934 resultados para HTML5, MVC, GIS
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The Biogeography Branch’s Sampling Design Tool for ArcGIS provides a means to effectively develop sampling strategies in a geographic information system (GIS) environment. The tool was produced as part of an iterative process of sampling design development, whereby existing data informs new design decisions. The objective of this process, and hence a product of this tool, is an optimal sampling design which can be used to achieve accurate, high-precision estimates of population metrics at a minimum of cost. Although NOAA’s Biogeography Branch focuses on marine habitats and some examples reflects this, the tool can be used to sample any type of population defined in space, be it coral reefs or corn fields.
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Land-based pollution is commonly identified as a major contributor to the observed deterioration of shallow-water coral reef ecosystem health. Human activity on the coastal landscape often induces nutrient enrichment, hypoxia, harmful algal blooms, toxic contamination and other stressors that have degraded the quality of coastal waters. Coral reef ecosystems throughout Puerto Rico, including Jobos Bay, are under threat from coastal land uses such as urban development, industry and agriculture. The objectives of this report were two-fold: 1. To identify potentially harmful land use activities to the benthic habitats of Jobos Bay, and 2. To describe a monitoring plan for Jobos Bay designed to assess the impacts of conservation practices implemented on the watershed. This characterization is a component of the partnership between the U.S. Department of Agriculture (USDA) and the National Oceanic and Atmospheric Administration (NOAA) established by the Conservation Effects Assessment Project (CEAP) in Jobos Bay. CEAP is a multi-agency effort to quantify the environmental benefits of conservation practices used by private landowners participating in USDA programs. The Jobos Bay watershed, located in southeastern Puerto Rico, was selected as the first tropical CEAP Special Emphasis Watershed (SEW). Both USDA and NOAA use their respective expertise in terrestrial and marine environments to model and monitor Jobos Bay resources. This report documents NOAA activities conducted in the first year of the three-year CEAP effort in Jobos Bay. Chapter 1 provides a brief overview of the project and background information on Jobos Bay and its watershed. Chapter 2 implements NOAA’s Summit to Sea approach to summarize the existing resource conditions on the watershed and in the estuary. Summit to Sea uses a GIS-based procedure that links patterns of land use in coastal watersheds to sediment and pollutant loading predictions at the interface between terrestrial and marine environments. The outcome of Summit to Sea analysis is an inventory of coastal land use and predicted pollution threats, consisting of spatial data and descriptive statistics, which allows for better management of coral reef ecosystems. Chapters 3 and 4 describe the monitoring plan to assess the ecological response to conservation practices established by USDA on the watershed. Jobos Bay is the second largest estuary in Puerto Rico, but has more than three times the shoreline of any other estuarine area on the island. It is a natural harbor protected from offshore wind and waves by a series of mangrove islands and the Punta Pozuelo peninsula. The Jobos Bay marine ecosystem includes 48 km² of mangrove, seagrass, coral reef and other habitat types that span both intertidal and subtidal areas. Mapping of Jobos Bay revealed 10 different benthic habitats of varying prevalence, and a large area of unknown bottom type covering 38% of the entire bay. Of the known benthic habitats, submerged aquatic vegetation, primarily seagrass, is the most common bottom type, covering slightly less than 30% of the bay. Mangroves are the dominant shoreline feature, while coral reefs comprise only 4% of the total benthic habitat. However, coral reefs are some of the most productive habitats found in Jobos Bay, and provide important habitat and nursery grounds for fish and invertebrates of commercial and recreational value.
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Gray’s Reef National Marine Sanctuary (GRNMS) is exploring the concept of a research area (RA) within its boundaries. The idea of a research area was first suggested in public scoping meetings held prior to the review of the Gray’s Reef Management Plan. An RA is a region specifically designed for conducting controlled scientific studies in the absence of confounding factors. As a result, a multidisciplinary group gathered by GRNMS was convened to consider the issue. This Research Area Working Group (RAWG) requested that a suite of analyses be conducted to evaluate the issue quantitatively. To meet this need, a novel selection procedure and geographic information system (GIS) was created to find the optimal location for an RA while balancing the needs of research and existing users. This report and its associated GIS files describe the results of the requested analyses and enable further quantitative investigation of this topic by the RAWG and GRNMS.
