基于遥感信息的农作物生物质可获取量评估及空间分布研究

发布时间：2018-09-14 10:16

【摘要】：科学准确的估算农作物生物量及生物质能利用潜力是生物质能源开发利用战略的必要前提。随着遥感技术的不断发展,可获取的遥感数据的时间、空间、光谱分辨率都在不断提高,为大时间跨度和大空间尺度的农作物生物量估算提供了有力的数据支撑。本文首先对基于遥感信息的对农作物生物量估算方法的原理和典型应用进行了分析总结,并在此基础上归纳总结出了基于植被指数(Vegetation Index)的小麦生物量估算方法。本文以2015年5月1日和4月25的Landsat8影像数据,以安徽省六安市为例,采用基于植被指数的小麦生物量估算方法,对六安市2015年小麦生物量进行估算,并对其小麦生物质能利用潜力进行估算和分析。首先,对研究区域的影像进行预处理和图像分类处理,通过对非监督分类和监督分类6类常用分类算法分类结果的对比分析,选取分类效果最好的最小距离分类法对六安市进行土地利用监督分类,并对分类结果进行后处理,提取六安市小麦种植区域,并对其提取精度进行分析。然后,将实测小麦生物量数据与对应采样点的差值植被指数(DVI)、归一化绿度指数(NDGI)、归一化差异植被指数(NDVI)、绿色植被指数(RI)和比值植被指数(RVI)进行相关分析,分析结果证明,NDVI与小麦生物量相关性最高,相关系数r达到0.760,其次是RVI,相关系数r为0.655,在此基础上进一步将上述5种植被指数分别实测小麦生物量进行指数、线性、对数、二次多项式、幂函数五种方式的拟合,选取决定系数最高的植被指数和拟合类型建立小麦生物量估算模——基于NDVI的指数估算模型,并将估算结果与基于谷草比的小麦生物量进行对比分析,结果表明,两者误差仅为10.115万吨,误差百分比为6.89%；同时,对六安市各区县小麦生物量空间分析并对其单位小麦生物量密度进行计算,结果表明寿县的生物量及生物量密度都为最高分别为63.79万吨和213.6吨／平方千米。最后,通过对前人研究中小麦可收集系数和折标煤系数进行加权平均得到本文中小麦可收集系数和折标煤系数分别为0.766和0.490,利用上述系数对六安小麦生物质能利用潜力进行估算和分析,结果表明,六安市2015年小麦生物质能利用潜力约55.14万吨标煤,约占2015年六安全年能源消耗总量的13.85%。基于以上研究发现,基于遥感信息的小麦生物量及生物质能估算方法具有一定的有效性和实用性,能够实现一定精度要求下的小麦生物量估算,可以为生物质能源战略提供必要的技术支持。
[Abstract]:Scientific and accurate estimation of crop biomass and biomass energy utilization potential is a necessary prerequisite for biomass energy development and utilization strategy. With the development of remote sensing technology, the time, space and spectral resolution of the available remote sensing data are improving, which provides a powerful data support for the estimation of crop biomass in long time span and large spatial scale. In this paper, the principle and typical application of crop biomass estimation method based on remote sensing information are analyzed and summarized, and the wheat biomass estimation method based on vegetation index (Vegetation Index) is summarized. Based on the Landsat8 image data of May 1 and April 25, 2015, and taking Lu'an City, Anhui Province as an example, the wheat biomass estimation method based on vegetation index was used to estimate wheat biomass in 2015 in Lu'an City. The potential of biomass energy utilization in wheat was estimated and analyzed. First of all, the image preprocessing and image classification of the study area are processed, and the results of the six common classification algorithms are compared and analyzed by the unsupervised classification and supervised classification. The least distance classification method with the best classification effect was selected to classify the land use in Luan city, and the classification results were post-processed to extract the wheat planting area of Luan city, and the precision of the extraction was analyzed. Then, the correlation between the measured wheat biomass data and the difference vegetation index (DVI), normalized greening index (NDGI), difference vegetation index (NDVI), green vegetation index (NDVI), and ratio vegetation index (RVI) of wheat were analyzed. The results showed that the correlation between wheat biomass was the highest, the correlation coefficient was 0.760, and the correlation coefficient of RVI, was 0.655. On the basis of this, the wheat biomass was measured on the basis of the above five planting indices, and the index was linear and logarithmic, respectively. Fitting the quadratic polynomial and power function, selecting the vegetation index and fitting type with the highest determinant coefficient, establishing the wheat biomass estimation model based on NDVI exponential estimation model. The results show that the error is only 101150 tons and the error percentage is 6.89. The spatial analysis of wheat biomass and the calculation of biomass density per unit wheat in various districts and counties of Lu'an City showed that the highest biomass and biomass density of Shouxian were 637900 tons and 213.6 tons / square kilometer, respectively. Last According to the weighted average of wheat collectible coefficient and broken coal coefficient in previous studies, the wheat collectible coefficient and the broken coal coefficient in this paper are 0.766 and 0.490, respectively. The utilization potential of these coefficients to wheat biomass energy in Liuan is obtained. For estimation and analysis, The results showed that the potential of wheat biomass energy utilization in 2015 was about 551400 tons of standard coal, accounting for 13.85% of the total energy consumption in 2015. Based on the above research, it is found that the estimation method of wheat biomass and biomass energy based on remote sensing information has certain validity and practicability, and can realize wheat biomass estimation under certain precision requirements. It can provide necessary technical support for biomass energy strategy.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：S127

【相似文献】