Mapping vegetation in a late Quaternary landform of the Amazonian wetlands using object-based image analysis and decision tree classification

Fan-shaped morphologies related to late Quaternary residual megafan depositional systems are common features over wide areas in northern Amazonia. These features were formed by ancient distributary drainage systems that are in great contrast to tributary drainage networks that typify the modern Amazon basin. The surfaces of the Amazonian megafans constitute vegetacional mosaic wetlands with different campinarana types. A fine-scale-resolution investigation is required to provide detailed classification maps for the various campinarana and surrounding forest types associated with the Amazonian megafans. This approach remains to be presented, despite its relevance for analysing the relationship between stages of plant succession and sedimentary dynamics associated with the evolution of megafans. In this work, we develop a methodology for classifying vegetation over a fan-shaped megafan palaeoform from a northern Amazonian wetland. The approach included object-based image analysis (OBIA) and data-mining (DM) techniques combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, land-cover fractions derived by the linear spectral mixing model, synthetic aperture radar (SAR) images, and the digital elevation model (DEM) acquired during the Shuttle Radar Topography Mission (SRTM). The DEM, vegetation fraction, and ASTER band 3 were the most useful parameters for defining the forest classes. The normalized difference vegetation index (NDVI), ASTER band 1, vegetation fraction, and the Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) transmitting and receiving horizontal polarization (HH) and transmitting horizontal and receiving vertical polarization (HV) were all effective in distinguishing the wetland classes campinarana and Mauritia. Tests of statistical significance indicated the overall accuracies and kappa coefficients (κ) of 88% and 0.86 for the final map, respectively. The allocation disagreement coefficient of 5% and a quantity disagreement value of 7% further attested the statistical significance of the classification results. Hence, in addition to water, exposed soil, and deforestation areas, OBIA and DM were successful for differentiating a large number of open (forest, wood, shrub, and grass campinaranas), forest (terra firme, várzea, igapó, and alluvial), as well as Mauritia wetland classes in the inner and outer areas of the studied megafan.     

Spring thaw classification based on AMSR-E brightness temperature in the central Tibetan Plateau

Changes in soil freeze/thaw dynamics in the Tibetan Plateau (TP) had significant influences on regional hydrology and ecosystem processes. We developed an algorithm to detect spring thaw onset in the central TP using microwave brightness temperature (T_b) data. We assumed that the T_b at lower frequencies is more sensitive to changes in soil freeze/thaw status, while the T_b at higher frequencies is subjected more to scattering effects of snow cover and vegetation. Therefore, the standard deviation of brightness temperature at 6-GHz horizontal polarization was used to detect soil F/T status, and a scattering index based on higher frequencies was used to constrain the scattering effects of snow cover. The algorithm was calibrated and validated with surface ground temperature and daily minimum air temperature. Results showed that our algorithm had a mean bias of 3.7 ~ 17.6 days and a RMSE of 7.5 ~ 19.7 days, and this method behaved better than the previous one based on all-frequency standard deviation of microwave T_b, which had a mean bias of 8.3 ~ 41.2 days and a RMSE of 13.4 ~ 27.6 days. Further validation is needed over more extensive area with diverse surface conditions.     

Comparing the effects of classification techniques on landscape-level assessments: pixel-based versus object-based classification

Landscape-level assessments, particularly the quantification of forest fragmentation, often involve calculating landscape metrics from classified remotely sensed images. The utility of these derived metrics is often assumed to be dependent on the quality of the classified images. We compared conventional, pixel-based classification and a newer method of object-based classification to determine the effects of these two methods on fragmentation analysis of Cockpit Country, Jamaica, West Indies. Both methods showed similar trends in fragmentation metrics; however, there were significant differences between the methods for the metrics that quantified landscape configuration. The object-based classification allowed for the easy inclusion of roads into the analysis, which produced more accurate maps that showed a significant difference in the size of the largest forest patch. The object-based method also allowed classification of forests to show the location and extent of core forest areas; we were therefore able to identify an area of core forest that had remained consistent over the study period as a significant area for conservation focus. We recommend that the object-based method be the method chosen for landscape analyses, particularly forest-fragmentation studies.     

Comparison of pixel- and object-based classification in land cover change mapping

Land use/land cover (LULC) change occurs when humans alter the landscape, and this leads to increasing loss, fragmentation and spatial simplification of habitat. Many fields of study require monitoring of LULC change at a variety of scales. LULC change assessment is dependent upon high-quality input data, most often from remote sensing-derived products such as thematic maps. This research compares pixel- and object-based classifications of Landsat Thematic Mapper (TM) data for mapping and analysis of LULC change in the mixed land use region of eastern Ontario for the period 1995–2005. For single date thematic maps of 10 LULC classes, quantitative and visual analyses showed no significant accuracy difference between the two methods. The object-based method produced thematic maps with more uniform and meaningful LULC objects, but it suffered from absorption of small rare classes into larger objects and the incapability of spatial parameters (e.g. object shape) to contribute to class discrimination. Despite the similar map accuracies produced by the two methods, temporal change maps produced using post-classification comparison (PCC) and analysed using intensive visual analysis of errors of omission and commission revealed that the object-based maps depicted change more accurately than maximum likelihood classification (MLC)-derived change maps.     

Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification

Reference polygons are homogenous areas that aim to provide the best available assessment of ground condition that the user can identify. Delineation of such polygons provides a convenient and efficient approach for researchers to identify training and validation data for supervised classification. However, the spatial dependence of training and validation data should be taken into account when the two data sets are obtained from a common set of reference polygons. We investigate the effect on classification accuracy and the accuracy estimates derived from the validation data when training and validation data are obtained from four selection schemes. The four schemes are composed of two sampling designs (simple random and systematic) and two methods for splitting sample points between training and validation (validation points in separate polygons from training points and validation points and training points split within the same polygons). A supervised object-based classification of the study region was repeated 30 times for each selection scheme. The selection scheme did not impact classification accuracy, but estimates of overall (OA), user's (UA), and producer's (PA) accuracies produced from the validation data overestimated accuracy for the study region by about 10%. The degree of overestimation was slightly greater when the validation sample points were allowed to be in the same polygons as the training data points. These results suggest that accuracy estimates derived from splitting training and validation within a limited set of reference polygons should be regarded with suspicion. To be fully confident in the validity of the accuracy estimates, additional validation sample points selected from the region outside the reference polygons may be needed to augment the validation sample selected from the reference polygons.     

A performance evaluation of a burned area object-based classification model when applied to topographically and non-topographically corrected TM imagery

Operational use of remote sensing as a tool for post-fire Mediterranean forest management has been limited by problems of classification accuracy arising from confusion between burned and non-burned land, especially within shaded areas. Object-oriented image analysis has been developed to overcome the limitations and weaknesses of traditional image processing methods for feature extraction from high spatial resolution images. The aim of this work was to evaluate the performance of an object-based classification model developed for burned area mapping, when applied to topographically and non-topographically corrected Landsat Thematic Mapper (TM) imagery for a site on the Greek island of Thasos. The image was atmospherically and geometrically corrected before object-based classification. The results were compared with the forest perimeter map generated by the Forest Service. The accuracy assessment using an error matrix indicated that the removal of topographic effects from the image before applying the object-based classification model resulted in only slightly more accurate mapping of the burned area (1.16% increase in accuracy). It was concluded that topographic correction is not essential prior to object-based classification of a burned Mediterranean landscape using TM data.     

Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium

Obtaining detailed information about the amount of forest cover is an important issue for governmental policy and forest management. This paper presents a new approach to update the Flemish Forest Map using IKONOS imagery. The proposed method is a three-step object-oriented classification routine that involves the integration of 1) image segmentation, 2) feature selection by Genetic Algorithms (GAs) and 3) joint Neural Network (NN) based object-classification. The added value of feature selection and neural network combination is investigated. Results show that, with GA-feature selection, the mean classification accuracy (in terms of Kappa Index of Agreement) is significantly higher (p < 0.01) than without feature selection. On average, the summed output of 50 networks provided a significantly higher (p < 0.01) classification accuracy than the mean output of 50 individual networks. Finally, the proposed classification routine yields a significantly higher (p < 0.01) classification accuracy as compared with a strategy without feature selection and joint network output. In addition, the proposed method showed its potential when few training data were available.     

Simulation modelling and analysis of part and tool flow control decisions in a flexible manufacturing system

This paper presents the details of a simulation study carried out for analyzing the impact of scheduling rules that control part launching and tool request selection decisions of a flexible manufacturing system (FMS) operating under tool movement along with part movement policy. Two different scenarios have been investigated with respect to the operation of FMS. In scenario 1, the facilities such as machines, tool transporter and part transporter are assumed to be continuously available without breakdowns, whereas in scenario 2, these facilities are prone to failures. For each of these scenarios, a discrete-event simulation model is developed for the purpose of experimentation. A number of scheduling rules are incorporated in the simulation models for the part launching and tool request selection decisions. The performance measures evaluated are mean flow time, mean tardiness, mean waiting time for tool and percentage of tardy parts. The results obtained through the simulation have been statistically analyzed. The best possible scheduling rule combinations for part launching and tool request selection have been identified for the chosen FMS.     

Tree-based modelling for the classification of mammographic benign and malignant micro-calcification clusters

Multimedia Tools and Applications - Computer Aided Detection (CAD) systems are being developed to assist radiologists in diagnosis. For breast cancer the emphasis is shifting from detection to...     

