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Leaf area index estimation in maize and soybean using UAV LiDAR data

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Abstract

Leaf area index (LAI) is a vital input variable for crop growth and yield prediction models. Therefore, rapid and accurate crop LAI estimates can offer important information for monitoring and managing the quantity and quality of food production. Here, LAI values of maize and soybean were predicted applying height metrics and intensity metrics calculated through unmanned aerial vehicle (UAV) LiDAR data. Moreover, we compared the prediction performance of physical model with that of empirical model for estimating crop LAI. The physical model based on Beer–Lambert law yielded reliable estimation results using LiDAR height data (maize: R2 = 0.815, RMSE = 0.385; soybean: R2 = 0.627, RMSE = 0.515) and LiDAR intensity data (maize: R2 = 0.719, RMSE = 0.474; soybean: R2 = 0.548, RMSE = 0.567). However, the linear regression model obtained a higher estimation accuracy. The single linear regression model derived from LiDAR height data had an R2 value of 0.837 (RMSE = 0.361) for maize and 0.658 (RMSE = 0.493) for soybean, and derived from LiDAR intensity data had an R2 value of 0.749 (RMSE = 0.448) for maize and 0.460 (RMSE = 0.619) for soybean, respectively. We found that the random forest (RF) regression model yielded the lowest estimation accuracy in this study. Moreover, the RF regression model in our study was not able to reliably estimate soybean LAI whether using LiDAR height metrics (R2 = 0.294) or intensity metrics (R2 = 0.180). Our results show that both LiDAR intensity and height metrics are capable of reliably predicting maize and soybean LAIs, although LiDAR intensity data yielded lower estimation accuracy than LiDAR height data. In conclusion, the results presented in this study demonstrate that using UAV-LiDAR technology to predict crop LAI is a flexible, practical, and cost-effective method.

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References

  • Afrasiabian, Y., Noory, H., Mokhtari, A., Nikoo, M. R., Pourshakouri, F., & Haghighatmehr, P. (2020). Effects of spatial, temporal, and spectral resolutions on the estimation of wheat and barley leaf area index using multi- and hyper-spectral data (case study: Karaj, Iran). Precision Agriculture, 22, 660–688.

    Article  Google Scholar 

  • Ahmed, O. S., Franklin, S. E., Wulder, M. A., & White, J. C. (2015). Characterizing stand-level forest canopy cover and height using landsat time series, samples of airborne LiDAR, and the Random Forest algorithm. Isprs Journal of Photogrammetry and Remote Sensing : Official Publication of the International Society for Photogrammetry and Remote Sensing (Isprs), 101, 89–101.

    Article  Google Scholar 

  • Alonzo, M., Bookhagen, B., McFadden, J. P., Sun, A., & Roberts, D. A. (2015). Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry. Remote Sensing of Environment, 162, 141–153.

    Article  Google Scholar 

  • Bouvier, M., Durrieu, S., Fournier, R. A., & Renaud, J. P. (2015). Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sensing of Environment, 156, 322–334.

    Article  Google Scholar 

  • Cao, L., Coops, N. C., Sun, Y., Ruan, H., Wang, G., Dai, J., & She, G. (2019). Estimating canopy structure and biomass in bamboo forests using airborne LiDAR data. Isprs Journal of Photogrammetry and Remote Sensing : Official Publication of the International Society for Photogrammetry and Remote Sensing (Isprs), 148, 114–129.

    Article  Google Scholar 

  • Chen, J. M., & Cihlar, J. (1996). Retrieving leaf area index of boreal conifer forests using landsat TM images. Remote Sensing of Environment, 55, 153–162.

    Article  Google Scholar 

  • Comba, L., Biglia, A., Ricauda Aimonino, D., Tortia, C., Mania, E., Guidoni, S., & Gay, P. (2019). Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery. Precision Agriculture, 21, 881–896.

