基于站点的生物多样性星空地一体化遥感监测

科学制定生物多样性保护和恢复政策, 需要空间上连续、时间上高频的物种和生境分布以及物种迁移信息支持, 遥感是目前能满足该要求的有效技术手段。近年来, 遥感平台和载荷技术高速发展, 综合多平台、多尺度、多模式遥感技术, 开展基于站点的星空地一体化遥感观测试验, 可以对地表进行时空多维度、立体连续观测, 为生物多样性遥感监测提供了新的契机。本文总结了使用遥感技术监测生物多样性的主要方法, 回顾了典型的星空地一体化遥感观测试验。综述以往研究发现, 一方面, 现有遥感试验还缺少对生物多样性直接监测指标的观测, 另一方面, 生物多样性遥感监测方法也缺少星空地多维立体观测平台的支撑, 亟需加强两者的融合, 开展基于站点的生物多样性星空地一体化遥感监测研究。以设于我国四川王朗大熊猫国家级自然保护区内的王朗山地生态遥感综合观测试验站为例, 展示了星空地一体化遥感综合观测试验平台在生物多样性监测中的应用潜力。星空地一体化遥感观测可以提供物种和生境的综合定量信息, 与生态模型有机结合, 可以刻画生物多样性的时空格局与动态过程, 有助于挖掘过程机理, 提高生物多样性监测的信息化水平。     

Definition and application of expert knowledge on vegetation pattern,phenology, and seasonality for habitat mapping,as exemplified in a Mediterranean coastal site

Habitats are effective indicators of biodiversity. Remote sensing data and techniques are of great utility for their long-term monitoring. Habitat maps can be derived from land cover (LC) maps through rules obtained from expert knowledge and integrated with in situ data. Spatial (vegetation pattern) and temporal (phenology and water seasonality) relationships were explored and documented to infer reliable rules for LC (according to the Food and Agricultural Organization Land Cover Classification System (FAO-LCCS) taxonomy) to habitat (Annex I to the 92/43 EEC Directive and EUNIS) class translation. A coastal site in southern Italy was considered as study site for the definition and validation of such rules. Phenological data of the plant communities were collected on the basis of vegetation plots randomly distributed within the study site. Water seasonality was extracted from periodical observation of the water surface. Vegetation pattern was analyzed by means of vegetation survey along transects. The potentiality of rules, based on this specific expert knowledge, was tested in an experimental setting for habitat mapping. The overall accuracy of the habitat map was 75.1%. Such a result supports the usefulness of prior expert knowledge for habitat mapping from LCCS classes and disambiguation on one-to-many relations between LC/LU and habitat types.     

A review and a framework for the integration of biodiversity monitoring at the habitat level

The monitoring of biodiversity at the level of habitats is becoming widespread in Europe and elsewhere as countries establish national habitat monitoring systems and various organisations initiate regional and local schemes. Parallel to this growth, it is increasingly important to address biodiversity changes on large spatial (e.g. continental) and temporal (e.g. decade-long) scales, which requires the integration of currently ongoing monitoring efforts. Here we review habitat monitoring and develop a framework for integrating data or activities across habitat monitoring schemes. We first identify three basic properties of monitoring activities: spatial aspect (explicitly spatial vs. non-spatial), documentation of spatial variation (field mapping vs. remote sensing) and coverage of habitats (all habitats or specific habitats in an area), and six classes of monitoring schemes based on these properties. Then we explore tasks essential for integrating schemes both within and across the major classes. Finally, we evaluate the need and potential for integration of currently existing schemes by drawing on data collected on European habitat monitoring in the EuMon project. Our results suggest a dire need for integration if we are to measure biodiversity changes across large spatial and temporal scales regarding the 2010 target and beyond. We also make recommendations for an integrated pan-European habitat monitoring scheme. Such a scheme should be based on remote sensing to record changes in land cover and habitat types over large scales, with complementary field mapping using unified methodology to provide ground truthing and to monitor small-scale changes, at least in habitat types of conservation importance.     