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The National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) initiated a coral reef research program in 1999 to map, assess, inventory, and monitor U.S. coral reef ecosystems (Monaco et al. 2001). These activities were implemented in response to requirements outlined in the Mapping Implementation Plan developed by the Mapping and Information Synthesis Working Group (MISWG) of the Coral Reef Task Force (CRTF) (MISWG 1999). As part of the MISWG of the CRTF, NOS' Biogeography Branch has been charged with the development and implementation of a plan to produce comprehensive digital coral-reef ecosystem maps for all U.S. States, Territories, and Commonwealths within five to seven years. Joint activities between Federal agencies are particularly important to map, research, monitor, manage, and restore coral reef ecosystems. In response to the Executive Order 13089 and the Coral Reef Conservation Act of 2000, NOS is conducting research to digitally map biotic resources and coordinate a long-term monitoring program that can detect and predict change in U.S. coral reefs, and their associated habitats and biological communities. Most U.S. coral reef resources have not been digitally mapped at a scale or resolution sufficient for assessment, monitoring, and/or research to support resource management. Thus, a large portion of NOS' coral reef research activities has focused on mapping of U.S. coral reef ecosystems. The map products will provide the fundamental spatial organizing framework to implement and integrate research programs and provide the capability to effectively communicate information and results to coral reef ecosystem managers. Although the NOS coral program is relatively young, it has had tremendous success in advancing towards the goal to protect, conserve, and enhance the health of U.S. coral reef ecosystems. One objective of the program was to create benthic habitat maps to support coral reef research to enable development of products that support management needs and questions. Therefore this product was developed in collaboration with many U.S. Pacific Territory partners. An initial step in producing benthic habitat maps was the development of a habitat classification scheme. The purpose of this document is to outline the benthic habitat classification scheme and protocols used to map American Samoa, Guam and the Commonwealth of the Northern Mariana Islands. Thirty-two distinct benthic habitat types (i.e., four major and 14 detailed geomorphological structure classes; eight major and 18 detailed biological cover types) within eleven zones were mapped directly into a geographic information system (GIS) using visual interpretation of orthorectified IKONOS satellite imagery. Benthic features were mapped that covered an area of 263 square kilometers. In all, 281 square kilometers of unconsolidated sediment, 122 square kilometers of submerged vegetation, and 82.3 square kilometers of coral reef and colonized hardbottom were mapped.
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A density prediction model for juvenile brown shrimp (Farfantepenaeus aztecus) was developed by using three bottom types, five salinity zones, and four seasons to quantify patterns of habitat use in Galveston Bay, Texas. Sixteen years of quantitative density data were used. Bottom types were vegetated marsh edge, submerged aquatic vegetation, and shallow nonvegetated bottom. Multiple regression was used to develop density estimates, and the resultant formula was then coupled with a geographical information system (GIS) to provide a spatial mosaic (map) of predicted habitat use. Results indicated that juvenile brown shrimp (<100 mm) selected vegetated habitats in salinities of 15−25 ppt and that seagrasses were selected over marsh edge where they co-occurred. Our results provide a spatially resolved estimate of high-density areas that will help designate essential fish habitat (EFH) in Galveston Bay. In addition, using this modeling technique, we were able to provide an estimate of the overall population of juvenile brown shrimp (<100 mm) in shallow water habitats within the bay of approximately 1.3 billion. Furthermore, the geographic range of the model was assessed by plotting observed (actual) versus expected (model) brown shrimp densities in three other Texas bays. Similar habitat-use patterns were observed in all three bays—each having a coefficient of determination >0.50. These results indicate that this model may have a broader geographic application and is a plausible approach in refining current EFH designations for all Gulf of Mexico estuaries with similar geomorphological and hydrological characteristics.
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Two halfbeak species, ballyhoo (Hemiramphus brasiliensis) and balao (H. balao), are harvested as bait in south Florida waters, and recent changes in fishing effort and regulations prompted this investigation of the overlap of halfbeak fishing grounds and spawning grounds. Halfbeaks were sampled aboard commercial fishing vessels, and during fishery-independent trips, to determine spatial and temporal spawning patterns of both species. Cyclic patterns of gonadosomatic indices (GSIs) indicated that both species spawned during spring and summer months. Histological analysis demonstrated that specific stages of oocyte development can be predicted from GSI values; for example, female ballyhoo with GSIs >6.0 had hydrated oocytes that were 2.0−3.5 mm diameter. Diel changes in oocyte diameters and histological criteria demonstrated that final oocyte maturation occurred over a 30- to 36-hour period and that ballyhoo spawned at dusk. Hydration of oocytes began in the morning, and ovulation occurred at sunset of that same day; therefore females with hydrated oocytes were ready to spawn within hours. We compared maps of all locations where fish were collected to maps of locations where spawning females (i.e. females with GSIs >6.0) were collected to determine the degree of overlap of halfbeak fishing and spawning grounds. We also used geographic information system (GIS) data to describe the depth and bottom type of halfbeak spawning grounds. Ballyhoo spawned all along the coral reef tract of the Atlantic Ocean, inshore of the reef tract, and in association with bank habitats within Florida Bay. In the Atlantic Ocean, balao spawned along the reef tract and in deeper, more offshore waters than did ballyhoo; balao were not found inshore of the coral reef tract or in Florida Bay. Both halfbeak species, considered together, spawned throughout the fishing grounds of south Florida.
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In the face of dramatic declines in groundfish populations and a lack of sufficient stock assessment information, a need has arisen for new methods of assessing groundfish populations. We describe the integration of seafloor transect data gathered by a manned submersible with high-resolution sonar imagery to produce a habitat-based stock assessment system for groundfish. The data sets used in this study were collected from Heceta Bank, Oregon, and were derived from 42 submersible dives (1988–90) and a multibeam sonar survey (1998). The submersible habitat survey investigated seafloor topography and groundfish abundance along 30-minute transects over six predetermined stations and found a statistical relationship between habitat variability and groundfish distribution and abundance. These transects were analyzed in a geographic information system (GIS) by using dynamic segmentation to display changes in habitat along the transects. We used the submersible data to extrapolate fish abundance within uniform habitat patches over broad areas of the bank by means of a habitat classification based on the sonar imagery. After applying a navigation correction to the submersible-based habitat segments, a good correlation with major boundaries on the backscatter and topographic boundaries on the imagery were apparent. Extrapolation of the extent of uniform habitats was made in the vicinity of the dive stations and a preliminary stock assessment of several species of demersal fish was calculated. Such a habitat-based approach will allow researchers to characterize marine communities over large areas of the seafloor.
