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| 1 | Remote sensing imagery in vegetation mapping: a review显示文摘Aims Mapping vegetation through remotely sensed images involves various considerations,processes and techniques.Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources.Various sources of imagery are known for their differences in spectral,spatial,radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping.Generally,it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level.Then,correlations of the vegetation types(communities or species)within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified.These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process,which is also called image processing.This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically,this paper focuses on the comparisons of popular remote sensing sensors,commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts,available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced,analyzed and compared.The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures,which can be utilized to study vegetation cover from remote sensed images. | Yichun Xie Zongyao Sha Mei Yu | 2008 | Journal of Plant Ecology2008，1，1： | 39 |
| 2 | Spatio-temporal patterns of satellite-derived grassland vegetation phenology from 1998 to 2012 in Inner Mongolia, China显示文摘Spatio-temporal variations of vegetation phenology, e.g. start of green-up season(SOS) and end of vegetation season(EOS), serve as important indicators of ecosystems. Routinely processed products from remotely sensed imagery, such as the normalized difference vegetation index(NDVI), can be used to map such variations. A remote sensing approach to tracing vegetation phenology was demonstrated here in application to the Inner Mongolia grassland, China. SOS and EOS mapping at regional and vegetation type(meadow steppe, typical steppe, desert steppe and steppe desert) levels using SPOT-VGT NDVI series allows new insights into the grassland ecosystem. The spatial and temporal variability of SOS and EOS during 1998–2012 was highlighted and presented, as were SOS and EOS responses to the monthly climatic fluctuations. Results indicated that SOS and EOS did not exhibit consistent shifts at either regional or vegetation type level; the one exception was the steppe desert, the least productive vegetation cover, which exhibited a progressive earlier SOS and later EOS. Monthly average temperature and precipitation in preseason(February, March and April) imposed most remarkable and negative effects on SOS(except for the non-significant impact of precipitation on that of the meadow steppe), while the climate impact on EOS was found to vary considerably between the vegetation types. Results showed that the spatio-temporal variability of the vegetation phenology of the meadow steppe, typical steppe and desert steppe could be reflected by the monthly thermal and hydrological factors but the progressive earlier SOS and later EOS of the highly degraded steppe desert might be accounted for by non-climate factors only, suggesting that the vegetation growing period in the highly degraded areas of the grassland could be extended possibly by human interventions. | SHA Zongyao ZHONG Jialin BAI Yongfei TAN Xicheng Jonathan LI | 2016 | Journal of Arid Land2016，8，3： | 13 |
| 3 | Assessing the impacts of human activities and climate variations on grassland productivity by partial least squares structural equation modeling(PLS-SEM)显示文摘The cause-effect associations between geographical phenomena are an important focus in ecological research. Recent studies in structural equation modeling(SEM) demonstrated the potential for analyzing such associations. We applied the variance-based partial least squares SEM(PLS-SEM) and geographically-weighted regression(GWR) modeling to assess the human-climate impact on grassland productivity represented by above-ground biomass(AGB). The human and climate factors and their interaction were taken to explain the AGB variance by a PLS-SEM developed for the grassland ecosystem in Inner Mongolia, China. Results indicated that 65.5% of the AGB variance could be explained by the human and climate factors and their interaction. The case study showed that the human and climate factors imposed a significant and negative impact on the AGB and that their interaction alleviated to some extent the threat from the intensified human-climate pressure. The alleviation may be attributable to vegetation adaptation to high human-climate stresses, to human adaptation to climate conditions or/and to recent vegetation restoration programs in the highly degraded areas. Furthermore, the AGB response to the human and climate factors modeled by GWR exhibited significant spatial variations. This study demonstrated that the combination of PLS-SEM and GWR model is feasible to investigate the cause-effect relation in socio-ecological systems. | SHA Zongyao XIE Yichun TAN Xicheng BAI Yongfei LI Jonathan LIU Xuefeng | 2017 | Journal of Arid Land2017，9，4： | 8 |