基于多源遥感信息融合的广东省土地利用分类方法——以雷州半岛为例

准确高效的获取土地利用信息对生态环境评价非常重要。广东省地处华南热带和亚热带季风气候区,经济作物种类繁多,土地覆盖破碎,为土地利用精确分类带来很大不确定性,而常年多云雨的天气也为有效光学影像的获取带来困难。为提高土地覆盖分类精度,以雷州半岛为实验区,综合应用Landsat-TM/ETM、多时相HJ光学影像,以及X波段Terra SAR数据,通过分析不同地物类型在光谱、极化以及多时相特征上的差别,对原始图像进行特征提取。在此基础上融合多源遥感信息的地物特征运用面向对象土地覆盖分类方法获取研究区高精度的土地利用信息。结果显示这一方法能有效提高土地覆盖利用信息获取精度,为研究生态环境变化提供更准确的数据支持。     

Soil carbon stocks,deforestation and land‐cover changes in the Western Ghats biodiversity hotspot (India)

Habitat loss and soil organic carbon (SOC) stock variations linked to land‐cover change were estimated over two decades in the most densely populated biodiversity hotspot in the world, in order to assess the possible influence of conservation practices on the protection of SOC. For a study area of 88 484 km², 70% of which lie inside the Western Ghats Biodiversity Hotspot (WGBH), land‐cover maps for two dates (1977, 1999) were built from various data sources including remote sensing images and ecological forest maps. SOC stocks were calculated from climatic parameters, altitude, physiography, rock type, soil type and land‐cover, with a modelling approach used in predictive learning and based on Multiple Additive Regression Tree. The model was trained on 361 soil profiles data, and applied to estimate SOC stocks from predictor variables using a Geographical Information System (GIS). Comparison of 1977 and 1999 land‐cover maps showed 628 km² of dense forests habitat loss (6%), corresponding to an annual deforestation rate of 0.44%. This was found consistent with other studies carried out in other parts of the WGBH, but not with FAO figures showing an increase in forest area. This could be explained by the different forest definitions used, based on ecological classification in the former, and on percentage tree cover in the latter. Unexpectedly, our results showed that despite ongoing deforestation, overall SOC stock was maintained (～0.43 Pg). But a closer examination of spatial differences showed that soil carbon losses in deforested areas were compensated by sequestration elsewhere, mainly in recent plantations and newly irrigated croplands. This suggests that more carbon sequestration in soils could be achieved in the future through appropriate wasteland management. It is also expected that increasing concerns about biodiversity loss will favour more conservation and reinforce the already prevailing protective measures, thus further maintaining C stocks.     

红树林生态系统遥感监测研究进展

随着现代遥感技术的迅速发展,遥感监测已经成为红树林生态系统变化监测的重要手段和方法。从遥感技术在生态系统变化监测应用领域入手,综述了国内外红树林遥感监测的发展历程,系统总结了遥感技术在红树林湿地动态、种间分类、群落结构(叶面积指数、冠幅、树高等)、生物量、灾害灾情(病虫害、风暴潮等)、景观格局动态、驱动力、红树林湿地保护与管理等领域应用现状,归纳了不同应用领域遥感监测的理论、方法及研究现状。指出我国在红树林遥感监测中存在的不足。提出红树林遥感监测应在分类标准体系规范化、分类精度提升、红树林生态学特征参数(物种多样性、优势度等)、生态系统环境空间演变过程及遥感监测的尺度效应方面加大研究力度。充分发挥区域综合监测模型在红树林生态系统变化遥感监测中的作用。     

A method for assessing evergreen habitats using phytodiversity and geospatial techniques in tropical rain forests of Southern Western Ghats (India)

