智慧林业发展现状与展望

doi:10.12302/j.issn.1000-2006.202209052

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南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (6): 83-95.doi: 10.12302/j.issn.1000-2006.202209052

所属专题：南京林业大学120周年校庆特刊

智慧林业发展现状与展望

曹林(), 周凯, 申鑫, 杨晓明, 曹福亮, 汪贵斌()

南京林业大学,南方现代林业协同创新中心,江苏南京 210037

收稿日期:2022-09-23 修回日期:2022-10-18 出版日期:2022-11-30 发布日期:2022-11-24
通讯作者: 汪贵斌
基金资助:
国家自然科学基金项目(31922055);江苏省现代教育技术研究课题(2022-R-102708)

The status and prospects of smart forestry

CAO Lin(), ZHOU Kai, SHEN Xin, YANG Xiaoming, CAO Fuliang, WANG Guibin()

Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

Received:2022-09-23 Revised:2022-10-18 Online:2022-11-30 Published:2022-11-24
Contact: WANG Guibin

摘要/Abstract

摘要：

信息技术是人类文明发展的重要推动力,也是当今世界发展最快、影响最大的高新技术之一。随着现代信息技术在林业领域的广泛应用,智慧林业成为了现代林业发展的必由之路。智慧林业是物联网、大数据、云计算、人工智能、移动互联网等新一代信息技术与3S技术、智能装备及林木育种、森林培育、森林经营、森林保护等林业生产和管理业务深入融合新模式。我国智慧林业的发展,在现代林业发展中具有重要的里程碑意义。笔者首先介绍了智慧林业产生的背景、内涵、特征、理论基础和研究方法,以及针对智慧林业发展所进行的顶层设计、项目部署实施、科研平台建设及人才培养概况;然后系统介绍了林业智能感知、空间信息技术、大数据及云计算、虚拟现实和智能装备技术等智慧林业关键核心技术的研究现状;进一步介绍了智慧林业在林木遗传育种、森林精准培育、森林资源监测与经营决策、林火监测预测及病虫害防治、野生动植物保护方向上的应用进展;最后,分析了未来智慧林业的发展目标,展望了智慧林业技术体系的主要发展方向。笔者认为,智慧林业的发展需要进一步推进智能算法及硬件的研发和应用,并加强智慧林业理论基础研究;同时,还需在精准多源数据获取的基础上,将现代数据挖掘、模型模拟、智能分析技术融入林业生产的业务流程中,服务林业生产的全产业链,引领林业高质量发展。

关键词: 智慧林业, 林业信息化, 人工智能, 林木表型, 森林培育与监测, 森林经营决策, 森林保护与灾害防治

Abstract:

Information technology is an important driving force for the development of human civilization, and it is also one of the fastest growing and most influential high-techs in the world today. With the wide application of modern information technology in the field of forestry, smart forestry has become the route one must take for the development of modern forestry. Smart forestry is the deep integration of new generation information technologies such as Internet of Things, big data, cloud computing, artificial intelligence, mobile Internet and 3S technology, intelligent equipment as well as forestry production and management businesses such as and forest breeding, forest cultivation, forest management and forest protection. The development of smart forestry in China is a milestone in the development of modern forestry. The author introduces the background, connotation, characteristics, theoretical basis and research methods of smart forestry, as well as top-level design, project deployment and implementation, scientific research platform construction and talent training for smart forestry development; the research status of key core technologies of intelligent forestry, such as forestry intelligent perception, spatial information technology, big data and cloud computing, virtual reality and intelligent equipment technology, are systematically introduced. The application progress of smart forestry in forest tree genetics and breeding, forest precision silviculture, forest resource monitoring and management decision-making, forest fire monitoring and prediction, pest control, and wildlife protection were further introduced. Finally, the development goals of smart forestry in the future are pointed out, and the main development directions of smart forestry technology system are prospected. The author believes that the development of smart forestry needs to further promote the research, development and application of intelligent algorithms and hardware, as well as to strengthen the research of its theoretical basis. At the same time, it is also necessary to integrate modern data mining, model simulation and intelligent analysis technologies into the forestry production business process on the basis of obtaining accurate multi-source data, to provide services for the whole industry chain of forestry production and lead the high-quality development of forestry.

