Remote sensing monitoring and analysis of the vegetation phenological characteristics of the Qinling Mountains-Huanghuai Plain ecotone from 2002 to 2020

WANG Yating; ZHU Changming; ZHANG Tao; ZHANG Xin; SHI Zhiyu

doi:10.6046/zrzyyg.2021400

2022 Vol. 34, No. 4

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Article navigation > Remote Sensing for Natural Resources > 2022 > 34(4): 225-234

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WANG Yating, ZHU Changming, ZHANG Tao, ZHANG Xin, SHI Zhiyu. 2022. Remote sensing monitoring and analysis of the vegetation phenological characteristics of the Qinling Mountains-Huanghuai Plain ecotone from 2002 to 2020. Remote Sensing for Natural Resources, 34(4): 225-234. doi: 10.6046/zrzyyg.2021400

Citation:

WANG Yating, ZHU Changming, ZHANG Tao, ZHANG Xin, SHI Zhiyu. 2022. Remote sensing monitoring and analysis of the vegetation phenological characteristics of the Qinling Mountains-Huanghuai Plain ecotone from 2002 to 2020. Remote Sensing for Natural Resources, 34(4): 225-234. doi: 10.6046/zrzyyg.2021400

Remote sensing monitoring and analysis of the vegetation phenological characteristics of the Qinling Mountains-Huanghuai Plain ecotone from 2002 to 2020

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

Received Date: 22 November 2021

Revised Date: 15 December 2022

Publish Date: 27 December 2022

Abstract

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.
- vegetation phenology /
- Qinling Mountains-Huanghuai Plain /
- ecotone /
- spatio-temporal change /
- remote sensing

