Volume 16, Issue 3 (11-2022)
مرتع 2022, 16(3): 497-509 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
ALimahmodi Sarab S, Tarnian F. Mapping of forage Production in Poor Rangelands Haftkel Rangelands Using Sentile-2 Images. مرتع 2022; 16 (3) :497-509
URL: http://rangelandsrm.ir/article-1-1057-en.html
URL: http://rangelandsrm.ir/article-1-1057-en.html
Abstract: (981 Views)
Background and objectives: Determination of the exact rangelands forage production could be of great help to rangeland managers in sake of proper stocking rate. With implementing proper sampling design, remote sensing data could be used to estimate forage production due to the extent of rangelands areas, cost, time consumption and other problems in field data gathering. The objectives of this study were to select the best model and the best remote sensing index in order to map forage production using field data and vegetation indices of NDVI, SAVI, MSAVI2, DVI and GCI extracted from satellite images of Sentinel 2.
Methodology: A sampling network with a total of 58 plots (1×1 meters) were established in the studied area and cut and weight method was used to measure forage production. Then, vegetation indices of NDVI, SAVI, MSAVI2, DVI and GCI were created with SNAP software. The values of the mentioned indices were extracted from the location of the plots, using the ArcGIS 10.4 software. The normality of the data was checked by the Kolmogorov Smirnov test. Then their relationships were analyzed with regression in SPSS 16 software. Also, multiple linear regression was used to investigate the relationship between plant indicators and forage production. The train model was created by 70% of the total plots and 30% of the data were used to test the model. Coefficient of determination (R2) and root mean square error (RMSE) were used to select the best model and index. Finally, the selected model was used to create the map of forage production (Kg/hec), using ArcGIS 10.4. The values of final map as the estimated data and a total of 58 plots as observed data were evaluated by independent t-test.
Results: The results related to the relationship between forage production and plant indices with univariate linear regression showed that all tested indices had a significant relationship with forage production. The univariate linear regression model with MSAVI2 index had the highest coefficient of determination and the lowest RMSE (Y= 649.3-8523.7×MSAVI2; R2= 0.68 and RMSE= 16). The results also showed that the accuracy of the DVI index (R2= 0.66; RMSE= 19) was higher than the NDVI index (R2= 0.58; RMSE= 22) for estimating forage production in studied area. By applying the assumptions of multivariate linear regression model, only two indices of GCI and MSAVI2 were included in the model, and the amount of R2 and RMSE were the same as univariate linear model with MSAVI2 index. The results of independent t-test indicated that there were not significant differences between observed data and the ones estimated by selected model (p<0.05). The minimum, mean and maximum of forage production in the final map were 10, 220 and 475 kg/hec., respectively.
Conclusion: According to the equality of the root mean square error and the coefficient of determination of the multiple and linear regression models and also the results of independent t-test that indicated no significant difference between observed and estimated forage production, we suggest using the MSAVI2 index to estimate forage production in warm semi-arid rangelands.
Methodology: A sampling network with a total of 58 plots (1×1 meters) were established in the studied area and cut and weight method was used to measure forage production. Then, vegetation indices of NDVI, SAVI, MSAVI2, DVI and GCI were created with SNAP software. The values of the mentioned indices were extracted from the location of the plots, using the ArcGIS 10.4 software. The normality of the data was checked by the Kolmogorov Smirnov test. Then their relationships were analyzed with regression in SPSS 16 software. Also, multiple linear regression was used to investigate the relationship between plant indicators and forage production. The train model was created by 70% of the total plots and 30% of the data were used to test the model. Coefficient of determination (R2) and root mean square error (RMSE) were used to select the best model and index. Finally, the selected model was used to create the map of forage production (Kg/hec), using ArcGIS 10.4. The values of final map as the estimated data and a total of 58 plots as observed data were evaluated by independent t-test.
Results: The results related to the relationship between forage production and plant indices with univariate linear regression showed that all tested indices had a significant relationship with forage production. The univariate linear regression model with MSAVI2 index had the highest coefficient of determination and the lowest RMSE (Y= 649.3-8523.7×MSAVI2; R2= 0.68 and RMSE= 16). The results also showed that the accuracy of the DVI index (R2= 0.66; RMSE= 19) was higher than the NDVI index (R2= 0.58; RMSE= 22) for estimating forage production in studied area. By applying the assumptions of multivariate linear regression model, only two indices of GCI and MSAVI2 were included in the model, and the amount of R2 and RMSE were the same as univariate linear model with MSAVI2 index. The results of independent t-test indicated that there were not significant differences between observed data and the ones estimated by selected model (p<0.05). The minimum, mean and maximum of forage production in the final map were 10, 220 and 475 kg/hec., respectively.
Conclusion: According to the equality of the root mean square error and the coefficient of determination of the multiple and linear regression models and also the results of independent t-test that indicated no significant difference between observed and estimated forage production, we suggest using the MSAVI2 index to estimate forage production in warm semi-arid rangelands.
Type of Study: Research |
Subject:
Special
Received: 2021/09/24 | Accepted: 2021/12/29 | Published: 2022/11/1
Received: 2021/09/24 | Accepted: 2021/12/29 | Published: 2022/11/1
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
