Multitemporal SPOT Images Research Articles

Derivation of vegetation density and land-use type pattern in mountain regions of Jordan using multi-seasonal SPOT images

The mountainous region represents the most important agricultural and biodiversity haven in Jordan. The objective of this study is to characterize the seasonal pattern of land use and vegetation using multi-temporal SPOT images. Multi-temporal SPOT images were analyzed to characterize the land use and cropping pattern in the mountain regions of Jordan. The images were radiometrically corrected using invariant objects located on the image, and a linear inter-calibration method was used to calibrate the other images. A hybrid classification approach was used in the classification; the spectral signatures of the land-use classes were derived in an iterative procedure using the ISODATA and field survey data. Then, the maximum likelihood classification was applied on all images to classify the class signatures into thematic land-use types. The hybrid classification approach gives more accurate classification accuracy especially for the multi-seasonal image classification. The overall accuracy of the multi-temporal data set was achieved with 87.9%, while classification accuracy for single-date classifications were 61.3, 76.8, 72.2, and 65.5 for months of October, February, April, and June, respectively. In addition, the scene combinations that were derived from February and April were classified the land-use types almost as well as those combinations including more scenes. Regarding the classification details, the multi-temporal images enable higher level of classification for land-use types such as Anderson level 2, and produce accurate boundaries for the different cropping and farming systems.

Digital zoning of South African viticultural terroirs using bootstrapped decision trees on morphometric data and multitemporal SPOT images

The digital mapping of homogeneous zones that are likely to produce grapes or wines of similar composition (viticultural terroirs) is currently developed at the field scale but not at the regional scale. This study proposes to map viticultural terroirs using bootstrapped regression trees on distinct combinations of morphometric data (elevation, slope, aspect and wetness index) and/or 20-m SPOT satellite images at four dates over the Stellenbosch viticultural area (South Africa). Expert knowledge on previously characterized grape and vine quality at 55 distinct Sauvignon blanc vineyards was available. Randomly sampled fractions of these observations were used as a basis for tree calculation for the mapping of 8 terroir units through the QUEST algorithm. For each of the 7 data layer combinations, the running of 100 QUEST iterations resulted in 100 classified images which were stacked in a “hyperclassified” image. The modal class of the hyperclassified image was computed at each pixel providing a “mode image” proposed as the final zoning result, whereas the class assignment frequencies provided a map of classification uncertainty. This approach is well adapted to the mixed 20-m pixels typical of viticultural environments. Provided that both radiometric and morphometric information are used to construct the model, no confusion occurs with non-viticultural land use. Four of the 8 terroir units could be modelled with a higher frequency of correct prediction. The combination of data layers that gave the highest percentage of correctly classified pixels in the validation sets (52–78% median accuracy) consisted of the 4 satellite images, elevation, slope and aspect.

Automatic cloud removal from multi-temporal SPOT images

Partial cloud cover is a severe problem in the optical remote sensing images. The problem can be mostly overcome by mosaicking the cloud-free areas of the multi-temporal images. In this paper, multidisciplinary methods are proposed to generate cloud-free mosaic images from multi-temporal SPOT images in three steps. At first, the original images are enhanced in both brightness and chromaticity. Secondly, the linear spectral unmixing method (LSU) is used to extract all cloud cover regions. Then, we choose the base image that has the least thin-cloud cover and divide the base image into grid zones. We find the thin-cloud and cloud-shadow zones in the eight neighbors of the thick-cloud zones based on the relative locations and the sun elevation angle. At last, the cloud and cloud-shadow zones of the base image are replaced by the same-location cloud-free zones on other images. Between each two zones of the base image and the replacing image, we create a transition zone. The multiscale wavelet-based fusion method is then used to fuse the pixels in the zones to generate cloud-free satellite images. Based on our complete and sophisticated approach, the high-quality fused results are produced from the source images that have variant brightness.

