摘要
以定量遥感的手段实现对内蒙古乌梁素海湿地生物量的估算。在定点监测芦苇生长周期内的生物量基础上,分析了各监测点生物量(鲜重和干重)实测数据与同期4种植被指数NDVI、DVI、PVI、RVI的相关关系。建立一元线性估算模型及多种非线性估算模型并进行对比分析。结果表明:对于乌梁素海而言,芦苇地上生物量(鲜重与干重)与所选4种植被指数均存在显著正相关关系,鲜重、干重的最优模型均是基于NDVI的三次多项式估算模型。精度检验结果显示:用NDVI三次多项式估算模型计算出的鲜重和干重的预测值与实测值较接近,鲜重的平均误差为19.90%,拟合精度达到80.10%;干重的平均误差为18.71%,拟合精度达到81.29%,可以满足乌梁素海地区芦苇生物量宏观估测的需要。通过分析2013年7月研究区芦苇总生物量干鲜重的空间分布图可得,乌梁素海地区芦苇干重在1 000~1 500 g·m^(-2),鲜重在3 000~4 500 g·m^(-2),且高生物量和低生物量相对较少。
In this study, the biomass values of Phragmites communis in the Ulansuhai Lake in Inner Mongolia were estimated by the means of quantitative remote sensing. Based on monitoring the biomass in growing season of Phragmites communis on the fixed locations, the correlations between the measured data of fresh weight and dry weight at different monitoring points and the four vegetation indexes ( NDVI, DVI, PVI and RVI) in the same period were analyzed. The linear estimation model and a variety of non-linear estimation models were developed. The results showed that there were the significant positive correlations between the aboveground biomass (including fresh weight and dry weight) of Phragmites communis in the Ulansuhai Lake and the selected four vegetation indices. The optimal models of fresh weight and dry weight were all based on the cubic polynomial estimation model of NDVI. The accuracy test results showed that the predicted values were close to the measured ones by using NDVI cubic polynomial model to calculate the fresh weight and dry weight. The average error of fresh weight was 19.90%, and the fitting accuracy was as high as 80.10%. The average error of dry weight was 18.71%, and the fitting accuracy reached 81.29%. These could meet the need of macro estimation of regional biomass of Phragmites communis in the Ulansuhai Lake. Through analyzing the spatial distribution of biomass of Phragmites communis in the study area in July 2013, it could be obtained that the dry and fresh weights of biomass of Phragmites communis varied in ranges of 1 000 - 1 500 g ·m^-2 and 3 000 -4 500 g ·m^-2 respectively.
出处
《干旱区研究》
CSCD
北大核心
2016年第5期1028-1035,共8页
Arid Zone Research
基金
国家自然科学基金项目(31560146
41562020
41571090)
国家科技支撑计划项目(2011BAC02B03)
内蒙古教育厅项目(NJYT-15-A02)
内蒙古科技厅项目(20140707)资助
关键词
TM影像
芦苇
生物量
植被指数
反演模型
乌梁素海
TM image
Phragmites communis
biomass
vegetation index
inversion model
Ulansuhai Lake