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A new regional vegetation mapping method based on terrain-climate-remote sensing and its application on the Qinghai-Xizang Plateau

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Science China Earth Sciences 2023年第2期66卷 237-246页

作者： Guangsheng ZHOU Hongrui REN Tong LIU Li ZHOU Yuhe JI Xingyang SONG Xiaomin LV State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing100081China Department of Geomatics Taiyuan University of TechnologyTaiyuan030024China

Understanding the impact of climate change on vegetation and its evolution trend requires long-term accurate data on regional vegetation types and their geographical distribution.Currently,land use and land cover types are mainly obtained based on remote sensing information.Little research has been conducted on remote sensing interpretation of vegetation types and their geographical distributions in terms of the comprehensive utilization of remote sensing,climate,and terrain.A new region vegetation mapping method based on terrain-climate-remote sensing was developed in this study,supported by the Google Earth Engine(GEE)and the random forest algorithm,which is a new generation of earth science data and analysis application platform,together with optimal vegetation mapping features obtained from the average impure reduction method and out-of-bag error value,using different information from remote sensing,climate,and terrain.This vegetation of Qinghai-Xizang Plateau with 10 m spatial resolution in 2020 was mapped,in terms of this new vegetation mapping method,Sentinel-2A/B remotely sensed images,climate,and terrain.The accuracy verification of vegetation mapping on the Qinghai-Xizang Plateau showed an overall accuracy of 89.5%and a Kappa coefficient of 0.87.The results suggest that the regional vegetation mapping method based on terrain-climate-remote sensing proposed in this study can provide technical support for obtaining long-term accurate data on vegetation types and their geographical distributions on the Qinghai-Xizang Plateau and the globe.

关键词： vegetation mapping Random forest algorithm GEE remote sensing Qinghai-Xizang Plateau

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Novel vegetation mapping Through Remote Sensing Images Using Deep Meta Fusion Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 2915-2931页

作者： S.Vijayalakshmi S.Magesh Kumar Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiTamilnaduIndia

Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions.It is challenging to determine vegetation using traditional map classification approaches.The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties.It is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed images.The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping.The architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation area.The novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight architecture.The system considers detailing feature areas to improve classification accuracy and reduce processing time.The proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 s.The training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM model.The system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.

关键词： vegetation mapping deep learning machine learning remote sensing data image processing

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vegetation mapping of the Mond Protected Area of Bushehr Province(South-west Iran)

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Journal of Integrative Plant Biology 2009年第3期51卷 251-260页

作者： Ahmadreza Mehrabian Alireza Naqinezhad Abdolrassoul Salman Mahiny Hossein Mostafavi Homan Liaghati Mohsen Kouchekzadeh Department of Biodiversity and Ecosystems Management Environmental Sciences Research InstituteShahid Beheshti University Department of Biology Faculty of SciencesUniversity of Mazandaran College of the Environment Gorgan University of Agriculture and Natural Resources Sciences Department of Ecological Agriculture Environmental Sciences Research InstituteShahid Beheshti University Department of Planning and Design of Environment Environmental Research InstituteShahid Beheshti University

Arid regions of the world occupy up to 35% of the earth＇s surface, the basis of various definitions of climatic conditions, vegetation types or potential for food production. Due to their high ecological value, monitoring of arid regions is necessary and modern vegetation studies can help in the conservation and management of these areas. The use of remote sensing for mapping of desert vegetation is difficult due to mixing of the spectral reflectance of bright desert soils with the weak spectral response of sparse vegetation. We studied the vegetation types in the semiarid to arid region of Mond Protected Area, south-west Iran, based on unsupervised classification of the Spot XS bands and then produced updated maps. Sixteen map units covering 12 vegetation types were recognized in the area based on both field works and satellite mapping. Halocnemum strobilaceum and Suaeda fruticosa vegetation types were the dominant types and Ephedra foliata, Salicornia europaea-Suaeda heterophylla vegetation types were the smallest. vegetation coverage decreased sharply with the increase in salinity towards the coastal areas of the Persian Gulf. The highest vegetation coverage belonged to the riparian vegetation along the Mond River, which represents the northern boundary of the protected area. The location of vegetation types was studied on the separate soil and habitat diversity maps of the study area, which helped in final refinements of the vegetation map produced.

关键词： arid region coast line halophytes island vegetation Iran Persian Gulf vegetation mapping vegetation types.

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vegetation mapping USING REMOTE SENSING AND GIS

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Journal of Geographical Sciences 1994年第Z2期4卷 112-123页

作者： Wu Bingfang huang Xuan Tian Zhigang(LREIS, Institute of Geography, CAS, Beijing 100101 People’s Republic of China)

Classification accuracy of satellite imagery in complex terrain environments can be improvd by using ancillary daa and imasery spaial features extracted from the images. The classification mny be accomplished by using spaial analysis methods of geographic information System (GIS) that provide a tool for integrating all Kinds of ancillare data, or using ancillare data as an augmented subset of bands in processing imagery. The purpose of the study is to test the role of GIS spatial and spectra analysis medel in aiding the classification of satellite data and to compare the ability Of two satellite systems, SPOT and Landsat Thematic Mapper (TM) in vegetation mapping in mountainous region.

关键词： vegetation mapping classification remote sensing GIS

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Remote sensing imagery in vegetation mapping: a review

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Journal of Plant Ecology 2008年第1期1卷 9-23页

作者： Yichun Xie Zongyao Sha Mei Yu Department of Geography and Geology Eastern Michigan UniversityYpsilantiMI 48197USA International Software School Wuhan UniversityWuhanHubei Province 430079China Key Laboratory of vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijing 100093China

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.

