本文選題：土壤鹽漬化 + 遙感�。� 參考：《新疆大學(xué)》2017年博士論文

【摘要】：土壤作為一種重要的自然資源,為人類生產(chǎn)食物和纖維,并維持地球生態(tài)系統(tǒng)平衡;同時(shí),土壤作為人類社會(huì)及一切社會(huì)活動(dòng)的基本條件,承擔(dān)重要的載體作用。然而,土壤鹽漬化是干旱、半干旱區(qū)所面臨的重要生態(tài)環(huán)境問(wèn)題之一,土壤鹽漬化引起的土壤板結(jié)、肥力下降、酸堿失衡、土地退化等后果,嚴(yán)重制約我國(guó)農(nóng)業(yè)發(fā)展,影響當(dāng)前我國(guó)可持續(xù)發(fā)展的戰(zhàn)略大局。遙感技術(shù)因其尺度大、范圍廣、時(shí)效性強(qiáng)、經(jīng)濟(jì)性強(qiáng)等特點(diǎn),很好的彌補(bǔ)了傳統(tǒng)鹽漬化現(xiàn)象監(jiān)測(cè)方法的不足,為定量監(jiān)測(cè)土壤鹽漬化現(xiàn)象提供了嶄新的途徑。在前人利用遙感技術(shù)對(duì)土壤鹽漬化現(xiàn)象進(jìn)行監(jiān)測(cè)等研究與應(yīng)用的基礎(chǔ)上,本研究選取位于新疆維吾爾自治區(qū)準(zhǔn)噶爾盆地西南緣艾比湖流域的典型鹽漬化土壤為研究對(duì)象,通過(guò)土壤樣本采集、室內(nèi)光譜測(cè)定、理化性質(zhì)分析等野外與室內(nèi)工作,明確該流域土壤理化性質(zhì)特征,從光譜維和空間維探討分?jǐn)?shù)階微分在遙感技術(shù)監(jiān)測(cè)土壤鹽漬化中的應(yīng)用的可行性。在光譜維度上,利用Grünwald-Letnikov分?jǐn)?shù)階微分公式對(duì)可控光源條件下測(cè)定的鹽漬化土壤光譜反射率數(shù)據(jù)進(jìn)行0~2階微分(階數(shù)間隔0.1)處理,結(jié)合極差、相關(guān)系數(shù)、變異系數(shù)、光譜信息散度等指標(biāo),探討分?jǐn)?shù)階微分對(duì)光譜反射率數(shù)據(jù)的影響,并以此為基礎(chǔ),利用偏最小二乘回歸及組合建模理論建立土壤含鹽量定量估算模型。在空間維度上,構(gòu)建分?jǐn)?shù)階微分算子,對(duì)Landsat 8 OLI獲取的多光譜遙感影像進(jìn)行濾波處理,結(jié)合銳化程度、信息熵、結(jié)構(gòu)相似性指數(shù)、峰值信噪比等圖像質(zhì)量評(píng)價(jià)指標(biāo),研究分?jǐn)?shù)階微分算子在圖像增強(qiáng)中的作用,并在選取較為適合艾比湖保護(hù)區(qū)鹽漬化信息空間可視化表征的鹽分指數(shù)后,探討分?jǐn)?shù)階微分濾波對(duì)鹽分指數(shù)提取時(shí)產(chǎn)生的影響。主要結(jié)論如下:(1)艾比湖流域表層土壤含鹽量變異性強(qiáng),均值為33.838 g/kg,大于鹽土20 g/kg的分級(jí)標(biāo)準(zhǔn),流域內(nèi)土壤鹽漬化現(xiàn)象普遍而又嚴(yán)重;陽(yáng)離子含量Na~+Ca~(2+)K~+Mg~(2+),陰離子含量SO_4~(2-)Cl~-HCO_3~-,結(jié)合相關(guān)性分析結(jié)果,NaCl和CaSO_4是主要的鹽分,艾比湖流域土壤主要以堿性和強(qiáng)堿性為主,鹽化類型主要以Cl~-鹽型和SO_4~(2-)鹽型為主。(2)艾比湖流域內(nèi),土壤中砂粒含量最多,黏粒含量最少,根據(jù)國(guó)際制土壤質(zhì)地分類標(biāo)準(zhǔn),流域內(nèi)土壤可分為粉質(zhì)壤土、壤土、砂質(zhì)壤土、砂土及壤質(zhì)砂土;分形維數(shù)與黏粒、粉粒、砂粒均呈現(xiàn)較好的非線性關(guān)系;利用逐步多元回歸方法,建立基于土壤機(jī)械組成的分形維數(shù)估算模型,該模型為2.844 0.002 0.006D clay sandy(28)(10)x-x,決定系數(shù)0.720;根據(jù)描述性統(tǒng)計(jì)結(jié)果,黏粒、粉粒與分形維數(shù)隨著鹽漬化程度的加深而增加,砂粒含量隨著鹽漬化程度的加深而減少。(3)不同鹽漬化程度土壤光譜曲線形態(tài)基本相似,在400~500 nm、550~700 nm、1000~1850 nm以及2250~2400 nm這4個(gè)范圍內(nèi),區(qū)分明顯;在1400 nm和1900 nm附近這兩個(gè)吸收帶,隨著鹽分含量的增加,光譜中水的吸收谷加深;在2340 nm這個(gè)吸收帶,吸收深度隨著鹽漬化程度的降低而加深;在400~1875 nm范圍內(nèi),反射率與土壤含鹽量相關(guān)系數(shù)均通過(guò)了0.01極顯著性檢驗(yàn),而在1876~2400 nm,則沒(méi)有波段通過(guò)檢驗(yàn)。(4)對(duì)于光譜反射率的5種數(shù)學(xué)形式,與土壤含鹽量的相關(guān)系數(shù)通過(guò)顯著性檢驗(yàn)的波段數(shù)量:未微分一階微分二階微分;將階數(shù)間隔進(jìn)行細(xì)化后,相關(guān)系數(shù)曲線呈現(xiàn)一定的漸變趨勢(shì),部分細(xì)節(jié)信息得以體現(xiàn),相關(guān)系數(shù)通過(guò)顯著性檢驗(yàn)的波段數(shù)量呈現(xiàn)先減少,再增加,而后再減少的趨勢(shì);隨著微分階數(shù)的增加,光譜微分值趨近于0,數(shù)據(jù)范圍減小;結(jié)合消除量綱影響的變異系數(shù),雖然數(shù)據(jù)范圍減小,但是數(shù)據(jù)的離散程度總體上呈現(xiàn)增加的趨勢(shì);光譜信息散度隨著階數(shù)的增加呈現(xiàn)非線性的增加趨勢(shì),且從0階到1階增加的幅度大于從1階到2階的幅度,這表明分?jǐn)?shù)階微分能夠細(xì)化相關(guān)系數(shù)、極差、變異系數(shù)、光譜信息散度等指標(biāo)的變化趨勢(shì),并且在一定程度上可以增加光譜數(shù)據(jù)的離散程度,增強(qiáng)光譜間的差異性。(5)針對(duì)光譜反射率的5種數(shù)學(xué)形式0~2階微分?jǐn)?shù)據(jù),利用偏最小二乘回歸建立的105個(gè)土壤鹽分估算模型中,基于原始光譜反射率均方根變換1.9階微分?jǐn)?shù)據(jù)建立的模型最優(yōu),該模型的RMSE_C=26.206 g/kg,R~2C=0.787,RMSEP=24.955 g/kg,R~2P=0.819,RPD=2.352。對(duì)簡(jiǎn)單平均法進(jìn)行改進(jìn),根據(jù)RPD的值對(duì)每個(gè)子模型賦予權(quán)重,通過(guò)對(duì)21個(gè)RPD≥2的模型進(jìn)行加權(quán)性質(zhì)的組合,組合后的模型RMSE_C=26.291 g/kg,R~2C=0.786,RMSEP=24.332 g/kg,R~2P=0.828,RPD=2.413,定量估算土壤含鹽量的精度與預(yù)測(cè)能力得到進(jìn)一步提升。