Volume 10, Issue 19 (5-2022)
ifej 2022, 10(19): 88-98 |
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Sari Agricultural Sciences and Natural Resources University
Abstract: (2228 Views)
Introduction and Objective: In a proper planning of the forest sector, the preparation of quantitative and qualitative maps is a management requirement and is unavoidable for sustainable development. For this effective planning, the latest information on the types of characteristics that are important in decisions regarding the optimal use and protection of forests are needed. This study conducted in order to the modelling spatial estimation of some quantitative characteristics forest using topographic features and its spatial mapping in District-3 of Sangdeh Forests.
Material and Methods: Characteristics of number, basal area and volume per hectare were calculated by surveying 150 sample plots (1000 m2). Primary topographic features of altitude, slope, aspect, profile curvature, flat curvature and tangential curvature and secondary topographic features including wetness index and solar radiation were extracted from digital elevation model with 10m resolution. Then, the relationships between forest quantitative characteristics and topographic features were analyzed and modeled using non-parametric method random forest, support vector machine and also parametric multiple linear regressions. Models were evaluated using 30% of the samples.
Results: Bias and root mean square error percentages were calculated to select the appropriate model and the results showed that the support vector machine method had the best results for estimating all three measured characteristics. In estimating number per hectare, the polynomial-3 function with mean square error values and skewness is RMSE = 9.59 and Bias = 1.62, ground cover with radial base function (RBF) and values of 53.5, respectively. 30% RMSE and 1.32 - =% Bias and volume per hectare with polynomial function of third degree and values of RMSE = 37.62 and Bias = -0.51 were selected as the most appropriate model. The results also showed that the topographic variables of aspect, altitude, solar radiation and tangential curvature had the most influence on the modeling process.
Conclusion: The selected model in this study was able to provide some of the necessary information for forest management, but the model alone cannot explain all the reasons affecting the characteristics, so it is recommended to combine other factors such as climatic conditions, latitude, geology, and remote sensing techniques, which play a major role in explanation and interpretation, to improve the accuracy of the forecast.
Material and Methods: Characteristics of number, basal area and volume per hectare were calculated by surveying 150 sample plots (1000 m2). Primary topographic features of altitude, slope, aspect, profile curvature, flat curvature and tangential curvature and secondary topographic features including wetness index and solar radiation were extracted from digital elevation model with 10m resolution. Then, the relationships between forest quantitative characteristics and topographic features were analyzed and modeled using non-parametric method random forest, support vector machine and also parametric multiple linear regressions. Models were evaluated using 30% of the samples.
Results: Bias and root mean square error percentages were calculated to select the appropriate model and the results showed that the support vector machine method had the best results for estimating all three measured characteristics. In estimating number per hectare, the polynomial-3 function with mean square error values and skewness is RMSE = 9.59 and Bias = 1.62, ground cover with radial base function (RBF) and values of 53.5, respectively. 30% RMSE and 1.32 - =% Bias and volume per hectare with polynomial function of third degree and values of RMSE = 37.62 and Bias = -0.51 were selected as the most appropriate model. The results also showed that the topographic variables of aspect, altitude, solar radiation and tangential curvature had the most influence on the modeling process.
Conclusion: The selected model in this study was able to provide some of the necessary information for forest management, but the model alone cannot explain all the reasons affecting the characteristics, so it is recommended to combine other factors such as climatic conditions, latitude, geology, and remote sensing techniques, which play a major role in explanation and interpretation, to improve the accuracy of the forecast.
Type of Study: Research |
Subject:
Special
Received: 2020/01/12 | Accepted: 2020/03/9 | Published: 2022/06/13
Received: 2020/01/12 | Accepted: 2020/03/9 | Published: 2022/06/13
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