We have used data generated using remote sensing and geographical information systems to categorize habitats, and then determined the relationship between the habitat categorizations and species-distribution patterns. A biologically rich hotspot—Kalakad-Mundanthurai Tiger Reserve, located at Southern Western Ghats, India, was chosen for this study. In order to spatially delineate areas of high species richness/diversity and endemic habitat zones, we have identified evergreen habitats in conjunction with landscape metrics, species assemblage, micro-habitats like slope, topography, species endemism, and proportion of core and edge species. A total of 236 species and 2,920 individuals were recorded using systematic stratified plots of 0.1 ha covering 47 plots. Hierarchical cluster analysis was done using Ward’s method. Plot information was used to identify clusters based on species density. The analysis showed five species assemblages that are quite distinct from each other in terms of dominant species. The distribution of endemic and edge species, land cover heterogeneity, and continuity of patches in these clusters were evaluated to understand the degree of disturbance and intactness at landscape scale. Integration of species assemblages and topography brought out four major elevation-slope complexes. Information on species composition (robust field survey) with spectral (hybrid classification) properties has shown 72% overall accuracy and distinguished four evergreen sub-groups and other land cover classes. The developed approach assumes great importance in the assessment of biodiversity and prioritizing the areas of conservation.     

Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review

Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced epidemiological data, collaboration between remote sensing scientists and biologists, and the availability of sophisticated, statistical geographic information system and image processing algorithms in a desktop environment creates a fertile research environment. The use of remote sensing techniques to map vector-borne diseases has evolved significantly over the past 25 years. In this paper, we review the status of remote sensing studies of arthropod vector-borne diseases due to mosquitoes, ticks, blackflies, tsetse flies, and sandflies, which are responsible for the majority of vector-borne diseases in the world. Examples of simple image classification techniques that associate land use and land cover types with vector habitats, as well as complex statistical models that link satellite-derived multi-temporal meteorological observations with vector biology and abundance, are discussed here. Future improvements in remote sensing applications in epidemiology are also discussed.     

Enrichment of land-cover polygons with eco-climatic information derived from MODIS NDVI imagery

Aim The FAO land‐cover classification system (LCCS) represents an innovative approach to standardizing and harmonizing land‐cover classifications based on remote sensing data. The thematic information considered by the LCCS, however, is intrinsically related to vegetation physiognomy and does not report important eco‐climatic features. Our aim is to develop a methodology to enrich LCCS maps with information on vegetation productivity and phenology derived from Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. Location The LCCS has recently been applied in East Africa by the Africover project. The proposed methodology is developed and tested in Tanzania using MODIS NDVI data for a 5‐year period (2001–05). Methods Annual NDVI profiles of Africover polygons were extracted from MODIS imagery. These profiles, composed of 23 NDVI values per year, were averaged over the study period, purified for possible land‐cover errors and converted into a more manageable format composed of 24 half‐month values. The resulting NDVI profiles were first analysed visually and then evaluated statistically against rainfall measurements taken at 12 Tanzanian stations. The steps involved were as follows: NDVI values were aggregated on a monthly basis and represented with a one‐digit integer to obtain an extended code; a subset of parameters describing vegetation development and phenology was identified, thus obtaining a restricted codification; and finally, the information loss resulting from both the extended and restricted codification was evaluated with respect to the original NDVI profiles. Results NDVI profiles of different Africover classes can differ in mean values but tend to have a similar shape, linked to the seasonality of local vegetation. Both NDVI annual averages and seasonal variations are strictly dependent on rainfall patterns, particularly in arid zones. The tested codifications effectively summarize the eco‐climatic information contained in the polygon NDVI profiles, with the extended and restricted codifications retaining > 90% and 80% of such information, respectively. Main conclusions The proposed methodology is capable of enriching LCCS polygons with eco‐climatic information derived from MODIS NDVI data. Such information is related to vegetation development and seasonality, and can be efficiently condensed at various levels of detail.     

Capturing landscape changes and ecological processes in Nikko National Park (Japan) by integrated use of remote sensing images

Landscape change is of crucial ecological importance because of its impacts on biodiversity and the sustainability of habitats and landscapes. The ability to infer ecological processes from landscape changes provides a powerful new tool for large-scale and regional ecological research. Accordingly, we investigated landscape changes in Nikko National Park (Japan) from 1981 to 2006 using an integration of remote sensing images to track ecological processes in the park. We showed that natural succession and anthropogenic disturbance have caused an enormous expansion of coniferous forest (mostly Larix kaempferi and Tsuga diverifolia), which has resulted in an alteration of sika deer (Cervus nippon) habitat structure. The deer population has vastly increased in size, with concomitantly negative effects on the biodiversity of forest-floor vegetation. All changes that we detected have degraded landscape and ecosystem sustainability. Our integrated use of remote sensing images in 2006 accurately identified and classified landscape pattern in the study area. We achieved an overall accuracy of 96.48%, demonstrating that the procedure has an enormous potential for synthetic processing of multitemporal and multiresource remote sensing images.     

