高精度DEM支持下的多时期航片杉木人工林树高生长监测

doi:10.11707/j.1001-7488.20190411

Abstract

Abstract: [Objective] This study integrated multi-temporal aerial photographs and DEM derived from airborne LiDAR data to calculate the forest canopy height of Cunninghamia lanceolata and monitor the variation of growth quantitatively.[Method] First of all, high accuracy digital elevation model beneath canopy and forestry digital surface model were constructed based on classified LiDAR point cloud data. Digital surface models were then created by applying an automated stereo-matching algorithm to the scanning copy of aerial photographs. These multi-temporal canopy heights were obtained by subtracting the LiDAR ground elevations from the two kinds of DSM. Using historical aerial photographs of 1996, 2004 and digital aerial photographs, LiDAR data of 2014, multi-temporal CHMs were reconstructed within a period of 18 years, and the accuracy was evaluated and analyzed.[Result] 1) The R² between the canopy height models acquired by LiDAR data and corresponding digital aerial photographs in 2014 is 0.52, and the root mean square error is 1.79 m. 2) Compared with the measurements from field plots, our data showed an accuracy of 85.00% with mean absolute errorand mean relative error of 1.59 m and 15.00%, and the maximum absolute error and maximum relative error of 3.45 m and 30.80% respectively. 3) Combined with the aerial photos of year 1996, 2004 and 2014, these multi-temporal canopy height models of Cunninghamia lanceolata plantation have a similar growth trend to the predicted growth curve.[Conclusion] Based on the results, utilizing aerial photographs can characterize the variation of canopy height in the sunny slope of mountainous terrain. However, for forests located in the valley bottom, the canopy height would be under estimated with aerial photographs. Multi-temporal aerial photographs combining with the high accuracy DEM can reflect the variation of overstory's height, which provide the possibility for monitoring the forest's growth trend and access the forest's productivity.

Key words: historical aerial photographs, dense matching, LiDAR, DEM, DSM, CHM, growth monitoring

CLC Number:

S757

Xia Yongjie, Pang Yong, Liu Luxia, Chen Bowei, Dong Bin, Huang Qingfeng. Forest Height Growth Monitoring of Cunninghamia lanceolata Plantation Using Multi-Temporal Aerial Photography with the Support of High Accuracy DEM[J]. Scientia Silvae Sinicae, 2019, 55(4): 108-121.

