叙利亚战争对植被的影响

doi:10.6046/zrzyyg.2021233

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摘要

战争不仅会造成大量的人员伤亡和经济损失,还可能对环境带来不利影响。利用2001—2018年长时间序列卫星遥感数据探究叙利亚地区植被生长对战争所引起环境变化的响应,结果表明,叙利亚战争冲突最为激烈区域的植被指数存在较为明显的下降趋势,从战争开始的2011—2015年,土地类型变化并不显著,但在2015—2018年之间,土地类型发生了较大的变化。18 a间,草地面积减少了10.08%,农作物种植区面积减少21.87%。研究进一步探索了人为因素对植被状态的影响,发现东部幼发拉底河两岸及延伸区域受人为因素影响最为显著。研究揭示了战争给植被生长带来的负面影响,对于军事冲突地区粮食安全等方面研究与战略制定具有参考意义。

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	曾晖
	任华忠
	朱金顺
	郭金鑫
	叶昕
	滕沅建
	聂婧
	秦其明

关键词 ： NDVI, 时间序列, 植被, 叙利亚战争

Abstract：

Besides numerous casualties and economic losses, wars may cause damage to the environment. Using a long time series of satellite remote sensing data from 2001 to 2018, this study explored the response of vegetation growth to the environmental changes in Syria caused by the Syrian Civil War. The results are as follows. The vegetation index significantly decreased in regions that experienced the most intense conflict in the war. The land types changed slightly from 2011 when the war started to 2015 but changed significantly from 2015 to 2018, with the grassland area decreasing by 10.08% and the crop planting area decreasing by 21.87%. This study further explored the impacts of human activities on the vegetation status, revealing that both sides of the Euphrates River in the east and their extensional areas are most significantly affected by human activities. This study discovered the negative impacts of the war on vegetation growth and can be utilized as a reference for the research and strategy formulation on food security in areas with military conflicts.

Key words： NDVI time series vegetation Syrian Civil War

收稿日期: 2021-07-28 出版日期: 2022-09-21

ZTFLH:

TP79

基金资助:国家重点研发计划项目“城乡生态资源高分遥感与地面协同监测信息服务应用示范”(2017YFB0503905-05);国家自然科学基金项目“高空间分辨率城市地表温度遥感反演方法研究”(41771369);及国家对地观测科学数据中心开放基金项目“基于高分五号热红外数据的高空间分辨率地表温度产品集生成”(NODAOP2020001)

通讯作者: 任华忠

作者简介: 曾晖(1990-),男,硕士研究生,研究方向为热红外遥感应用与研究。Email: zenghui@pku.edu.cn。

引用本文:

曾晖, 任华忠, 朱金顺, 郭金鑫, 叶昕, 滕沅建, 聂婧, 秦其明. 叙利亚战争对植被的影响[J]. 自然资源遥感, 2022, 34(3): 121-128.
ZENG Hui, REN Huazhong, ZHU Jinshun, GUO Jinxin, YE Xin, TENG Yuanjian, NIE Jing, QIN Qiming. Impacts of the Syrian Civil War on vegetation. Remote Sensing for Natural Resources, 2022, 34(3): 121-128.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021233 或 https://www.gtzyyg.com/CN/Y2022/V34/I3/121

Fig.1 2001年研究区地表分类示意图

Fig.2 AG算法重建后动态阈值法提取叙利亚作物区物候年际变化

$β$	Z	NDVI变化趋势
>0	≥1.96	显著增长
>0	(-1.96,1.96)	轻微增长
0	(-1.96,1.96)	稳定或无植被区域
<0	(-1.96,1.96)	轻微退化
<0	≤-1.96	显著退化

