THE COMPARISON OF THE CURRENT REMOTE SENSING—BASED VEGETATION GREENUP DETECTION METHODS WITH THE COMPUTER SIMULATION
Received date: 2004-09-06
Revised date: 2005-03-28
Online published: 2005-07-25
Vegetation greenup is closely related to seasonal dynamics of the lower atmosphere and is therefore an important variable in influences and feedback mechanisms of global climate variation to terrestrial ecosystem. Detecting vegetation greenup using remotely sensed data at regional or global scales has become an advanced topic in geography and ecology. However, current remote sensing-based vegetation greenup detection methods are short of valid comparison and do not have a consistent understanding. So, This paper utilizes computer simulation technique to study four remote sensing-based detecting methods (Moving average method, NDVI ratio threshold method, the greatest change slope method and Logistic function fitting method) after summarizing advanced developments of traditional measuring methods and remote sensing-based detecting methods of vegetation greenup. Simulation results indicate that:①simulated results of greenup from the four methods make a difference of 20 days, which strongly reflect all method's differences in building principle. Moving average method defines vegetation greenup as the period beginning at the point where the smoothed time-series data suddenly increases. NDVI Ratio Threshold method defines vegetation greenup as the period beginning at the point of the greatest slope. The greatest change slope method defines vegetation greenup as the period beginning at the point of the maximum change in the NDVI slope angle. Logistic Function Fitting method defines vegetation greenup as the period beginning at the point where the rate of change of curvature exhibits a local maximum in the fitting curve. In addition, the four methods have a same character that the NDVI time-series data should maintains the greatest sustained increase after vegetation greenup begins. ②Computer simulating technique is helpful to understand the great potential of Remote sensing-based vegetation greenup detection methods in the future. ③Validation of the results is very important in the course of using remote sensing-based detecting methods, which this paper is short of because of lacking in practical observation data.
WU Yongfeng, HE Chunyang, Ma Ying,LI Jing, Zhang Yeli . THE COMPARISON OF THE CURRENT REMOTE SENSING—BASED VEGETATION GREENUP DETECTION METHODS WITH THE COMPUTER SIMULATION[J]. Advances in Earth Science, 2005 , 20(7) : 724 -731 . DOI: 10.11867/j.issn.1001-8166.2005.07.0724
[1] Michael A White, Peter E Thomton, Steven W Running. A continental phenology model for monitoring vegetation responses to interannual climatic variability [J]. Global Biogeochemical Cycles, 1997,11:217-234.
[2] Zhang Xiaoyang, Mark A Friedl, Crystal B Schaaf, et al. Monitoring vegetation phenology using MODIS [J]. Remote Sensing of Environment, 2003, 84:471-475.
[3] Fang Xiuqi, Yu Weihong. Progress in the studies on the phenological responding to global warming [J]. Advances in Earth Science, 2002,17(5): 714-719. [方修琦，余卫红. 物候对全球变暖响应的研究综述[J]. 地球科学进展，2002,17(5):714-719.]
[4] White M A, Nemani R R, Thornton P E, et al. Satellite evidence of phenological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest [J]. Ecosystems, 2002,5:260-277.
[5] Sinkyu Kang, Steven W Runninga, Jong-Hwan Lim, et al. A regional phenology model for detecting onset of greenness in temperate mixed forests, Korea: An application of MODIS leaf area index [J]. Remote Sensing of Environment, 2003, 86:232-242.
[6] Zhang Xuexia, Ge Quansheng, Zheng Jingyun. Overview on the vegetation on phenology using the remote sensing [J]. Advances in Earth Science, 2003, 18(4):534-544. [张学霞，葛全胜，郑景云. 遥感技术在植物物候研究中的应用综述[J]. 地球科学进展, 2003, 18(4):534-544.]
[7] Justice C O, Townshend J R G, Holben B N, et al. Analysis of the phenology of global vegetation using meteorological satellite data [J]. International Journal of Remote Sensing, 1985, 6:1 271-1 318.
[8] Goward S N, Tucker C J, Dye D G. North American vegetation patterns observed with the NOAA advanced very high resolution radiometer [J]. Vegetation, 1985, 64:3-14.
[9] Stone T A, Schlesinger S, Houghton R A, et al. A map of the vegetation of South America based on satellite imagery [J]. Photogrammetric Engineering and Remote Sensing, 1994, 60:541-551.
[10] Duncan I, Stow D, Franklin I, et al. Assessing the relationship between spectral vegetation indices and shrub cover in the Iornada Basin, New Mexico [J]. International Journal of Remote Sensing, 1993, 14:3 395-3 416.
[11] Fontes J, Gastellu-Etchegorry J P, Amram O, et al. A global phenological model of the African continent [J]. Ambio, 1995, 24:297-303.
[12] Moulin S, Kergoat L, Viovy N, et al. Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements [J]. Journal of Climate, 1997, 10:1 154-1 170.
[13] Yu Fangfang, Kevin P Price, James Ellis, et al. Response of seasonal vegetation development to climatic variations in eastern central Asia [J]. Remote Sensing of Environment, 2003, 87:42-54.
[14] Hicken R P, Vittum M T. The importance of soil and air temperature in spring phenoclimatic modeling [J]. International Journal of Biometeorology, 1976, 20:200-206.
[15] Cannel M I R, Smith R I. Climatic warming, spring budburst and frost damage on trees [J]. Journal of Applied Ecology, 1986, 23:177-191.
[16] Hari P, Hiikkinen R. The utilization of old phenological time series of budburst to compare models describing annual cycles of plants [J]. Tree Physiology, 1991, 8:281-287.
[17] Caprio I M.Flowering dates, potential evapotranspiration and water use efficiency of Syringa vulgaris L. at different elevations in the western United States of America [J]. Agricultural and Forest Meteorology, 1993, 63:55-71.
[18] Baker J T, Leskovar D I, Reddy V R, et al. A simple phenological model of muskmelon development [J]. Annals of Botany, 2001, 87: 615-621.
[19] Wu Bingfang. Operational remote sensing methods for agricultural statistics [J]. Acta Geographica Sinica, 2000, 55(1):25-35. [吴炳方. 全国农情监测与估产的运行化遥感方法[J]. 地理学报, 2000, 55(1):25-35.]
[20] Daniel Lloyd. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery [J]. International Journal of Remote Sensing, 1990, 11:2 269-2 279.
[21] Fischer A. A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters [J]. Remote Sensing of Environment, 1994, 48:220-230.
[22] Markon Col, MoDo Fleming, Binnian E F. Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data [J]. Polar Recognition, 1995, 31:179-190.
[23] Kaduk J, Heimann M. A prognostic phenology model for global terrestrial carbon cycle models [J]. Climate Research, 1996, 6:1-19.
[24] Reed Bradley C, Brown Jesslyn F, et al. Measuring phenological variability from satellite imagery [J]. Journal of Vegetation Science, 1994, 5:703-714.
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