Key words: smart forestry, forestry informatization, artificial intelligence, phenotyping of forest trees, forest silviculture and monitoring, forest management decision, forest protection and disaster prevention

中图分类号:

曹林,周凯,申鑫,等. 智慧林业发展现状与展望[J]. 南京林业大学学报（自然科学版）, 2022, 46(6): 83-95.

CAO Lin, ZHOU Kai, SHEN Xin, YANG Xiaoming, CAO Fuliang, WANG Guibin. The status and prospects of smart forestry[J].Journal of Nanjing Forestry University (Natural Science Edition), 2022, 46(6): 83-95.DOI: 10.12302/j.issn.1000-2006.202209052.

图/表 2

参考文献 102

[1]	褚君浩. 科技创新开拓智能时代[J]. 科技导报, 2019, 37(2):31-33.
	CHU J H. Science and innovation leading the intelligent age[J]. Sci & Technol Rev, 2019, 37(2):31-33.
[2]	江泽慧. 论林业新科技革命[J]. 世界林业研究, 1999, 12(4):1-5.
	JIANG Z H. On the new forestry scientific and technological revolution[J]. World For Res, 1999, 12(4):1-5.
[3]	李世东. 论第六次信息革命[J]. 中国新通信, 2014, 16(14):3-6.
	LI S D. On the sixth information revolution[J]. China New Telecommun, 2014, 16(14):3-6.DOI:10.3969/j.issn.1673-4866.2014.14.002.
[4]	陈述彭. 数字地球:挑战与思考[J]. 遥感信息, 1999, 14(2):2-4.
	CHEN S P. Digital earth:challenges and thoughts[J]. Remote Sens Inf, 1999, 14(2):2-4. DOI:10.3969/j.issn.1000-3177.1999.02.001.
[5]	冯仲科, 张晓勤. 发展我国的数字林业体系[J]. 北京林业大学学报, 2000, 22(5):102-103.
	FENG Z K, ZHANG X Q. Development of digital forestry system in China[J]. J Beijing For Univ, 2000, 22(5):102-103.DOI:10.13332/j.1000-1522.2000.05.024.
[6]	陈述彭, 程维明. 世界森林的数字地球监测[J]. 遥感学报, 2001, 5(5):321-326,401.
	CHEN S P, CHENG W M. World forest watching by digital earth[J]. J Remote Sens, 2001, 5(5):321-326,401.
[7]	李增元, 张怀清, 陆元昌. 数字林业建设与进展[J]. 中国农业科技导报, 2003, 5(2):7-9.
	LI Z Y, ZHANG H Q, LU Y C. Establishment and development of digital forestry[J]. Rev China Agric Sci Technol, 2003, 5(2):7-9.DOI:10.3969/j.issn.1008-0864.2003.02.002.
[8]	李德仁. 展望大数据时代的地球空间信息学[J]. 测绘学报, 2016, 45(4):379-384.
	LI D R. Towards geo-spatial information science in big data era[J]. Acta Geod Cartogr Sin, 2016, 45(4):379-384.DOI:10.11947/j.AGCS.2016.20160057.
[9]	李世东. 中国智慧林业路线图[J]. 林业经济, 2014, 36(10):54-57.
	LI S D. China’s forestry roadmap wisdom[J]. For Econ, 2014, 36(10):54-57.DOI:10.13843/j.cnki.lyjj.2014.10.012.
[10]	徐国祯. 森林的系统观与林业系统工程[J]. 林业资源管理, 1988(4):16-22.
	XU G Z. System view of forest and forestry system engineering[J]. For Resour Manag, 1988(4):16-22.DOI:10.13466/j.cnki.lyzygl.1988.04.005.
[11]	RUSTAD L, CAMPBELL J L, RANDALL J, et al. Environmental sensor applications at USDA forest service experimental forests: the smart forest network[R]. Long Beach, CA: ASA, CSSA and SSSA, 2014.
[12]	PAPPAS C, BÉLANGER N, BERGERON Y, et al. Smartforests Canada: a network of monitoring plots for forest management under environmental change[M]. //TOGNETTIR, SMITHM, PANZACCHIP. Climate-Smart Forestry in mountain regions. Switzerland: Springer, 2022. DOI:10.1007/978-3-030-80767-2_16.
[13]	GABRYS J. Smart forests and data practices: from the internet of trees to planetary governance[J]. Big Data & Society, 2020, 7(1): 1-10. DOI: 10.1177/2053951720904871.
