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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (4) : 225-234     DOI: 10.6046/zrzyyg.2021400
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Remote sensing monitoring and analysis of the vegetation phenological characteristics of the Qinling Mountains-Huanghuai Plain ecotone from 2002 to 2020
WANG Yating1(), ZHU Changming1(), ZHANG Tao2, ZHANG Xin3, SHI Zhiyu1
1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
2. Changsha Natural Resources Comprehensive Survey Center, China Geological Survey, Changsha, 410600, China
3. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, CAS, Beijing 100101, China
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Abstract  

Vegetation phenology shows non-linear and regionally different responses to global changes. Typical differences exist in the climates between the north and the south of the Qinling Mountains. Accordingly, this study investigated the Qinling Mountains - Huanghuai Plain ecotone zone. Based on the MOD09Q1 remote sensing data from 2002 to 2020, this study extracted key parameters of the phenological characteristics of the Qinling Mountains-Huaihe Plain ecotone zone using the adaptive dynamic threshold method. Then, it described in detail the spatio-temporal change process of vegetation phenology in the study area to reveal the spatio-temporal differentiation characteristics. Furthermore, the responses of vegetation phenology to climate changes in the study area were analyzed by combining the temperature data. The study results show that: Significant spatial differentiation characteristics of vegetation phenology existed in the Qinling Mountains - Huanghuai Plain ecotone. Both the start of the growing season (SOS) and the end of the growing season (EOS) of the forest vegetation were later than those of farmland vegetation. Specifically, the SOS and EOS were Day 67-Day 116 and Day 280-Day 340 for forest vegetation and were Day 49-Day 92 and Day 195-Day 328 for farmland vegetation. The length of the growing season (LOS) was 215~262 days for forest vegetation and was 147~261 days for farmland vegetation. In addition, the forest vegetation phenology was affected by altitude. A higher altitude corresponds to a later SOS and an earlier EOS. From 2002 to 2020, the Qinling Mountains-Huaihe Plain ecotone zone generally had early SOS and EOS and shortened LOS. The changing trends of SOS and EOS were -0.14 d·a-1and -0.78 d·a-1, respectively for forest vegetation and 0.1 d·a-1 and -1.43 d·a-1, respectively for farmland vegetation. The vegetation phenological characteristics of the Qinling Mountains-Huaihe Plain ecotone were significantly correlated with regional temperature, especially the temperatures in March and September. The analysis of the data from the existent observation sites shows that the rising temperature advanced the regional phenophases.

Keywords vegetation phenology      Qinling Mountains-Huanghuai Plain      ecotone      spatio-temporal change      remote sensing     
ZTFLH:  TP79  
  Q948  
  X17  
Issue Date: 27 December 2022
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Yating WANG
Changming ZHU
Tao ZHANG
Xin ZHANG
Zhiyu SHI
Cite this article:   
Yating WANG,Changming ZHU,Tao ZHANG, et al. Remote sensing monitoring and analysis of the vegetation phenological characteristics of the Qinling Mountains-Huanghuai Plain ecotone from 2002 to 2020[J]. Remote Sensing for Natural Resources, 2022, 34(4): 225-234.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021400     OR     https://www.gtzyyg.com/EN/Y2022/V34/I4/225
Fig.1  Study area
Fig.2  Typical sample fitting curve of NDVI
Fig.3  Spatial distribution of vegetation phenology in the study area
Fig.4  Spatial distribution of interannual change trend of vegetation phenology in the study area
Fig.5  Annual and seasonal changes of temperature in study area
站点 物候始期 物候末期
冬季 春季 1月 2月 3月 夏季 秋季 8月 9月 10月
固始 -0.144 0.229 -0.008 -0.208 -0.150 0.019 0.012 -0.221 -0.404 -0.488*
信阳 -0.096 -0.642** 0.100 0.099 -0.286 -0.337 -0.356 -0.607** -0.604** -0.364
许昌 0.317 -0.059 0.509 0.403 0.262 -0.171 -0.349 -0.051 0.201 -0.158
宝丰 0.157 0.376 0.037 0.225 0.131 0.298 0.024 0.160 0.114 -0.026
桐柏 0.006 -0.407 0.236 0.027 -0.117 0.119 0.093 -0.037 -0.304 -0.306
驻马店 -0.463 -0.739** 0.027 -0.158 -0.811** -0.233 -0.428 -0.140 0.261 -0.512*
南阳 -0.111 -0.152 -0.294 -0.014 -0.129 -0.053 -0.342 -0.369 -0.244 -0.287
西峡 -0.034 -0.165 -0.083 -0.145 -0.201 -0.121 -0.150 -0.282 -0.669** 0.090
Tab.1  Correlation coefficient of phenology and temperature at each period
Fig.6  Relationship between the vegetation phenology and the temperature in study area
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