上海滩涂植被资源遥感分析

利用2003年8月2日L andsat5-TM多光谱遥感影像,运用遥感处理软件ERDA S Im ag ine 8.6,经几何校正分幅裁剪等图像预处理后,采用监督分类和目视解译相结合对上海市滩涂植被进行解译分析。结合全球定位系统(GPS)样点定位,对分类结果进行全面的野外核实和修正,同时利用地理信息系统(G IS)对解译结果进行数据合成,统计出滩涂各类植被的分布区域及面积等数据。实际调查及其分析统计显示,上海滩涂植物群落总面积为21302.1hm2,主要植被组成为芦苇、海三棱草及互花米草三大群落,滩涂植物群落具有明显的高程梯度分布规律。大尺度的上海市滩涂植被的空间分布现状及其数量调查为上海市滩涂资源的合理规划、生物多样性保护和可持续开发利用提供科学依据。     

基于QUEST决策树兼容多源数据的淡水沼泽湿地信息提取

以三江平原东北部为例,探讨了中国典型淡水沼泽湿地信息的提取方法.利用TM卫星影像数据,基于半方差分析和z检验方法对比研究区典型地物不同尺度的各种纹理特征,从而遴选最优的窗口大小、纹理特征及其派生波段以提高地物之间的可分性.采用快速、无偏、高效统计树(quick,unbiased,and efficient statistical tree,QUEST)算法集成遥感影像的光谱特征、多尺度纹理特征和地学辅助数据建立研究区湿地信息提取的决策树模型.基于实测的GPS样本点采用混淆矩阵的方法对分类结果进行精度验证,并与传统的最大似然监督分类方法(maximum likelihood classification,MLC)进行对比.结果表明,基于QUEST的决策树分类结果的总精度和Kappa系数分别为84.58%和0.816,分类精度较MLC监督分类方法有明显提高,是内陆淡水沼泽湿地信息提取的有效手段.     

多尺度遥感综合监测我国北方典型草原区植被盖度

利用多尺度遥感影像综合进行全球和区域尺度的土地利用/覆盖变化(LUCC)研究是最近全球变化研究的重要方向之一.本文综合利用野外群落样方、数字相机、ETM+影像、NOAA/AVHRR影像,在遥感、GIS和GPS支持下,对我国北方典型草原区植被盖度进行了综合监测、模拟与分析.结果表明:(1)利用经处理后的数字相机影像测量盖度的结果准确性较高,可以作为植被盖度测量的标准结果,反映真实的覆盖特征,并用以验证利用其它方法测量结果的精度.(2)利用野外1 m2样方网格法目视估测的植被盖度结果变化较大,不稳定.本次实验中,与数字相机测量结果相比,样方估测的盖度普遍偏高,平均偏差为9.92%;但两者相关性较好(r2=0.89).(3)采用Gutman模型ETM+影像、NOAA/AVHRR影像反演植被盖度的结果与数字相机测量结果偏差分别为7.03%、7.83%,ETM+像元分解NOAA像元后得到的植被盖度与数字相机测量结果偏差5.68%.三者与数字相机测量结果的相关系数r2分别为0.78、0.6l和0.76.(4)利用野外实测植被盖度数据直接与NOAA-NDVI影像建立统计模型估算植被盖度的精度较低(r2=0.65),而通过空间分辨率介于两者之间的ETM+影像进行转换后,该精度得到一定的提高(r2=0.80).利用像元分解的方法提高了大尺度植被盖度监测的精度,是利用遥感数据进行尺度转换研究的重要实践.多尺度遥感影像的综合对地观测对大区域上反演植被盖度有很好的促进作用.     

