鹽池縣草地退化及地上生物量遙感反演研究
發(fā)布時間：2018-12-15 22:15
【摘要】：本文以鹽池縣草地為研究對象,通過野外試驗和室內(nèi)試驗測定并分析了不同退化草地土壤養(yǎng)分和土壤酶活性的變化情況,同時,使用TM影像提取了8種常用的植被指數(shù),并建立了6種植被指數(shù)與地上生物量的回歸模型。草地生物量反演能夠及時、準確地獲取草地生長狀況,并為鹽池縣草地地上生物量的遙感估算和草地可持續(xù)發(fā)展提供一定的理論依據(jù),為今后開展大面積草地估產(chǎn)和動態(tài)監(jiān)測提供了有效途徑。論文的主要研究結(jié)果如下：1)隨著草地退化程度的增加,土壤各養(yǎng)分含量隨之下降,除有效磷含量是潛在退化草地輕度退化草地嚴重退化草地中度退化草地極嚴重退化草地外,全磷、全氮、全鉀、有機碳、堿解氦和速效鉀均為潛在退化草地輕度退化草地中度退化草地嚴重退化草地極嚴重退化草地。四種土壤酶活性含量均隨土壤退化程度加重而降低。對土壤酶活性與土壤養(yǎng)分做相關(guān)性分析,可知土壤過氧化氫酶與有機碳、全鉀含量相關(guān)性不顯著(P0.05),但與其他養(yǎng)分含量相關(guān)性均達到顯著或極顯著水平(P0.05或P0.01)。蛋白酶、磷酸酶、蔗糖酶與土壤養(yǎng)分含量相關(guān)性也達到顯著或極顯著水平(P0.05或P0.01)。說明在草地退化的過程中土壤酶活性與土壤肥力因素的變化是一致的。2)利用2013年8月鹽池縣TM影像,借助ENVI和ArcGis軟件,提取比值植被指數(shù)(RVI)、歸一化植被指數(shù)(NDVI)、轉(zhuǎn)換型植被指數(shù)(TVI)、修改型土壤調(diào)整植被指數(shù)(MSAVI)、土壤調(diào)整植被指數(shù)(SAVI)、差值植被指數(shù)(DVI)、濕度植被指數(shù)(WVI)和亮度植被指數(shù)(BVI)8種植被指數(shù),并與同期草地地上生物量作相關(guān)性分析,研究結(jié)果表明：除WVI和BVI外,其它6種植被指數(shù)與地上生物量均呈極顯著相關(guān)。RVI與草地生物量的相關(guān)系數(shù)達到了0.956。分別對6種植被指數(shù)與地上生物量做回歸分析,共建立36種回歸模型。3)通過對植被指數(shù)與地上生物量做相關(guān)分析,并建立回歸模型,發(fā)現(xiàn)擬合精度最好的是比值植被指數(shù)RVI,其次是NDVI。最優(yōu)關(guān)系模型為三次多項式回歸模型,然后是二次多項式回歸模型,模擬效果最差的是指數(shù)函數(shù)回歸模型。三次多項式回歸模型為：y=4.7539RVI3-38.708RVI2+213.04RVI-161.714)對RVI的三次多項式回歸模型進行精度驗證,結(jié)果顯示草地地上生物量實測值和預測值的平均誤差系數(shù)為16.56%,回歸擬合精度為83.44%,由此可見,應用遙感植被指數(shù)得到的回歸模型,可以用來監(jiān)測鹽池縣草地地上生物量。
[Abstract]:In this paper, the changes of soil nutrients and soil enzyme activities in different degraded grassland were determined and analyzed by field and laboratory experiments. At the same time, eight common vegetation indices were extracted by TM image. A regression model was established between six cropping indices and aboveground biomass. The inversion of grassland biomass can obtain the grassland growth status in time and accurately, and provide a certain theoretical basis for the remote sensing estimation of grassland aboveground biomass and the sustainable development of grassland in Yanchi County. It provides an effective way to carry out large area grassland yield estimation and dynamic monitoring in the future. The main results are as follows: 1) with the increase of grassland degradation, the nutrient content of soil decreased. The total phosphorus, total nitrogen, total potassium, organic carbon, total phosphorus, total nitrogen, total potassium, organic carbon, total phosphorus, total nitrogen, total potassium, organic carbon, total phosphorus, total nitrogen, total potassium, organic carbon, total phosphorus, total nitrogen, total potassium, organic carbon, Both alkali-hydrolyzed helium and available potassium were moderately degraded grassland, moderately degraded grassland and extremely severely degraded grassland. The four kinds of soil enzyme activities decreased with the increase of soil degradation. The correlation analysis between soil enzyme activity and soil nutrients showed that the correlation between soil catalase and organic carbon and total potassium content was not significant (P0.05). But the correlation with other nutrient content was significant or extremely significant (P0.05 or P0.01). The correlation between protease, phosphatase, sucrase and soil nutrient content was significant or extremely significant (P0.05 or P0.01). The results show that the changes of soil enzyme activity and soil fertility factors are consistent in the course of grassland degradation. 2) using the TM image of Yanchi County in August 2013, using ENVI and ArcGis software, the ratio vegetation index (RVI),) normalized vegetation index (NDVI),) is extracted. Conversion vegetation index (TVI), modified soil adjustment vegetation index (MSAVI), soil adjustment vegetation index (SAVI), difference value vegetation index (DVI), humidity vegetation index (WVI) and brightness vegetation index (BVI) 8 planting cover index; The correlation analysis was made with the aboveground biomass of grassland at the same time. The results showed that, except WVI and BVI, the other 6 indices were significantly correlated with the aboveground biomass, and the correlation coefficient between RVI and grassland biomass was 0.956. A total of 36 regression models were established. 3) correlation analysis between vegetation index and aboveground biomass was made and a regression model was established. It was found that the ratio vegetation index RVI, was the best fitting precision, followed by NDVI.. The optimal relation model is cubic polynomial regression model, then quadratic polynomial regression model. The worst simulation effect is exponential function regression model. The cubic polynomial regression model (y=4.7539RVI3-38.708RVI2 213.04RVI-161.714) was used to verify the accuracy of RVI's cubic polynomial regression model. The results showed that the average error coefficient of measured and predicted aboveground biomass of grassland was 16.56. The precision of regression fitting is 83.44. It can be seen that the regression model obtained by remote sensing vegetation index can be used to monitor the aboveground biomass of grassland in Yanchi County.
