Evaluation of sustainable forest management of Iran's Zagros forests

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).Keywords: Fuzzy logic, Criteria, indicators, Sustainability Index (SI

Evaluation of the crisis severity in forests of Kohgiluye and Boyerahmad province (Case study: Tang-e Solak)

In this study, some forest biometric parameters were studied to evaluate the decline severity in Tang-e Solak forest area in Kohgiluyeh and Boyerahmad province. Hundred plots of one hectare each were used. The Fierke method was used to evaluate the decline severity. In this method, crown condition classes (CCC) and Basal Emergence hole Classes (BEC) are divided into 6 groups. Finally Rapid Estimation Index (REI) was calculated by aggregating CCC and BEC. REI was then classified into tree classes for determining the dieback severity. The result of this study showed that 88% of broad-leaved trees are in weak severity, while only 12% include medium severity of decline

Application of aerial photographs and satellite images for visualization of forest cover changes (Case study: Zagros forests, Iran)

Visualization is one of the best methods to represent data from natural environments. In this research, the variation of the forest cover was visualized at a part of Zagros forests (741 hectare) using aerial photographs and satellite images. Geometric correction and relief displacement was done on 1997 aerial photographs. Aerial photographs of 1955 and 1969 were corrected by approximate image correction method using georefrenced images. Forest boundaries were determined on all of the images by visual interpretation techniques, However classification of 1997 aerial photographs and satellite image were done automatically using Maximum likelihood classifier. Aerial photographs of 1955 and 1969 were classified by dot grid. Shadow index was specified by determining the accurate canopy amount on 74 random trees and compare them with data deduction from images. Likewise, field data were collected using 141 circular 1200m sample plots in a systematic random grid (100m×300m). With preparation of essential data, visualizations were done on different scale in the 3DNature software. Results indicated the notable reduce of forest area (37%) since 50 years ago. Maximum amount of canopy drop (45%) have been occurred from 1955 to 1969. Canopy percent on forest region indicate ascending trend (+15%) from 1969 to 1997 and lightly descending trend from 1997 to 2003

Evaluation level of tree diversity in the Hyrcanian forests using complex structural diversity index (Case study: beech-hornbeam type, Nav-e Asalem, Gilan)

Stand structure and species diversity are two useful parameters for complex assessment of forest biodiversity, and provide important information for management and decision making for appropriate silvicultural system. For this purpose, five one ha plots were selected randomly in homogeneous ecological units of beech-hornbeam type in Nav-e Asalem, Gilan province. In order to determine complex structural diversity index (SI) in the studied forest type, uniform angle, mingling and DBH dimensions difference indicators were calculated. Also in order to determine density of trees, the nearest neighbors distance index was used. According to the results, the average value of nearest neighbors distance index was calculated to 5.58 meter. Mean uniform angle index was 0.52, which indicates clumped distribution of trees. Mean mingling index (0.45) indicates moderate mixture of the studied mixed stand. The amount of DBH dimensions difference index was 0.47, indicates moderate difference between trees diameter at breast height. The complex structural diversity index which presents three dimensions of structure, including diversity of spatial pattern, species diversity, and diversity of DBH dimensions) was calculated to 0.475. This value confirms a high level of tree diversity. The results of this study provide key information for management and maintaining of tree diversity in the studied mixed beech-hornbeam stand. Also with monitoring and management of tree diversity level we can reduce the negative impact of natural and human factors

Analysis of Zagros forest structure using neighborhood-based indices (Case study: Ghalehgol forest, Khorramabad)

Analysis of forest structure is essential for enhanced understanding of forest ecology and management. In this study, the spatial structure of the existing species in Perk district of Ghaleh Gol site in Lorestan Province was explored. To this end, a 32-hectare region was 100% surveyed. To investigate the spatial structure, we used a set of indices including the Clark and Evans, uniform angles, Shannon-Wiener, mingling, Crown canopy and Crown canopy differentiation indices. The results showed the average values of 0.8 and 0.47 for Clark and Evans and uniform angles indices, respectively. This indicated random and cluster distribution patterns. In addition, Mean values of 0.25 and 0.06 were returned by Shannon - Weiner and mingling indices. Due to the dominant Oak coverage within the study area, Blend Low Index was additionally calculated. The mean Crown Canopy Index of 0.5 turned out a canopy dominance of Quercus  brantii, Acer cineracense, Crataegus sp. and Pyrus syriaca  over Lonicera nomularifolia and Amygdalus sp.. Moreover, the Crown Canopy Differentiation Index was calculated to quantify the differences between the levels of crown canopy in adjacent trees. This returned a mean value of 0.48 for the entire trees, which reflects the difference between the tree canopy levels. The results showed that the study site is currently undergoing an inappropriate biodiversity for woody species which yet shows better conditions compared to similarly-structured stands in the region. As a conclusion, proper forest planning measures should be carried out to prevent the recently experienced consistent loss of biodiversity

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)

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

Simulation of plantation map using the spatial pattern of natural trees to restore degraded forests in Zagros region

