【作者】 杨永顺；

【导师】 陈全功；

【作者基本信息】 兰州大学 ， 草业科学， 2007， 硕士

【摘要】 草地植被作为我国最重要的植被类型之一，是重要的可更新资源和畜牧业的原料基地，也是地球上最大的天然绿色屏障，对于人类的生存和发展起着重要的作用。但是草地的退化问题一直在警钟长鸣，其中1989～1999年全国退化草地的面积从8667万hm~2增加到1.3亿hm~2，增加了4333万hm~2，草地平均以每年433.3万hm~2的速度在退化。草地的退化将导致草地生态系统生产力和NEP(净生态系统生产力)的显著下降，并引发自然资源不断减少和生态环境的恶化。过度放牧和草原垦殖是导致草地退化的主要人为因素。因此，监测调查天然草地资源，阐明草地数量、质量、生产力状况、利用价值和发展潜力，利用可操作的方法明确各地草地的具体产草量、草地载畜量等，对于贯彻执行《草原法》，科学利用和开发草地资源，改良和保护草地，指导畜牧业生产以及制定宏观经济发展战略和生产发展规划等具有极重要的意义。3S技术(GIS、RS、GPS)在资源调查、环境监测、作物估产、区域规划等领域展示着至关重要和不可替代的应用前景。在草地资源方面，国内外很多学者利用LANDSAT的MSS、TM、NOAA／AVHRR资料以及MODIS等资料做了大量工作，尤其对草地生产能力的评价。然而，在检索到的文献资料里，多是对于某区域特定时间段或序列草地承载能力的研究，仅仅是使用计算机桌面程序对其计算分析。本文综合应用3S技术(GIS、RS、GPS)，结合草业科学的专业知识，以回答在什么地方(以县为单位)，有多少产草量，可以承载多少载畜量为目标，设计研究了基于WebGIS的草原监测系统，具体的研究工作主要有以下两部分：1、依据陈全功教授关于草地载畜量关键场理论的思想，利用美国NASA中心提供的MODIS／TERRA VGETATION INDICES 16-DAY L3 GLOBAL 250M SIN GRID V004产品，配合新疆阿勒泰地区2006年草地野外实测数据，根据相关性原则回归出适合该地区的草地遥感估产模型：Y=-6860.252+632.415X-8.337X~2+0.034X~3其中Y代表草地产草量，X代表植被指数(ARNDVI)。根据草地遥感估产模型计算了2006年新疆阿勒泰地区各市县的理论产草量、理论载畜量与关键场载畜量。2、从当前的草地监理与草业发展的需求出发，设计开发了基于WebGIS的中国草原监测系统：1)该系统结合桌面GIS与WebGIS的长处，定期发布以省、地区以及县为单位的地面植被MODIS卫星遥感监测结果的专题图件，其分辨率为250M，并提供下载共享。2)该系统根据现有资料，以县为单位，对2006年新疆阿勒泰地区的草地理论产草量、理论载畜量和关键场载畜量的计算结果予以发布，并拟在以后每年九月份对该地区以上指标进行预警，为冬春季草地利用与家畜管理决策提供依据。3)该系统通过网络环境，收集草地地面监测点的数据与信息，为及时了解地面实况，准确解译卫星遥感图像，以及支持决策提供保障。更多还原

【Abstract】 Grassland is one of the most important vegetation forms in China. It is an important renewal resource and raw material base for the animal husbandry. To man, the grassland plays a key role in living and development. But the grassland deterioration has alerted us. From 1989 to 1999, the deterioration area had increased by 4333 thousands hm~2. The deterioration can cause the descent with grassland ecosystem productivity and net ecosystem productivity. Overgrazing and reclaiming cause the deterioration.Therefore, it is of great important for us to investigate natural grassland resource and clarity the quantity, quality, productivity status and development potential of the resource, furthermore, to improve the productivity and economic benefit of grassland resource, so that we can make use of and develop grassland resource more scientifically."3S" technique (GIS、RS、GPS) shows its charming in investigating resources, monitoring environment and planning region etc. A great deal of work had been done with MSS、TM、NOAA/AVHRR and MODIS data in grassland resource investigation both at home and abroad, especially in the assessment of grassland productivity. However, we acquired from the literature review that most of their effort focused on the research of carrying capacity of grassland during certain period, what is more, little working dealt with WebGIS.This paper integrates "3S" technique (GIS、RS、GPS) with the special knowledge to answer questions as following : Where is, how many the grass yield is and how many the carrying capacity is .The research involves two parts:1. According to The Key Pasture Theory advised by Prof. Chen Quangong, we developed an regression model which is suitable for productivity assessment based on remote sensing data as MODIS/TERRA VGETATION INDICES 16-DAY L3 GLOBAL 250M SIN GRID V004 product obtained from NASA and some field observation data collected from Aletai district of Sinkiang province in 2006, which is suggested the following equation:Y = -6860.252 + 632.415X - 8.337X~2 + 0.034X~3 Where Y is grass yield, X is vegetation index (ARNDVI)。2. We designed WebGIS-based grassland monitoring system for China to meet the needs of grassland monitoring and development.1) The system integrated the characteristics of DesktopGIS and WebGIS will release ground vegetation monitoring results obtained from MODIS data (with the ground resolution of 250M and can be downloaded for free) in the form of theme maps reflecting glass yield periodically. 2) The system had released theoretical carrying capacity of grassland and key pasture carrying capacity at Aletai district of Sinkiang province in 2006, and will give warning every September.3) The system will collect the data and information of grassland monitoring.As a result assist interpret remote sensing imageries precisely, so that can promise the success of the decision support system.更多还原