Forest type mapping using PRISMA imagery in the Kheyrud forest

Document Type : Research Paper

Authors

1 Department of Forestry and Forest Economics, Faculty of Natural Resources, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.

2 Department of Forest Resources Management, Faculty of Forestry, University of Agriculture in Krakow, Krakow, Poland.

10.22059/jfwp.2024.381635.1312

Abstract

Mapping forest types is essential for sustainable management, however, field methods are time-consuming and costly. Therefore, Modern remote sensing, especially hyperspectral imagery like PRISMA with 243 spectral bands, is valuable for mapping Hyrcanian mixed forests. However, its accuracy and capabilities must be assessed. In this study, the capability of PRISMA data was assessed using SVM, SAM, and RF classification approaches to create forest type maps for the Gorazbon district of Kheyrud Forest. A ground truth map consisting of 131 field sample plots, each measuring 45 × 45 m, was generated using Kuchler's abundance method based on the 100 trees with the largest diameters. Five forest types were identified: pure beech, mixed-beech, beech-hornbeam, hornbeam-beech, and mixed hornbeam. Geometric correction, smoothing and noise filtering was applied, then, Illumination correction was performed. Anomalies were detected using the RX algorithm. The minimum noise fraction (MNF) transformation and pixel purity index (PPI) were applied to the remaining 103 spectral bands and then used in the classification process. Additionally, indices such as NDVI, ReNDVI, MTCI, NDIred, and RTVI were incorporated into the classification process. The results showed that the SVM and RF algorithms achieved overall accuracies of 53.09% and 43.19%, with kappa coefficients of 0.38 and 0.25, respectively. The best combination of input data was derived from the spectral bands obtained through the MNF transformation. Based on the findings, high noise in PRISMA imagery and the similar spectral behavior of forest types in this region hindered the species discrimination and classification performance.

