运用机载LiDAR数据对橡胶林地上生物量估测的采样尺度效应分析
收稿日期: 2019-10-18
修回日期: 2019-11-27
网络出版日期: 2020-05-18
基金资助
国家自然科学基金项目(31860205);国家自然科学基金项目(31460194);云南“唐守正”院士工作站项目(2018IC066)
Analysis of Sampling Size Effect of Aboveground Biomass Estimation of Rubber Forest Using Airborne LiDAR Data
Received date: 2019-10-18
Revised date: 2019-11-27
Online published: 2020-05-18
气候变化下,森林生物量遥感监测是当前研究的热点,机载LiDAR作为重要的遥感信息源,其采样大小对生物量估测精度有着一定的影响。以机载LiDAR数据为信息源,以44块30m×30m的方形橡胶林实测样地数据为基础,对机载激光雷达数据进行不同尺寸采样(共21个采样尺寸,边长从10m至30m,间隔为1m),提取不同采样尺寸下的激光雷达参数,并与橡胶林地上生物量建立PLSR模型,就机载激光雷达采样大小对橡胶林地上生物量估测精度的影响进行研究。研究表明:当采样尺寸小于18m时,估测精度随着采样尺寸的增大而增大;而当采样尺寸大于18m时,估测精度随着采样尺寸的增大而减小,进而趋于平缓。结果虽然呈现出一定的规律性,但是差异并不是很明显。当采样尺寸为18m时估测效果最佳,模型决定系数(R 2)为0.718,均方根误差(RMSE)为17.830 t/hm 2;交叉验证精度 P 和 RMSEcv 分别为82.741%和18.874t/hm 2。相较于实际样地(30m)尺寸下的估测结果,18m采样尺寸下的 R 2 提高了1.989%, RMSEcv 降低了2.611%。因此,生物量的估测精度受机载激光雷达数据采样尺寸大小的影响,在生物量估测过程中需结合研究对象和研究区的实际情况对采样尺寸进行选择,从而提高生物量估测精度。
罗洪斌 , 舒清态 , 庞勇 , 王强 , 王冬玲 . 运用机载LiDAR数据对橡胶林地上生物量估测的采样尺度效应分析[J]. 林草资源研究, 2020 , 0(1) : 136 -142 . DOI: 10.13466/j.cnki.lyzygl.2020.01.017
Remote sensing monitoring of forest biomass under climate change is a hot topic of current research.As an important remote sensing information source,airborne LiDAR's sampling size has a certain influence on the estimation accuracy of biomass.In this paper,the airborne LiDAR data is sampled in different sizes(21 sampling sizes,side length from 10m to 30m,interval is 1m),the LiDAR parameters of different sampling sizes are extracted,and the PLSR model is established with the biomass on the rubber forest.The effect of airborne LiDAR sampling size on the estimation accuracy of biomass on rubber forest was studied.The results show that the estimation accuracy of the aboveground biomass of rubber forest is affected by the sampling size of airborne LiDAR data.The results showed a certain regularity,but the difference was not significant.When the sampling size is less than 18m,the estimation accuracy increases with the increase of sampling size,while the sampling size is larger than 18m.The estimation accuracy decreases as the sampling size increases,and thus tends to be gentle.When the sampling size is 18m,the estimation result is the best.The model determination coefficient R 2 is 0.718,the root mean square error RMSE is 17.830 t/hm 2,and the cross-validation accuracy P and RMSEcv are 82.741% and 18.874t/hm 2.Compared to the estimated results at the actual sample size(30m), R 2 increased by 1.989% and RMSE decreased by 2.611% at the 18m sample size.Thus,the biomass estimation process for an actual case study and research of the sampling area size is selected,thereby increasing the biomass estimation accuracy.
Key words： sample; LiDAR; biomass; rubber forest
| [1] | 李海奎, 雷渊才 . 中国森林植被生物量和碳储量评估[M]. 北京: 中国林业出版社, 2010,28. |
| [2] | 陈迪 . 基于遥感技术的甘南州陆地生态系统NEP研究[D]. 甘肃:兰州大学, 2016. |
| [3] | 张瑞英, 庞勇, 李增元 , 等. 结合机载LiDAR和LANDSAT ETM+数据的温带森林郁闭度估测[J]. 植物生态学报, 2016,40(2):102-115. |
| [4] | Gibbs H K, Brown S, Niles J O , et al. Monitoring and estimating tropical forest carbon stocks:making REDD a reality[J]. Environmental Research Letters, 2007,2(4):1-13. |
| [5] | Duncanson L I, Niemann K O, Wulder M A . Integration of GLAS and Landsat TM data for aboveground biomass estimation[J]. Canadian Journal of Remote Sensing, 2010,36(2):129-141. |
| [6] | Lefsky M A, Cohen W B, Parker G G , et al. Lidar Remote Sensing for Ecosystem Studies[J]. BioScience, 2002,52(1):19-30. |
| [7] | Nelson R, Krabill W, Maclean G . Determining forest canopy characteristics using airborne laser data.[J]. Remote Sensing of Environment, 1984,15(3):201-212. |
| [8] | Nelson R, Krabill W, Tonelli J . Estimating forest biomass and volume using airborne laser data[J]. Remote Sensing of Environment, 1988,24(2):247-267. |
| [9] | Popescu S C . Estimating biomass of individual pine trees using airborne LiDAR[J]. Biomass and Bioenergy, 2007,31(9):646-655. |
| [10] | N?sset E, Gobakken T . Estimation of above and below-ground biomass across regions of the boreal forest zone using airborne laser[J]. Remote Sensing of Environment, 2008,112(6):3079-3090. |
| [11] | 庞勇, 李增元 . 基于机载激光雷达的小兴安岭温带森林组分生物量反演[J]. 植物生态学报, 2012,36(10):1095-1105. |
| [12] | Luo Shezhou, Chen Jing M, Wang Cheng , et al. Effects of LiDAR point density,sampling size and height threshold on estimation accuracy of crop biophysical parameters[J]. Optics Express, 2016,24(11):1578-1593. |
| [13] | Streutker D R, Glenn N F . Li DAR measurement of sagebrush steppe vegetation heights[J]. Remote Sensing of Environment, 2006,102(1-2):135-145. |
| [14] | Luo Shezhou, Wang Cheng, Xi Xiaohuan , et al. Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation[J]. Ecological Indicators, 2017,73:378-387. |
| [15] | Estornell J, Ruiz L A, B. Velázquez-Martí, et al. Estimation of shrub biomass by airborne LiDAR data in small forest stands[J]. Forest Ecology and Management, 2011,262(9):1697-1703. |
| [16] | 唐建维, 庞家平, 陈明 , 等. 西双版纳橡胶林的生物量及其模型[J]. 生态学杂志, 2009,28(10):1942-1948. |
| [17] | 罗洪斌, 舒清态, 王强 , 等. 运用机载激光雷达和陆地卫星数据对橡胶林地上生物量的估测[J]. 东北林业大学学报, 2019(7):56-61. |
| [18] | 赵晓庆, 杨贵军, 刘建刚 , 等. 基于无人机载高光谱空间尺度优化的大豆育种产量估算[J]. 农业工程学报, 2017(1):110-116. |
| [19] | 舒清态, 唐守正 . 国际森林资源监测的现状与发展趋势[J]. 世界林业研究, 2005(3):33-37. |
/
| 〈 |
|
〉 |