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本文利用地理信息系统(GIS)技术、景观生态学理论和方法、分形理论以及统计分析方法对北京地区植被景观的空间分布特征进行了分析,并对景观格局和景观多样性的分析方法进行了探讨,结果表明: (1)对几乎所有的斑块类型,其斑块大小的分布都不是对称的,而是右偏的。4种概率分布(Г—分布、对数正态分布、Weibull分布和(负)指数分布)都只能刻划部分斑块类型,并且服从对数正态分布的斑块类型最多,服从(负)指数分布的斑块类型最少。 (2)随着斑块面积的增加,边界效应越来越小,而斑块形状越来越不紧凑。 (3)分形分析识别出本地区植被景观中的两个尺度域:一个是斑块面积小于(大约)2.7km2,另一个是斑块面积大于(大约)2.7km2。两个域中的斑块复杂程度有很大差异,后一个域中的斑块明显比前一个域中的斑块复杂,并且随着斑块面积的增加,斑块形状越来越复杂。 (4)用斑块数作为多度指标时,该景观的斑块类型一多度分布服从(截断)对数正态分布和(截断)负二项分布,不服从对数级数分布和几何分布。用斑块面积作为多度指标时,该景观的斑块类型一多度分布服从对数正态分布、Weibull分布和Г—争布,不服从正态分布。从而该景观的斑块类型一多度分布不是对称的,也是右偏的。在4个优势度/多样性模型中,“生态位优先占领”模型和Zipf-Mandelbrot模型可以较好地刻划该景观的斑块类型一多度关系。 (5)样本大小对多样性测度有直接的影响。如果这种影响比较小,就说明测度指标比较稳定。三个丰富度指数中,Ri比R2和R3更稳定;五个多样性性指数中,D和Di最稳定,OD最不稳定,因此,OD是用于景观多样性监测的理想指标;五个均匀度指数中,Jgi最稳定。根据设计的3种计算临界样方数量(即多样性测度指标达到稳定时的样方数量)方法的计算结果,上述几个最稳定的测度指标在通常情况下只需要几个样方(即总抽样面积为数百km2)就达到稳定状态。 (6)斑块类型数目随面积的增加而增加。根据四个评价指标的评价结果,认为双曲线对该景观的斑块类型一面积关系的拟合效果最好。 (7)样本较大(对于一阶刀切估计,大于30个样方;对于二阶刀切估计,大于60个样方)时,刀切法能够给出斑块类型数目(NPT)较好的估计;样本较小(小于30个样方)时,Mingoti和Meeden提出的经验贝叶斯方法能够对NPT给出比刀切法和自助法更好的估计。斑块类型一面积曲线外推虽然也能给出NPT较好的估计,但这种方法需要慎重使用,不能外推得很远。 (8)列联表分析表明,该植被景观中的斑块类型与土壤类型、岩石类型、海拔高度和坡向各因子之间均存在显著的相关性。植被景观多样性与岩石类型多样性和地形多样性之间也均呈显著的正相关关系,即植被景观多样性随岩石类型多样性和地形多样性的增加而增加。但植被景观多样性与土壤类型多样性之间不存在显著的线性相关或秩相关关系,这可能是由于二者的分类体系不吻合。植被景观多样性与总的道路密度和第二类道路密度之间均呈显著的负相关关系,而与第一类和第三类道路密度之间的关系都不显著。这反映出景观样本单元(10kmxlOkm)的尺度对应于第二类道路的影响尺度。而道路密度在一定程度上反映了人类活动的强度,因此,在10kmxlOkm这个尺度上,人类活动愈剧烈,景观多样性就愈小。
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鄂尔多斯高原是一个多层次、复杂的生态过渡带,具有复杂多样的环境条件、生态特点,因此也就具有复杂多样的植物与环境关系。本文从群落和景观两个尺度水平上研究鄂尔多斯高原植物或植被与环境关系及景观空间格局。利用鄂尔多斯高原野外植物群落样方调坦数据、微生境环境数据、气候数据,以典范对应分析(Canonical Correspondence Analysis, CCA)的方法分析了鄂尔多斯高原植物分布空间格局与环境要素的关系并对秋类环境要素对鄂尔多斯高原植物空间分布格局的贡献进行了定量分解;利用1:500 000 鄂尔多斯高原植被、土壤、土地利用、土地沙漠化类型等专题地图在GIS支持下分析了鄂尔多斯高原景观空间格局、并利用上述专题图数据加上鄂尔多斯高原气候数据库分析了土壤、土地利用、土地利用、土地沙漠化和气候等对鄂尔多斯高原植被空间分布格局的作用。通过分析,得到了以下主要结论: 1 在分析方法上、利用典范对应分析的方法,把植物分布的空间因素与环境因素分离的方法发展为植物分布空间格局不同类型影响因素作用的定量分离,提出了相应的概念模型和实现方法。 2 影响鄂尔多斯高原植物分布空间格局的主要微生境环境要素是基质类型、地下水位、覆沙厚度等,而影响鄂尔多斯高原分布空间格局的主要气候要素中,降水和干湿指标的作用大于温度和热量指标的作用。 