(6)對(duì)于真彩色和標(biāo)準(zhǔn)假彩色合成后的圖像,在分?jǐn)?shù)階微分算子的階數(shù)大于0.8時(shí),R、G、B三個(gè)通道的平均峰值信噪比小于20 d B,圖像失真程度不可接受;對(duì)于分?jǐn)?shù)階微分濾波后的圖像,與原圖相比,雖然在一定程度上丟失了圖像信息,破壞了與原圖的結(jié)構(gòu)相似性,但是PSNR和銳化程度在0.6~0.7階之間達(dá)到平衡,圖像增強(qiáng)的效果達(dá)到最優(yōu)。此外,當(dāng)微分階數(shù)大于1時(shí),分?jǐn)?shù)階微分算子對(duì)圖像中的某些邊緣雙重響應(yīng),產(chǎn)生雙像素邊界。(7)針對(duì)艾比湖保護(hù)區(qū),鹽分指數(shù)SI_5(28)(B′R)/G與土壤含鹽量的相關(guān)系數(shù)為0.3532,通過(guò)了0.01顯著性檢驗(yàn),較為適合保護(hù)區(qū)內(nèi)鹽漬化信息空間可視化表征;從經(jīng)過(guò)0.1~0.8階微分濾波增強(qiáng)后圖像提取鹽分指數(shù)SI_5,與土壤含鹽量的相關(guān)系數(shù)呈現(xiàn)先增加再減少的趨勢(shì),并在微分階數(shù)為0.4時(shí),達(dá)到最大值0.3583;在微分階數(shù)為0.8時(shí),相關(guān)系數(shù)大幅降低且僅通過(guò)0.05顯著性檢驗(yàn),表明分?jǐn)?shù)階微分算子在進(jìn)行圖像增強(qiáng)處理時(shí),微分階數(shù)在一定范圍內(nèi)有利于提取土壤鹽漬化信息;而超過(guò)這個(gè)范圍,會(huì)對(duì)鹽分信息的提取產(chǎn)生不利影響。另外,隨著微分濾波器階數(shù)的增加,鹽分指數(shù)空間分布的部分細(xì)節(jié)信息得以體現(xiàn),這與分?jǐn)?shù)階微分算子圖像增強(qiáng)的能力息息相關(guān)。本研究將分?jǐn)?shù)階微分引入到遙感數(shù)據(jù)預(yù)處理中,為基于遙感數(shù)據(jù)提取地物信息提供更多的輔助性方法,也為機(jī)載、星載遙感技術(shù)在大尺度上對(duì)鹽漬化專題信息提取、土壤鹽漬化現(xiàn)象監(jiān)測(cè)以及遙感專題制圖等提供應(yīng)用參考,以期滿足未來(lái)對(duì)土壤鹽漬化現(xiàn)象監(jiān)測(cè)、精準(zhǔn)農(nóng)業(yè)等應(yīng)用的更高要求。
[Abstract]:Soil, as an important natural resource, produces food and fiber for human beings and maintains the balance of the earth's ecosystem. At the same time, soil is an important carrier for human society and all social activities. However, soil salinization is one of the important ecological environmental problems faced by drought and semi dry areas, soil salt. The results of soil consolidation, fertility decline, acid-base imbalance, land degradation, etc., seriously restrict the development of China's agriculture and affect the current strategic situation of sustainable development in China. Remote sensing technology has made up for the shortcomings of the traditional salinization monitoring method because of its large scale, wide range, strong timeliness and strong economy. On the basis of the research and application of remote sensing technology for monitoring soil salinization, this study selected typical salinized soil in the EBI Lake Basin in the southwest edge of Junggar basin, the Xinjiang Uygur Autonomous Region, as the research object, and collected the soil samples. In the field and indoor work, the characteristics of soil physical and chemical properties of the basin are clearly defined. The feasibility of applying fractional differential in remote sensing technology to monitor soil salinization is discussed from spectral dimension and space dimension. On the spectral dimension, the Gr u nwald-Letnikov fractional differential equation is used to determine the controlled light source. The spectral reflectance data of salinized soil are processed by 0~2 order differential (order interval 0.1), and the influence of fractional differential on spectral reflectance data is discussed in combination with the extreme difference, correlation coefficient, coefficient of variation and spectral information divergence. On the basis of this, the soil salt content quantitation is established by using the least two multiplicative regression and combined modeling theory. On the spatial dimension, the fractional differential operator is constructed, and the multi spectral remote sensing images obtained by Landsat 8 OLI are filtered, combined with the sharpening degree, the information entropy, the structural similarity index, the peak signal