[14]	SFI. SmartForest Bringing Industry 4.0 to the Norwegian forest sector annual report 2020[R]. HØGSKOLEVEIEN, Norway: SFI SmartForest, 2020.
[15]	恭映璧. 推进以智慧林业为主导的林业创新——战后日本林业发展及其启示(十二)[J]. 林业与生态, 2021(5):22-26.
[16]	中国林草年度盘点:改革创新,变局中开新局[N].2021-01-23(07).
[17]	李世东. 智慧林业几个基本问题的探讨[J]. 林草政策研究, 2021, 1(3):24-31.
	LI S D. Several basic issues of smart forestry[J]. J For Grassland Policy, 2021, 1(3):24-31.DOI:10.12344/lczcyj.2021.07.10.0001.
[18]	吴振江, 李俊枝, 李顺龙. “互联网+”智慧林业的发展策略[J]. 东北林业大学学报, 2019, 47(5):105-107,117.
	WU Z J, LI J Z, LI S L. “Internet +” smart forestry in green development[J]. J Northeast For Univ, 2019, 47(5):105-107,117.DOI:10.13759/j.cnki.dlxb.2019.05.020.
[19]	国家林业局. 关于加快中国林业大数据发展的指导意见[EB/OL].[2016-07-13](2022-09-20). http://www.forestry.gov.cn/.
[20]	国家林业和草原局. 国家林业和草原局关于促进林业和草原人工智能发展的指导意见[J]. 自然资源通讯, 2019(23):11-14.
[21]	杨秀好, 秦江林, 李有海, 等. 林业有害生物灾害监测预警与应急防控关键技术研究与应用[Z]. 南宁:广西壮族自治区林业有害生物防治检疫站, 2016.
[22]	贵阳市林业局. 林业+大数据!贵阳智慧林业云平台实现智能管理[N]. 潇湘晨报,2021-12-02(1).
[23]	“5G+智慧林业”上线全力守护绿水青山[Z]. 广西移动, 2022-08-02.
[24]	教育部办公厅. 关于公布新农科研究与改革实践项目的通知[Z].(2020-09-08)[2022-09-04]. http://www.edu.gov.cn.
[25]	教育部. 关于公布2021年度普通高等学校本科专业备案和审批结果的通知[Z].(2021-12-10)[2022-09-04]. http://www.edu.gov.cn.
[26]	MIRAZ M H, ALI M, EXCELL P S, et al. A review on Internet of Things (IoT),Internet of everything (IoE) and Internet of nano things (IoNT)[C]//2015 Internet Technologies and Applications (ITA).Wrexham,UK:IEEE, 2015:219-224.DOI:10.1109/ITechA.2015.7317398.
[27]	ZHAO C, LI X S, CHEN J S. Study on the application of Internet of Things in the Logistics in forest industry[J]. Appl Mech Mater, 2011, 97/98:664-668.DOI:10.4028/www.scientific.net/amm.97-98.664.
[28]	ZHANG Q, ZHOU C L, CHEN Z T, et al. Application of weighted cetroid location algorithm in forest fire monitring[C]// 2014 9th International Conference on Computer Science & Education.Vancouver BC,Canada:IEEE, 2014:986-988.DOI:10.1109/ICCSE.2014.6926610.
[29]	SHIM K, BARCZAK A, REYES N, et al. Small mammals and bird detection using IoT devices[C]// 2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ).Tauranga, New Zealand: IEEE, 2021:1-6.DOI:10.1109/IVCNZ54163.2021.9653430.
[30]	LUO K, SAEEDI S, BADGER J, et al. Using the internet of things to monitor human and animal uses of industrial linear features[C]// Lecture Notes in Computer Science. Switzerland AG: Springer, 2018:85-89. DOI:10.1007/978-3-319-90053-7_9.
[31]	GHAZALI M A, OTHMAN K A, HAKIMIE MOHD ISA M A, et al. Wireless sensor development for Malaysian forest monitoring and tracking system[C]// 2017 21st International Computer Science and Engineering Conference (ICSEC). Bangkok Thailand: IEEE, 2017:1-5.DOI:10.1109/ICSEC.2017.8443938.
[32]	ZHAO W, YI L. Research on the evolution of the innovation ecosystem of the Internet of Things:a case study of Xiaomi(China)[J]. Procedia Comput Sci, 2022, 199:56-62.DOI:10.1016/j.procs.2022.01.008.
[33]	高德民, 林海峰, 刘云飞, 等. 基于无线传感网的森林火灾FWI系统分析[J]. 林业科技开发, 2015, 29(1):105-109.
	GAO D M, LIN H F, LIU Y F, et al. Study on forest fire FWI system based on wireless sensor networks[J]. China For Sci Technol, 2015, 29(1):105-109.DOI:10.13360/j.issn.1000-8101.2015.01.030.
