TAILIEUCHUNG - Biomass and Remote Sensing of Biomass Part 11

Tham khảo tài liệu 'biomass and remote sensing of biomass part 11', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Application of Artificial Neural Network ANN to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems 191 model improved the MAE and RMSE which were and for rangeland and and for forested land respectively. Overall the ANN models explained greater variability and had higher capacity to predict SOM because these models use the non-linear relationships among inputs and output variables. The developed ANN model for predicting the soil organic matter in the present study explained 84 and 91 of the total SOM variability in the rangeland and forest landscapes receptively. Overall the results implied that the ANN modeling was successful in identifying most of the remote sensing data which influence soil organic matter. However our results also suggest that this methodology used for analyzing the data has wider applicability and can be applied to other sites. Fig. 4. Scatter plot displaying the relationships between measured and estimated value of the SOM in MLR and ANN models at the two sites studied in west and central Iran. a MLR for rangeland b MLR for forested land c ANN for rangeland d ANN for forested land. Determining the most important bands for explaining variability in SOM The results on the relative importance of digital numbers and vegetation index using sensitivity analysis based upon coefficients of sensitivity of the ANN model for soil organic matter are shown in Fig. 5. The variables with high values made contributions to explain the variability in SOM. Band 1 of ETM was identified as the most important band for detecting SOM variability in the study area of rangeland Fig. 5a . Other important factors for predicting SOM included 192 Biomass and Remote Sensing of Biomass band 2 and 5 with relative coefficients of sensitivity ranking as and respectively. Two other selected variables included band 7 and the NDVI showed sensitivity coefficient of less than 1 implying that they make lower contribution in .

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