Effect of topographic aspect and polarization in estimation of some forest variables using ALOS-2 data (Case study: Shastkalateh forest, Gorgan)

Document Type : Research Paper

Authors

1 Department of Forestry, Gorgan University of Agricultural Sciences and Natural Resources

2 Forest science faculty, Gorgan University of Agricultural Sciences and Natural Resources

3 Faculty of Geodesy & Geomatics Engineering, Khajeh Nasir Toosi University of Technology

4 Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources

Abstract

SAR data have been widely used for estimation of forest variables. These data have a high potential in flat terrain, however, there are some problems such as the effect of topography on backscatters in mountainous regions. The goal of this study was to evaluate the effect of oriented and non-oriented aspect slopes and polarization in the estimation of stand Lorey’s mean tree height, volume, and basal area variables using dual-polarization (HH, HV) data of ALOS-2 PALSAR-2 in a part of Shastkalateh forest of Gorgan. Modeling was performed in three modes including without considering the aspect slope, in the slope-aspect orientated with the transmitted waves and non-orientated with waves, and also in the polarizations as separately using linear regression and support vector machine methods. The results of correlation between the backscattering of the PALSAR-2 and studied variables showed that in the slope-aspect orientated with the transmittance, the correlations were significant for basal area and volume, but it was not significant for Lorey's height. While in the non-orientated slopes, the correlation rate was very low for all variables and no significant. Also, the results showed that the HV polarization and the support vector machine method were very sensitive to the Lorey’s mean tree height, basal area, and volume variables with r and relative RMSE (-0.14, 12.44), (-0.48, 29.35) and (-0.44, 36.40), in the area in the direction of alignment with the waves, respectively. While the lower sensitivity is (-0.18, 14.14), (-0.05, 35.85), and (-0.04, 38.40), respectively, in the non-orientated with waves.