[34]	刘海洋, 于新文, 张旭. 基于无线传感网的森林环境因子采集系统性能测试[J]. 林业科技通讯, 2016(1):68-70.
	LIU H Y, YU X W, ZHANG X. Performance test of forest environmental factor acquisition system based on wireless sensor network[J]. For Sci Technol, 2016(1):68-70.DOI:10.13456/j.cnki.lykt.2016.01.027.
[35]	周成虎. 全空间地理信息系统展望[J]. 地理科学进展, 2015, 34(2):129-131.
	ZHOU C H. Prospects on pan-spatial information system[J]. Prog Geogr, 2015, 34(2):129-131.
[36]	骆剑承, 周成虎, 梁怡, 等. 时空数据智能化处理与分析的理论和方法探讨[J]. 中国图象图形学报, 2001, 6(9):836-841.
	LUO J C, ZHOU C H, LIANG Y, et al. Theoretical and technical issues on intelligent processing and analyzing models for temporal and spatial data[J]. J Image Graph, 2001, 6(9):836-841. DOI:10.3969/j.issn.1006-8961.2001.09.004.
[37]	庞丽峰, 黄水生, 李万里, 等. 全球卫星系统在我国林业中的应用[J]. 世界林业研究, 2019, 32(5):41-46.
	PANG L F, HUANG S S, LI W L, et al. Application of GNSS in forestry sector in China[J]. World For Res, 2019, 32(5):41-46.DOI:10.13348/j.cnki.sjlyyj.2019.0053.y.
[38]	杨元喜, 汤静. 智慧城市与北斗卫星导航系统[J]. 卫星应用, 2014(2):7-10.
	YANG Y X, TANG J. Smart city and Beidou satellite navigation system[J]. Satell Appl, 2014(2):7-10.
[39]	杨必胜, 陈驰, 董震. 面向智能化测绘的城市地物三维提取[J]. 测绘学报, 2022, 51(7):1476-1484.
	YANG B S, CHEN C, DONG Z. 3D geospatial information extraction of urban objects for smart surveying and mapping[J]. Acta Geod Cartogr Sin, 2022, 51(7):1476-1484.
[40]	刘春, 贾守军, 吴杭彬, 等. 点云场景认知模式:泛化点云[J]. 测绘学报, 2022, 51(4):556-567.
	LIU C, JIA S J, WU H B, et al. Scene cognition pattern of point cloud-generalization point cloud[J]. Acta Geod Cartogr Sin, 2022, 51(4):556-567.
[41]	黄华国. 林业定量遥感研究进展和展望[J]. 北京林业大学学报, 2019, 41(12):1-14.
	HUANG H G. Progress and perspective of quantitative remote sensing of forestry[J]. J Beijing For Univ, 2019, 41(12):1-14.
[42]	HOWE D, COSTANZO M, FEY P, et al. The future of biocuration[J]. Nature, 2008, 455(7209):47-50.DOI:10.1038/455047a.
[43]	REICHMAN O J, JONES M B, SCHILDHAUER M P. Challenges and opportunities of open data in ecology[J]. Science, 2011, 331(6018):703-705.DOI:10.1126/science.1197962.
[44]	CHEBBI I, BOULILA W, FARAH I R. Improvement of satellite image classification: approach based on Hadoop/MapReduce[C]// 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).Monastir,Tunisia:IEEE, 2016:31-34.DOI:10.1109/ATSIP.2016.7523046.
[45]	HASSABIS D. Artificial intelligence:chess match of the century[J]. Nature, 2017, 544(7651):413-414.DOI:10.1038/544413a.
[46]	CRUZ A S, LINS H C, MEDEIROS R V A, et al. Artificial intelligence on the identification of risk groups for osteoporosis,a general review[J]. Biomed Eng Online, 2018, 17(1):12.DOI:10.1186/s12938-018-0436-1.
[47]	MATTHEWS G, HANCOCK P A, LIN J C, et al. Evolution and revolution:personality research for the coming world of robots,artificial intelligence,and autonomous systems[J]. Pers Individ Differ, 2021, 169:109969.DOI:10.1016/j.paid.2020.109969.
[48]	吴保国, 温亮宝. 森林重大病虫害诊治专家咨询系统的研究[J]. 北京林业大学学报, 2006, 28(6):113-118.
