Iranian Journal of Forest

Iranian Journal of Forest

Oak Forest Canopy Cover Estimation using Landsat 9 data in the Northern Zagros Forests

Document Type : Research Paper

Authors
S Ahmadi 1
P. Fatehi 2
M. Namiranian 3
N. Miri 4
1 M.Sc., Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, I. R. Iran
2 Assistant Prof., Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran. I.R. Iran
3 Prof., Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran. I.R. Iran
4 Ph.D. Student, Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran. I.R. Iran
10.22034/ijf.2025.457774.1989
Abstract
Introduction: Forest Canopy Cover (FCC) is one of the most important structural characteristics in monitoring health and quality of forest stands. Measuring this characteristic based on ground methods requires a lot of cost and time, and it is extremely difficult to implement on a large scale. Today, use of remote sensing techniques can compensate for the limitations of terrestrial methods. The present study was conducted with the aim of investigating the efficiency of Landsat 9 satellite in modeling and estimating the canopy cover of the northern Zagros oak forests.
Materials and methods: In order to collect FCC field data, 79 square plots with dimensions of 45×45 m were implemented based on systematic-random sampling method with 200×200 m grid size in summer 2023 in a part of Baneh forests, Kurdistan province. In each plot, the large diameter and the perpendicular diameter of the crown of trees or shoots were measured using a tape measure. Finally, FCC values were calculated for each plot based on the crown area of each tree or stem. In the present study, a frame of the OLI-2 sensor data of the Landsat 9 satellite at the Collection 2 Level 1 correction level was received on July 23, 2023. After verifying the geometrical quality of the image and performing atmospheric correction using FLAASH method, Vegetation Indices, Principal Component Analysis (PCA), and Tasseled Cap Transformation were performed on the image. In total, the number of 35 computational bands and 7 original usage bands and the spectral values of these 42 spectral variables obtained from Landsat 9 data were extracted using the map of field plots. In the following, FCC using stepwise regression (MLR) and non-parametric Random Forest (RF) and Support Vector Machine (SVM) statistical models based on three sets of spectral variables (original bands, computational bands and combination of original and computational bands) and 70% of the data were modeled. Finally, the regression models were evaluated and fitted using the coefficient of determination (R2), root mean square error (RMSE) and root mean square relative error (rRMSE) statistics using cross-validation method based on 30% of data.
Results: Investigation of FCC data measured on the field data on descriptive statistics showed that the researched forest is in a semi-dense condition. Pearson's correlation analysis conducted to investigate the relationship between FCC and spectral variables showed that vegetation indices are more sensitive to FCC changes. The results of modeling using 70% of the samples showed that the model obtained from RF is based on the combination of the original and computational bands with R2 = 0.88 and rRMSE = 16.95 as the optimal model in estimating the amount of canopy. The evaluation of the relative importance of the spectral variables used in the obtained model showed that NDNIR.SWIR2 has the highest influence in the RF modeling process and PCA of band 7 (PCA.B7) has the least importance. Validation of the obtained models using cross-validation showed that the RF model obtained from the original bands of the Landsat 9 image with of R2 = 0.85 and rRMSE = 15.65 was selected as the best model for forest canopy estimation and mapping.
Conclusion: In general, this research showed that by using the original bands of Landsat 9 satellite data based on the RF machine learning algorithm, it is possible to estimate and map the FCC in the traditionally managed forests of North Zagros with reasonable accuracy.
Keywords
Forest Canopy Cover
Landsat 9
Machine Learning Approach
Zagros Oak Forest

Subjects

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Iranian Journal of Forest
Volume 16, Issue 4 - Serial Number 4
Winter 2025
Pages 471-488

  • Receive Date 21 May 2024
  • Revise Date 28 September 2024
  • Accept Date 16 August 2024