Assessment of grassland use intensity by remote sensing to support conservation schemes

Grassland is a land cover in the area of conflict between agriculture and conservation, where intensification of land use is a major threat to grassland biodiversity. Grassland use intensity is a key factor for the conservation value of grassland, and detailed spatial data on grassland use intensity is needed to improve strategies for biodiversity conservation. A new remote sensing-based approach using multi-temporal high resolution RapidEye satellite data was developed in the present study that makes a large-scale assessment of grassland use intensity possible. RapidEye is a constellation of five satellites with 6.5 m spatial resolution, which allows frequent and timely image acquisition targeted at specific growing seasons. Semi-natural grassland, extensively used grassland, intensively used grassland and tilled grassland could be reliably differentiated at the management plot level in a study area in southern Germany. Various combinations of images from different observation dates have been tested as classification input and their overall classification accuracies were validated by field data. Best results were achieved using a combination of five multi-temporal scenes with an overall accuracy of 85.7%. A three-scene combination resulted in an overall accuracy of 82.2%. The analysis showed that seasonal aspects are very important when selecting adequate observation dates. Grassland use intensity was also assessed on peatlands using a peat soil map, since land use intensity significantly affects greenhouse gas emissions from peatlands. The results demonstrate the potential of targeted multi-spectral, high spatial resolution remote sensing for the large-scale monitoring of dynamic habitats, which is of vital importance to support various environmental conservation schemes through improved monitoring and reporting capabilities.     

测量的区域土地覆盖格局研基于多尺度遥感究

 利用1km、4km和8km 3种空间分辨率的NOAA/AVHRR数字影像,对中国NECT样带西部地区进行了土地覆盖分类及其景观特征的比较研究。重点比较了几种空间分辨率遥感数据分类结果边界的一致性和空间差异,以及影像所记录的景观格局的差异。为进一步在不同尺度上研究景观变化过程以及尺度转换研究奠定了基础。研究表明：3种空间分辨率的遥感影像所反映的区域土地覆盖的宏观空间格局是一致的,但类型的边界、每一类型斑块的形状和数量均产生较大的差异;经过对反映景观空间结构的4种指标（分维数、破碎度、多样性、优势度）的比较显示出随着遥感影像空间分辨率的变化,影像所反映的景观结构发生了较大的变化。其中,各覆盖类型的分维数表现出最大差异,表征着空间分辨率的变化对斑块复杂程度的影响最大。     

长江三峡库区生态环境变化遥感研究进展

长江三峡库区是我国最重要的生态敏感区之一,受三峡水利工程和相关人类活动影响,其生态环境变化显著.揭示三峡库区生态系统结构、功能和生态过程的变化对于维护库区生态安全具有重要意义.生态环境遥感为此提供了关键途径,并在三峡库区生态环境研究中受到重视.现有的三峡库区生态环境变化遥感研究往往存在较大差异,难以有效反映生态环境变化和响应特征,加之生态本底和人类干扰活动的复杂性,三峡库区生态环境变化遥感研究仍面临诸多挑战.本文对库区环境变化遥感研究的发展阶段、研究尺度、遥感数据和方法等进行了系统总结,并从土地利用/覆盖变化、植被变化、水土安全、生态服务价值评价、生态系统健康与生态规划5个方面对库区生态环境变化遥感研究进展进行综述,在此基础上探讨了当前三峡库区生态环境变化遥感研究存在的问题,并提出未来研究中需重点关注的主要科学问题,以期为三峡库区生态环境管理和类似地区生态环境变化遥感研究提供参考.
     

