[1]林春穆.基于多光谱数据初探野生长叶榧分布光谱特征[J].江苏林业科技,2022,49(01):28-33.[doi:10.3969/j.issn.1001-7380.2022.01.005]
 Lin Chunmu.Identification of distribution of wild Torreya jackii Chun in Jiangshi Nature Reserve based on spectral characteristics[J].Journal of Jiangsu Forestry Science &Technology,2022,49(01):28-33.[doi:10.3969/j.issn.1001-7380.2022.01.005]
点击复制

基于多光谱数据初探野生长叶榧分布光谱特征()
分享到:

《江苏林业科技》[ISSN:1001-7380/CN:32-1236/S]

卷:
第49卷
期数:
2022年01期
页码:
28-33
栏目:
试验研究
出版日期:
2022-03-05

文章信息/Info

Title:
Identification of distribution of wild Torreya jackii Chun in Jiangshi Nature Reserve based on spectral characteristics
文章编号:
1001-7380(2022)01-0028-06
作者:
林春穆
邵武将石省级自然保护区,福建 邵武 354011
Author(s):
Lin Chunmu
Shaowu Jiangshi Provincial Nature Reserve, Shaowu 354011, China
关键词:
长叶榧遥感无人机多光谱特征分布调查
Keywords:
Torreya jackiiRemote sensingUnmanned aerial vehicle (UAV)Multispectral characteristicDistribution Investigation
分类号:
S712S718.54S757.2S791.53
DOI:
10.3969/j.issn.1001-7380.2022.01.005
文献标志码:
A
摘要:
福建省邵武将石省级自然保护区为野生长叶榧的重要分布区域,对其在该区域内的分布特征及生存条件调查是长叶榧保护工作的重要依据。多光谱识别特征的构建是进行大范围调查的主要手段。该研究将无人机遥感和卫星遥感相结合,利用多光谱数据,对野生长叶榧的特异光谱特征进行分析。根据差异显著的植被指数,开发了用于区分长叶榧及其他伴生树种的新植被指数,即Y=1.292 369+0.011 708 36X3+84 849.3X4-495 776.2X8-44 008.38X10+35 988.67X12+16.241 29X14-0.201 897 2X16X33为比值植被指数,X4为土壤调节植被指数,X8为标准差异植被指数,X10为修正非线性指数,X12为近红外百分比植被指数,X14为绿色归一化植被指数,X16为绿差植被指数)。基于Y(新植被指数),对多光谱遥感影像进行均一化,人工判别野生长叶榧的分布特征,同时随机挑选了50个目标野生长叶榧群落,通过野外探查校正结果,准确率达92%。发现该自然保护区中共存在6个主要分布群落,均生长在研究区域内山势陡峭、峡谷深邃或多基岩裸露的陡峭坡,或山地沟谷2侧,陡坡密林中,悬崖上或溪流两旁的常绿阔叶林或次生灌木丛中,且多生长在海拔250—500 m地带,平均Y在100 000—200 000范围内。
Abstract:
Shaowu Jiangshi Provincial Nature Reserve, Fujian Province, is an important distribution area for Torreya jackii Chun. The investigation of distribution characteristics and living condition of wild T. jackii in this region provides a significant basis for conservation. The construction of multispectral identification features is the main means of a large scale investigation. In this article, the specific spectral characteristics were analyzed by using multi-spectral data combined with UAV(unmanned aerial vehicle) remote sensing and satellite remote sensing. Considering significant differences among vegetation indices, a new vegetation index, Y was developed to distinguish T. jackii and other associated tree species, equal to 1.292 369+0.011 708 36X3+84 849.3X4-495 776.2X8-44 008.38X10+35 988.67X12+16.241 29X14-0.201 897 2X16, among which, Simple Vegetation Ratio presented as X3, Soil Adjusted Vegetation Index as X4, Normalized Difference Vegetation Index as X8, Modified Non-Linear Index as X10, Infrared Percentage Vegetation Index as X12, Green Normalized Difference Vegetation Index as X14, and Green Difference Vegetation Index as X16. Based on Y, the multi-spectral remote sensing images of the Nature Reserve were homogenized, and artificial methods were used to distinguish the distribution characteristics of wild T. jackii. Meanwhile, fifty target wild T. jackii communities were randomly selected, with an accuracy of 92%, through field exploration. At a result, 6 main distribution communities were found in the Nature Reserve, within steep hills, either on steep slopes covered with an evergreen broad-leaved forest, secondary shrub, or on sides of deep gullies exposed to bedrock, or in dense forests with steep slopes, on cliffs and along streams. Wild T. jackii community distributes between 250—500 m above sea level. The average Y ranged from 100 000 to 200 000.

