Issues in modified problem‐based learning: A self‐study in pre‐service science‐teacher education

Abstract:

In this self‐study, the author gained in‐depth understanding of how to plan and implement problem‐based learning (PBL), a student‐centred approach to teaching and learning that is driven by messy, open‐ended problems. This paper focuses primarily on the issues and concerns that arose as she developed and implemented a modified form of traditional PBL (Barrows, 1996) in large, pre‐service science‐teacher education classes. To view the research from many perspectives, a variety of data collection methods and sources were used, including field notes, semi‐structured interviews, student‐generated documents, and student journals. The outcomes of this study describe challenges (problem development, facilitation of groups, and assessment) encountered by the author as she planned for and implemented PBL. Furthermore, changes in the author's classroom practice, the connection between these changes and constructivist learning principles, and implications for science‐teacher education are addressed.     

Reflectance characteristics of major land surfaces in slash‐and‐burn ecosystems in Laos

The in situ reflectance spectra in the 400–2500 nm wavelength region were obtained using a portable radiometer over a range of land surfaces including burnt fields, crop canopies, and fallow vegetation at different community ages in slash‐and‐burn ecosystems in Laos. Normalized difference spectral indices (NDSI[i,j] = [R_j ?R_i ]/[R_j +R_i ]) were derived using reflectance R_i and R_j at i and j nm wavelengths for a thorough combination (14 706 pairs) of 172 wavebands (10‐nm resolution). The separability of burnt fields from dry/senescent vegetation was highest at NDSI[1090, 2390], whereas it was highly discriminated from fallow and crop vegetation by NDSI[760, 1970]. NDSIs using 730–760 nm with 1970–1990 nm showed the largest differences between dry/senescent vegetation and fallow or crop vegetation. None of the NDSIs was useful in discriminating between fallow and crop vegetations or between slashed/senescent vegetation and crop residue/abandoned field. Community age and biomass of fallow vegetation could not be inferred directly from spectral information, since no NDSIs showed any significant differences among crop and fallow vegetation that had a large variability in the amount of green vegetation. Results would provide useful information for various applications of optical satellite sensor images especially in assessments of land use or post‐fire regeneration of vegetation.     

Fire‐induced variability in satellite SPOT‐VGT NDVI vegetational data

Is it possible to quantitatively characterize the dynamic variability induced by fires on vegetation? To answer this question, we performed a fluctuation analysis of time series of satellite SPOT‐VEGETATION Normalized Difference Vegetation Index (NDVI) data from 1998 to 2003 in order to discriminate fire‐induced variability in the vegetation dynamics of forests in southern Italy. We used the Detrended Fluctuation Analysis (DFA), which permits the detection of persistent properties in non‐stationary signal fluctuations. We analysed two forest sites, one fire‐unaffected and the other fire‐affected. Our findings suggest that fires play an important role in the temporal evolution of forests, increasing the persistence of the vegetation dynamics.     

Multi‐criteria decision evaluation in groundwater zones identification in Moyale‐Teltele subbasin,South Ethiopia

The conventional approaches such as ground‐based surveys and exploratory drilling for groundwater investigation are time‐consuming and uneconomical. Systematic organisation of data of characteristics of any terrain, evaluation of inter‐thematic, interclass dependencies and variability, and also analysing cumulative effect on the development of groundwater regime is the best approach which the current decade requires. This is proved by generation of the thematic information on the above factors through remote sensing technique, and integration in geographic information system (GIS) for evaluation using multi‐criteria decision‐making techniques. This paper presents the results of the attempt made to identify the groundwater potential zones in the Moyale‐Teltele Sub basin of the Genale Dawa River Basin in South Ethiopia using the integrated approaches of remote sensing and geographic information systems. Six geologic, physiographic, and hydrologic factors were applied namely: lithology, structure, geomorphology, slope, land cover, and drainage. Weighted Overlay Analysis using multicriteria decision technique is implemented to produce the groundwater potential map of the area. This result is further verified by groundwater yield data of boreholes and springs collected in the field and from previous reports. The validation revealed that the result is in good conformity with the actual yield of the wells and springs.     