【學位授予單位】：寧夏大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：S812
本文編號：2381369
[Abstract]:In this paper, the changes of soil nutrients and soil enzyme activities in different degraded grassland were determined and analyzed by field and laboratory experiments. At the same time, eight common vegetation indices were extracted by TM image. A regression model was established between six cropping indices and aboveground biomass. The inversion of grassland biomass can obtain the grassland growth status in time and accurately, and provide a certain theoretical basis for the remote sensing estimation of grassland aboveground biomass and the sustainable development of grassland in Yanchi County. It provides an effective way to carry out large area grassland yield estimation and dynamic monitoring in the future. The main results are as follows: 1) with the increase of grassland degradation, the nutrient content of soil decreased. The total phosphorus, total nitrogen, total potassium, organic carbon, total phosphorus, total nitrogen, total potassium, organic carbon, total phosphorus, total nitrogen, total potassium, organic carbon, total phosphorus, total nitrogen, total potassium, organic carbon, total phosphorus, total nitrogen, total potassium, organic carbon, Both alkali-hydrolyzed helium and available potassium were moderately degraded grassland, moderately degraded grassland and extremely severely degraded grassland. The four kinds of soil enzyme activities decreased with the increase of soil degradation. The correlation analysis between soil enzyme activity and soil nutrients showed that the correlation between soil catalase and organic carbon and total potassium content was not significant (P0.05). But the correlation with other nutrient content was significant or extremely significant (P0.05 or P0.01). The correlation between protease, phosphatase, sucrase and soil nutrient content was significant or extremely significant (P0.05 or P0.01). The results show that the changes of soil enzyme activity and soil fertility factors are consistent in the course of grassland degradation. 2) using the TM image of Yanchi County in August 2013, using ENVI and ArcGis software, the ratio vegetation index (RVI),) normalized vegetation index (NDVI),) is extracted. Conversion vegetation index (TVI), modified soil adjustment vegetation index (MSAVI), soil adjustment vegetation index (SAVI), difference value vegetation index (DVI), humidity vegetation index (WVI) and brightness vegetation index (BVI) 8 planting cover index; The correlation analysis was made with the aboveground biomass of grassland at the same time. The results showed that, except WVI and BVI, the other 6 indices were significantly correlated with the aboveground biomass, and the correlation coefficient between RVI and grassland biomass was 0.956. A total of 36 regression models were established. 3) correlation analysis between vegetation index and aboveground biomass was made and a regression model was established. It was found that the ratio vegetation index RVI, was the best fitting precision, followed by NDVI.. The optimal relation model is cubic polynomial regression model, then quadratic polynomial regression model. The worst simulation effect is exponential function regression model. The cubic polynomial regression model (y=4.7539RVI3-38.708RVI2 213.04RVI-161.714) was used to verify the accuracy of RVI's cubic polynomial regression model. The results showed that the average error coefficient of measured and predicted aboveground biomass of grassland was 16.56. The precision of regression fitting is 83.44. It can be seen that the regression model obtained by remote sensing vegetation index can be used to monitor the aboveground biomass of grassland in Yanchi County.
【學位授予單位】：寧夏大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：S812
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