Satellite image analysis using crowdsourcing data for collaborative mapping: current and opportunities     

Wei Su  Xiaodong Zhang 《International Journal of Digital Earth》2020,13(6):645-660

ABSTRACT

Researchers are continually finding new applications of satellite images because of the growing number of high-resolution images with wide spatial coverage. However, the cost of these images is sometimes high, and their temporal resolution is relatively coarse. Crowdsourcing is an increasingly common source of data that takes advantage of local stakeholder knowledge and that provides a higher frequency of data. The complementarity of these two data sources suggests there is great potential for mutually beneficial integration. Unfortunately, there are still important gaps in crowdsourced satellite image analysis by means of crowdsourcing in areas such as land cover classification and emergency management. In this paper, we summarize recent efforts, and discuss the challenges and prospects of satellite image analysis for geospatial applications using crowdsourcing. Crowdsourcing can be used to improve satellite image analysis and satellite images can be used to organize crowdsourced efforts for collaborative mapping.  相似文献   

陆源碎屑岩中的古土壤在地层分析中的应用          下载免费PDF全文

魏钦廉  伊海生  肖玲 《古地理学报》2006,8(2):211-218

对各种古土壤的分类标准进行比较之后,重点介绍了Mack的古土壤分类及其土壤的演变序列。Mack的古土壤分类主要依据稳定矿物和保存在土壤中的古地貌属性,将古土壤划分为9种。在地层序列中古土壤的识别标志主要包括颜色、粒度、生物成因构造（植物生长痕迹）、原生沉积构造退化特征、层次化和边界特征、宏观结构特征、矿物及地球化学特征等。可以通过野外观察、室内显微镜观察、实验室化验及地震、测井资料来进行鉴别。陆相盆地具有很强的分隔性,盆地相变又非常复杂,寻找区域性较稳定的层序界面比较困难。越来越多的研究证实用古土壤划分陆相成因地层层序、解决地区及全球对比具有重要的实用价值。  相似文献   

遥感技术在黑竹沟风景旅游资源调查分类与评价中的应用   总被引：2，自引：1，他引：1  

张瑞英  何政伟  张雪峰 《干旱区地理》2006,29(6):909-914

随着信息技术的发展,遥感成为了旅游资源调查评价的新手段。阐述了运用遥感新技术对四川省乐山市黑竹沟风景旅游区旅游资源进行调查的过程,并结合现场调研和相关资料对其旅游资源进行了分类和评价。首先对黑竹沟风景旅游区2003年的ETM影像数据遥感图像进行了处理,然后建立了解译标志进行旅游资源信息的提取和分析,最后对黑竹沟风景旅游区的旅游资源进行了客观全面的分类和评价。黑竹沟的旅游资源可分为2大类、7亚类、21个基本类型,是一处景源内容丰富、景象多变的旅游区,发展潜力巨大。  相似文献   

全球及区域模式中陆面过程的地表植被覆盖分类方法   总被引：1，自引：0，他引：1  

姚凤梅  张佳华 《干旱区地理》2006,29(6):872-877

全球和区域尺度上陆面生态系统与气候密切相关。全球和区域模式的发展对于我们认识气候与陆地生态的变化起到重要作用。在这些模型中,植被覆盖是影响大气-植被间热量、水分和CO2等交换的重要陆面参数。在分析了陆地植被覆盖分类原理基础上,介绍了目前全球不同植被覆盖分类方案,包括基于地基观测的植被分布、基于生物气候特征的分类方案（如：Holdridge的方案）;特别近年来陆面过程试验表明,各种遥感数据源（如：NOAA—AVHRR,EOS—MODIS,Landsat—TM）等为我们提供了有利的工具来监测全球植被动态,完善植被分类,并且采用高时空分辨率的全球土地覆盖状况特征,在不同时空尺度揭示植被-大气相互作用。本文分析了代表性的3种基于卫星遥感技术的陆面植被分类方案,分别是BATS（18类）、SIB（9类）、SIB2（12类）和BIOME—BGC（31类）陆地模式的植被覆盖分类方案．最后分析了目前可用于全球植被覆盖分类的新的遥感数据库。  相似文献   

海洋经济红藻江蓠科的分类研究现状及存在的问题          下载免费PDF全文

丁兰平  杨楠  刘金梅  姜晶晶  黄冰心 《应用海洋学学报》2020,39(1):144-151

江蓠科红藻是重要的经济海藻,用途十分广泛。然而,近年来受分子生物学技术引入等的影响,其分类学地位引起了极大争议。针对这个问题,本研究以争议比较大的江蓠属(Gracilaria)、拟江蓠(龙须菜)属(Gracilariopsis)和多穴藻属(Polycavernosa)为对象,总结归纳了其物种多样性、国内外研究进展、存在的问题、学者试图解答的问题以及部分自己的研究结果。以期为该类群的研究提供相对详细、客观的参考数据。  相似文献   

An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering     

Xiaojing Wu  Raul Zurita-Milla  Changqing Song 《International journal of geographical information science》2020,34(9):1822-1848

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Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Information on tree species composition is crucial in forest management and can be obtained using remote sensing. While the topic has been addressed frequently over the last years, the remote sensing-based identification of tree species across wide and complex forest areas is still sparse in the literature. Our study presents a tree species classification of a large fraction of the Białowieża Forest in Poland covering 62 000 ha and being subject to diverse management regimes. Key objectives were to obtain an accurate tree species map and to examine if the prevalent management strategy influences the classification results. Tree species classification was conducted based on airborne hyperspectral HySpex data. We applied an iterative Support Vector Machine classification and obtained a thematic map of 7 individual tree species (birch, oak, hornbeam, lime, alder, pine, spruce) and an additional class containing other broadleaves. Generally, the more heterogeneous the area was, the more errors we observed in the classification results. Managed forests were classified more accurately than reserves. Our findings indicate that mapping dominant tree species with airborne hyperspectral data can be accomplished also over large areas and that forest management and its effects on forest structure has an influence on classification accuracies and should be actively considered when progressing towards operational mapping of tree species composition.  相似文献   

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.  相似文献   

Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands,South Africa     

《International Journal of Applied Earth Observation and Geoinformation》2020

Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively.  相似文献   

基于自然间断点分级法的土地利用数据网格化分析   总被引：4，自引：0，他引：4  

李乃强  徐贵阳 《测绘通报》2020,(4):106

土地利用在自然资源统一管理中扮演着重要角色,面对不同区域和年份的数据,统一分析比对口径尤为重要,同时也应反映出相互之间的差异。本文以宜兴市2009年和2017年土地利用现状数据为数据源,首先使用统一的分类标准提取用地类型中的3大类,通过不同大小的单元划分尝试和结果分析,发现适用于该数据的网格尺度大小;然后基于自然间断点分级法进行分级范围划定,对宜兴市三类用地类型的分布和变化趋势进行综合分析,较为真实地反映了宜兴市用地情况;最后通过选用合适的空间尺度和分级范围划定方法,进而构建一个兼具操作性和科学性的土地利用数据网格化方法,为自然资源部门统筹管理和综合治理提供依据。  相似文献