Adv Search
Volume 30 Issue 3
. 
Turn off MathJax
Article Contents
Article Navigation >  Chinese Geographical Science  > 2020  >  30(3): 410-426

LIU Yuanxin, LYU Yihe, BAI Yingfei, ZHANG Buyun, TONG Xiaolin. Vegetation Mapping for Regional Ecological Research and Management: A Case of the Loess Plateau in China[J]. Chinese Geographical Science, 2020, 30(3): 410-426. doi: 10.1007/s11769-020-1120-5
Citation: LIU Yuanxin, LYU Yihe, BAI Yingfei, ZHANG Buyun, TONG Xiaolin. Vegetation Mapping for Regional Ecological Research and Management: A Case of the Loess Plateau in China[J]. Chinese Geographical Science, 2020, 30(3): 410-426. doi: 10.1007/s11769-020-1120-5
shu

Vegetation Mapping for Regional Ecological Research and Management: A Case of the Loess Plateau in China

doi: 10.1007/s11769-020-1120-5

  • 1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China;

  • 2. University of Chinese Academy of Sciences, Beijing 100049, China;

  • 3. The Grain for Green Project Management Office of Yan'an Municipality, Yan'an 716000, China;

  • 4. The Grain for Green Project Management Office of Ansai District, Yan'an 717400, China

Funds:

Under the auspices of National Key Research and Development Program of China (No. 2016YFC0501601), Key Science and Technology Project of Yan'an Municipality (No. 2016CGZH-14-03)

