

33. ZHUART: Pioneering Innovation to Drive Bamboo Industry Growth // ZhuArt Bamboo. – URL: https://zhuartbamboo.com/zhuart-pioneering-innovation-to-drive-bamboo-industry-growth/ (accessed: 31.05.2025).

34. Qi S., Song B., Liu C., Gong P., Luo J., Zhang M., Xiong T. Bamboo Forest Mapping in China Using the Dense Landsat 8 Image Archive and Google Earth Engine // Remote Sensing. – 2022. – Vol. 14, Article 762. – URL: https://doi.org/10.3390/rs14030762 (accessed: 31.05.2025).

35. He A., Xu Z., Li Y., Li B., Huang X., Zhang H., Guo X., Li Z. Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV hyperspectral images // Geo-spatial Information Science. – 2025. – Published online: 04.02.2025. – URL: https://doi.org/10.1080/10095020.2025.2454521 (accessed: 31.05.2025).

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