基于长时间序列数据定量分析林木对气象参数的响应特征

Monitoring of Forest Growth Based on Object-oriented and Long-term Sequence

本文以廊坊市安次区为研究区,依据人工林种植实际情况,以地块为基本单元,利用2013-2016年Landsat8遥感影像,结合气象数据分析研究平原林木生长对气象因子的响应特征;通过分析植被生长状况指示因子归一化植被指数(NDVI)、比值植被指数(RVI)与气象因子的定量关系,分析气象因子对林木生长发育的影响,并构建林木植被指数与气候因子的关系模型,用以分析植被指数与气象因子间的定量关系.研究表明:(1)植被指数对气象因子具有较好的敏感性,该敏感性随着树龄增加而呈现逐渐降低的趋势;(3)一类树和二类树的植被指数对气象因子的敏感性较高,五类树的植被指数值与前期气象因子的敏感性最差;(4)2013-2016年间平均温度波动不大,平均降水量出现较大波动,其中2014年降水量明显偏低,导致2014-2015年间植物生长量明显偏低.通过该文研究表明采用Landsat8多光谱数据开展对林木长势监测具有科学、可靠性.

Forest land resource change monitoring is a basic and long-terml work.This study uses Landsat8 remote sensing image data and meteorological factors to analyze the monitoring methods of forest growth at the county level in the periodfrom 2013 to 2016.The forest land in the middle and northern part of Anci District,Langfang City,Hebei Province was se-lected as the research object.Normalized Difference Vegetation Index(NDVI)and Ratio Vegetation Index(RVI)were generat-ed by using remote sensing image data to analyze the relationship between vegetation index and meteorological factors,and the relationships between forest vegetation index and climatic factors were constructed.In addition,the study and analysis f the changes of forest growth with the meteorological data from 2013 to 2016 were carried out.Results showed that:(1 Vegetation index and meteorological factors were mostly polynomial relations,and with the increase of tree age,the correla tion between vegetation index and meteorological factors was gradually reduced;(2)The sensitivity of younger trees to meteorological factors was high while older trees had low sensitivity(3)The average temperature fluctuated little between 2013 and 2016 and the average precipitation fluctuated a lot,of which the precipitation in 2014 was lower than that of the previ ous meteorological factors The amount was significantly lower,leading to significantly lower plant growth from 2014 to 2015.This research showed that Landsat8 multi-spectral data was scientific and reliable for forest growth monitoring.