Small‐footprint,waveform‐resolving lidar estimation of submerged and sub‐canopy topography in coastal environments

The experimental advanced airborne research lidar (EAARL) is an airborne lidar instrument designed to map near‐shore submerged topography and adjacent land elevations simultaneously. This study evaluated data acquired by the EAARL system in February 2003 and March 2004 along the margins of Tampa Bay, Florida, USA, to map bare‐earth elevations under a variety of vegetation types and submerged topography in shallow, turbid water conditions. A spatial filtering algorithm, known as the iterative random consensus filter (IRCF), was used to extract ground elevations from a point cloud of processed last‐surface EAARL returns. Filtered data were compared with acoustic and field measurements acquired in shallow submerged (0–2.5 m water depth) and sub‐canopy environments. Root mean square elevation errors (RMSEs) ranged from 10–14 cm for submerged topography to 16–20 cm for sub‐canopy topography under a variety of vegetation communities. The effect of lidar sampling angles and global positioning system (GPS) satellite configuration on accuracy was investigated. Results show high RMSEs for data acquired during periods of poor satellite configuration and at large sampling angles along the edges of the lidar scan. The results presented in this study confirm the cross‐environment capability of a green‐wavelength, waveform‐resolving lidar system, making it an ideal tool for mapping coastal environments.     

Developing classroom‐focused research in technology education

Abstract

During the last 10 years, curriculum documents in Australia, the United Kingdom, the United States, Canada, Hong Kong, and New Zealand have emphasized the importance of students’ developing technological literacy. In utilizing research findings to consider future curriculum needs, there is the danger that the field may come to be understood in light of the research undertaken, not in light of what needs to be done. Past research has tended to focus on curriculum issues and the defining of the subject. If technology education is to advance as a curriculum area of worth and as a focus of research, then much more of our research effort must be on student and teacher learning in technology. This paper argues that classroom‐based research must become the focus of research over the next 10 years. While there is published research on what students do when involved in technological activities, we still lack significant research on students’ learning in technology and on ways in which this learning can be enhanced. Teacher and student conceptualization of technology is a complex issue and requires an understanding of the many factors that influence it. Classroom culture and student expectations appear to influence strongly the way in which students carry out their technological activities. Student learning in technology can be enhanced by effective formative interactions occurring between teacher and student and between student and student. Part of technology assessment should provide evidence of progression in learning, about which we currently know very little. This paper describes some fruitful areas of classroom‐based research that could inform technology curriculum development.     

Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery

The extraction of texture features from high‐resolution remote sensing imagery provides a complementary source of data for those applications in which the spectral information is not sufficient for identification or classification of spectrally similar landscape features. This study presents the results of grey‐level co‐occurrence matrix (GLCM) and wavelet transform (WT) texture analysis for forest and non‐forest vegetation types differentiation in QuickBird imagery. Using semivariogram fitting, the optimal GLCM windows for the land cover classes within the scene were determined. These optimal window sizes were then applied to eight GLCM texture measures (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) for the scene classification. Using wavelet transformation, up to five levels of macro‐texture were computed and tested in the classification process. Comparing the classification results, (1) the spectral‐only bands classification gave an overall accuracy of 58.69%; (2) the statistically derived 21×21 optimal mean texture combined with spectral information gave the best results among the GLCM optimal windows with an accuracy of 73.70%; and (3) the combined optimal WT‐texture levels 4 and 5 gave an accuracy of 63.56%. The combined classification of these three optimal results gave an overall accuracy of 77.93%. The results indicate that even though vegetation texture was generally measured better by the GLCM‐mean texture (micro‐textures) than by WT‐derived texture (macro‐textures), the results show that the micro–macro texture combination would improve the differentiation and classification of the overall vegetation types. Overall, the results suggests that computer‐assisted classification of high‐spatial‐resolution remotely sensed imagery has a good potential to augment the present ground‐based forest inventory methods.     