References

蔡文峰, 李凤日. 2010. 基于VrituoZo系统对林木冠幅信息的提取. 森林工程, 26(2):4-7.
(Cai W F, Li F R. 2010. Extraction of tree' crown diameter information based on VirtuoZo system. Forest Engineering, 26(2):4-7.[in Chinese])
陈德良. 1998. 航空摄影测量像片的几何关系分析. 福建农业大学学报, 27(2):206-210.
(Chen D L. 1998. Analysis of geometric relationships of aerial photographs. Journal of FuJian Agricultural University,27(2):206-210.[in Chinese])
樊仲谋. 2015. 摄影测树原理与技术方法研究.北京:北京林业大学博士学位论文.
(Fan Z M. 2015. Photogrammetric principles and techniques for tree measurement. Beijing:PhD thesis of Beijing Forestry University.[in Chinese])
宫鹏, 梅雪良, Biging G S,等. 1999. 利用数字摄影测量探测橡树草原变化. 遥感学报, 3(4):285-289.
(Gong P, Mei X L,Biging G S,et al. 1999. Monitoring oak woodland change using digital photogrammetry. Journal of Remote Sensing, 3(4):285-289.[in Chinese])
李明泽, 范文义, 张元元. 2009. 基于全数字摄影测量的林分立木高度量测. 北京林业大学学报, 31(2):74-79.
(Li M Z, Fan W Y, Zhang Y Y. 2009. Measuring heights of standing trees based on digital photogrammetry. Journal of Beijing Forestry University, 31(2):74-79.[in Chinese])
吕丽萍. 2006. 双像法与单片法生成DOM的比较. 工程地球物理学报, 3(1):45-48.
(Lü L P. 2006. Comparison of DOM production between stereo pair plotting and single photodifferential correction. Chinese Journal of Engineering Geophysics, 3(1):45-48.[in Chinese])
庞勇, 李增元,陈尔学,等. 2005. 激光雷达技术及其在林业上的应用. 林业科学,41(3):129-136.
(Pang Y, Li Z Y, Chen E X, et al. 2005. LiDAR remote sensing technology and its application in forestry. Scientia Silvae Sinicae, 41(3):129-136.[in Chinese])
汪承义. 2007. 基于航空激光雷达数据的建筑物重建技术研究. 北京:中国科学院研究生院博士学位论文.
(Wang C Y. 2007. Research on building reconstruction based on airborne LiDAR Data. Beijing:PhD thesis of Beijing Graduate University of Chinese Academy of Sciences.[in Chinese])
汪少俊. 2015. 绩溪县杉木生长规律研究. 安徽林业科技, 41(3):12-16.
(Wang S J. 2015. Analysis on the growth rhythm of Cunninghamia lanceolata in Jixi County. Anhui Forestry Science and Technology, 41(3):12-16.[in Chinese])
王佳, 冯仲科. 2011. 航空数字摄影测量对林分立木测高及精度分析. 测绘科学,36(6):77-79.
(Wang J, Feng Z K. 2011. Stand tree height measured by digital photogrammetry. Science of Surveying and Mapping, 36(6):77-79.[in Chinese])
杨战辉, 张力. 1998. 用VirtuoZo数字摄影测量工作站生产DEM、DOM的试验. 测绘通报, (11):10-11,30.
(Yang Z H, Zhang L. 1998. Experiment of producing DEM, DOM with VirtuoZo digital photogrammetry workstation.Bulletin of Surveying and Mapping,(11):10-11,30.[in Chinese])
袁晓红, 李际平. 2012. 杉木人工林南北坡向树高胸径生长曲线研究. 西北林学院学报, 27(2):180-183.
(Yuan X H, Li J P. 2012. Height, DBH growth model of fir artificial forest on northern and southern slopes. Journal of Northwest Forestry University, 27(2):180-183.[in Chinese])
张剑清. 2011. 摄影测量学. 武汉:武汉大学出版社, 150-171.
(Zhang J Q. 2011. Photogrameetry. Wuhan:Wuhan University Press, 150-171.[in Chinese])
张永军, 张勇. 2005. 大重叠度影像的相对定向与前方交会精度分析. 武汉大学学报:信息科学版, 30(2):126-130.
(Zhang Y J, Zhang Y.2005. Analysis of precision of relative orientation and forward intersection with high-overlap images. Editorial Board of Geomatics and Information Science of Wuhan University,30(2):126-130.[in Chinese])
智长贵, 秦学军, 韩爱惠. 2006. 基于光束法空中三角测量理论的树高测量方法研究. 林业资源管理, 8(4):92-95.
(Zhi C G, Qin X J, Han A H. 2006. Study on measurement of tree height based on theory of bundle aerotriangulation. Forest Resources Management, 8(4):92-95.[in Chinese])
Bohlin J, Wallerman J, Fransson J E S. 2016. Deciduous forest mapping using change detection of multi-temporal canopy height models from aerial images acquired at leaf-on and leaf-off conditions. Scandinavian Journal of Forest Research, 31(5):1-27.
Ginzler C, Hobi M. 2015. Countrywide stereo-image matching for updating digital surface models in the framework of the Swiss national forest inventory. Remote Sensing, 7(4):4343-4370.
Harwin S, Lucieer A. 2012.Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sensing, 4(6):1573-1599.
Lisein J, Pierrot-Deseilligny M,Bonnet S, et al. 2013. A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests, 4(4):922-944.
Micheletti N, Lane S N, Chandler J H. 2015. Application of archival aerial photogrammetry to quantify climate forcing of alpine landscapes. The Photogrammetric Record, 30(15):143-165.
Nurminen K, Litkey P, Honkavaara E, et al. 2015. Automation aspects for the georeferencing of photogrammetric aerial image archives in forested scenes. Remote Sensing,7(2):1565-1593.
Pang Y, Li Z Y, Ju H B, et al. 2016. LiCHy:the CAF's LiDAR, CCD and hyperspectral integrated airborne observation system. Remote Sensing, 8(5):398.
St-Onge B, Audet F A, Bégin J. 2015. Characterizing the height structure and composition of a boreal forest using an individual tree crown approach applied to photogrammetric point clouds. Forests, 6(11):3899-3922.
St-Onge B, Jumelet J, Cobello M, et al. 2011. Measuring individual tree height using a combination of stereo photogrammetry and LiDAR. Canadian Journal of Forest Research, 34(10):2122-2130.
St-Onge B, Véga C, Fournier R A, et al. 2008. Mapping canopy height using a combination of digital stereo photogrammetry and LiDAR. International Journal of Remote Sensing, 29(11):3343-3364.
Véga C, St-Onge B. 2009. Mapping site index and age by linking a time of canopy height models with growth curves. Forest Ecology and Management, 257(3):951-959.
Véga C, St-Onge B. 2013. Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and LiDAR models. Remote Sensing of Environment, 112(4):1784-1794.
White J C, Wulder M A, Vastaranta M, et al. 2013. The utility of image-based points clouds for forest inventory:a comparison with airborne laser scanning. Forests, 4(3):518-536.
Zhang J Q, Zhang Z X, Shen W M, et al. 1996. VirtuoZo digital photogrammetry system and its theoretical foundation and key algorithms. International Archives of Photogrammetry and Remote Sensing, XXXI(part B2):424-429.

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Forest Height Growth Monitoring of Cunninghamia lanceolata Plantation Using Multi-Temporal Aerial Photography with the Support of High Accuracy DEM

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