多尺度遥感综合监测我国北方典型草原区植被盖度

利用多尺度遥感影像综合进行全球和区域尺度的土地利用/覆盖变化(LUCC)研究是最近全球变化研究的重要方向之一。本文综合利用野外群落样方、数字相机、ETM+影像、NOAA/AVHRR影像,在遥感、GIS和GPS支持下,对我国北方典型草原区植被盖度进行了综合监测、模拟与分析。结果表明:(1) 利用经处理后的数字相机影像测量盖度的结果准确性较高,可以作为植被盖度测量的标准结果,反映真实的覆盖特征,并用以验证利用其它方法测量结果的精度。(2) 利用野外1 m2样方网格法目视估测的植被盖度结果变化较大,不稳定。本次实验中,与数字相机测量结果相比,样方估测的盖度普遍偏高,平均偏差为9.92%;但两者相关性较好(r2=0.89)。(3) 采用Gutman模型ETM+影像、NOAA/AVHRR影像反演植被盖度的结果与数字相机测量结果偏差分别为7.03%、7.83%,ETM+像元分解NOAA像元后得到的植被盖度与数字相机测量结果偏差5.68%。三者与数字相机测量结果的相关系数r2分别为0.78、0.61和0.76。(4)利用野外实测植被盖度数据直接与NOAA-NDVI影像建立统计模型估算植被盖度的精度较低(r2=0.65),而通过空间分辨率介于两者之间的ETM+影像进行转换后,该精度得到一定的提高(r2=0.80)。利用像元分解的方法提高了大尺度植被盖度监测的精度,是利用遥感数据进行尺     

地形对植被生物量遥感反演的影响——以广州市为例

目前植被生物量遥感反演研究中的地形校正主要是校正地形变化对地表反射率的影响,较少考虑地形起伏引起的像元面积与实际地表面积的差异,而这种差异将导致植被生物量估算结果的偏差.在生物量遥感反演的基础上,结合地表面积计算模型和物质守恒定律,建立了生物量地形校正模型,定量分析和讨论了地形起伏对广州市植被类型面积提取和生物量准确估算的影响.结果表明:地形校正前后全市针叶林、阔叶林、草地、灌木林和园地面积分别增加6.18％、3.70％、2.86％、1.92％和1.29％;在综合分析区域生物量遥感反演中的各种不确定性的基础上,建立的各植被类型的生物量模型均具有较高精度,相关系数均接近或者超过0.9,可以满足生物量反演的要求;全市植被生物量呈现出东、北高,西、南低的分布格局,像元实际代表的林地(阔叶林和针叶林)平均生物量为61.86t/hm2,高于珠三角区域生物量平均值,但与亚热带林的顶级群落生物量水平有较大差距,林地生物量还有较大的增长空间;经过校正地形变化引起的像元面积和实际地表面积差异对生物量提取结果的影响后,植被总生物量比校正前增加了5.82％,5种植被类型的总生物量有不同程度的增加,阔叶林、针叶林、草地、灌木林和园地分别增加了7.74％,4.76％、3.34％、2.50％和1.58％.与其它的表面积计算模型相比,利用的像元地表面积模型具有较高的精度,可以满足生物量遥感估算中地形校正的需要.     

用支持向量机识别 β-发夹模体

基于蛋白质序列,提出了一种新的超二级结构模体β-发夹的预测方法。利用离散增量构成的向量来表示序列信息,并将6个离散增量输入支持向量机,在六维向量空间中寻找最优超平面,将β-发夹和非β-发夹进行分类。计算结果表明,利用所设计的算法预测β-发夹,有较高的预测能力。对于训练集,5-交叉检验的预测总精度为81.24％,相关系数为0．57,β-发夹敏感性为83．06％;对于独立的检验集,预测总精度为78．34％,相关系数0．56,β-发夹敏感性为77．24％。将此预测模型应用于CASP6的63个蛋白质进行检验,得到较好结果。     

