Leaf area index estimation in the Zagros forests of Iran using Sentinel-2 image and Gaussian Process Regression

Document Type : Research article

Authors

1 Ph.D. Student, Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Assistant Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran

3 Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran

4 Associate Prof., Department of Forestry, Academic Member of Dr. Hedayat Ghazanfari Center for Research & Development of Northern Zagros Forestry, University of Kurdistan, Sanandaj, Iran

5 Assistant Prof., Department of Remote Sensing, Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic

10.22092/ijfpr.2023.364041.2129

Abstract

Background and objectives: Leaf area index (LAI) is a vital biophysical characteristic to assess the condition, describe forest structure and function of forest ecosystems. LAI is a key input in modeling global climate change, carbon fluxes, water cycle, photosynthesis, and interception processes. The estimation of LAI in forests through remote sensing data, using machine learning models, has gained widespread attention, particularly for large-scale LAI mapping. This method is favored for its efficiency, involving minimal time investment, cost-effectiveness, and a non-destructive approach. This study aimed to investigate the potential of Sentinel-2 data for estimating the LAI of northern Zagros forests, employing the Gaussian Process Regression (GPR) method.
Methodology: LAI field data were collected in June and July 2023 from a coppice forest in the Marivan and Sarvabad counties of Kurdistan province, Iran. A total of 93 square plots, each measuring 20×20 square meters, were randomly selected. The location of each plot was recorded using a DGPS device. The LAI within each plot was measured using the hemispherical photography method. Five photos were captured within each sample using a Coolpix4500+FC-E8 camera equipped with a fisheye lens. The LAI was then calculated for each hemispherical photo and averaged for each sample plot using the “hemispheR” package in the R programming language. A cloud-free Sentinel-2B image with L1C correction level was acquired on July 2, 2023. After verifying the radiometric and geometric quality of the image, the Sen2Cor processor was used to apply atmospheric correction. Different input data, including spectral bands and spectral indices (Vegetation Indices, Tasseled Cap Transformation, and Principal Component Analysis) were generated from the Sentinel-2 image. These datasets, i.e., the spectral bands, spectral indices, and a combination of spectral bands and spectral indices, were used to estimate LAI. The modeling process was carried out using the GPR algorithm based on 65 sample plots (70% of the dataset). The performance of the models was finally evaluated using 28 plots (30% of the dataset) with different metrics such as the coefficient of determination (R2), root mean square error (RMSE), relative root mean square error (rRMSE), and Akaike Information Criterion (AIC).
Results: The descriptive statistics for the measured LAI showed that the minimum, maximum, average, and standard deviation values of the leaf area index over the study area were 0.33, 3.88, 2.129, and 0.627 m2.m-2, respectively. The Pearson correlation analysis between forest LAI and spectral variables (including original bands and spectral indices) indicated a stronger correlation between LAI and spectral indices (i.e., GNDVI, SAVI, and TCTV) than the original bands. Thirty percent of field sample plots were randomly selected and used to evaluate the forest LAI model generated using the GPR machine learning algorithm based on three datasets: original bands, spectral indices, and a combination of original bands and spectral indices, all derived from Sentinel-2 imagery. The evaluation outcomes revealed that the model derived from the main bands of the Sentinel-2 satellite achieved R2 = 0.81, RMSE = 0.21 m2.m-2, rRMSE = 9.14%, and AIC = 103.65. This performance was deemed satisfactory when compared to the performance of models built using the other two datasets (i.e., spectral indices, and a combination of original bands and spectral indices) to estimate LAI. Using the best-performing model, a comprehensive LAI map of the study area was generated using data derived from the main bands of Sentinel-2 imagery.
Conclusion: This study provides preliminary evidence of the potential of Sentinel-2 satellite data in evaluating the leaf area index in the North Zagros coppice forests. However, the integration of ground data of leaf area index and Sentinel-2 data from various growing seasons could potentially enhance the robustness of the results and mitigate uncertainties, thereby paving the way for future research endeavors. This approach could lead to more accurate and reliable assessments of forest health and productivity.
 
 

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References

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