to noise ratio and so on, the function of the fractional differential operator in image enhancement is studied, and it is more suitable for the selection of the image enhancement. The effect of fractional differential filtering on the extraction of salt index is discussed after the salinization index of the salinization information space of the Ebinur lake protection area. The main conclusions are as follows: (1) the salinity variation of the surface soil in the Ebinur Lake Basin is strong, the mean value is 33.838 g/kg, the soil salinity is higher than the salt soil 20 g/kg, and the soil salinization in the basin The cation content Na~+Ca~ (2+) K~+Mg~ (2+) and anion content SO_4~ (2-) Cl~-HCO_3~-, combined with correlation analysis results, NaCl and CaSO_4 are the main salts, the main soil in the Ebinur Lake Basin is alkaline and strong alkaline, and the main types of salinization are Cl~ - salt and SO_4~ (2-) salt. (2) the sand in the lake basin According to the international classification standard of soil texture, the soil in the basin can be divided into silty loam, loam, sandy loam, sandy soil and loam sandy soil, and the fractal dimension has a good nonlinear relationship with the clay particles, powder and sand, and the fractal dimension based on the soil mechanical composition is established by the stepwise multiple regression method. The model was estimated to be 2.844 0.002 0.006D clay Sandy (28) (10) x-x, and the determining coefficient was 0.720. According to the descriptive statistics, the clay particles, powder and fractal dimension increased with the deepened salinization degree, and the sand content decreased with the deepening of the salinization degree. (3) the soil spectral curves of different salinization degree were basically similar, at 400. The 4 ranges of ~500 nm, 550~700 nm, 1000~1850 nm, and 2250~2400 nm are distinct; the two absorption bands near 1400 nm and 1900 nm increase with the increase of salt content, the absorption valley of the water is deepened; in the 2340 nm absorption band, the absorption depth is deepened with the decrease of the salinization range. The correlation coefficient of soil salt content passed the 0.01 polarity test, while in 1876~2400 nm, there was no wave band through test. (4) the 5 mathematical forms of spectral reflectance, the number of bands associated with the correlation coefficient of soil salt content through the saliency test: the two order differential of the first order of the first order; after refining the interval of the order, the correlation system The number curve presents a certain trend of gradual change, some details can be reflected, the correlation coefficient is first reduced, then increased, then decreasing, and the spectral differential value tends to 0 and the data range decreases with the increase of the differential order. However, the degree of dispersion of the data is increasing in general, and the spectral information divergence increases with the increase of the order, and the increase from the 0 order to the 1 order is greater than that from the 1 to the 2 order, which indicates that the fractional differential can refine the correlation coefficient, the difference, the coefficient of variation, the spectral information divergence and so on. The trend, and to a certain