    Article  Google Scholar 

  • Corte, A. P. D., Souza, D. V., Rex, F. E., Sanquetta, C. R., Mohan, M., Silva, C. A., Zambrano, A. M. A., Prata, G., de Alves, D. R., Trautenmüller, J. W., Klauberg, C., de Moraes, A., Sanquetta, M. N., Wilkinson, B., & Broadbent, E. N. (2020). Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, 179, 105815.

    Article  Google Scholar 

  • De Rosa, D., Basso, B., Fasiolo, M., Friedl, J., Fulkerson, B., Grace, P. R., & Rowlings, D. W. (2021). Predicting pasture biomass using a statistical model and machine learning algorithm implemented with remotely sensed imagery. Computers and Electronics in Agriculture, 180, 105880.

    Article  Google Scholar 

  • Fieber, K. D., Davenport, I. J., Tanase, M. A., Ferryman, J. M., Gurney, R. J., Becerra, V. M., Walker, J. P., & Hacker, J. M. (2015). Validation of Canopy Height Profile methodology for small-footprint full-waveform airborne LiDAR data in a discontinuous canopy environment. Isprs Journal of Photogrammetry and Remote Sensing : Official Publication of the International Society for Photogrammetry and Remote Sensing (Isprs), 104, 144–157.

    Article  Google Scholar 

  • García, M., Riaño, D., Chuvieco, E., & Danson, F. M. (2010). Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sensing of Environment, 114, 816–830.

    Article  Google Scholar 

  • Gebbers, R., & Adamchuk, V. I. (2010). Precision Agriculture and Food Security. Science, 327, 828.

    Article  CAS  PubMed  Google Scholar 

  • Gilliot, J. M., Michelin, J., Hadjard, D., & Houot, S. (2021). An accurate method for predicting spatial variability of maize yield from UAV-based plant height estimation: A tool for monitoring agronomic field experiments. Precision Agriculture, 22, 897–921.

    Article  Google Scholar 

  • Harkel, T. J., Bartholomeus, H., & Kooistra, L. (2020). Biomass and crop height estimation of different crops using UAV-Based Lidar. Remote Sens, 12, 17.

    Article  Google Scholar 

  • Hmida, S. B., Kallel, A., Gastellu-Etchegorry, J. P., & Roujean, J. L. (2017). Crop Biophysical Properties Estimation based on LiDAR full-waveform inversion using the DART RTM. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 4853–4868.

    Article  Google Scholar 

  • Höfle, B., & Pfeifer, N. (2007). Correction of laser scanning intensity data: Data and model-driven approaches. Isprs Journal of Photogrammetry and Remote Sensing : Official Publication of the International Society for Photogrammetry and Remote Sensing (Isprs), 62, 415–433.

    Article  Google Scholar 

  • Hopkinson, C., & Chasmer, L. (2009). Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sensing of Environment, 113, 275–288.

    Article  Google Scholar 

  • Hu, T., Ma, Q., Su, Y., Battles, J. J., Collins, B. M., Stephens, S. L., Kelly, M., & Guo, Q. (2019). A simple and integrated approach for fire severity assessment using bi-temporal airborne LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 78, 25–38.

    Article  Google Scholar 

  • Korhonen, L., Hadi, Packalen, P., & Rautiainen, M. (2017). Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sensing of Environment, 195, 259–274.

    Article  Google Scholar 

  • Kross, A., McNairn, H., Lapen, D., Sunohara, M., & Champagne, C. (2015). Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34, 235–248.

    Article  Google Scholar 

  • Lei, L., Qiu, C., Li, Z., Han, D., Han, L., Zhu, Y., Wu, J., Xu, B., Feng, H., Yang, H., & Yang, G. (2019). Effect of Leaf occlusion on Leaf Area Index Inversion of Maize using UAV–LiDAR Data. Remote Sensing, 11, 1067.

    Article  Google Scholar 

  • Li, W., Niu, Z., Chen, H., & Li, D. (2016). Characterizing canopy structural complexity for the estimation of maize LAI based on ALS data and UAV stereo images. International Journal of Remote Sensing, 38, 2106–2116.

    Article  Google Scholar 

  • Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News 23.