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References

[1]	Parmesan C, Gary Y. A globally coherent fingerprint of climate change impacts across natural systems[J]. Nature, 2003, 421(6918):37-42. Google Scholar
[2]	Ge Q S, Wang H J, Rutishauser T, et al. Phenological response to climate change in China:A meta-analysis[J]. Global Change Biology, 2015, 21(1):265-274. Google Scholar
[3]	代武君, 金慧颖, 张玉红, 等. 植物物候学研究进展[J]. 生态学报, 2020, 40(19):6705-6719. Google Scholar
[4]	Dai W J, Jin H Y, Zhang Y H, et al. Advances in plant phenology[J]. Acta Ecologica Sinica, 2020, 40(19):6705-6719. Google Scholar
[5]	Walkovszky A. Changes in phenology of the locust tree (Robinia pseudoacacia L.) in Hungary[J]. International Journal of Biometeorology, 1998, 41(4):155-160. Google Scholar
[6]	Sparks T H, Jeffree E P, Jeffree C E. An examination of the relationship between flowering times and temperature at the national scale using long-term phenological records from the UK[J]. International Journal of Biometeorology, 2000, 44(2):82-87. Google Scholar
[7]	Tucker C J, Slayback D A, Pinzon J E, et al. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999[J]. International Journal of Biometeorolo-gy, 2001, 45(4):184-190. Google Scholar
[8]	郑景云, 葛全胜, 郝志新. 气候增暖对我国近40年植物物候变化的影响[J]. 科学通报, 2002 (20):1582-1587. Google Scholar
[9]	Zheng J Y, Ge Q S, Hao Z X. Effects of global warming on plant phenological changes for the last 40 years in China[J]Chinese Science Bulletin, 2002 (20):1582-1587. Google Scholar
[10]	Maignan F, Bréon F M, Bacour C, et al. Interannual vegetation phenology estimates from global AVHRR measurements[J]. Remote Sensing of Environment, 2008, 112(2):496-505. Google Scholar
[11]	范德芹, 赵学胜, 朱文泉, 等. 植物物候遥感监测精度影响因素研究综述[J]. 地理科学进展, 2016, 35(3):304-319. Google Scholar
[12]	Fan D Q, Zhao X S, Zhu W Q, et al. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data[J]. Progress in Geography, 2016, 35(3):304-319. Google Scholar
[13]	Wu X C, Liu H Y. Consistent shifts in spring vegetation green-up date across temperate biomes in China,1982—2006[J]. Global Change Biology, 2013, 19(3):870-880. Google Scholar
[14]	刘玲玲, 刘良云, 胡勇. 1982—2006年欧亚大陆植被生长季开始时间遥感监测分析[J]. 地理科学进展, 2012, 31(11):1433-1442. Google Scholar
[15]	Liu L L, Liu L Y, Hu Y. Assessment and intercomparison of satellite-derived start-of-season (SOS) measures in Eurasia for 1982—2006[J]. Progress in Geography, 2012, 31(11):1433-1442. Google Scholar
[16]	Ge Q S, Dai J H, Cui H J, et al. Spatiotemporal variability in start and end of growing season in China related to climate variability[J]. Remote Sensing, 2016, 8(5):433. Google Scholar
[17]	高洪文. 生态交错带(Ecotone)理论研究进展[J]. 生态学杂志, 1994, 13(1):32-38. Google Scholar
[18]	Gao H W. Advancement of theoretical research in Ecotone[J]. Chinese Journal of Ecology, 1994, 13(1):32-38. Google Scholar
[19]	朱芬萌, 安树青, 关保华, 等. 生态交错带及其研究进展[J]. 生态学报, 2007(7):3032-3042. Google Scholar
[20]	Zhu F M, An S Q, Guan B H, et al. A review of ecotone:Concepts,attributes,theories and research advances[J]. Acta Ecological Sinica, 2007(7):3032-3042. Google Scholar
[21]	高翔, 白红英, 张善红, 等. 1959—2009年秦岭山地气候变化趋势研究[J]. 水土保持通报, 2012, 32(1):207-211. Google Scholar
[22]	Gao X, Bai H Y, Zhang S H, et al. Climatic change tendency in Qinling Mountains from 1959 to 2009[J]. Bulletin of Soil and Water Conservation, 2012, 32(1):207-211. Google Scholar
[23]	邓晨晖, 白红英, 马新萍, 等. 2000—2017年秦岭山地植被物候变化特征及其南北差异[J]. 生态学报, 2021, 41(3):1068-1080. Google Scholar
[24]	Deng C H, Bai H Y, Ma X P, et al. Variation characteristics and its north-south differences of the vegetation phenology by remote sensing monitoring in the Qinling Mountains during 2000—2017[J]. Acta Ecologica Sinica, 2021, 41(3):1068-1080. Google Scholar
[25]	邓晨晖, 白红英, 高山, 等. 1964—2015年气候因子对秦岭地区植物物候的综合影响效应[J]. 地理学报, 2018, 73(5):917-931. Google Scholar
[26]	Deng C H, Bai H Y, Gao S, et al. Comprehensive effect of climatic factors on plant phenology in Qinling Mountains region during 1964—2015[J]. Acta Geographica Sinica, 2018, 73(5):917-931. Google Scholar
