中国南方稻区褐飞虱灾变分析与预警系统的研究及应用
|
摘要
南方稻区是我国水稻主产区，近20年来水稻褐飞虱的发生、危害严重地威胁着该地区的水稻生产，因此及时、准确地预测褐飞虱的发生，不仅对于种植区水稻的正常发展，而且对区域农村经济可持续发展都具有重要意义。
传统的褐飞虱灾变预测方法主要是针对某一个县市进行的，缺乏空间分析因子。本研究以中国南方稻区为研究区域，采用地统计学和地理信息系统(GIS)方法，通过分析1981-1987年的虫情资料，模拟出褐飞虱越冬北界，分析其灾变发生程度的空间分布状况，并建立模拟模型。在此基础之上，设计开发出基于Web-GIS和ASP的中国南方稻区褐飞虱灾变预警系统，并初步应用到浙江地区。此系统是一个集成网络地理信息系统、模拟模型、网络数据库、人工智能和网络多媒体技术的网络系统。此外还初步研究了稻叶瘟对水稻冠层光谱特性的影响。
本研究主要内容如下：
1．中国南方稻区褐飞虱地理信息系统数据库的组建
采用Arc View 3.2和FoxPro 6.0软件组建1981-1987年中国南方稻区褐飞虱地理信息系统数据库，内容包括褐飞虱种群动态空间数据库和属性数据库的构建、发生程度、气象因子。此数据库具有可扩充性，为进一步分析大尺度南方稻区褐飞虱灾变发生的时空动态变化提供基础数据。
2．基于GIS的中国数字地形模型建立：
根据GIS数字地形模型(DTM)的构造原理和方法，用离散点移动拟合距离加权平均插值方法生成中国数字高程模型(DEM)。以DEM为基础，用离散点插值数值拟合方法构造了DTM的数学模型。用该模型从DEM中提取了中国不同地貌的地面单元平均高程、相对高程、高差、高程变异、坡度、坡向、地面粗糙度、坡面形态、谷脊特征线、沟谷密度、沟谷深度等各种地形结构线，由DEM产生了三维立体地形模型和各种地形剖面，以及地面辐照度的计算和地形阴阳坡的划分等，为全国一月均温的模拟和褐飞虱越冬北界的划定提供模型基础。
3．基于GIS的褐飞虱越冬北界的划定与模拟：
以地统计学的变差函数理论和 Kropng插值方法为工具，结合气候统计
学，探讨地统计学在气象因于和褐飞虱越冬北界的划定与模拟上的应用途
径和方法，与历史研究成果进行比较，在地理位置和总体趋势上具有一致
性。
4．褐飞虱灾变发生程度的空间分布格局与动态：
以GIS和地统计学为工具，以1981－1987年褐飞虱发生程度为研究对
象，分别从不同年份内和年份间的发生程度方面，在时空角度上分析褐飞
虱空间分布及发生程度的规律性，发现大尺度中国南方稻区褐飞虱发生程
度的主间分布类型呈规则分布，但发生程度均存在一定的空问相关性，主
间相关强弱差异较大，且发生程度均存在不等的随机性变异。198卜1987
年的褐飞虱发生程度均表现为由西南向东北方向发生程度增加的趋势，发
生程度大的区域主要集中在福建、浙江、湖南和）－西。褐飞虱的发生程度、
所在区域、方向趋势在同一年内及不同年份间均存在差别。褐飞虱发生程
度在1981－1987年的基本分布格局表现为福建、浙江、湖南、广西东部发
生偏重；四川、重庆、云南、贵州发生偏轻；厂 东、海南、江西、安徽、
湖北、江苏、上海为中等发生。7年间的发生趋势，即198年至1983年
发生程度趋于加重，但1984年却骤减，1985、1986年发生程度又加重、
1987年出现严重的趋势。最终建立褐飞虱发生程度空间半方差变异函数模
拟模型。
5．基于摸糊匹配的水稻病虫害诊断专家系统的设计与实现：
针对症状诊断知识的特点，提出了一种采用模糊匹配获取知识的方法
未设计水稻病虫害诊断专家系统。在ASP网络服务器端脚本语言下实现，
应用于基于Internet中国南方稻区褐飞虱灾变预警系统中，能通过模糊匹
配产生实用的诊断规则，对缺少诊断知识的病虫害症状进行尝试性推理，
达得从模糊到精确的诊断目的。
6．基于Internet的中国南方稻区褐飞虱灾变预警系统的组建：
以Internet为系统平台，以网络地理信息系统、网络数据库、人工智能、
网络多媒体和动画制作为技术支撑的中国南方稻区褐飞虱灾变预警系统，
具有时空动态分析、预警发布、预测预报、数据传输、病虫诊断、辅助决
策和远程教育等功能，界面友好。
IX
7．稻叶瘟对水稻冠层光谱特异性影响的探讨：
水稻感染稻叶瘟后，绿光区、红光区和近红外区的水稻冠层光谱反射
率随病情程度的加重分别呈现下降、上升和下降的趋势；绿光吸收边缘的
特征波长值发生红移，红光吸收边缘和近红外吸收边缘的特征波长值发生
蓝移。受害轻时近红外区反射率变化幅度大，受害重时绿光区和红光区反
射率变化幅度大。研究为应用遥感技术早期探测重大病虫害的发生提供了
实验依据。
本研究的技术发展和创新点有以下几个方面：
，提出应用引 S技术模拟农作物病虫害越冬区域：利用 GIS技术和
气候统计学方法并结合农业气象学和病虫害及寄主的生物学特性能够预
测当年病虫害的越冬环境和地理分布区域，为中长期预测病虫害灾变发
生提供快
China South is one of the main regions for rice production in China, where in recent 20 years Brown Planthopper (Nilaparvata lugens (Stal)) (BPH) damaged the rice production seriously. Therefore, the timely and accurate prediction of BPH occurrence is important not only to the rice normal development of the planting region, but also to the sustainable development of regional rural economy.
The traditional method used for prediction of BPH disaster is only aimed at one county and lack of spatial factors. Geostatistics methods and GIS were used to simulated the northern boundary of BPH overwintering sites, analyze spatial distribution of BPH disaster levels and built its dynamic model in the whole region from 1981 to 1987 in this study. As a result, China South BPH Disaster Early-warning System based on Web-GIS and Active Sever Page, as well as its preliminary application in Zhejiang region, were designed and developed. The web was a internet software system, which integrated GIS, simulation model, expert system and multimedia technology. Besides, effects of rice leaf blast on spectrum reflectance of rice was studied simply.
The main contents of this study are as follows:
1. Establishment of GIS database for BPH in China South
Arc View 3.2 GIS and FoxPro 6.0 were used to develop database for BPH. The database included spatial and attribute database. The spatial database included China's boundary map, river map, road map, elevation map, soil category map, etc. and the attributes database included BPH disaster levels, meteorologic phenomena. The GIS database provided primary data for BPH spatial analysis.
2. Establishment of China digital terrain model (DTM) based on GIS
Using the principles and approaches of building DTM based on GIS, China Digital Elevation Model (DEM) was generated with the distance weighted average interpolation of shifting imitation for dispersed elevation points. On the basis of the DEM, mathematical models for DTM were built with numerical value interpolating imitation for dispersed elevation points. The average elevation of ground
135
unit, relative elevation, elevation difference, elevation variation, slope, aspect, ground roughness, ground formation, gully feature lines and hill ridge, valley density, gully depth, and others were extracted. Moreover, 3D model of China topographic map with shade slope and sunny slope was structured. The result was applied to the simulation of January average temperature in China and the computation of northern boundary of BPH overwintering sites.
3. Definement and simulation of northern boundary of BPH overwintering sites based on CIS: As a tool of Geostatistical theory and Kriging interpolation method, the simulation of
ineteorologic factors and northern boundary of BPH overwintering sites were discussed with climatic statistics. The results are consistent with historical research on the whole.
