EGI OpenIR
基于多源遥感数据的塔里木河下游植被变化监测
Alternative TitleMonitoring of Vegetation Changes in the Lower Reaches of Tarim River based on Multi-Source Remote Sensing Data
肖昊
Subtype硕士
Thesis Advisor李均力
2019-06-30
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Discipline工程硕士
Keyword塔里木河下游 植被格局时空变化 植被长势监测 SAVI 时序数据 多方法分类 Lower Reaches of Tarim River Spatio-Temporal Change of Vegetation Pattern Monitoring of Vegetation Growth SAVI Time Series Data Multi-method Classification
Abstract干旱区荒漠河岸带植被在维持极端干旱区生态稳定中起着极其重要的作用,对其进行长时间的连续监测具有重要的意义。近年来,随着遥感技术的日益成熟,使得数据类型日益多元化、数据量显著增加。尽管遥感技术在干旱区植被信息提取与植被长势监测中取得了较大进展,但针对极端干旱区的稀疏荒漠河岸带植被的精确识别与区分、时空格局变化均有待进一步研究。本 研 究 以 塔 里 木 河 下 游 地 区 为 研 究 区 , 选 择 多 源 遥 感 影 像 数 据(Sentinel-2A/B、 Landsat8 OLI、 GF-2 卫星遥感影像数据),结合野外获取的无人机影像数据、 RTK 地面真实点数据,采用基于土壤调节指数时序数据的分类方法、支持向量机等方法对塔里木河下游地区地表覆被信息进行提取,并对典型监测断面内的植被进行长势变化监测,最后基于获取的地表覆被信息,采用面积变化、双向动态度等指标对塔里木河下游 2013-2018 年干旱区荒漠河岸带植被时空变化进行分析,得到极端干旱区内植被的变化状态与变化趋势,为相关决策部门提供有意义的参考信息。本文主要结论如下:(1)针对极端干旱区的稀疏荒漠河岸带植被的信息提取,利用 SAVI 时序数据能够实现对植被信息的最优识别与区分,其总体精度达到了 88.3%, Kappa系数为 0.722;在无法构建 SAVI 时序数据时,支持向量机能够较好的实现对植被信息的识别与区分,其总体精度到达了 85.8%, Kappa 系数为 0.739,基于 CART算法的面向对象分类方法与最小距离法不适用于稀疏分布的荒漠河岸带植被的识别与区分,其总体分类精度低于 56%。(2)塔里木河下游在 2013-2018 年之间,植被面积处于增长的状态,地表覆被转移类型主要集中于水体-植被、植被-水体、裸地-植被、植被-裸地等四类,发生区域集中于其文库勒湖与台特玛湖区域。(3) 胡杨主要分布于塔里木河下游的上中段,其中英苏、老英苏等区域胡杨分布最为密集,同时胡杨分布的末端位于库尔干附近,且胡杨呈现出缓慢增长的趋势,胡杨恢复区主要出现在英苏东侧、阿拉干西侧与依干不及麻北部区域,主要分布于距河道 0~1km 与 4.5~6km 范围内,在这两个范围内的胡杨恢复面积增长分别占总增长面积的 56%、 39%;灌木为塔里木河下游的主要建群种,自大西海子水库向下分布密度逐渐减小,其中英苏至阿拉干区间段内,密度最高,灌木恢复区主要分布于距河道 1~4km 的范围内,占总增长面积的 51%,其次,灌木面积变化率最小值为 0.72,灌木总体一直处于稳定增长的状态;草本植物面积随河道距离的增加逐渐减小,主要分布于距河道 2.5km 的范围内,草本植物恢复区主要出现于英苏北侧、其文库勒湖与老塔里木河下段,在距河道 2km、 10.5km处,出现两次小幅度的增加,面积分别增加了为 4.88 km2、 4.93 km2。(4)塔里木河下游在 2013-2018 年间,植被长势总体处于改善的状态,同时,沿塔里木河河道由上至下,植被生长趋势大致一致,也从侧面反映了塔里木河下游两岸的水文条件在 2013-2018 年得到了改善。英苏、老英苏、博孜库勒、阿拉干、依干不及麻监测区在 2014-2015 年、 2016-2017 年间出现了两次显著的植被长势变好的现象,库尔干监测区在 2015-2017 年间植被长势一直处于较好的状态,仅有个别监测区出现小面积的长势变差现象;胡杨与柽柳的变化较小,仅在个别年份出现生长衰退的现象,草本植物的变化因监测区的不同而具有差异性,在英苏、老英苏、博孜库勒、阿拉干等区域,由于水文条件年变化差异小,草本植物处于稳定生长的状态,在依干不及麻、库尔干等水文条件较好且年变化较大的区域,草本植物在 2016-2017 年间出现了一个极大涨幅。
Other AbstractVegetation in desert riparian zone plays an extremely important role inmaintaining the ecological stability of arid zone. It is of great significance to carry outlong-term continuous monitoring of the desert riparian vegetation. At the same time,with the maturity of remote sensing technology in recent years, on the one hand, moresatellite remote sensing images can be obtained for the lower reaches of the TarimRiver, on the other hand, remote sensing technology has made some progress in theextraction of vegetation information and monitoring of vegetation growth in aridregions. However, there are still many difficulties: First, the precise identification anddifferentiation of sparse desert riparian vegetation in arid regions. Secondly, thespatial and temporal patterns of vegetation in the desert riparian zone in the lowerreaches of the Tarim River need further studied.In this study, multi-source remote sensing image data (Sentinel-2A/B, Landsat 8OIL, GF-2 satellite remote sensing image data) were selected, combined with the fieldacquired UAV image data and ground real point data.First, the land cover informationin the study region was extracted by using many classification methods, includingbased on soil adjustment vegetation index time series data , support vector machineand other methods, at the same tiome, monitor the growth of the vegetation in thedesignated monitoring area. Finally, based on the acquired land cover information, thespatial and temporal patterns of land cover in the lower reaches of the Tarim Riverbetween 2013 and 2018 were analyzed by using multiple indicators, such as areachange and bidirectional dynamic, etc, which to analyze the temporal and spatialpatterns of surface overburden in the lower reaches of the Tarim River between 2013and 2018. To provide meaningful reference information for relevant decision-makingdepartments.The main conclusions are as follows:(1) For the information extraction of sparse desert riparian vegetation in arid regions, the optimal