  • Liu, Q., Liang, S., Xiao, Z., & Fang, H. (2014). Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data. Remote Sensing of Environment, 145, 25–37.

    Article  Google Scholar 

  • Lumley, T., & Miller, A. (2020). Leaps: Regression Subset Selection. R Package Version 3.1.

  • Luo, S., Chen, J. M., Wang, C., Gonsamo, A., Xi, X., Lin, Y., Qian, M., Peng, D., Nie, S., & Qin, H. (2018). Comparative Performances of Airborne LiDAR Height and Intensity Data for Leaf Area Index Estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 300–310.

    Article  Google Scholar 

  • Luo, S., Wang, C., Xi, X., Nie, S., Fan, X., Chen, H., Yang, X., Peng, D., Lin, Y., & Zhou, G. (2019). Combining hyperspectral imagery and LiDAR pseudo-waveform for predicting crop LAI, canopy height and above-ground biomass. Ecological Indicators, 102, 801–812.

    Article  Google Scholar 

  • Maimaitijiang, M., Ghulam, A., Sidike, P., Hartling, S., Maimaitiyiming, M., Peterson, K., Shavers, E., Fishman, J., Peterson, J., Kadam, S., Burken, J., & Fritschi, F. (2017). Unmanned aerial system (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. Isprs Journal of Photogrammetry and Remote Sensing : Official Publication of the International Society for Photogrammetry and Remote Sensing (Isprs), 134, 43–58.

    Article  Google Scholar 

  • Manuri, S., Andersen, H. E., McGaughey, R. J., & Brack, C. (2017). Assessing the influence of return density on estimation of lidar-based aboveground biomass in tropical peat swamp forests of Kalimantan, Indonesia. International Journal of Applied Earth Observation and Geoinformation, 56, 24–35.

    Article  Google Scholar 

  • Mesas-Carrascosa, F. J., Castillejo-González, I. L., de la Orden, M. S., & Porras, A. G. F. (2012). Combining LiDAR intensity with aerial camera data to discriminate agricultural land uses. Computers and Electronics in Agriculture, 84, 36–46.

    Article  Google Scholar 

  • Mielcarek, M., Stereńczak, K., & Khosravipour, A. (2018). Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation. International Journal of Applied Earth Observation and Geoinformation, 71, 132–143.

    Article  Google Scholar 

  • Nie, S., Wang, C., Dong, P., & Xi, X. (2016). Estimating leaf area index of maize using airborne full-waveform lidar data. Remote Sensing Letters, 7, 111–120.

    Article  Google Scholar 

  • Pablo, C. P., Piotr, T., Nicholas, C., Luis, C., & Ángel, R. (2018). Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data. Remote Sensing of Environment, 217, 400–413.

    Article  Google Scholar 

  • Pearse, G. D., Morgenroth, J., Watt, M. S., & Dash, J. P. (2017). Optimising prediction of forest leaf area index from discrete airborne lidar. Remote Sensing of Environment, 200, 220–239.

    Article  Google Scholar 

  • Qin, Y., Yao, W., Vu, T. T., Li, S., Niu, Z., & Ban, Y. (2015). Characterizing Radiometric attributes of Point Cloud using a normalized reflective factor derived from small footprint LiDAR Waveform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 740–749.

    Article  Google Scholar 

  • Richardson, J. J., Moskal, L. M., & Kim, S. H. (2009). Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR. Agricultural and Forest Meteorology, 149, 1152–1160.

    Article  Google Scholar 

  • Rosso, P., Nendel, C., Gilardi, N., Udroiu, C., & Chlebowski, F. (2022). Processing of remote sensing information to retrieve leaf area index in barley: A comparison of methods. Precision Agriculture, 23, 1449–1472.

    Article  Google Scholar 

  • Sadeghi, Y., St-Onge, B., Leblon, B., Prieur, J. F., & Simard, M. (2018). Mapping boreal forest biomass from a SRTM and TanDEM-X based on canopy height model and Landsat spectral indices. International Journal of Applied Earth Observation and Geoinformation, 68, 202–213.

