塞罕坝机械林场落叶松锉叶蜂遥感监测与预测研究
|
摘要
森林害虫作为一种典型的生物自然灾害,其造成的危害日益严重。应用遥感、地理信息系统等高新技术及时和准确地进行森林害虫灾害的监测与预测,对于森林害虫灾害的监测预警、防灾减灾都有重要意义。本文以河北省塞罕坝机械林场为研究区,以研究区落叶松锉叶蜂大发生年为研究背景,应用实地调查样地虫情、地形及林分因子数据,研究了落叶松锉叶蜂发生与生境因子之间的关系,掌握了其空间分布规律;同时将遥感数据与地面实测光谱数据结合,分析了不同受害程度的针叶光谱曲线特征变化,建立并筛选最优的落叶松锉叶蜂发生遥感监测模型;在此基础上,应用GIS技术的信息量模型对该区落叶松锉叶蜂灾害进行了空间定量预测,依据信息量法的结果编制了该区的危险程度预测分区图,并对信息量模型法预测结果的精度进行了分析。
通过本研究,得到了以下主要研究结果:
(1)根据森林资源二类调查小班数据、落叶松锉叶蜂发生数据、气象数据、地物光谱数据、GPS定位数据、地形图、森林资源分布图、虫情分布图及遥感数据等,建立了研究区的GIS数据库。
(2)对影响落叶松锉叶蜂发生的生境因子进行了调查研究,结果表明:林分及地形因子与落叶松锉叶蜂灾害的发生关系密切,表现为纯林重于混交林,疏林重于密林,林缘重于林内,成熟林重于中幼龄林,寄主下层重于中上层,高海拔区重于低海拔区,阳坡重于阴坡,山脊重于山谷。气候因子与落叶松锉叶蜂的发生有密切的关系,温度、湿度和降雨量这三个因子共同影响落叶松锉叶蜂灾害的发生。同时人类活动因子(防治措施)能显著降低幼虫的虫口密度。
(3)通过对不同危害程度的落叶松反射光谱进行测定,研究了不同危害程度的落叶松在绿光区、红光区和近红外区反射光谱的变化特征,并对光谱反射曲线进行微分分析。结果表明,绿光区、红光区和近红外区的落叶松光谱反射率随危害程度的加重分别呈现下降、上升和下降的趋势;对反射率曲线进行微分分析,健康落叶松、轻度、中度和重度为害后的一阶导数光谱反射率最大值随着危害程度增加而下降,并且向短波方向移动(蓝移)。实测光谱数据提取的归一化植被指数(NDVI)与落叶松锉叶蜂危害程度呈显著负相关。该研究结果对应用遥感技术早期探测落叶松锉叶蜂的发生提供了实验依据。
(4)分析了不同危害程度区遥感数据的光谱信息变化特点,同时采用统计方法,研究了落叶松锉叶蜂虫口密度与植被指数NDVI之间的关系,建立并筛选最优的落叶松锉叶蜂发生的遥感监测模型:虫口密度=?1287. 878+1946.733/NDVI。结果表明,落叶松锉叶蜂虫口密度与植被指数NDVI呈负相关关系,即随着虫口密度的增加,植被指数NDVI呈减小趋势。表明植被指数NDVI对落叶松锉叶蜂发生区域有较强的指示性,可用于落叶松锉叶蜂发生的遥感监测。
(5)应用GIS技术的信息量模型对该区落叶松锉叶蜂灾害进行了空间定量预测,依据信息量法的结果编制了该区的危险程度预测分区图,并将其划分为5个等级。信息量模型法的预测精度达到83.1%,说明所建立的模型具有较高的预测精度,是可以用于预测分析的,其预测结果为该虫的防治提供了科学依据。
Forest pests are considered as one of the typical biological and natural disasters, the infestation of which was more and more serious. Remote sensing, GIS (Geographic Information System) and GPS were effective technologies for monitoring and predicting of forest pests, which have important significance for monitoring and precaution, disaster prevention and reduction of forest pests. In this paper, Saihanba Mechanical Forestry Farm of Hebei Province was taken as the study area, and the outbreaks of Pristiphora laricis(Hartig) was taken as the study background. Using the survey data for Pristiphora laricis(Hartig) as well as stand factors and topographic data, the relationship between the occurrence of Pristiphora laricis(Hartig) and habitat factors were analyzed; Using remotely sensed images combined the measured spectral data at the same time, this paper also analyzed the change of the spectral features under different damaged degrees, and built a monitoring model of Pristiphora laricis(Hartig) infestation based on the vegetation index; Then, spatial prediction for Pristiphora laricis(Hartig) infestation in the study area were performed based on GIS and information value model. Supported by this model, a spatial prediction map with 5 classes (extremely high, high, middle, low, and none) was obtained, and the prediction model precision was also analyzed.
The main research contents and results in this paper are as follows:
1) According to forest resource inventory data for management, the occurrence of Pristiphora laricis(Hartig),meteorological data, ground measured spectral data, GPS data, topographic map, forest resource map, damage degree map, and remote sensing images, A GIS database of study area were built.