测量的区域土地覆盖格局研基于多尺度遥感究

利用1km、4km和8km 3种空间分辨率的NOAA/AVHRR数字影像,对中国NECT样带西部地区进行了土地覆盖分类及其景观特征的比较研究。重点比较了几种空间分辨率遥感数据分类结果边界的一致性和空间差异,以及影像所记录的景观格局的差异。为进一步在不同尺度上研究景观变化过程以及尺度转换研究奠定了基础。研究表明：3种空间分辨率的遥感影像所反映的区域土地覆盖的宏观空间格局是一致的,但类型的边界、每一类型斑块的形状和数量均产生较大的差异;经过对反映景观空间结构的4种指标（分维数、破碎度、多样性、优势度）的比较显示出随着遥感影像空间分辨率的变化,影像所反映的景观结构发生了较大的变化。其中,各覆盖类型的分维数表现出最大差异,表征着空间分辨率的变化对斑块复杂程度的影响最大。     

基于长时间序列landsat数据的科尔沁沙地土地利用演变分析

以科尔沁沙地沙丘-草甸过渡带区域主要土地覆被类型为研究对象,以1987-2017年多时相Landsat TM/OLI遥感影像解译分类为基础,参考生态学植被演替研究方法,系统分析研究区30年来的土地利用/覆被动态演变规律,研究结果表明：（1）决策树法在复杂下垫面不同覆被类型的同步识别效果较好,所有影像分类精度均达到88%以上,分类效果较好,其中2017年分类精度最高为95.24%,达到了分类研究的要求;（2）研究区存在着"半灌丛-草甸地-灌丛"的植被结构特征,且整体表现为"南进北退"的变化趋势。结合土地利用动态度分析结果表明人类活动干涉下,研究区整体上遵循了半干旱区植被条件改善的一般规律,侧面反映该研究区域生态环境的持续不稳定性和脆弱性;（3）研究区覆被类型发生变化的总面积达到2623.59 hm²,总变化强度为63.76%。其中正向演替的比例为52.61%,以半灌丛面积的持续减小与沙地草甸面积的持续扩张为主要变化特征。但同时,半灌丛转为沙地的面积为184.95 hm²,表明以放牧为主的研究区同时发生着局部的逆行演变;（4）质心迁移结果反映了1987-2017年间,除人为影响较大的林地、草地以及耕地向北迁移外,其他植被类型的质心都有很明显的南迁,主要植被类型重心迁移距离依次由大到小为耕地 > 半灌丛 > 灌丛 > 沙地草甸 > 湿地草甸 > 林地。研究通过记录科尔沁沙地连续扩展的时空模式,展示了遥感-生态和时间序列影像在30 m分辨率下跟踪土地利用/覆被变化的潜力,为提高干旱半干旱区土地利用情况的动态监测效率,开展土地利用/覆被动态演变研究提供参考。     

遥感用于森林生物多样性监测的进展

随着物种和栖息地的丧失,全球范围的生物多样性保护已经成为迫切的需要。航空航天技术的迅猛发展使遥感成为能提供跨越不同时空尺度监测陆地生态系统生物多样性的重要工具,这方面的研究在欧美等国已经有了小范围的开展,在国内刚刚起步。国外关于生物多样性遥感探测的方法基本有3种:1.利用遥感数据直接对物种或生境制图,进而估算生物多样性;2 .建立遥感数据的光谱反射率与地面观测物种多样性的关系模型;3.与野外调查数据结合直接在遥感数据上进行生物多样性指数制图。研究表明,物种直接制图法只能应用于较小的范围;生境制图的方法,应用广泛,技术相对成熟,研究范围局限于几百公里的范畴,但不能获取生境内部的多样性信息。光谱模型技术目前正处于探索阶段,对于植被复杂、生物多样性高的地域,具有较大的应用潜力。在遥感数据上直接进行生物多样性制图在加拿大已经得到了应用。     

Habitat Diversity in the Conservation of the Grassland Auchenorrhyncha (Homoptera: Cercopidae,Cicadellidae, Cixidae,Delphacidae) of Northern Britain