Keywords


[1]. Zahriban, M., Fallah, A., Shataee, S., and Kalbi, S. (2015). Estimating quantitative forest attributes using Pleiades satellite data and non-parametric algorithms in Darabkola forests, Mazandaran. Iranian Journal of Forest and Poplar Research, 23(3): 1735-0883.
[2]. Mohammadi, J., Shataee, S., Namiranian, M., and Næsset, E. (2017). Modeling biophysical properties of broad-leaved stands in the hyrcanian forests of Iran using fused airborne laser scanner data and UltraCam-D images. International journal of applied earth observation and geoinformation, 61: 32-45.
[3]. Järnstedt, J., Pekkarinen, A., Tuominen, S., Ginzler, C., Holopainen, M., and Viitala, R. (2012). Forest variable estimation using a high-resolution digital surface model. ISPRS Journal of Photogrammetry and Remote Sensing, 74: 78-84.
[4]. Tsui, O. W., Coops, N. C., Wulder, M. A., and Marshall, P. L. (2013). Integrating airborne LiDAR and space-borne radar via multivariate kriging to estimate above-ground biomass. Remote Sensing of Environment, 139: 340-352.
[5]. Askne, J. I., Soja, M. J., and Ulander, L. M. (2017). Biomass estimation in a boreal forest from TanDEM-X data, lidar DTM, and the interferometric water cloud model. Remote Sensing of Environment, 196: 265-278.
[6]. Soja, M. J., Quegan, S., d’Alessandro, M. M., Banda, F., Scipal, K., Tebaldini, S., and Ulander, L. M. (2020). Mapping above-ground biomass in tropical forests with ground-cancelled P-band SAR and limited reference data. Remote Sensing of Environment, 112153.
[7]. Akhtar, A. M., Qazi, W. A., Ahmad, S. R., Gilani, H., Mahmood, S. A., and Rasool, A. (2020). Integration of high-resolution optical and SAR satellite remote sensing datasets for aboveground biomass estimation in subtropical pine forest, Pakistan. Environmental Monitoring and Assessment, 192 (9): 1-17.
[8]. Hirschmugl, M., Deutscher, J., Sobe, C., Bouvet, A., Mermoz, S., and Schardt, M. (2020). Use of SAR and optical time series for tropical forest disturbance mapping. Remote Sensing, 12 (4): 727.
[9]. Pulliainen, J. T., Heiska, K., Hyyppa, J., and Hallikainen, M. T. (1994). Backscattering the C-and X-bands. IEEE Transactions on Geoscience properties of boreal forests at and Remote Sensing, 32 (5): 1041–1050.
[10]. Tanase, M.A., Santoro, M., de La Riva, J., Fernando, P., and Le Toan, T. (2010). Sensitivity of X-, C-, and L-band SAR backscatter to burn severity in Mediterranean pine forests. IEEE Transactions on Geoscience and Remote Sensing, 48(10): 3663–3675.
[11]. Wu, S. T., and Sader, S. A. (1987). Multi polarization SAR data for surface feature delineation and forest vegetation characterization. IEEE transactions on geoscience and remote sensing, (1): 67-76.
[12]. Rignot, E., Way, J., Williams, C., and Viereck, L. (1994). Radar estimates of aboveground biomass in boreal forests of interior Alaska. IEEE Transactions on Geoscience and Remote Sensing, 32 (5): 1117-1124.
[13]. Lone, J. M., Sivasankar, T., Sarma, K., Qadir, A., and Raju, P. (2017). Influence of slope aspect on above ground biomass estimation using ALOS-2 data. International Journal of Science and Research, 6 (6): 1422-1428.
 [14]. Golshani, P., Maghsoudi, Y., and Sohrabi, H. (2019). Relating ALOS-2 PALSAR-2 parameters to biomass and structure of temperate broadleaf Hyrcanian forests. Journal of the Indian Society of Remote Sensing, 47(5): 749-761.
[15]. Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T. D., and Tien Bui, D. (2018). Improving accuracy estimation of Forest Aboveground Biomass based on incorporation of ALOS-2 PALSAR-2 and Sentinel-2A imagery and machine learning: A case study of the Hyrcanian forest area (Iran). Remote Sensing, 10 (2): 172.
[16]. Yazdani, M. Shataee, Sh., Mohammadi, J.; and Maghsoudi, Y. (2020). Comparison of different machine learning and regression methods for estimation and mapping of forest stand attributes using ALOS/PALSAR data in complex Hyrcanian forests. Journal of Applied Remote Sensing, 14 (2): 024509.
[17]. Sharifi, A., and Amini, J. (2015). Forest biomass estimation using synthetic aperture radar polarimetric features. Journal of Applied Remote Sensing, 9 (1): 097695.
[18]. Ramezani, M. R., and Sahebi, M. R. (2015). Forest biomass estimation using SAR and optical images. Journal of Geospatial Information Technology, 3: 15-26.
[19]. Amini, J., and. Sadeghi, Y. (2013). Performance of SAR and optical images in modeling forest biomass. Iranian Journal of Remote Sensing and GIS, 4: 69-82.
[20]. Nouri, M., Shataee, S. S., and Mohammadi, J. (2020). Capability of Alos-Palsar-2 radar quad polarization data for estimation of structural quantitative characteristics of planted forest, Arabdagh region, Iran. Iranian Journal of Forest and Poplar Research, 27 (4):451-463
[21]. Ataee, M.S., Maghsoudi, Y., Latifi, H., and Fadaie, F. (2019). Improving estimation accuracy of growing stock by multi-frequency SAR and multi-spectral data over Iran’s heterogeneously-structured broadleaf Hyrcanian forests. Forests, 10 (8):  641.
[22]. Austin, J. M., Mackey, B. G., and Van Niel, K. P. (2003). Estimating forest biomass using satellite radar: a exploratory study in a temperate Australian Eucalyptus forest. Forest Ecology and Management, 176 (1-3): 575-583.
[23]. Sun, G., Ranson, K. J., and Kharuk, V. I. (2002). Radiometric slope correction for forest biomass estimation from SAR data in the western Sayani Mountains, Siberia. Remote Sensing of Environment, 79 (2-3): 279–287.
[24]. Kim, C. (2012). Quantitative analysis of relationship between ALOS PALSAR backscatter and forest stand volume. Journal of Marine Science and Technology, 20 (6): 624-628.
[25]. Luckman, A. J. (1998). The effects of topography on mechanisms of radar backscatter from coniferous forest and upland pasture. IEEE Transactions on Geoscience and Remote Sensing, 36 (5): 1830-1834.
[26]. Rauste, Y. (1990). Incidence-angle dependence in forested and non-forested areas in Seasat SAR data. International Journal of Remote Sensing, 11(7): 1267-1276.
[27]. Van Zyl, J. J. (1993). The effect of topography on radar scattering from vegetated areas, IEEE Transactions on Geosciences and Remote Sensing, 31 (1): 153–160.
[28]. Soenen, S. A., Peddle, D. R., Hall, R. J., Coburn, C. A., and Hall, F. G. (2010). Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain. Remote Sensing of Environment, 114 (7): 1325-1337.
[29]. Van Zyl, J. J. (1993). The effect of topography on radar scattering from vegetated areas. IEEE Transactions on Geosciences and Remote Sensing, 31(1): 153–160.
[30]. Zahriban Hesari, M., Shataee, Sh., Maghsoudi, Y., Mohammadi, J., Fransson, J. E. S., and Persson, H.J. (2020). Forest variable estimations using TanDEM-X data in Hyrcanian forests. Canadian Journal of Remote Sensing, 46 (2): 166–176.
[31]. Lee, J., and Pottier, E. (2009). Introduction to the polarimetric target decomposition concept. Polarimetric Radar Imaging: From Basics to Applications; CRC Press: Boca Raton, FL, USA, 1-422.
[32]. JAXA. (2014). Calibration Result of ALOS-2/PALSAR-2 JAXA Standard Products. Japan Aerospace Exploration Agency, Earth Observation Research Center. https://www.eorc.jaxa.jp/ALOS-2/en/calval/calval_index.htm.
[33]. Lee, J. S. (1994). Jurkevich, L., Dewaele, P., Wambacq, P., and Oosterlinck, A. Speckle filtering of synthetic aperture radar images: A review. Remote Sensing Reviews, 8 (4): 313-340.
[34]. Mitchard, E. T., Saatchi, S. S., Woodhouse, I. H., Nangendo, G., Ribeiro, N., Williams, M., Ryan, C. M., Lewis, S. L., Feldpausch, T., and Meir, P. (2009). Using satellite radar backscatter to predict above‐ground woody biomass: A consistent relationship across four different African landscapes. Geophysical Research Letters, 36, L23401.
[35]. Carreiras, J. M., Vasconcelos, M. J., and Lucas, R. M. (2012). Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sensing of Environment, 121: 426-442.
[36]. Yu, Y., and Saatchi, S. (2016). Sensitivity of L-band SAR backscatter to aboveground biomass of global forests. Remote Sensing, 8(6): 522.