Abstract: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 (R 2 ), 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 (R 2 = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R 2 = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R 2 = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R 2 = 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.

Sustainable Forest Management (SFM) means management of forest resources that consideration the needs of the current generation without risking ability of future generations to attain their needs. Evaluation of SFM needs to design a feedback information system to monitoring of forest resources. In this research, sustainability indicators based on the SMART&D framework were prepared in Tange-Solak local area in Zagros forest, Iran. Based on this, 7 indicators of ecosystem features were provided for evaluation of SFM. Here, Sustainability Index (SI) was used in evaluating SFM via fuzzy membership function. The results reveal that, Forest SI was eventually obtained as 0.15. This number (0.15) was obtained from the Fuzzy approach used in this study for an SI value far lower for forest sustainability compared to the number 1 (maximum value).© JASEM https://dx.doi.org/10.4314/jasem.v21i5.3

ABSTRACT:The aim of this study is to solve the forest park site selection problem using a Fuzzy analytic hierarchy process (FAHP) framework in the Galegol Basin, Lorestan province, Iran. The Delphi screening method was used to select the most relevant criteria and sub-criteria to the forest park problem. Using the FAHP weighting approach, the weight of each criterion and sub-criterion was calculated. Then, the suitability map of forest park location was mapped by the weighted linear combination (WLC) method. The results revealed that 7 criteria (climate, water resources, physiography, landscape, vegetation cover, wildlife and economic criteria) and 16 sub-criteria received the required values and can be involved into the decision-making process of the forest park site selection problem. Using the derived weights of sub-criteria by FAHP and the WLC method, the final results showed that most of the study area is moderately suitable for the forest park location problem. The results of this study can be valuable in the planning of local forest park and future land use planning.

The magnitude and duration of ongoing global warming affects tree growth, especially in semi-arid forest landscapes, which are typically dominated by a few adapted tree species. We investigated the effect of climatic control on the tree growth of Persian oak (Quercus brantii Lindl.), which is a dominant species in the Central Zagros Mountains of western Iran. A total of 48 stem discs was analyzed from trees at three sites, differing in local site and stand conditions (1326 to 1704 m a.s.l.), as well as the level and type of human impact (high human intervention for the silvopastoral site, moderate for the agroforestry site, and low for the forest site). We used principal component analysis (PCA) to investigate the common climatic signals of precipitation, air temperature, and drought (represented by SPEI 1 to 48 months) across the site chronologies. PC1 explains 83% of the total variance, indicating a dominant common growth response to regional climatic conditions that is independent of the local environmental conditions (i.e., forest stand density and land-use type). Growth–climate response analyses revealed that the radial growth of Q. brantii is positively affected by water availability during the growing season (r = 0.39, p < 0.01). Precipitation during April and May has played an ever-important role in oak growth in recent decades. Our study provides evidence that hydroclimatic conditions control tree-ring formation in this region, dominating the effects of topography and human impact. This finding highlights the great potential for combining historical oak samples and living trees from different forest stands in order to generate multi-centennial tree-ring-based hydroclimate reconstructions.

Oak decline has been observed periodically in the different parts of the world. We conducted this study to evaluate the project control in this phenomenon. In this paper, the project control methods have proposed to be useful tools to deal with oak decline. The aim of the study is twofold: (i) define and schedule a set of activities and determine times for those activities in the Control of Forest Decline Project (CFDP) using the Project Evaluation and Review Technique (PERT) method; (ii) apply the Critical Path Method (CPM) within the context on how to reduce the project time by increasing operating costs and crashing the activities. In crisis management, “golden time” is defined for doing activities and controlling the crisis, which has a greater role than other times. The analysis confirmed that the problem of forest decline is an ecological problem and its root lies in participatory management with the local community. We also found that the time crashing is not economically efficient to the CFDP except for two activities: public information and stakeholder analysis.