Definition of northern British grassland Auchenorrhyncha habitats was carried out using a classification based on analysis of data from 351 sites, involving 121 species, located between Greater Manchester and northern Scotland. Ten habitats were identified showing little influence of geographical position and exhibiting a basic upland-lowland trend. Other factors influencing habitat and species assemblage distribution were soil water, vegetation structure and land cover. An analysis of the species data with satellite-derived land cover data indicated that the lowland covers of tilled land, coast and urban and the upland covers of heath grassland and shrub heath were most important in affecting both species and assemblage distribution. The large-scale survey of grassland sites provided new information on both the ecology and distribution of individual Auchenorrhyncha species. Some were limited to specific habitat types but a considerable number were generalist species found in most or all of the 10 habitat types but showing preferences within upland to lowland or wet to dry site gradients. The ability to generate a subtle grassland Auchenorrhyncha habitat classification with large-scale survey results from standardised and reproducible sampling increases the potential for using habitat diversity for the conservation of grassland Auchenorrhyncha. Habitat preservation would also ensure that species richness (biodiversity) is maintained and that the habitats of rare species are conserved.     

基于遥感的湿地景观格局季相分析

以中国东北地区三江平原北部为研究区域,利用2012年多季相遥感影像作为数据源,结合野外调查数据,应用面向对象的分类方法,根据影像的物候、时相等特征,提取不同月份的湿地信息,进行景观格局季相分析。结果表明:(1)研究区湿地面积、类型格局在同一年不同季节不同月份会有不同幅度的变化,总体呈现缓增骤减的态势。湿地主要分布在低洼地区,主要湿地类型为草本沼泽,其次为河流,其他湿地占总面积比例较小。(2)研究区各阶段湿地都有转化,主要发生在湿地和非湿地之间,多数表现在草本沼泽和草地之间的转化。(3)湿地分布和湿地转化面积主要集中在低海拔区域和低坡度区域,其中海拔100 m和坡度5°以下范围内的湿地分布面积和湿地转化面积占湿地总面积及湿地转化面积的绝大部分。(4)年内季节性湿地转化与降水、温度和湿地植被物候关系密切。     

Remote sensing of biodiversity: what to measure and monitor from space to species?

Global biodiversity monitoring systems through remote sensing can support consistent assessment, monitoring, modelling and reporting on biodiversity which are key activities intended for sustainable management. This work presents an overview of biodiversity monitoring components, i.e. biodiversity levels, essential biodiversity variables, biodiversity indicators, scale, biodiversity inventory, biodiversity models, habitat, ecosystem services, vegetation health and biogeochemical heterogeneity and discusses what remote sensing through Earth Observations has contributed to the study of biodiversity. The technological advancements in remote sensing have enabled information-rich data on biodiversity. Remote sensing data are making a strong contribution in providing unique information relevant to various biodiversity research and conservation applications. The extensive use of Earth observation data are not yet realized in biodiversity assessment, monitoring and conservation. The development of direct remote sensing approaches and the techniques for quantifying biodiversity at the community to species level is likely to be a great challenge for comprehensive earth observation-based monitoring strategy.

     

基于NDVI_Ts特征空间的中国土地覆盖分类研究

 归一化植被指数（NDVI）与地表温度（Ts）是描述地表覆盖特征的两个重要参数, 其构成的NDVI_Ts特征空间具有丰富的地学和生态学内涵。该文在NOAA/AVHRR连续时间序列数据反演Ts的基础上,通过主成分分析、非监督分类和基于DEM的分类后处理等方法,以Ts/NDVI为指标对中国土地覆盖进行分类。结果表明,Ts/NDVI对中国较大尺度上不同土地覆盖类型的差异具有较强的敏感性,其对中国土地覆盖分类结果的野外抽样检验精度比传统的单独利用NDVI时间序列进行非监督分类提高了3.3%,Kappa系数提高了0.020 2;在综合其它反映植被特征及其环境的指标（如气候、地形等）的基础上,利用Ts/NDVI将有可能较为准确 地提取中国植被或土地覆盖的信息,有利于对其进行分类和变化监测,具有深远的研究潜力 和应用价值。