3 通过对鄂尔多斯高原植物分布空间格局与环境关系的研究,以植物对微生境环境要素的反应为根据,把鄂尔多斯高原主要植物划分为4个大类群:梁地植物、沙地植物、草甸植物和耐盐植物。根据它们对气候要素的反应,把鄂尔多斯高原主要植物划分为典型草原植物、荒漠草原植物和草原化荒漠植物3大类。进一步,根据鄂尔多劳动保护高原植物与环境关系的研究,进行了鄂尔多斯高原植被功能型划分的尝试,得到了鄂尔多斯高原的12种主要植被功能型。 4 对鄂尔多斯高原植物分布的空间格局的影响环境因素的贡献作了定量地分解。分析结果显示:鄂尔多斯高原植物分布空间格局中有27.02%可由已知环境变量得到解释,其中21.56%与微生境环境要素相关,7.51%与气候要素的作用有关,而气候与微生境环境要素的耦合作用的份额为2.05%。根据植物生长是否直接受到地下水的影响,鄂尔多斯高原存在两大类型生态特点差异明显的生境类型:中性立地和隐域生境,对两大类型生境上影响植物空间分布格局的环境要素的作用也进行了定量分解。分析结果表明:对于植物生长不直接受地下水影响的中性立地,已知环境要素的作用可以解释植物空间分布格局总信息的29.36%,稍大于对总体上鄂尔多斯高原植物分布空间格局的解释,其中9.23%与气候要素相关,22.08%与微生境环境要素相关,而两种类型环境要素的耦合作用则占1.95%。对于植物生长直接受到地下水影响的隐域生境,所有已知环境要素对植物分布空间格局的贡献率为72.28%,其中气候要素的作用为30.31%,微生境环境要素的作用为49.08%,两类环境要素的耦合作用为7.11%。 5 描述景观空间格局的指数多种多样,这些能数在描述特定区域的景观空间格局时是有信息冗余的。本文对利用FRAGSTATS所获得的鄂尔多斯高原植被、土壤、土地利用、土地沙漠化等景观分量的20个景观指数实施了因子分析。通过因子分析,我们可以把描述鄂尔多斯高原景观空间格局的景观指数归并为以下8类:多样性指数、斑块多度指数、斑块类型丰富度指数、斑块面积指数、斑块形状指数、分形维数、空间配置指数和斑块面积变异指数。通过因子分析,还得到了这些景观指数对描述鄂尔多斯景观格局的共性特征:在描述鄂尔多斯高原景观空间格局时,作用最大的是多样性指数、斑块多度指数、面积加权平均斑块形状指数和面积加权平均分形维数,其次是斑块类型丰富度指数、平均分维指数、平均形状指数和斑块面积指数,而空间配置指数(扩散与毗连指数)和斑块面积变异指数的作用则比较微弱。 6 对鄂尔多斯高原景观指数的因子分析是非常有效和成功的。因子分析对鄂尔多斯高原植被、土壤、土地利用、土地沙漠化景观指数的分析分别得到了5-6个主要因子,可以表达原有20个景观指数所表达信息的91.1-96.0%,即可以反应鄂尔多斯高原景观空间格局的大部分信息。本文所进行的因子分析对因子进行了方差最大化(Varimax)正交旋转的处理,因子分析得到的每一个主要因子都有一个或几个与之相关性非常高的景观指数与之对应,因此,就可以用与因子分析所得主要因子相关性最高的景观指数代替该主要因子来表达鄂尔多斯高原的景观空间格局。另外还因为有些景观指数之间具有极高的相关系数,所以对因子分析所得到的景观指数可以进一步精减,最后利用因子分析成功地把原有20个景观指数减少到了11个。最后被选来描述鄂尔多斯高原景观格局的景观指数有下列11个:MSIEI(修正的Simpson均匀度指数)、AWMPFD(面积加权平均斑块分形维数)、AWMSI(面积加权平均形状指数)、NP(斑块数目)、PR(斑块类型丰富度)、MSI(平均形状指数)、MPFD(平均斑块分形维数)、MPS(平均斑块面积)、PSCV(斑块面积变异系数)、DLFD(双对数分形维数)和IJI(扩散与毗连指数)。 7 鄂尔多斯高原植被、土壤、土地利用在景观组成结构上具有一个共同特点,就是各种类型的面积差异极大,少数类型占有极大比重,而其余面积则很小。产生这一情形的原因主要与人为活动的强烈影响有关,表现在地带性的植被与土壤面积所占的比重不高,沙地、沙生植被与风沙土则占有很大比重。 8 以地带性植被和滩地隐域性植被表示的鄂尔多斯高原的原生植被仅占高原面积的不足30%,而以地带性土壤和滩地隐域性土壤表示的原生性土壤占鄂尔多斯高原总面积的近40%,说明土壤退化不如植被退化严重,或滞后于植被退化。 9 鄂尔多斯高原各景观指数的空间变化曲线,植被与土壤很相近,具有非常相似的格局;土地利用景观格局空间变化特征与植被、土壤等明显不同;土地沙漠化的景观格局空间变化曲线介植被曲线、土壤曲线与土地利用曲线之间,说明土地沙漠化不仅是一个受人为活动影响的过程,而且与自然过程密切相关。 10 鄂尔多斯高原景观格局的空间梯度变化表现出了东西向和南北向的梯度,但总体上以东西向的变化比较明显。 11 通过鄂尔多斯高原土壤类型、土地利用、土地沙漠化等景观要素、气候、空间要素与鄂尔多斯高原植被空间分布格局的CCA分析,探讨了它们之间的相互关系。