	WU B G, WEN L B. Expert consulting system for the diagnosis,prevention and control of important forest diseases and insect pests[J]. J Beijing For Univ, 2006, 28(6):113-118.DOI:10.3321/j.issn:1000-1522.2006.06.020.
[49]	张钹. 人工智能进入后深度学习时代[J]. 智能科学与技术学报, 2019, 1(1):4-6.
	ZHANG B. Artificial intelligence is entering the post deep-learning era[J]. Chin J Intell Sci Technol, 2019, 1(1):4-6.
[50]	BRANDT M, TUCKER C J, KARIRYAA A, et al. An unexpectedly large count of trees in the West African Sahara and Sahel[J]. Nature, 2020, 587(7832):78-82.DOI:10.1038/s41586-020-2824-5.
[51]	周焱, 刘文萍, 骆有庆, 等. 基于深度学习的小目标受灾树木检测方法[J]. 林业科学, 2021, 57(3):98-107.
	ZHOU Y, LIU W P, LUO Y Q, et al. Small object detection for infected trees based on the deep learning method[J]. Sci Silvae Sin, 2021, 57(3):98-107.
[52]	余茂源. 智慧林业的关键技术及其应用策略研究[J]. 林业调查规划, 2017, 42(5):121-123,131.
	YU M Y. Research on key technology of intelligent forestry and its application strategy[J]. For Invent Plan, 2017, 42(5):121-123,131.DOI:10.3969/j.issn.1671-3168.2017.05.024.
[53]	ORLAND B. Smartforest:3-D interactive forest visualization and analysis[C]// Proceedings of Decision Support-2001,Resource Technology 94,Toronto Bethesda, Maryland: American Society for Photogrammetry and Remote Sensing, 1994: 181-190.
[54]	刘海, 张怀清, 林辉. 森林经营可视化管理系统设计与实现[J]. 科技资讯, 2010, 8(17):240-242.
	LIU H, ZHANG H Q, LIN H. Forest management visual management systems design and implementation[J]. Sci & Technol Inf, 2010, 8(17):240-242.DOI:10.16661/j.cnki.1672-3791.2010.17.127.
[55]	王万富, 王琢, 刘佳鑫, 等. 基于Qt/Embedded的农林智能装备导航定位算法研究及软件设计[J]. 国外电子测量技术, 2022, 41(3):63-68.
	WANG W F, WANG Z, LIU J X, et al. Research on navigation and positioning algorithms and software design of agricultural and forestry intelligent equipment based on Qt/Embedded[J]. Foreign Electron Meas Technol, 2022, 41(3):63-68.DOI:10.19652/j.cnki.femt.2103457.
[56]	刘延鹤, 傅万四, 张彬, 等. 林业机器人发展现状与未来趋势[J]. 世界林业研究, 2020, 33(1):38-43.
	LIU Y H, FU W S, ZHANG B, et al. Development state and future trend of forestry robot[J]. World For Res, 2020, 33(1):38-43.DOI:10.13348/j.cnki.sjlyyj.2019.0076.y.
[57]	罗梅, 刘延鹤, 蒋鹏飞, 等. 我国林业装备的发展现状及未来趋势[J]. 林业机械与木工设备, 2021, 49(1):8-11,17.
	LUO M, LIU Y H, JIANG P F, et al. Development status and future trend of forestry machinery in China[J]. For Mach & Woodwork Equip, 2021, 49(1):8-11,17.DOI:10.13279/j.cnki.fmwe.2021.0002.
[58]	CHEN S, CHIU Y C, CHANG Y C. Development of a tubing-grafting robotic system for fruit-bearing vegetable seedlings[J]. Appl Eng Agric, 2010, 26(4):707-714.DOI:10.13031/2013.32055.
[59]	SICILIANO B, KHATIB O. Introduction[M]//Springer Handbook of Robotics.Berlin, Heidelberg:Springer, 2008:1-4.DOI:10.1007/978-3-540-30301-5_1.
[60]	LEE J, HWANGBO J, WELLHAUSEN L, et al. Learning quadrupedal locomotion over challenging terrain[J]. Sci Robot, 2020, 5(47):eabc5986.DOI:10.1126/scirobotics.abc5986.
[61]	RASHID H, AHMED I U, ULLAH A, et al. Multiple sensors based fire extinguisher robot based on DTMF,bluetooth and GSM technology with multiple mode of operation[C]//2016 International Workshop on Computational Intelligence (IWCI).Dhaka, Bangladesh:IEEE, 2016:41-46.DOI:10.1109/IWCI.2016.7860336.