2) The factors influencing the population of the pest were analyzed. The results indicated that the occurrence of Pristiphora laricis(Hartig) was closely related to the forest stand conditions, topographic and climatic factors. The infestation of Pristiphora laricis(Hartig) was more serious in pure forest, open forest land, mature forest, higher altitude stands, southern slope and ridge than in mixed forest, dense stands, half-mature and young forest, lower altitude stands, northern slope and valley; it was also more serious along the forest boundary than in the forest; the infestation was more serious under the circumstance of small perpendicular height; And the higher was the temperature, the earlier the larva hatched. In addition, the activities of human (controlling measures) can reduce the population density effectively.
3) Larix principis-rupprechtii spectrum features at green, red and near infrared bands were studied by measuring and analyzing its canopy reflectance and differential spectrum under different level damaged of Pristiphora laricis(Hartig). The results showed that the spectral reflectance rate at green, red and near infrared bands will respectively descend, ascend and descend with Pristiphora laricis(Hartig)’s outbreak, and the maximal value of first derivative will descend with the damage increased. Moreover, the spectrum moves to the direction of short wave. Another result was that NDVI extracted from spectral data was negatively correlated with damage degrees, which had an important indication significance for the early infection forecast of Pristiphora laricis(Hartig) with remote sensing.
4) The relationship between NDVI and the population density was studied, and monitoring model of Pristiphora laricis(Hartig) infestation based on the vegetation index was built as p opulation density= ?1287.878+1946.733/NDVI, which can be used in monitoring Pristiphora laricis(Hartig) based on the vegetation index. The results indicated that a negative correlation existed in the relation between NDVI and the population density. In other words, with the decrease of NDVI, the population density of Pristiphora laricis(Hartig) appears linearly increased. This implicated that NDVI is a good indicators of Pristiphora laricis(Hartig) location, so that can be used as a predictor for Pristiphora laricis(Hartig) remote monitoring.
5) Spatial prediction for Pristiphora laricis(Hartig) infestation in the study area was studied based on GIS and information value model.Supported by this model, a spatial prediction map with 5 classes(extremely high, high, middle, low, and none)was obtained. and the precision can be up to 83.1% which indicated that prediction model is very practical. The prediction map can provide scientific references for Pristiphora laricis(Hartig) controlling.
通过本研究,得到了以下主要研究结果:
(1)根据森林资源二类调查小班数据、落叶松锉叶蜂发生数据、气象数据、地物光谱数据、GPS定位数据、地形图、森林资源分布图、虫情分布图及遥感数据等,建立了研究区的GIS数据库。
(2)对影响落叶松锉叶蜂发生的生境因子进行了调查研究,结果表明:林分及地形因子与落叶松锉叶蜂灾害的发生关系密切,表现为纯林重于混交林,疏林重于密林,林缘重于林内,成熟林重于中幼龄林,寄主下层重于中上层,高海拔区重于低海拔区,阳坡重于阴坡,山脊重于山谷。气候因子与落叶松锉叶蜂的发生有密切的关系,温度、湿度和降雨量这三个因子共同影响落叶松锉叶蜂灾害的发生。同时人类活动因子(防治措施)能显著降低幼虫的虫口密度。
(3)通过对不同危害程度的落叶松反射光谱进行测定,研究了不同危害程度的落叶松在绿光区、红光区和近红外区反射光谱的变化特征,并对光谱反射曲线进行微分分析。结果表明,绿光区、红光区和近红外区的落叶松光谱反射率随危害程度的加重分别呈现下降、上升和下降的趋势;对反射率曲线进行微分分析,健康落叶松、轻度、中度和重度为害后的一阶导数光谱反射率最大值随着危害程度增加而下降,并且向短波方向移动(蓝移)。实测光谱数据提取的归一化植被指数(NDVI)与落叶松锉叶蜂危害程度呈显著负相关。该研究结果对应用遥感技术早期探测落叶松锉叶蜂的发生提供了实验依据。
(4)分析了不同危害程度区遥感数据的光谱信息变化特点,同时采用统计方法,研究了落叶松锉叶蜂虫口密度与植被指数NDVI之间的关系,建立并筛选最优的落叶松锉叶蜂发生的遥感监测模型:虫口密度=?1287. 878+1946.733/NDVI。结果表明,落叶松锉叶蜂虫口密度与植被指数NDVI呈负相关关系,即随着虫口密度的增加,植被指数NDVI呈减小趋势。表明植被指数NDVI对落叶松锉叶蜂发生区域有较强的指示性,可用于落叶松锉叶蜂发生的遥感监测。
(5)应用GIS技术的信息量模型对该区落叶松锉叶蜂灾害进行了空间定量预测,依据信息量法的结果编制了该区的危险程度预测分区图,并将其划分为5个等级。信息量模型法的预测精度达到83.1%,说明所建立的模型具有较高的预测精度,是可以用于预测分析的,其预测结果为该虫的防治提供了科学依据。
Forest pests are considered as one of the typical biological and natural disasters, the infestation of which was more and more serious. Remote sensing, GIS (Geographic Information System) and GPS were effective technologies for monitoring and predicting of forest pests, which have important significance for monitoring and precaution, disaster prevention and reduction of forest pests. In this paper, Saihanba Mechanical Forestry Farm of Hebei Province was taken as the study area, and the outbreaks of Pristiphora laricis(Hartig) was taken as the study background. Using the survey data for Pristiphora laricis(Hartig) as well as stand factors and topographic data, the relationship between the occurrence of Pristiphora laricis(Hartig) and habitat factors were analyzed; Using remotely sensed images combined the measured spectral data at the same time, this paper also analyzed the change of the spectral features under different damaged degrees, and built a monitoring model of Pristiphora laricis(Hartig) infestation based on the vegetation index; Then, spatial prediction for Pristiphora laricis(Hartig) infestation in the study area were performed based on GIS and information value model. Supported by this model, a spatial prediction map with 5 classes (extremely high, high, middle, low, and none) was obtained, and the prediction model precision was also analyzed.