参考文献/References:

[1]原雅楠,李正才,王斌,等.不同品种榧树针叶-土壤C、N、P生态化学计量特征研究[J].林业科学研究, 2020,33(6):49-56.
[2]王昌腾.野生长叶榧树生物学特性与保护研究[J].林业科技通讯, 2005(10):6-7.
[3]李建辉,金则新,李钧敏.长叶榧黄酮类化合物含量及成分分析[J].植物研究, 2007,27(1):50-54.
[4]金珊珊,李建辉,金则新,等.濒危植物长叶榧种子化学成分分析[J].浙江林业科技,2008, 27(3):22-25.
[5]李钧敏,金则新,周扬.长叶框叶片次生代谢产物含量分析[J].西北林学院学报,2007, 22(2):123-126.
[6]王昌腾.浙江省野生长叶榧资源现状及保护对策[J].安徽农业科学, 2005,33(3):432-450.
[7]周炜伦,陈水飞,李垚,等.浙江仙居长叶榧树、刺叶栎资源现状调查及分析[J].中国野生植物资源, 2020,39(8):65-71.
[8]刘杏娥.基于遥感技术预测小黑杨人工林木材性质的研究[D].北京:中国林业科学研究院, 2005.
[9]裴浩杰,冯海宽,李长春,等.基于综合指标的冬小麦长势无人机遥感监测[J].农业工程学报, 2017,33(20):74-82.
[10]刘杨,冯海宽,黄珏,等.基于无人机高光谱影像的马铃薯株高和地上生物量估算[J].农业机械学报,2021,52(2):188-198.
[11]王娟,陈永富,陈巧,等.基于无人机遥感的森林参数信息提取研究进展[J].林业资源管理, 2020(5):144-151.
[12]郑晓岚,张显峰,程俊毅,等.利用无人机多光谱影像数据构建棉苗株数估算模型[J].中国图象图形学报, 2020,25(3):520-534.
[13]李维.无人机遥感技术在林业资源调查与病虫害防治中的应用[J].中国农业文摘,2019(5):45-60.
[14]李浩,郑恒宇,陈学永.无人机遥感技术在森林病虫害监测中的应用[J].南方论坛,2019(17):55-59.
[15]高娟婷,孙飞达,霍霏,等.无人机遥感技术在草地动植物调查监测中的应用与评价[J].草地学报, 2021,29(1):1-9.
[16]顾泽鑫,王白娟,苏文苹,等.高光谱无人机遥感影像识别技术在茶园病虫害防治中的应用研究[J]. 经济师, 2020 (12):61-62.
[17]翟东昌,陈红梅.基于邻域熵的高光谱波段选择算法[J].计算机应用,20121,42(2):485-492.
[18]杨志.陕北榆神矿区生态地质环境特征及煤炭开采影响机理研究[D].徐州:中国矿业大学,2019.
[19]丁艳玲.植被覆盖度遥感估算及其真实性检验研究[D].长春:中国科学院研究生院(东北地理与农业生态研究所),2015.
[20]刘秀英.玉米生理参数及农田土壤信息高光谱监测模型研究[D].杨凌:西北农林科技大学,2016.
[21]李昭阳.多源遥感数据支持下的松嫩平原生态环境变化研究[D].长春:吉林大学,2006.
[22]赵成义.陆地不同生态系统土壤呼吸及土壤碳循环研究[D].北京:中国农业科学院,2004.
[23]王克如.基于图像识别的作物病虫草害诊断研究[D].北京:中国农业科学院,2005.

相似文献/References:

[1]崔立,闫保银.基于遥感技术的森林蓄积量估测研究进展[J].江苏林业科技,2020,47(04):45.[doi:10.3969/j.issn.1001-7380.2020.04.011]
[2]刘云鹏,解春霞,李莉,等.无人机遥感监测技术在松材线虫病疫情监测中的应用探讨[J].江苏林业科技,2022,49(02):52.[doi:10.3969/j.issn.1001-7380.2022.02.010]
 Liu Yunpeng,Xie Chunxia,Li Li,et al.Discussion on the application of UAV remote sensing technology in monitoring of PWD[J].Journal of Jiangsu Forestry Science &Technology,2022,49(01):52.[doi:10.3969/j.issn.1001-7380.2022.02.010]

备注/Memo

备注/Memo:
收稿日期:2021-11-29修回日期:2021-12-20
基金项目:福建省林业科技项目"天然长叶榧种群现状与保护对策的研究"(闽林科便函〔2019〕16号)
作者简介:林春穆(1983- ),男,福建三明人,高级工程师,硕士。主要从事自然保护区管理及野生动植物保护工作。E-mail:luomu168@163.com
更新日期/Last Update: 2022-04-04