高分辨率卫星遥感数据在白衣庵滑坡调查研究中的应用

利用Quickbird-2卫星遥感数据对白依庵滑坡进行了调查研究。首先选取321和432波段进行彩色合成,并且与pan波段进行了分辨率融合,对融合后的图像进行正射校正作为遥感解译底图。从遥感影像上提取了白依庵滑坡的各要素,进行了稳定性评价和危害性分析,并提出了防治措施建议。根据研究结果可知,在现阶段利用Quickbird-2高分辨率卫星遥感数据进行滑坡研究具有无比的优越性、实用性和经济性。     

Relative density of finds for assessing similarity-based maps of orchid occurrence

Similarity-based mapping of the expected distribution of 10 orchid species was conducted in a study area covering 300 km² in south-eastern Estonia. The observation track and species finds were recorded during fieldwork. Absence locations were generated on the line of observation track. Both presence and absence sites having an in-between distance of at least 100 m were used as training data. Expected presence/absence of a species was calculated according to similarity between the predictable location and selected observations (examples) of presence and absence sites. For each species, the machine learning system identified the best predictive sets by selecting the most useful variables out of 136 map and remote sensing features. Similarity-based estimations were evaluated both by training fit and by independent verification data. Reliability of the predictive maps was expressed also by usefulness ratios—the densities of validation find sites (1) in the predicted presence area relative to the density of those in the predicted absence area, and (2) relative to the share of the observation track in the predicted presence area and in the predicted absence area. The predictive mapping was most efficient for Dactylorhiza incarnata, D. russowii, Epipactis palustris, and Goodyera repens. We conclude that the profound coverage of observations on any larger area is unrealistic and the reliability of similarity-based predictive maps depends on the representativity of existing records relative to the diversity of the study area. The investigation showed that the studied species are much more common in nature than the records in the national database indicate.     

热岛效应季节动态随城市化进程演变的遥感监测

作为城市特有的一种环境问题,热岛效应动态发展变化的规律是城市热岛研究的基础工作.遥感技术在城市热岛动态变化监测方面的有用性已经得到了证实.然而当前城市热岛遥感研究基本都取少数几景进行对比分析,这使得遥感在城市热岛时空动态监测方面不能充分发挥作用.采用50景长时间序列Landsat TM和ETM+SLC-on/off影像,采取定性和定量分析相结合的方法,使用热岛强度、热岛范围等指标和热岛显著区的概念对厦门市1987-2008年20年间热岛季节动态随城市化进程演变的趋势进行分析,结果表明:厦门城市热岛在2003、2004年之后已由春夏秋扩展到冬季,且冬季热岛的高等级斑块在数量、个体面积和总面积上均有明显增长趋势.引起这种变化的原因还需要进一步研究和分析.     

气温遥感估算方法研究综述

气温作为重要的气候资源之一,在植被长势、农业灾害和气候变化研究中发挥着举足轻重的作用。目前大尺度农业、气候模型都需要空间分布的气温作为输入参数。除了用有限的台站数据空间插值获取气温栅格化数据外,遥感技术更为连贯精细的气温空间观测提供了有力的数据支持。为服务区域尺度气温相关的科研及业务工作,论文首先介绍了目前国内外气温遥感估算的主要方法,包括简单统计法、高级统计法、温度-植被指数分析法（TVX法）、地表能量平衡法及大气温度廓线外推法。再根据实际应用需要,重点总结比较了最高、最低和平均气温及不同时空尺度气温的遥感估算特点。最后讨论了气温遥感估算在实际应用中存在的问题并探讨了未来的研究趋势。     

基于IKONOS影像的城市住宅区中水回用空间分布估算

中水回用是解决城市水资源短缺和水环境污染问题的有效途径。开展分散式中水可行性研究,需要考虑中水原水与回用需求的数量、空间分布及平衡关系。论文以南京市住宅区为例,探索了基于IKONOS高分辨率遥感影像分类,估算中水回用空间分布的方法。通过引入单位面积用水定额,将遥感和GIS 分析得到的用地信息与中水原水产量和回用需求关联起来,计算了研究区域中27 个地块的中水原水产量和回用需求量,并分析了两者在空间上的水量平衡关系。结果表明,研究区域存在较大的中水原水供给潜力3.52 L/（m²·d）和较大的利用需求5.37 L/（m²·d）。住宅小区的单位面积原水产量与其建筑容积率成正比;以中高层住宅区为主的地区,中水原水产量能够基本满足本地区的中水回用需求。因此,在这些地区建立分散式污水处理系统,可减少废污水排放,节约自来水取用量,提高水资源利用率。     

应用B-IBI和UAV遥感技术评价辽河上游生态健康

采用B-IBI(benthic index of biotic integrity,底栖动物完整性指数)结合UAV(unmanned aerial vehicle,无人机)遥感技术,对辽河保护区干流上游河流生态系统健康进行评价. 根据16个采样点的UAV遥感正射影像、水质参数、水体理化指标和土地利用类型等信息,从中筛选出4个采样点作为参照点,其余为受损点;通过分布范围、判别能力和相关性分析,从32个候选生物指标中筛选出6个核心指标,包括总分类单元数、摇蚊分类单元数、寡毛类分类单元数、敏感类群、前3位优势分类单元和均匀度指数. B-IBI评价结果显示,辽河干流上游处于健康(B-IBI>3.90)的河段占0.82%,亚健康(2.93相似文献   