identification and differentiation of vegetation information can beachieved by using SAVI time series data and phenological information, with anoverall accuracy of 88.3% and a Kappa coefficient of 0.722. When the SAVI timeseries data cannot be constructed, the support vector machine can better realize therecognition and differentiation of vegetation information. The overall accuracyreaches 85.8% and the Kappa coefficient is 0.739. Meanwhile, the object-orientedclassification method based on CART algorithm and the minimum distance methodare not suitable for the identification and differentiation of vegetation in sparsedistributed desert riparian zone, and its overall classification accuracy is less than56%.(2) In the lower reaches of the Tarim River, the vegetation growth area isgrowing between 2013 and 2018. The main types of surface cover transfer arewater-vegetation and vegetation-sandy land. The main reason is that aquatic herbs aregreatly affected by water. When the hydrological conditions are good, aquaticvegetation grows in large areas in succession, while other types of vegetation growmore smoothly between 2013 and 2018.(3) Populus euphratica is mainly distributed in the upper middle section of thelower reaches of the Tarim River. The Populus euphratica is the most denselydistributed in the Yingsu and Old-yingsu areas. At the same time, the end of thePopulus euphratica distribution is located near Kurgan, and the Populus euphraticashows a slow growth trend. Fresh Populus euphratica is mainly distributed in the eastside of Yingsu, the west side of Alagan and the north of the area of Yiganbujima,which are mainly distributed in the range of 0~1km and 4.5~6km away from the river.The area growth of Populus euphratica in these two areas accounts for 56% 、 39%ofthe total growth area respectively. Shrubs are the main building species in the lowerreaches of the Tarim River. The density of the downward distribution from theDaxihaizi Reservoir is gradually decreasing. The density is the highest in theYingsu-Alagan section. The new shrubs are mainly distributed in the range of 1~4kmaway from the river. Within the scope of the total growth area of 51%, secondly, the minimum change rate of shrub area is 0.72, which mean the shrubs has been in asteady growth state; the herbs area gradually decreases with the increase of the riverchannel distance, mainly distributed in the range of 2.5km away from the river. Thenew herbs mainly appeared in the north side of the Yingsu and the old Tarim River,meanwhile, herbs appeared two significant increase at 2km and 10.5km from the river,which area added 4.88 km2, 4.93 km2, respectively.(4) Between 2013 and 2018, the vegetation growth in the lower reaches of theTarim River is generally in a stable state. In 2014-2105 and 2016-2017, there are twosignificant improvements in vegetation growth in the monitoring areas of Yingsu,Old-Yingsu, Bozikule, Alagan and Yiganbujima. The monitoring areas of the Kurganare in an excellent state of growth between 2015 and 2017.At the same time, there is alittle area of growth variation in individual monitoring areas; For specific vegetationtypes, the overall growth is in a stable state, and the changes of Poplar and Tamarixare small, the phenomenon of growth decline occurs only in individual years, and thechanges of herbs are different because of the differences in the monitoring area, in theYingsu, Old-Yingsu, BoziKule, Alagan, due to the small differences in the annualvariation of hydrological conditions, Herbs are in a stable state of growth, in the dryless than hemp, Kurgan and other areas with better hydrological conditions and largeannual changes, herbs area age changes greatly, of which in 2016-2017 years therehas been a great increase.
Subject Area测绘工程
Language中文
Document Type学位论文
Identifierhttp://ir.xjlas.org/handle/365004/15333
Collection中国科学院新疆生态与地理研究所
研究系统
Affiliation中国科学院新疆生态与地理研究所
First Author Affilication中国科学院新疆生态与地理研究所
Recommended Citation
GB/T 7714
肖昊. 基于多源遥感数据的塔里木河下游植被变化监测[D]. 北京. 中国科学院大学,2019.
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