Application of airborne LiDAR in the estimation of the mean height of mangrove stand
DENG Jingwen1(), TIAN Yichao1,2(), ZHANG Qiang1, TAO Jin1,3, ZHANG Yali1, HUANG Shengguang3
1. Collage of Resources and Environment, Beibu Gulf University, Qinzhou 535000, China 2. Guangxi Key Laboratory of Marine Disaster in the Beibu Gulf, Beibu Gulf University, Qinzhou 535000, China 3. Key Laboratory of Marine Geographic Information Resources Development and Utilization, Beibu Gulf University, Qinzhou 535000, China
This study constructed a stand mean height inversion model based on LiDAR data, aiming to provide dynamic monitoring of the growth of Sonneratia apetala. Taking the mangrove wetland of Sonneratia apetala in the Maowei Sea of Beibu Gulf as the study object and based on the height and intensity parameters extracted using airborne LiDAR data, this study compared three models, namely random forest, support vector machine, and neural network, based on the coefficient of determination (R2), root mean square error (RMSE), Akachi information criterion (AIC), and Bayesian information criterion (BIC) and then estimated the mean height and spatial distribution of Mangrove in the study area using the optimal model. The results are as follows. The stand mean height of Sonneratia apetala in the study area is 3.90~11.58 m, and Sonneratia apetala with a higher tree height and a larger diameter at breast height is mainly distributed near the tidal trench and the middle part of the study area. In the estimation of the stand mean height of Sonneratia apetala, the maximum percentile height (hmax) had the highest contribution rate, followed by 75%~99% percentile height. The random forest regression model yielded the highest precision (R2=0.938 1,RMSE=0.58 m,AIC=80.50, and BIC=49.05), followed by the support vector machine model (R2 = 0.766 5 and RMSE = 1.27 m in the test stage), and the neural network regression model yielded the worst fitting effects. Overall, the random forest model is the optimal model for the inversion of the stand mean height of Sonneratia apetala in the study area.
邓静雯, 田义超, 张强, 陶进, 张亚丽, 黄升光. 机载LiDAR在红树林林分平均高估算中的应用[J]. 自然资源遥感, 2022, 34(3): 129-137.
DENG Jingwen, TIAN Yichao, ZHANG Qiang, TAO Jin, ZHANG Yali, HUANG Shengguang. Application of airborne LiDAR in the estimation of the mean height of mangrove stand. Remote Sensing for Natural Resources, 2022, 34(3): 129-137.
Pang Y, Li Z Y, Chen E X, et al. LiDAR remote sensing technology and its application in forestry[J]. Scientia Silvae Sinicae, 2005(3):129-136.
[2]
Næsset E, Økland T. Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve[J]. Remote Sensing of Environment, 2002, 79(1):105-115.
doi: 10.1016/S0034-4257(01)00243-7
[3]
Andersen H E, Mcgaughey R J, Reutebuch S E. Estimating forest canopy fuel parameters using LiDAR data[J]. Remote Sensing of Environment, 2005, 94(4):441-449.
doi: 10.1016/j.rse.2004.10.013
Jiao Y T, Xing Y Q, Huo D, et al. Study on mean canopy height estimation from airborne LiDAR point cloud date[J]. Journal of Northwest Forestry University, 2015, 30(3):170-174.
Hong Y F, Zhang S G, Chen W, et al. Inversion of biomass components for Larix olgensis plantation using airborne LiDAR[J]. Forest Research, 2019, 32(5):83-90.
[6]
Li M, Im J, Quackenbush L J, et al. Forest biomass and carbon stock quantification using airborne LiDAR data:A case study over Huntington Wildlife Forest in the Adirondack Park[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 7(7):3143-3156.
doi: 10.1109/JSTARS.2014.2304642
[7]
Monnet J M, Chanussot J, Berger F. Support vector regression for the estimation of forest stand parameters using airborne laser scanning[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(3):580-584.
doi: 10.1109/LGRS.2010.2094179
Lu L, Zhou X C, Yu Z Z, et al. Plot-level forest height inversion using airborne LiDAR data based on the random forest[J]. Journal of Geo-Information Science, 2016, 18(8):1133-1140.
Zhao X, Yue C R, Li C G, et al. Estimation of forest stand mean height based on airborne LiDAR point cloud data[J]. Forest Research, 2020, 33(4):59-66.
