Estimating oak forest parameters in the western mountains of Iran using satellite-based vegetation indices

Abstract

Remote sensing is an important tool for studying and modeling forest stands. Vegetation indices obtained from Landsat-8 remotely sensed data were used to estimate the forest parameters in the western mountains of Iran. Thirty-four sample points were selected on the map of Bayangan County, Kermanshah province, Iran. At each point, a cluster of five rectangular plots of 8100 m² and 200 m apart was established. Some clusters were primarily sampled to determine the variance of the forest parameters. The coefficient of variation was used as a criterion for sample allocation. Stand density, canopy cover and basal area per hectare were calculated for each plot. Vegetation indices were extracted from the Landsat-8 images for each plot. Simple linear and nonlinear regressions were conducted to develop the models. The models were validated using an independent data set. Stability of model parameters was statistically evaluated. The results showed that Normalized difference vegetation index (NDVI) and Transformed normalized difference vegetation index (TNDVI) followed by Simple ratio vegetation index (SRVI) were the best predictors, explaining up to 91% of the variations with high precision. For NDVI, Soil adjusted vegetation index 2 (SAVI2) and SRVI, the cubic model was more appropriate than the linear model for predicting the forest parameters. For this model, the R-square value increased while NRMSE decreased significantly. For Infrared percentage vegetation index (IPVI), the quadratic model was better, but, for other vegetation indices, nonlinear models were not superior to linear ones. Finally, it can be concluded that Landsat-8 imagery is suitable for predicting forest parameters in the oak forests of western Iran. Of course, large plots must be selected, and pre-classification is necessary to gain accurate and precise estimations.

Keywords

Basal area / Canopy cover / Density / Landsat-8 / Modeling

References

[1] Abdollahnejad A, Panagiotidis D, Peter Surovy P. Estimation and extrapolation of tree parameters using spectral correlation between UAV and Pléiades data. Forests, 2018, 9(2): 85-100.

[2] Akdim N, Alfieri SM, Habib A, Choukri A, Cheruiyot E, Labbassi K, Menenti M. Monitoring of irrigation schemes by remote sensing: phenology versus retrieval of biophysical variables. Remote Sens, 2014, 6: 5815-5851.

[3] Barati S, Rayegan B, Saati M, Sharifi A, Nasri M. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. Egypt J Remote Sens Space Sci, 2011, 14: 49-56.

[4] Carreiras JMB, Pereira JMC, Pereira JS. Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. For Ecol Manag, 2006, 223(1–3): 45-53.

[5] Chen WB, Zhao XF. Estimation of forest parameters based on TM imagery and statistical analysis. J For Res, 2007, 18(3): 241-244.

[6] Clevers JGPW. Application of a weighted infrared-red vegetation index for estimating leaf area index by correcting soil moisture. Remote Sens Environ, 1989, 29: 25-37.

[7] Crippen RE. Calculating the vegetation index faster. Remote Sens Environ, 1990, 34: 71-73.

[8] Deering DW, Rouse JW, Haas RH, Schell JA (1975) Measuring forage production of grazing units from Landsat MSS data. In: Proceedings of the 10th international symposium on remote sensing of environment, vol. II, pp 1169–1178

[9] Delpasand S, Kiani B, Mokhtari M. Evaluation of the capability of Landsat 8 data for predicting tree density in the Bagh-Shadi Herat forests. Adv Environ Biol, 2014, 8(5): 1449-1452.

[10] Fassnacht FE, Hartig F, Latifi H, Berger C, Hernández J, Corvalán P, Koch B. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sens Environ, 2014, 154: 102-114.

[11] Gizachew B, Solberg S, Naesset E, Gobakken T, Bollandsas OM, Breidenbach J, Zahabu E, Mauya EW. Mapping and estimating the total living biomass and carbon in low-biomass woodland using Landsat 8 CDR data. Carbon Balance Manag, 2016, 11: 13.

[12] Gunlu A, Ercanli I, Sengurt M, Tezcan Yayla A (2012) Modeling stand volume using Landsat TM data for fir stands (Abies bornmuelleriana) located in Buyukuz planning unit, Turkey. J Kast For 65–71

[13] Gunlu A, Ercanli I, Baskent EZ, Gakir G. Estimating aboveground biomass using Landsat TM imagery: a case study of Anatolian Crimean pine, forest in turkey. Ann For Res, 2014, 57(2): 289-298.

[14] Hatfield JL, Prueger JH. Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sens, 2010, 2: 562-578.

[15] Hudak AT, Evans JS, Falkowski MJ, Crookston NL, Gessler PE, Morgan P, Smith AMS (2005) Predicting plot basal area and tree density in mixed-conifer forest from LIDAR and Advanced Land Imager (ALI) data. In: Proceedings of the 26th Canadian symposium on remote sensing in Wolfville, 14–16 June, Nova Scotia, pp 201–208

[16] Huete H. Soil-adjusted vegetation index (SAVI). Remote Sens Environ, 1988, 25: 295-309.

[17] Irons JR, Dwyer JL, Barsi JA. The next Landsat satellite: the Landsat data continuity mission. Remote Sens Environ, 2012, 122: 11-21.

[18] Jordan CF. Derivation of leaf area index from quality of light on the forest floor. Ecology, 1969, 50: 663-666.

[19] Karlson M, Ostwald M, Reese H, Sanou J, Tankoano B, Mattsson E. Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest. Remote Sens, 2015, 7(8): 10017-10041.

[20] Khorrami R, Darvishsefat AA, Namiranian M. Investigation on the capability of Landsat 7 ETM⁺ data for standing volume estimation of Beach stands (case study: Sangdeh Forests). Iran J Nat Resour, 2008, 60(4): 1281-1289.