4. Analysis on BPH disaster spatial distribution and its dynamic models:
Statistical theory and GIS method were used to analyze BPH disaster level spatial distribution and its dynamics with inner-years and inter-years in China South from 1981 to 1987. The result of traditional statistics showed that the distribution pattern of BPH disaster level is regular on large scale, and the result of Geostatistics indicated that BPH disaster level was correlated to the spatial factor., but the range changed greatly in 1981-1987. The trend of BPH disaster is serious with the direction from south-west to north-east. The BPH high risk regions located mainly Fujian, zhejiang, Hunan and Guangxi, and lower risk ones lied in Sichuan, Chongqing, yunnan, Guizhou, and others were middle risk. The disaster level, regional distribution, directional trend of BPH were different in inner-years and inter-years. The disaster trend was that the BPH risk increased from 1981 to 1983, decreased greatly in 1984, aggravated in 1985 and 1986, and became serious in 1987. The spatial semivariance models of BPH were built on the basis of these result
传统的褐飞虱灾变预测方法主要是针对某一个县市进行的，缺乏空间分析因子。本研究以中国南方稻区为研究区域，采用地统计学和地理信息系统(GIS)方法，通过分析1981-1987年的虫情资料，模拟出褐飞虱越冬北界，分析其灾变发生程度的空间分布状况，并建立模拟模型。在此基础之上，设计开发出基于Web-GIS和ASP的中国南方稻区褐飞虱灾变预警系统，并初步应用到浙江地区。此系统是一个集成网络地理信息系统、模拟模型、网络数据库、人工智能和网络多媒体技术的网络系统。此外还初步研究了稻叶瘟对水稻冠层光谱特性的影响。
本研究主要内容如下：
1．中国南方稻区褐飞虱地理信息系统数据库的组建
采用Arc View 3.2和FoxPro 6.0软件组建1981-1987年中国南方稻区褐飞虱地理信息系统数据库，内容包括褐飞虱种群动态空间数据库和属性数据库的构建、发生程度、气象因子。此数据库具有可扩充性，为进一步分析大尺度南方稻区褐飞虱灾变发生的时空动态变化提供基础数据。
2．基于GIS的中国数字地形模型建立：
根据GIS数字地形模型(DTM)的构造原理和方法，用离散点移动拟合距离加权平均插值方法生成中国数字高程模型(DEM)。以DEM为基础，用离散点插值数值拟合方法构造了DTM的数学模型。用该模型从DEM中提取了中国不同地貌的地面单元平均高程、相对高程、高差、高程变异、坡度、坡向、地面粗糙度、坡面形态、谷脊特征线、沟谷密度、沟谷深度等各种地形结构线，由DEM产生了三维立体地形模型和各种地形剖面，以及地面辐照度的计算和地形阴阳坡的划分等，为全国一月均温的模拟和褐飞虱越冬北界的划定提供模型基础。
3．基于GIS的褐飞虱越冬北界的划定与模拟：
以地统计学的变差函数理论和 Kropng插值方法为工具，结合气候统计
学，探讨地统计学在气象因于和褐飞虱越冬北界的划定与模拟上的应用途
径和方法，与历史研究成果进行比较，在地理位置和总体趋势上具有一致
性。
4．褐飞虱灾变发生程度的空间分布格局与动态：
以GIS和地统计学为工具，以1981－1987年褐飞虱发生程度为研究对
象，分别从不同年份内和年份间的发生程度方面，在时空角度上分析褐飞
虱空间分布及发生程度的规律性，发现大尺度中国南方稻区褐飞虱发生程
度的主间分布类型呈规则分布，但发生程度均存在一定的空问相关性，主
间相关强弱差异较大，且发生程度均存在不等的随机性变异。198卜1987
年的褐飞虱发生程度均表现为由西南向东北方向发生程度增加的趋势，发
生程度大的区域主要集中在福建、浙江、湖南和）－西。褐飞虱的发生程度、
所在区域、方向趋势在同一年内及不同年份间均存在差别。褐飞虱发生程
度在1981－1987年的基本分布格局表现为福建、浙江、湖南、广西东部发
生偏重；四川、重庆、云南、贵州发生偏轻；厂 东、海南、江西、安徽、
湖北、江苏、上海为中等发生。7年间的发生趋势，即198年至1983年
发生程度趋于加重，但1984年却骤减，1985、1986年发生程度又加重、
1987年出现严重的趋势。最终建立褐飞虱发生程度空间半方差变异函数模
拟模型。
5．基于摸糊匹配的水稻病虫害诊断专家系统的设计与实现：
针对症状诊断知识的特点，提出了一种采用模糊匹配获取知识的方法
未设计水稻病虫害诊断专家系统。在ASP网络服务器端脚本语言下实现，
应用于基于Internet中国南方稻区褐飞虱灾变预警系统中，能通过模糊匹
配产生实用的诊断规则，对缺少诊断知识的病虫害症状进行尝试性推理，
达得从模糊到精确的诊断目的。
6．基于Internet的中国南方稻区褐飞虱灾变预警系统的组建：
以Internet为系统平台，以网络地理信息系统、网络数据库、人工智能、
网络多媒体和动画制作为技术支撑的中国南方稻区褐飞虱灾变预警系统，
具有时空动态分析、预警发布、预测预报、数据传输、病虫诊断、辅助决
策和远程教育等功能，界面友好。
IX
7．稻叶瘟对水稻冠层光谱特异性影响的探讨：
水稻感染稻叶瘟后，绿光区、红光区和近红外区的水稻冠层光谱反射
率随病情程度的加重分别呈现下降、上升和下降的趋势；绿光吸收边缘的
特征波长值发生红移，红光吸收边缘和近红外吸收边缘的特征波长值发生
蓝移。受害轻时近红外区反射率变化幅度大，受害重时绿光区和红光区反
射率变化幅度大。研究为应用遥感技术早期探测重大病虫害的发生提供了
实验依据。
本研究的技术发展和创新点有以下几个方面：
，提出应用引 S技术模拟农作物病虫害越冬区域：利用 GIS技术和
气候统计学方法并结合农业气象学和病虫害及寄主的生物学特性能够预
测当年病虫害的越冬环境和地理分布区域，为中长期预测病虫害灾变发
生提供快
China South is one of the main regions for rice production in China, where in recent 20 years Brown Planthopper (Nilaparvata lugens (Stal)) (BPH) damaged the rice production seriously. Therefore, the timely and accurate prediction of BPH occurrence is important not only to the rice normal development of the planting region, but also to the sustainable development of regional rural economy.
The traditional method used for prediction of BPH disaster is only aimed at one county and lack of spatial factors. Geostatistics methods and GIS were used to simulated the northern boundary of BPH overwintering sites, analyze spatial distribution of BPH disaster levels and built its dynamic model in the whole region from 1981 to 1987 in this study. As a result, China South BPH Disaster Early-warning System based on Web-GIS and Active Sever Page, as well as its preliminary application in Zhejiang region, were designed and developed. The web was a internet software system, which integrated GIS, simulation model, expert system and multimedia technology. Besides, effects of rice leaf blast on spectrum reflectance of rice was studied simply.
The main contents of this study are as follows:
1. Establishment of GIS database for BPH in China South
Arc View 3.2 GIS and FoxPro 6.0 were used to develop database for BPH. The database included spatial and attribute database. The spatial database included China's boundary map, river map, road map, elevation map, soil category map, etc. and the attributes database included BPH disaster levels, meteorologic phenomena. The GIS database provided primary data for BPH spatial analysis.
2. Establishment of China digital terrain model (DTM) based on GIS
Using the principles and approaches of building DTM based on GIS, China Digital Elevation Model (DEM) was generated with the distance weighted average interpolation of shifting imitation for dispersed elevation points. On the basis of the DEM, mathematical models for DTM were built with numerical value interpolating imitation for dispersed elevation points. The average elevation of ground
135
unit, relative elevation, elevation difference, elevation variation, slope, aspect, ground roughness, ground formation, gully feature lines and hill ridge, valley density, gully depth, and others were extracted. Moreover, 3D model of China topographic map with shade slope and sunny slope was structured. The result was applied to the simulation of January average temperature in China and the computation of northern boundary of BPH overwintering sites.
