Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology

被引:39
|
作者
Tian, Jiaqi [1 ]
Zhu, Xiaolin [1 ]
Wu, Jin [2 ]
Shen, Miaogen [3 ]
Chen, Jin [4 ]
机构
[1] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong 999077, Peoples R China
[2] Univ Hong Kong, Fac Sci, Sch Biol Sci, Hong Kong 999077, Peoples R China
[3] Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Alpine Ecol & Biodivers, 16 Lincui Rd, Beijing 100101, Peoples R China
[4] Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China
基金
中国国家自然科学基金;
关键词
urbanization effects; vegetation spring phenology; spatial resolution; satellite image; time series; GREEN-UP DATE; MODIS SURFACE REFLECTANCE; CLIMATE-CHANGE; TIME-SERIES; CARBON UPTAKE; FUSION MODEL; RIVER DELTA; TEMPERATURE; IMPACTS; ECOSYSTEMS;
D O I
10.3390/rs12010117
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Numerous investigations of urbanization effects on vegetation spring phenology using satellite images have reached a consensus that vegetation spring phenology in urban areas occurs earlier than in surrounding rural areas. Nevertheless, the magnitude of this rural-urban difference is quite different among these studies, especially for studies over the same areas, which implies large uncertainties. One possible reason is that the satellite images used in these studies have different spatial resolutions from 30 m to 1 km. In this study, we investigated the impact of spatial resolution on the rural-urban difference of vegetation spring phenology using satellite images at different spatial resolutions. To be exact, we first generated a dense 10 m NDVI time series through harmonizing Sentinel-2 and Landsat-8 images by data fusion method, and then resampled the 10 m time series to coarser resolutions from 30 m to 8 km to simulate images at different resolutions. Afterwards, to quantify urbanization effects, vegetation spring phenology at each resolution was extracted by a widely used tool, TIMESAT. Last, we calculated the difference between rural and urban areas using an urban extent map derived from NPP VIIRS nighttime light data. Our results reveal: (1) vegetation spring phenology in urban areas happen earlier than rural areas no matter which spatial resolution from 10 m to 8 km is used, (2) the rural-urban difference in vegetation spring phenology is amplified with spatial resolution, i.e., coarse satellite images overestimate the urbanization effects on vegetation spring phenology, and (3) the underlying reason of this overestimation is that the majority of urban pixels in coarser images have higher diversity in terms of spring phenology dates, which leads to spring phenology detected from coarser NDVI time series earlier than the actual dates. This study indicates that spatial resolution is an important factor that affects the accuracy of the assessment of urbanization effects on vegetation spring phenology. For future studies, we suggest that satellite images with a fine spatial resolution are more appropriate to explore urbanization effects on vegetation spring phenology if vegetation species in urban areas is very diverse.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series
    Bajocco, Sofia
    Dragoz, Eleni
    Gitas, Ioannis
    Smiraglia, Daniela
    Salvati, Luca
    Ricotta, Carlo
    PLOS ONE, 2015, 10 (03):
  • [2] COARSE-RESOLUTION SATELLITE DATA FOR ECOLOGICAL SURVEYS
    ROLLER, NEG
    COLWELL, JE
    BIOSCIENCE, 1986, 36 (07) : 468 - 475
  • [3] Urbanization effects on the spatial patterns of spring vegetation phenology depend on the climatic background
    Yin, Peiyi
    Li, Xuecao
    Zhou, Yuyu
    Mao, Jiafu
    Fu, Yongshuo H.
    Cao, Wenting
    Gong, Peng
    He, Wanru
    Li, Baoguo
    Huang, Jianxi
    Liu, Xiaoping
    Shi, Zitong
    Liu, Donglie
    Guo, Jinchen
    AGRICULTURAL AND FOREST METEOROLOGY, 2024, 345
  • [4] Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series (vol 10, e0119811, 2015)
    Bajocco, S.
    Dragoz, E.
    Gitas, I
    Smiraglia, D.
    Salvati, L.
    Ricotta, C.
    PLOS ONE, 2015, 10 (04):
  • [5] Improving the Spatial Resolution of Land Surface Phenology by Fusing Medium- and Coarse-Resolution Inputs
    Frantz, David
    Stellmes, Marion
    Roeder, Achim
    Udelhoven, Thomas
    Mader, Sebastian
    Hill, Joachim
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (07): : 4153 - 4164
  • [6] A NEW APPROACH FOR THE VALIDATION OF COARSE-RESOLUTION SATELLITE SOIL MOISTURE PRODUCTS
    Yan, Shuang
    Jiang, Lingmei
    Kou, Xiaokang
    2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2015, : 661 - 664
  • [7] VALIDATION OF COARSE-RESOLUTION FRACTIONAL VEGETATION COVER PRODUCT IN HEIHE BASIN, CHINA
    Huang, Shuai
    Mu, Xihan
    Yan, Guangjian
    2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2013, : 2102 - 2105
  • [8] Effects of Spectral Nudging on Oceanic States in a Coarse-Resolution Model
    Wang, Zeliang
    Han, Guoqi
    Dupont, Frederic
    ATMOSPHERE-OCEAN, 2015, 53 (03) : 351 - 362
  • [9] Artificial neural network response to mixed pixels in coarse-resolution satellite data
    Moody, A
    Gopal, S
    Strahler, AH
    REMOTE SENSING OF ENVIRONMENT, 1996, 58 (03) : 329 - 343
  • [10] Validating GEOV1 Fractional Vegetation Cover Derived From Coarse-Resolution Remote Sensing Images Over Croplands
    Mu, Xihan
    Huang, Shuai
    Ren, Huazhong
    Yan, Guangjian
    Song, Wanjuan
    Ruan, Gaiyan
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2015, 8 (02) : 439 - 446