Management of plant litter or crop residues in agricultural fields is an important consideration for reducing soil erosion and increasing soil organic C. Current methods of quantifying crop residue cover are inadequate for characterizing the spatial variability of residue cover within fields or across large regions. Our objectives were to evaluate several spectral indices for measuring crop residue cover using satellite multispectral and hyperspectral data and to categorize soil tillage intensity in agricultural fields. Landsat Thematic Mapper (TM) and EO-1 Hyperion imaging spectrometer data were acquired over agricultural fields in central Iowa in May and June 2004. Crop residue cover was measured in corn (Zea mays L.) and soybean (Glycine max Merr.) fields using line-point transects. Spectral residue indices using Landsat TM bands were weakly related to crop residue cover. With the Hyperion data, crop residue cover was linearly related to the cellulose absorption index (CAI), which measures the relative intensity of cellulose and lignin absorption features near 2100 nm. Coefficients of determination (r2) for crop residue cover as a function of CAI were 0.85 for the May and 0.77 for the June Hyperion data. Three tillage intensity classes, corresponding to intensive (<15% residue cover), reduced (15–30% cover) and conservation (>30% cover) tillage, were correctly identified in 66–68% of fields. Classification accuracy increased to 80–82% for two classes, corresponding to conventional (intensive + reduced) and conservation tillage. By combining information on previous season's (2003) crop classification with crop residue cover after planting in 2004, an inventory of soil tillage intensity by previous crop type was generated for the whole Hyperion scene. Regional surveys of soil management practices that affect soil conservation and soil C dynamics are possible using advanced multispectral or hyperspectral imaging systems. 相似文献
The present study aims to explore the potential and effectiveness of new Earth Observation data for mapping the vegetation composition and structure and thus provide accurate forest maps to be used in fire propagation simulation models and fire risk assessment. Land cover classification of ASTER and Hyperion images is performed in a detailed nomenclature including different vegetation types and densities since the same vegetation type may give fires with different behaviour as a result of differences in fuel continuity.
The results suggest that both datasets can provide highly accurate maps with an overall accuracy of 85% for ASTER and 93% for Hyperion classification. Although Hyperion is superior to ASTER in terms of overall accuracy, the latter provided a higher thematic accuracy identifying one additional class compared to Hyperion. The evaluation of the classification results in terms of cost and technical characteristics suggest that both datasets are suitable for use in wildfire management tools, depending on the specific user needs, and they could also be used complementary if a combination of high thematic accuracy and locally high spatial accuracy is needed. 相似文献