Tab.1 NDVI变化趋势的变化程度划分

$β$	Z	H	NDVI变化趋势	$β$	Z	H	NDVI变化趋势
>0	≥1.96	>0.5	一致且显著增长	<0	(-1.96,1.96)	<0.5	从增长到退化
>0	(-1.96,1.96)	>0.5	一致且轻微增长	<0	(-1.96,1.96)	>0.5	一致且轻微退化
>0	(-1.96,1.96)	<0.5	从退化到增长	<0	≤-1.96	>0.5	一致且显著退化
0	(-1.96,1.96)	0.5	稳定无变化

Tab.2 NDVI变化趋势的变化程度划分

Fig.3 研究期趋势分析结果示意图

Fig.4 叙利亚多种地表类型生长季内NDVI变化趋势(2001—2018)

Tab.3 叙利亚土地覆盖转移矩阵

Fig.5 荒原和作物地类像素变化(2001—2018)

Fig.6 未来变化趋势分析结果示意图

Fig.7 叙利亚地区生长季降雨量数据和残差分析结果示意图

[1]	孙红雨, 王长耀. 中国地表植被覆盖变化及其与气候因子关系──基于NOAA时间序列数据分析[J]. 遥感学报, 1998, 2(3):204-210.
	Sun H Y, Wang C Y. Analysis of the vegetation cover change and the relationship between NDVI and environmental factors by using NOAA time series data[J]. Journal of Remote Sensing, 1998, 2(3):204-210.
[2]	Rouse J W, Haas R H, Schell J A, et al. Monitoring vegetation systems in the Great Plains with ERTS[J]. NASA Special Publication, 1974, 351(1974):309.
[3]	田庆久, 闵祥军. 植被指数研究进展[J]. 地球科学进展, 1998, 13(4):327-333.
	Tian Q J, Min X J. Advances in study on vegetation indices[J]. Advance in Earth Sciences, 1998, 13(4):327-333.
[4]	Li X, Li D. Can night-time light images play a role in evaluating the Syrian crisis?[J]. International Journal of Remote Sensing, 2014, 35(18):6648-6661. doi: 10.1080/01431161.2014.971469
[5]	Li X, Chen F, Chen X. Satellite-observed nighttime light variation as evidence for global armed conflicts[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(5):2302-2315. doi: 10.1109/JSTARS.2013.2241021
[6]	Huete A, Didan K, van Leeuwen W.MODIS vegetation indices[M]// Land remote sensing and global environmental change. New York: Springer, 2010:579-602.
[7]	Beck H E, McVicar T R, van Dijk A I J M. Global evaluation of four AVHRR-NDVI data sets:Intercomparison and assessment against Landsat imagery[J]. Remote Sensing of Environment, 2011, 115(10):2547-2563. doi: 10.1016/j.rse.2011.05.012
[8]	Huete A, Justice C, Van Leeuwen W.MODIS vegetation index(MOD13)[J]. Algorithm Theoretical Basis Document, 1999, 3(213):295-309.
[9]	Harris I, Jones P D, Osborn T J. Updated high-resolution grids of monthly climatic observations:The CRU TS3.10 Dataset[J]. International Journal of Climatology, 2014, 34(3):623-642. doi: 10.1002/joc.3711
[10]	Fick S E, Hijmans R J. WorldClim 2:New 1-km spatial resolution climate surfaces for global land areas[J]. International Journal of Climatology, 2017, 37(12):4302-4315. doi: 10.1002/joc.5086
[11]	柯灵红, 王正兴, 宋春桥. 青藏高原东北部 MODIS LST 时间序列重建及与台站地温比较[J]. 地理科学进展, 2011, 30(7):819-826.
	Ke L H, Wang Z X, Song C Q. Reconstruction of MODIS LST time series and comparison with land surface temperature among observation stations in the northeast Qinghai-Tibet Plateau[J]. Progress in Geography, 2011, 30(7):819-826.
[12]	曹云锋, 王正兴, 邓芳萍. 3种滤波算法对 NDVI 高质量数据保真性研究[J]. 遥感技术与应用, 2010, 25(1):118-125.
	Cao Y F, Wang Z X, Deng F P. Fidelity performance of three filters for high quality NDVI Time-series analysis[J]. Remote Sensing Technology and Application, 2010, 25(1):118-125.
[13]	Jönsson P, Eklundh L. TIMESAT:A program for analyzing time-series of satellite sensor data[J]. Computers and Geosciences, 2004, 30(8):833-845. doi: 10.1016/j.cageo.2004.05.006
