Effects of different filter algorithms on deriving leaf area index (LAI)

Liang Yongqi; Li Mingze; Yang Ruixia; Geng Tong; Li Huan

doi:10.12171/j.1000-1522.20180268

Journal of Beijing Forestry University > 2020 > 42(1): 54-64. > DOI: 10.12171/j.1000-1522.20180268

Liang Yongqi, Li Mingze, Yang Ruixia, Geng Tong, Li Huan. Effects of different filter algorithms on deriving leaf area index (LAI)[J]. Journal of Beijing Forestry University, 2020, 42(1): 54-64. DOI: 10.12171/j.1000-1522.20180268

Citation:

PDF (1421 KB)

Effects of different filter algorithms on deriving leaf area index (LAI)

Liang Yongqi^1,2,3,,
Li Mingze^4, ,,
Yang Ruixia^1,3,
Geng Tong^1,2,3,
Li Huan⁴

1.
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
4.
School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, China

More Information

Received Date: August 20, 2018
Revised Date: October 18, 2018
Available Online: October 15, 2019
Published Date: January 13, 2020

Graphical Abstract

Abstract

Abstract

ObjectiveFiltering is an important part of data preprocessing when using discrete-return LiDAR to derive leaf area index (LAI). Laser penetration index (LPI), which responses to the canopy’s gap fraction, is a pivotal argument, and can be defined by echoes intensity or count, and is directly influenced by filter precision. So, filter algorithms can affect deriving LAI indirectly.
MethodIn this paper, we used the open source filter algorithms without manual operation to filter the error points. Using the LPI defined on count, we built model in larch forest and elm forest, Maor Mountain National Park, based on Beer-Lambert law. We compared the filter algorithm of adaptive triangulated irregular network, morphology, local slope, using hybrid filtering as standard. In order to avoid the subjective influence during modelling, we built 100 models by choosing samples randomly.
ResultIn larch forest, the models’ R-squared under larch was 0.900 3, 0.876 3, 0.892 5,0.877 0, root mean squared error (RMSE) was 0.105 6, 0.134 5, 0.109 7,0.133 2; in elm forest, the models’ R-squared was 0.914 4, 0.903 0, 0.887 2, 0.900 0, root mean squared error (RMSE) was 0.269 0, 0.201 7, 0.189 4, 0.207 0, respectively.
ConclusionConsidering the sample’s topography, when using discrete-return LiDAR data derive LAI based on LPI, the hybrid algorithm has a better performance on deriving LAI. II error has more influence on deriving LAI than I error.
- LiDAR,
- leaf area index (LAI),
- filter algorithm,
- forest vegetation

FullText(HTML)

References (21)

