EGI OpenIR
基于改进型 TVDI 在干旱区旱情监测中的应用研究
Alternative TitleApplication Research Based on modified TVDI in Drought Monitoring in Arid Areas
陈丙寅
Subtype硕士
Thesis Advisor陈曦
2019-06-30
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Discipline工程硕士
Keyword干旱区 旱情监测 TVDI GPM Arid area Drought monitoring TVDI GPM
Abstract干旱是全球范围内影响最为广泛的自然灾害之一, 其所导致的土壤沙漠化、荒漠化和盐碱化给生态环境造成不可逆的危害, 同时其所具有影响范围大、持续时间长、破坏性大等特点。 近年来,遥感信息技术的发展使得旱情监测方式方法取得了显著的发展,但是仍面临着诸多困难与挑战:一是在旱情监测过程中,未考虑植被生长周期,二是针对旱情监测,并未考虑植被生理特性对旱情的影响。因此利用遥感手段进行旱情监测,需要进一步深入研究。本文选择时间序列数据 MODIS 数据,结合野外土壤湿度调查数据进行旱情监测。 首先对 MODIS 数据进行投影转换、裁剪拼接等预处理,通过地形校正和干边校正对 TVDI 模型进行改进,构建了改进型的温度植被干旱指数(mTVDI)用于新疆干旱区旱情监测, 利用土壤实测数据对 mTVDI 及传统的 TVDI 模型进行对比验证,同时利用降水数据、高程数据和土地利用数据对旱情影响因素进行分析。 研究结果表明:(1) 改进后的 TVDI 模型即 mTVDIE 对干旱区旱情的敏感度最高,与实测土壤水分数据的相关性 R²为 0.74, mTVDIE 能够很好的指示旱情状况。旱情监测结果表明: 新疆 2002-2016 年旱情分布以塔里木盆地和准噶尔盆地边缘植被区为干旱中心,旱情状况由严重逐步向周围山区递减至湿润状态。 与此同时,吐鲁番盆地和哈密盆地边缘植被区长期处于严重干旱的状态。(2) 从植被生长季看,研究区在整个生长季的前期和末期即第 97, 113,129, 145, 257, 273, 289 和 305 天旱情较为严重,生长季中期即第 161, 177,193, 209, 225, 241 天较为湿润;从 2002-2016 年看,旱情状况表现出一定的周期性,旱情发生以 4, 8 年为周期。(3) 降水对旱情状况具有重要影响作用。 研究利用 GPM 降水数据对基于mTVDIE 结果进行对比分析,发现二者相关性较强, R²达到 0.6216。 Pearson 相关系数为-0.788,通过了 P<0.01 的显著性检验。 可以看出,降水对新疆旱情状况影响较大,降水的多少直接影响植被生长季旱情广度和严重程度。(4) 旱情与 DEM 关系密切,利用 mTVDIE 结果与海拔高度、坡度、坡向等要素进行对比可知: 海拔高度与旱情呈逆相关,海拔高度越高, mTVDIE 结果越小,旱情状况随着海拔的升高由干旱逐步转为湿润状态; 坡度不断增大会使得土壤保水性逐步下降, 旱情状况保持一个稳定的状态;坡向与旱情的相关性并不明显, 坡向的变化并未引起 mTVDIE 结果有明显分化。(5) 不同土地利用类型所表现出的旱情响应有所不同。旱地和水田由于受人为因素影响,旱情状况处于一个相对稳定的状态;高密度覆盖的草地和林地土壤湿度大、低密度覆盖的草地和林地土壤湿度小; 城镇建设用地植被覆盖度低,旱情表现结果不一,但总体处于干旱状态。综上,基于增强型植被指数与经过校正后的地表温度数据所构建的 mTVDIE模型能够满足干旱区植被生长季旱情监测的目的。 mTVDIE 模型所表现出的旱情状况与土壤水分之间显示出较强的相关性,对全球干旱区旱情研究具有很强的参考意义。
Other AbstractDrought is one of the most influential natural disasters on a global scale. Droughtcauses soil desertification, desertification and salinization to cause irreversibledamage to the ecological environment. At the same time, it has a wide range ofinfluence,long duration and great destructiveness. In recent years, the deveploment ofremote sensing information technology has made significant progress in the method ofmonitoring drought conditions, but it still faces many difficulties and challenges:first,in the process of drought monitoring, the vegetation growth cycle is not considered;second, it has not considered the effects of vegetation physiological characteristics ondrought conditions at drought monitoring. Therefore, the use of remote sensing fordrought monitoring requires further research.This paper selects the time series MODIS data, combined with the field soilmoisture survey data for drought monitoring. Firstly, the MODIS data ispre-processed by projection transformation, cutting , splicing and so on. The TVDImodel is modified by terrain correction and dry edge correction. A modifiedtemperature vegetation drought index (mTVDI) is constructed for drought monitoringin arid regions of Xinjiang. The data was compared and verified by mTVDI andtraditional TVDI models. At the same time, precipitation data, elevation data and landuse data were used to analyze the factors affecting drought. Research indicates:(1)The modified TVDI, mTVDIE, has the highest sensitivity to drought in aridareas, and the correclation with measured soil moisture data is R2=0.74. mTVDIE canindicate the drought condition well. The drought monitoring results show that the thesevere drought area is in the marginal vegetation area of the Junggar Basin and theTarim Basin in Xinjiang from 2002 to 2016. And the drought conditions are graduallydecreased from the surrounding mountainous area to the mountainous area. At thesame time, the marginal vegetation areas of the Turpan Basin and the Hami Basinhave been in a state of severe drought for a long time.(2) From the spatial point of view, the distribution of drought in Xinjiang from2002 to 2016 is based on the Tarim Basin and the Junggar Basin as two arid centers,and the drought conditions have gradually decreased from the mountainous area to thesurroundings. At the same time, the Turpan Basin and the Hami Basin have been in astate of severe drought for a long time.(3) Precipitation plays an important role in the drought situation. The study usedGPM precipitation data to compare and analyze the results based on mTVDIE, and found that the correlation between them was strong, and R2 reached 0.6216. ThePearson correlation coefficient was -0.788, and a significance test of P < 0.01 waspassed. It can be seen that precipitation has a great impact on the drought situation inXinjiang, and the amount of precipitation directly affects the breadth and severity ofdrought in the growing season.(4) Drought is closely related to DEM. Using mTVDIE results to compare withaltitude, slope and aspect, we can see that altitude is inversely related to drought. Thehigher of altitude, the smaller of the mTVDIE result. With the increase of altitude, thedrought gradually turned into a humid state. The increasing slope will graduallyreduce the soil water retention, and the drought situation will maintain a stable state.The correlation between the slope direction and the drought is not obvious, and thechange of the slope direction does not cause obvious differentiation of the mTVDIEresults.(5) The drought response shown by different land use types is different. Due tohuman factors, dryland and paddy fields are in a relatively stable state. High-densitygrassland and woodland soil moisture is high, low-density grassland and woodlandsoil moisture is small. Urban construction land vegetation coverage is low so thedrought performance results were mixed, but overall it was in a state of drought.In summary, the mTVDIE model based on the enhanced vegetation index and thecorrected surface temperature data can meet the purpose of drought monitoring in thearid area. The mTVDIE model showed a strong correlation between droughtconditions and soil moisture, and it has strong reference significance for droughtresearch in global arid regions.
Subject Area测绘工程
Language中文
Document Type学位论文
Identifierhttp://ir.xjlas.org/handle/365004/15278
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
陈丙寅. 基于改进型 TVDI 在干旱区旱情监测中的应用研究[D]. 北京. 中国科学院大学,2019.
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