南方红壤侵蚀区土壤肥力质量的突变——以福建省长汀县为例

以南方红壤侵蚀区典型区域福建省长汀县为研究区,将土壤肥力质量10个因子作为内部因子,坡度、植被覆盖度、水土流失强度等作为外部因子,构建土壤肥力质量演变的尖点突变模型,并分析土壤肥力质量演变分别与土壤肥力质量等级、水土流失强度、坡度和植被覆盖度的关系.研究结果表明:1)90个样点中,突变的样点共27个,占30％,稳定的样点共63个,占70％;2)突变样点主要对应于土壤肥力质量等级1和等级2(分别占突变样点总数的48.15％和33.33％)、水土流失微度和轻度(分别占37.04％和44.44％)、坡度5-10.和10-15.(分别占37.04％和40.74％)、植被覆盖度＞0.4和0.3-0.4(分别占48.15％和37.04％);分叉集△与土壤肥力质量等级、水土流失强度、坡度和植被覆盖度都呈极显著(P＜0.01)相关关系,皮尔逊相关系数绝对值的大小顺序为:水土流失强度＞植被覆盖度＞土壤肥力质量等级＞坡度;突变主要发生于土壤肥力质量等级较高、水土流失强度较轻、坡度中等、植被覆盖较好的地点;3)土壤肥力质量演变时间相对较长,应根据中间过渡状态来判断是否产生突变;土壤肥力质量处于突变状态时可用较小投入产生较大效益,在关注严重水土流失区生态恢复与重建的同时,不应忽视突变区的治理.     

基于面向对象及光谱特征的植被信息提取与分析

面向对象分类方法是实现精确详细提取遥感信息的新方法.以2010年TM影像作为基础数据,采用面向对象方法着重提取了西辽河流域平原的植被信息(耕地、林地、中生偏旱和中生偏湿草).通过对不同地物的光谱和空间信息(位置、形状)进行比较分析,建立适当的隶属度函数和阈值范围,在草地的分类规则中,参考野外采集的草地植被光谱信息,辅助草地属性特征选择.最后构建分类决策树,完成自动分类,分类总体精度达到82.13％.在分类结果的基础上,讨论了植被分布特点,对不同类型的植被面积、不同区县植被面积以及植被与一级河流关系三方面进行分析.结果显示:研究区植被以林地和耕地为主,分别占总面积的38.9％和23.3％;耕地、林地、中生偏湿草地多分布于河流中下游区县,受人为因素影响较大的林地、耕地主要集中在科尔沁左翼中旗和科尔沁左翼后旗;林地、中生偏湿草较集中分布在一级河流的10km缓冲区内,耕地主要集中在5km缓冲区内,中生偏旱草与一级河流的关系不显著.     

基于中分辨率TM数据的湿地水生植被提取

利用湿地水生植被生长旺盛、光谱反射较强、光谱信息比较丰富的8月份中分辨率Landsat TM和ETM+多光谱遥感影像,采用面向对象的分类方法,进行野鸭湖湿地水生植被的提取。研究表明:在提取过程中,通过对原始影像进行主成分变换和穗帽变换,将主要信息与噪声分离,不仅减小了数据冗余和波段间的相关性,而且增大了影像上湿地水生植被与其他地物类型光谱和空间信息的差异性,并结合野外水生植被光谱特征分析,选择归一化植被指数NDVI与归一化水体指数NDWI辅助分类,构建特征波段或波段组合,然后,确定适当的隶属度函数和阈值范围,构建分类决策树,完成湿地水生植被的自动分类,提高了影像分割与面向对象分类的精度,取得了较为理想的湿地水生植被提取结果。2002年和2008年两景影像的总体分类精度分别达到86.5%和85.44%,表明中分辨率TM影像可以满足湿地水生植被提取的需要,又因为其具有较高的波谱分辨率、极为丰富的信息量、相对较低的价格、长时间序列,可以作为近20a湿地水生植被提取和动态变化监测的主要数据源。     

点格局分析中边缘校正的一种新算法及其应用

在点格局分析中,通常选取一个矩形作为研究区域,而K(d)函数估计值的方差倾向于随着距离尺度的增加而增加.作为一种粗略的指导,距离尺度的最大值一般为矩形最小边长度的一半.在这种情况下,边缘校正的权重最小值为0.25.通过在校正圆上等弧长取点,用校正圆上落在研究区域之内的点数除以整个校正圆上的点数,作为边缘校正权重的近似值.点数越多,这种近似算法越接近传统的精确算法.这种近似算法不仅适用于计算研究区域为矩形的边缘校正权重,而且适用于计算研究区域为任意多边形的边缘校正权重.此外,当矩形研究区域中点事件的信息不足时,这种算法可以允许计算接近到距离尺度的上限(即矩形对角线长度的一半)对应的K(d)函数.     