  • Received Date: 2019-06-17
  • Rev Recd Date: 2019-10-11
  • Vegetation maps are fundamental for regional-scale ecological research. However, information is often not sufficiently up to date for such research. The Loess Plateau is a key area for vegetation restoration projects and a suitable area for regional ecological research. To carry out regional vegetation mapping based on the principles of hierarchical classification, object-oriented methods, visual interpretation, and accuracy assessment, this study integrated land cover, high-resolution remote sensing images, background environmental data, bioclimate zoning data, and field survey data from the Loess Plateau. To further clarify the implications of vegetation mapping, we compared the deviation of the 2015 vegetation map of the Loess Plateau (VMLP) and the widely used vegetation map of China (VMC) (1:1 000 000) for the expressed vegetation information and the evaluation of ecosystem services. The results indicated that 1) the vegetation of the Loess Plateau could be divided into 9 vegetation type groups and 18 vegetation types with classification accuracies of 87.76% and 83.97%, respectively; 2) the distribution of vegetation had obvious zonal regularity; 3) a deviation of 29.56×104km2 occurred when the vegetation coverage area was quantified with the VMC; 4) the vegetation classification accuracy affected the ecosystem service assessment, the total water yield of the Loess Plateau calculated by the VMC and other required parameters was overestimated by 2.2×106 mm in 2015. Because vegetation mapping is a basic and important activity, that requires greater attention, this study provides supporting data for subsequent multivariate vegetation mapping and vegetation management for conservation and restoration.
  • [1] Alamgir M, Turton S M, Macgregor C et al., 2016. Ecosystem services capacity across heterogeneous forest types:understanding the interactions and suggesting pathways for sustaining multiple ecosystem services. Science of the Total Environment, 566-567:584-595. doi:10.1016/j.scitotenv.2016. 05.107
    [2] Bonan G B, 2008. Forests and climate change:forcings, feedbacks, and the climate benefits of forests. Science, 320:1444-1449. doi: 10.1126/science.1155121
    [3] Brinkmann K, Patzelt A, Schlecht E et al., 2011. Use of environmental predictors for vegetation mapping in semi-arid mountain rangelands and the determination of conservation hotspots. Applied Vegetation Science, 14(1):17-30. doi: 10.1111/j.1654-109x.2010.01097.x
    [4] Brown de Colstoun E C, Story M H, Thompson C et al., 2003. National park vegetation mapping using multi-temporal LANDSAT 7 data and a decision tree classifier. Remote Sensing of Environment, 85(3):316-327. doi: 10.1016/S0034-4257(03)00010-5
    [5] Cai D L, Guan Y N, Guo S et al., 2014. Mapping plant functional types over broad mountainous regions:a hierarchical soft time-space classification applied to the Tibetan Plateau. Remote Sensing, 6(4):3511-3532. doi: 10.3390/rs6043511
    [6] Cawsey E M, Austin M P, Baker B L, 2002. Regional vegetation mapping in Australia:a case study in the practical use of statistical modelling. Biodiversity and Conservation, 11(12):2239-2274. doi: 10.1023/a:1021350813586
    [7] Ebrahimi M, Khosravi H, Rigi M, 2016. Short-term grazing exclusion from heavy livestock rangelands affects vegetation cover and soil properties in natural ecosystems of southeastern Iran. Ecological Engineering, 95:10-18. doi:10.1016/j. ecoleng.2016.06.069
    [8] Eppink F V, van den Bergh J C J M, Rietveld P, 2004. Modelling biodiversity and land use:urban growth, agriculture and nature in a wetland area. Ecological Economics, 51(3-4):201-216. doi: 10.1016/j.ecolecon.2004.04.011
    [9] Fang J Y, Yu G R, Liu L L et al., 2018. Climate change, human impacts, and carbon sequestration in China. Proceedings of the National Academy of Sciences of the United States of America, 115(16):4015-4020. doi: 10.1073/pnas.1700304115
    [10] Friedl M A, Sulla-Menashe D, Tan B et al., 2010. MODIS collection 5 global land cover:algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1):168-182. doi: 10.1016/j.rse.2009.08.016
    [11] Fu B J, Liu Y, Lü Y H et al., 2011. Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China. Ecological Complexity, 8(4):284-293. doi: 10.1016/j.ecocom.2011.07.003
    [12] Gallet S, Sawtschuk J, 2014. Restoration dynamics evaluation by vegetation mapping and transition matrix modelling:analysis of 20 yr of restoration and management at the megalithic site of Carnac (Brittany, France). Applied Vegetation Science, 17(2):225. doi: 10.1111/avsc.12080
