草地蝗虫发生遥感监测的一种新算法

草地蝗虫发生的遥感指标分析是建立草地蝗虫发生预报模型的基础。以Landsat-5 TM为遥感信息源，根据草地蝗虫发生的遥感机理，提出了一种基于比值的草地蝗虫发生监测的遥感新算法，即R／G=(TM4 TM7)／TM6。在青海湖地区，结合草地蝗虫发生的野外样点数据，应用此新算法以及常用的标准植被指数[归一化植被指数(NDVI)和土壤调节植被指数(SAVI)]，分别进行了此3种指标对草地蝗虫发生的差异显著性检验。结果表明，在草地蝗虫密度≥5头／m^2时，常规的标准算法(NDVI，SAVI)优于新算法(RIG)；而在草地蝗虫密度≥15头／m^2和≥25头／m^2时，RIG优于NDVI和SAVI，可用于草地蝗虫发生的遥感监测。     

草地光谱分类最佳时相选择分析

利用2003年5-10月在环青海湖地区获取的典型天然草地与人工草地多时相地面高分辨率光谱数据，首先分析了最大似然分类法、支持向量机分类法、光谱角分类法、最小距离分类法和人工神经网络分类法所对应的最佳光谱变换方案；通过16个时相光谱数据的分类对比实验，分别确定了天然草地与人工草地分类、人工草地分类、天然草地分类的最佳时相；最后利用TM遥感数据对地面光谱数据分析结果进行了补充验证。     

基于地理国情监测的石家庄自然生态环境状况研究

利用2009、2018年分辨率为1 m的卫星遥感影像,依据地理国情监测的分类体系,获取了两个年份石家庄8个区县的地表覆盖数据,展示了该区域林地、草地和水域三类自然生态空间要素10 a间的变化情况,并采用生境质量指数、水源涵养指数两项指标对该区域的生态环境状况进行了评价.归因于该区域固有的自然地理特征,大部分地区生境质量...     

基于光谱特征的雅鲁藏布江源区草地类型识别

以雅鲁藏布江源区为研究对象,以Landsat5 TM图像为数据源,根据不同草地类型的波段组合特征,结合源区1∶100万植被类型图、DEM和NDVI数据,构建草地判别规则,利用决策树分类法对雅鲁藏布江源区草地类型进行遥感识别。研究结果表明:①不同类型草地因其生境不同,利用不同波段组合特征进行草地类型识别能够达到较好的效果;②与传统的监督分类法相比,基于波段组合特征的决策树分类法具有较高的识别精度(总体精度提高了15.4%,Kappa系数提高了0.225);③在海拔4 400～5 000 m区域内,固沙草草原面积最大,其次为矮嵩草和小嵩草混生草甸,再次为变色锦鸡儿和金露梅灌丛,藏北嵩草草甸面积最小。     

遥感与GIS支持下环青海湖地区草地蝗虫测报方法与模型研究

草地蝗灾是我国北方牧区草地重要自然灾害之一,对草地破坏性很大.及时和准确地进行草地蝗虫发生的预测,对于牧区畜牧业的健康发展和地区经济可持续发展和社会安定都有重要意义.     

基于GIS和神经网络的森林植被分类

本文综述了国际遥感分类研究,使用Landsat7 ETM＋遥感数据和地理辅助数据,应用BP神经网络方法,将莽汉山林场作为研究区进行了遥感影像的分类研究。比较了BP神经网络分类与最大似然、简单和复杂非监督分类法之间的类型与数量精度。BP神经网络分类的总类型精度是70.5%,总数量精度为84.65%,KAPPA系数是0.6455。结果说明BP神经网络的分类质量优于其他方法,其总的类型精度与其他三种分类方法相比分别增加了10.5%、32%和33%,总的质量精度增加了5.3%。因此,辅以地理参考数据的BP神经网络分类可以作为一种有效的分类方法。     