3. Definement and simulation of northern boundary of BPH overwintering sites based on CIS: As a tool of Geostatistical theory and Kriging interpolation method, the simulation of
ineteorologic factors and northern boundary of BPH overwintering sites were discussed with climatic statistics. The results are consistent with historical research on the whole.
4. Analysis on BPH disaster spatial distribution and its dynamic models:
Statistical theory and GIS method were used to analyze BPH disaster level spatial distribution and its dynamics with inner-years and inter-years in China South from 1981 to 1987. The result of traditional statistics showed that the distribution pattern of BPH disaster level is regular on large scale, and the result of Geostatistics indicated that BPH disaster level was correlated to the spatial factor., but the range changed greatly in 1981-1987. The trend of BPH disaster is serious with the direction from south-west to north-east. The BPH high risk regions located mainly Fujian, zhejiang, Hunan and Guangxi, and lower risk ones lied in Sichuan, Chongqing, yunnan, Guizhou, and others were middle risk. The disaster level, regional distribution, directional trend of BPH were different in inner-years and inter-years. The disaster trend was that the BPH risk increased from 1981 to 1983, decreased greatly in 1984, aggravated in 1985 and 1986, and became serious in 1987. The spatial semivariance models of BPH were built on the basis of these result
引文
1．丁岩饮．昆虫数学生态学．北京：科学出版社，1994
2．王人潮，王珂，黄敬峰，史舟．农业资源信息系统．北京：中国农业出版社，2000
3．王人潮，史舟，胡月明．浙江红壤资源信息系统的研制与应用．北京：中国农业出版社，1999
4．王正军．基于GIS的早稻二化螟种群动态的时空分析与预测研究．浙江大学研究生院(博士学位论文)，2000
5．王亚东，陶海军，王塞等．大豆病虫害诊断专家系统中知识库的建造．计算机与农业，2000，(5)：34-39
6．王秀尔，王永春．加快我国网络农业建设的对策措施．农业经济，2001，(8)：17-18
7．王国荣，张宝刚．ASP网页制作教程．北京：人民邮电出版社，2000
8．王政权．地统计学及在生态学中的应用．北京：科学出版社，1999
9．王海扣，王群等．应用地理信息系统分析江苏褐飞虱的发生动态．西南农业大学学报，1998，20(5)：432-437
10．王磊．建设农业信息网络，促进农业信息化发展．安徽农业科学，2000，28(3)：389-390，393
11．冯定原．农业气象预报和情报分析．北京：气象出版社，1988
12．卢其尧．山区年月平均气温推算方法的研究．地理学报，1988，43(3)：213-222．
13．史舟．遥感数据更新、系统集成以及农业种植决策支持系统研制——基于红壤资源信息系统．浙江大学研究生院(博士学位论文)，1999
14．石根生，李典谟．马尾松毛虫越冬代蛹空间格局的地统计学描述，生态环境研究与可持续发展．北京：中国环境科学出版社，1997
15．石根生，李典谟．不同松林马尾松毛虫蛹及天敌群子的空间格局分析．生态学报，1997，17(4)：386-392
16．关蔚禾，王璐．遥感技术和地理信息系统及其在自然资源研究中的应用，当代生态学博论(李建国主编)，北京：中国科学技术出版社，1992
17．农业部全国植物保护总站．稻飞虱监测与治理年报(内部资料)，1993
18．刘有才，刘增良．模糊专家系统原理与设计。北京：北京航空航天大学出版社，1995
19．刘斌，尹红征，毕庆生等．中国农业信息网络技术体系的建立及发展刍议．河南农业大学学报，2000，34(2)：184-188
20．孙洪志．生物种群动态模型．沈阳：东北林业大学出版社，1997
21．邬伦，任伏虎等．地理信息系统教程．北京：北京大学出版社，1994
22．张左生．粮油作物病虫鼠害预测预报．上海：上海科技出版社，1995
23．张国庆，生物种群数量动态研究．北京：农业出版社，1995
24．李友常，夏乃斌等．杨树光肩天牛种群空间割据的地统计学研究．生态学报，1997，17(4)：393-401
25．李永平．建全农业信息网络提高农业管理和服务效能．农业信息探索，2000，(6)：15-17
26．李汝铎，丁锦华等．褐飞虱及其种群管理．上海：复旦大学出版社，1996
27．李保华，赵美琦．梨黑星病预测与管理专家系统(ESPSPM)的研制．计算机农业应用专刊，1996：120-123
28．李哈滨，伍业刚．景观生态学的数量研究方法，刘建国主编，当代生态学博沦．北京：中国科学技术出版社，1992，209-230
29．李晓，郑业鲁．农业电子商务的理性思考——现实与前景，农业图书情报学刊，2001，(1)：12-15
30．李鹄鸣，林昌善．标准积温法则——有效积温模型的改进．昆虫生态学研空，北京： 中国科学技术出版社，1992
31．畅叔子．基于知识的诊断推理．北京：清华大学出版社，1993
32．汪信庆，程家安等．二化螟种群时间动态预测模拟系统．浙江农业大学学报，1995，21(1)：71-76
33．沈国权．考虑宏观地形的小网格温度场分析方法及应用．气象，1984，(6)：22-27
34．邹钟琳．江苏竹水稻之花飞虱．江苏省昆虫局专刊，1926，(4)：1-35
35．陆鑫．利用ASP技术实现WEB数据库的访问．电子科技大学学报，2000，29(1)：87-90
36．陈国权．知识工程中自然语义的模糊表达．北京：科学出版社，1989
37．陈若篪，丁锦华，谈涵秋 等．迁飞昆虫学．北京：农业出版社，1989
38．陈若篪，徐秀媛等．褐飞虱越冬温度指标的研究．昆虫学报，1982，25(4)：390-395
39．陈述彭，鲁学军等，地理信息系统导论．北京：科学出版社，2000
40．陈述彭，遥感大辞典．北京：科学出版社，1990
41．陈述彭．地理系统与地理信息系统．地理学报，1991，46(1)：1-7
42．陈述彭．地理信息系统的探索与试验．地理科学，1983，3(4)：297-302
43．陈维博，昆虫中群死亡过程的数学模拟．生态学报，1981，1(2)：159-167
44．周心铁，刘毓化．组件技术与GIS的发展．计算机世界，1998，7：41
45．周立阳，石根生．地理信息系统与害虫的综合治理，生态环境研究与可持续发展．北京：中国环境科学出版社，1997
46．周国法，徐汝梅．生物地理统计学．北京：科学出版社，1998
47．周强，张润杰等．地质统计学在昆虫种群空间结构研究中的应用概述．动物学研究，1998，19(6)：482-488．
48．庞雄飞，梁广文．害虫中群系统的控制，广州：广东科技出版社，1995
49．於崇文．数学地质的方法与应用——地质与化探工作中的多元分析．北京：冶金工业出版社，1980
50．侯景儒，郭光裕．矿床统计预测及地质统计学的理论与应用．北京：冶金工业出版社，1993
51．柯正谊，何建邦，池天河．数字地面模型．北京：中国科学技术出版社，1993
52．胡伯海．全国农作物重大病虫预测预报系统．计算机与农业，1996，6：27-31
53．胡伯海．国外农业电子信息应用技术．计算机与农业，1996，5：18-20
54．胡国文，唐启义，马巨法等．中国褐飞虱的分布和危害．内部资料汇编，1995，39-42
55．赵圣菊，1983，用气象因子组建一代粘虫发生区越冬代蛾量及迁入期的长期预测模式初探，生态学报，3(4)：377-378
56．赵艳，张敏，姜小毅等．多媒体网络教学系统的建立及在农业技术推广中的应用．河北科技大学学报，2000，21(2)：59-63
57．赵霈生，杨崇俊．Web-GIS的设计与实现．中国图象图形学报，2000，A，5(1)：75-79
58．唐启义，冯明光．实用统计分析及其计算机处理平台，北京：中国农业出版社，1997
59．徐汝梅，变维矩阵模型在温室白粉虱中群动态模拟中的应用．生态学报，1981，1(2)：147-158
60．徐汝梅．昆虫种群生态学．北京：北京师范大学出版社，1987
61．徐秀嫒，赵健等．以游草饲养褐稻虱的结果和讨论，昆虫知识，1982，19(2)：48
62．袁德辉，翁笃鸣，县级山区月平均气温推算方法．地理研究，1992，11(3)：32-36
63．贾善刚．我国西部农业科技信息网络系统发展策略．中国农业资源与区划，2000，21(4)：47-50
64．高灵旺，沈佐锐，李志红．计算机软件技术在植保软件开发中的应用．农业工程学报，2000，16(2)：32-35
65．黄杏元，汤勤．地理信息系统概论，北京：高等教育出版社，1989
66．黄雪樵．三峡地区GIS中坡面太刚辐射模型的研究．见：中国科学院-水利部成都山地灾害与环境研究所．遥感·地图·地理信息系统在资源与环境研究中的应用．成都：成都地图出版社，1991，79-87．
67．傅抱璞．山地气候．北京：科学出版社，1983
68．曾仕迈。植保系统工程导论．北京：北京农业大学出版社，1994
69．湖南省褐飞虱科研协作组．湖南褐飞虱越冬虫源及发生，植物保护，1981，7(2)：21-30
70．程家安．水稻害虫．北京：中国农业出版社，1996
71．程家安．褐飞虱智力计算机决策支持系统．浙江农业大学学报，1990，16(2)：129-133
72．程家安，章连观等．迁入种群对褐飞虱种群动态影响的模拟研究．中国水稻科学，1991，5(4)：163-168
73．程家安，章连观等．气温对褐飞虱种群动态影响的模拟．中国水稻科学，1992，6(1)：21-26
74．程登发，我国植保信息技术的发展与展望。植物保护，1998，24(2)：27-30
75．程遐年，陈若篪等．稻褐飞虱迁飞规律的研究．昆虫学报，1979，22(1)：1-21
76．蒲蛰龙，农作物害虫管理数学模型与应用．广州：广东科技出版社，1990
77．翟保平，张孝羲．水稻重大害虫的灾变规律及其预警：回顾与展望．昆虫知识，2000，37(1)：41-42
78．蔡自兴，徐光佑．人工智能及其应用(第二版)．北京：清大学出版社，1996
79． Allan, L. J., Peter, S. B., et al., 2000, Pl@ntelnfo-a web-based system for peronalised decision support in crop management. Computers and Electronics in Agriculture, 2000, 25:271-293
80． Agrios，G．N．．植物病理学(第三版)．陈永萱，陆家云，许志刚译，北京：中国农 业出版社．1995
81． Bilonick, R. A., monthly hydrogen ion deposition maps for the northeastern U. S. from July 1982 to September 1984． Atoms. Environ., 1988, 22: 1909-1924
82． Blakerman, R. H.. The identification of crop disease and stress by aerial photography. In: M. D. Steven, J. A. Clark (ed.), Applications of remote sensing in agriculture. Cambridge: Great Britain at the University Press, 1990, 229-254