The main research contents and results in this paper are as follows:
1) According to forest resource inventory data for management, the occurrence of Pristiphora laricis(Hartig),meteorological data, ground measured spectral data, GPS data, topographic map, forest resource map, damage degree map, and remote sensing images, A GIS database of study area were built.
2) The factors influencing the population of the pest were analyzed. The results indicated that the occurrence of Pristiphora laricis(Hartig) was closely related to the forest stand conditions, topographic and climatic factors. The infestation of Pristiphora laricis(Hartig) was more serious in pure forest, open forest land, mature forest, higher altitude stands, southern slope and ridge than in mixed forest, dense stands, half-mature and young forest, lower altitude stands, northern slope and valley; it was also more serious along the forest boundary than in the forest; the infestation was more serious under the circumstance of small perpendicular height; And the higher was the temperature, the earlier the larva hatched. In addition, the activities of human (controlling measures) can reduce the population density effectively.
3) Larix principis-rupprechtii spectrum features at green, red and near infrared bands were studied by measuring and analyzing its canopy reflectance and differential spectrum under different level damaged of Pristiphora laricis(Hartig). The results showed that the spectral reflectance rate at green, red and near infrared bands will respectively descend, ascend and descend with Pristiphora laricis(Hartig)’s outbreak, and the maximal value of first derivative will descend with the damage increased. Moreover, the spectrum moves to the direction of short wave. Another result was that NDVI extracted from spectral data was negatively correlated with damage degrees, which had an important indication significance for the early infection forecast of Pristiphora laricis(Hartig) with remote sensing.
4) The relationship between NDVI and the population density was studied, and monitoring model of Pristiphora laricis(Hartig) infestation based on the vegetation index was built as p opulation density= ?1287.878+1946.733/NDVI, which can be used in monitoring Pristiphora laricis(Hartig) based on the vegetation index. The results indicated that a negative correlation existed in the relation between NDVI and the population density. In other words, with the decrease of NDVI, the population density of Pristiphora laricis(Hartig) appears linearly increased. This implicated that NDVI is a good indicators of Pristiphora laricis(Hartig) location, so that can be used as a predictor for Pristiphora laricis(Hartig) remote monitoring.
5) Spatial prediction for Pristiphora laricis(Hartig) infestation in the study area was studied based on GIS and information value model.Supported by this model, a spatial prediction map with 5 classes(extremely high, high, middle, low, and none)was obtained. and the precision can be up to 83.1% which indicated that prediction model is very practical. The prediction map can provide scientific references for Pristiphora laricis(Hartig) controlling.
引文
[1] 张时敏,黄孝运,周淑芷.落叶松两种叶蜂的研究[J].林业科学研究,1990,3(5):518-523.
[2] 李梦楼,唐章友,宋荫群.落叶松锉叶蜂生物学及发生规律的研究[J].西北林学院学报,1994,9(2):30-33.
[3] 杨春.王建超.刘瑞祥.落叶松锉叶蜂的发生与防治技术[J].河北林业科技,2007,2:54.
[4] 曾兵兵,张晓丽,路常宽等.落叶松锉叶蜂为害的松林光谱特征分析[J].山东林业科技,2007,(6):1-3.
[5] 潘宏阳.我国森林病虫害预防工作存在的问题与对策[J].中国森林病虫,2002,21(1):42-47.
[6] 李天生.松毛虫监测与防治方法研究进展[J].昆虫知识,2003,7(2):122-128.
[7] 池天河,苏亚芳.重大自然灾害遥感监测评估集成系统[M].北京:中国科学技术出版社,1995:71-75.
[8] 戴昌达,雷莉萍,胡德永,等.卫星遥感监测松毛虫灾害[J].遥感信息,1991(3):32-34.
[9] 武红敢,石进.松毛虫灾害的 TM 影像监测技术[J].遥感学报,2004(2):172-177.