    [13] Garzón-Machado V, Otto R, del Arco Aguilar M J, 2013. Bioclimatic and vegetation mapping of a topographically complex oceanic island applying different interpolation techniques. International Journal of Biometeorology, 58(5):887-899. doi: 10.1007/s00484-013-0670-y
    [14] Graves S J, Caughlin T T, Asner G P et al., 2018. A tree-based approach to biomass estimation from remote sensing data in a tropical agricultural landscape. Remote Sensing of Environment, 218:32-43. doi: 10.1016/j.rse.2018.09.009
    [15] Hall F G, Bergen K, Blair J B et al., 2011. Characterizing 3D vegetation structure from space:mission requirements. Remote Sensing of Environment, 115(11):2753-2775. doi: 10.1016/j.rse.2011.01.024
    [16] Hao R F, Yu D Y, Wu J G, 2017. Relationship between paired ecosystem services in the grassland and agro-pastoral transitional zone of China using the constraint line method. Agriculture, Ecosystems & Environment, 240:171-181. doi: 10.1016/j.agee.2017.02.015
    [17] Hlatshwayo S T, Mutanga O, Lottering R T et al., 2019. Mapping forest aboveground biomass in the reforested Buffelsdraai landfill site using texture combinations computed from SPOT-6 pan-sharpened imagery. International Journal of Applied Earth Observation and Geoinformation, 74:65-77. doi: 10.1016/j.jag.2018.09.005
    [18] Huang H B, Liu C X, Wang X Y et al., 2017. Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data. ISPRS Journal of Photogrammetry and Remote Sensing, 129:189-199. doi: 10.1016/j.isprsjprs.2017.04.020
    [19] Jiang C, Wang F, Zhang H Y et al., 2016. Quantifying changes in multiple ecosystem services during 2000-2012 on the Loess Plateau, China, as a result of climate variability and ecological restoration. Ecological Engineering, 97:258-271. doi: 10.1016/j.ecoleng.2016.10.030
    [20] Joy S M, Reich R M, Reynolds R T, 2003. A non-parametric, supervised classification of vegetation types on the Kaibab National Forest using decision trees. International Journal of Remote Sensing, 24(9):1835-1852. doi:10.1080/01431160 210154948
    [21] Karami M, Westergaard-Nielsen A, Normand S et al., 2018. A phenology-based approach to the classification of Arctic tundra ecosystems in Greenland. ISPRS Journal of Photogrammetry and Remote Sensing, 146:518-529. doi: 10.1016/j.isprsjprs.2018.11.005
    [22] Lü Y H, Fu B J, Feng X M et al., 2012. A policy-driven large scale ecological restoration:quantifying ecosystem services changes in the Loess Plateau of China. PloS One, 7(2):e31782. doi: 10.1371/journal.pone.0031782
    [23] Lü Y H, Zhang L W, Zeng Y et al., 2017. Representation of critical natural capital in China. Conservation Biology, 31(4):894-902. doi: 10.1111/cobi.12897
    [24] Li Z, Zhou T, Zhao X et al., 2015. Assessments of drought impacts on vegetation in China with the optimal time scales of the climatic drought index. International Journal of Environmental Research and Public Health, 12(7):7615-7634. doi: 10.3390/ijerph120707615
    [25] Liu L L, Zhang X Y, Donnelly A et al., 2016a. Interannual variations in spring phenology and their response to climate change across the Tibetan Plateau from 1982 to 2013. International Journal of Biometeorology, 60(10):1563-1575. doi: 10.1007/s00484-016-1147-6
    [26] Liu Shuangna, Zhou Tao, Wei Linyan et al., 2012. The spatial distribution of forest carbon sinks and sources in China. Chinese Science Bulletin, 57(14):1699-1707. (in Chinese)
    [27] Liu Y X, Lü Y H, Fu B J et al., 2019. Quantifying the spatio-temporal drivers of planned vegetation restoration on ecosystem services at a regional scale. Science of the Total Environment, 650(1):1029-1040. doi:10.1016/j.scitotenv.2018. 09.082
    [28] Liu Y X, Zhao W W, Zhang X et al., 2016b. Soil water storage changes within deep profiles under introduced shrubs during the growing season:evidence from semiarid Loess Plateau, China. Water, 8(10):475. doi: 10.3390/w8100475
    [29] Manies K L, Mladenoff D J, 2000. Testing methods to produce landscape-scale presettlement vegetation maps from the U.S. public land survey records. Landscape Ecology, 15(8):741-754. doi: 10.1023/a:1008115200471
    [30] Massetti A, Sequeira M M, Pupo A et al., 2016. Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal). European Journal of Remote Sensing, 49(1):643-672. doi:10.5721/eujrs 20164934
    [31] Mohamed M A, Babiker I S, Chen Z M et al., 2004. The role of climate variability in the inter-annual variation of terrestrial net primary production (NPP). Science of the Total Environment, 332(1-3):123-137. doi:10.1016/j.scitotenv.2004. 03.009
