Description of a pure seed fraction of oat through usual evaluations and radiographic images

The objective of this work was to evaluate the width and length incidence in a single seed fraction of oat [Avena sativa (L.)] cv. Cristal. The seeds were selected by a mechanical divider and by hand, and their correspondence to radiographic images in seeds with glumes and their caryopses. The width and length of the seeds with glumes and their caryopses were measured with electronic calliper, and their weight, with precision balance. Radiographic images of seeds with glumes were taken with an X-ray experimental equipment. The analyst selected seeds with glumes by the width and by the length previously determined and so with more weight, than that obtained by hand selection was slightly narrower, larger and lighter. The presence of the glumes masked the caryopses real dimensions (width and length), and conduced the analyst to select seeds that differed more by the width than by the length. The radiographic images showed the presence, or not, of caryopses inside the seed and its real dimensions. The mechanical partition method for seeds showed to be more efficient because the analyst subjectivity was not considered when the selection upon its dimensions was done. The X-ray analysis was a useful tool that complements the pure seed fraction selection as another factor of seed quality.

Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil

The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.