Earthquake-induced landslide hazard and vegetation recovery assessment using remotely sensed data and a neural network-based classifier: a case study in central Taiwan

A catastrophic earthquake with a Richter magnitude of 7.3 occurred in the Chi-Chi area of Nantou County on 21 September 1999. Large-scale landslides were generated in the Chiufenershan area of Nantou County in central Taiwan. This study used a neural network-based classifier and the proposed NDVI-based quantitative index coupled with multitemporal SPOT images and digital elevation models (DEMs) for the assessment of long-term landscape changes and vegetation recovery conditions at the sites of these landslides. The analyzed results indicate that high accuracy of landslide mapping can be extracted using a neural network-based classifier, and the areas affected by these landslides have gradually been restored from 211.52 ha on 27 September 1999 to 113.71 ha on 11 March 2006, a reduction of 46.24%, after six and a half years of assessment. In accordance with topographic analysis at the sites of the landslides, the collapsed and deposited areas of the landslide were 100.54 and 110.98 ha, with corresponding debris volumes of 31,983,800 and 39,339,500 m3. Under natural vegetation succession, average vegetation recovery rate at the sites of the landslides reached 36.68% on 11 March 2006. The vegetation recovery conditions at the collapsed area (29.17%) are shown to be worse than at the deposited area (57.13%) due to topsoil removal and the steep slope, which can be verified based on the field survey. From 1999 to 2006, even though the landslide areas frequently suffered from the interference of typhoon strikes, the vegetation succession process at the sites of the landslides was still ongoing, which indicates that nature, itself, has the capability for strong vegetation recovery for the denudation sites. The analyzed results provide very useful information for decision-making and policy-planning in the landslide area.

A Normalization Scheme of Bidirectional Reflectances and Normalized Vegetation Indices for Short-Term Multi-Angular Spot Satellite Images

Though the view angle range of SPOT HRV is smaller as compared to those of NOAA AVHRR, EOS MODIS/MISR and ADEOS POLDER, numerous researchers had showed significant bi-directional effect of ground reflectance for decades. Currently, there’s no existing normalization method of bidirectional reflectances for multi-temporal SPOT images. In this study, a normalization scheme for multi-angular SPOT data is proposed. This scheme will correct both atmospheric and bidirectional effect for short-term multi-temporal SPOT data. Five SPOT images scanned within twelve days are used. The results show that the variation of trees reflectance to viewing zenith angle is very significant. The proposed normalization scheme can satisfactorily correct viewing zenith angle effect of bidirectional reflectances of trees. Normalized vegetation indices are also developed and successfully shown to reduce the viewing angle effect of Normalized Difference Vegetation Index (NDVI). Soil Adjusted Vegetation Index (SAVI) is found to be less dependent on viewing zenith angle than NDVI and normalized VIs in the azimuthal plane away from the principal plane. This may be due to the effectiveness of SAVI in the reduction of viewing zenith angle effect induced by the soil and shadow and the error of the atmospheric correction model.

Interpretation of vegetation indices derived from multi-temporal SPOT images

A temporal sequence of 15 SPOT HRV images and low altitude aircraft reflectance data were obtained over soil and cotton fields at the Maricopa Agricultural Center, Arizona from April to October 1989. The SPOT data included different view angles (− 28° − + 24°) while the aircraft data were obtained with a nadir-viewing radiometer equipped with SPOT filters. View angle/direction, atmosphere, and soil influences on individual band and vegetation indices were observed in the SPOT data. The relative magnitude among the three influences was dependent on surface conditions, varied with canopy growth, and was different for red and near-infrared (NIR) reflectances and vegetation indices (VIs). View angle effects were most pronounced in red and NIR reflectance data and were secondary with the use of VIs. View angle variations, and to some extent soil variations, influenced VI responses from partial canopies while relative atmospheric influences became most dominant at higher densities of vegetation. The results suggest that the current compositing routine for temporal vegetation index imagery may not be entirely adequate.

Road extraction from multi-temporal satellite images by an evidential reasoning approach

Road networks extracted from multi-temporal SPOT images of the same scene are matched to collect evidence for individual road segments. The Dempster—Shafer theory is applied to find a degree of confirmation for a road segment in one network based on its corresponding lines in the other networks.