[10] 武红敢,乔彦友,黄建文.利用陆地卫星 TM 数据评估森林病虫害[J].遥感技术与应用,1994(4):46-51.
[11] 刘 志 明 , 晏 明 . 用 气 象 卫 星 监 测 大 范 围 森 林 虫 害 方 法 研 究 [J]. 自 然 灾 害 学报,2002,11(3):109-114.
[12] 金瑞华,张宏民,林培.浅谈生物灾害遥感监测[J].遥感信息,1991(3):35-37.
[13] 薛贤清,陈瑞蘼.应用雷达监测马尾松毛虫踪迹的探讨[J].林业科技通讯,1987(6):28-32.
[14] 赵春燕,杨志高.高光谱遥感技术及其在森林监测中的应用探讨[J].林业调查规划,2006,31(1):4-6.
[15] 武红敢.卫星遥感技术在森林病虫害监测中的应用[J].世界林业研究,1995(2):24-26.
[16] 黄麟,张晓丽,石韧.森林病虫害遥感监测技术研究的现状与问题[J].遥感信息,2006 (2):71-75.
[17] 童庆禧.高光谱遥感的现在与未来.遥感学报[J],2003,7(增刊):1–12.
[18] 马建文,韩秀珍,哈斯巴干,等.东亚飞蝗灾害的遥感监测实验[J].国土资源遥感,2003,55(1):51-55.
[19] 杨存建,陈德清,魏一鸣.遥感和 GIS 在森林病虫害监测管理中的应用模式[J].灾害学,1999 (1):6-10.
[20] 雷莉萍,胡德永,江平,等.森林虫害的遥感监测模式研究[J].遥感信息,1995 (3):12-14.
[21] 王海扣,王群,程遐年.应用地理信息系统分析江苏褐飞虱的发生动态[J].西南农业大学学报,1998,20(5):432～437.
[22] 闫 宏 娟 , 李 典 漠 . 地 理 信 息 系 统 及 其 在 昆 虫 生 态 学 中 的 应 用 [J]. 昆 虫 知识,1999,36(1):41-45.
[23] 张洪亮,倪绍祥,邓自旺.GIS 支持下青海湖地区草地蝗虫发生与月均温的相关性[J].应用生态学报,2002,13(7):837-840.
[24] 翟连荣.空间分析技术在害虫种群管理中的应用[J].昆虫知识,1997,34(5):314-318.
[25] 王 海扣 , 周 保 华 , 程 遐 年 . 地 理 信 息 系 统 及 其 在 害 虫 治 理 中 的 应 用 [J]. 昆 虫 知识,1997,34(6):366-370.
[26] 王正军,张爱兵,程家安,等.基于 GIS 的种群动态的失控分析与模拟研究的方法进展[J].生态学报,2002,22(1):104-109.
[27] 赵友福,林伟.应用地理信息系统对梨火疫病可能分布区的初步研究[J].植物检疫,1995,6:321-326.
[28] 康华春,张润杰.应用区域灾害系统论、地统计学和 GIS 研究病虫灾害[A]. 李典谟.走向 21世纪的中国昆虫学-中国昆虫学会 2000 年学术学会论文集[C].北京:中国科学技术出版社,2000,10.
[29] 马 小 明 , 叶 文 虎 , 李 天 生 , 等 . 松 毛 虫 综 合 管 理 信 息 系 统 研 究 [J]. 林 业 科 学 研究,1993,(6):28-32.
[30] 曹 文田 , 刘 猛 , 马 小 明 等 . 全 国 森 林 病 虫 害 防 治 管 理 信 息 系 统 [J]. 中 国 森 林 病虫,2000,(4):27-31.
[31] 薛冬娟,张冬冬,蒋文科,等.作物病虫害综合治理地理信息系统[J].河北农业大学学报,2004,27(5):104-109.
[32] 王福贵,吴坚.松毛虫灾害点的航空录像图像判别和定位研究[J].林业科学研究,1997,10(3):270-276.
[33] 方 书 清 , 陈 文 茂 . 航 空 录 像 技 术 在 松 毛 线 虫 病 监 测 中 的 应 用 [J]. 林 业 科 技 开发,2003,17(5):42-43.
[34] 蒋建军,倪绍祥,韦玉春.GIS 辅助下的环青海湖地区草地蝗虫生境分类研究[J].遥感学报,2002,6(5):387-392.
[35] 杨秀好.全球定位系统(GPS)在林业飞防上的应用初探[J].林业科技开发,1999,(3):17-19.
[36] 黄向东,陈良昌,邵荣华,等.GPS 和生物农药在飞机防治松毛虫中的应用[J].森林病虫通讯,1999,(5):9-12.
[37] 黄向东,张央炳,尹华顺,等.海燕 650C 轻型飞机喷药防治马尾松毛虫试验[J].湖南林业科技,2003,30(3):53-54.
[38] 王庆锁,李玉中,路端正,等.河北省塞罕坝地区种子植物区系的过渡性分析[J].生态学杂志,2005,24(5):473-477.
