本文選題：地理加權(quán)回歸模型 + 產(chǎn)草量估算�。� 參考：《遼寧工程技術(shù)大學(xué)》2013年碩士論文

【摘要】：產(chǎn)草量是草地生產(chǎn)力的體現(xiàn),也是制定畜牧業(yè)生產(chǎn)管理策略的基礎(chǔ)。利用RS技術(shù)估算產(chǎn)草量已經(jīng)成為草場生產(chǎn)力研究和草場管理的重要途徑之一。產(chǎn)草量可以通過遙感數(shù)據(jù)驅(qū)動,采用生物-物理模型來估算,也可以通過建立經(jīng)驗?zāi)Ｐ蛠砉浪恪；谏?物理的估算模型多用于大尺度宏觀估算,但是估算精度不高；經(jīng)驗?zāi)Ｐ椭饕谶b感數(shù)據(jù)與地面實測數(shù)據(jù)建立統(tǒng)計模型,簡單易行。常用的經(jīng)驗?zāi)Ｐ鸵话愣己雎粤水a(chǎn)草量的空間異質(zhì)性,導(dǎo)致估算精度比較低。產(chǎn)草量與地理位置有關(guān),鄰近地理位置間的產(chǎn)草量具有空間相關(guān)性。本研究充分考慮產(chǎn)草量的空間相關(guān)性,將地理加權(quán)回歸(Geographically Weighter Regression,簡稱GWR)的思想引入產(chǎn)草量遙感估算建模中,基于GWR模型,利用與產(chǎn)草量密切相關(guān)的植被指數(shù)、氣象因子、草地類型等因子構(gòu)建產(chǎn)草量估算模型。 論文以三江源區(qū)為試驗區(qū),首先分析植被指數(shù)、氣象因子、草地類型數(shù)據(jù)與地面實測產(chǎn)草量數(shù)據(jù)的相關(guān)性,利用相關(guān)性較高的因子和GWR模型構(gòu)建試驗區(qū)的產(chǎn)草量估算模型。然后利用驗證點對模型進(jìn)行精度評價,并與多元線性回歸模型進(jìn)行比較,說明已構(gòu)建模型的可用性及優(yōu)越性。最后以三江源區(qū)果洛藏族自治州為例,構(gòu)建區(qū)域產(chǎn)草量GWR模型,并使用國產(chǎn)環(huán)境衛(wèi)星影像估算整個區(qū)域的產(chǎn)草量。 本文主要取得了如下的進(jìn)展和貢獻(xiàn)： (1)將地理加權(quán)回歸的思想引入產(chǎn)草量估算建模過程中,構(gòu)建了基于GWR的產(chǎn)草量遙感估算模型。試驗結(jié)果證明基于GWR的產(chǎn)草量估算模型可以提高產(chǎn)草量估算模型的擬合優(yōu)度,模型的擬合r2從不足0.3提高到0.8以上,估算精度明顯優(yōu)于多元線性回歸模型,可以提高20%左右。 (2)通過試驗發(fā)現(xiàn)與三江源區(qū)產(chǎn)草量遙感估算密切相關(guān)的因子包括：5-8月累積降水量、5-8月干燥度、修正型土壤植被指數(shù)(Modified Soil Adjusted Vegetation Index,簡稱MSAVI)。本研究基于GWR構(gòu)建的三江源區(qū)產(chǎn)草量模型中,r2達(dá)到0.858,調(diào)整r2為0.772,可信度比較高,實地驗證精度為71.61%。該估算模型的參數(shù)少,可直接由HJ影像和氣象數(shù)據(jù)獲得,應(yīng)用方便,可直接用于三江源區(qū)實際產(chǎn)草量的估算。 (3)本研究在構(gòu)建基于GWR的產(chǎn)草量估算模型的基礎(chǔ)上,建立了利用國產(chǎn)環(huán)境衛(wèi)星數(shù)據(jù)估算區(qū)域產(chǎn)草量的技術(shù)方法。以果洛藏族自治州為例,其中產(chǎn)草量估算GWR模型的r2為0.845,調(diào)整r2為0.727,并達(dá)到P值小于0.001的顯著水平,精度為67.47%。利用2010年8月的環(huán)境衛(wèi)星數(shù)據(jù)和5-8月的氣象數(shù)據(jù)和已建好的模型,估算得到果洛藏族自治州的總產(chǎn)草量(鮮重)為3260.01×104t,產(chǎn)草量空間呈現(xiàn)自東向西逐漸減少的趨勢。
[Abstract]:The yield of grass is the embodiment of grassland productivity and the basis of formulating the strategy of animal husbandry production and management.The estimation of grass yield by RS technology has become one of the important ways to study grassland productivity and grassland management.The yield of grass can be estimated by remote sensing data, biophysical model and empirical model.Bio-physical estimation models are mostly used in large-scale macroscopic estimation, but the estimation accuracy is not high, and the empirical model is mainly based on remote sensing data and ground measured data to establish statistical model, which is simple and easy to carry out.The spatial heterogeneity of grass yield is neglected in common empirical models, which leads to low estimation accuracy.The yield of grass was related to geographical location, and there was a spatial correlation between the yield of grass near geographical location.In this study, the spatial correlation of grass yield was fully considered, and the idea of geographical weighted regression was introduced into the remote sensing estimation modeling of grass yield. Based on the GWR model, the vegetation index and meteorological factors, which were closely related to grass yield, were used.Grassland type and other factors were used to estimate the yield of grass.Firstly, the correlation between vegetation index, meteorological factors, grassland type data and the measured grass yield data was analyzed, and the estimation model of grass yield in the experimental area was constructed by using the highly correlated factors and GWR model.Then the accuracy of the model is evaluated by the verification point, and compared with the multivariate linear regression model, the usability and superiority of the constructed model are illustrated.Finally, taking Guoluo Tibetan Autonomous Prefecture in Sanjiangyuan region as an example, the GWR model of regional grass yield is constructed, and the total grass yield of the whole region is estimated by using domestic environmental satellite images.The main achievements of this paper are as follows:1) the idea of geographical weighted regression is introduced into the modeling process of grass yield estimation, and the remote sensing estimation model of grass yield based on GWR is constructed.The experimental results show that the estimation model based on GWR can improve the goodness of fit of the estimation model. The fitting R2 of the model is improved from less than 0.3 to more than 0.8, and the estimation accuracy is obviously better than that of the multivariate linear regression model, and it can be increased by about 20%.(2) it was found that the factors closely related to the remote sensing estimation of grass yield in the source region of the three rivers included the accumulated precipitation in May to August and the drying degree in May to August, and the modified Soil Adjusted Vegetation Index (MSAVIX).In this study, the model of grass yield in Sanjiangyuan region based on GWR was 0.858, and adjusted to 0.772.The reliability of the model was high, and the accuracy of field verification was 71.611.The estimated model has few parameters and can be obtained directly from HJ image and meteorological data. It is convenient to be applied and can be directly used to estimate the actual grass yield in the source region of the three Rivers.3) based on the GWR model, a technical method for estimating regional grass yield using domestic environmental satellite data was established.Taking Guoluo Tibetan Autonomous Prefecture as an example, the estimated yield of grass in GWR model is 0.845, the adjusted R2 is 0.727, and the P value is less than 0.001, and the precision is 67.47.Based on the environmental satellite data of August, 2010, meteorological data of May-August and established models, the total grass yield (fresh weight) of Guoluo Tibetan Autonomous Prefecture is estimated to be 3260.01 脳 10 ~ 4 t, and the space of grass yield is gradually decreasing from east to west.
【學(xué)位授予單位】：遼寧工程技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：S812;P237

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