Abstract
It is crucial to maintain the balance of economic development and ecosystem protection. The value of ecosystem services is an indicator to help people understand the importance of ecosystem protection. Traditional models estimate ecosystem service values only according to land use/cover data while ignoring vegetation status differences in the same land use/cover. This study uses the normalized difference vegetation index (NDVI), the leaf area index (LAI), and net primary productivity (NPP) as vegetation status data to describe the differences in the same land use/cover type. The principal component analysis (PCA) approach is used to reduce the correlations among the three types of vegetation status data. Then, the calculated vegetation status index after PCA is input into the estimation model. The case study in China shows that the improved model has two major advantages. First, it can clearly distinguish the differences in ecosystem service values even for the same land use/cover type. Second, it can clearly describe the transitional zones between different land use/cover types through continuous changes in ecosystem service values. This improved model can provide a more detailed description of the distribution characteristics of ecosystem service values in China and help policymakers balance economic development and ecosystem protection.
Similar content being viewed by others
References
Asner, G. P., J. M. O. Scurlock, and J. A. Hicke, 2003: Global synthesis of leaf area index observations: Implications for ecological and remote sensing studies. Glob. Ecol. Biogeogr., 12, 191–205, doi: https://doi.org/10.1046/j.1466-822X.2003.00026.x.
Baldi, G., M. D. Nosetto, R. Aragón, et al., 2008: Long-term satellite NDVI data sets: Evaluating their ability to detect ecosystem functional changes in South America. Sensors, 8, 5397–5425, doi: https://doi.org/10.3390/s8095397.
Brauman, K. A., and G. C. Daily, 2008: Ecosystem services. Encyclopedia of Ecology, Jørgensen, S. E., and B. D. Fath, Eds., Elsevier, Amsterdam, 1148–1154.
Chen, W. X., G. Q. Chi, and J. F. Li, 2019a: The spatial association of ecosystem services with land use and land cover change at the county level in China, 1995–2015. Sci. Total Environ., 669, 459–470, doi: https://doi.org/10.1016/j.scitotenv.2019.03.139.
Chen, W. X., X. Y. Ye, J. F. Li, et al., 2019b: Analyzing requisition-compensation balance of farmland policy in China through telecoupling: A case study in the middle reaches of Yangtze River Urban Agglomerations. Land Use Policy, 83, 134–146, doi: https://doi.org/10.1016/j.landusepol.2019.01.031.
Costanza, R., R. d’Arge, R. de Groot, et al., 1997: The value of the world’s ecosystem services and natural capital. Nature, 387, 253–260, doi: https://doi.org/10.1038/387253a0.
Daily, G. C., 1997: Nature’s Services: Societal Dependence on Natural Ecosystems. Island Press, Washington, DC, 274 pp.
Douglas, I., 2015: Ecosystems and human well-being. Reference Module in Earth Systems and Environmental Sciences, Encyclopedia of the Anthropocene, Elsevier, 185–197, doi: https://doi.org/10.1016/B978-0-12-409548-9.09206-X.
Feng, X. M., B. J. Fu, X. J. Yang, et al., 2010: Remote sensing of ecosystem services: An opportunity for spatially explicit assessment. Chinese Geogr. Sci., 20, 522–535, doi: https://doi.org/10.1007/s11769-010-0428-y.
Hao, C. Y., S. H. Wu, and C. Y. Xu, 2008: Comparison of some vegetation indices in seasonal information. Chinese Geogr. Sci., 18, 242–248, doi: https://doi.org/10.1007/s11769-008-0242-y.
He, G. Z., Y. L. Lu, A. P. J. Mol, et al., 2012: Changes and challenges: China’s environmental management in transition. Environ. Dev., 3, 25–38, doi: https://doi.org/10.1016/j.envdev.2012.05.005.
Hu, X. S., W. Hong, R. Z. Qiu, et al., 2015: Geographic variations of ecosystem service intensity in Fuzhou City, China. Sci. Total Environ., 512–513, 215–226, doi: https://doi.org/10.1016/j.scitotenv.2015.01.035.
Hyer, E. J., and S. J. Goetz, 2004: Comparison and sensitivity analysis of instruments and radiometric methods for LAI estimation: Assessments from a boreal forest site. Agric. For. Meteor., 122, 157–174, doi: https://doi.org/10.1016/j.agrformet.2003.09.013.
Ifarraguerri, A., and C.-I. Chang, 2000: Unsupervised hyperspectral image analysis with projection pursuit. IEEE Trans. Geosci. Remote Sens., 38, 2529–2538, doi: https://doi.org/10.1109/36.885200.
Konarska, K. M., P. C. Sutton, and M. Castellon, 2002: Evaluating scale dependence of ecosystem service valuation: A comparison of NOAA-AVHRR and Landsat TM datasets. Ecol. Econ., 41, 491–507, doi: https://doi.org/10.1016/S0921-8009(02)00096-4.
Li, G. D., C. L. Fang, and S. J. Wang, 2016: Exploring spatiotemporal changes in ecosystem-service values and hotspots in China. Sci. Total Environ., 545–546, 609–620, doi: https://doi.org/10.1016/j.scitotenv.2015.12.067.
Liu, J. Y., Z. X. Zhang, X. L. Xu, et al., 2010: Spatial patterns and driving forces of land use change in China during the early 21st century. J. Geogr. Sci., 20, 483–494, doi: https://doi.org/10.1007/s11442-010-0483-4.