新疆北塔山雪豹对秋季栖息地的选择

2004年9~10月,对新疆北塔山地区雪豹(Unicaunica)栖息地选择进行了调查。在选定的15条样带上测定了59个利用样方及30个任意样方的6种生境特征(海拔、地形、植被类型、生境平坦度、放牧状况和坡向),其结果如下:Vanderploeg和Scavia选择指数表明:雪豹对海拔、地形、植被类型、生境平坦度和坡向存在选择性:(1)雪豹偏好利用海拔2000~2200m,而避开2600~3000m区域;(2)倾向于悬崖底部和山嵴,避开山坡和山谷;(3)偏好于灌丛,避开森林;(4)避开平坦的开阔地;(5)倾向于选择非放牧区域活动;(6)倾向于选择北坡,避开南坡。对生境特征的主成分分析显示:前3个主成分(海拔、地形和植被类型)的累积贡献率达到75·76%,可以反映雪豹的栖息地特征,同时表明:影响雪豹栖息地选择的主要因素依次为放牧状况、植被类型、地形和生境平坦度。     

Remote Sensing and Geographic Information Systems and risk of American visceral leishmaniasis in Bahia, Brazil

The spatial distribution of American visceral leishmaniasis (VL) was studied within the context of the environmental characteristics of northwest Bahia State in Brazil during an epidemic year. Geographic Information Systems (GIS) and Remote Sensing (RS) were used to characterize the landscape epidemiology of VL in order to identify and map high risk areas and endemic zones in a northwestern Bahia study area. Normalized Difference Vegetation Index (NDVI) was shown to be one of the most important risk factors in the area of study. Low NDVI values were related to high numbers of sand flies and high numbers of human and canine VL positive cases. Caatinga vegetation type was the dominant vegetation type in the endemic area. The use of RS and GIS allowed the identification of classes of VL risk that may be useful information to guide control program interventions.     

Machine learning and GIS-RS-based algorithms for mapping the groundwater potentiality in the Bundelkhand region,India

Mapping the groundwater potential zones with high accuracy is always a difficult task. The combination of Geographic Information System (GIS) and Remote Sensing (RS) with machine learning techniques provide a reliable method to map the groundwater prospective areas. This research used support vector machine (SVM) learning and random forest (RF) regression algorithms to predict the groundwater potential areas in the Bundelkhand craton region. All the parameters that affect the groundwater occurrence in this region, such as altitude, aspect, distance to drainage, distance to road, distance to faults, geomorphology, distance to river, lithology, normalized difference vegetation index (NDVI), rainfall, slope, soil, drainage density, land use land cover (LULC), topographic wetness index (TWI), lineament density and curvatures have been prepared by the remote sensing data as well as data taken from different departments and organizations. The training and testing dataset is generated from the groundwater potential zones map prepared through the frequency ratio (FR) technique. A total of 23,917-pixel locations have been selected in the research region. These locations contain the location of groundwater points and non-groundwater points equally. These points were randomly portioned into 70:30 for training and testing the model, respectively. The 2417 unknown points were taken from the study area and given to the trained model to predict the groundwater prospective areas. The maps of groundwater potential zones obtained using machine learning models were categorized into five classes: very low, low, moderate, high, and very high. The outcome of the employed algorithm is validated through the well discharge data and area under the receiver operating characteristics curve (AUC-ROC) method. The developed model's outcome gives valuable information regarding the effective management of groundwater in a particular region to government agencies and private sectors.     