| 4 | Design of Service-Oriented Architecture for Spatial Data Integration and Its Application in Building Web-based GIS Systems显示文摘In this paper we propose a service-oriented architecture for spatial data integration (SOA-SDI) in the context of a large number of available spatial data sources that are physically sitting at different places, and develop web-based GIS systems based on SOA-SDI, allowing client applications to pull in, analyze and present spatial data from those available spatial data sources. The proposed architecture logically includes 4 layers or components; they are layer of multiple data provider services, layer of data in-tegration, layer of backend services, and front-end graphical user interface (GUI) for spatial data presentation. On the basis of the 4-layered SOA-SDI framework, WebGIS applications can be quickly deployed, which proves that SOA-SDI has the potential to reduce the input of software development and shorten the development period. | SHA Zongyao XIE Yichun | 2010 | Geo-Spatial Information Science2010，13，1： | 4 |
| 5 | Geoscience model service integrated workflow for rainstorm waterlogging analysis显示文摘This paper proposed a geoscience model service integrated workflowbased rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis systems.In this research,we studied a general OGC WPS service invoking strategy,an automatic asynchronous invoking mechanism of WPS services in the BPEL workflow,and a distributed waterlogging analysis services integrated workflow to realize the reconstruction of a waterlogging analysis model based on the proposed method.The proposed method can make use of the flexible adjustment capability of the workflow and not only overcomes the inherent defects of conventional geoscience analysis methods but also realizes the integration and calculation of distributed geospatial data,models and computing resources automatically.The method has better construction convenience,execution reliability,extensibility and intelligence potential than a conventional method and has important value for dealing with more natural disasters and environmental challenges. | Xicheng Tan Jingguo Jiao Nengcheng Chen Fang Huang Liping Di Jinchuan Wang Zongyao Sha Jin Liu | 2021 | International Journal of Digital Earth2021，14，7： | 1 |
| 6 | Comparison of leaf area index inversion for grassland vegetation through remotely sensed spectra by unmanned aerial vehicle and field-based spectroradiometer显示文摘Aims Remote sensing technology has been proved useful in mapping grass-land vegetation properties.Spectral features of vegetation cover can be recorded by optical sensors on board of different platforms.With increas-ing popularity of applying unmanned aerial vehicle(UAV)to mapping plant cover,the study aims to investigate the possible applications and potential issues related to mapping leaf area index(LAI)through integra-tion of remote sensing imagery collected by multiple sensors.Methods This paper applied the collected spectral data through field-based(FLD)and UAV-borne spectroradiometer to map LAI in a Sino-German experiment pasture located in the Xilingol grassland,Inner Mongolia,China.Spectroradiometers on FLD and UAV platforms were taken to measure spectral reflectance related to the targeted vegetation proper-ties.Based on eight vegetation indices(VIs)computed from the col-lected hyperspectral data,regression models were used to inverse LAI.The spectral responses between FLD and UAV platforms were com-pared,and the regression models relating LAI with VIs from FLD and UAV were established.The modeled LAIs by UAV and FLD platforms were analyzed in order to evaluate the feasibility of potential integra-tion of spectra data for mapping vegetation from the two platforms.Important Findings Results indicated that the spectral reflectance between FLD and UAV showed critical gaps in the green and near-infrared regions of the spec-trum over densely vegetated areas,while the gaps were small over sparsely vegetated areas.The VI values from FLD spectra were greater than their UAV-based counterparts.Out of all the VIs,broadband gen-eralized soil-adjusted vegetation index(GESAVI)and narrow-band nNDVI2 were found to achieve the best results in terms of the accuracy of the inversed LAIs for both FLD and UAV platforms.We conclude that GESAVI and nNDVI2 are the two promising VIs for both platforms and thus preferred for LAI inversion to carry spectra integration of the two platforms.We suggest that accuracy on the LAI inversion could be improved by applying more advanced functions(e.g.non-linear)con-sidering the observed bias for the difference between the UAV-and FLD-inversed LAIs,especially when LAI was low. | Zongyao Sha Yuwei Wang Yongfei Bai Yujin Zhao Hua Jin Ya Na Xiaoliang Meng | 2019 | Journal of Plant Ecology2019，12，3： | 1 |
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