A FORTRAN V program for a new classification technique: the g-mode central method

The G-mode central method is described. This method allows a large number of samples to be classified based on several variables. The classification method works without a priori knowledge of the taxonomic homogeneous units forming the statistical sample.     

Hybrid modelling of batch centrifuges as part of a generic object oriented beet Sugar Mill library

Sugar crystallizers deliver a slurry where the sugar grains are suspended in the so called mother liquor. The batch centrifuges are the units in charge of mechanically separating the former from the latter guaranteeing the quality and efficiency of the process. A realistic first principles model of a batch sugar centrifuge is described in the context of a Sugar House object oriented generic library, specially designed for the training of control room personnel. The challenges posed by the hybrid continuous–discrete character of the model are particularly addressed.     

A classification and review of non-vision sensors used in discrete part manufacturing

The control of an automated manufacturing system relies heavily on the use of sensors. A review of sensors used in discrete part manufacturing is presented.     

Study of the Preprocessing Influence in the Accuracy of Semi-automated Shadow Detection Approach

Shadows may occupy a significant portion of the image mainly in urban scenes. This research has the objective to detect shadows in high resolution orbital images using morphological operators. In order to verify preprocessing contribution in this shadow detection methodology, we have tested the median, morphological, bilateral and mean curvature filters to evaluate which one has the characteristic of mitigate the noise of the images and contribute to enhance detection performance. During the study, 10 panchromatic images of Worldview II satellite from the urban area of Presidente Prudente, in the state of Sao Paulo, were used. According to the shadow detection methodology by mathematical morphology, we checked the accuracy value using the images resulting of the smoothing methods applied in the preprocessing step. Finally, we evaluated all the smoothing levels in order to select the most appropriated according to accuracy and if images preserves the elements of interest. By analyzing the obtained results it is easy to see that the bilateral filter has presented satisfactory results, since it considers the spatial domain in the smooth ing process, despite incorporating the pixels intensity domain as well. Therefore, we can conclude that the bilateral filter is a good alternative considering an adequate choice of the parameters.     

Accommodating interference in the formal design of concurrent object-based programs

This paper is about formal development methods for concurrent programs. Interference is the bane of the quest for compositional methods for concurrency. Concepts from object-oriented languages are argued to be a promising way of taming interference. Two approaches to development are described which are applicable to differing degrees of interference.     

SODAR pattern classification and its dependence on meteorological parameters over a semiarid region of India

The variability of the atmospheric boundary layer together with meteorological parameters has been investigated over the semi-arid region Delhi. Two sources of the dataset have been used: sound detection and ranging (SODAR) and automatic weather station during the period from December 2013 to November 2014. A Laboratory Virtual Instrument Engineering Workbench (LabVIEW)-based programme has been developed to plot the stability class from A to F directly from the mixing height dataset. Based on the SODAR echograms and mixing height, temporal and seasonal variability of stability classes has been estimated. It is observed that the convective boundary layer height advances and decreases during the daytime depending on the increase and decrease of surface temperature due to solar heating of the ground. From seasonal classification of the stability class, it is observed that the class A and class E are dominated in convection and nocturnal periods in all seasons, whereas class F is not found during the winter and pre-monsoon seasons. Impact of meteorological parameters, that is, wind speed, temperature, and relative humidity on mixing height during different seasons has also been studied.     

Improving the discrimination of vegetation and landform patterns in sandy rangelands: a synergistic approach

Soil erosion is a key factor in land degradation processes in the sandy rangelands of the Peninsula Valdés of Patagonia, Argentina. Mapping landform and vegetation patterns is important for improving prediction, monitoring and planning of areas threatened by sand and shrub encroachment. This paper investigates the contribution of optical sensors, such as the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and textural measures derived from microwave Radarsat Advanced Synthetic Aperture Radar (ASAR) to their discrimination. An evaluation is undertaken to compare the classification accuracy achieved by specific regions of the spectrum and their synergistic use in an object‐oriented approach. Image segmentation and object‐oriented classifications were applied to the datasets. This required defining appropriate fuzzy membership functions for characterizing active and stabilized lineal dunes and the main vegetation classes. Improvements in the discrimination of active and stabilized dunes (vegetated by either scrub or grass) were achieved by using an object‐oriented classification that integrated microwave and visible near‐infrared (NIR) data. Changes in surface roughness caused by different vegetation types stabilizing the dunes affected the radar backscattering. Whereas Radarsat enabled a clear separation of scrub‐stabilized dunes, Terra‐ASTER showed superior performance in the cartography of grass‐stabilized dunes. The synergistic use of microwave and visible and near‐infrared (VNIR) data yielded a substantial increase in the discrimination and mapping of landform/vegetation patterns.