    Article  Google Scholar 

  • Shao, G., Shao, G., Gallion, J., Saunders, M. R., Frankenberger, J. R., & Fei, S. (2018). Improving Lidar-based aboveground biomass estimation of temperate hardwood forests with varying site productivity. Remote Sensing of Environment, 204, 872–882.

    Article  Google Scholar 

  • Sinha, S. K., Padalia, H., Dasgupta, A., Verrelst, J., & Rivera, J. P. (2020). Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India. International Journal of Applied Earth Observation and Geoinformation, 86, 102027.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in Precision Agriculture: A review. Remote Sensing, 12, 3136.

    Article  Google Scholar 

  • Solberg, S., Brunner, A., Hanssen, K. H., Lange, H., Næsset, E., Rautiainen, M., & Stenberg, P. (2009). Mapping LAI in a Norway spruce forest using airborne laser scanning. Remote Sensing of Environment, 113, 2317–2327.

    Article  Google Scholar 

  • Tesfamichael, S. G., van Aardt, J., Roberts, W., & Ahmed, F. (2018). Retrieval of narrow-range LAI of at multiple lidar point densities: Application on Eucalyptus grandis plantation. International Journal of Applied Earth Observation and Geoinformation, 70, 93–104.

    Article  Google Scholar 

  • Wing, B. M., Ritchie, M. W., Boston, K., Cohen, W. B., Gitelman, A., & Olsen, M. J. (2012). Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest. Remote Sensing of Environment, 124, 730–741.

    Article  Google Scholar 

  • Wittke, S., Yu, X., Karjalainen, M., Hyyppä, J., & Puttonen, E. (2019). Comparison of two-dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest. International Journal of Applied Earth Observation and Geoinformation, 76, 167–178.

    Article  Google Scholar 

  • Yan, W. Y., & Shaker, A. (2018). Airborne LiDAR intensity banding: Cause and solution. Isprs Journal of Photogrammetry and Remote Sensing : Official Publication of the International Society for Photogrammetry and Remote Sensing (Isprs), 142, 301–310.

    Article  Google Scholar 

  • You, H., Wang, T., Skidmore, A., & Xing, Y. (2017). Quantifying the effects of Normalisation of Airborne LiDAR Intensity on Coniferous Forest Leaf Area Index estimations. Remote Sensing, 9, 163.

    Article  Google Scholar 

  • Zhao, Y., Liu, X., Wang, Y., Zheng, Z., Zheng, S., Zhao, D., & Bai, Y. (2021). UAV-based individual shrub aboveground biomass estimation calibrated against terrestrial LiDAR in a shrub-encroached grassland. International Journal of Applied Earth Observation and Geoinformation, 101, 102358.

    Article  Google Scholar 

  • Zhu, X., Liu, J., Skidmore, A. K., Premier, J., & Heurich, M. (2020). A voxel matching method for effective leaf area index estimation in temperate deciduous forests from leaf-on and leaf-off airborne LiDAR data. Remote Sensing of Environment, 240, 111696.

    Article  Google Scholar 

  • Zhu, W., Sun, Z., Huang, Y., Yang, T., Li, J., Zhu, K., Zhang, J., Yang, B., Shao, C., Peng, J., Li, S., Hu, H., & Liao, X. (2021). Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping. Precision Agriculture, 22, 1768–1802.

    Article  CAS  Google Scholar 

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Acknowledgements

The authors would like to thank the anonymous reviewers for their thoughtful comments and suggestions on the manuscript.

Funding

This work was supported by the Natural Science Foundation of Fujian Province, China (No. 2022J01151), the National Natural Science Foundation of China (No. 42101325).

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Correspondence to Shezhou Luo or Weiwei Liu.

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Luo, S., Liu, W., Ren, Q. et al. Leaf area index estimation in maize and soybean using UAV LiDAR data. Precision Agric (2024). https://doi.org/10.1007/s11119-024-10146-9

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