Characterising the spatial pattern of phenology for the tropical vegetation of India using multi-temporal MERIS chlorophyll data

被引:0
|
作者
C. Jeganathan
J. Dash
Peter M. Atkinson
机构
[1] University of Southampton,Global Environmental Change and Earth Observation Group, School of Geography
来源
Landscape Ecology | 2010年 / 25卷
关键词
Phenology; Remote sensing; Tropical vegetation; Fourier; MERIS; Chlorophyll; India;
D O I
暂无
中图分类号
学科分类号
摘要
The annual growth cycles of terrestrial ecosystems are related to long-term regional/global climatic patterns. Understanding vegetation phenology and its spatio-temporal variation is required to reveal and predict ongoing changes in Earth system dynamics. The study attempts to characterize the phenology of the major tropical vegetation types in India, since such information is not yet available for India. Multi-temporal Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data were utilized to derive onset of greenness (OG) and end of senescence (ES) for four major tropical vegetation types. The study found that Fourier-smoothed results using the first four components revealed adequately the annual phenological variation of the natural vegetation types in India. From these smoothed data, inflection points were located iteratively through a spatio-temporal search, spanning over 18 months of 8-day composite data, per pixel such as to derive the OG and ES. The median OG and ES was extracted from the available annual results for the years 2003–04, 2004–05, 2005–06 and 2006–07. The GLC2000 land cover map (1 km spatial resolution) was utilized to determine the locations of the major vegetation types. The percentage of each vegetation type falling beneath a MTCI composite pixel (4.6 km spatial resolution) was calculated. MTCI composite pixels with homogeneity of ≥80% vegetative cover were used for examining pattern of phenology in different regions, different years and at different latitudes. The most common dates for the occurrence of OG for the tropical evergreen, semi-evergreen, moist-deciduous, and dry-deciduous vegetation types were found to be during February–April, January–April, March–May, and February–May, respectively. Similarly, for ES the most common dates were in February–April, January–April, February–April, and December–April, respectively. The phenological pattern was uniquely different for each vegetation type, as expected, and also differed with regions and latitudes. A general trend of early occurrence of OG in the lower latitudes was observed.
引用
收藏
页码:1125 / 1141
页数:16
相关论文
共 50 条
  • [41] Spatial pattern of temporal trend of crop phenology matrices over India using timeseries gimms NDVI data (1982-2006)
    Agricultural Sciences and Applications Group, RS and GIS - Applications Area, National Remote Sensing Centre, Hyderabad, India
    Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci. - ISPRS Arch., 8W20 (113-118):
  • [42] Multi-temporal profiles of Vegetation Indices of Mediterranean habitats: an analysis of data provided by the VEGETATION instrument
    Lobo, A
    Carreras, J
    Ninot, JM
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY II, 2001, 4171 : 336 - 339
  • [43] Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images
    Xiao, Xiangming
    Hagen, Stephen
    Zhang, Qingyuan
    Keller, Michael
    Moore, Berrien, III
    REMOTE SENSING OF ENVIRONMENT, 2006, 103 (04) : 465 - 473
  • [44] Vegetation structure and greenness in Central Africa from Modis multi-temporal data
    Gond, Valery
    Fayolle, Adeline
    Pennec, Alexandre
    Cornu, Guillaume
    Mayaux, Philippe
    Camberlin, Pierre
    Doumenge, Charles
    Fauvet, Nicolas
    Gourlet-Fleury, Sylvie
    PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2013, 368 (1625)
  • [45] A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients
    Dudley, Kenneth L.
    Dennison, Philip E.
    Roth, Keely L.
    Roberts, Dar A.
    Coates, Austin R.
    REMOTE SENSING OF ENVIRONMENT, 2015, 167 : 121 - 134
  • [46] MULTI-TEMPORAL ANALYSIS OF NORTHEAST VEGETATION BY MEANS OF MODIS-EVI DATA
    Formigoni, Mileide de Holanda
    Xavier, Alexandre Candido
    De Souza Lima, Juliao Soares
    CIENCIA FLORESTAL, 2011, 21 (01): : 1 - 8
  • [47] Multi-temporal classification of TerraSAR-X data for wetland vegetation mapping
    Betbeder, Julie
    Rapinel, Sebastien
    Corpetti, Thomas
    Pottier, Eric
    Corgne, Samuel
    Hubert-Moy, Laurence
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XV, 2013, 8887
  • [48] Multi-Temporal Trend Analysis of Coastal Vegetation Using Metrics Derived from Hyperspectral and LiDAR Data
    Suir, Glenn M.
    Jackson, Sam
    Saltus, Christina
    Reif, Molly
    REMOTE SENSING, 2023, 15 (08)
  • [49] Urban vegetation land covers change detection using multi-temporal MODIS Terra/Aqua data
    Zoran, Maria A.
    Savastru, Roxana S.
    Savastru, Dan M.
    Dida, Adrian I.
    Ionescu, Ovidiu M.
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XV, 2013, 8887
  • [50] Quantifying understory vegetation density using multi-temporal Sentinel-2 and GEDI LiDAR data
    Xi, Yanbiao
    Tian, Qingjiu
    Zhang, Wenmin
    Zhang, Zhichao
    Tong, Xiaoye
    Brandt, Martin
    Fensholt, Rasmus
    GISCIENCE & REMOTE SENSING, 2022, 59 (01) : 2068 - 2083