基于HSI模型的吉林省东部地区生境质量动态评价

生境质量评价对生物多样性保护具有重要意义,能够为生态环境的恢复和保护、区域可持续发展政策的制定提供科学依据。以吉林省东部地区为研究区,基于2000年和2015年2期Landsat TM/OLI影像和数字高程模型(digital elevation model,DEM)数据,选取水源状况、干扰因子、遮蔽条件及食物来源作为评价因子,采用层次分析法确定权重,建立了生境质量评价模型,对吉林省东部地区进行生境质量动态评价,并从评价因子、土地覆被变化、社会经济及气候变化等方面分析变化原因。结果表明,吉林省东部地区2000年生境质量最好和较好的区域分别占研究区总面积的37. 69%和46. 88%,2015年生境质量最好和较好的区域分别占研究区总面积的41. 20%和44. 66%; 2000—2015年间生境质量变好的区域为9 414. 19 km2,变差的区域为5 627. 5 km2;土地覆被变化是导致生境质量变化的主要原因之一,吉林省大力发展生态旅游、退耕还林是生境质量变好的主要驱动力,气候条件适宜为吉林省东部地区生态环境保持长期稳定提供了有力保障。     

2000—2020年烟台市生境质量时空演变分析

文章以烟台市为研究区,基于土地利用类型,威胁元和威胁程度等数据,通过转移矩阵,运用InVEST模型获得2000、2010、2020年生境质量指数。结果表明：耕地和草地为研究区主要用地类型,建设用地和湿地面积逐年增加,以草地、耕地、林地流向建设用地和湿地为主。2000—2020年生境质量整体水平不高,呈现先下降后上升的趋势,不同区域生境质量差异较大。研究结果确定了烟台市生境质量时空状况,为维护烟台市生物多样性具有一定的参考价值。     

基于GEE平台的1991-2020年环洱海地带土地利用时空变化分析

洱海作为我国重点保护湖泊“新三湖”之一,近30年间环洱海地带经济发展与人地矛盾的问题日益突出. 研究环洱海地区长时间序列的土地利用变化规律,分析人类活动的影响程度对保护治理洱海具有重要意义. 基于谷歌地球引擎(GEE)云平台,以1991—2020年7期Landsat TM/OLI影像为基础数据,融合光谱、归一化差异指数和增强型植被指数等特征,采用随机森林方法对环洱海10 km范围进行了土地利用分类,结合土地利用变化图谱、人类活动指数模型定量分析了城镇化背景下环洱海地带土地利用类型的演变趋势及人类活动强度. 结果表明:1991—2020年林地、草地面积整体呈减少趋势,主要转出方向为耕地;建设用地面积持续增长,主要转入来源为耕地;水域面积变化较小,湿地呈先增加后减少趋势,上述变化趋势与环洱海地区城镇化快速推进有关;人类活动强度总体逐年上升,以低影响区为主且保持相对稳定.高影响区和中高影响区主要集中于环湖南侧和环湖西侧,中低影响区呈零星块状分布且一直呈减少趋势.      

基于Google Earth Engine与机器学习的省级尺度零散分布草地生物量估算

大区域草地地上生物量估算对草地资源利用管理及全球碳循环研究具有重要意义。为高效快速地估算大区域零散分布草地地上生物量,本文选取安徽省为研究区,在谷歌地球云引擎（Google Earth Engine）平台的支撑下,通过机器学习方法建立Landsat 8 OLI及其他辅助数据与地面实测草地地上生物量之间的联系,开展了草地零散分布地区省级尺度草地地上生物量高分辨率估算,并与传统的基于归一化植被指数（NDVI）回归模型进行了比较。研究结果表明,综合利用光谱与地形因子的机器学习方法,估算零散化分布草地地上生物量的精度可以达到65%以上,其中分类回归树（CART）模型R²=0.57,预测精度为68.60%,支持向量机（SVM）模型R²=0.59,预测精度为75.74%,而使用NDVI的回归分析产生的误差较大,R²=0.37,预测精度为57.51%,因此机器学习方法相对于传统基于NDVI的回归分析具有明显优势。另外,谷歌地球云引擎平台数据来源广泛、获取方便,可以高效地实现海量影像数据的预处理及计算分析,大大提升了工作效率,与地面调查数据的结合可实现更大区域乃至全国尺度上的零散分布草地地上生物量高分辨率遥感估算。     

GIS-based Indicators of Montana Grasshopper Communities

We evaluated two digital data sources that might be helpful in characterizing grasshopper habitat using plant and grasshopper species composition data collected at 128 sites in three areas of Montana. A GIS was used to associate each sampling site with Omernik's ecoregions and the Montana State Soil Geographic Database (MTSTATSGO). Detrended Correspondence Analysis (DCA) and statistical analyses were used to test for correlations among grasshopper species, available water capacity, and soil permeability across sampling areas and ecoregions. Four grasshopper species were correlated with soil permeability and six were correlated with available water capacity. MTSTATSGO plant cover percentages did not correlate with cover measured in the field, indicating inadequate resolution for the scale of this study. Ecoregions were useful in distinguishing grasshopper community gradients across Montana, from mountains to plains. These georeferenced data should be considered as input for grasshopper forecasting and decision-making models. Our results show how GIS can be used to evaluate relationships between digital data sets and ecological data gathered in the field.     

