【摘要】：本研究的研究范圍是遼寧省阜新市彰武縣,運用ENVI軟件對研究區(qū)2016年6月和4月的Landsat 8衛(wèi)星OLI遙感影像圖進(jìn)行預(yù)處理。同時,獲取研究區(qū)相應(yīng)月份的實測土壤含水率數(shù)據(jù)。本研究選取了溫度植被指數(shù)法和垂直干旱指數(shù)法作為旱情監(jiān)測的指標(biāo),將各指標(biāo)分別與土壤含水率進(jìn)行擬合,運用SPSS軟件進(jìn)行顯著性分析,最終得出適用于彰武地區(qū)的旱情監(jiān)測模型。本研究的主要內(nèi)容和結(jié)論如下:1.對基于溫度植被指數(shù)的模型進(jìn)行構(gòu)建和分析。通過對比分析溫度植被指數(shù)與土壤含水率相關(guān)性和顯著性,結(jié)果表明:溫度植被干旱指數(shù)與不同深度的土壤含水率擬合效果較差,其中與30cm 土壤含水率的擬合效果相對較好,其復(fù)相關(guān)系數(shù)為0.3837;利用SPSS軟件對二者的顯著性進(jìn)行分析,分析結(jié)果表明,土壤深度為10cm和20cm的土壤含水率與TVDI在0.05水平上顯著相關(guān),深度為30cm的土壤含水率與TVDI在0.01水平上顯著相關(guān)。2.對基于垂直干旱指數(shù)的模型進(jìn)行構(gòu)建和分析。在基于垂直干旱指數(shù)法旱情監(jiān)測模型構(gòu)建中,由PDI反演數(shù)據(jù)與土壤含水率的擬合結(jié)果可知,深度20cm的土壤含水率擬合效果最好,復(fù)相關(guān)系數(shù)為0.5555,10cm與30cm深度的土壤含水率擬合效果相對較差。顯著性分析結(jié)果表明,土壤深度為1Ocm和20cm的土壤含水率與垂直干旱指數(shù)在0.01水平上顯著相關(guān),深度為30cm的土壤含水率與垂直干旱指數(shù)在0.05水平上顯著相關(guān)。3.對兩個模型進(jìn)行對比分析,選擇最優(yōu)模型作為彰武地區(qū)的旱情監(jiān)測模型。由兩個模型的相關(guān)性和顯著性對比分析結(jié)果可知,垂直干旱指數(shù)與土壤含水率的擬合結(jié)果要好于土壤含水率與TVDI的擬合結(jié)果。因此,基于垂直植被指數(shù)法的旱情監(jiān)測模型更適用于彰武地區(qū)的旱情監(jiān)測。4.對構(gòu)建的模型進(jìn)行驗證分析。通過對2016年4月份衛(wèi)星影像圖進(jìn)行反演,得到土壤含水率反演值,通過對不同深度實測土壤含水率與反演出來土壤含水率的對比分析,結(jié)果表明,深度為1Ocm和20cm的土壤含水率的相關(guān)性分別高出深度為30cm 土壤含水率相關(guān)性0.1041和0.1064。其中,深度為30cm的土壤含水率相關(guān)性最差,其相關(guān)系數(shù)為0.4945;深度為10cm的反演土壤含水率與實測土壤含水率的復(fù)相關(guān)系數(shù)為0.5986;深度為20cm的反演與實測土壤含水率的復(fù)相關(guān)系數(shù)為0.6009。通過精度和相對誤差分析,10cm、20cm反演效果最理想,10cm反演精度平均值為91.25%,20cm反演精度平均值為88.53%,分別高出30cm反演精度平均值6.96%和4.24%。在相對誤差分析中10cm、20cm的相對誤差平均值分別低于30cm相對誤差8.9%和5.34%,30cm的相對誤差平均值偏大為18.49%。綜上所述,在旱情監(jiān)測中,運用遙感手段和垂直干旱指數(shù)模型進(jìn)行監(jiān)測時,深度小于20cm的干旱監(jiān)測效果比較理想。
[Abstract]:The research scope of this study is Zhangwu County, Fuxin City, Liaoning Province. The Landsat 8 satellite OLI remote sensing images in June and April 2016 are preprocessed by ENVI software. At the same time, the measured data of soil moisture content in corresponding months in the study area were obtained. In this study, the temperature vegetation index method and vertical drought index method were selected as indicators of drought monitoring. The indexes were fitted with soil moisture content, and the significance was analyzed by SPSS software. Finally, a drought monitoring model suitable for Zhangwu area is obtained. The main contents and conclusions of this study are as follows: 1. The model based on temperature vegetation index is constructed and analyzed. The correlation and significance between temperature vegetation index and soil moisture content were compared and analyzed. The results showed that the fitting effect of temperature vegetation drought index and soil moisture content at different depths was poor, and the fitting effect with 30cm soil moisture content was relatively good. The multiple correlation coefficient was 0.3837; The significance of the two was analyzed by using SPSS software. The results showed that the soil moisture content with the depth of 10cm and 20cm was significantly correlated with TVDI at the level of 0. 05. Soil moisture content with depth of 30cm was significantly correlated with TVDI at 0.01 level. The model based on vertical drought index is constructed and analyzed. In the construction of drought monitoring model based on vertical drought index method, the fitting results of PDI inversion data and soil moisture content show that the soil moisture content fitting effect of depth 20cm is the best. The fitting effect of soil moisture content with the depth of 30cm was relatively poor when the complex correlation coefficient was 0. 5555 ~ 10 cm and the depth of 30cm was 10 cm. The results of significant analysis showed that soil moisture content with 1Ocm and 20cm depth was significantly correlated with vertical drought index at 0.01 level, and soil moisture content with depth 30cm was significantly correlated with vertical drought index at 0. 05 level. The two models were compared and the optimal model was chosen as the drought monitoring model in Zhangwu area. The correlation and significance of the two models showed that the fitting results of vertical drought index and soil moisture content were better than that of soil moisture content and TVDI. Therefore, the model based on vertical vegetation index method is more suitable for drought monitoring in Zhangwu area. 4. The model is validated and analyzed. Through the inversion of satellite image in April 2016, the inversion value of soil moisture content is obtained. Through the comparison and analysis of soil moisture content measured at different depths and the inversion soil moisture content, the results show that, The correlation of soil moisture with depth of 1Ocm and 20cm was higher than that of 30cm with depth of 0.1041 and 0.1064, respectively. Among them, the correlation coefficient of soil moisture content with depth of 30cm is the worst, the correlation coefficient is 0.4945, the complex correlation coefficient between soil moisture content and measured soil moisture content is 0.5986 when the depth is 10cm, and the correlation coefficient is 0.4945 when the depth is 30cm. The complex correlation coefficient between the inversion of depth of 20cm and the measured soil moisture content is 0.6009. Through the analysis of accuracy and relative error, the inversion effect of 10cm ~ (20) cm is the most ideal. The average inversion accuracy of 10cm is 91.25 ~ (20) cm and the average of inversion accuracy is 88.53, which is 6.96% and 4.24% higher than the average value of 30cm inversion precision respectively. In the relative error analysis, the average relative error of 10 cm ~ (20) cm is lower than that of 30cm by 8.9% and 5.34 ~ (30) cm, respectively. The average relative error of 10 cm ~ (20) cm is 18.49. In conclusion, in drought monitoring, the effect of drought monitoring with depth less than 20cm is ideal when remote sensing and vertical drought index model are used to monitor drought.
【學(xué)位授予單位】：沈陽農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S423

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