[39] 吴美蓉.中巴地球资源卫星应用及其发展[J].测绘科学,2000,25(2):25-30.
[40] 丁暄,童庆禧,郑兰芬,等.光谱遥感找矿理论、技术与应用[J].地球化学,1992,(1):1-8.
[41] 浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版,2000.
[42] 甘莆平,王润生,马蔼乃,等.光谱遥感岩矿识别基础与技术研究进展[J].遥感技术与应用,2002,17(3):140-147.
[43] 张宗贵,工润生,郭小方,等.基于地物光谱特征的成像光谱遥感矿物识别方法[J].地学前缘,2003,10(2):437-443.
[44] 寇有观,萧秫.农业遥感[M].北京:农业出版社,1988.
[45] 刘清旺,武红敢,石进,等.基于 TM 影像的森林病虫灾害遥感监测系统[J].遥感信息,2007,(2):46-49.
[46] 申广荣,王人潮.植被光谱遥感数据的研究现状及其展望[J].浙江大学学报(农业与生命科学版).2001,27(6):682-690.
[47] 韦玉春.基于遥感和数量分析方法的环青海湖地区草地蝗虫发生研究[Dl.南京:南京师范大学地理科学学院,2001.
[48] 罗元华,张梁,张业成.地质灾害风险评估方法[M].北京:地质出版社 1998.
[49] 阮沈勇,黄润秋.基于 GIS 的信息量法模型在地质灾害危险性区划中的应用[J].成都理工学院学报,2001,28(1):89-92.
[50] Haara A, Nevalainen S. Detection of dead or defoliated spruces using digital aerial data [J].For Ecol Manage,2002(160):97–107.
[51] Appel D N, Maggio R C,Nelson E L,et al. Measurement of expanding oak wilt centers in live oak [J].Photophthology,1987,79:1318–1322.
[52] Leckie D G, Ostaff D P. Classification of airborne multispectral scanner data for mapping current defoliation caused by the spruce budworm [J].For. Sci.1988(34):259–279.
[53] Olthof I, King D J. Development of a forest health index using multispectral airborne digital camera imagery [J]. Canadian J Remote Sensing, 2000,26 (3):166–176.
[54] Rencz A N, Nemeth J. Detection of Mountain Pine Beetle Infestation Using Landsat Mss and Simulated Thematic Mapper Data [J]. Canadian Journal of Remote Sensing, 1985 (11):50–58.
[55] Adams M. L, Philpot W D. Yellowness index: an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation [J]. International Journal of Remote Sensing,1999,20(18):3663–3675.
[56] Ekstrand S P. Detection of Moderate Damage on Norway Spruce Using Landsat TM and Digital Stand Data [J].IEEE Transaction on Geoscience and Remote Sensing,1990,28(4):685–692.
[57] Muchoney D M, Haack B N. Change detection for monitoring forest defoliation [J].PE & RS,1994(60): 1243–1251.
[58] Franklin S E, Raske A G. Satellite remote sensing of spruce budworm forest defoliation in western Newfoundland [J]. Can. J Remote Sens,1994,20(1):30–48.
[59] Nelson R F. Detecting forest canopy change due to insect activity using Landsat MSS [J].PE & RS,1983(49):1303–1314.
[60] Vogelmann J E, Rock B N. Use of Thematic Mapper Data for the Detection of Forest Damage Caused by the Pear Thrips [J].Remote Sensing of Environment,1989(30):217–225.
[61] Ekstrand S. Assessment of Forest Damage with Landsate TM: correction for varying forest stand characteristics [J]. Remote Sensing of Environment,1994,47 (3): 291–302.
[62] Ahern F, T. Erdle, I D Kneppeck. A quantitative relationship between Landsat TM spectral response and forest growth rates [J]. International Journal of Remote Sensing ,1991,12(3):387–400.
[63] Bowers W. Forest structural damage assessment using image semi-variance [J].Canadian Journal of Remote Sensing,1994(20):28–36.
[64] Jose’e Le’vesquea, Douglas King J. Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modeling individual tree crown and forest canopy structure and health [J].Remote Sensing of Environment,2003(84):589–602.
[65] Radeloff V C, Boyce M S, Mladenoff D J. Effects of interacting disturbances on landscape patterns: Budworm defoliation and salvage logging [J].Ecological Applications ,2000,10 (1):233–247.
[66] Brockhaus J A. A comparision of Landsat TM and SPOT HRV data for use in the development of forest defoliation modes[J].International Journal of Remote Sensing,1992,13(6):3235–3240.
[67] Gage S H. Predicting regional gypsy moth (Lymantriidae) population trends in an expanding population using pheromone trap catch and spatial analysis[J].Environ Entomol,1991,19(2):370-377.
[68] Song Y H, Heong K L. Use of Geographical Information System in analyzing large area distribution and dispersal of rice insects in South Korea[J]. J Appl Entomol, 1993,32(3):307-316.
[69] Tiansheng Li,Guofa Zhou,Jian Wu.Evaluating the impact of insect community on pine caterpillardensity in different stand conditions[J].Entomologyia Sinica, 1998, 5(2):166-176.