[14]	Jonsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEE Transactions on Geo-science and Remote Sensing, 2002, 40(8):1824-1832. doi: 10.1109/TGRS.2002.802519
[15]	Eklundh L, Jönsson P. TIMESAT 3.3 with seasonal trend decomposition and parallel processing software manual[R]. Sweden: Lund and Malmö University, 2017.
[16]	宋春桥, 柯灵红, 游松财. 基于 TIMESAT 的 3 种时序 NDVI 拟合方法比较研究[J]. 遥感技术与应用, 2011, 26(2):147-154.
	Song C Q, Ke L H, You S C. Comparison of three NDVI time-series fitting methods based on TIMESAT:Taking the grassland in Northern Tibet as case[J]. Remote Sensing Technology and Application, 2011, 26(2):147-154.
[17]	Mann H B. Nonparametric tests against trend[J]. Econometrica:Journal of the Econometric Society, 1945:245-259.
[18]	Sen P K. Estimates of the regression coefficient based on Kendall’s tau[J]. Journal of the American Statistical Association, 1968, 63(324):1379-1389. doi: 10.1080/01621459.1968.10480934
[19]	Gilbert R O. Statistical methods for environmental pollution monitoring[M]. New York: John Wiley and Sons, 1987.
[20]	王佃来, 刘文萍, 黄心渊. 基于 Sen+ Mann-Kendall 的北京植被变化趋势分析[J]. 计算机工程与应用, 2013, 49(5):13-17.
	Wang D L, Liu W P, Huang X Y. Trend analysis in vegetation cover in Beijing based on Sen+ Mann-Kendall method[J]. Computer Engineering and Applications, 2013, 49(5):13-17.
[21]	Hurst H E. Long-term storage capacity of reservoirs[J]. Transactions of the American Society of Civil Engineers, 1951, 116(1):770-799. doi: 10.1061/TACEAT.0006518
[22]	Mandelbrot B B, Wallis J R. Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence[J]. Water Resources Research, 1969, 5(5):967-988. doi: 10.1029/WR005i005p00967
[23]	Evans J, Geerken R. Discrimination between climate and human-induced dryland degradation[J]. Journal of Arid Environments, 2004, 57(4):535-554. doi: 10.1016/S0140-1963(03)00121-6
[24]	Wessels K J, van den Bergh F, Scholes R J. Limits to detectability of land degradation by trend analysis of vegetation index data[J]. Remote Sensing of Environment, 2012, 125:10-22. doi: 10.1016/j.rse.2012.06.022
[25]	Abdo H G. Impacts of war in Syria on vegetation dynamics and erosion risks in Safita area,Tartous,Syria[J]. Regional Environmental Change, 2018, 18(6):1707-1719. doi: 10.1007/s10113-018-1280-3
[26]	Jaafar H H, Zurayk R, King C. Impact of the Syrian conflict on irrigated agriculture in the Orontes basin[J]. International Journal of Water Resources Development, 2015, 31(3):436-449. doi: 10.1080/07900627.2015.1023892
[27]	Wessels K J, Prince S D, Malherbe J. Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa[J]. Journal of Arid Environments, 2007, 68(2):271-297. doi: 10.1016/j.jaridenv.2006.05.015
[28]	Kelley C P, Mohtadi S, Cane M A. Climate change in the Fertile Crescent and implications of the recent Syrian drought[J]. Proceedings of the National Academy of Sciences, 2015, 112(11):3241-3246. doi: 10.1073/pnas.1421533112

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