江西省赣州市南康区松材线虫病发生特征

doi:10.13287/j.1001-9332.202402.030

应用生态学报 ›› 2024, Vol. 35 ›› Issue (2): 507-515.doi: 10.13287/j.1001-9332.202402.030

江西省赣州市南康区松材线虫病发生特征

袁佳玉^1,2,3, 熊立⁴, 吴志伟^1,2,3*, 朱诗豪^1,2,3, 康平^1,2,3, 李顺^1,2,3

¹江西师范大学鄱阳湖湿地与流域研究教育部重点实验室, 南昌 330022;
²江西师范大学江西省自然灾害监测预警与评估重点实验室, 南昌 330022;
³江西师范大学地理与环境学院, 南昌 330022;
⁴江西省减灾备灾中心, 南昌 330022

收稿日期:2023-05-23 修回日期:2023-12-17 出版日期:2024-02-18 发布日期:2024-08-18
通讯作者: ^*E-mail: wuzhiwei@jxnu.edu.cn
作者简介:袁佳玉, 女, 2000年生, 硕士研究生。主要从事景观生态学研究。E-mail: jiayuyuan@jxnu.edu.cn
基金资助:
国家自然科学基金项目(31960253)和江西省教育厅研究生创新基金项目(YC2022-s249)

Characteristics of pine wood nematode disease in Nankang District, Ganzhou, Jiangxi Province, China

YUAN Jiayu^1,2,3, Xiong Li⁴, WU Zhiwei^1,2,3*, ZHU Shihao^1,2,3, KANG Ping^1,2,3, LI Shun^1,2,3

¹Ministry of Education Key Laboratory of Poyang Lake Wetland and Watershed Research, Jiangxi Normal University, Nanchang 330022, China;
²Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Pro-vince, Jiangxi Normal University, Nanchang 330022, China;
³School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China;
⁴Jiangxi Disaster Reduction and Preparedness Center, Nanchang 330022, China

Received:2023-05-23 Revised:2023-12-17 Online:2024-02-18 Published:2024-08-18

摘要/Abstract

摘要： 松材线虫病是我国南方森林面临的主要灾害之一。本文基于野外调查和高分一号(GF-1)卫星WFV影像数据,采用随机森林模型构建松材线虫病空间识别模型,探究地形、人类活动和林分因子等对病害发生的影响,监测病害空间分布,并采用空间自相关性分析评估江西省赣州市南康区松材线虫病发生特征。结果表明: 构建模型对松材线虫病的识别效果良好(AUC值=0.99,总体精度=0.96),可以实现对区域松材线虫病空间分布情况的有效监测;归一化差异绿度指数(NDGI)、距高速公路的距离、归一化植被指数(NDVI)是重要的建模因子;空间自相关性分析表明,松材线虫病的发生存在明显的空间正相关性即空间聚集性特征;南康区松材线虫病高发生区集中于赤土乡、朱坊镇和十八塘乡,低发生区集中于蓉江街道附近;分析变量的边际效应发现,离高速公路远、离县道近的低海拔地段是松材线虫病易发区域。研究结果可服务于区域松材线虫病分布的快速监测,对该病害防治和管理具有一定的指导意义。

关键词: 松材线虫病, 监测模型, 随机森林, 多光谱数据, GF-1 WFV

Abstract: Pine wood nematode (PWN) disease is one of the major disasters in forests of southern China, causing substantial forest resources and ecological and economic losses. Based on field surveys and WFV image data from the GF-1 satellite, we constructed a spatial identification model of PWN disease with the random forest model to explore the relative influences of topography, human activities and stand factors on the occurrence of diseases and predict their spatial distribution. We then used the spatial autocorrelation analysis to assess the distribution characteristics of PWN disease at the regional scale. The results showed that the random forest model constructed in this study was effective in identifying pine nematode diseases (AUC value=0.99, overall accuracy=0.96). The norma-lized difference greenness index (NDGI), the distance to the highway, and normalized vegetation index (NDVI) were important factors in explaining the spatial variations of PWN disease occurrence. There was a positive spatial correlation in the occurrence of PWN disease (not randomly distributed but with obvious spatial aggregation characteristics). The high occurrence areas of pine wood nematode disease concentrated in Chitu Township, Zhufang Township and Shibatang Township, low occurrence areas concentrated in the vicinity of Rongjiang Street. The areas far away from the highway, low in elevation, and close to county roads were suffered to PWN disease. The results could serve the regional monitoring of pine nematode disease occurrence and provide practical guidance for PWN disease management.

Key words: pine wood nematode disease, monitoring model, random forest, multispectral data, GF-1 WFV

袁佳玉, 熊立, 吴志伟, 朱诗豪, 康平, 李顺. 江西省赣州市南康区松材线虫病发生特征[J]. 应用生态学报, 2024, 35(2): 507-515.

YUAN Jiayu, Xiong Li, WU Zhiwei, ZHU Shihao, KANG Ping, LI Shun. Characteristics of pine wood nematode disease in Nankang District, Ganzhou, Jiangxi Province, China[J]. Chinese Journal of Applied Ecology, 2024, 35(2): 507-515.