References

[1]	Coppin P, Jonckheere I, Nackaerts K, et al. Digital change detection methods in ecosystem monitoring: a review[J]. International Journal of Remote Sensing, 2004, 25(9): 1565−1596. doi: 10.1080/0143116031000101675
[2]	You H, Wang T, Skidmore A K, et al. Quantifying the effects of normalisation of airborne LiDAR intensity on coniferous forest leaf area index estimations[J]. Remote Sensing, 2017, 9(2): 163−179. doi: 10.3390/rs9020163
[3]	Peduzzi A, Wynne R H, Fox T R, et al. Estimating leaf area index in intensively managed pine plantations using airborne laser scanner data[J]. Forest Ecology & Management, 2012, 270(4): 54−65.
[4]	Sumnall M J, Fox T R, Wynne R H, et al. Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return LiDAR[J]. International Journal of Remote Sensing, 2016, 37(1): 78−99. doi: 10.1080/01431161.2015.1117683
[5]	骆社周, 王成, 张贵宾, 等. 机载激光雷达森林叶面积指数反演研究[J]. 地球物理学报, 2013, 56(5):1467−1475. doi: 10.6038/cjg20130505 Luo S Z, Wang C, Zhang G B, et al. Forest leaf area index (LAI) inversion using airborne LiDAR data[J]. Geophys, 2013, 56(5): 1467−1475. doi: 10.6038/cjg20130505
[6]	Chen T, Akciz S O, Hudnut K W, et al. Fault-slip distribution of the 1999 mw 7.1 hector mine earthquake, California, estimated from postearthquake airborne LiDAR data[J]. Bulletin of the Seismological Society of America, 2015, 105: 776−790. doi: 10.1785/0120130108
[7]	黄作维, 刘峰, 胡光伟. 基于多尺度虚拟格网的LiDAR点云数据滤波改进方法[J]. 光学学报, 2017, 37(8):346−355. Huang Z W, Liu F, Hu G W. Improved method for LiDAR point cloud data filtering based on hierarchical pseudo-grid[J]. Acta Optic Sin, 2017, 37(8): 346−355.
[8]	Solberg S. Comparing discrete echoes counts and intensity sums from ALS for estimating forest LAI and gap fraction[C/OL]//International Conference on Silvilaser, Sept. 17−19, 2008: 247−256[2018−05−06]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.591.1374&rep=rep1&type=pdf.
[9]	Sithole G, Vosselman G. Experimental comparison of filter algorithms for Bare-Earth extraction from airborne laser scanning point clouds[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2004, 59: 85−101.
[10]	Favorskaya M N, Jain L C. Handbook on advances in remote sensing and geographic information systems[M]. Cham:Springer International Publishing, 2017.
[11]	Pingel T J, Clarke K C, Mcbride W A. An improved simple morphological filter for the terrain classification of airborne LiDAR data[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2013, 77: 21−30.
[12]	Zhao X, Guo Q, Su Y, et al. Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2016, 117: 79−91.
[13]	Polat N, Uysal M. Investigating performance of airborne LiDAR data filtering algorithms for DTM generation[J]. Measurement, 2015, 63: 61−68. doi: 10.1016/j.measurement.2014.12.017
[14]	Axelsson P. DEM generation from laser scanner data using adaptive TIN models[J]. International Archives of Photogrammetry & Remote Sensing, 2000(33): 110−116.
[15]	Zhang K Q, Chen S C, Whitman D, et al. A progressive morphological filter for removing non-ground measurements from airborne LiDAR data[C]. IEEE Transactions on Geoscience and Remote Sensing, 2003 (41): 872−882.
[16]	Vosselman G. Slope based filtering of laser altimetry data[C/OL]. Amsterdam: International Archives of Photogrammetry & Remote Sensing, 2000[2018−05−06]. https://www.researchgate.net/publication/228719860_Slope_based_filtering_of_laser_altimetry_data.
[17]	Zhao K, García M, Liu S, et al. Terrestrial LiDAR remote sensing of forests: maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution[J]. Agricultural & Forest Meteorology, 2015, 209−210: 100−113.
[18]	Solberg S, Hill R, Suarez R. Mapping gap fraction, LAI and defoliation using various ALS penetration variables[J]. International Journal of Remote Sensing, 2010, 31(5): 1227−1244. doi: 10.1080/01431160903380672
[19]	Morsdorf F, Kötz B, Meier E, et al. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction[J]. Remote Sensing of Environment, 2006, 104(1): 50−61. doi: 10.1016/j.rse.2006.04.019
[20]	Hyyppä J, Hyyppä H, Leckie D, et al. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests[J]. International Journal of Remote Sensing, 2008, 29(5): 1339−1366. doi: 10.1080/01431160701736489
[21]	Deng S S, Shi W Z. Integration of different filter algorithms for improving the ground surface extraction from airborne LiDAR data[C/OL]. Proceedings of 8th International Symposium on Spatial Data Quality Implementation Science. Hong Kong: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, XL-2/W1(2): 105−110[2018−05−06]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.917.8968&rep=rep1&type=pdf.