[62]	GERASIMOV Y, SOKOLOV A, SYUNEV V. Development trends and future prospects of cut-to-length machinery[J]. Adv Mater Res, 2013, 705:468-473.DOI:10.4028/www.scientific.net/amr.705.468.
[63]	蔡硕, 邢艳秋, 端木嘉龙. 背包式激光雷达滤除低强度点云提取林木胸径[J]. 森林工程, 2021, 37(5):12-19.
	CAI S, XING Y Q, DUANMU J L. Extraction of DBH from filtering out low intensity point cloud by backpack laser scanning[J]. For Eng, 2021, 37(5):12-19.DOI:10.16270/j.cnki.slgc.2021.05.012.
[64]	SU Y J, GUO Q H, JIN S C, et al. The development and evaluation of a backpack LiDAR system for accurate and efficient forest inventory[J]. IEEE Geosci Remote Sens Lett, 2021, 18(9):1660-1664.DOI:10.1109/LGRS.2020.3005166.
[65]	张慧颖. 现场环境智能巡检机器人设计[J]. 现代电子技术, 2016, 39(18):135-138.
	ZHANG H Y. Design of intelligent inspection robot for field environment[J]. Mod Electron Tech, 2016, 39(18):135-138.DOI:10.16652/j.issn.1004-373x.2016.18.034.
[66]	姜树海, 张楠. 六足仿生森林消防机器人机构设计与分析[J]. 机械设计与制造, 2015(12):208-212.
	JIANG S H, ZHANG N. Analysis and design of forest fire-fighting hexapod bionic robot mechanism[J]. Mach Des & Manuf, 2015(12):208-212.DOI:10.19356/j.cnki.1001-3997.2015.12.059.
[67]	魏占国, 刘晋浩. 轮式林木联合采伐机底盘的设计与研究[J]. 广西大学学报(自然科学版), 2010, 35(2):263-268.
	WEI Z G, LIU J H. Design of the chassis for a wheeled forest combined harvester[J]. J Guangxi Univ (Nat Sci Ed),2010, 35(2):263-268.DOI:10.13624/j.cnki.issn.1001-7445.2010.02.025.
[68]	RINCENT R, CHARPENTIER J P, FAIVRE-RAMPANT P, et al. Phenomic selection is a low-cost and high-throughput method based on indirect predictions:proof of concept on wheat and poplar[J]. G3 (Bethesda Md), 2018, 8(12):3961-3972.DOI:10.1534/g3.118.200760.
[69]	AITKEN S N, BEMMELS J B. Time to get moving:assisted gene flow of forest trees[J]. Evol Appl, 2016, 9(1):271-290.DOI:10.1111/eva.12293.
[70]	GRATTAPAGLIA D, SILVA-JUNIOR O B, RESENDE R T, et al. Quantitative genetics and genomics converge to accelerate forest tree breeding[J]. Front Plant Sci, 2018, 9:1693.DOI:10.3389/fpls.2018.01693.
[71]	DUNGEY H S, DASH J P, PONT D, et al. Phenotyping whole forests will help to track genetic performance[J]. Trends Plant Sci, 2018, 23(10):854-864.DOI:10.1016/j.tplants.2018.08.005.
[72]	BIAN L M, ZHANG H C, GE Y F, et al. Closing the gap between phenotyping and genotyping:review of advanced,image-based phenotyping technologies in forestry[J]. Ann For Sci, 2022, 79(1):22.DOI:10.1186/s13595-022-01143-x.
[73]	边黎明, 张慧春. 表型技术在林木育种和精确林业上的应用[J]. 林业科学, 2020, 56(6):113-126.
	BIAN L M, ZHANG H C. Application of phenotyping techniques in forest tree breeding and precision forestry[J]. Sci Silvae Sin, 2020, 56(6):113-126.
[74]	TARDIEU F, CABRERA-BOSQUET L, PRIDMORE T, et al. Plant phenomics,from sensors to knowledge[J]. Curr Biol, 2017, 27(15):R770-R783.DOI:10.1016/j.cub.2017.05.055.
[75]	MILELLA A, MARANI R, PETITTI A, et al. In-field high throughput grapevine phenotyping with a consumer-grade depth camera[J]. Comput Electron Agric, 2019, 156:293-306.DOI:10.1016/j.compag.2018.11.026.