[70] Liebhold A M, Halverson J A, Elmes G A. Gypsy moth invasion in North America: a quantitative analysis[J].Biogeog,1992, 19:513-520.
[71] Crowley J K. Visible and near-infrared(0.4-2.5mm) reflectance spectra of playa evaporite minerals[J].Journal of Geophysical Research,1991,96(16):231-240.
[72] Curran P J. Imaging spectrometry[J]. Progress in Physical Geography,1994, 18 (2): 247-266.
[73] Drake N A.Reflectance spectra of evaporite minerals(400-2500mn):applications for remote sensing[J]. International Journal of Remote Sensing,1995,16(14): 2555-2571.
[74] Resmini R G, Karpus M E, Aldrich W S ,et al.Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite,Nevada,USA[J]. International Journal of Remote Sensing,1997,18(7): 1553-1570.
[75] Rouse J.W., Haas R.H., Schell J.A., and Deering D.W. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of Third Earth Resources Technology Satellite-1 Symposium, Greenbelt: NASA SP-351.1974:310-317.
[76] Deering D W, Rouse J W, Haas R H ,et al.Measuring forage production of grazing units from Landsat MSS data[A].Proceedings of Tenth International Symposium on Remote Sensing of Environment[C].Ann Arbor, ERIM,1975,2: 1169-1178.
[77] Roujean J L, Breon F M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements[J]. Remote Sens Environ, 1995, 51: 375-384.
[78] Gitelson A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote sens Environ,1996, 58(3): 289-298.
[79] Richardson A J, Wiegand C L. Distinguishing vegetation from soil background information[J]. Photogrammetric Engineering and Remote Sensing. 1977,43:1541-1552.
[80] Huete A R. A soil-adjusted vegetaion index (SAVI)[J]. Remote Sensing of Environment,1988, 25:295-309.
[2] 李梦楼,唐章友,宋荫群.落叶松锉叶蜂生物学及发生规律的研究[J].西北林学院学报,1994,9(2):30-33.
[3] 杨春.王建超.刘瑞祥.落叶松锉叶蜂的发生与防治技术[J].河北林业科技,2007,2:54.
[4] 曾兵兵,张晓丽,路常宽等.落叶松锉叶蜂为害的松林光谱特征分析[J].山东林业科技,2007,(6):1-3.
[5] 潘宏阳.我国森林病虫害预防工作存在的问题与对策[J].中国森林病虫,2002,21(1):42-47.
[6] 李天生.松毛虫监测与防治方法研究进展[J].昆虫知识,2003,7(2):122-128.
[7] 池天河,苏亚芳.重大自然灾害遥感监测评估集成系统[M].北京:中国科学技术出版社,1995:71-75.
[8] 戴昌达,雷莉萍,胡德永,等.卫星遥感监测松毛虫灾害[J].遥感信息,1991(3):32-34.
[9] 武红敢,石进.松毛虫灾害的 TM 影像监测技术[J].遥感学报,2004(2):172-177.
[10] 武红敢,乔彦友,黄建文.利用陆地卫星 TM 数据评估森林病虫害[J].遥感技术与应用,1994(4):46-51.
[11] 刘 志 明 , 晏 明 . 用 气 象 卫 星 监 测 大 范 围 森 林 虫 害 方 法 研 究 [J]. 自 然 灾 害 学报,2002,11(3):109-114.
[12] 金瑞华,张宏民,林培.浅谈生物灾害遥感监测[J].遥感信息,1991(3):35-37.
[13] 薛贤清,陈瑞蘼.应用雷达监测马尾松毛虫踪迹的探讨[J].林业科技通讯,1987(6):28-32.
[14] 赵春燕,杨志高.高光谱遥感技术及其在森林监测中的应用探讨[J].林业调查规划,2006,31(1):4-6.
[15] 武红敢.卫星遥感技术在森林病虫害监测中的应用[J].世界林业研究,1995(2):24-26.
[16] 黄麟,张晓丽,石韧.森林病虫害遥感监测技术研究的现状与问题[J].遥感信息,2006 (2):71-75.
[17] 童庆禧.高光谱遥感的现在与未来.遥感学报[J],2003,7(增刊):1–12.
[18] 马建文,韩秀珍,哈斯巴干,等.东亚飞蝗灾害的遥感监测实验[J].国土资源遥感,2003,55(1):51-55.
[19] 杨存建,陈德清,魏一鸣.遥感和 GIS 在森林病虫害监测管理中的应用模式[J].灾害学,1999 (1):6-10.
[20] 雷莉萍,胡德永,江平,等.森林虫害的遥感监测模式研究[J].遥感信息,1995 (3):12-14.
[21] 王海扣,王群,程遐年.应用地理信息系统分析江苏褐飞虱的发生动态[J].西南农业大学学报,1998,20(5):432～437.
