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The present study adopts a game theory approach analyzing land-use planning in Zagros forests, Iran. A Static Game of Incomplete Information (SGII) was applied to the evaluation of participatory forest management in the study area. This tool allows a complete assessment of sustainable forest planning producing two modeling scenarios based on (i) high and (ii) low social acceptance. According to the SGII results, the Nash Bayesian Equilibrium (NBE) strategy suggests the importance of landscape protection in forest management. The results of the NBE analytical strategy show that landscape protection with barbed wires is the most used strategy in local forest management. The response to the local community includes cooperation in conditions of high social acceptance and noncooperation in conditions of low social acceptance. Overall, social acceptance is an adaptive goal in forest management plans.
Mehdi Zandebasiri; José António Filipe; Javad Soosani; Mehdi Pourhashemi; Luca Salvati; Mário Nuno Mata; Pedro Neves Mata. An Incomplete Information Static Game Evaluating Community-Based Forest Management in Zagros, Iran. Sustainability 2020, 12, 1750 .
AMA StyleMehdi Zandebasiri, José António Filipe, Javad Soosani, Mehdi Pourhashemi, Luca Salvati, Mário Nuno Mata, Pedro Neves Mata. An Incomplete Information Static Game Evaluating Community-Based Forest Management in Zagros, Iran. Sustainability. 2020; 12 (5):1750.
Chicago/Turabian StyleMehdi Zandebasiri; José António Filipe; Javad Soosani; Mehdi Pourhashemi; Luca Salvati; Mário Nuno Mata; Pedro Neves Mata. 2020. "An Incomplete Information Static Game Evaluating Community-Based Forest Management in Zagros, Iran." Sustainability 12, no. 5: 1750.
The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R2), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R2 = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R2 = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R2 = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R2 = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only.
Sasan Vafaei; Javad Soosani; Kamran Adeli; Hadi Fadaei; Hamed Naghavi; Tien Dat Pham; Dieu Tien Bui. 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 2018, 10, 172 .
AMA StyleSasan Vafaei, Javad Soosani, Kamran Adeli, Hadi Fadaei, Hamed Naghavi, Tien Dat Pham, Dieu Tien Bui. 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. 2018; 10 (2):172.
Chicago/Turabian StyleSasan Vafaei; Javad Soosani; Kamran Adeli; Hadi Fadaei; Hamed Naghavi; Tien Dat Pham; Dieu Tien Bui. 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, no. 2: 172.
Topography is the most factor that has the greatest impact of all factor that affect the distribution. To study the diversity of trees and shrub species in the Perc forest situated in Khorramabad, Lorestan, 140 circular plots of 1200 m2 in a grid of 300 m × 250 m were surveyed, using a systematic random sampling method. In each plot, the Margalef richness index, Shannon–Wiener diversity index, Hill’s N 1 and Simpson indices and the evenness index of Simpson and Smith-Wilson were calculated and ordered on the basis of different classes of elevation, exposition and slope. The results indicated that slope did not have any significant effect on the indices. Exposition and elevation classes significant impacted the richness and diversity indices, but did not influence evenness. In general, the highest plant diversity was observed for slopes less than 15 %, northern aspects, without geographical direction, and elevations of 2100–2200 m. This information can be very useful in achieving the goals for sustainable management of forests. In addition to greater protection for regions with high diversity and reforestation (compatible species) in degraded area, we can help increase diversity in forests.
Ramin Hosseinzadeh; Javad Soosani; Vahid Alijani; Sheyda Khosravi; Hamdieh Karimikia. Diversity of woody plant species and their relationship to physiographic factors in central Zagros forests (Case study: Perc forest, Khorramabad, Iran). Journal of Forestry Research 2016, 27, 1137 -1141.
AMA StyleRamin Hosseinzadeh, Javad Soosani, Vahid Alijani, Sheyda Khosravi, Hamdieh Karimikia. Diversity of woody plant species and their relationship to physiographic factors in central Zagros forests (Case study: Perc forest, Khorramabad, Iran). Journal of Forestry Research. 2016; 27 (5):1137-1141.
Chicago/Turabian StyleRamin Hosseinzadeh; Javad Soosani; Vahid Alijani; Sheyda Khosravi; Hamdieh Karimikia. 2016. "Diversity of woody plant species and their relationship to physiographic factors in central Zagros forests (Case study: Perc forest, Khorramabad, Iran)." Journal of Forestry Research 27, no. 5: 1137-1141.