Image‐object detectable in multiscale analysis on high‐resolution remotely sensed imagery

Landscapes are complex systems composed of a large number of heterogeneous components as well as explicit homogeneous regions that have similar spectral character on high‐resolution remote sensing imagery. The multiscale analysis method is considered an effective way to study the remotely sensed images of complex landscape systems. However, there remain some difficulties in identifying perfect image‐objects that tally with the actual ground‐object figures from their hierarchical presentation results. Therefore, to overcome the shortcomings in applications of multiresolution segmentation, some concepts and a four‐step approach are introduced for homogeneous image‐object detection. The spectral mean distance and standard deviation of neighbouring object candidates are used to distinguish between two adjacent candidates in one segmentation. The distinguishing value is used in composing the distinctive feature curve (DFC) with object candidate evolution in a multiresolution segmentation procedure. The scale order of pixels is built up by calculating a series of conditional relative extrema of each curve based on the class separability measure. This is helpful in determining the various optimal scales for diverse ground‐objects in image segmentation and the potential meaningful image‐objects fitting the intrinsic scale of the dominant landscape objects. Finally, the feasibility is analysed on the assumption that the homogeneous regions obey a Gaussian distribution. Satisfactory results were obtained in applications to high‐resolution remote sensing imageries of anthropo‐directed areas.     

Assessment of stand‐wise stem volume retrieval in boreal forest from JERS‐1 L‐band SAR backscatter

JERS‐1 L‐band SAR backscatter from test sites in Sweden, Finland and Siberia has been investigated to determine the accuracy level achievable in the boreal zone for stand‐wise forest stem volume retrieval using a model‐based approach. The extensive ground‐data and SAR imagery datasets available allowed analysis of the backscatter temporal dynamics. In dense forests the backscatter primarily depended on the frozen/unfrozen state of the canopy, showing a ～4 dB difference. In sparse forests, the backscatter depended primarily on the dielectric properties of the forest floor, showing smaller differences throughout the year. Backscatter modelling as a function of stem volume was carried out by means of a simple L‐band Water Cloud related scattering model. At each test site, the model fitted the measurements used for training irrespective of the weather conditions. Of the three a priori unknown model parameters, the forest transmissivity coefficient was most affected by seasonal conditions and test site specific features (stand structure, forest management, etc.). Several factors determined the coefficient's estimate, namely weather conditions at acquisition, structural heterogeneities of the forest stands within a test site, forest management practice and ground data accuracy. Stem volume retrieval was strongly influenced by these factors. It performed best under unfrozen conditions and results were temporally consistent. Multi‐temporal combination of single‐image estimates eliminated outliers and slightly decreased the estimation error. Retrieved and measured stem volumes were in good agreement up to maximum levels in Sweden and Finland. For the intensively managed test site in Sweden a 25% relative rms error was obtained. Higher errors were achieved in the larger and more heterogeneous forest test sites in Siberia. Hence, L‐band backscatter can be considered a good candidate for stand‐wise stem volume retrieval in boreal forest, although the forest site conditions play a fundamental role for the final accuracy.     

Radiometric correction effects in Landsat multi‐date/multi‐sensor change detection studies

Radiometric corrections serve to remove the effects that alter the spectral characteristics of land features, except for actual changes in ground target, becoming mandatory in multi‐sensor, multi‐date studies. In this paper, we evaluate the effects of two types of radiometric correction methods (absolute and relative) for the determination of land cover changes, using Landsat TM and Landsat ETM+ images. In addition, we present an improvement made to the relative correction method addressed. Absolute correction includes a cross‐calibration between TM and ETM+ images, and the application of an atmospheric correction protocol. Relative correction normalizes the images using pseudo‐invariant features (PIFs) selected through band‐to‐band PCA analysis. We present a new algorithm for PIFs selection in order to improve normalization results. A post‐correction evaluation index (Quadratic Difference Index (QD)), and post‐classification and change detection results were used to evaluate the performance of the methods. Only the absolute correction method and the new relative correction method presented in this paper show good post‐correction and post‐classification results (QD index ≈ 0; overall accuracy >80%; kappa >0.65) for all the images used. Land cover change estimations based on uncorrected images present unrealistic change rates (two to three times those obtained with corrected images), which highlights the fact that radiometric corrections are necessary in multi‐date multi‐sensor land cover change analysis.     