[22] 闫 宏 娟 , 李 典 漠 . 地 理 信 息 系 统 及 其 在 昆 虫 生 态 学 中 的 应 用 [J]. 昆 虫 知识,1999,36(1):41-45.
[23] 张洪亮,倪绍祥,邓自旺.GIS 支持下青海湖地区草地蝗虫发生与月均温的相关性[J].应用生态学报,2002,13(7):837-840.
[24] 翟连荣.空间分析技术在害虫种群管理中的应用[J].昆虫知识,1997,34(5):314-318.
[25] 王 海扣 , 周 保 华 , 程 遐 年 . 地 理 信 息 系 统 及 其 在 害 虫 治 理 中 的 应 用 [J]. 昆 虫 知识,1997,34(6):366-370.
[26] 王正军,张爱兵,程家安,等.基于 GIS 的种群动态的失控分析与模拟研究的方法进展[J].生态学报,2002,22(1):104-109.
[27] 赵友福,林伟.应用地理信息系统对梨火疫病可能分布区的初步研究[J].植物检疫,1995,6:321-326.
[28] 康华春,张润杰.应用区域灾害系统论、地统计学和 GIS 研究病虫灾害[A]. 李典谟.走向 21世纪的中国昆虫学-中国昆虫学会 2000 年学术学会论文集[C].北京:中国科学技术出版社,2000,10.
[29] 马 小 明 , 叶 文 虎 , 李 天 生 , 等 . 松 毛 虫 综 合 管 理 信 息 系 统 研 究 [J]. 林 业 科 学 研究,1993,(6):28-32.
[30] 曹 文田 , 刘 猛 , 马 小 明 等 . 全 国 森 林 病 虫 害 防 治 管 理 信 息 系 统 [J]. 中 国 森 林 病虫,2000,(4):27-31.
[31] 薛冬娟,张冬冬,蒋文科,等.作物病虫害综合治理地理信息系统[J].河北农业大学学报,2004,27(5):104-109.
[32] 王福贵,吴坚.松毛虫灾害点的航空录像图像判别和定位研究[J].林业科学研究,1997,10(3):270-276.
[33] 方 书 清 , 陈 文 茂 . 航 空 录 像 技 术 在 松 毛 线 虫 病 监 测 中 的 应 用 [J]. 林 业 科 技 开发,2003,17(5):42-43.
[34] 蒋建军,倪绍祥,韦玉春.GIS 辅助下的环青海湖地区草地蝗虫生境分类研究[J].遥感学报,2002,6(5):387-392.
[35] 杨秀好.全球定位系统(GPS)在林业飞防上的应用初探[J].林业科技开发,1999,(3):17-19.
[36] 黄向东,陈良昌,邵荣华,等.GPS 和生物农药在飞机防治松毛虫中的应用[J].森林病虫通讯,1999,(5):9-12.
[37] 黄向东,张央炳,尹华顺,等.海燕 650C 轻型飞机喷药防治马尾松毛虫试验[J].湖南林业科技,2003,30(3):53-54.
[38] 王庆锁,李玉中,路端正,等.河北省塞罕坝地区种子植物区系的过渡性分析[J].生态学杂志,2005,24(5):473-477.
[39] 吴美蓉.中巴地球资源卫星应用及其发展[J].测绘科学,2000,25(2):25-30.
[40] 丁暄,童庆禧,郑兰芬,等.光谱遥感找矿理论、技术与应用[J].地球化学,1992,(1):1-8.
[41] 浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版,2000.
[42] 甘莆平,王润生,马蔼乃,等.光谱遥感岩矿识别基础与技术研究进展[J].遥感技术与应用,2002,17(3):140-147.
[43] 张宗贵,工润生,郭小方,等.基于地物光谱特征的成像光谱遥感矿物识别方法[J].地学前缘,2003,10(2):437-443.
[44] 寇有观,萧秫.农业遥感[M].北京:农业出版社,1988.
[45] 刘清旺,武红敢,石进,等.基于 TM 影像的森林病虫灾害遥感监测系统[J].遥感信息,2007,(2):46-49.
[46] 申广荣,王人潮.植被光谱遥感数据的研究现状及其展望[J].浙江大学学报(农业与生命科学版).2001,27(6):682-690.
[47] 韦玉春.基于遥感和数量分析方法的环青海湖地区草地蝗虫发生研究[Dl.南京:南京师范大学地理科学学院,2001.
[48] 罗元华,张梁,张业成.地质灾害风险评估方法[M].北京:地质出版社 1998.
[49] 阮沈勇,黄润秋.基于 GIS 的信息量法模型在地质灾害危险性区划中的应用[J].成都理工学院学报,2001,28(1):89-92.
[50] Haara A, Nevalainen S. Detection of dead or defoliated spruces using digital aerial data [J].For Ecol Manage,2002(160):97–107.
[51] Appel D N, Maggio R C,Nelson E L,et al. Measurement of expanding oak wilt centers in live oak [J].Photophthology,1987,79:1318–1322.
[52] Leckie D G, Ostaff D P. Classification of airborne multispectral scanner data for mapping current defoliation caused by the spruce budworm [J].For. Sci.1988(34):259–279.