以对鄂尔多斯高原植被组成数据的总方差解释的百分率为标准,土壤对鄂尔多斯植被分布的空间格局的作用最大,其方差贡献率可达44.28%,其次是土地利用与鄂尔多斯高原植被的关系也很密切,土地利用对鄂尔多斯高原植被空间分布格局的方差贡献率为22.45%,空间因素对鄂尔多斯高原植被空间分布格局的贡献率为17.51%,土地沙漠化对鄂尔多斯高原植被空间分布格局的贡献为15.65%,排在第四位;气候因素对鄂尔多斯高原植被空间格局的贡献率为11.95%,居第五位。 12 在气候要素对鄂尔多斯高原植被空间分布格局的作用中,降水与干湿指标的作用大于温度与热量指标的作用。这一点与利用野外调查样方的群落数据植物与气候关系的分析是完全一致的。CCA分析还表明鄂尔多斯高原植被空间格局的东西向变化大于南北向分异。 13 在群落和景观水平上,鄂尔多斯高原植物空间分布或植被格局的影响因素的作用具有相似的格局,即气候因子的作用明显地小于地质、土壤、水文等微生境环境要素(群落水平)或土壤(景观水平)的作用,并在这两个尺度上气候要素对植物空间分布或植被格局的定量解释份额上也是非常相近的,都仅有10%左右。气候因子对鄂尔多斯高原植物空间分布格局的这种弱的解释能力,从侧面说明了人为活动等非自然因素对鄂尔多斯高原植物空间分布格局的强烈作用。 14 在鄂尔多斯高原生态系统管理上,应协调人与自然的关系;加强鄂尔多斯高原的生物多样性保育,对于本区生态和经济对非常重要的滩地,应协调好对其开发利用与保护的关系;在鄂尔多斯高原土地沙漠化防治方面,应把调整人地关系与自然生态背景与条件相结合,如使用“三圈”模式等生态系统管理模式等。
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神农架位于湖北省西部,长江以北、汉水以南的广阔地带,属北亚热带向暖温带的过渡区域。本文依据该地区所处地理位置的植被分布规律等资料,绘制了1:20万的植被复原图。并在此基础上,运用ERDAS imagine 8.4和Maplnfoprofessional 6.0软件,分别对神农架地区的TM影像(5、4、3波段)进行监督分类及目视解译,同时结合野外的样方调查,绘制了神农架地区1:20万的植被类型图,并建立了相应的属性数据库。最后,根据野外的GPS定位点对制图精度进行了Kappa检验。 制图结果表明,制图区总面积3476.67 km2,共计504个斑块。据统计,林地面积2607.45 km2,森林覆盖率75%;山地灌丛及亚高山灌丛总面积358.62 km2,占总面积的10.3%:草甸面积156.84 km2,占4.51%。自然植被划分为10个植被型,46个群系,以及农田(包括居民点)和茶园两种农业土地利用类型。其中针叶、落叶阔叶混交林面积最大(6个群系),约908 km2,占总面积的26.12%;其它依次为落叶阔叶林(1 1个群系),针叶林(4个群系),常绿阔叶林(3个群系),山地灌丛(5个群系),常绿阔叶、落叶阔叶混交林(3个群系),亚高山灌丛(6个群系),草甸(4个群系)以及亚高山针叶林(3个群系)等。另外,两种农业土地利用类型面积共计430 kn12,占总面积的12.37%。 植被类型图与复原图叠加分析表明:①常绿阔叶林的理论分布区域,由常绿阔叶林,常绿、落叶阔叶混交林等7种植被型以及农田(含居民点)等土地利用类型共同组成。因处低海拔区域,人口集中,所以农田(含居民点)分布最广,所占面积最大,占到该区域面积的35.28%;加上长期的人为干扰,常绿阔叶林面积缩小至48.76 krr12,占到该区域面积的13.93%;②常绿、落叶阔叶混交林的理论分布区域内,因干扰后落叶阔叶林恢复较快,逐渐占据优势。另外,该区域海拔较低,人类活动也较频繁,农田(含居民点)面积仍有相当的比例;③针阔混交林理论分布区海拔位置高,人为活动影响少,原地带性植被保存较好,分布面积最大;其余部分为落阔林等7种植被型共同组成。④针叶林理论分布区域应是以巴山冷杉林为单优种的亚高山针叶林带,但因历史上的皆伐及火烧等原因,现面积仅有17 kfr12,占该区域的19.8%,其余则为亚高山灌丛及亚高山草甸所替代。
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研究了新疆阜康地区森林植被资源与环境的特征和其30年来的变化,利用Arcinfo强大的空间分析功能,对资源、DEM模型、景观指数、环境价值和新疆降水量的地统计学规律进行较全面的分析。本文分为五个部分: 1、新疆阜康地区森林资源与环境空间数据库的建立森林资源与环境空间数据库的建立是它们空问分析的基础。利用多期的遥感图象和该区的地形图,建立森林分类图形和属性库(包括森林和环境自变量集)一体化的GIS空间数据库。