83． Borth, P. W., Huber. R. T., Modelling pink bollworm establishment and sipersion in cotton with the kriging technique. Proc.Beltwide Cotton Prodection Res. Conf. 1987, 2: 67-74
84． Coulson, R. N.. Intelligent geographic information systems and integrated pest management Crop Protection. 1992, 11:507-516．
85． Cressie, N. A. C.. Statistics fot spatial data. John-Wiley and Sons. New York., USA, 1991
86． Dawson, T. P., Curran, P. J., North, P. R. J.. The Propagation of Foliar Biochemical Absorption Features in Forest Canopy reflectance: a theoretical analysis. Remote Sensing of Environment, 1999,67(2) : 147-159
87． Denno, R. K, Roderick, G. k.. Population biology of planthoppers. Annu. Rev. Entomol., 1990,35:489-520
88． Dyck, V. A., Thomas, B.. The brown planthopper problem, In: Brown Planthopper Threat to Rice Production in Asia, 1RRI, Los Banos, Philippines, 1979, 3-17
89． ESR1, Inc. 2000, ArcIMS 3． 1 Installation Guide, ESRI, Inc.
90． ESRI, Inc. 2000, Using ArcIMS 3． 1, ESRI, Inc.
91． ESRI, Inc. Arclnfo 8． 0 用户手册, ESRI, Inc.
92． ESRI, Inc. Arc View 3． 2 用户手册, ESRI, Inc.
93． FAO, Guidelines for pest risk analysis. 1993
94． Fielding, D.J., Brusven, M. A.. Spatial analysis of grasshopper density and ecological disturbance on southern Idaho rangeland. Agriculture, Eecosytsems and Environment. 1993,43(1) : 31-47
95． Gage, M. W., Sequeira, R. A., et al.. Prediction regional gypsy moth (Lymantriidae) population trends in an expanding population using pheromone trap catch and spatial analysis. Environ. Entomol., 1990, 19(2) : 370-377
96． Gammadesign. GS+ 3． 11 用户手册, http://www.gammadesign.com
97． Goudriaan, J., Dispersion in simulation models of population growth and salt movement in the soil. Neth. J. Agric. Sci., 1973, 21: 269-281
98． Harvorson, J.. Geostatistics and ecology. Bulletin of the Ecol. Soc. Am., 1994, 75: 36-38
99． Hohn, M. E., Liebhold, A. M., Gribko, L. S.. Geostatistical model for forecasting spatial dynamics of defoliation caused by gypsy moth (Lepidoptera: Lymantriidae). Environ. Entomol., 1993,22(5) : 1066-1075
100． Holsapple, C.W., Winston, A.B.. Decision Support Systems-A Knowledge-Based Approach. West, New York, 1996
101． Hopper, B.E.. Ecological aspects of pest risk assessment. Bulletin OEPP/EPPO, 1991, 21:587-594．
102． Issaks, E. H., Srivastava, R. M.. An introduction to applied geostatistics. Oxford University Press, New York, New York, USA, 1989
103． Jackson, R. B., Caldwell, M. M.. Geostatistical patterns of soil heterogeneity around individual perennial plants. Journal of Ecology, 1993, 81: 683-692
104． Jensen, A. L., Thysen, I., Secher, B. J. M., Decision support in crop production via the Internet. Petria, 1997, 7(Suppl. 1) 147-154
105． Johnson, D. L., Spatial autocorrelation, spatial modeling, and improvements in grasshopper survey methodology. Can. Entomol., 1989, 121: 579-588
106． Johnson, D. L., Worobec. A.. Spatial and temporal computer analysis of insects and weather: grasshoppers and rainfall in Alberta. Mem. Entomol. Soc. Can., 1988, 146: 33-48
107． Journal, A. G, Huijbregts, C. J.. Mining geostatistics. London Academic Press. UK, 1978
108． Journal, A. G.. Nonparametric estimation of ecological theory. Ecology, 1983, 75:1-15
109． Kemp, W. P., Kalaris, T. M.. Rangeland grasshopper spatial variability: macroscale population assessment. J. Econ. Entomol., 1989, 82: 1270-1276
110． Kemp, W. P., probabiling of outbreak for rangeland grasshopper (Orthoptera: Acrididae) in Montana: application of Markwvian principle. J. Econ. Entomol. 1987, 80(6) : 1100-1105
111． Krige, D. G, A statistical approach to some basic mine valuation problems on the Witwatersrand. J. Chem. Metal 1． And Min. Soc. South Africa, 1951, 52(6) : 119-139
112． Legg, C., A geographic information system for planning and managing the conservation of tropical forests in the Knuckles Range. Remote sensing, 1995, No. Special Issue. 37-62
113． Lewis, D. M.. Importance of GIS to community-based management of wildlife lesson from Zanbia. Ecological Applications, 1995, 5(4) : 861-871
114． Liebhold, A. M., Elkinton, J. S., Zhou, G.. Regional correlation of gypsy moth (Lepidptera: Lymantriidae) defoliation with counts of eggs, pupae and male moths. Environ. Entomol., 1995,2(2) : 193-203
115． Liebhold, A. M., Elkinton, J. S.. Characterizing spatial patterns of gypsy moth regional defoliation. Forest Science, 1989, 35(2) : 557-568
116． Liebhold, A. M., Elmes, G.A., Halverson, J. A., Quimby, J.. Landscape characterization of forest susceptibility to gypsy moth defoliation. Forest Science, 1994, 40: 18-29
117． Liebhold, A. M., Halverson, J. A., Elmes, G. A.. Gypsy moth invasion in North America: a quantitative analysis. J. Biogeogr., 1992, 19: 513-520
118． Liebhold, A. M., Luzader, E., Reardon, R.. Forecasting gypsy moth (Lepidoptera: Lymntriidae) defoliation with a geographic information system. J. Econ. Entomol. 1998, 91(2) : 464-472
119． Liebhold, A. M., Zhang, X., Hohn, M. E., Elkinton, J. S., Tice-hurst, M., Benzon, G. L., Campbell, R. W.. Geostatistical analysis of gypsy moth (Lepidoptera: Lymantriidae) egg mass populations. Environ. Entomol. 1991, 20 (5) : 1407-1417
120． Liebhold, A.M.. Geographic Information System in Applied Insect Ecology. Annu. Re. Entomol. 1993,38:303-327
121． Liu J.G., Jery, J. B., Pulliam, H. R.. Potential effects of a forest management plan on Bachman's sparrows (Aimophila aestivalis ) : linking a spatially explicit model with G1S. Cornservation-Biology. 1998, 9(1) : 62-75