[1]	Zhou Lai, Cheng Xiaofang, Zhang Mengtao. Effects of forest gaps on understory species diversity of Larix principis-rupprechtii natural secondary forest[J]. Journal of Beijing Forestry University, 2024, 46(6): 48-56. DOI: 10.12171/j.1000-1522.20220223
[2]	Fan Xiuhua, Zhang Baoquan, Fan Chunyu. Effects of species diversity and structural diversity on productivity in different succession stages of typical natural forest in Changbai Mountains of northeastern China[J]. Journal of Beijing Forestry University, 2021, 43(12): 1-8. DOI: 10.12171/j.1000-1522.20210071
[3]	Jiang Xiaolei, Hao Qing, Li Wei, Sun Zhenyuan. Species distribution and diversity characteristics of secondary plant communities in Laoshan Mountain of Qingdao, Shandong Province of eastern China[J]. Journal of Beijing Forestry University, 2020, 42(8): 22-33. DOI: 10.12171/j.1000-1522.20190414
[4]	Jin Suo, Bi Haojie, Liu Jia, Liu Yuhang, Wang Yu, Qi Jinqiu, Hao Jianfeng. Effects of stand density on community structure and species diversity of Cupressus funebris plantation in Yunding Mountain, southwestern China[J]. Journal of Beijing Forestry University, 2020, 42(1): 10-17. DOI: 10.12171/j.1000-1522.20190202
[5]	LIU Sheng-dong, GAO Wen-tao, LI Yan, SHI Ying, MENG Qing-fan. Comparative study of Cerambycidae species diversity in different forest stands of southern Zhangguangcai Mountains, northeastern China.[J]. Journal of Beijing Forestry University, 2015, 37(5): 110-118. DOI: 10.13332/j.1000-1522.20140287
[6]	WANG Bing, SONG Qing-feng.. Value assessing methods of species diversity conservation in forest ecosystem[J]. Journal of Beijing Forestry University, 2012, 34(2): 155-160.
[7]	WEI Tian-xing, WANG Jing-jing.. Diversity of riparian vegetation in the Caijiachuan Watershed of the Loess Plateau, southwestern Shanxi Province, northern China[J]. Journal of Beijing Forestry University, 2009, 31(6): 49-53.
[8]	YUE Yong-jie, , YU Xin-xiao, NIU Li-li, SUN Qing-yan, LI Jin-hai, WU Jun. Structural characteristics of plant communities and species diversity in Wuling Mountain, Beijing.[J]. Journal of Beijing Forestry University, 2008, 30(supp.2): 165-170.
[9]	LI Rui, ZHANG Ke-bin, WANG Bai-tian, YANG Xiao-hui, QIAO Feng, YANG Jun-jie, YANG Li. Plant species diversity of wetland-dry grassland ecosystem: Taking Yanchi County, Ningxia Hui Autonomous Region as an example[J]. Journal of Beijing Forestry University, 2006, 28(5): 12-17.
[10]	NAN Hai-long, HAN Hai-rong, MA Qin-yan, YI Li-ta, KANG Feng-feng. Species diversity of herb and shrub layers in gaps of a mixed conifer broad-leaved forest in Taiyue Mountain of Shanxi Province[J]. Journal of Beijing Forestry University, 2006, 28(2): 52-56.

Cited By

Cited by

Periodical cited type(8)

1.	黎明慧，李远发. 径阶距对评估南盘江流域天然林物种多样性的影响. 南京林业大学学报(自然科学版). 2023(04): 166-174 .
2.	刘丹，郭忠玲，崔晓阳，范春楠. 5种东北红豆杉植物群丛及其物种多样性的比较. 生物多样性. 2020(03): 340-349 .
3.	徐艺文，陈浩，赵洪波. 洈水国家湿地公园植物多样性、主要群落及数量特征. 湖北林业科技. 2019(04): 13-18+68 .
4.	李秀宇，郭琪，董黎，孙宇涵，牛东升，刘佳平，王红生，李云. 山西省吉县刺槐无性系种质遗传多样性的EST-SSR分析. 北京林业大学学报. 2019(07): 39-48 . 本站查看
5.	范冬冬，张健飞，苑美燕，张志东. 不同经营模式华北落叶松人工林草本物种多样性差异分析. 林业与生态科学. 2018(02): 159-164 .
6.	张学礼. 晋北半干旱地区乔木树种基因资源的收集和利用. 花卉. 2018(12): 191-192 .
7.	刘建荣. 云顶山自然保护区植物群落物种多样性研究. 中南林业科技大学学报. 2018(10): 79-85 .
8.	苏红华，王红，陈宗杰，余传文，陈晓熹，吴建辉，周璋，李意德，许涵. 海南尖峰岭热带半落叶季雨林物种组成、结构及其多样性特征. 林业与环境科学. 2018(06): 21-27 .

Other cited types(12)

Get Citation

PDF

XML

Article views (2012) PDF downloads (100) Cited by(20)

Turn off MathJax

Article Contents

Abstract

References

Effects of different filter algorithms on deriving leaf area index (LAI)