To design an optimal plantation map for reforestation, a convenient and accurate method is required to analyze plant communities as well as to simulate the plantation map using the spatial pattern of natural trees. In this study, a new algorithm was designed to simulate a plantation map in Zagros forests. To accomplish this, ten hectares of intact stands located in 45 km south of Khorramabad city in Lorestan province was selected. A set of previously-developed algorithms were evaluated to design a new algorithm. The map extracted from the algorithm showed that the required conditions in drawing the map were met. In addition, an appropriate pattern of plantation in different areas can be reached by using the results of this algorithm. Since this study is the first study of its own in Iran, further investigations are required to remove the weak points. Therefore, it is suggested that the presumably existing weak points may be best removed by applying this algorithm in various regions, as well as by integrating effective indexes such as soil type, height from the sea level and physiographic factors. Being based on computer simulations, the results obtained here should be further experimented in different study sites within Zagros region, followed by a thorough evaluation of their plausibility under natural forest conditions

Estimation of aboveground biomass using optical and radar images (Case study: Nav-e Asalem forests, Gilan)

Using remote sensing data is an applied method to estimate above ground biomass. In this study, satellite radar data of ALOS-2, with the full polarization and the optical data of Sentinel-2, has been used to estimate the aboveground biomass in the Nav-e Asalem forests, Gilan province. The backscattering coefficients at different polarization, the texture measures and target decomposition features of SAR images, obtained original and synthetic bands from optical images in three different combinations of radar images, optical images and the composition of radar and optical images, as inputs to the Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models were used. In order to measure aboveground biomass, 149 sample plots were laid out. Evaluation of ANN and MLR models using R2 and RMSE statistics showed that in all cases the ANN was better performance to estimate the aboveground biomass than MLR. The best results showed that the ANN from combined optical and radar data with R2 and RMSE, 0.86 and 31.62 Mg/ha (15.34%), respectively, can be the best applied method to estimate the aboveground biomass. The results of radar images and optical separately, with the R2 and RMSE for the modeling of aboveground biomass have been shown, respectively, 0.57 and 49.17 Mg/ha (23.85%) by radar images and 0.7 and 39.53 Mg/ha (19.17%) by the optical images, superior modeling to estimate aboveground biomass represents by optical imaging. The overall and more accurate results to estimate of aboveground biomass have been shown when we used combined radar and optical images with the ANN model

Capability investigation on spectral images of Ikonos from leaveless season for Box (Buxus hyrcana Pojark.) understory distribution mapping in the Hyrcanian forest (Case study: Khiboos-Anjilsi Buxus reserved area, Mazandaran)

As one of the most important understory evergreen species in Hyrcanian forests of Iran, information on the distribution of  Box  (Buxus Hyrcana Pojark.) are essential for both forest research and practice. Here, the capability of very high spatial resolution IKONOS satellite imagery acquired in leaf-off condition was tested for mapping Box distribution in a part of Khiboos-Anjili forest reserve in Mazandaran province. The IKONOS imagery was geometrically corrected with a georefrenced panchromatic Pleaides scene, which was orthorectified using 3D ground control points obtained using differential GPS (RMSE less than one pixel). Reference data samples from three classes of non-forested area, deciduous stands without Box understory and deciduous stands with Box understory were recorded using DGPS-supported field survey. By means of a number of vegetation indices, classes seperabilities were evaluated on main and synthetic image channels by partitioning 75% training area and transformed divergence. IKONOS image was classified using both main and best-selected bands and a number of nonparametric (Maximum Likelihood, Mahalonobis distance, Minimum distance to mean and Paralell piped) and parametric (Suport Vector Machine) classifiers. Then the classified images were assessed using 25 percent of unused sample points. Results of validation using the 25% left-out test data showed the highest performance by SVM algorithm compared to other algorithms, with overall accuracy and Kappa coefficient of 97.87% and 0.96, respectively. The results also showed the potential of IKONOS imagery from leaf-off season has to map Box trees in understory layer

Canopy cover estimation across semi-Mediterranean woodlands: application of high-resolution earth observation data

"The semi-Mediterranean Zagros forests in western Iran are a crucial source of environmental services, but are severely threatened by climatic and anthropological constraints. Thus, an adequate inventory of existing tree cover is essential for conservation purposes. We combined ground samples and Quickbird imagery for mapping the canopy cover in a portion of unmanaged Quercus brantii stands. Orthorectified Quickbird imagery was preprocessed to derive a set of features to enhance the vegetation signal by minimizing solar irradiance effects. A recursive feature elimination was conducted to screen the predictor feature space. The random forest (RF) and support vector machines (SVMs) were applied for modeling. The input datasets were composed of four sets of predictors including the full set of predictors, the four original Quickbird bands, selected vegetation indices, and the soil line-based vegetation indices. The highest r2 and lowest relative root mean square error (RMSE) were observed in modeling with total indices and the full data set in both modeling methods. Regardless of the input dataset used, the RF models outperformed the SVM by returning higher r2 and lower relative RMSEs. It can be concluded that applying these methods and vegetation indices can provide useful information for the retrieval of canopy cover in mountainous, semiarid stands which is crucial for conservation practices in such areas.