جستجوی مقالات مرتبط با کلیدواژه "remote sensing indices" در نشریات گروه "اکولوژی"

Remote sensing data provide a high ability to represent habitat characteristics and use in species distribution models. The purpose of this study is to determine the most important of remote sensing predictors, including climatic indices (precipitation and temperature), primary and secondary topographic indices (elevation, slope, direction, Topographic Position Index (TPI) and Terrain Roughness Index (TRI) and remote sensing indices (Global Environmental Monitoring Index (GEMI), Leaf Area Index (LAI), Modified Normalized Difference Water Index (MNDWI), Modified Simple Ratio Index (MSR), Normalized Burn Ratio Index (NBR) and Visible Atmospherically Resistant Index (VARI)) using two species distribution models (Boosted Regression Tree and Random Forest) to predict the presence of J. excelsa in Khalkhal County of Ardabil province and northern part of Zanjan province using SAHM software. The evaluation of prediction models using AUC chart (Area under curve) showed that it is at an excellent level for both the BRT model (0.991) and the RF model (0.974). The most important affecting habitat desirability based on the BRT method include annual precipitation, slope, digital elevation model, temperature, GEMI index and TRI index variables respectively. The most important variables affecting habitat desirability based on the RF method, respectively, include annual precipitation, digital elevation model, GEMI index, slope, VARI index, temperature, MSR index, TRI index, TPI index, NBR index, MNDWI index, LAI index and aspect. The region mapped in the study as suitable habitats’ for the species could be used in the planning strategies with the aim of evaluating the susceptible habitats, the possibility of conservation, reproduction and breeding. Considering that the modeling method choice is the main source of variability in predictions and choosing the best prediction model is not simple, therefore, it is suggested to use a combination of these models instead of relying on the outputs of a single model.

This study aimed to evaluate and compare the structure and function status of some areas with different desertification classes around Gavkhoni International Wetland using Landscape Function Analysis (LFA), and Cover Directional Leakiness Index (CDLI) extracted from Landsat 8 OLI sensor and NDVI, SAVI-A, PD54, and STVI-1 vegetation indices. In this research, six different areas were selected, and two or three transects with a length of 50 meters were established in each area. The length and width of the patches and inter-patches were continuously measured. Then 11 soil surface indicators were assessed in all ecological patches, and inter-patch areas with three replications. The results showed that although range places did not differ significantly in terms of soil stability and nutrient cycling indices, the infiltration index in the areas covered by Haloxylon ammodendron, Seidlitzia rosmarinus and Halocnemum strobilaceum varied significantly compared with the other areas (p < 0.05). According to the results, there were no significant differences between the structure and function indices in the areas with various desertification classes (p > 0.05). The PD54 index showed the best performance among vegetation indices due to its better differentiation of resource leakage between different areas, and it had a high relationship with CDLI (R2 = 71.9 %). There was a negative correlation between CDLI and vegetation cover percentage (p < 0.05). The present study indicated that integrating the results of LFA method with CDLI can be used to evaluate the structure and function status of desert ecosystems appropriately.