extent, can increase the degree of dispersion of spectral data and enhance the difference between spectra. (5) for the 5 mathematical form 0~2 differential data of spectral reflectance, 105 soil salinity estimation models established by partial least squares regression are built on the basis of the 1.9 order differential data of the original spectral reflectance mean square root transformation. The model is optimal, and the model's RMSE_C=26.206 g/kg, R~2C=0.787, RMSEP=24.955 g/kg, R~2P=0.819, RPD=2.352. are improved for the simple mean method. According to the value of RPD, each sub model is weighted, and the weighted properties of the 21 RPD > 2 models are combined, and the model RMSE_C=26.291 g/kg, R~2C=0.786, etc. G, R~2P=0.828, RPD=2.413, the accuracy and prediction ability of quantitative estimation of soil salt content is further improved. (6) the average peak signal to noise ratio of the three channels of R, G, B is less than 20 d B when the order of fractional differential operator is greater than 0.8, and the degree of image distortion is unacceptable. The image after differential filtering is compared with the original image, although the image information is lost to a certain extent and the structural similarity between the original image is destroyed, but the PSNR and the sharpening degree are balanced between the 0.6~0.7 order, and the effect of the image enhancement is optimal. In addition, when the differential order is greater than 1, the fractional differential operator has some edge double in the image. (7) the correlation coefficient of the salt index SI_5 (28) (B 'R) /G and the soil salt content is 0.3532 for the Ebinur lake protection area, and it passes the 0.01 significant test. It is more suitable for the visualization of the salinization information space in the protected area. The salt index SI_5 is extracted from the image after the 0.1~0.8 order differential filtering enhancement, and the soil is extracted from the soil. The correlation coefficient of soil salt content increases first and then decreases, and reaches the maximum value of 0.3583 when the differential order is 0.4. When the differential order is 0.8, the correlation coefficient is greatly reduced and only through 0.05 significant tests. It shows that the fractional order differential operator is beneficial to the extraction of soil in a certain range when the image enhancement is carried out. In addition, with the increase of the order of differential filter, some details of the spatial distribution of the salt index are reflected, which is related to the ability interest rate of the fractional differential operator image enhancement. The fractional differential is introduced to remote sensing data in this study. In preprocessing, it provides more auxiliary methods for extracting ground information based on remote sensing data. It also provides reference for airborne, spaceborne remote sensing technology to extract salinization information, soil salinization monitoring and remote sensing thematic mapping in large scale, in order to meet the future monitoring of soil salinization, precision agriculture and so on. Higher requirements for application.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：S156.41

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