122． MacDonald, A. B., Russell, L. R.. A method to determine the computer system requirements for a large-scale geographic information system (GIS). Computer and Electronics in Agriculture. 1989,9: 111-122
123． Matheron, G.. Principles of geostatistics. Economic Geology, 1963, 58:1246-1266
124． McNulty S.G., et al., Regional-scale forest ecosystem modeling: database development, mode! predictions and validation using a Geographic Information System. Climate-Research. 1994, 4(3) : 223-231．
125． Moss, K.. JAVA Servlets. McGraw-Hill, New York, 1998．
126． Nault, L. R., Rodriguez, J. G. The Leafhoppers and Planthoppers, Wiley, New York, 1985
127． Nicholson, M. C. Methods for evaluating Lyme disease risks using geographic information systems and geospatial analysis. Journal of Medical Entomology, 1996, 33(5) : 711-720
128． Oden, N. L., Sokal, R. R.. Directional autocorrelation: an extension of spatial correlograms to two dimensions. Syst. Zool., 1986, 35: 608-617
129． Perfect, T. J., Cook, A. G. Rice planthopper population dynamics: A comparison between temperate and tropical regions, In: Planthoppers: Their Ecology and Management (R. F. Denno, T. J. Perfect.), Chapman & Hall, New York/London, 1994, 282-301
130． Pottera, W. D., Denga, X. Lia, J.. et al., A web-based expert system for gypsy moth risk assessment. Computers and Electronics in Agriculture, 2000, 27: 95-105
131． Power, J. M., Saarenmaa, H., Object oriented modeling and GIS Integration in a decision support system for the management of eastern hemlock looper in Newfoundland. Computers and Electronics in Agriculture, 1995, 12(1) : 1-18
132． Ravin, F.W., Development of monitoring and decision-support systems for integrated pest management of forest defoliation in North America. For. Ecol. Manage. 1990, 39: 3-13
133． Rence, A. N., Nemeth. J.. Defection of Mountain Pine Beetle Infection Using Landsat MSS and Simulated Thematic Mapper Data. Canadian Journal of Remote Sensing, 1985, 11(1) : 50-58
134． Roberts, E. A., Ravlin, F. W. Heischer, S. J.. Am. Entomol., 1993, summer: 93-107
135． Robertson, G. P., Freckman, O. W.. The spatial distribution of nematode tropic groups across a cultivated ecosystem. Ecology, 1995, 76: 1425-1432
136． Robertson, G. P., Huston, M., Evans, F., Tiedje, J.. Spatial variability in a successional plant community: pattern of nitrogen availability.Ecology, 1988, 69:1517-1524
137． Robertson, G. P., Klingensmith, K. M., Klug, M. J., Paul, E. A., et al. Soil resources, microbial activity, and primary production across an agriculture ecosystem. Ecological Application, 1997,7: 158-170
138． Robertson, G. P.. Geostatistics in ecology: interpolating with know variance. Ecology, 1987, 68(3) : 744-748
139． Rossi, R. E., Mulla, D. J.. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecological Monographs, 1992, 62(2) : 277-314
140． Schotzko, D. J., Jane. S. K.. Effect of sample placement on the geostatistical analysis of spatial distribution of Lygus Hesperus in lentils. J. Econ. Entomol., 1990, 83: 1888-1990
141． Schotzko, D. J., O'Keefte L. E.. Geostatistical description of the spatial distribution of Lygus Hesperus in lentils. Environ. Entomol., 1989, 82: 1277-1288
142． Schotzko, D. J., Quisenberry, S. S.. Spatio-Temporal dynamics of tritropoic interactions: using the Russian Wheat Aphid (Homoptera: Aphididae) on a preferred and nonpreferred host, with the introduction of a predator as the model. In: Russian wheat aphid. Thomas Say Publictions. 1997,337-365
143． Schotzko, D. J., Smith, C. M.. Effects of host plant on the plant distribution of the Russian Wheat Aphid (Homoptera: Miridae) in Lentils. J. Econo. Entomol., 1991, 84(5) : 846-855
144． Shelstad, D., Describing the spread of oak wilt using a geographic information system. Journal of Agroculture. 1991, 17(7) :192-199
145． Shepherd, et al., Predicting the Forest Susceptibility to the Gypsy Moth (Lymantria dispar (L.)) Defoliation. Forest Ecology and Management. 1997, 98(1) : 26-34
146． Sokal, R. R., Oden, N. L., Baker, J. S. F.. Spatial structure in Drosophila buzzatii populations: simple and spatial autocorrelation. Am. Nat. 1987, 129: 122-142
147． Sokal, R. R., Oden, N. L.. Spatial autocorrelation analysis as an inferential tool in population genetics. Am. Nat., 1991, 138:518-521
148． Song, Y. H., Heong, K. L., Use of geographical information systems in analyzing large area distribution and dispersal of rice insects in South Korea. Korean Journal of Applied Entomology, 1993, 32(3) : 307-316
149． SPSS, Inc. SPSS 10． 0 用户手册, SPSS, Inc. 1999
150． Stefik, M.. Introduction to Knowledge Systems. Morgan Kaufmann, San Mateo, CA., 1995
151． Taylor, L. T.. Aggregation, variance and the mean. Nature, 1961, 189: 732-735
152． Thomas, S.J., Fosbroke, S.L.C., Cumming, A.B.. GypsES: Decision Support and Project Management-User's Guide, Version 1． 0． Radnor, PA: USDA Forest Service, Northeastern Forest Experiment Station, NA-TP-01-98． 1998．
153． Turban, E.. Decision Support and Expert Systems: Management Support Systems, third ed. Macmillan, New York, 1993
154． Twery, M. J. et al, Using G1S to assess gypsy moth hazard. Proc. ASPRS/ACSM. Annu.Comv. 1990,3:284-290
155． Vencill, A. M., Zehnder, G. W., Heatwole, C. D., Potato insect expert system: computerized approach to insecticide management for Colorado potato beetle (Coleoptera: Chrysomelidae). Journal of Economic Entomology, 1995, 88(4) : 944-954
156． Weisz, R., Saunders, M., Smilowitz, Z., et al., Knowledge-based reasoning in integrated resistance management: the Colorado potato beetle (Coleoptera: Chrysomelidae). Journal of Economic Entomology, 1994, 87(6) : 1384-1399