[53] Olthof I, King D J. Development of a forest health index using multispectral airborne digital camera imagery [J]. Canadian J Remote Sensing, 2000,26 (3):166–176.
[54] Rencz A N, Nemeth J. Detection of Mountain Pine Beetle Infestation Using Landsat Mss and Simulated Thematic Mapper Data [J]. Canadian Journal of Remote Sensing, 1985 (11):50–58.
[55] Adams M. L, Philpot W D. Yellowness index: an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation [J]. International Journal of Remote Sensing,1999,20(18):3663–3675.
[56] Ekstrand S P. Detection of Moderate Damage on Norway Spruce Using Landsat TM and Digital Stand Data [J].IEEE Transaction on Geoscience and Remote Sensing,1990,28(4):685–692.
[57] Muchoney D M, Haack B N. Change detection for monitoring forest defoliation [J].PE & RS,1994(60): 1243–1251.
[58] Franklin S E, Raske A G. Satellite remote sensing of spruce budworm forest defoliation in western Newfoundland [J]. Can. J Remote Sens,1994,20(1):30–48.
[59] Nelson R F. Detecting forest canopy change due to insect activity using Landsat MSS [J].PE & RS,1983(49):1303–1314.
[60] Vogelmann J E, Rock B N. Use of Thematic Mapper Data for the Detection of Forest Damage Caused by the Pear Thrips [J].Remote Sensing of Environment,1989(30):217–225.
[61] Ekstrand S. Assessment of Forest Damage with Landsate TM: correction for varying forest stand characteristics [J]. Remote Sensing of Environment,1994,47 (3): 291–302.
[62] Ahern F, T. Erdle, I D Kneppeck. A quantitative relationship between Landsat TM spectral response and forest growth rates [J]. International Journal of Remote Sensing ,1991,12(3):387–400.
[63] Bowers W. Forest structural damage assessment using image semi-variance [J].Canadian Journal of Remote Sensing,1994(20):28–36.
[64] Jose’e Le’vesquea, Douglas King J. Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modeling individual tree crown and forest canopy structure and health [J].Remote Sensing of Environment,2003(84):589–602.
[65] Radeloff V C, Boyce M S, Mladenoff D J. Effects of interacting disturbances on landscape patterns: Budworm defoliation and salvage logging [J].Ecological Applications ,2000,10 (1):233–247.
[66] Brockhaus J A. A comparision of Landsat TM and SPOT HRV data for use in the development of forest defoliation modes[J].International Journal of Remote Sensing,1992,13(6):3235–3240.
[67] Gage S H. Predicting regional gypsy moth (Lymantriidae) population trends in an expanding population using pheromone trap catch and spatial analysis[J].Environ Entomol,1991,19(2):370-377.
[68] Song Y H, Heong K L. Use of Geographical Information System in analyzing large area distribution and dispersal of rice insects in South Korea[J]. J Appl Entomol, 1993,32(3):307-316.
[69] Tiansheng Li,Guofa Zhou,Jian Wu.Evaluating the impact of insect community on pine caterpillardensity in different stand conditions[J].Entomologyia Sinica, 1998, 5(2):166-176.
[70] Liebhold A M, Halverson J A, Elmes G A. Gypsy moth invasion in North America: a quantitative analysis[J].Biogeog,1992, 19:513-520.
[71] Crowley J K. Visible and near-infrared(0.4-2.5mm) reflectance spectra of playa evaporite minerals[J].Journal of Geophysical Research,1991,96(16):231-240.
[72] Curran P J. Imaging spectrometry[J]. Progress in Physical Geography,1994, 18 (2): 247-266.
[73] Drake N A.Reflectance spectra of evaporite minerals(400-2500mn):applications for remote sensing[J]. International Journal of Remote Sensing,1995,16(14): 2555-2571.
[74] Resmini R G, Karpus M E, Aldrich W S ,et al.Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite,Nevada,USA[J]. International Journal of Remote Sensing,1997,18(7): 1553-1570.
[75] Rouse J.W., Haas R.H., Schell J.A., and Deering D.W. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of Third Earth Resources Technology Satellite-1 Symposium, Greenbelt: NASA SP-351.1974:310-317.
[76] Deering D W, Rouse J W, Haas R H ,et al.Measuring forage production of grazing units from Landsat MSS data[A].Proceedings of Tenth International Symposium on Remote Sensing of Environment[C].Ann Arbor, ERIM,1975,2: 1169-1178.
[77] Roujean J L, Breon F M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements[J]. Remote Sens Environ, 1995, 51: 375-384.
[78] Gitelson A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote sens Environ,1996, 58(3): 289-298.
[79] Richardson A J, Wiegand C L. Distinguishing vegetation from soil background information[J]. Photogrammetric Engineering and Remote Sensing. 1977,43:1541-1552.
[80] Huete A R. A soil-adjusted vegetaion index (SAVI)[J]. Remote Sensing of Environment,1988, 25:295-309.