基于不同立地质量的香格里拉高山松林郁闭度遥感反演
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摘要
以香格里拉市为研究区,基于2006年TM遥感影像和2006年森林资源规划设计调查数据,以小班各遥感因子为自变量,每个小班的郁闭度为因变量,对香格里拉高山松林按区分立地质量等级和不区分地位级2种策略建立郁闭度神经网络遥感反演模型,并进行精度评价。基于小班平均高和平均年龄建立的地位级表将立地质量等级划分为好、中、差3种类型。研究结果表明,红外、近红外、植被指数和第一主成分等遥感因子对郁闭度的解释能力较强;从模型独立样本验证结果得出,不区分地位级郁闭度估测精度为67.64%,区分地位级后,好、中、差3种立地类型的郁闭度估测精度分别为74.14%、75.32%、72.38%,区分立地质量类型模型的精度优于不区分地位级模型的精度。
Taking Shangri-La City as the study area,the remote sensing factors of subcompartment as the independent variables,the canopy density as the dependent variable,this paper classified the site qualities and made indiscriminate site class to respectively establish the neural network inversion models for the canopy density of Pinus densat based on TM remote sensing image and forest resource planning and design data in 2006,as well as evaluated the accuracy of the models. The site qualities were divided into three levels of good,medium and poor in site class table according to the average height and the average age. The results showed that remote sensing factors such as infrared,near infrared,vegetation index,and first principal component,had stronger ability to explain canopy density. According to the validation results of independent sample,the estimation accuracy was 67. 64% regardless of site class,the estimation accuracy of good,medium and poor sites were 74. 14% 、75. 32% 、72. 38% as differentiating site class,which indicated that the accuracy was better than that of the model regardless of site class.
Taking Shangri-La City as the study area,the remote sensing factors of subcompartment as the independent variables,the canopy density as the dependent variable,this paper classified the site qualities and made indiscriminate site class to respectively establish the neural network inversion models for the canopy density of Pinus densat based on TM remote sensing image and forest resource planning and design data in 2006,as well as evaluated the accuracy of the models. The site qualities were divided into three levels of good,medium and poor in site class table according to the average height and the average age. The results showed that remote sensing factors such as infrared,near infrared,vegetation index,and first principal component,had stronger ability to explain canopy density. According to the validation results of independent sample,the estimation accuracy was 67. 64% regardless of site class,the estimation accuracy of good,medium and poor sites were 74. 14% 、75. 32% 、72. 38% as differentiating site class,which indicated that the accuracy was better than that of the model regardless of site class.
引文
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[2]姚智,张晓丽.基于Web GIS平台的森林郁闭度遥感反演信息系统研建[J].浙江农林大学学报,2015,32(3):392-398.
[3]赵蕊.Hyperion EO-1森林郁闭度反演研究[M].哈尔滨:东北林业大学出版社,2007.
[4]李崇贵,蔡体久.森林郁闭度对蓄积量估测的影响规律[J].东北林业大学学报,2006,34(1):15.
[5]谷金应,冯仲科,葛钟强,等.基于TM影像数据的林分冠层郁闭度反演技术研究[J].山东林业科技,2014,44(1):9-12.
[6]牛战勇,冯娟,谷建才,等.基于叶面积指数的森林郁闭度遥感反演研究[J].林业资源管理,2014(1):46-51.
[7]谭炳香,李增元,陈尔学,等.Hyperion高光谱数据森林郁闭度定量估测研究[J].北京林业大学学报,2006,28(3):94-100.
[8]XU B,GONG P,PU R L.Crown closure estimation of oaksavannah in a dry season with Landsat TM imagery:comparison of various in-dices through correlation analysis[J].Int.J.Of Remote Sensing,2003,24(9):1811-1822.
[9]王聪,杜华强,周国模,等.基于几何光学模型的毛竹林郁闭度无人机遥感定量反演[J].应用生态学报,2015(5):1501-1509.
[10]吴飚,张登荣,张汉奎,等.结合图像纹理特征的森林郁闭度遥感估测[J].林业科学,2012,48(2):48-53.
[11]岳彩荣.香格里拉县森林生物量遥感估测研究[D].北京:北京林业大学,2012.
[12]孟宪宇.测树学[M].北京:中国林业出版社,2006:99-111.
[13]周春国,徐雁南.地位级表编制的新方法[J].华东森林经理,1997(11):11-13.
[14]林昌庚,周春国,林俊钦,等.关于地位级表[J].林业资源管理,1997(5):30-33.
[15]夏克文,李昌彪,沈钧毅.前向神经网络隐含层节点数的一种优化算法[J].计算机科学,2005,32(10):143-145.