Tree spatial pattern determination by satellite data, object-based classification and O-ring function (Case study: Sangedeh forest - Sari city)

Document Type : Research Paper

Authors

1 department environmental and natural resources , Islamic Azad university, science and research branch, Tehran.

2 Associated professor of forestry, department of environmental and natural resources, science and research branch of Azad University, Tehran, Iran.

3 Head of remote sensing institute, Associated professor of faculty of Geodesy & Geomatics Engineering, K.N. Toosi university of technology, Tehran.

4 Prof of forestry, department of natural resources and environmental, Science and Research branch of Islamic Azad University, Tehran

Abstract

Determination of the spatial pattern of trees is one of the important structural parameters of the forest for identifying changes in forest communities and it's monitoring in recent years. On the one hand, extracting this structural parameter using satellite data has a significant impact on reducing cost of inventory and makes it easy to evaluate forests of forest managers. In this study, the spatial pattern of trees in the natural forest of Farim of Mazandaran province was determined by using SPOT7 satellite data. Thus, some tree`s characteristics such as species, diameter at breast height, the diameter of canopies, gaps and the position of each tree were measured in 12 plot with area about one ha (100 m x 100 m). On the other hand, after radiometric and geometric corrections of satellite data, the tree`s canopy and gaps were classified, using the object-based method, and tree position per plot also was determined by canopy gravity center. Then, the spatial distribution pattern of the trees and structure, distinguished and extracted by field inventory and satellite data was compared by O-ring function. The results showed that the overall accuracy of canopy and gaps extracted from satellite data by object-based classification was about 91% and kappa coefficient equaled 0.7. The spatial pattern of trees were random except of the small radius and similar to the pattern of trees in-ground inventory. In total, results showed that SPOT-7 data and the object-based classification have an acceptable potential for determining the spatial pattern of forest stands.

Keywords


[1]. Bozali, N., Sivrikaya, F., and Akay, A. E. (2015). Use of spatial pattern analysis to assess forest cover changes in the Mediterranean region of Turkey. Journal of Forest Research, 20 (4): 365-374.
[2]. Mataji, A., Babaie Kafaki, S., Safaee, H., and Kiadaliri, H. (2008). Spatial pattern of regeneration gaps in managed and unmanaged stands in natural Beech (Fagus orientalis) forests.Iranian Journal of Forest and Poplar Research, 16(1): 149-157.
[3]. Kazempour Larsary, M., Abkenar Taheri, K., Pourbabaei, H., Pothier, D., and Amanzadeh, B. (2018). Spatial patterns of trees from different development stages in mixed temperate forest in the Hyrcanian region of Iran. Journal of Forest Science, 64 (6): 260-270.
[4]. Bayat, M., Thanh Noi, P., Zare, R., and Tien Bui, D. (2019). A semi-empirical approach based on genetic programming for the study of biophysical controls on diameter-growth of Fagus orientalis in Northern Iran. Remote Sensing, 11(14): 1680.
[5]. Daghestani, M., Babaei Kafaki, S., Mataji, A., and Adeli, E. (2010). Surveying the Relationship of Beech Stand Diameter and Its Spectral Signature on Satellite Data. Journal of Wood and Forest Science and Technology,17(3): 137-149.
[6]. Omidvar Hosseini, F., Akhavan, R., Kia Daliri, H., and Mataji, A. (2015). Spatial patterns and intra-specific competition of Chestnut-leaved oak (Quercus castaneifolia C. A. Mey.) using O- ring statistic (Case study: Neka Forest, Iran). Journal of Forest and Poplar Research, 23 (2): 294-306.
[7]. Karimi, M., Pormajidian, M.R., Jalilvand, H., and Safari, A. (2012). Preleminary study for application of O-ring function in determination of small-scale spatial pattern and interaction species (Case study: Bayangan forests, Kermanshah). Iranian Journal of Forest and Poplar Research, 20(4): 608-621.
[8]. Wiegand, T., and Moloney, K.A. (2004). Rings, circles, and null-models for points pattern analysis in ecology. Oikos, 104 (2): 209–229.
[9]. Akhavan, R., Sagheb-Talebi, K., Zenner, E. K., and Safavimanesh, F. (2012). Spatial patterns in different forest development stages of an intact old-growth Oriental beech forest in the Caspian region of Iran. European Journal of Forest Research, 131(5), 1355-1366.
[10]. Batoubeh, P., Akhavan, R., Pourhashemi, M., and Kiadaliri, H. (2013). Determining the minimum plot size to study the spatial patterns of manna Oak trees (Quercus brantii lindl.) using Ripley –K function at less-disturbance stands in Marivan forests. Forest and Wood Products, 66 (1): 27-38.
[11]. Rafieyan. O., Darvishsefat. A. A., Babaii. S., and Mataji, A. (2011). Object-Based Classification of UltraCamD Imagery for Identification of Tree Species in the Mixed Planted Forest. Caspian Journal of Environmental Sciences, 9 (1): 67-79.
[12]. Amini, S., Homayouni, S., Safari, A., and Darvishsefat A., A. (2018). Object-based classification of hyperspectral data using Random Forest algorithm. Geo-spatial Information Science, 21(2):127–138.
[13]. Rahimizadeh, N., Kafaky, S. B., Sahebi, M. R., and Mataji, A. (2020). Forest structure parameter extraction using SPOT-7 satellite data by object-and pixel-based classification methods. Environmental Monitoring and Assessment, 192(1): 43.
[14]. Akhavan, R., Momeni Moghaddam, T., Akbarinia, M., and Hoseini, S. M. (2017). Spatial patterns and intra-specific competition of Juniper tree in different life stages using O- ring statistic in Layen Forests, Iran. Forest and Wood Products, 70 (1): 111-125.
[15]. Zhang, L., Shen, H., Gong, W., and Zhang, H. (2012). Adjustable model-based fusion method for multispectral and panchromatic images. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(6): 1693-1704.
[16]. Jensen, J. R. (2000). Remote Sensing of the Environment an Earth Resource Perspective Prentice Hall. Upper Saddle River (NJ), USA. 544 p.
[17]. Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8 (2):127-150.
[18]. Nourian, N., Joibary, S. S., and Mohammadi, J. (2016). Assessment of different remote sensing data for forest structural attributes estimation in the Hyrcanian forests. Forest systems, 25(3): 9.
[19]. Lu, D., Mausel, P., Brondızio, E., and Moran, E. (2004). Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198(1-3): 149-167.
[20]. Malahlela, O., Cho, M. A., and Mutanga, O. (2014). Mapping canopy gaps in an indigenous subtropical coastal forest using high-resolution WorldView-2 data. International Journal of Remote Sensing, 35(17): 6397-6417.