为了提高TM遥感图象的分类精度,利用ERDAS图象处理软件,对它进行包括主成分、降噪、去条带和自然色彩变换等增强处理,采用监督分类和人工判读相结合的方法进行分类,采用R2V、ERDAS、Arcview、Arcinfo等软件的集成,使得小班面层与某些线层的无缝联接。成功地形成一套适于西部GIS的森林资源与环境空间数据库的技术路径。此外,对新疆阜康北部地区森林资源动态进行初步分析。 2、新疆阜康地区数字高程模型(DEM)及其粗差检测分析为了提高生态建模的精度,模拟和提取该区的地面特征至关重要。在已建立的森林资源与环境空间数据库的支持下,利用Arcinfo和ERDAS,建立了新疆阜康地区的1:5万数字高程模型(DEM)。通过提取地形的海拔、坡度、坡向特征因子,分析森林植被的垂直分布。通过对DEM的粗差检测分析,分析阜康地区的数字高程模型精度。 3、新疆阜康地区景观格局变化分析在1977年、1987年、1999年森林资源与环境空间数据库的支持下,利用景观分析软件编制三个时段的新疆阜康地区植被景观类型图,并分析了近30年来新疆阜康地区景观动态与景观格局变化。结果表明:①在此期间整个研究区的斑块数减少,斑块平均面积扩大,景观中面积在不同景观要素类型之间的分配更加不均衡,景观面积向少数几种类型聚集。说明了在这期间阜康地区的景观类型有向单一化方向发展的趋势;②农耕地分布呈破碎化的趋势,斑块平均面积变小,斑块间离散程度也更高:这些变化说明人为的经济活动在阜康地区的加剧,③天然林面积减少较多,水域的面积却呈现上升的趋势,冰川及永久积雪的面积呈下降趋势, 4、新疆阜康地区森林生态效益的初步分析从广义森林生态效益定义出发,针对12种森林生态效益因变量不完全独立、且各自的自变量集不完全相同,引入具有多对多特征且整体上相容的似乎不相关广义线性模型。通过构造12种森林生态效益的“有效面积系数”和“市场逼近系数”,在森林资源与环境空间数据库的支持下,对新疆阜康地区两期的森林生态效益进行科学的计量。结果表明:新疆阜康地区的森林生态效益货币量1987年是90673.8万元,1999年是84134.4万元,总体上呈下降趋势。 5、利用新疆气象站资料研究年降雨量的空间分布规律利用ArcGIS地统计学模块,在2000年新疆气候信息空间数据库和新疆DEM模型的支持下,做出了新疆地区的年降水量空间分布图。根据新疆气候资料建立趋势而分析模型、模拟了新疆降水量空间分布的趋势值。采用3种算法(距离权重法、普通Kriging法、协同Kriging方法)计算并比较分析了研究区多年的平均降水量的时空变化。利用模拟产生的精度最优的栅格降水空间数据库,建立的多年平均降水资源信息系统,可快速计算研究区内任一地域单元中降水的总量及其空间变化,可以生成高精度的气候要素空间分布图。
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EXTRACT (SEE PDF FOR FULL ABSTRACT): Potential (clear-sky) radiation receipt is modeled for the slopes of the H.J. Andrews Experimental Forest Long-Term Ecological Research site in the foothills of the southern Cascade mountains of central Oregon. The modeling method developed by Williams is selected and applied to the forest area for the times of the solstices and equinox as well as mid-month times in January, February, April, and May in order to completely characterize the seasonal change of potential radiation at the location. ... It seems that Lookout Creek approximately divides the Andrews Forest into an area of relatively high potential radiation to the north of the creek and relatively lower potential radiation values to the south of the creek. Potential radiation values seem to be associated with the Andrews GIS data layers of debris flows and predominant tree species zones.