Liu, R., J. M. Chen, J. Liu, et al., 2007: Application of a new leaf area index algorithm to China’s landmass using MODIS data for carbon cycle research. J. Environ. Manage., 85, 649–658, doi: https://doi.org/10.1016/j.jenvman.2006.04.023.
Millennium Ecosystem Assessment, 2005: Ecosystems and Human Well-being. Island Press, Washington, DC, 371 pp.
Myneni, R. B., F. G. Hall, P. J. Sellers, et al., 1995: The interpretation of spectral vegetation indexes. IEEE Trans. Geosci. Remote Sens., 33, 481–486, doi: https://doi.org/10.1109/TGRS.1995.8746029.
Nelson, E., G. Mendoza, J. Regetz, et al., 2009: Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Front. Ecol. Environ., 7, 4–11, doi: https://doi.org/10.1890/080023.
Ning, J., J. Y. Liu, W. H. Kuang, et al., 2018: Spatiotemporal patterns and characteristics of land-use change in China during 2010–2015. J. Geogr. Sci., 28, 547–562, doi: https://doi.org/10.1007/s11442-018-1490-0.
Norgaard, R. B., 2010: Ecosystem services: From eye-opening metaphor to complexity blinder. Ecol. Econ., 69, 1219–1227, doi: https://doi.org/10.1016/j.ecolecon.2009.11.009.
Ozanne, C. M. P., D. Anhuf, S. L. Boulter, et al., 2003: Biodiversity meets the atmosphere: A global view of forest canopies. Science, 301, 183–186, doi: https://doi.org/10.1126/science.1084507.
Plummer, S., 2006: On validation of the MODIS gross primary production product. IEEE Trans. Geosci. Remote Sens., 44, 1936–1938, doi: https://doi.org/10.1109/TGRS.2006.872521.
Polasky, S., E. Nelson, J. Camm, et al., 2008: Where to put things? Spatial land management to sustain biodiversity and economic returns. Biol. Conserv., 141, 1505–1524, doi: https://doi.org/10.1016/j.biocon.2008.03.022.
Rahman, A. F., J. A. Gamon, D. A. Fuentes, et al., 2001: Modeling spatially distributed ecosystem flux of boreal forest using hyperspectral indices from AVIRIS imagery. J. Geophys. Res. Atmos., 106, 33579–33591, doi: https://doi.org/10.1029/2001JD900157.
Robinson, D. A., N. Hockley, D. M. Cooper, et al., 2013: Natural capital and ecosystem services, developing an appropriate soils framework as a basis for valuation. Soil Biol. Biochem., 57, 1023–1033, doi: https://doi.org/10.1016/j.soilbio.2012.09.008.
Sellers, P. J., J. A. Berry, G. J. Collatz, et al., 1992: Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sens. Environ., 42, 187–216, doi: https://doi.org/10.1016/0034-4257(92)90102-P.
Serna-Chavez, H. M., C. J. E. Schulp, P. M. van Bodegom, et al., 2014: A quantitative framework for assessing spatial flows of ecosystem services. Ecol. Indic., 39, 24–33, doi: https://doi.org/10.1016/j.ecolind.2013.11.024.
Tucker, C. J., J. R. G. Townshend, and T. E. Goff, 1985: African land-cover classification using satellite data. Science, 227, 369–375, doi: https://doi.org/10.1126/science.227.4685.369.
Turner, D. P., W. D. Ritts, W. B. Cohen, et al., 2005: Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring. Glob. Change Biol., 11, 666–684, doi: https://doi.org/10.1111/j.1365-2486.2005.00936.x.
Verbyla, D. L., 2005: Assessment of the MODIS leaf area index product (MOD15) in Alaska. Int. J. Remote Sens., 26, 1277–1284, doi: https://doi.org/10.1080/01431160412331330194.
Vitousek, P. M., 1994: Beyond global warming: Ecology and global change. Ecology, 75, 1861–1876, doi: https://doi.org/10.2307/1941591.
Xie, G. D., L. Zhen, C. X. Lu, et al., 2008: Expert knowledge based valuation method of ecosystem services in China. J. Nat. Resour., 23, 911–919, doi: https://doi.org/10.11849/zrzyxb.2008.05.019. (in Chinese)
Zhang, H. Q., and E. Q. Xu, 2017: An evaluation of the ecological and environmental security on China’s terrestrial ecosystems. Sci. Rep., 7, 811, doi: https://doi.org/10.1038/s41598-017-00899-x.
Zhao, M. S., F. A. Heinsch, R. R. Nemani, et al., 2005: Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ., 95, 164–176, doi: https://doi.org/10.1016/j.rse.2004.12.011.
Zorrilla-Miras, P., I. Palomo, E. Gómez-Baggethun, et al., 2014: Effects of land-use change on wetland ecosystem services: A case study in the Doñana marshes (SW Spain). Landsc. Urban Plan., 122, 160–174, doi: https://doi.org/10.1016/j.landurbplan.2013.09.013.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Key Research and Development Program of China (2018YFC1506503) and Meteorological Collaborative Innovation Foundation in Huadong Area (QYHZ201815).
Rights and permissions
About this article
Cite this article
Zhou, FC., Han, X., Tang, S. et al. An Improved Model for Evaluating Ecosystem Service Values Using Land Use/Cover and Vegetation Parameters. J Meteorol Res 35, 148–156 (2021). https://doi.org/10.1007/s13351-021-9199-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13351-021-9199-x