Modeling for Prediction of Land Cover Changes Based on Bio-physical and Human Factors in Zagros Mountains, Iran

The rapid population growth and ongoing development activities has resulted in natural resources demolition. However, the dynamics of the natural resources in relation to different biophysical and socio-economic factors are still remains poorly understood. The present study investigates the basic natural resources i.e. forest, rangeland and surface water bodies’ status using satellite data for the years 1990, 1998, and 2006, and their change detection in relation to biophysical and socio-economic factors. Monitoring land-use/cover change detection using remotely sensed data has been a well recognized technique. The analysis of change detection revealed eleven important land cover changes, which occurred during the past 16 years (1990–2006) in the region. The rate of land cover change was observed to vary across the sub periods and a general decline of forest cover and increase in rangelands and water bodies was observed. Logistic regression model was employed to analyze the relationship between changes and explanatory factors. The land cover change results and logistic models developed in this study are useful in supporting natural resources management efforts and provide useful information for managers/policy makers in formulation of sustainable management strategies for the region.     

Estimating the Effects of Map Error on Habitat Delineation for the California Spotted Owl in Southern California

The reliability of habitat maps that have been generated using Geographic Information Systems (GIS) and image processing of remotely sensed data can be overestimated. Habitat suitability and spatially explicit population viability models are often based on these products without explicit knowledge of the effects of these mapping errors on model results. While research has considered errors in population modeling assumptions, there is no standardized method for measuring the effects of inaccuracies resulting from errors in landscape classification. Using landscape‐scale maps of existing vegetation developed for the USDA Forest Service in southern California from Landsat Thematic Mapper satellite data and GIS modeling, we performed a sensitivity analysis to estimate how mapping errors in vegetation type, forest canopy cover, and tree crown size might affect delineation of suitable habitat for the California spotted owl (Strix occidentalis occidentalis). The resulting simulated uncertainty maps showed an increase in the estimated area of suitable habitat types. Further analysis measuring the fragmentation of the additional patches showed that they were too small to be useful as habitat areas.     

Integrating optical satellite data and airborne laser scanning in habitat classification for wildlife management

Wildlife habitat selection is determined by a wide range of factors including food availability, shelter, security and landscape heterogeneity all of which are closely related to the more readily mapped landcover types and disturbance regimes. Regional wildlife habitat studies often used moderate resolution multispectral satellite imagery for wall to wall mapping, because it offers a favourable mix of availability, cost and resolution. However, certain habitat characteristics such as canopy structure and topographic factors are not well discriminated with these passive, optical datasets. Airborne laser scanning (ALS) provides highly accurate three dimensional data on canopy structure and the underlying terrain, thereby offers significant enhancements to wildlife habitat mapping. In this paper, we introduce an approach to integrate ALS data and multispectral images to develop a new heuristic wildlife habitat classifier for western Alberta. Our method combines ALS direct measures of canopy height, and cover with optical estimates of species (conifer vs. deciduous) composition into a decision tree classifier for habitat – or landcover types. We believe this new approach is highly versatile and transferable, because class rules can be easily adapted for other species or functional groups. We discuss the implications of increased ALS availability for habitat mapping and wildlife management and provide recommendations for integrating multispectral and ALS data into wildlife management.     

三江源典型区草地退化Hyperion高光谱遥感识别研究

三江源草地退化直接影响当地及长江、黄河和澜沧江中上游地区的生态安全、水资源合理利用、经济可持续发展和社会稳定,一直是人们关注的焦点问题。近年来,该地区草地群落毒杂草组分增大,退化严重。高光谱遥感具有精细识别草地种群的潜在能力,如何利用其进行草地群落组成信息探测并进行退化识别是当前研究的热点和难点问题。利用卫星高光谱Hyperion影像和地面实测高光谱遥感数据,通过多端元混合像元分解技术,提取可食牧草、毒杂草以及裸土组分信息;以此为指标对研究区草地退化程度进行分类识别,依次为:未退化、轻度退化、中度退化、重度退化和极度退化。利用野外样方实测数据验证监测结果,精度达到84.2%,表明高光谱遥感对草地退化探测具有良好的应用潜力。     