The use of step‐wise density slicing in classifying high‐resolution panchromatic photographs

The present paper provides an improvised, simple and easy‐to‐follow methodology in which step‐wise density slicing was used to classify high‐resolution panchromatic photographs to levels of spatial detail and accuracy that offer end products comparable with what is obtainable from conventional signature‐based classification of multispectral images. By tapping on a range of manipulative remote sensing and GIS techniques, we provide an illustrated example that takes the reader from on‐screen image display to final compilation and accuracy assessment of a classified map output. Comprising digital classification by image segmentation and visually‐guided grey scale coding, the methodology we offer was practically tested in a change detection investigation around the Okavango Delta in northern Botswana. We conveniently selected one site from this broad study and sampled results over four time‐slices for illustrative purposes. With this improvisation giving results with an accuracy level of 69%, we suggest that this methodology may be used for detailed classification of high‐resolution panchromatic images.     

First algorithm for chlorophyll‐a retrieval from MODIS‐Terra imagery of Sun‐induced fluorescence in the Southern Ocean

The measurements of in situ samplers, the ENEA Light Detection and Ranging (Lidar) Fluorosensor (ELF) and Moderate Resolution Imaging Spectroradiometer on‐board the Terra satellite (MODIS‐Terra), carried out in the Southern Ocean during the Austral summer 2002–2003, were used to provide the first algorithm for chlorophyll‐a (Chl‐a) retrieval from MODIS‐Terra imagery of Sun‐induced fluorescence in the Southern Ocean. The results of the algorithm indicate that the standard MODIS‐Terra algorithm underestimated Chl‐a. The discrepancy (20%) is below the expected error of MODIS (35%).     

Evaluation and accuracy assessment of high‐resolution IFSAR DEMs in low‐relief areas

The use of digital elevation models from remotely sensing systems has been restricted in the past to high‐relief areas due to the lack of appropriate resolution and accuracy to map micro‐relief variability in low relief areas. Interferometric synthetic aperture radar, a new technology that provides detailed elevation models from remotely sensed data, is evaluated. Main characteristics of this data are highlighted. Accuracy assessment is tested in detail for two high‐resolution acquisition modes using higher accuracy sources of data. The accuracy results using the root mean square (rms) error were better than expected according to mission specifications. However, at the checkpoint locations where the signal backscatter generates an elevation measure, the accuracy depends considerably upon the site‐specific surface characteristics, such as the land use, above ground biomass, adjacent forest areas and infrastructure features located within surrounding pixels.     

Long‐range persistence in global Aerosol Index dynamics

Detrended fluctuation analysis (DFA) was applied to zonal mean daily Aerosol Index (AI) values derived from satellite observations during 1979–2003 to search for self‐similarity properties. The results show that the detrended and deseasonalized AI fluctuations in both hemispheres and globally obey persistent long‐range power‐law correlations for time scales longer than about 4 days and shorter than about 2 years. This suggests that the AI fluctuations in small time intervals are related to the AI fluctuations in longer time intervals in a power‐law fashion (when the time intervals vary from about 4 days to about 2 years). In other words, an anomaly in AI in one time frame continues into the next, exhibiting a power‐law evolution. The influence of the annual and semiannual cycles on the scaling behaviour of the AI time series in both hemispheres is discussed. A plausible mechanism for the time scale of about 2 years in AI time series could be the modulation of the Brewer–Dobson cell by the quasi‐biennial oscillation at the equatorial stratosphere in the zonal wind. The synoptic‐scale meteorological systems probably give rise to the time scale of about 4 days. These findings could prove useful in testing the results of existing models, which should be examined to determine if they demonstrate the scaling behaviour mentioned above.     