157． Williams, D. W., Liebhold, A. M. Herbivorous insects and global change: potential changes in the spatial distribution of forest defoliator outbreaks. Journal of Biogeography, 1995,22 (4-5) : 665-671
158． Wood, B. L., Beck, L. R., Washino, R. K. et al., Spectral and spatial characterization of rice field mosquito habitats. International Journal of Remote Sensing, 1991, 12: 621-626
159． Zadoks, J. C. EPIPRE: research, development and application of an integrated pest and disease management system for wheat. Bulletin SROP, 1998, 11(2) : 82-90
160． Zhou, G, Liebhold, M. A., and Kurt, G. Predicting the Forest Susceptibility to the Gypsy Moth Defoliation from the inventory and Geographic information, proceedings of the National Symposium on IPM in China, pp112-122, China Agriculture Science Press, 1996
2．王人潮，王珂，黄敬峰，史舟．农业资源信息系统．北京：中国农业出版社，2000
3．王人潮，史舟，胡月明．浙江红壤资源信息系统的研制与应用．北京：中国农业出版社，1999
4．王正军．基于GIS的早稻二化螟种群动态的时空分析与预测研究．浙江大学研究生院(博士学位论文)，2000
5．王亚东，陶海军，王塞等．大豆病虫害诊断专家系统中知识库的建造．计算机与农业，2000，(5)：34-39
6．王秀尔，王永春．加快我国网络农业建设的对策措施．农业经济，2001，(8)：17-18
7．王国荣，张宝刚．ASP网页制作教程．北京：人民邮电出版社，2000
8．王政权．地统计学及在生态学中的应用．北京：科学出版社，1999
9．王海扣，王群等．应用地理信息系统分析江苏褐飞虱的发生动态．西南农业大学学报，1998，20(5)：432-437
10．王磊．建设农业信息网络，促进农业信息化发展．安徽农业科学，2000，28(3)：389-390，393
11．冯定原．农业气象预报和情报分析．北京：气象出版社，1988
12．卢其尧．山区年月平均气温推算方法的研究．地理学报，1988，43(3)：213-222．
13．史舟．遥感数据更新、系统集成以及农业种植决策支持系统研制——基于红壤资源信息系统．浙江大学研究生院(博士学位论文)，1999
14．石根生，李典谟．马尾松毛虫越冬代蛹空间格局的地统计学描述，生态环境研究与可持续发展．北京：中国环境科学出版社，1997
15．石根生，李典谟．不同松林马尾松毛虫蛹及天敌群子的空间格局分析．生态学报，1997，17(4)：386-392
16．关蔚禾，王璐．遥感技术和地理信息系统及其在自然资源研究中的应用，当代生态学博论(李建国主编)，北京：中国科学技术出版社，1992
17．农业部全国植物保护总站．稻飞虱监测与治理年报(内部资料)，1993
18．刘有才，刘增良．模糊专家系统原理与设计。北京：北京航空航天大学出版社，1995
19．刘斌，尹红征，毕庆生等．中国农业信息网络技术体系的建立及发展刍议．河南农业大学学报，2000，34(2)：184-188
20．孙洪志．生物种群动态模型．沈阳：东北林业大学出版社，1997
21．邬伦，任伏虎等．地理信息系统教程．北京：北京大学出版社，1994
22．张左生．粮油作物病虫鼠害预测预报．上海：上海科技出版社，1995
23．张国庆，生物种群数量动态研究．北京：农业出版社，1995
24．李友常，夏乃斌等．杨树光肩天牛种群空间割据的地统计学研究．生态学报，1997，17(4)：393-401
25．李永平．建全农业信息网络提高农业管理和服务效能．农业信息探索，2000，(6)：15-17
26．李汝铎，丁锦华等．褐飞虱及其种群管理．上海：复旦大学出版社，1996
27．李保华，赵美琦．梨黑星病预测与管理专家系统(ESPSPM)的研制．计算机农业应用专刊，1996：120-123
28．李哈滨，伍业刚．景观生态学的数量研究方法，刘建国主编，当代生态学博沦．北京：中国科学技术出版社，1992，209-230
29．李晓，郑业鲁．农业电子商务的理性思考——现实与前景，农业图书情报学刊，2001，(1)：12-15
30．李鹄鸣，林昌善．标准积温法则——有效积温模型的改进．昆虫生态学研空，北京： 中国科学技术出版社，1992
31．畅叔子．基于知识的诊断推理．北京：清华大学出版社，1993
32．汪信庆，程家安等．二化螟种群时间动态预测模拟系统．浙江农业大学学报，1995，21(1)：71-76
33．沈国权．考虑宏观地形的小网格温度场分析方法及应用．气象，1984，(6)：22-27
34．邹钟琳．江苏竹水稻之花飞虱．江苏省昆虫局专刊，1926，(4)：1-35
35．陆鑫．利用ASP技术实现WEB数据库的访问．电子科技大学学报，2000，29(1)：87-90
36．陈国权．知识工程中自然语义的模糊表达．北京：科学出版社，1989
37．陈若篪，丁锦华，谈涵秋 等．迁飞昆虫学．北京：农业出版社，1989
38．陈若篪，徐秀媛等．褐飞虱越冬温度指标的研究．昆虫学报，1982，25(4)：390-395
39．陈述彭，鲁学军等，地理信息系统导论．北京：科学出版社，2000
40．陈述彭，遥感大辞典．北京：科学出版社，1990
41．陈述彭．地理系统与地理信息系统．地理学报，1991，46(1)：1-7
42．陈述彭．地理信息系统的探索与试验．地理科学，1983，3(4)：297-302
43．陈维博，昆虫中群死亡过程的数学模拟．生态学报，1981，1(2)：159-167
44．周心铁，刘毓化．组件技术与GIS的发展．计算机世界，1998，7：41
45．周立阳，石根生．地理信息系统与害虫的综合治理，生态环境研究与可持续发展．北京：中国环境科学出版社，1997
46．周国法，徐汝梅．生物地理统计学．北京：科学出版社，1998
47．周强，张润杰等．地质统计学在昆虫种群空间结构研究中的应用概述．动物学研究，1998，19(6)：482-488．
48．庞雄飞，梁广文．害虫中群系统的控制，广州：广东科技出版社，1995
49．於崇文．数学地质的方法与应用——地质与化探工作中的多元分析．北京：冶金工业出版社，1980
50．侯景儒，郭光裕．矿床统计预测及地质统计学的理论与应用．北京：冶金工业出版社，1993
51．柯正谊，何建邦，池天河．数字地面模型．北京：中国科学技术出版社，1993
52．胡伯海．全国农作物重大病虫预测预报系统．计算机与农业，1996，6：27-31
53．胡伯海．国外农业电子信息应用技术．计算机与农业，1996，5：18-20
54．胡国文，唐启义，马巨法等．中国褐飞虱的分布和危害．内部资料汇编，1995，39-42
55．赵圣菊，1983，用气象因子组建一代粘虫发生区越冬代蛾量及迁入期的长期预测模式初探，生态学报，3(4)：377-378
56．赵艳，张敏，姜小毅等．多媒体网络教学系统的建立及在农业技术推广中的应用．河北科技大学学报，2000，21(2)：59-63
57．赵霈生，杨崇俊．Web-GIS的设计与实现．中国图象图形学报，2000，A，5(1)：75-79
58．唐启义，冯明光．实用统计分析及其计算机处理平台，北京：中国农业出版社，1997
59．徐汝梅，变维矩阵模型在温室白粉虱中群动态模拟中的应用．生态学报，1981，1(2)：147-158
60．徐汝梅．昆虫种群生态学．北京：北京师范大学出版社，1987
61．徐秀嫒，赵健等．以游草饲养褐稻虱的结果和讨论，昆虫知识，1982，19(2)：48
62．袁德辉，翁笃鸣，县级山区月平均气温推算方法．地理研究，1992，11(3)：32-36
63．贾善刚．我国西部农业科技信息网络系统发展策略．中国农业资源与区划，2000，21(4)：47-50
64．高灵旺，沈佐锐，李志红．计算机软件技术在植保软件开发中的应用．农业工程学报，2000，16(2)：32-35
65．黄杏元，汤勤．地理信息系统概论，北京：高等教育出版社，1989
66．黄雪樵．三峡地区GIS中坡面太刚辐射模型的研究．见：中国科学院-水利部成都山地灾害与环境研究所．遥感·地图·地理信息系统在资源与环境研究中的应用．成都：成都地图出版社，1991，79-87．
67．傅抱璞．山地气候．北京：科学出版社，1983
68．曾仕迈。植保系统工程导论．北京：北京农业大学出版社，1994
69．湖南省褐飞虱科研协作组．湖南褐飞虱越冬虫源及发生，植物保护，1981，7(2)：21-30
70．程家安．水稻害虫．北京：中国农业出版社，1996
71．程家安．褐飞虱智力计算机决策支持系统．浙江农业大学学报，1990，16(2)：129-133
72．程家安，章连观等．迁入种群对褐飞虱种群动态影响的模拟研究．中国水稻科学，1991，5(4)：163-168
73．程家安，章连观等．气温对褐飞虱种群动态影响的模拟．中国水稻科学，1992，6(1)：21-26
74．程登发，我国植保信息技术的发展与展望。植物保护，1998，24(2)：27-30
75．程遐年，陈若篪等．稻褐飞虱迁飞规律的研究．昆虫学报，1979，22(1)：1-21
76．蒲蛰龙，农作物害虫管理数学模型与应用．广州：广东科技出版社，1990
77．翟保平，张孝羲．水稻重大害虫的灾变规律及其预警：回顾与展望．昆虫知识，2000，37(1)：41-42
78．蔡自兴，徐光佑．人工智能及其应用(第二版)．北京：清大学出版社，1996
79． Allan, L. J., Peter, S. B., et al., 2000, Pl@ntelnfo-a web-based system for peronalised decision support in crop management. Computers and Electronics in Agriculture, 2000, 25:271-293
80． Agrios，G．N．．植物病理学(第三版)．陈永萱，陆家云，许志刚译，北京：中国农 业出版社．1995
81． Bilonick, R. A., monthly hydrogen ion deposition maps for the northeastern U. S. from July 1982 to September 1984． Atoms. Environ., 1988, 22: 1909-1924
82． Blakerman, R. H.. The identification of crop disease and stress by aerial photography. In: M. D. Steven, J. A. Clark (ed.), Applications of remote sensing in agriculture. Cambridge: Great Britain at the University Press, 1990, 229-254
83． Borth, P. W., Huber. R. T., Modelling pink bollworm establishment and sipersion in cotton with the kriging technique. Proc.Beltwide Cotton Prodection Res. Conf. 1987, 2: 67-74
84． Coulson, R. N.. Intelligent geographic information systems and integrated pest management Crop Protection. 1992, 11:507-516．
85． Cressie, N. A. C.. Statistics fot spatial data. John-Wiley and Sons. New York., USA, 1991
86． Dawson, T. P., Curran, P. J., North, P. R. J.. The Propagation of Foliar Biochemical Absorption Features in Forest Canopy reflectance: a theoretical analysis. Remote Sensing of Environment, 1999,67(2) : 147-159
