Please wait a minute...
img

官方微信

遥感技术与应用  2018, Vol. 33 Issue (5): 923-931    DOI: 10.11873/j.issn.1004-0323.2018.5.0923
模型与反演     
新疆棉花物候时空变化遥感监测及气温影响分析
茹皮亚·西拉尔1,2,杨辽1
(1.中国科学院新疆生态与地理研究所,新疆 乌鲁木齐 830011;
2.中国科学院大学,北京 100049)
Monitoring Spatial-temporal Change of Cotton Phenology in Xinjiang and Its Response to Climate Change
Rupiya Xilaer1,2,Yang Liao1
(1.Xinjiang Institute of Ecology and Geography,CAS,Urumqi 830011,China;
2.University of Chinese Academy of Sciences,Beijing 100049,China)
 全文: PDF 
摘要:
基于2001~2016连续16 a的MOD09Q1数据计算获取NDVI时序数据,利用Savitzky-Golay(S-G)滤波重构NDVI时序曲线,以动态阈值法提取新疆大地块棉花的生长季开始期、生长季结束期和生长季长度信息,并分析新疆棉花物候时空变化特征及其对气温变化的响应。结果表明:① 南北疆棉花物候空间差异显著:生长季开始期由南向北逐渐推迟,南疆集中于第151 d之前,北疆于第151~163 d之间;生长季结束期逐渐提前,北疆大部分于第292 d前结束生长,而南疆集中发生在第298 d之后;生长季长度逐渐缩短,南疆普遍长于150 d,北疆通常短于150 d。② 南北疆物候变化趋势不一致:阿克苏生长季开始期和结束期表现为“推迟—提前—推迟—提前”变化趋势,生长季长度不明显延长;沙湾县生长季开始期表现为微弱提前趋势,生长季结束期则为先推迟再提前趋势,生长季长度表现为缩短、延长、缩短的波动变化。③ 原因分析表明棉花物候主要受气温影响:生长季开始期与10 ℃初日、15 ℃初日、生长初期平均气温表现显著的相关性;生长季结束期与10 ℃终止日相关性较好。
关键词: NDVI物候时空变化气温变化棉花新疆    
Abstract: Observations of vegetation phenology provide valuable information regarding ecosystem response to environmental conditions,especially to climate change.Cotton is one of the most important economic crops in Xinjiang,and its phenological change can directly reflect the change of climate in Xinjiang.This research was an attempt to extract cotton phenological parameters in Xinjiang by using 16 years’(2001 to 2016) time series MODIS Normalized Difference Vegetation Index(NDVI):firstly,filtering noise from the time-series data using Savitzky-Golay filtered method;then detecting cotton phenology parameters (Start of Growth Season(SOS),End of Growth Season(EOS),Long of Growth Season(LOS)) using Dynamic Threshold method;finally,the spatial patterns and temporal trends of observed cotton phenological characteristics were analyzed over the past 16 years and the relationship between cotton phenology and temperature changes was also discussed.The result of this study showed that the spatial patterns of cotton phenology were significantly different in study region:SOS delayed gradually from Nanjiang to Beijiang,and mainly occurred before 151st and after 151st days respectively;EOS gradually advanced,most areas of northern Xinjiang ended up 292nd days ago,while the southern Xinjiang happened 298th days later;LOS shortened,Nanjiang is generally longer than 150 days while Beijiang is usually shorter than 150 days.The trend of cotton phenology(2001~2016) under climate change in northern and southern Xinjiang were not completely similar:SOS and EOS in southern Xinjiang showed a delay-advancing-delay-advancing trend,and LOS was unsignificantly delayed;While SOS in northern Xinjiang were slightly advanced and EOS exhibited a delay trend followed by an advancing,LOS showed a shorten-lengthen-shorten trend.In addition,cotton phenology showed a strong correlation with the temperature:SOS and EOS were positively correlated with the beginning date of 15℃ and the end date of 10℃ respectively;SOS was negatively correlated with the spring temperature,while EOS had a positive correlation with autumn temperature.
Key words: NDVI    Phenology    Temporal and spatial variation    Temperature variation    Cotton    Xinjiang
收稿日期: 2017-11-23 出版日期: 2019-02-22
ZTFLH:  S162.5  
基金资助: 国家科技支撑项目(2015BAJ02B02)。

作者简介: 茹皮亚·西拉尔(1991-),女,新疆伊犁人,硕士研究生,主要从事摄影测量与遥感研究。Email:rupiyaxilaer15@mails.ucas.ac.cn。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

茹皮亚·西拉尔, 杨辽. 新疆棉花物候时空变化遥感监测及气温影响分析[J]. 遥感技术与应用, 2018, 33(5): 923-931.

