Estimation of birch forest LAI based on single laser penetration index of airborne LiDAR data.

doi:10.13287/j.1001-9332.201611.020

Abstract

Abstract: Forest leaf area index (LAI) is an important indicator to describe the forest canopy structure and growth status of trees. In this paper, the Yigen area of Inner Mongolia was selected as the study area. Taken full account of the differences among different echo types, the LiDAR point cloud data were split into different single lasers. Then, intensity normalization was implemented for LiDAR point cloud data with the range between sensor and target. Based on the normalized intensity data, a new laser penetration index, called single laser beam penetration index (LPI_s), was calculated along with the calculation of traditional LPI. These two laser penetration indexes were used to estimate the forest LAI based on the theoretical model and empirical model on four different sampling scales (5, 10, 15, and 20 m), respectively, which aimed to improve the retrieval accuracy of forest LAI through laser beam splitting. The results showed that the forest LAI estimated from mean LPI_s (LPI_mean) was obviously better than that from traditional LPI. In addition, both of the empirical [R²=0.80, mean absolute deviation (MAD)=0.11] and theoretical models (R²=0.77, MAD=0.16) achieved the best performances with sampling scale of 15 m. The mapping of birch forest LAI for the study area was derived by integrating both the advantages of best empirical and theoretical models.

Key words: airborne LiDAR, single laser penetration index, leaf area index (LAI), laser beam splitting, intensity normalization

XING Yan-qiu, HUO Da, YOU Hao-tian, TIAN Xin, JIAO Yi-tao, XIE Jie, YAO Song-tao. Estimation of birch forest LAI based on single laser penetration index of airborne LiDAR data.[J]. Chinese Journal of Applied Ecology, 2016, 27(11): 3469-3478.

References

[1] Watson DJ. Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Annals of Botany,1947, 11: 41-76
[2] Shin Y, Seguchi M, Koriyama M, et al. Estimation of LAI in the forested watershed using ASTER data based on Price’s model in summer and winter. European Journal of Forest Research, 2010, 129: 1237-1245
[3] Ma H, Song J, Wang J, et al. Improvement of spatially continuous forest LAI retrieval by integration of discrete airborne LiDAR and remote sensing multi-angle optical data. Agricultural and Forest Meteorology, 2014, 189: 60-70
[4] Gray J, Song C. Mapping leaf area index using spatial, spectral, and temporal information from multiple sensors. Remote Sensing of Environment, 2012, 119: 173-183
[5] Liu J-Y (刘婧怡), Tang X-G (汤旭光), Chang S-Z (常守志), et al. Application of remote sensing to inverse the forest leaf area index and regional estimation. Remote Sensing Technology and Application (遥感技术与应用), 2014, 29(1): 18-25 (in Chinese)
[6] Bréda NJ. Ground-based measurements of leaf area index: A review of methods, instruments and current controversies. Journal of Experimental Botany, 2003, 54: 2403-2417
[7] Han T-T (韩婷婷), Xi X-H (习晓环), Wang C (王成), et al. Forest leaf area index inversion based on TM data in Xishuangbanna area. Remote Sensing Information (遥感信息), 2014, 29(2): 26-30 (in Chinese)
[8] Anderson MC, Neale CM, Li F, et al. Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote Sensing of Environment, 2004, 92: 447-464
[9] You H-T (尤号田), Xing Y-Q (邢艳秋), Wang Z (王铮), et al. A method of leaf area index retrieval for coniferous forests based on LiDAR discrete point cloud intensity information. Journal of Central South University of Forestry & Technology (中南林业科技大学学报), 2014, 34(10): 39-44 (in Chinese)
[10] Luo S, Wang C, Xi X, et al. Estimating FPAR of maize canopy using airborne discrete-return LiDAR data. Optics Express, 2014, 22: 5106-5117
[11] Kankare V, Räty M, Yu X, et al. Single tree biomass modelling using airborne laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 85: 66-73
[12] You H-T (尤号田), Xing Y-Q (邢艳秋), Wang Z (王铮), et al. Estimation of the leaf area index of coniferous forests using LiDAR discrete point cloud. Journal of Northwest Forestry University (西北林学院学报), 2014, 29(3): 41-47 (in Chinese)
[13] Solberg S. Mapping gap fraction, LAI and defoliation using various ALS penetration variables. International Journal of Remote Sensing, 2010, 31: 1227-1244
[14] Solberg S, Brunner A, Hanssen KH, et al. Mapping LAI in a Norway spruce forest using airborne laser scanning. Remote Sensing of Environment, 2009, 113: 2317-2327
[15] Alonzo M, Bookhagen B, McFadden JP, et al. Mapping urban forest leaf area index with airborne LiDAR using penetration metrics and allometry. Remote Sensing of Environment, 2015, 162: 141-153
[16] Luo S-Z (骆社周), Wang C (王成), Zhang G-B (张贵宾), et al. Forest leaf area index (LAI) inversion using airborne LiDAR data. Chinese Journal of Geophysics (地球物理学报), 2013, 56(5): 1467-1475 (in Chinese)
[17] Sumnall M, Peduzzi A, Fox TR, et al. Assessing the transferability of statistical predictive models for leaf area index between two airborne discrete return LiDAR sensor designs within multiple intensely managed loblolly pine forest locations in the south-eastern USA. Remote Sen-sing of Environment, 2016, 176: 308-319
[18] Höfle B, Pfeifer N. Correction of laser scanning intensity data: Data and model-driven approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 62: 415-433
[19] Tompalski P, Coops NC, White JC, et al. Simulating the impacts of error in species and height upon tree vo-lume derived from airborne laser scanning data. Forest Ecology and Management, 2014, 327: 167-177
[20] Coren F, Sterzai P. Radiometric correction in laser scanning. International Journal of Remote Sensing, 2006, 27: 3097-3104
[21] Kvålseth TO. Cautionary note about R². The American Statistician, 1985, 39: 279-285
[22] Solberg S, Hill R, Rosette J, et al. Comparing discrete echoes counts and intensity sums from ALS for estimating forest LAI and gap fraction. Proceedings of SilviLa-ser 2008: 8th International Conference on LiDAR Applications in Forest Assessment and Inventory, Edinburgh, UK, 2008: 446-455
[23] Luo S, Wang C, Zhang G, et al. Forest leaf area index (LAI) estimation using airborne Discrete-Return LiDAR data. Chinese Journal of Geophysics, 2013, 56: 233-242
[24] Lefsky MA, Cohen WB, Acker SA, et al. Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sen-sing of Environment, 1999, 70: 339-361
[25] Zhou J-J (周靖靖), Zhao Z (赵忠), Liu J-L (刘金良), et al. Estimating leaf area index of black locust (Robinia pseudoacacia L.) plantations based on texture parameters of Quickbird imagery. Chinese Journal of Applied Ecology (应用生态学报), 2014, 25(5): 1266-1274 (in Chinese)