参考文献

[1] 陈宏伟, 胡远满, 常禹, 等. 落叶松毛虫对大兴安岭呼中林区森林的景观长期影响模拟. 应用生态学报, 2010, 21(5): 1090-1096
[2] Vollenweider P, Gunthardt-Goerg MS. Diagnosis of abio-tic and biotic stress factors using the visible symptoms in foliage. Environmental Pollution, 2006, 140: 562-571
[3] 孙永春. 南京中山陵发现松材线虫. 江苏林业科技, 1982(4): 27-47
[4] 国家林业和草原局. 国家林业和草原局公告2023年第7号: 2023年松材线虫病疫区[EB/OL]. (2023-02-17) [2023-12-14]. https://www.forestry.gov.cn
[5] Cohen WB, Spies TA, Alig RJ, et al. Characterizing 23 years (1972-95) of stand replacement disturbance in western oregon forests with landsat imagery. Ecosystems, 2002, 5: 122-137
[6] 国家林业和草原局. 全国松材线虫病疫情防控五年攻坚行动计划(2021—2025) [EB/OL]. (2021-07-07) [2023-12-14]. http://www.forestry.gov.cn
[7] 马云强, 赵宁, 王华, 等. 基于BJ-2号卫星的马尾松松材线虫病监测研究. 农业与技术, 2021, 41(10): 76-79
[8] Huang JX, Lu X, Chen LY, et al. Accurate identification of pine wood nematode disease with a deep convolution neural network. Remote Sensing, 2022, 14: 913
[9] 方国飞, 黄文江, 牟晓伟, 等. 松材线虫病疫情精准监测实践与展望. 中国森林病虫, 2022, 41(4): 16-23
[10] 黄丽明, 王懿祥, 徐琪, 等. 采用YOLO算法和无人机影像的松材线虫病异常变色木识别. 农业工程学报, 2021, 37(14): 197-203
[11] 邱万林, 宗世祥. 基于多光谱卫星影像与机器学习算法的松材线虫病受害林分识别研究. 环境昆虫学报, 2023, 45(2): 408-420
[12] Zhang Y, Dian Y, Zhou J, et al. Characterizing spatial patterns of pine wood nematode outbreaks in subtropical zone in China. Remote Sensing, 2021, 13: 4682
[13] Park Y, Chung Y, Moon Y. Hazard ratings of pine forests to a pine wilt disease at two spatial scales (indivi-dual trees and stands) using self-organizing map and random forest. Ecological Informatics, 2013, 13: 7
[14] 刘婷婷, 杨晋帆, 周汝良, 等. 基于地理栅格变量与机器学习的松材线虫病扩散风险分析. 浙江农林大学学报, 2023, 40(3): 617-626
[15] Liu H, Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 2005, 17: 12
[16] Guyon I, Weston J, Barnhill S, et al. Gene selection for cancer classification using support vector machines. Machine Learning, 2002, 46: 389-422
[17] Nicodemus KK. Editor: On the stability and ranking of predictors from random forest variable importance mea-sures. Briefings in Bioinformatics, 2011, 12: 5
[18] Breiman L. Random forests. Machine Learning, 2001, 45: 5-32
[19] Wurm M, Taubenboeck H, Weigand M, et al. Slum mapping in polarimetric SAR data using spatial features. Remote Sensing of Environment, 2017, 194: 190-204
[20] 栗旭升, 李虎, 陈冬花, 等. 联合GF-5与GF-6卫星数据的多分类器组合亚热带树种识别. 林业科学, 2020, 56(10): 93-104
[21] 董灵波, 梁凯富, 张一帆, 等. 基于Landsat 8时间序列数据的翠岗林场森林类型划分. 应用生态学报, 2022, 33(9): 2339-2346
[22] 徐丽, 欧阳勋志, 潘萍, 等. 基于GF-1 WFV与MODIS时空融合的南方森林植被类型识别. 应用生态学报, 2022, 33(7): 1948-1956
[23] 朱子明, 祁新华. 基于Moran’I的闽南三角洲空间发展研究. 经济地理, 2009, 29(12): 1977-1980
[24] Moran PAP. Notes on continuous stochastic phenomena. Biometrika, 1950, 37: 17-23
[25] Li X, Liu Y, Huang P, et al. Integrating multi-scale remote-sensing data to monitor severe forest infestation in response to pine wilt disease. Remote Sensing, 2022, 14: 5164
[26] Robinet C, Roques A, Pan H, et al. Role of human-mediated dispersal in the spread of the pinewood nematode in China. PLoS One, 2009, 4(2): 4646
[27] 傅银贞, 汪小钦. 基于北京一号CCD数据的植被指数特性分析. 测绘科学, 2010, 35(6): 35-38
[28] 罗先轶, 张永光. 基于多源遥感数据的森林虫害监测及驱动力分析. 航天返回与遥感, 2022, 43(6): 129-140
[29] 黄芳芳, 雷鸣, 张力, 等. 基于随机森林和决策树的马尾松松材线虫病监测方法. 信息通信, 2019(12): 32-36
[30] Douma JC, van der Werf W, Hemerik L, et al. Deve-lopment of a pathway model to assess the exposure of European pine trees to pine wood nematode via the trade of wood. Ecological Applications, 2017, 27: 17
[31] 刘强, 吴志伟, 林世滔, 等. 松材线虫病发生点格局及影响因素. 应用生态学报, 2022, 33(9): 2530-2538
[32] 巨云为, 李明阳, 吴文浩. 江苏省松材线虫发生的预测方法. 林业科学, 2010, 46(12): 91-96
[33] 柏龙, 田呈明, 洪承昊, 等. 湖北宜昌松林景观格局对松材线虫流行及扩散的影响. 生态学报, 2015, 35(24): 8107-8116
[34] 王娟, 牛树奎, 骆有庆, 等. 浙江省富阳市森林景观格局与松材线虫病发病程度的关系. 科学技术与工程, 2006(19): 3047-3052
[35] 吴敏娟, 尤誉杰, 张晓红, 等. 不同干扰模式对受害马尾松人工纯林林分结构的影响. 应用生态学报, 2019, 30(1): 58-66