Rupiya Xilaer, Yang Liao. Monitoring Spatial-temporal Change of Cotton Phenology in Xinjiang and Its Response to Climate Change. Remote Sensing Technology and Application, 2018, 33(5): 923-931.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2018.5.0923        http://www.rsta.ac.cn/CN/Y2018/V33/I5/923

[1] 李长春, 徐轩, 包安明, 刘雪峰, 杨文攀.  基于FY3B-MWRI数据新疆区域积雪深度反演[J]. 遥感技术与应用, 2018, 33(6): 1030-1036.
[2] 冯姣姣, 王维真, 李净, 刘雯雯. 基于BP神经网络的华东地区太阳辐射模拟及时空变化分析[J]. 遥感技术与应用, 2018, 33(5): 881-889.
[3] 高莎, 林峻, 马涛, 吴建国, 郑江华. 新疆巴音布鲁克草原马先蒿光谱特征提取与分析[J]. 遥感技术与应用, 2018, 33(5): 908-914.
[4] 张滔,唐宏. 基于Google Earth Engine的京津冀2001~2015年植被覆盖变化与城镇扩张研究[J]. 遥感技术与应用, 2018, 33(4): 593-599.
[5] 郝斌飞,韩旭军,马明国,刘一韬,李世卫. Google Earth Engine在地球科学与环境科学中的应用研究进展[J]. 遥感技术与应用, 2018, 33(4): 600-611.
[6] 汪航,师茁. 基于MODIS时间序列数据的春尺蠖虫害遥感监测方法研究—以新疆巴楚胡杨为例[J]. 遥感技术与应用, 2018, 33(4): 686-695.
[7] 苗茜,王昭生,王荣,黄玫,孙佳丽. 基于NDVI数据评估O3污染对华北地区夏季植被生长的影响[J]. 遥感技术与应用, 2018, 33(4): 696-702.
[8] 周玉科,刘建文. 基于MODIS NDVI和多方法的青藏高原植被物候时空特征分析[J]. 遥感技术与应用, 2018, 33(3): 486-498.
[9] 王佳鹏,施润和,张超,刘浦东,曾毓燕. 基于光谱分析的长江口湿地互花米草叶片叶绿素含量反演研究[J]. 遥感技术与应用, 2017, 32(6): 1056-1063.
[10] 杨涛,黄法融,李倩,白磊,李兰海. 新疆北部植被生长季NDVI时空变化及其与冬季降雪的关系[J]. 遥感技术与应用, 2017, 32(6): 1132-1140.
[11] 包刚,包玉龙,阿拉腾图娅,包玉海,覃志豪,王牧兰,周义. 1982~2011年蒙古高原植被物候时空动态变化[J]. 遥感技术与应用, 2017, 32(5): 866-874.
[12] 郝建盛,张飞云,赵鑫,刘云霄,李兰海. 基于GRACE监测数据的伊犁—巴尔喀什湖盆地水储量变化特征及影响因素[J]. 遥感技术与应用, 2017, 32(5): 883-892.
[13] 孙晓,吴孟泉,何福红,张安定,赵德恒,李勃 . 2015年黄海海域浒苔时空分布及台风“灿鸿”影响研究[J]. 遥感技术与应用, 2017, 32(5): 921-930.
[14] 秦俊,冷寒冰,赵广琦,景军,周坚华. 物候和波谱—位置分析在城镇绿化植物群分类中的应用[J]. 遥感技术与应用, 2017, 32(5): 948-957.
[15] 周金霖,马明国,肖青,闻建光. 西南地区植被覆盖动态及其与气候因子的关系[J]. 遥感技术与应用, 2017, 32(5): 966-972.