Canopy Cover Estimation in Lowland Forest in South Sumatera, Using LiDAR and Landsat 8 OLI imagery
Abstract
Canopy cover is one of the most important variables in ecology, hydrology, and forest management, and useful as a basis for defining forests. LiDAR is an active remote sensing method that provides the height information of an object in three-dimensional space. The method allows for the mapping of terrain, canopy height and cover. Its only setback is that it has to be integrated with Landsat to cover a large area. The main objective of this study is to generate the canopy cover estimation model using Landsat 8 OLI and LiDAR. Landsat 8 OLI vegetation indices and LiDAR-derived canopy cover estimation, through First Return Canopy Index (FRCI) method, were used to obtain a regression model. The performance of this model was then assessed using correlation, aggregate deviation, and raster display. Lastly, the best canopy cover estimation was obtained using equation, FRCI = 2.22 + 5.63Ln(NDVI), with R2 at 0.663, standard deviation at 0.161, correlation between actual and predicted value at 0.663, aggregate deviation at -0.182 and error at 56.10%.
References
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Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite-1 Symposium (pp. 309–317). NASA Goddard Space Flight Center.
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Smirnov, N. (1948). Table for estimating the goodness of fit of empirical distributions. The Annals of Mathematical Statistics, 19, 279–281.
Tan, B., Masek, J. G., Wolfe, R., Gao, F., Huang, C., Vermote, E. F., ..., & Ederer, G. (2013). Improved forest change detection with terrain illumination corrected Landsat images. Remote Sensing of Environment, 136, 469–483. https://doi.org/10.1016/j.rse.2013.05.013
Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker III, C. J. (2015). Use of the normalized difference vegetation index (NDVI) to assess land degradation at multiple scales: Current status, future trends, and practical considerations. New York: Springer.
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Chen, G., Hay, G. J., & St-Onge, B. (2012). A GEOBIA framework to estimate forest parameters from LiDAR transects, Quickbird imagery and machine learning: A case study in Quebec, Canada. International Journal of Applied Earth Observation Geoinformation, 15, 28–37. https://doi.org/10.1016/j.jag.2011.05.010
Das, K. R., & Imon, A. (2016). A brief review of tests for normality. American Journal of Theoretical Applied Statistics, 5, 5–12. http://doi.org/10.11648/j.ajtas.20160501.12
Draper, N. R., & Smith, H. (1998). Applied regression analysis (3th ed.). New York: John Wiley & Sons.
[FAO] Food and Agriculture Organization. (2000). On definitions of forest and forest change: FRA Working Paper 33. Rome: FAO.
Furno, M. (2005). The Glejser test and the median regression. Sankhyā: The Indian Journal of Statistics, 67, 335–358.
Glejser, H. (1969). A new test for heteroscedasticity. Journal of the American Statistical Association, 64, 316–323.
Green, E. P., Mumby, P. J., Edwards, A. J., & Clark, C. D. (2000). Remote sensing handbook for tropical coastal management (A. J. Edwards, Ed.). Paris: United Nations Educational, Scientific and Cultural Organization (UNESCO).
Hudak, A. T., Lefsky, M. A., Cohen, W. B., & Berterretche, M. (2002). Integration of LiDAR and Landsat ETM+ data for estimating and mapping forest canopy height. Remote Sensing of Environment, 82, 397–416. https://doi.org/10.1016/S0034-4257(02)00056-1
Hudjimartsu, S., Prasetyo, L. B., Setiawan, Y., & Suyamto, D. (2017). Illuminating modelling for topographic correction of Landsat 8 and Sentinel-2A imageries. European Modelling Symposium (EMS), 2017, 95–99, https://doi.org/10.1109/EMS.2017.27
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
Hyyppa, J., Kelle, O., Lehikoinen, M., & Inkinen, M. (2001). A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on Geoscience Remote Sensing, 39, 969–975. http://doi.org/10.1109/36.921414
Irlan, Saleh, M. B., Prasetyo, L. B., & Setiawan, Y. (2020). Evaluation of tree detection and segmentation algorithms in peat swamp forest based on LiDAR point clouds data. Jurnal Manajemen Hutan Tropika, 26, 123–132. https://doi.org/10.7226/jtfm.26.2.123
Jakubowski, M. K., Guo, Q., & Kelly, M. (2013). Tradeoffs between LiDAR pulse density and forest measurement accuracy. Remote Sensing of Environment, 130, 245–253. https://doi.org/10.1016/j.rse.2012.11.024
Jaya, I. N. S. (2010). Analisis citra digital: Perspektif penginderaan jarak jauh untuk pengelolaan sumberdaya alam. Bogor: IPB Press.