江西省赣州市南康区松材线虫病发生特征

Characteristics of pine wood nematode disease in Nankang District, Ganzhou, Jiangxi Province, China

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	赵允格, 吉静怡, 张万涛, 明姣, 黄琬雲, 高丽倩. 黄土高原生物土壤结皮分布时空分异特征 [J]. 应用生态学报, 2024, 35(3): 739-748.
[2]	任孟林, 郭妍, 陈伯轩, 范佳乐, 胡同欣, 孙龙. 红松人工林地表可燃物火蔓延速率预测模型 [J]. 应用生态学报, 2023, 34(8): 2091-2100.
[3]	杨玉丽, 肖辉杰, 辛智鸣, 范光鹏, 李俊然, 贾肖肖, 汪立韬. 基于激光雷达与高光谱的荒漠绿洲农田防护林衰退程度评估 [J]. 应用生态学报, 2023, 34(4): 1043-1050.
[4]	王怡婧, 丁启东, 张俊华, 陈睿华, 贾科利, 李小林. 基于无人机高光谱遥感和机器学习的土壤水盐信息反演 [J]. 应用生态学报, 2023, 34(11): 3045-3052.
[5]	董灵波, 梁凯富, 张一帆, 刘兆刚. 基于Landsat 8时间序列数据的翠岗林场森林类型划分 [J]. 应用生态学报, 2022, 33(9): 2339-2346.
[6]	刘强, 吴志伟, 林世滔, 李顺, 方志斌. 松材线虫病发生点格局及影响因素 [J]. 应用生态学报, 2022, 33(9): 2530-2538.
[7]	崔晏华, 刘淑德, 张云雷, 徐宾铎, 纪毓鹏, 张崇良, 任一平, 薛莹. 海州湾春季短蛸的栖息分布特征及其与环境因子的关系 [J]. 应用生态学报, 2022, 33(6): 1686-1692.
[8]	魏宇宸, 赵美芳, 朱昌达, 张秀秀, 潘剑君. 基于景观及微地形特征的丘陵区土壤属性预测 [J]. 应用生态学报, 2022, 33(2): 467-476.
[9]	邱吉丽, 张弥, 蒲旖旎, 张圳, 贾磊, 赵佳玉, 肖薇, 刘寿东. 涡度相关观测的太湖CH₄通量数据插补方法评价 [J]. 应用生态学报, 2022, 33(10): 2785-2795.
[10]	王深华, 江军, 刘丰彩, 俞梦笑, 陈洋, 许萍萍, 常中兵, 王应平. 中国成熟天然林土壤有机碳垂直分异特征 [J]. 应用生态学报, 2021, 32(7): 2371-2377.
[11]	秦雯怡, 陈果, 李小臻, 王翔, 王鹏. 基于机器语言的岷江上游流域表层土壤氢氧稳定同位素空间分布模拟 [J]. 应用生态学报, 2021, 32(12): 4327-4338.
[12]	李艳大, 叶春, 曹中盛, 孙滨峰, 舒时富, 黄俊宝, 田永超, 何勇. 基于作物生长监测诊断仪的双季稻叶片氮含量和氮积累量监测 [J]. 应用生态学报, 2020, 31(9): 3040-3050.
[13]	王世航, 卢宏亮, 赵明松, 周玲美. 基于不同特征挖掘方法结合广义提升回归模型估测安徽省土壤pH [J]. 应用生态学报, 2020, 31(10): 3509-3517.
[14]	李哲, 张沁雨, 邱新彩, 彭道黎. 基于高分二号遥感影像树种分类的时相及方法选择 [J]. 应用生态学报, 2019, 30(12): 4059-4070.
[15]	吴敏娟,尤誉杰,张晓红,王懿祥,邱烷婷,吕玉龙,应彬彬,陈时跃. 不同干扰模式对受害马尾松人工纯林林分结构的影响 [J]. 应用生态学报, 2019, 30(1): 58-66.