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苋属(Amaranthus)约40种,世界均有分布。我国有20种,分布很广,其中外来种为17种(11种为入侵种),危害旱田作物、果树、茶树和蔬菜。反枝苋(Amaranthus retroflexus L.)是苋属入侵种中发生频率最多、分布最广、危害最严重的杂草。本文首先基于反枝苋在世界范围内4207个实际分布点及其对应的气候、地形和土壤三类要素28个环境因子的定量关系,利用主成分分析确定了影响其分布的主要环境因子,据此估测其中心可能分布区和最大可能分布区,并与实际分布点进行比较;然后利用GARP生态位模型和地理信息系统(GIS)对影响苋属8个入侵种地理分布的环境因子进行分析并对其全球可能分布区进行预测,并根据苋属入侵种与环境因子的关系对8个苋属入侵种进行聚类分析;最后基于Receiver Operating Characteristics(ROC)分析对GARP模型及GIS模型对反枝苋全球可能分布区的预测结果进行精度检验和比较,结果表明: (1) GIS模型预测显示14个环境因子在决定反枝苋全球分布格局中起着重要作用。反枝苋中心可能分布区位于新西兰南部、澳大利亚东南部、南美洲北部少数地区、北美洲西北部及东南部部分地区、欧洲大部分地区和中国东南部。最大可能分布区位于南美洲中南部、北美洲大部分、非洲北部小部分、澳大利亚南部及北部少数区域、欧洲大部分地区和亚洲大部分地区及中国除西藏、青海、新疆、四川西部以外的地区。中心可能分布区的预测结果与实际分布点吻合较好,而最大可能分布区则过于广阔。 (2) GARP模型预测显示14个环境因子中雨日频率,极端低温,海拔这三个环境因子的影响较为重要,是苋属8个入侵种分布的主要限制因子。聚类分析表明8种苋属入侵种按欧式距离的长度可分为三类:第一类:反枝苋、凹头苋;第二类:刺苋、皱果苋、尾穗苋;第三类:绿穗苋、白苋、北美苋。 ROC分析结果显示GARP模型对反枝苋的可能分布区模拟效果(AUCGARP=0.857)好于GIS模型,其中GIS模型对反枝苋中心可能分布区的模拟效果(AUCGIS-CENTER=0.832)好于最大可能分布区(AUCGIS-MAX=0.778)。 苋属8个入侵种均有分布的地区为澳大利亚沿海地区,新西兰,中国东南沿海,欧洲西部,南美洲部分国家,美国,非洲中部。 (3)两种模型所预测的反枝苋的可能分布区有很大程度的重合性,GARP模型预测的可能分布区大于GIS模型预测出的中心可能分布区,但小于GIS模型预测出的最大可能分布区,且和实际分布点拟合程度较好。
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在我国,小叶锦鸡儿(Caragana microphylla)、中间锦鸡儿(Caragana intermedia)、柠条锦鸡儿(Caragana korshinskii)、藏锦鸡儿(Caragana tibetica)和狭叶锦鸡儿(Caragana stenophylla)主要分布于北方温带地区,是造林、固沙、饲用及观赏的重要灌木,而贝加尔针茅(Stipa baicalensisi)、大针茅(Stipa grandis)、克氏针茅(Stipa krylovii)、本氏针茅(Stipa bungeana)、短花针茅(Stipa breviflora)、沙生针茅(Stipa glareosa)和戈壁针茅(Stipa gobica)是我国北方温带地区具有重要饲用价值和水土保持功效的多年生密丛禾草。这5种锦鸡儿灌木和7种针茅草本植物在东北、华北和西北地区的生态环境建设及社会经济发展中发挥特殊作用。关于它们地理分布与气候关系的深入研究十分必要,可以为其种质资源的开发和改善生态环境提供理论依据。 本研究首先全面收集这12个物种在中国北方温带干旱-半干旱地区的全部地理分布资料,利用ArcGIS 9.0软件绘制现状分布图。通过分析其现实分布格局,发现小叶锦鸡儿、中间锦鸡儿和柠条锦鸡儿在空间上呈现出从东到西的地理替代分布格局,继续向西南方向则分布有藏锦鸡儿,向西北方向分布有狭叶锦鸡儿。贝加尔针茅、大针茅、克氏针茅和戈壁针茅也在空间上呈现出自东向西的地理替代分布格局,克氏针茅向南被本氏针茅替代,短花针茅和沙生针茅没有明显的地理替代分布现象。5种锦鸡儿和7种针茅的分布范围分别又都有一定的重叠。 整理12个物种分布区内的气象台站长期记录,选择计算16个具有重要生物学意义的水热指标值;进而用方差分析、多重比较和因子分析相结合的方法,研究控制这5种锦鸡儿和7种针茅地理分布的主导驱动因子。结果表明:控制小叶锦鸡儿和中间锦鸡儿间地理分布差异的主导因子是水分因子,特别是湿度;水分因子同样是控制中间锦鸡儿和柠条锦鸡儿间地理分布差异的主导因子,特别是生长季及年降水量;控制柠条锦鸡儿和藏锦鸡儿间地理分布差异的主导因子是夏季高温,控制柠条锦鸡儿和狭叶锦鸡儿地理分布差异的是冬季低温。控制贝加尔针茅、大针茅、克氏针茅和戈壁针茅间替代分布的主导气候因子是年降水量和生长季降水量。控制克氏针茅和本氏针茅间替代分布的主导气候因子是温暖指数。 运用耦合BIOCLIM模型的软件包“DIVA-GIS”模拟预测这5种锦鸡儿和7种针茅的现状潜在分布区及未来气候变化的影响。