[76]	SALVATORI E, FUSARO L, MANES F. Chlorophyll fluorescence for phenotyping drought-stressed trees in a mixed deciduous forest[J]. Ann Di Bot, 2016, 6:39-49.
[77]	TSUCHIKAWA S. A review of recent near infrared research for wood and paper[J]. Appl Spectrosc Rev, 2007, 42(1):43-71.DOI:10.1080/05704920601036707.
[78]	WATT M S, BUDDENBAUM H, LEONARDO E M C, et al. Using hyperspectral plant traits linked to photosynthetic efficiency to assess N and P partition[J]. ISPRS J Photogramm Remote Sens, 2020, 169:406-420.DOI:10.1016/j.isprsjprs.2020.09.006.
[79]	COHEN Y, ALCHANATIS V, PRIGOJIN A, et al. Use of aerial thermal imaging to estimate water status of palm trees[J]. Precision Agric, 2012, 13(1):123-140.DOI:10.1007/s11119-011-9232-7.
[80]	JIN S C, SUN X L, WU F F, et al. Lidar sheds new light on plant phenomics for plant breeding and management:recent advances and future prospects[J]. ISPRS J Photogramm Remote Sens, 2021, 171:202-223.DOI:10.1016/j.isprsjprs.2020.11.006.
[81]	NEALE D B, MARTÍNEZ-GARCÍA P J, DE LA TORRE A R, et al. Novel insights into tree biology and genome evolution as revealed through genomics[J]. Annu Rev Plant Biol, 2017, 68:457-483.DOI:10.1146/annurev-arplant-042916-041049.
[82]	FÉRET J B, BERGER K, DE BOISSIEU F, et al. PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents[J]. Remote Sens Environ, 2021, 252:112173.DOI:10.1016/j.rse.2020.112173.
[83]	WANG Z H, WANG T J, DARVISHZADEH R, et al. Vegetation indices for mapping canopy foliar nitrogen in a mixed temperate forest[J]. Remote Sens, 2016, 8(6):491.DOI:10.3390/rs8060491.
[84]	张峰, 周广胜. 植被含水量高光谱遥感监测研究进展[J]. 植物生态学报, 2018, 42(5):517-525.
	ZHANG F, ZHOU G S. Research progress on monitoring vegetation water content by using hyperspectral remote sensing[J]. Chin J Plant Ecol, 2018, 42(5):517-525.DOI:10.17521/cjpe.2017.0313.
[85]	CHENG T, RIAÑO D, USTIN S L. Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis[J]. Remote Sens Environ, 2014, 143:39-53.DOI:10.1016/j.rse.2013.11.018.
[86]	BUDDENBAUM H, ROCK G, HILL J, et al. Measuring stress reactions of beech seedlings with PRI,fluorescence,temperatures and emissivity from VNIR and thermal field imaging spectroscopy[J]. Eur J Remote Sens, 2015, 48(1):263-282.DOI:10.5721/EuJRS20154815.
[87]	师志刚, 刘群昌, 白美健, 等. 基于物联网的水肥一体化智能灌溉系统设计及效益分析[J]. 水资源与水工程学报, 2017, 28(3):221-227.
	SHI Z G, LIU Q C, BAI M J, et al. Water and fertilization integrated intelligent irrigation system design and benefit analysis based on the Internet of Things[J]. J Water Resour Water Eng, 2017, 28(3):221-227.DOI:10.11705/j.issn.1672-643X.2017.03.40.
[88]	陈尔学, 刘健, 王晓慧. 重点防护林工程监测技术[M]. 北京: 中国林业出版社, 2012.
	CHEN E X, LIU J, WANG X H. Monitoring technology of the key shelter-forest project[M]. Beijing: China Forestry Publishing House, 2012.
[89]	李硕明. 一种基于物联网技术的森林资源监测系统[J]. 物联网技术, 2016, 6(5):11-13,16.
	LI S M. Forest resource monitoring system based on Internet of Things technology[J]. Internet Things Technol, 2016, 6(5):11-13,16.DOI:10.16667/j.issn.2095-1302.2016.05.001.
