研究目的
To explore the capability of airborne hyperspectral sensors and simulated spaceborne multispectral sensors to indirectly predict and map soil properties of tidal marshes, and to evaluate modern image processing techniques including object-based image analysis, machine learning, and ensemble analysis.
研究成果
The framework effectively quantified and mapped marsh soil properties using imaging spectroscopy. Object-based modeling outperformed pixel-based methods. Hyperspectral data provided better accuracy than multispectral data, but WorldView-2 and QuickBird showed promise for monitoring salinity and water content. Ensemble analysis improved robustness and provided uncertainty maps. This approach is a viable alternative to traditional soil data acquisition for carbon cycle research and marsh conservation.
研究不足
Potential sources of error include upscaling from field plots to image objects, time gaps between imagery and field sampling, radiometric mismatch between flight lines, and limited field samples. The models may not be directly applicable across time due to temporal variability in soil properties and vegetation response.
1：Experimental Design and Method Selection:
A framework combining object-based image analysis (OBIA), machine learning modeling, and ensemble analysis was developed. Minimum Noise Fraction (MNF) transformation was used for feature selection. Four machine learning algorithms (ANN, SVM, RF, k-NN) and one parametric method (MLR) were compared.
2：Sample Selection and Data Sources:
346 salt marsh plots (1x1 m) were surveyed along 24 transects at Sapelo Island, Georgia, USA, with soil samples collected for lab analysis of salinity, water content, and organic matter. Hyperspectral data from the AISA Eagle sensor and a marsh species map were used.
3：List of Experimental Equipment and Materials:
Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor, GPS unit for field sampling, laboratory equipment for gravimetric, rehydration, and ignition methods. Software: eCognition Developer
4：0 for image segmentation, WEKA for machine learning modeling. Experimental Procedures and Operational Workflow:
Hyperspectral imagery was radiometrically and geometrically corrected. MNF transformation applied to reduce dimensionality. Image segmentation performed using multi-resolution segmentation. In-situ samples matched to image objects or pixels. Models developed and evaluated using k-fold cross-validation. Ensemble analysis used for final predictions and uncertainty mapping.
5：Data Analysis Methods:
Statistical metrics (correlation coefficient, mean absolute error, percent mean absolute error, root mean squared error) calculated for model performance. ANOVA F-test used for ensemble model selection.
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