    [32] Molnár Z, Bartha S, Seregélyes T et al., 2007. A grid-based, satellite-image supported, multi-attributed vegetation mapping method (MÉTA). Folia Geobotanica, 42(3):225-247. doi: 10.1007/BF02806465
    [33] Muchoney D, Strahler A, 2002. Regional vegetation mapping and direct land surface parameterization from remotely sensed and site data. International Journal of Remote Sensing, 23(6):1125-1142. doi: 10.1080/01431160110070771
    [34] Nijland W, Reshitnyk L, Rubidge E, 2019. Satellite remote sensing of canopy-forming kelp on a complex coastline:a novel procedure using the Landsat image archive. Remote Sensing of Environment, 220:41-50. doi: 10.1016/j.rse.2018.10.032
    [35] Novo-Fernández A, Franks S, Wehenkel C et al., 2018. Landsat time series analysis for temperate forest cover change detection in the Sierra Madre Occidental, Durango, Mexico. International Journal of Applied Earth Observation & Geoinformation, 73(1):230-244. doi: 10.1016/j.jag.2018.06.015
    [36] Ouyang Zhiyuan, Zhang Lu, Wu Bingfang et al., 2015. An ecosystem classification system based on remote sensor information in China. Acta Ecologica Sinica, 35(2), 219-226. (in Chinese)
    [37] Peng J, Hu Y N, Liu Y X et al., 2018. A new approach for urban-rural fringe identification:integrating impervious surface area and spatial continuous wavelet transform. Landscape and Urban Planning, 175:72-79. doi: doi.org/10.1016/j.landurbplan.2018.03.008
    [38] Potter C S, Randerson J T, Field C B et al., 1993. Terrestrial ecosystem production:a process model based on global satellite and surface data. Global Biogeochemical Cycles, 7:811-841. doi: 10.1029/93GB02725
    [39] Qiu B W, Liu Z, Tang Z H et al., 2016. Developing indices of temporal dispersion and continuity to map natural vegetation. Ecological Indicators, 64:335-342. doi:10.1016/j.ecolind. 2016.01.006
    [40] Ren Y J, Lü Y H, Fu B J, 2016. Quantifying the impacts of grassland restoration on biodiversity and ecosystem services in China:a meta-analysis. Ecological Engineering, 95:542-550. doi: 10.1016/j.ecoleng.2016.06.082
    [41] Sader S A, Bertrand M, Wilson E H, 2003. Satellite change detection of forest harvest patterns on an industrial forest landscape. Forest Science, 49(3):341-353. doi:10.1046/j.1439-0329. 2003.00323.x
    [42] Schlaepfer D R, Bradford J B, Lauenroth W K et al., 2017. Climate change reduces extent of temperate drylands and intensifies drought in deep soils. Nature Communications, 8:14196. doi: 10.1038/ncomms14196
    [43] Shamsoddini A, Raval S, 2018. Mapping red edge-based vegetation health indicators using Landsat TM data for Australian native vegetation cover. Earth Science Informatics, 11:1-8. doi: 10.1007/s12145-018-0347-5
    [44] Sui X H, Zhou G S, 2012. Carbon dynamics of temperate grassland ecosystems in China from 1951 to 2007:an analysis with a process-based biogeochemistry model. Environmental Earth Sciences, 68(2):521-533. doi: 10.1007/s12665-012-1756-2
    [45] Walker K J, Stevens P A, Stevens D P et al., 2004. The restoration and re-creation of species-rich lowland grassland on land formerly managed for intensive agriculture in the UK. Biological Conservation, 119(1):1-18. doi:10.1016/j.biocon.2003.10. 020
    [46] Wang S, Fu B J, Piao S L et al., 2015a. Reduced sediment transport in the Yellow River due to anthropogenic changes. Nature Geoscience, 9(1):38-41. doi: 10.1038/ngeo2602
    [47] Wang Q F, Zheng H, Zhu X J et al., 2015b. Primary estimation of Chinese terrestrial carbon sequestration during 2001-2010. Science Bulletin, 60(6):577-590. doi: 10.1007/s11434-015-0736-9
    [48] Wu B F, Zeng Y, Qian J K et al., 2017. Land Cover Atlas of the People's Republic of China (1:1 000 000). Beijing:SinoMaps Press.
    [49] Wu Bingfang, Yuan Quanzhi, Yan Changzhen et al., 2014. Land cover changes of China from 2000 to 2010. Quaternary Sciences, 34(4):723-731. (in Chinese)
    [50] Zhang Y W, Shangguan Z P, 2016. The coupling interaction of soil water and organic carbon storage in the long vegetation restoration on the Loess Plateau. Ecological Engineering, 91(9):574-581. doi: 10.1016/j.ecoleng.2016.03.033
    [51] Zhao D S, Wu S H, 2013. Responses of vegetation distribution to climate change in China. Theoretical and Applied Climatology, 117(1-2):15-28. doi: 10.1007/s00704-013-0971-4
    [52] Zhao M Y, Peng J, Liu Y X et al., 2018. Mapping watershed-level ecosystem service bundles in the Pearl River Delta, China. Ecological Economics, 152:106-117. doi:10.1016/j.ecolecon. 2018.04.023
    [53] Zhu Wenquan, Pan Yaozhong, Zhang Jinshui, 2007. Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing. Journal of Plant Ecology, 31(3):413-424. (in Chinese)
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Get Citation
PDF
XML