基于3S技术的野生动物生境评价与保护研究——以高黎贡山羚牛为例

在3S技术和数理统计分析技术支持下,应用羚牛行为生态学研究成果,在研究羚牛生境分布与空间格局的基础上,通过空间分析,开展高黎贡山北段羚牛生境适宜性评价与保护研究。研究结果表明:适宜羚牛生存的生境面积有限,并有逐年减少的趋势,为有效保护高黎贡山羚牛,有必要加强羚牛现有生境保护,尤其是最适宜生境的保护;消除羚牛生境阻隔因素,增强生境连通性。     

A Research on the Monitoring of Coastal Land use with Aerial Photographs in the Example of Kadırga bay (Çanakkale)

In coastal areas, environmental monitoring is an important tool in acquiring data to achieve the goal of sustainable usage relating to the character of changes in quantity and quality of land cover / land use. Studies can reveal the diversity of species and the natural factors and human influence affecting this diversity, occurring as a result of habitat fragmentation or destruction. As well as comparing the past and current state of environmental resources, monitoring helps to determine trends in the use and changes of resources. It was in this context that Kad?rga Bay, located on the northwest coast of Canakkale province, Turkey was studied. Also situated in this location is Assos, a significant site in ancient history with great tourism potential. This study determined changes in land cover / land use by means of photogrammetrical interpretation of data from 1957 to 2002. The research further aimed to determine pressure on the components of the ecological structure of the area. Based on the data obtained, projections can be made to protect the future, and will provide an important source for management of the area. Within the 45-year study period, it was determined that agriculture and secondary housing construction exerted the most pressure on the habitat of the area. In the main, an increase in agricultural activities and secondary house construction emerged as the cause of a decrease in wetlands and forest areas.     

Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type classification

We developed a classification workflow for boreal forest habitat type mapping. In object-based image analysis framework, Fractal Net Evolution Approach segmentation was combined with random forest classification. High-resolution WorldView-2 imagery was coupled with ALS based canopy height model and digital terrain model. We calculated several features (e.g. spectral, textural and topographic) per image object from the used datasets. We tested different feature set alternatives; a classification accuracy of 78.0% was obtained when all features were used. The highest classification accuracy (79.1%) was obtained when the amount of features was reduced from the initial 328 to the 100 most important using Boruta feature selection algorithm and when ancillary soil and land-use GIS-datasets were used. Although Boruta could rank the importance of features, it could not separate unimportant features from the important ones. Classification accuracy was bit lower (78.7%) when the classification was performed separately on two areas: the areas above and below 1 m vertical distance from the nearest stream. The data split, however, improved the classification accuracy of mire habitat types and streamside habitats, probably because their proportion in the below 1 m data was higher than in the other datasets. It was found that several types of data are needed to get the highest classification accuracy whereas omitting some feature groups reduced the classification accuracy. A major habitat type in the study area was mesic forests in different successional stages. It was found that the inner heterogeneity of different mesic forest age groups was large and other habitat types were often inside this heterogeneity.     

遥感与GIS在蝗虫监测和预报中的应用前景

蝗灾是世界上重要自然灾害之一 ,蝗虫监测与预报因此成为各国管理部门的重要工作内容。新兴的遥感与GIS技术由于其在空间定位、信息获取、信息处理等方面的优势 ,必将成为该领域的重要研究手段。文中回顾和总结了国内外研究者利用遥感和GIS进行蝗虫监测和预报等方面的研究进展 ,并对其技术路线、发展趋势及存在问题进行了初步探讨     

基于地表覆盖/土地利用数据的沁河源区生态保护红线边界处理

边界处理是指借助地理信息系统软件,将生态保护红线从栅格形式的生态评估结果落实到具有明确边界的地块上,也称为红线的“落图”过程,是生态保护红线划定的重要流程,也是红线精准落地和有效管控的必要条件。本文以沁河源区为例,利用地理信息系统软件,基于地表覆盖、土地利用两种数据分别对生态功能极重要区红线进行落图。结果表明：两种数据均适用于红线边界处理,并且联合应用时处理结果更佳;沁河源区生态功能极重要区栅格面积为877.87 km²,联合应用两种数据处理后的红线矢量面积为877.73 km²,占研究区总面积的64.51%;研究结果可为生态功能重要区域生态保护红线边界的优化、调整提供参考。