盐池县草地退化及地上生物量遥感反演研究
发布时间：2018-12-15 22:15
【摘要】：本文以盐池县草地为研究对象,通过野外试验和室内试验测定并分析了不同退化草地土壤养分和土壤酶活性的变化情况,同时,使用TM影像提取了8种常用的植被指数,并建立了6种植被指数与地上生物量的回归模型。草地生物量反演能够及时、准确地获取草地生长状况,并为盐池县草地地上生物量的遥感估算和草地可持续发展提供一定的理论依据,为今后开展大面积草地估产和动态监测提供了有效途径。论文的主要研究结果如下：1)随着草地退化程度的增加,土壤各养分含量随之下降,除有效磷含量是潜在退化草地轻度退化草地严重退化草地中度退化草地极严重退化草地外,全磷、全氮、全钾、有机碳、碱解氦和速效钾均为潜在退化草地轻度退化草地中度退化草地严重退化草地极严重退化草地。四种土壤酶活性含量均随土壤退化程度加重而降低。对土壤酶活性与土壤养分做相关性分析,可知土壤过氧化氢酶与有机碳、全钾含量相关性不显著(P0.05),但与其他养分含量相关性均达到显著或极显著水平(P0.05或P0.01)。蛋白酶、磷酸酶、蔗糖酶与土壤养分含量相关性也达到显著或极显著水平(P0.05或P0.01)。说明在草地退化的过程中土壤酶活性与土壤肥力因素的变化是一致的。2)利用2013年8月盐池县TM影像,借助ENVI和ArcGis软件,提取比值植被指数(RVI)、归一化植被指数(NDVI)、转换型植被指数(TVI)、修改型土壤调整植被指数(MSAVI)、土壤调整植被指数(SAVI)、差值植被指数(DVI)、湿度植被指数(WVI)和亮度植被指数(BVI)8种植被指数,并与同期草地地上生物量作相关性分析,研究结果表明：除WVI和BVI外,其它6种植被指数与地上生物量均呈极显著相关。RVI与草地生物量的相关系数达到了0.956。分别对6种植被指数与地上生物量做回归分析,共建立36种回归模型。3)通过对植被指数与地上生物量做相关分析,并建立回归模型,发现拟合精度最好的是比值植被指数RVI,其次是NDVI。最优关系模型为三次多项式回归模型,然后是二次多项式回归模型,模拟效果最差的是指数函数回归模型。三次多项式回归模型为：y=4.7539RVI3-38.708RVI2+213.04RVI-161.714)对RVI的三次多项式回归模型进行精度验证,结果显示草地地上生物量实测值和预测值的平均误差系数为16.56%,回归拟合精度为83.44%,由此可见,应用遥感植被指数得到的回归模型,可以用来监测盐池县草地地上生物量。
[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.
【学位授予单位】：宁夏大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：S812
本文编号：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.
【学位授予单位】：宁夏大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：S812
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