Jaya, I. N. S., Saleh, M. B., Noventasari, D., Santi, N. A., Anggraini, N., Sutrisno, D., ..., & Qian, L. (2019). The examination of the satellite image-based growth curve model within mangrove forest. Jurnal Manajemen Hutan Tropika, 25, 44–50. https://doi.org/10.7226/jtfm.25.1.44
Jennings, S., Brown, N., & Sheil, D. (1999). Assessing forest canopies and understorey illumination: Canopy closure, canopy cover and other measures. Forestry: An International Journal of Forest Research, 72, 59–74.
Jeronimo, S., Kane, van R., Churcill, D. J., Mcgaughey, R. J., & Franklin, J. F. (2018). Applying LiDAR individual tree detection to management of structurally diverse forest landscapes. Journal of Forestry, 116(4), 336–46. https://doi.org/10.1093/jofore/fvy023
Kim, E., Lee, W. K., Yoon, M., Lee, J. Y., Lee, E. J., & Moon, J. (2016). Detecting individual tree position and height using airborne LiDAR data in Chollipo arboretum, South Korea. Terrestrial, Atmospheric and Oceanic Sciences, 27(4), 593–604. https://doi.org/10.3319/TAO.2016.03.29.01(ISRS)
Kolmogorov, A. (1933). Sulla determinazione empirica di una legge di distribuzione. Giornale dell'Istituto Italiano degli Attuari, 4, 8391.
Korhonen, L., Korpela, I., Heiskanen, J., & Maltamo, M. (2011). Airborne discrete-return LiDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index. Remote Sensing of Environment, 115, 1065–1080. https://doi.org/10.1016/j.rse.2010.12.011
Korhonen, L., & Morsdorf, F. (2014). Forestry applications of airborne laser scanning. New York: Springer.
Ma, Q., Su, Y., & Guo, Q. (2017). Comparison of canopy cover estimations from airborne LiDAR, aerial imagery, and satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, 10, 422–54236. http://doi.org/10.1109/JSTARS.2017.2711482
Nakamura, A., Kitching, R. L., Cao, M., Creedy, T. J., Fayle, T. M., Freiberg, M., ..., & Ma, K. (2017). Forests and their canopies: Achievements and horizons in canopy science. Trends in Ecology Evolution, 32, 438–451. https://doi.org/10.1016/j.tree.2017.02.020
Prasetyo, L. B., Nursal, W. I., Setiawan, Y., Rudianto, Y., Wikantika, K., & Irawan, B. (2019). Canopy cover of mangrove estimation based on airborne LiDAR & Landsat 8 OLI. IOP Conference Series: Earth and Environmental Science, 335, 012029. https://doi.org/10.1088/1755-1315/335/1/012029
[REKI] Restorasi Ekosistem. (2020). RKUPHHK-Restorasi Ekosistem 2011–2020. Jambi: PT REKI.
Richter, R., Kellenberger, T., & Kaufmann, H. (2009). Comparison of topographic correction methods. Remote Sensing, 1, 184–196. https://doi.org/10.3390/rs1030184
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite-1 Symposium (pp. 309–317). NASA Goddard Space Flight Center.
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia Analgesia, 126, 1763–1768. https://doi.org/10.1213/ANE.0000000000002864
Smirnov, N. (1948). Table for estimating the goodness of fit of empirical distributions. The Annals of Mathematical Statistics, 19, 279–281.
Tan, B., Masek, J. G., Wolfe, R., Gao, F., Huang, C., Vermote, E. F., ..., & Ederer, G. (2013). Improved forest change detection with terrain illumination corrected Landsat images. Remote Sensing of Environment, 136, 469–483. https://doi.org/10.1016/j.rse.2013.05.013
Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker III, C. J. (2015). Use of the normalized difference vegetation index (NDVI) to assess land degradation at multiple scales: Current status, future trends, and practical considerations. New York: Springer.
Zhen, Z., Quackenbush, L. J., & Zhang, L. (2016). Trends in automatic individual tree crown detection and delineation Evolution of LiDAR data. Remote Sensing, 8, 333. https://doi.org/10.3390/rs8040333
Authors
SalehM. B., DewiR. W., PrasetyoL. B., & SantiN. A. (2021). Canopy Cover Estimation in Lowland Forest in South Sumatera, Using LiDAR and Landsat 8 OLI imagery . Jurnal Manajemen Hutan Tropika, 27(1), 50. https://doi.org/10.7226/jtfm.27.1.50
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