Article Metrics

Article views(306) PDF downloads(72) Cited by()

Proportional views
Related

Vegetation Mapping for Regional Ecological Research and Management: A Case of the Loess Plateau in China

doi: 10.1007/s11769-020-1120-5
  • 1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China;
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China;
  • 3. The Grain for Green Project Management Office of Yan'an Municipality, Yan'an 716000, China;
  • 4. The Grain for Green Project Management Office of Ansai District, Yan'an 717400, China
Funds:

Under the auspices of National Key Research and Development Program of China (No. 2016YFC0501601), Key Science and Technology Project of Yan'an Municipality (No. 2016CGZH-14-03)

  • Received Date: 2019-06-17
  • Rev Recd Date: 2019-10-11

Abstract: Vegetation maps are fundamental for regional-scale ecological research. However, information is often not sufficiently up to date for such research. The Loess Plateau is a key area for vegetation restoration projects and a suitable area for regional ecological research. To carry out regional vegetation mapping based on the principles of hierarchical classification, object-oriented methods, visual interpretation, and accuracy assessment, this study integrated land cover, high-resolution remote sensing images, background environmental data, bioclimate zoning data, and field survey data from the Loess Plateau. To further clarify the implications of vegetation mapping, we compared the deviation of the 2015 vegetation map of the Loess Plateau (VMLP) and the widely used vegetation map of China (VMC) (1:1 000 000) for the expressed vegetation information and the evaluation of ecosystem services. The results indicated that 1) the vegetation of the Loess Plateau could be divided into 9 vegetation type groups and 18 vegetation types with classification accuracies of 87.76% and 83.97%, respectively; 2) the distribution of vegetation had obvious zonal regularity; 3) a deviation of 29.56×104km2 occurred when the vegetation coverage area was quantified with the VMC; 4) the vegetation classification accuracy affected the ecosystem service assessment, the total water yield of the Loess Plateau calculated by the VMC and other required parameters was overestimated by 2.2×106 mm in 2015. Because vegetation mapping is a basic and important activity, that requires greater attention, this study provides supporting data for subsequent multivariate vegetation mapping and vegetation management for conservation and restoration.

LIU Yuanxin, LYU Yihe, BAI Yingfei, ZHANG Buyun, TONG Xiaolin. Vegetation Mapping for Regional Ecological Research and Management: A Case of the Loess Plateau in China[J]. Chinese Geographical Science, 2020, 30(3): 410-426. doi: 10.1007/s11769-020-1120-5
Citation: LIU Yuanxin, LYU Yihe, BAI Yingfei, ZHANG Buyun, TONG Xiaolin. Vegetation Mapping for Regional Ecological Research and Management: A Case of the Loess Plateau in China[J]. Chinese Geographical Science, 2020, 30(3): 410-426. doi: 10.1007/s11769-020-1120-5
shu

    HTML

Reference (53)

Catalog

    Copyright © Editorial office of Chinese Geographical Science

    Tel: +86-0431-85542219   Email: egeoscien@neigae.ac.cn  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return