关键词： vegetation mapping remote sensing sensors image processing image classification

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Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images

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Frontiers of Earth Science 2013年第1期7卷 43-54页

作者： Yan HUANG Bailang YU Jianhua ZHOU Chunlin HU Wenqi TAN Zhiming HU Jianping WU Key Laboratory of Geographic Information Science (Ministry of Education) East China Normal University Shanghai 200241 China Shanghai Landscape and City Appearance Administration Information Center Shanghai 200040 China

Urban green volume is an important indicator for analyzing urban vegetation structure, ecological evaluation, and green-economic estimation. This paper proposes an object-based method for automated estimation of urban green volume combining three-dimensional （3D） information from airborne Light Detection and Ranging （LiDAR） data and vegetation information from high resolution remotely sensed images through a case study of the Lujiazui region, Shanghai, China. High resolution airborne near-infrared photographs are used for identifying the urban vegetation distribution. Airborne LiDAR data offer the possibility to extract individual trees and to measure the attributes of trees, such as tree height and crown diameter. In this study, individual trees and grassland are identified as the independent objects of urban vegetation, and the urban green volume is computed as the sum of two broad portions： individual trees volume and grassland volume. The method consists of following steps： generating and filtering the normalized digital surface model （nDSM）, extracting the nDSM of urban vegetation based on the Normalized Difference vegetation Index （NDVI）, locating the local maxima points, segment- ing the vegetation objects of individual tree crowns and grassland, and calculating the urban green volume of each vegetation object. The results show the quantity and distribution characteristics of urban green volume in the Lujiazui region, and provide valuable parameters for urban green planning and management. It is also concluded from this paper that the integrated application of LiDAR data and image data presents an effective way to estimate urban green volume.

关键词： Urban areas vegetation mapping Remote sensing Environmental quality

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Characterizing Shorea robusta communities in the part of Indian Terai landscape

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Journal of Forestry Research 2014年第1期25卷 121-128页

作者： V.S.Chitale M.D.Behera S.Matin P.S.Roy V.K.Sinha Spatial Analysis and Modeling Laboratory Centre for Oceans RiversAtmosphere and Land Sciences (CORAL)Indian Institute of Technology (IIT) Kharagpur Center for Earth and Space Sciences University of Hyderabad Uttar Pradesh State Forest Dept.

Shorea robusta Gaertn. f.（Sal）is one of the important tim-ber-yielding plants in India, which dominates the vegetation of Terai landscape of Uttar Pradesh state in India forming various communities based on its associations. The present study deals with delineation, map-ping and characterization of various communities of Sal （Shorea robusta） forests in Terai landscape of Uttar Pradesh, India ranging across over 16 districts. Field survey and visual interpretation based forest vegetation type classification and mapping was carried out as part of the project entitled ‘Biodiversity characterization at landscape level using remote sensing and GIS’. Indian Remote Sensing-P6 （Resourcesat-1） Linear Imaging Self Scanner-III satellite data was used during the study. The total area covered by different Sal forests was found to be approximately 2256.77 km2. Sal communities were identified and characterized based on their spectral properties, physiognomy and phytosociological charac-teristics. Following nine Sal communities were identified, delineated and mapped with reasonable accuracyviz.,Chandar,Damar, dry plains, moist plains, western alluvium, western alluvium plains, mixed moist deciduous, mixed dry deciduous andSiwalik. It is evident from the area estimates that mixed moist deciduous Sal is the most dominant commu-nity in the region covering around （1613.90 km2）, other major communi-ties were found as western alluvium plains Sal （362.44 km2）, mixed dry deciduous Sal （362.44 km2） and dry plains Sal （107.71 km2）. The Terai landscape of Uttar Pradesh faces tremendous anthropogenic pressure leading to deterioration of the forests. Community level information could be used monitoring the status as well as for micro level conserva-tion and planning of the Sal forests in Terai Landscape of Uttar Pradesh.

关键词： vegetation mapping LISS III Forest management Microlevel Conservation

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Future of Abies pindrow in Swat district,northern Pakistan

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Journal of Forestry Research 2014年第1期25卷 211-214页

作者： Kishwar Ali Habib Ahmad Nasrullah Khan Stephen Jury Department of Plant Sciences School of Biological SciencesUniversity of Reading Department of Genetics Hazara University Mansehra Laboratory of Plant Ecology and Department of Botany University of Malakand Chakdara

Swat district is a biodiversity hub of Pakistan. The plant species, especially trees, in the Swat District are exposed to extinction threat from global climate change. Maximum entropy （MaxEnt） modelling of species distribution, using HADCM3 A2a global climate change scenario, pre-dicted a considerable change in the future distribution ofAbies pindrow （Royle ex D.Don） Royle. AUC （area under the curve）values of 0.972 and 0.983 were significant for the present and future distribution models of the species, respectively. It is clear that bioclimatic variables such as the mean temperature of the warmest quarter （bio_10） and the annual temperature range （bio_7） contribute significantly to the model and thus affect the predicted distribution and density of the species. The future model predicts that by the year 2080 population density will have decreased significantly. The highest density of the species is recorded in the eastern and western borders of the Valley in the areas of Sulatanr and Mankial. The changes in density and distribution of the species can have considerable impact, not only on the tree species itself, but on the associated subflora as well.

关键词： vegetation mapping Abies pindrow climate change predictive models Swat Valley

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