Wang D Z. Integration of UAV-LiDAR data and satellite imagery for multi-scale mangrove observation[D]. Wuhan: China University of Geosciences(Wuhan), 2020.
[11]
Tian Y C, Huang H, Zhang G Q, et al. Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing[J]. Science of the Total Environment, 2021, 781:146816-146834.
doi: 10.1016/j.scitotenv.2021.146816
Liao B W, Zheng S F, Chen Y J, et al. Biological characteristics and ecological adaptability for non-indigenous mangrove species Sonneratia apetala[J]. Chinese Journal of Ecology, 2004(1):10-15.
Deng B Y. A preliminary exploration on the eological invasion of Sonneratia apetala in Qinzhou area of Guangxi[J]. Hubei Forestry Science and Technology, 2020, 49(5):46-52.
Tian Y C, Huang Y L Zhang Q, et al. A comparative study of spatial heterogeneity of ecosystem service value in typical islands in Beibu Gulf[J]. Marine Sciences, 2019, 43(2):60-68.
[15]
周生祥. 林分平均树高的求算与分析[J]. 华东森林经理, 1989(3):37-45.
Zhou S X. Calculation and analysis of mean height of tree stand[J]. East China Forest Management, 1989(3):37-45.
[16]
Baltsavias E P. Airborne laser scanning:Basic relations and formulas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1999, 54(2-3):199-214.
doi: 10.1016/S0924-2716(99)00015-5
Shao W Z, Zhao F Y, Liang Z Y. The research of improved progressive triangulated irregular network densification filtering algorithm[J]. Beijing Surveying and Mapping, 2016(6):17-21.
Ou Q X. Modeling stand growth of natural conifer and broadleaf mixed forests in Jilin Province based on machine learning[D]. Beijing: Chinese Academy of Forestry, 2019.
[21]
Akaike H T. A new look at the statistical model identification[J]. IEEE Transactions on Automatic Control, 1974, 19(6):716-723.
doi: 10.1109/TAC.1974.1100705
[22]
Schwarz G E. Estimating the dimension of a model[J]. The Annals of Statistics, 1978, 6(2):461-464.
[23]
Vrieze, Scott I. Model selection and psychological theory:A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)[J]. Psychological methods, 2012, 17(2):228-243.
doi: 10.1037/a0027127
pmid: 22309957
Zhang X J, Guan W, Liao B W, et al. Comparison on the community characteristics between artificial and natural mangroves in Zhuhai City[J]. Ecological Science, 2014, 33(2):321-326.
Huang X M, Lu C Y. Studies on the characteristics of artificial population of Sonneratia apetala and the current recruitment and dispersal of the seedlings in Xiamen Bay[J]. Ecological Science, 2018, 37(5):1-6.
Wu R, Chen D D, Wang D R, et al. Survey of mangrove resources in Dongzhai Harbour of Hainan Province[J]. Chinese Journal of Tropical Agriculture, 2016, 36(9):62-71.
Zheng J M, Shu Z J, Fang X, et al. Discussion on techniques of mangrove afforestation and rehabilitation[J]. Protection Forest Science and Technology, 2016(1):99-103.
[28]
廖宝文. 中国红树林恢复与重建技术[M]. 北京: 科学出版社, 2010:20-44.
Liao B W. Restoration and teconstruction techniques of mangrove forests in China[M]. Beijing: Science Press, 2010:20-44.
[29]
Lu D S, Chen Q, Wang G X, et al. Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates[J]. International Journal of Forestry Research, 2012(2):436537-436553.
[30]
Brice M, Michael A W, Geordie W H, et al. Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with LiDAR plots[J]. International Journal of Remote Sensing, 2013, 34(12):4406-4424.
doi: 10.1080/01431161.2013.779041
[31]
Pourrahmati M R, Baghdadi N, Darvishsefat A A, et al. Mapping Lorey’s height over Hyrcanian forests of Iran using synergy of ICESat/GLAS and optical images[J]. European Journal of Remote Sensing, 2018, 51(1):100-115.
doi: 10.1080/22797254.2017.1405717
[32]
Waske B, Benediktsson J A, Árnason, et al. Mapping of hyperspectral AVIRIS data using machine-learning algorithms[J]. Canadian Journal of Remote Sensing, 2009, 35(1):106-116.
2022, Vol. 34 
), 田义超1,2(