[90]	冷天熙, 钱发斌, 胡文萍. 基于人工智能深度学习的卫星影像分类研究[J]. 林业调查规划, 2021, 46(1):1-4.
	LENG T X, QIAN F B, HU W P. Satellite image classification based on artificial intelligence In-depth learning[J]. For Invent Plan, 2021, 46(1):1-4.
[91]	尹丽丽. 黑龙江省森林资源监测遥感判读平台建设[J]. 林业勘查设计, 2021, 50(1):67-70.
	YIN L L. Construction of remote sensing interpretation platform for forest resources monitoring in Heilongjiang Province[J]. For Investig Des, 2021, 50(1):67-70.
[92]	张民侠, 郑怀兵. 基于智慧感知、分析、处置方法的森林智慧防火[J]. 南京理工大学学报, 2016, 40(6):694-699.
	ZHANG M X, ZHENG H B. Intelligent forest fire prevention based on intelligent perception,analysis and disposal method[J]. J Nanjing Univ Sci Technol, 2016, 40(6):694-699. DOI:10.14177/j.cnki.32-1397n.2016.40.06.009.
[93]	BO W H, LIU J, FAN X J, et al. BASNet:Burned area segmentation network for real-time detection of damage maps in remote sensing images[J]. IEEE Trans Geosci Remote Sens, 2022, 60:1-13.DOI:10.1109/TGRS.2022.3197647.
[94]	KOLTUNOV A, USTIN S L, QUAYLE B, et al. The development and first validation of the GOES Early Fire Detection (GOES-EFD) algorithm[J]. Remote Sens Environ, 2016, 184:436-453.DOI:10.1016/j.rse.2016.07.021.
[95]	MUTKA A M, BART R S. Image-based phenotyping of plant disease symptoms[J]. Front Plant Sci, 2015, 5:734.DOI:10.3389/fpls.2014.00734.
[96]	PÁDUA L, MARQUES P, MARTINS L, et al. Monitoring of chestnut trees using machine learning techniques applied to UAV-based multispectral data[J]. Remote Sens, 2020, 12(18):3032.DOI:10.3390/rs12183032.
[97]	HORNERO A, ZARCO-TEJADA P J, QUERO J L, et al. Modelling hyperspectral-and thermal-based plant traits for the early detection of Phytophthora-induced symptoms in oak decline[J]. Remote Sens Environ, 2021, 263:112570.DOI:10.1016/j.rse.2021.112570.
[98]	张宇, 李丽, 吴巩胜, 等. 基于生境斑块的滇金丝猴景观连接度分析[J]. 生态学报, 2016, 36(1):51-58.
	ZHANG Y, LI L, WU G S, et al. Analysis of landscape connectivity of the Yunnan snub-nosed monkeys(Rhinopithecus bieti) based on habitat patches[J]. Acta Ecol Sin, 2016, 36(1):51-58
[99]	张超. 基于“3S”技术的大熊猫潜在栖息地研究:以平武县小河沟自然保护区为例[D]. 成都: 成都理工大学, 2016.
	ZHANG C. Study on the giant pandas’ potential habitat based on “3S” technology: taking xiaohegou nature reserve in Pingwu as an example[D]. Chengdu: Chengdu University of Technology, 2016.
[100]	申凤伟. 基于物联网构架的野生动物监测信息管理系统研究[D]. 武汉: 武汉理工大学, 2012.
	SHEN F W. Research of monitoring IMS for wildlife preserve based on TIOT theory structure[D]. Wuhan: Wuhan University of Technology, 2012.
[101]	宫一男, 谭孟雨, 王震, 等. 基于深度学习的红外相机动物影像人工智能识别:以东北虎豹国家公园为例[J]. 兽类学报, 2019, 39(4):458-465.
	GONG Y N, TAN M Y, WANG Z, et al. AI recognition of infrared camera image of wild animals based on deep learning:Northeast Tiger and Leopard National Park for example[J]. Acta Theriol Sin, 2019, 39(4):458-465.DOI:10.16829/j.slxb.150333.
[102]	骆沙鸣. 应进一步提高我国森林质量和森林生态服务功能[J]. 中国产业, 2011(7):7.
	LUO S M. China’s forest quality and forest ecological service function should be further improved[J]. Ind China, 2011(7):7.

智慧林业发展现状与展望

The status and prospects of smart forestry

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