[27]	马新萍, 白红英, 贺映娜, 等. 基于NDVI的秦岭山地植被遥感物候及其与气温的响应关系——以陕西境内为例[J]. 地理科学, 2015, 35(12):1616-1621. Google Scholar
[28]	Ma X P, Bai H Y, He Y N, et al. The vegetation remote sensing phenology of Qinling Mountains based on NDVI and it’s response to temperature:Taking within the territory of Shaanxi as an example[J]. Scientia Geographica Sinica, 2015, 35(12):1616-1621. Google Scholar
[29]	夏浩铭, 李爱农, 赵伟, 等. 2001—2010年秦岭森林物候时空变化遥感监测[J]. 地理科学进展, 2015, 34(10):1297-1305. Google Scholar
[30]	Xia H M, Li A N, Zhao W, et al. Spatiotemporal variations of forest phenology in the Qinling zone based on remote sensing monitoring,2001—2010[J]. Progress in Geography, 2015, 34(10):1297-1305. Google Scholar
[31]	郭少壮, 白红英, 黄晓月, 等. 秦岭太白红杉林遥感物候提取及对气候变化的响应[J]. 生态学杂志, 2019, 38(4):1123-1132. Google Scholar
[32]	Guo S Z, Bai H Y, Huang X Y, et al. Remote sensing phenology of Larix chinensis forest in response to climate change in Qinling Mountains[J]. Chinese Journal of Ecology, 2019, 38(4):1123-1132. Google Scholar
[33]	李建豪, 陶建斌, 程波, 等. 秦岭山区植被春季物候的海拔敏感性[J]. 应用生态学报, 2021, 32(6):2089-2097. Google Scholar
[34]	Li J H, Tao J B, Cheng B, et al. Sensitivity of spring phenology to elevation in Qinling Mountains,China[J]. Chinese Journal of Applied Ecology, 2021, 32(6):2089-2097. Google Scholar
[35]	钟兆站, 李克煌. 秦岭—黄淮平原交界带气候边际效应初探[J]. 地理研究, 1996, 15(4):66-73. Google Scholar
[36]	Zhong Z Z, Li K H. A primary study on the climatic boundary effect of the join zone between Qinling Mountain and Huanghuai Plain[J]. Geographical Research, 1996, 15(4):66-73. Google Scholar
[37]	管华, 马建华. 秦岭—黄淮平原交界带土壤物质强淋溶效应分析[J]. 地域研究与开发, 2008, 27(3):117-120. Google Scholar
[38]	Guan H, Ma J H. Analysis of soil matrial strong leaching effect in the transitional region of Qinling Mountains and Huang-Huai Plain[J]. Areal Research and Development, 2008, 27(3):117-120. Google Scholar
[39]	何太蓉, 庄红娟, 刘存东. 秦岭—黄淮平原交界带中东部近50年气候变化特征与趋势[J]. 安徽农业科学, 2009, 37(14):6532-6534. Google Scholar
[40]	He T R, Zhuang H J, Liu C D. Charateristics and trend of climatic change in the east of transitional region between Qinling Mountains and Huanghuai Plain in resent 50 years[J]. Journal of Anhui Agricultural Sciences, 2009, 37(14):6532-6534. Google Scholar
[41]	王正兴, 刘闯, Huete A. 植被指数研究进展:从AVHRR-NDVI到MODIS-EVI[J]. 生态学报, 2003, 23(5):979-987. Google Scholar
[42]	Wang Z X, Liu C, Huete A. From AVHRR-NDVI to MODIS-EVI advances in vegetation index research[J]. Acta Ecologica Sinica, 2003, 23(5):979-987. Google Scholar
[43]	项铭涛, 卫炜, 吴文斌. 植被物候参数遥感提取研究进展评述[J]. 中国农业信息, 2018, 30(1):55-66. Google Scholar
[44]	Xiang M T, Wei W, Wu W B. Review of vegetation phenology estimation by using remote sensing[J]. China Agricultural Informatics, 2018, 30(1):55-66. Google Scholar
[45]	何月, 樊高峰, 张小伟, 等. 浙江省植被物候变化及其对气候变化的响应[J]. 自然资源学报, 2013, 28(2):220-233. Google Scholar
[46]	He Y, Fan G F, Zhan X W, et al. Vegetation phenological variation and its response to climate changes in Zhejiang Province[J]. Journal of Natural Resources, 2013, 28(2):220-233. Google Scholar
[47]	李净, 刘红兵, 李彩云, 等. 基于GIMMS 3g NDVI的近30年中国北部植被生长季始期变化研究[J]. 地理科学, 2017, 37(4):620-629. Google Scholar
[48]	Li J, Liu H B, Li C Y, et al. Changes of green-up day of vegetation growing season based on GIMMS 3g NDVI in Northern China in recent 30 years[J]. Scientia Geographica Sinica, 2017, 37(4):620-629. Google Scholar
[49]	宋春桥, 游松财, 柯灵红, 等. 藏北高原典型植被样区物候变化及其对气候变化的响应[J]. 生态学报, 2012, 32(4):1045-1055. Google Scholar
[50]	Song C Q, You S C, Ke L H, et al. Phenological variation of typical vegetation types in northern Tibet and its response to climate changes[J]. Acta Ecologica Sinica, 2012, 32(4):1045-1055. Google Scholar
[51]	李正国, 唐华俊, 杨鹏, 等. 植被物候特征的遥感提取与农业应用综述[J]. 中国农业资源与区划, 2012, 33(5):20-28. Google Scholar
[52]	Li Z G, Tang H J, Yang P, et al. Progress in remote sensing of vegetation phenology and its application in agriculture[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2012, 33(5):20-28. Google Scholar
[53]	J?nsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(8):1824-1832. Google Scholar