87． Denno, R. K, Roderick, G. k.. Population biology of planthoppers. Annu. Rev. Entomol., 1990,35:489-520
88． Dyck, V. A., Thomas, B.. The brown planthopper problem, In: Brown Planthopper Threat to Rice Production in Asia, 1RRI, Los Banos, Philippines, 1979, 3-17
89． ESR1, Inc. 2000, ArcIMS 3． 1 Installation Guide, ESRI, Inc.
90． ESRI, Inc. 2000, Using ArcIMS 3． 1, ESRI, Inc.
91． ESRI, Inc. Arclnfo 8． 0 用户手册, ESRI, Inc.
92． ESRI, Inc. Arc View 3． 2 用户手册, ESRI, Inc.
93． FAO, Guidelines for pest risk analysis. 1993
94． Fielding, D.J., Brusven, M. A.. Spatial analysis of grasshopper density and ecological disturbance on southern Idaho rangeland. Agriculture, Eecosytsems and Environment. 1993,43(1) : 31-47
95． Gage, M. W., Sequeira, R. A., et al.. Prediction regional gypsy moth (Lymantriidae) population trends in an expanding population using pheromone trap catch and spatial analysis. Environ. Entomol., 1990, 19(2) : 370-377
96． Gammadesign. GS+ 3． 11 用户手册, http://www.gammadesign.com
97． Goudriaan, J., Dispersion in simulation models of population growth and salt movement in the soil. Neth. J. Agric. Sci., 1973, 21: 269-281
98． Harvorson, J.. Geostatistics and ecology. Bulletin of the Ecol. Soc. Am., 1994, 75: 36-38
99． Hohn, M. E., Liebhold, A. M., Gribko, L. S.. Geostatistical model for forecasting spatial dynamics of defoliation caused by gypsy moth (Lepidoptera: Lymantriidae). Environ. Entomol., 1993,22(5) : 1066-1075
100． Holsapple, C.W., Winston, A.B.. Decision Support Systems-A Knowledge-Based Approach. West, New York, 1996
101． Hopper, B.E.. Ecological aspects of pest risk assessment. Bulletin OEPP/EPPO, 1991, 21:587-594．
102． Issaks, E. H., Srivastava, R. M.. An introduction to applied geostatistics. Oxford University Press, New York, New York, USA, 1989
103． Jackson, R. B., Caldwell, M. M.. Geostatistical patterns of soil heterogeneity around individual perennial plants. Journal of Ecology, 1993, 81: 683-692
104． Jensen, A. L., Thysen, I., Secher, B. J. M., Decision support in crop production via the Internet. Petria, 1997, 7(Suppl. 1) 147-154
105． Johnson, D. L., Spatial autocorrelation, spatial modeling, and improvements in grasshopper survey methodology. Can. Entomol., 1989, 121: 579-588
106． Johnson, D. L., Worobec. A.. Spatial and temporal computer analysis of insects and weather: grasshoppers and rainfall in Alberta. Mem. Entomol. Soc. Can., 1988, 146: 33-48
107． Journal, A. G, Huijbregts, C. J.. Mining geostatistics. London Academic Press. UK, 1978
108． Journal, A. G.. Nonparametric estimation of ecological theory. Ecology, 1983, 75:1-15
109． Kemp, W. P., Kalaris, T. M.. Rangeland grasshopper spatial variability: macroscale population assessment. J. Econ. Entomol., 1989, 82: 1270-1276
110． Kemp, W. P., probabiling of outbreak for rangeland grasshopper (Orthoptera: Acrididae) in Montana: application of Markwvian principle. J. Econ. Entomol. 1987, 80(6) : 1100-1105
111． Krige, D. G, A statistical approach to some basic mine valuation problems on the Witwatersrand. J. Chem. Metal 1． And Min. Soc. South Africa, 1951, 52(6) : 119-139
112． Legg, C., A geographic information system for planning and managing the conservation of tropical forests in the Knuckles Range. Remote sensing, 1995, No. Special Issue. 37-62
113． Lewis, D. M.. Importance of GIS to community-based management of wildlife lesson from Zanbia. Ecological Applications, 1995, 5(4) : 861-871
114． Liebhold, A. M., Elkinton, J. S., Zhou, G.. Regional correlation of gypsy moth (Lepidptera: Lymantriidae) defoliation with counts of eggs, pupae and male moths. Environ. Entomol., 1995,2(2) : 193-203
115． Liebhold, A. M., Elkinton, J. S.. Characterizing spatial patterns of gypsy moth regional defoliation. Forest Science, 1989, 35(2) : 557-568
116． Liebhold, A. M., Elmes, G.A., Halverson, J. A., Quimby, J.. Landscape characterization of forest susceptibility to gypsy moth defoliation. Forest Science, 1994, 40: 18-29
117． Liebhold, A. M., Halverson, J. A., Elmes, G. A.. Gypsy moth invasion in North America: a quantitative analysis. J. Biogeogr., 1992, 19: 513-520
118． Liebhold, A. M., Luzader, E., Reardon, R.. Forecasting gypsy moth (Lepidoptera: Lymntriidae) defoliation with a geographic information system. J. Econ. Entomol. 1998, 91(2) : 464-472
119． Liebhold, A. M., Zhang, X., Hohn, M. E., Elkinton, J. S., Tice-hurst, M., Benzon, G. L., Campbell, R. W.. Geostatistical analysis of gypsy moth (Lepidoptera: Lymantriidae) egg mass populations. Environ. Entomol. 1991, 20 (5) : 1407-1417
120． Liebhold, A.M.. Geographic Information System in Applied Insect Ecology. Annu. Re. Entomol. 1993,38:303-327
121． Liu J.G., Jery, J. B., Pulliam, H. R.. Potential effects of a forest management plan on Bachman's sparrows (Aimophila aestivalis ) : linking a spatially explicit model with G1S. Cornservation-Biology. 1998, 9(1) : 62-75
122． MacDonald, A. B., Russell, L. R.. A method to determine the computer system requirements for a large-scale geographic information system (GIS). Computer and Electronics in Agriculture. 1989,9: 111-122
123． Matheron, G.. Principles of geostatistics. Economic Geology, 1963, 58:1246-1266
124． McNulty S.G., et al., Regional-scale forest ecosystem modeling: database development, mode! predictions and validation using a Geographic Information System. Climate-Research. 1994, 4(3) : 223-231．
125． Moss, K.. JAVA Servlets. McGraw-Hill, New York, 1998．
126． Nault, L. R., Rodriguez, J. G. The Leafhoppers and Planthoppers, Wiley, New York, 1985
127． Nicholson, M. C. Methods for evaluating Lyme disease risks using geographic information systems and geospatial analysis. Journal of Medical Entomology, 1996, 33(5) : 711-720