区域及全球尺度的NPP过程模型和NPP对全球变化的响应

植被净第一性生产力（NPP）不仅是表征植被活动和生态过程的关键参数,而且是判定生态系统碳汇和反映生态系统对全球变化响应的主要因子。当前,模型模拟成为大尺度NPP研究的主要手段,而在众多NPP估算模型中,过程模型逐渐趋于主导地位。虽然目前有关NPP的研究有很多,但还没有关注于大尺度上应用的过程模型及其模拟的NPP对全球变化的响应。因此本文主要侧重于 NPP 过程模型在区域及全球尺度上的应用,具体包含以下内容,①进一步将区域及全球尺度的NPP过程模型分为静态植被模型和动态植被模型。②阐明这些模型间存在的区别与联系。③归纳出NPP过程模型在区域及全球尺度上应用的3大挑战：时空尺度转换、多源数据的获取与融合以及模型模拟结果的验证与评价,并根据其解决方案总结出通用的模型应用框架。④从气候变化、大气成分变化和土地利用/土地覆盖变化3个方面探讨NPP对全球变化的响应机制,以期找到NPP变化的规律与模式。最后根据NPP模型的发展对未来区域及全球尺度的NPP过程模型进行展望,认为未来模型的综合性将更高,机理性也将更强,同时与全球变化研究结合得更加紧密,且基于多个已有模型的混合模型也是未来NPP模型发展的一个重要方向。此外,本文认为对NPP模拟结果的尺度效应研究也是未来NPP研究的热点之一。     

基于Sentinel-2的江苏省植被生物物理特征反演

Sentinel-2作为新型独特的光学对地观测遥感卫星,因拥有对植被生物监测的红光边缘波段而备受关注.植被生物物理特征对监测植被生长状况及研究全球碳氮循环过程等都具有重要意义.然而,在小尺度上利用统计模型方法进行植被理化参数反演的研究比较多,在大尺度上利用生物物理参数快速、高精度反演的研究较少.因此,本文以江苏省为研究...     

Identifying the possibilities and pitfalls of conducting IUCN Red List assessments from remotely sensed habitat information based on insights from poorly known Cuban mammals

The International Union for Conservation of Nature's Red List of Threatened Species (RLS) is the key global tool for objective, repeatable assessment of species’ extinction risk status, and plays an essential role in tracking biodiversity loss and guiding conservation action. Satellite remote sensing (SRS) data sets on global ecosystem distributions and functioning show exciting potential for informing range-based RLS assessment, but their incorporation has been restricted by low temporal resolution and coverage of data sets, lack of incorporation of degradation-driven habitat loss, and noninclusion of assumptions related to identification of changing habitat distributions for taxa with varying habitat dependency and ecologies. For poorly known mangrove-associated Cuban hutias (Mesocapromys spp.), we tested the impact of possible assumptions regarding these issues on range-based RLS assessment outcomes. Specifically, we used annual (1985–2018) Landsat data and land-cover classification and habitat degradation analyses across different internal time series slices to simulate range-based RLS assessments for our case study taxa to explore potential assessment uncertainty arising from temporal SRS data set coverage, incorporating proxies of (change in) habitat quality, and assumptions on spatial scaling of habitat extent for RLS parameter generation. We found extensive variation in simulated species-specific range-based RLS assessments, and this variation was mostly associated with the time series over which parameters were estimated. However, results of some species-specific assessments differed by up to 3 categories (near threatened to critically endangered) within the same time series, due to the effects of incorporating habitat quality and the spatial scaling used in RLS parameter estimation. Our results showed that a one-size-fits-all approach to incorporating SRS information in RLS assessment is inappropriate, and we urge caution in conducting range-based assessments with SRS for species for which habitat dependence on specific ecosystem types is incompletely understood. We propose novel revisions to parameter spatial scaling guidelines to improve integration of existing time series data on ecosystem change into the RLS assessment process.     

A novel participatory and remote-sensing-based approach to mapping annual land use change on forest frontiers in Laos,Myanmar, and Madagascar

Tropical forests are under pressure from both commercial and smallholder agriculture. Forest frontiers are seeing dynamic land use changes that frequently lead to land system regime shifts, posing challenges for the sustainability of entire local social-ecological systems. Monitoring highly dynamic land use change and detecting land system regime shifts is methodologically challenging due to trade-offs between spatial and temporal data resolution. We propose an innovative approach that combines analysis of very-high-resolution satellite imagery with participatory mapping based on workshops and field walks. Applying it in Laos, Myanmar, and Madagascar, we were able to collect annual land use information over several decades. Unlike conventional land use change mapping approaches, which assess only few points in time, our approach provides information at a temporal resolution that enables detection of gradual and abrupt land system regime shifts.