Keywords

Main Subjects

References

[1] Rajabpour, M., Darvishsefat, A.A., & Khalilpour, A. (2009). Capability of SPOT5-HRG data for forest density mapping (Case study: Deilaman forests in Guilan province). Iranian Journal of Forest and Poplar Research, 1(18), 132-142. (In Persian)
[2] Gorji Bahri, Y. (2000). Investigation the classification of typology and planning of the research forest of Vaz, PhD thesis, Faculty of natural resources, University of Tehran. 139 p. (In Persian)
[3] Marvie Mohajer, M.R. (2005). Silviculture, University of Tehran Press, Tehran, 410 p. (In Persian)
[4] Shataei Joibari, S., Darvishsefat, A.A., & Sobhani, H. (2002). Comparison of pixel-based and object-based approaches for forest type mapping using satellite data. Journal of the Iraninan natural Resources, 3(60), 869-881. (In Persian)
[5] DarvishSefat, A.A., Abbasi, M., & Marvi Mohajer, M. (2009). Investigation on the possibility of beech forest type mapping using Landsat ETM+ data (case study: Kheyrud forest). Iranian Journal of Forest, 1(2), 105-113. (In Persian)
[6] Sheykhi, H., Darvishsefat, A.A., Fatehi, P., Rajab Pourrahmati, M., & Etemad, V. (2020). Investigation on the capability of Landsat-8 and Sentinel-2 data for mapping forest type in the Kojur watershed of Hyrcanian forests. Journal of Wood and Forest Science and Technology, 27(2), 79-98. (In Persian)
[7] Nasiri, V. (2020). Estimation forest attributes using sentinel-2 imagery and Unmanned Aerial Vehicle. PhD thesis, Agriculture and Natural Resources Campus Faculty of Natural Resources, University of Tehran, 130 p. (In Persian)
[8] Hennessy, A., Clarke, K., & Lewis, M. (2020). Hyperspectral classification of plants: a review of waveband selection generalisability. Remote Sensing, 1(12), 1-27.
[9] Lv, W., & Wang, X. (2020). Overview of hyperspectral image classification, Hindawi. Journal of Sensor, (2), 1-13.
[10] Vangi, E., D'Amico, G., Francini, S., Giannetti, F., Lasserre, B., Marchetti, M., & Chirici, G. (2021). The new hyperspectral satellite PRISMA: imagery for forest types discrimination. Sensors (Basel), 21(4), 1-19.
[11] George, R., Padalia, H., & Kushwaha, S.P.S. (2014). Forest tree species discrimination in western Himalaya using EO-1 Hyperion. International Journal of Applied Earth Observation and Geoinformation, (28), 140-149.
[12] Casa, R., Pignatti, S., Pascucci, S., Ionca, V., Mzid, N., & Veretelnikova, I. (2020). Assessment of PRISMA imaging spectrometer data for the estimation of topsoil properties of agronomic interest at the field scale. 22nd EGU General Assembly, held online. May. 4-8.
[13] Darvishsefat, A. A. (2007). Application of hyperspectral data for forest stand. Journal of the Iranian Natural Resources, 4(59), 831-841. (In Persian)
[14] Puletti N.A., Camarretta, N.A., & Corona, P.A. (2016). Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests. European Journal of Remote Sensing, (49), 157 – 169.
[15] Vaglio Laurin., Gaia, P., Nicola, H., William, L., Veraldo, C., Piermaria, P., Dario, C., Q., & Valentini, R. (2016). Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data. Remote Sensing of Environment (176), 163-176.
[16] Raczko, E., & Zagajewski, B. (2017). Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images. European Journal of Remote Sensing, 50(1), 144-154.
[17] Luo, X.A., & Xu, S.B. (2019). Forest mapping from hyperspectral image using deep belief network. 15th International Conference on Mobile Ad-Hoc and Sensor Networks, 15, 395- 398.
[18] Sabat-Tomala, A., Raczko, E., & Zagajewski, B. (2020). Comparison of support vector machine and random forest algorithms for invasive and expansive species classification using airborne hyperspectral data. Remote Sensing, 12(3), 1-21.
[19] Javanmiripour, M, Mohajer, J., Etemad, M., & Jourgholami, M. (2017). Determination of forest stands growth through Swiss control (Study area: Gorazbon district). Journal of Forest Research and Development, 3(3), 263-273. (In Persian)
[20] Bayat, M., Namiranian, M., Zobeiri, M., & Fathi, J. (2013). Determining growth increment and density of trees in forest, using permanent sample plots (Case study: Gorazbon district of Kheyrud Forest). Iranian Journal of Forest and Poplar Research, 3(21), 438-424. (In Persian).
[21] Savitzky, A., & Golay, M.J.E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem, 36(8), 1627-1639.
[22] Boroumand, N. (2023). The Influence of symbiotic Arbuscular Mycorrhizal fungi on the mount atlas Pistachio (Pistacia atlantica subsp. mutica) Seedling’s spectral reflectance. M.Sc. thesis, Faculty of Natural Resources, Department of Forestry and Forest Economic, University of Tehran, 101 p. (In Persian)
