Influence de l'état d'ameublissement et de la rugosité du sol des parcelles agricoles sur l'exactitude de l'altitude des points de contrôle positionnés au GPS

Agricultural soil tilth and roughness impact on the exactness of ground control points elevation surveyed by GPS. Our goal in this study is to estimate through ranges of variation, the impact of agricultural parcels soil's tilth and roughness, due to cultivation techniques, on the exactness of ground control points elevation surveyed by RTK (Real Time Kinematic) GPS (Global Positioning System). So, 16 point's elevations which were located each 100 mm on a transect have been surveyed first by using a Total Station (TS), and then a RTK GPS in 2 parcels (3 transects per parcel). Cultivation techniques on those parcels were different. The parcel 1 was tilled, and the soil of parcel 2 was prepared for cereal cropping. Then, the analysis of variance has been applied on the differences of TS and RTK GPS elevations data to estimate the confidence interval of ground control points elevation due to soil tilth, whereas the times series statistical method has been applied on elevation data to estimate the confidence interval due to soil roughness. The confidence intervals of points elevation are estimated being [51 mm; 57 mm], [-4 mm; 4 mm] for parcel 1, and [97 mm; 113 mm], [-35 mm; 23 mm], for parcel 2. Results show that ground control point's elevations exactness is influenced by soil tilth and soil roughness. In conclusion, we can admit that soil tilth and soil roughness have significant impact on the exactness of ground control points located on agricultural parcels. This impact must be considered in Digital Elevation Model (DEM) errors evaluation of agricultural watershed

An Adaptive Method of High Accuracy Surface Modeling and Its Application to Simulating Elevation Surfaces

An adaptive method is employed to speed up computation of high accuracy surface modeling (HASM), for which an error indicator and an error estimator are developed. Root mean-square error (RMSE) is used as the error estimator that is formulated as a function of gully density and grid cell size. The error indicator is developed on the basis of error surfaces for different spatial resolutions, which are interpolated in terms of the absolute errors calculated at sampled points while paying attention to the landform characteristics. The error surfaces indicate the magnitude and distribution of errors in each step of adaptive refinement and make spatial changes to the errors in the simulation process visualized. The adaptive method of high accuracy surface modeling (HASM-AM) is applied to simulating elevation surface of the Dong-Zhi tableland with 27.24 million pixels at a spatial resolution of 10 m 10 m. Test results show that HASM-AM has greatly speeded up computation by avoiding unnecessary calculations and saving memory. In addition, HASM-AM improves simulation accuracy

Representing and reducing error in natural-resource classification using model combination.

Artificial Intelligence (AI) models such as Artificial Neural Networks (ANNs), Decision Trees and Dempster - Shafer's Theory of Evidence have long claimed to be more error-tolerant than conventional statistical models, but the way error is propagated through these models is unclear. Two sources of error have been identified in this study: sampling error and attribute error. The results show that these errors propagate differently through the three AI models. The Decision Tree was the most affected by error, the Artificial Neural Network was less affected by error, and the Theory of Evidence model was not affected by the errors at all. The study indicates that AI models have very different modes of handling errors. In this case, the machine-learning models, including ANNs and Decision Trees, are more sensitive to input errors. Dempster - Shafer's Theory of Evidence has demonstrated better potential in dealing with input errors when multisource data sets are involved. The study suggests a strategy of combining AI models to improve classification accuracy. Several combination approaches have been applied, based on a 'majority voting system', a simple average, Dempster - Shafer's Theory of Evidence, and fuzzy-set theory. These approaches all increased classification accuracy to some extent. Two of them also demonstrated good performance in handling input errors. Second-stage combination approaches which use statistical evaluation of the initial combinations are able to further improve classification results. One of these second-stage combination approaches increased the overall classification accuracy on forest types to 54% from the original 46.5% of the Decision Tree model, and its visual appearance is also much closer to the ground data. By combining models, it becomes possible to calculate quantitative confidence measurements for the classification results, which can then serve as a better error representation. Final classification products include not only the predicted hard classes for individual cells, but also estimates of the probability and the confidence measurements of the prediction