128． Oden, N. L., Sokal, R. R.. Directional autocorrelation: an extension of spatial correlograms to two dimensions. Syst. Zool., 1986, 35: 608-617
129． Perfect, T. J., Cook, A. G. Rice planthopper population dynamics: A comparison between temperate and tropical regions, In: Planthoppers: Their Ecology and Management (R. F. Denno, T. J. Perfect.), Chapman & Hall, New York/London, 1994, 282-301
130． Pottera, W. D., Denga, X. Lia, J.. et al., A web-based expert system for gypsy moth risk assessment. Computers and Electronics in Agriculture, 2000, 27: 95-105
131． Power, J. M., Saarenmaa, H., Object oriented modeling and GIS Integration in a decision support system for the management of eastern hemlock looper in Newfoundland. Computers and Electronics in Agriculture, 1995, 12(1) : 1-18
132． Ravin, F.W., Development of monitoring and decision-support systems for integrated pest management of forest defoliation in North America. For. Ecol. Manage. 1990, 39: 3-13
133． Rence, A. N., Nemeth. J.. Defection of Mountain Pine Beetle Infection Using Landsat MSS and Simulated Thematic Mapper Data. Canadian Journal of Remote Sensing, 1985, 11(1) : 50-58
134． Roberts, E. A., Ravlin, F. W. Heischer, S. J.. Am. Entomol., 1993, summer: 93-107
135． Robertson, G. P., Freckman, O. W.. The spatial distribution of nematode tropic groups across a cultivated ecosystem. Ecology, 1995, 76: 1425-1432
136． Robertson, G. P., Huston, M., Evans, F., Tiedje, J.. Spatial variability in a successional plant community: pattern of nitrogen availability.Ecology, 1988, 69:1517-1524
137． Robertson, G. P., Klingensmith, K. M., Klug, M. J., Paul, E. A., et al. Soil resources, microbial activity, and primary production across an agriculture ecosystem. Ecological Application, 1997,7: 158-170
138． Robertson, G. P.. Geostatistics in ecology: interpolating with know variance. Ecology, 1987, 68(3) : 744-748
139． Rossi, R. E., Mulla, D. J.. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecological Monographs, 1992, 62(2) : 277-314
140． Schotzko, D. J., Jane. S. K.. Effect of sample placement on the geostatistical analysis of spatial distribution of Lygus Hesperus in lentils. J. Econ. Entomol., 1990, 83: 1888-1990
141． Schotzko, D. J., O'Keefte L. E.. Geostatistical description of the spatial distribution of Lygus Hesperus in lentils. Environ. Entomol., 1989, 82: 1277-1288
142． Schotzko, D. J., Quisenberry, S. S.. Spatio-Temporal dynamics of tritropoic interactions: using the Russian Wheat Aphid (Homoptera: Aphididae) on a preferred and nonpreferred host, with the introduction of a predator as the model. In: Russian wheat aphid. Thomas Say Publictions. 1997,337-365
143． Schotzko, D. J., Smith, C. M.. Effects of host plant on the plant distribution of the Russian Wheat Aphid (Homoptera: Miridae) in Lentils. J. Econo. Entomol., 1991, 84(5) : 846-855
144． Shelstad, D., Describing the spread of oak wilt using a geographic information system. Journal of Agroculture. 1991, 17(7) :192-199
145． Shepherd, et al., Predicting the Forest Susceptibility to the Gypsy Moth (Lymantria dispar (L.)) Defoliation. Forest Ecology and Management. 1997, 98(1) : 26-34
146． Sokal, R. R., Oden, N. L., Baker, J. S. F.. Spatial structure in Drosophila buzzatii populations: simple and spatial autocorrelation. Am. Nat. 1987, 129: 122-142
147． Sokal, R. R., Oden, N. L.. Spatial autocorrelation analysis as an inferential tool in population genetics. Am. Nat., 1991, 138:518-521
148． Song, Y. H., Heong, K. L., Use of geographical information systems in analyzing large area distribution and dispersal of rice insects in South Korea. Korean Journal of Applied Entomology, 1993, 32(3) : 307-316
149． SPSS, Inc. SPSS 10． 0 用户手册, SPSS, Inc. 1999
150． Stefik, M.. Introduction to Knowledge Systems. Morgan Kaufmann, San Mateo, CA., 1995
151． Taylor, L. T.. Aggregation, variance and the mean. Nature, 1961, 189: 732-735
152． Thomas, S.J., Fosbroke, S.L.C., Cumming, A.B.. GypsES: Decision Support and Project Management-User's Guide, Version 1． 0． Radnor, PA: USDA Forest Service, Northeastern Forest Experiment Station, NA-TP-01-98． 1998．
153． Turban, E.. Decision Support and Expert Systems: Management Support Systems, third ed. Macmillan, New York, 1993
154． Twery, M. J. et al, Using G1S to assess gypsy moth hazard. Proc. ASPRS/ACSM. Annu.Comv. 1990,3:284-290
155． Vencill, A. M., Zehnder, G. W., Heatwole, C. D., Potato insect expert system: computerized approach to insecticide management for Colorado potato beetle (Coleoptera: Chrysomelidae). Journal of Economic Entomology, 1995, 88(4) : 944-954
156． Weisz, R., Saunders, M., Smilowitz, Z., et al., Knowledge-based reasoning in integrated resistance management: the Colorado potato beetle (Coleoptera: Chrysomelidae). Journal of Economic Entomology, 1994, 87(6) : 1384-1399
157． Williams, D. W., Liebhold, A. M. Herbivorous insects and global change: potential changes in the spatial distribution of forest defoliator outbreaks. Journal of Biogeography, 1995,22 (4-5) : 665-671
158． Wood, B. L., Beck, L. R., Washino, R. K. et al., Spectral and spatial characterization of rice field mosquito habitats. International Journal of Remote Sensing, 1991, 12: 621-626
159． Zadoks, J. C. EPIPRE: research, development and application of an integrated pest and disease management system for wheat. Bulletin SROP, 1998, 11(2) : 82-90
160． Zhou, G, Liebhold, M. A., and Kurt, G. Predicting the Forest Susceptibility to the Gypsy Moth Defoliation from the inventory and Geographic information, proceedings of the National Symposium on IPM in China, pp112-122, China Agriculture Science Press, 1996