[23] Schmid, M., Rath, D., & Diebold, U. (2022). Why and how savitzky-golay filters should be replaced. ACS Measurement Science, (2), 185-196.
[24] Seidin, S.A., Veldanzoz, M.J., & Maghsoudi, Y. (2013). Discovery of hydrocarbon seeps using target detection methods in hyperspectral images. Oil and Gas Exploration and Production, (69), 111-63. (In Persian)
[25] Koloniatis, K., Andronis, V., & Karathanassi, V. (2020). Spectral smile correction for airborne imaging spectrometers. Hyperspectral Remote Sensing, 23-44.
[26] Boardman, J.W., & Huntington, J.F. (1995). Semi-quantitative mineralogical and geological mapping with m AVIRIS data. Proceedings of Spectral Sensing Research, 26-1.
[27] Schlerfa, M., Atzbergerb, C., Hilla, J. (2005). Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sensing of Environment, (95), 177-194.
[28] Fatehi, P., Damn, A., Schweiger, A.K., Schaepman M.E., & Kneubühler, M. (2015). Mapping Alpine aboveground biomass from imaging spectrometer data: a comparison of two approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(8), 3123-3129.
[29] Reed I., & X. Yu. (1990). Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustics, Speech and Signal Processing, (38), 1760-1770.
[30] Richards, J.A. (1999). Remote sensing digital image analysis, Springer-Verlag, Berlin, 240 p.
[31] Boardman, J.W., Kruse, F.A., & Green, R.O. (1995). Mapping target signatures via partial unmixing of AVIRIS data. Proceedings of the Fifth JPL Airborne Earth Science Workshop, Pasadena, California, JPL Publication, (1), 23-26.
[31] Green, A.A., Berman, M., Switzer, P., & Craig, M.D. (1988). A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 1(26), 65-74.
[33] Khanhdar Kolver, M. (2016). The mineral chemistry of intrusive rocks and the application of remote sensing in the identification of rocks in Ahoan pass, Northeast of Semnan. M.Sc. thesis, Faculty of Erath sciences, Shahrood University of Technoology. 200 p. (In Persian).
[34] Shahriari, H., Ranjbar, H., & Artman, M. (2012). The application of SMACC and PPI methods in the extraction of end members for the mapping of hydrothermal changes in the copper porphyry area of Dareh Zar. 31st Earth Science Conference, 1-7. (In Persian)
[35] Rouse, J.W., R.H. Haas, Jr., Schell, J.A., Deering, D.W., & Harlan, J.C. (1974). Monitoring the vernal advancement and retro gradation (green wave Effect) of natural vegetation. Texas A&M Univ., College Station, Texas, 390 p.
[36] Gitelson, A., & Merzlyak, M.N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology, (22), 247-252
[37] Clevers, J. G., Kooistra, L., & Brande, M. (2017). Using sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sensing, 9(5), 405.
[38] Delegido, J., Verrelst, J., Meza, C.M., Rivera, J.P., Alonso, L., & Moreno, J. (2012). A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. European Journal of Agronomy, (46), 42-52.
[39] Chen, P. F., Tremblay, N., Wang, J.H., Vigneault, P, Huang, W.J., & Li, B.G. (2010). New index for crop canopy fresh biomass estimation. Spectroscopy and Spectral Analysis, 30(2), 512-517.
[40] Hu, L., Wang, Q. & Xing, T. (2018). An algorithm of improved optimum index factor band selection from Hyperspectral remote sensing image. International conference of physics, computing and mathematical modeling, 1-6.
[41] Darvishsefat, A.A. (2023). Course booklet of remote sensing, University of Tehran.
[42] Kruse, F.A., Lefkoff, A.B., Boardman, J.B., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J., & Goetz, A.F.H. (1993). The spectral image processing system (SIPS) - interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, Special issue on AVIRIS, (44), 145-163.
[43] Ghahreman, A. (2007). Flora of Iran, Research institute of forest and rangelands, 1-26. (In Persian).
[44] Delogu, G., Caputi, E., Perretta, M., Nicolina, M., & Boccia, L. (2023). Using PRISMA Hyperspectral data for land cover classification with artificial intelligence support. Sustainability Journal, (15), 1-26.
[45] Shataei Joibari. (2012). Investigating the possibility of preparing a map of forest types using satellite data. PhD thesis, University of Tehran. 155 p. (In Persian)
[46] Abbasi, M. (2009). Investigation of the spectral signature of forest species leaf: Fagus orientalis, Quercus castaneifolia, Carpinus betulus, Alnus subcordata, Parotia persica using field spectroradiometry. PhD thesis, Agriculture and Natural Resources Campus Faculty of Natural Resources, University of Tehran, 135 p. (In Persian)
Forest and Wood Products
Volume 77, Issue 3
December 2024
Pages 249-264

Files

Share

How to cite

Statistics