结果表明:现状潜在分布区与实际分布区均有很好的一致性;在CO2浓度加倍的未来气候情景下,这些植物都会向北大幅度迁移,在我国的分布范围均缩小,分布格局发生显著变化。用ROC曲线和Kappa统计值法验证模型表明,BIOCLIM的模拟精度较高。利用BIOCLIM模型绘制了这12个物种的生物气候分室图,并根据生物气候分室确定了物种的最适气候范围。 为了研究锦鸡儿和针茅分布对气候变化的敏感性,本文在现实气象数据的基础上模拟预测了不同降水与温度变化情景下(保持年降水量不变,年均温分别增加1℃、2℃、3℃和4℃;保持年均温不变,年降水量分别增加和减少10%)的物种分布范围,发现随着气温升高和降水量增加,全部锦鸡儿和针茅都会向高纬度地区缓慢迁移,而当降水量减少时,它们将向低纬度地区迁移。不同气候情景下的物种分布范围迁移幅度表明,5种锦鸡儿中狭叶锦鸡儿和中间锦鸡儿的脆弱性相对较大,7种针茅中克氏针茅和贝加尔针茅的脆弱性相对较大。气温的单独变化控制这些物种分布区的未来迁移。 最后,本文探索了锦鸡儿和针茅的气候变化影响的阈值。就核心分布区而言,小叶锦鸡儿、贝加尔针茅、大针茅、沙生针茅的气候变化影响阈值是气温升高4℃,降水减少10%;中间锦鸡儿和狭叶锦鸡儿是气温升高4℃,降水增加10%;柠条锦鸡儿和本氏针茅是气温升高4℃,降水不变;藏锦鸡儿是气温升高2℃,降水增加10%;克氏针茅和短花针茅是气温升高3℃,降水不变;戈壁针茅是气温升高1℃,降水不变。
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通过群落生态学和景观生态学方法,结合GIS、RS技术对锡林河流域湿地植被进行了研究。结果表明:流域湿地面积为301.62km2,占流域面积的3%左右。尽管面积相对较小,但是物种丰富,群落结构多样。植被调查数据显示基本确定的植被型4个,植被亚型6个,群系组16个,群系68个,区系成分以泛北极种为主,占69%,相对简单;按照水分生态型划分,中生物种占最多,为44.32%;按生活型分以多年生草本为主占50%以上;科属分布相对复杂,隶属39个科,其中禾本科和菊科是最大的两个科,所占比例仅有17.30%和12.43%,其他科没有明显的优势性,充分说明湿地优越的生境可以满足多种植物共同生长。 多度分布是研究物种多样性分布的重要组分,同时反映了群落结构的特性。以常用的Lognormal、Logseries和Weibull、Exp、Power模型来拟和6个典型草甸群落和踏头草甸群落的物种多度分布,分log-相对多度-物种级数和物种-游程两种形式进行比较;同时,对于典型草甸群落和踏头群落区分常见种、偶然种等进行细化,深入分析群落多度的变化。结果表明,5个模型对于log-相对多度-物种级数在整个群落水平上均不能很好的拟和,50%以上的点都落在95%置信区间以外;但是对常见种和偶然种的拟和情况要好,Weibull、Power和Logseris模型分别对典型草甸群落常见种、偶然种和中间种能很好的拟和,而Logseries和Power模型对于踏头群落的常见种和偶然种拟和较好。5个模型都能较好的拟和物种-游程分布,其中K—S检验结果表明:Lognormal模型对于无脉苔草、针苔草和荸荠这类相对湿润环境下的典型草甸群落拟和较好,对于长叶火绒草和密花风毛菊群落Weibull拟和最好,Power 模型拟和箭叶橐吾最好,踏头草甸拟和最好的是Logseries模型,踏头间拟合最好的是Exp模型。不同的拟和模型应用于不同的群落类型,可以看出湿地群落的复杂性和生境的多样性。区分常见种和偶然种的拟合结果表明典型群落和踏头群落表现一致,即Lognormal模型对所有种拟和是最好的,而Power模型对偶然种的拟和是最好的,同时,Lognormal对典型草甸群落的中间种拟和也是最好。从中可以看出典型草甸群落和踏头群落尽管在表现形式上不同,但是群落的内部仍存在相似的联系,可能跟相似物种的作用有关。 根据湿地表观类型、植被水分状态和航片判别能力,结合实地调查,采用监督分类的方法将锡林河流域的湿地划分为低湿地草甸、盐化草甸和沼泽三种类型。自1984年以来20多年的时间中,锡林河流域的湿地发生了巨大的变化。尽管总的面积没有太大变化,但是湿地类型发生转化。中上游的低湿地草甸面积减少8.94%,沼泽面积减少30.82%,同时,盐化草甸的面积增加了15.98%。增加的盐化草甸主要是另外两种湿地类型转化而成的,中游水库截流,加速中下游草甸的盐化是锡林河流域湿地变化的主要原因。利用GIS技术依据探讨不同湿地的空间变化,分析沙化对湿地变化的影响,结果表明:沙化只对少数湿地有影响,发育良好的湿地即使处在相对强烈的沙化环境下,仍能保持不变。接着,分析了人类直接干扰对湿地变化的影响,缓冲区居民点分析结果表明:近20年来,位于湿地周边的居民点分布格局发生显著的变化。1980年代,居民点分布在盐化草甸周边的最多,到2004年,居民点在沼泽草甸分布数量为最多,该类湿地水、草和资源最为丰富,人类直接的干扰最大,进而转化成另外两类,减少面积最大。低湿地草甸是物种丰富,结构复杂的一种湿地,抗干扰能力强,恢复能力也强,因此相对的变化面积较小。以锡林浩特市水库上下游的湿地植被物种和群落结构的变化,证明了水量减少是湿地数量、结构改变的直接影响因子。