[21] Kiani B (2015) Applying modern statistics in natural resources, Yazd University Publications, p 522

[22] Kiani B (2017) Forest biometrics: sampling designs and measurement methods in forest sciences. Pelk Publications, Tehran, p 464

[23] Liang XL, Kankare V, Hyyppa J, Wang YS, Kukko A, Haggren H, Yu XW, Kaartinen H, Jaakkola A, Guan F, Holopainen M, Vastaranta M. Terrestrial laser scanning in forest inventories. ISPRS J Photogramm, 2016, 115: 63-77.

[24] Maire GL, Marsden C, Nouvellon Y, Stape JL, Ponzoni FJ. Calibration of a species-specific spectral vegetation index for leaf area index (LAI) monitoring: example with MODIS reflectance time-series on Eucalyptus plantations. Remote Sens, 2012, 4: 3766-3780.

[25] McRoberts RE, Cohen WB, Naesset E, Stehman SV, Tomppo EO. Using remotely sensed data to construct and assess forest attribute maps and related spatial products. Scand J For Res, 2010, 25: 340-367.

[26] Mohammadi J, Shataee SH, Babanezhad M. Estimation of forest stand volume, tree density and biodiversity using Landsat ETM + Data, comparison of linear and regression tree analyses. Procedia Environ Sci, 2011, 7: 299-304.

[27] Myers WL, Patil GP. Pattern-based compression of multi-band image data for landscape analysis, 2006, Boston: Springer 186

[28] Patel N, Majumdar A. Biomass estimation of Shorea robusta with principal component analysis of satellite data. J For Res, 2010, 21(4): 469-474.

[29] Pirbavaghar M. Evaluation of capability of IRS.P6 satellite data for predicting quantitative attributes of forests (case study: Northern Zagros forests). Iran J For, 2011, 3(4): 277-289.

[30] Qi JG, Chehbouni A, Huete AR, Kerry H, Sorooshian S. A modified soil adjusted vegetation index. Remote Sens Environ, 1994, 18: 119-126.

[31] Ramachandra TV. Comparative assessment of techniques for bioresource monitoring using GIS and remote sensing. ICFAI J Environ Sci, 2007, 1(2): 7-47.

[32] Renoud GH, Darvishsefat AA, Namiranian M (2015) Evaluation of Capability of Landsat 8 data for predicting basal area of Iranian Beach forests (case study: Kheirood forest). In: First national conference on spatial engineering, Tehran University, p 9

[33] Richardson AJ, Wieg CL. Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens, 1977, 43: 1451-1552.

[34] Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. In: Third ERTS symposium, NASA SP-351, vol I, pp 309–317

[35] Roy DP, Wulder MA, Loveland TR, Woodcock CE, Allen RG. Landsat-8: science and product vision for terrestrial global change research. Remote Sens Environ, 2014, 145: 154-172.

[36] Safari A, Shabanian A, Erfanifard SY, Heidari RH, Purreza M. Investigation of spatial pattern of wild pistachio (Pistacia atlantica Desf.), case study: Bayangan forests, Kermanshah. Iran J For, 2010, 2(2): 177-185.

[37] Shcherbakov MV, Brebels A, Shcherbakova NL, Tyukov AP, Janovsky TA, Kamaev VA. A survey of forecast error measures. World Appl Sci J, 2013, 24: 171-176.

[38] Sivanpillai R, Smith CHT, Srinivasan R, Messina MG, Wu XB. Estimating regional forest cover in east texas using enhanced thematic mapper (ETM+) data. For Ecol Manag, 2005, 218: 342-352.

[39] Skidmore JP, Matney THG, Schultz EB, Fan Z. Estimation of forest inventory required sample sizes from easily observed stand attributes. For Sci, 2015, 61(1): 123-127.

[40] Tian X, Li ZY, Su ZB, Chen EX, van der Tol C, Li X, Guo Y, Li LH, Ling FL. Estimating montane forest above-ground biomass in the upper reaches of the Heihe River Basin using Landsat-TM data. Int J Remote Sens, 2014, 35: 7339-7362.

[41] Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ, 1979, 8: 127-150.

[42] USGS (2016) Landsat 8 data users handbook, 106 p. https://landsat.usgs.gov/landsat-8-l8-data-users-handbook. Accessed 23 June 2018

[43] Watham T, Kushwaha SPS, Nady S, Patel NR, Ghosm S. Forest carbon stock assessment at barkot flux site using field inventory, Landsat 8 OLI data and geo-statistical techniques. Int J Multidiscip Res Dev, 2016, 3(5): 111-119.

[44] Watt PJ, Donoghue DNM, McManus KB, Dunford RW. Predicting forest height from IKONOS, LANDSAT and LIDAR imagery. Int Arch Photogramm, 2004, 5(8): 228-231.

[45] Weaver B, Weunsch KL. SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients. Behav Res Methods, 2013, 45(3): 880-895.

[46] Wilson BT, Knight JF, McRoberts RE. Harmonic regression of Landsat time series for modeling attributes from national forest inventory data. ISPRS J Photogramm, 2018, 137: 29-46.

[47] Woodcock CE, Allen R, Anderson M, Belward A, Bindschadler R, Cohen W, Gao F, Goward SN, Helder D, Helmer E, Nemani R, Oreopoulos L, Schott J, Thenkabail PS, Vermote EF, Vogelmann J, Wulder MA, Wynne R. Free access to Landsat imagery. Science, 2008 320 5879 1011

[48] Xu DD, Guo XL. A study of soil line simulation from Landsat images in mixed grassland. Remote Sens, 2013, 5(9): 4533-4550.