Application of self-organizing neural networks to classification of plant communities in Pangquangou Nature Reserve, North China

Vegetation classification is an important topic in plant ecology and many quantitative techniques for classification have been developed in the field. The arti-ficial neural network is a comparatively new tool for data analysis. The self-organizing feature map (SOFM) is powerful tool for clustering analysis. SOFM has been applied to many research fields and it was applied to the classification of plant communities in the Pangquangou Nature Reserve in the present work. Pangquangou Nature Reserve, located at 37°20'-38°20' N, 110°18'-111°18' E, is a part of the Luliang Mountain range. Eighty-nine samples (quadrats) of 10 m x 10 m for for-est, 4 m × 4 m for shrubland and 1m x 1m for grass-land along an elevation gradient, were set up and species data was recorded in each sample. After discussion of the mathematical algorism, clustering technique and the pro-cedure of SOFM, the classification was carried out by using NNTool box in MATLAB (6.5). As a result, the 89 samples were clustered into 13 groups representing 13 types of plant communities. The characteristics of each community were described. The result of SOFM clas-sification was identical to the result of fuzzy c-mean clus-tering and consistent with the distribution patterns of vegetation in the study area and shows significant eco-logical meanings. This suggests that SOFM may clearly describe the ecological relationships between plant com-munities and it is a very effective quantitative technique in plant ecology research.     

Resources Evaluation of Seed Plants in Yulong Snow Mountain,Lijiang, Northwestern Yunnan

Yulong Snow Mountain (Jade Dragon Snow Mountain), located in northwestern Yunnan, is belonging to one of three biodiversity centers of China. In this paper, we analyzed the composition of seed plant resource species in Yulong Snow Mountain based on databases and checklists of seed plants, and specimens collected recently in this area. More than 2815 seed plant species in Yulong Snow Mountain region were recorded, among which 96 species are endangered. Furthermore, we classified and evaluated these plant resources based on their use and biological characteristics. In addition, we propose a systematic and sustainable approach to solve the conflicts between the conservation and the economical development. This study updated the checklist of seed plants of Lijiang Alpine Botanical Garden, while also documented the basic information of plant resources in Yulong Snow Mountain. These findings provided scientific basis for future research and conservation.     

群落结构和叶面积指数在具茨山植被次生演替中的变化

采取3S(GIS,RS和GPS)技术和野外作业相结合的方法,研究了河南省具茨山人为干扰后植被次生演替过程。在此基础上,从宏观和微观尺度对不同演替阶段典型群落的结构变化和植被叶面积指数(LAI)动态进行分析。结果表明:乔木阶段的植被LAI均值为4.1～5.5;灌木阶段为3.0～3.7;草本阶段为1.0～1.5。部分灌木林在生长季的LAI和盖度高于乔木林。随着演替进行,群落结构也发生很大变化,乔木种类和数量逐步上升。植被系统的复杂度和稳定性不断增强,其发挥的生态功能和生态服务价值随之提高。     

西双版纳、白茫雪山、波罗的海南岸地衣纯培养放线菌多样性

【背景】在地衣共生系统中除了共生真菌、藻类以外,还蕴藏着丰富的放线菌资源。【目的】采集来自云南西双版纳、白茫雪山、德国波罗的海南岸3个地区的地衣,对获得的地衣纯培养放线菌进行多样性分析。【方法】采用3种放线菌选择分离培养基,通过平板稀释涂布法分离放线菌。通过比较16S rRNA基因序列相似性以及构建系统发育树,确定纯培养放线菌的分类地位。【结果】共分离菌株1 123株,鉴定417株。其中从西双版纳17份地衣样品中分离纯化到107株放线菌,分布在7个目14个科33个属,潜在新种18株,其中链霉菌为优势菌属;从白茫雪山7份地衣样品中分离纯化到103株放线菌,分布在4个目5个科9个属,潜在新种16株。其中链霉菌为优势菌属,占比39%;从波罗的海南岸5份地衣样品中分离纯化到65株放线菌,分布在4个目8个科18个属,潜在新种5株,潜在新种菌和链霉菌为优势菌属。【结论】在本研究条件下,西双版纳可培养地衣放线菌多样性较白茫雪山和波罗的海南岸丰富。白茫雪山地衣链霉菌居多,潜在新种占比15.5%。3个地区地衣放线菌的区系组成各不相同,这与3个地区地衣所处地理环境、完全不同的气候密切相关。     

