Monitoring on spatial-temporal vegetation dynamics based on Landsat time series

doi:10.13474/j.cnki.11-2246.2020.0345

Abstract

Abstract: The purpose of this paper is to analyze the spatial-temporal pattern of vegetation coverage and driving factors in Bayi district, using pixel dichotomy model and correlation analysis methods, from Landsat time series images between 1990 and 2019 based on GEE platform. Research results show: ①The overall trend on vegetation coverage emerged as an approximate stability with a little increasing, among which the valley regions increased significantly while the high mountain regions is relatively stable. ②Temperature in Bayi district increased significantly whereas the precipitation decreased slightly, demonstrating a “warm and dry” feature. The response of vegetation coverage to precipitation is stronger than temperature, but however the relationship between climate change and vegetation coverage is not significant generally. ③A good relationship is found between human activities and vegetation cover, where the non-high vegetation coverage areas presented in significant negative correlation while the high vegetation coverage areas had positive correlation. The proposed methods of dynamic monitoring on vegetation coverage and quantitative analysis using remote sensing big data and geographic cloud computing, can provide technical support and scientific data for ecological assessment and evolution analysis over alpine canyon region.

Key words: vegetation coverage, NDVI, GEE, Southeastern Tibet, Bayi district of Nyingchi city

CLC Number:

P237

WANG Hua, YANG Qianpeng, GUO Shanchuan, DAN Zeng, CIREN Duoji. Monitoring on spatial-temporal vegetation dynamics based on Landsat time series[J]. Bulletin of Surveying and Mapping, 2020, 0(11): 7-12,38.

References

[1] 徐兴奎,陈红,LEVY J K. 气候变暖背景下青藏高原植被覆盖特征的时空变化及其成因分析[J]. 中国科学,2008,53(4):456-462.
[2] PIAO S L, FANG J Y, JI W, et al. Variation in a satellite-based vegetation index in relation to climate in China[J]. Journal of Vegetation Science, 2004,15(2):219-226.
[3] 廖清飞,张鑫,马全,等.青海省东部农业区植被覆盖时空演变遥感监测与分析[J].生态学报,2014,34(20):5936-5943.
[4] 刘军会,高吉喜,王文杰.青藏高原植被覆盖变化及其与气候变化的关系[J].山地学报,2013,31(2):234-242.
[5] 李辉霞,刘国华,傅伯杰.基于NDVI的三江源地区植被生长对气候变化和人类活动的响应研究[J].生态学报,2011,31(19):5495-5504.
[6] 邓晨晖,白红英,高山,等. 秦岭植被覆盖时空变化及其对气候变化与人类活动的双重响应[J]. 自然资源学报, 2018, 33(3):425-438.
[7] HILER T, LYAPUSTIN A I, TUCKER C J, et al. Vegetation dynamics and rainfall sensitivity of the Amazon[J]. Proceedings of the National Academy of Sciences of the United States of America,2014,111(45):16041-16046.
[8] 阿多,赵文吉,宫兆宁,等. 1981-2013华北平原气候时空变化及其对植被覆盖度的影响[J].生态学报,2017,37(2):576-592.
[9] 符静,秦建新,张猛,等.2001-2013年洞庭湖流域植被覆盖度时空变化特征[J].地球信息科学学报,2016,18(9):1209-1216.
[10] 盛任,万鲁河.乌伊岭国家级自然保护区植被覆盖演变及其对气候突变的响应[J].生态学报,2019,39(9):3243-3256.
[11] 卓嘎,陈思蓉,周兵.青藏高原植被覆盖时空变化及其对气候因子的响应[J].生态学报,2018,38(9):3208-3218.
[12] 魏彦强,芦海燕,王金牛,等.近35年青藏高原植被待变化对气候变化及人类活动的响应[J].草业科学,2019,36(4):1163-1176.
[13] DU P J, BAI X Y, TAN K, et al. Advances of four machine learning methods for spatial data handling:a review[J]. Journal of Geovisualization and Spatial Analysis, 2020, 4(13):1-25.
[14] KE Y, IM J, LEE J, et al. Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations[J].Remote Sensing of Environment, 2015, 164(6):298-313.
[15] 贾坤, 姚云军, 魏香琴, 等. 植被覆盖度遥感估算研究进展[J]. 地球科学进展, 2013, 28(7):774-782.
[16] 许宁.基于遥感技术的2005-2014年西藏地区植被覆盖度变化分析[J].安徽农业科学,2016,44(19):33-37,52.
[17] 任德智,任毅华,卢杰,等.色季拉山区鲁朗河流域植被覆盖度遥感估算研究[J].西北林学报,2012,27(2):184-189.
[18] 李苗苗,吴炳方,颜长珍,等. 密云水库上游植被覆盖度的遥感估算[J].资源科学,2004,26(4):153-159.