[54]	武永峰, 何春阳, 马瑛, 等. 基于计算机模拟的植物返青期遥感监测方法比较研究[J]. 地球科学进展, 2005, 20(7):724-731. Google Scholar
[55]	Wu Y F, He C Y, Ma Y, et al. 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. Google Scholar
[56]	李铮, 柏延臣, 何亚倩. 遥感叶面积指数产品提取自然植被物候期对比[J]. 遥感技术与应用, 2015, 30(6):1103-1112. Google Scholar
[57]	Li Z, Bo Y C, He Y Q. Comparison of natural vegetation phenology metrics from remote sensing LAI products[J]. Romte Sensing Technology and Application, 2015, 30(6):1103-1112. Google Scholar
[58]	侯学会, 隋学艳, 梁守真, 等. 几种物候提取方法的小麦物候提取[J]. 遥感信息, 2017, 32(6):65-70. Google Scholar
[59]	Hou X H, Sui X Y, Liang S Z, et al. Comparison of five methods for phenology extraction of winter wheat[J]. Remote Sensing Information, 2017, 32(6):65-70. Google Scholar
[60]	吴文斌, 杨鹏, 唐华俊, 等. 基于NDVI数据的华北地区耕地物候空间格局[J]. 中国农业科学, 2009, 42(2):552-560. Google Scholar
[61]	Wu W B, Yang P, Tang H J, et al. Monitoring spatial patterns of cropland phenology in North China based on NOAA NDVI data[J]. Scientia Agricultura Sinica, 2009, 42(2):552-560. Google Scholar
[62]	于信芳, 庄大方. 基于MODIS NDVI数据的东北森林物候期监测[J]. 资源科学, 2006(4):111-117. Google Scholar
[63]	Yu X F, Zhuang D F. Monitoring forest phenophases of northeast China based on MODIS NDVI data[J]. Resources Science, 2006 (4):111-117. Google Scholar
[64]	宋怡, 马明国. 基于GIMMS_AVHRR_NDVI数据的中国寒旱区植被动态及其与气候因子的关系[J]. 遥感学报, 2008, 12(3):499-505. Google Scholar
[65]	Song Y, Ma M G. Variation of AVHRR NDVI and its relationship with climate in Chinese arid and cold regions[J]. Journal of Remote Sensing, 2008, 12(3):499-505. Google Scholar
[66]	李登科, 王钊. 基于MCD12Q2的秦岭植被物候时空变化及对气候的响应[J]. 生态环境学报, 2020, 29(1):11-22. Google Scholar
[67]	Li D K, Wang Z. Spatiotemporal variation of vegetation phenology and its response to climate in Qinling Mountains based on MCD12Q2[J]. Ecology and Environmental Sciences, 2020, 29(1):11-22. Google Scholar
[68]	李丹, 吴秀芹, 张靖宙, 等. 西南喀斯特断陷盆地植被物候动态变化及其与气候因子的响应[J]. 水土保持研究, 2020, 27(6):168-173. Google Scholar
[69]	Li D, Wu X Q, Zhang J Z, et al. Vegetation phenology change and response to climate change in the Karst faulted basin of Southwest China[J]. Research of Soil and Water Conservation, 2020, 27(6):168-173. Google Scholar
[70]	陈丽, 杨秋萍, 徐长春, 等. 2001—2017年开都—孔雀河流域植被物候特征及其对气候变化的响应[J]. 干旱区研究, 2020, 37(3):729-738. Google Scholar
[71]	Chen L, Yang Q P, Xu C C, et al. Phenological characteristics of vegetation and its response to climatic change in the Kaidu—Kongqi River basin,Xinjiang,during 2001—2017[J]. Arid Zone Research, 2020, 37(3):729-738. Google Scholar
[72]	李叶, 张艳红, 陈子琦, 等. 中高纬度山区气温空间化的方法比较研究——以大兴安岭北麓为例[J]. 山地学报, 2021, 39(2):174-182. Google Scholar
[73]	Li Y, Zhang Y H, Chen Z Q, et al. Comparative study on spatialization methods of air temperature in middle and high latitude mountainous areas:A case study of northern foot of the Daxing’anling Mountains[J]. Mountain Research, 2021, 39(2):174-182. Google Scholar
[74]	张晓东, 朱文博, 张静静, 等. 伏牛山地森林植被物候及其对气候变化的响应[J]. 地理学报, 2018, 73(1):41-53. Google Scholar
[75]	Zhang X D, Zhu W B, Zhang J J, et al. Phenology of forest vegetation and its response to climate change in the Funiu Mountains[J]. Acta Geographica Sinica, 2018, 73(1):41-53. Google Scholar
[76]	侯学会, 隋学艳, 姚慧敏, 等. 中国北方麦区冬小麦物候期对气候变化的响应[J]. 麦类作物学报, 2019, 39(2):202-209. Google Scholar
[77]	Hou X H, Sui X Y, Yao H M, et al. Response of winter phenology to climate in northern China[J]. Journal of Triticeae Crop, 2019, 39(2):202-209. Google Scholar
[78]	崔耀平, 肖登攀, 刘素洁, 等. 中国夏玉米和冬小麦近年生育期变化及其与气候的关系[J]. 中国生态农业学报, 2018, 26(3):388-396. Google Scholar
[79]	Cui Y P, Xiao D P, Liu S J, et al. Growth periods variation of summer maize and winter wheat and their correlations with hydrothermal conditions in recent years in China[J]. Chinese Journal of Eco-Agriculture, 2018, 26(3):388-396. Google Scholar
[80]	孙新素, 龙致炜, 宋广鹏, 等. 气候变化对黄淮海地区夏玉米-冬小麦种植模式和产量的影响[J]. 中国农业科学, 2017, 50(13):2476-2487. Google Scholar
[81]	Sun X S, Long Z W, Song G P, et al. Effects of climate change on cropping pattern and yield of summer maize-winter wheat in Huang-Huai-Hai Plain[J]. Scientia Agricultura Sinica, 2017, 50(13):2476-2487. Google Scholar