Integrating a participatory process with a GIS-based multi-criteria decision analysis for protected area zoning in China

Current management plans for nature reserves in China are ineffective and do not meet their intended goals of balancing conservation and development. Protected area zoning is an approach that can reduce conflict by partitioning the landscape into various land use units that are managed for different levels of human activity. In this paper, we present a systematic methodology that integrates a participatory process with Geographic Information Systems (GIS) using a multi-criteria decision analysis (MCDA) technique to guide a zoning scheme for the Meili Snow Mountain National Park (MSMNP), Yunnan. A participatory process involving multiple stakeholders and technical experts was used to establish park management objectives: nature conservation; tourism and recreation; and, community development. To meet these three objectives, criteria and impact factors for each objective were identified and weighted through a participatory process, and a GIS fuzzy set membership function was then used to generate gradient effects of each criterion. Three suitability maps of conservation, eco-tourism, and community development were generated. The three suitability maps were overlaid to generate a final combined suitability map, and five management zones (strict protected zone, ecosystem conservation zone, eco-tourism zone or buffer zone, park service zone, and traditional land use zone) were identified and mapped that corresponded to different protection and management strategies. Conservation targets (endangered species, alpine and forest ecosystems) and development zones were spatially segregated across the landscape, and the final zoning scheme was found to be acceptable by the various stakeholders.     

Accuracy,Precision, and Observation Rates of Global Positioning System Telemetry Collars

ABSTRACT We addressed concerns regarding performance of various Global Positioning System (GPS) collar configurations for describing habitat use by Rocky Mountain elk (Cervus elaphus) in rugged, forested terrain. We tested 8 GPS collars (Lotek Wireless, Newmarket, ON, Canada) in 4 different model and equipment configurations at 2 reference points (an open hilltop and a forested ravine) to determine habitat-specific differences in performance among collar configurations. We then placed individual collars at 60 additional points that were stratified randomly among 4 canopy-cover classes and 3 classes of available sky. All collars exhibited a locational bias of 4 m horizontally west and of 10 m vertically below a reference standard established by position-averaging with a handheld receiver (Garmin 12MAP) calibrated at National Geodetic Survey benchmarks. The GPS collar models that were programmed for longer satellite-acquisition times provided greater location precision than models that had been programmed for short acquisition times to preserve battery power. Canopy cover and available sky had a greater effect on collar location precision and observation rates than slope, slope position, aspect, conifer basal area, tree height, canopy depth, or elevation. Researchers should test collars at known reference points to confirm that location precision and rates of observation are adequate for their particular study objectives. Manufacturers of GPS collars should inform clients of their programming criteria for acquisition time so that customers can make informed decisions regarding trade-offs between precision of locations, data quantity, and battery life.     

玉龙雪山地区地表三维温度场时空变化分析

玉龙雪山属于海洋型冰川,对全球气候变化敏感,具有重要的生态研究价值。为了获取玉龙雪山地区的地表温度变化情况,提出了一种基于地表三维温度场的定量分析方法。首先,采用高分辨率立体测绘卫星影像构建数字表面模型,作为三维底图参考,并利用多时序热红外卫星影像数据反演地表温度场模型,提供地表温度变化依据;然后,在统一参考坐标系下将数字表面模型和多时序地表温度场模型套合,准确地分析地表温度时空变化情况。试验利用1987年至2018年间同一季节的Landsat卫星遥感影像反演地表温度,结合资源三号卫星立体影像构建的数字表面模型,并采用四阶温度区间分析多时序范围内玉龙雪山地区的地表温度变化情况。试验结果表明玉龙雪山低温区和次低温区面积逐渐减小,减小速度分别为2.096 km²/a和2.662 km²/a,中温区和暖温区面积逐渐增大,增大速度分别为2.902 km²/a和1.703km²/a,玉龙雪山地区的地表温度呈现整体上升的趋势,为全球生态环境变化提供参考。