Web‐based testing: A study in insecurity

Much has been written about the distinct ideas of online testing, in which students take tests and receive immediate feedback on their performance; distance learning, in which telecommunication technology extends the traditional classroom beyond the classroom walls; and multimedia, in which text, sound and graphics are integrated within an application. By using the World Wide Web (WWW) as a medium for administering online tests, immediate feedback can be provided, tests can be administered at a distance, and multimedia resources can be conveniently incorporated into questions. This paper describes Eval, an undergraduate research software prototype in which we explore security issues related to using WWW as a testing medium.     

Land use qualities identified in remotely‐sensed images

Land use can be defined as the intentional use of a specific piece of land resulting in patterns of ecological responses that are visible in land cover and landscape. The responses to land use often result in a heterogeneous combination of classes of land cover. Existing methods used in the classification of satellite imagery are limited in their capacity to handle categories consisting of heterogeneous or multiple land cover classes. Accordingly, a spatial relational post‐classification (SRPC) method has been developed which uses a spatial relational post‐classification of land cover classes based on the incorporation of information about identified land use qualities. This paper explains how this method works, and presents the results from a case study of the surroundings of Sötåsa village located in southern Sweden. Different land cover classes were aggregated semantically into two land use quality classes. In conclusion, it is argued that it is possible to make the semantic shift from reflectance to land use qualities using the developed method on satellite data, and that this provides considerable scope for the future analysis of land use.     

ASTER‐based study of the night‐time urban heat island effect in Metro Manila

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to derive land surface temperatures to quantify the night‐time urban heat island (UHI) effect in Metro Manila. Temperature differences between Metro Manila and its adjacent rural towns were compared to determine heat island intensity and analyse spatial variation of surface temperature. Transects were drawn across from the rural to the urban region to characterize the UHI profile and the Normalized Difference Vegetation Index (NDVI) was used to examine the relationship between amount of vegetation and temperature. The thermal images revealed the highest UHI intensity to be 2.96°C with the presence of a heat island existing in the central part of the city. The transects described the cross‐sectional heat island profile characterized by gradients of ‘cliffs’, ‘plateaus’ and a ‘peak’ occurring in the city centre. The study also showed an inverse relationship between NDVI and temperature, which suggests that increasing the amount of plants in cities can reduce the UHI effect.     

Satellite evidence of a topographic sink‐source system in a small semi‐arid watershed

Water is a limiting factor for biological processes in drylands and consequently it is expected that vegetation cover along slopes will be affected by runoff flow regime and shape of topography. Nevertheless, spaceborne remote sensing data have hardly been used to study the effect of hydrological processes on vegetation pattern at the slope scale. This Letter reports on the spatio‐temporal variation in spaceborne‐derived Normalized Difference Vegetation Index (NDVI) data, in physiographic units of unique pedo‐hydrological characteristics, in a semi‐arid watershed. It was found that NDVI values in the footslope and shoulder physiographic units are significantly (p<0.0001) higher than those of the interfluve and the backslope. This difference, observed during the entire phenological cycle, was enhanced towards the peak season but was of less significance during the early season and towards senescence. These results support the hypothesis that water redistribution can significantly increase plant production in sink areas, also in the slope‐scale.     

Optimization in multi‐scale segmentation of high‐resolution satellite images for artificial feature recognition

Multi‐resolution segmentation, as one of the most popular approaches in object‐oriented image segmentation, has been greatly enabled by the advent of the commercial software, eCognition. However, the application of multi‐resolution segmentation still poses problems, especially in its operational aspects. This paper addresses the issue of optimization of the algorithm‐associated parameters in multi‐resolution segmentation. A framework starting with the definition of meaningful objects is proposed to find optimal segmentations for a given feature type. The proposed framework was tested to segment three exemplary artificial feature types (sports fields, roads, and residential buildings) in IKONOS multi‐spectral images, based on a sampling scheme of all the parameters required by the algorithm. Results show that the feature‐type‐oriented segmentation evaluation provides an insight to the decision‐making process in choosing appropriate parameters towards a high‐quality segmentation. By adopting these feature‐type‐based optimal parameters, multi‐resolution segmentation is able to produce objects of desired form to represent artificial features.