Barry Haack

An assessment of Landsat MSS and TM data for urban and near-urban land-cover digital classification

The information content of Landsat TM and MSS data was examined to assess the ability to digitally differentiate urban and near-urban land covers around Miami, FL. This examination included comparisons of unsupervised signature extractions for various cover types, training site statistics for intraclass and interclass separability, and band and band combination selection from an 11-band multisensor data set. The principal analytical tool used in this study was transformed divergence calculations. The TM digital data are typically more useful than the MSS data in the homogeneous near-urban land-covers and less useful in the heterogeneous urban areas.

Integrating multisensor data and RADAR texture measures for land cover mapping

Abstract This study evaluated multisensor spaceborne data from Landsat Thematic Mapper (TM) and Shuttle Imaging Radar (SIR-C) for East African landscapes including settlements, natural vegetation, and agriculture. An extensive landscape that has been difficult to accurately map with spaceborne remote sensing has been subtropical areas of savanna or woodland, especially when mixed with scattered agricultural practices. These varied vegetation communities are often very difficult to spectrally differentiate with optical data. This study examines the utility of radar to accurately locate areas of natural vegetation, scattered agricultural, and settlements. Radar data were able to accurately map these features with approximately the same accuracy as TM. In addition to comparing these two sensor types, four different geospatial manipulations of the radar data were examined. Those manipulations included measures of texture, the size of the texture window, data filtering prior to extraction of texture values, and post-classification smoothing. The variance (2nd order) measure of texture provided the best classification accuracies. The optimum window size for texture was a 13×13 pixel array and both pre- and post-classification filtering of the radar data significantly improved results.

Radar spatial considerations for land cover extraction

One of the more recent developments in operational space‐borne remote sensing is the availability of radar. A disadvantage of currently available space‐borne radar is that data are almost entirely single wavelength and single polarization, limiting the ability to do traditional digital classification. Frequently radar‐derived values such as texture and combinations of these radar‐derived values with the original radar or optical data can improve digital classification accuracies. Various radar spatial components including texture, texture window size, speckle reduction and post‐classification filtering can have significant impact on classification. This study examined these various spatial components of space‐borne radar for feature identification in two sites in East Africa and one in Nepal. Relative accuracy of the resultant classifications was established by digital integration and comparison to ground reference information derived from field visitation. The extraction and use of these techniques, particularly texture, were found to be advantageous. There were, however, differences from site to site as to which technique was most effective.

Spectroscopic analysis of arsenic uptake in Pteris ferns.

Two arsenic-accumulating Pteris ferns (Pteris cretica mayii and Pteris multifida), along with a non-accumulating control fern (Nephrolepis exaltata) were grown in greenhouse conditions in clean sand spiked with 0, 20, 50, 100 and 200 ppm sodium arsenate. Spectral data were collected for each of five replicates prior to harvest at 4-week intervals. Fern samples were analyzed for total metals content and Partial Least Squares and Stepwise Linear Regression techniques were used to develop models from the spectral data. Results showed that Pteris cretica mayii and Pteris multifida are confirmed hyperaccumulators of inorganic arsenic and that reasonably accurate predictive models of arsenic concentration can be developed from the first derivative of spectral reflectance of the hyperaccumulating Pteris ferns. Both the arsenic uptake and spectral results indicate that there is some species-specific variability but the results compare favorably with previously published data and additional research is recommended.

Classification of California agriculture using quad polarization radar data and Landsat Thematic Mapper data

This study evaluated the accuracy of classifying California agriculture using spaceborne quad polarization radar from the Japanese ALOS PALSAR system and optical Landsat Thematic Mapper (TM) data. In addition, the study analyzed the utility of radar texture and sensor fusion techniques. The original radar had an overall accuracy of 74% but with individual crop producers accuracies ranging from 100% for almonds to 49% for alfalfa. Landsat provided a much higher overall classification accuracy of 91%. The merger of Landsat with radar texture increased overall accuracy to 97%, indicating the advantages of sensor integration.

An analysis of thematic mapper simulator data for urban environments

Abstract Thematic mapper simulator data collected for the Los Angeles Basin in 1980 were examined to assess their utility for urban and near-urban land-cover delimitations. Spectral data for six of the thematic mapper channels were reprojected to a UTM grid and aggregated to 30-m resolution, 120 m for the thermal band. Statistics for 21 training sites representing 8 land-cover types were obtained and examined using transformed divergence calculations for intraclass variability, optimal number of channels for classification, and best channels for classification. Four channels of data are adequate for classification with the best results obtained by selection of one channel from each of the available major portions of the electromagnetic spectrum. The thermal channel data is useful for urban land-cover delineations at 30-m resolution, but its utility at 120-m resolution is not clear from this study.

A Comparison of Land Use/Cover Mapping with Varied Radar Incident Angles and Seasons

This study examined the classification accuracy of specific land use/cover categories from original and derived spaceborne radar measures for a site in Nepal. Six RADARSAT images were compiled and registered to evaluate the effects of incident angle and seasonality on feature delineations. Examination of an initial RADARSAT image for Kathmandu provided poor classification results (56 percent overall accuracy) for the four generalized land covers of urban, residential, agriculture, and grass. Measures of Variance texture were applied to this original RADARSAT data over varied window sizes, improving overall classification accuracy to 75 percent. Dry-season results were generally better than wet-season along with a pattern of improved separability as the incident angle increased from averages of ca. 23.5° to 39.5 to 47°.

Remote sensing-derived national land cover land use maps: a comparison for Malawi

Reliable land cover land use (LCLU) information, and change over time, is important for Green House Gas (GHG) reporting for climate change documentation. Four different organizations have independently created LCLU maps from 2010 satellite imagery for Malawi for GHG reporting. This analysis compares the procedures and results for those four activities. Four different classification methods were employed; traditional visual interpretation, segmentation and visual labelling, digital clustering with visual identification and supervised signature extraction with application of a decision rule followed by analyst editing. One effort did not report classification accuracy and the other three had very similar and excellent overall thematic accuracies ranging from 85 to 89%. However, despite these high thematic accuracies there were very significant differences in results. National percentages for forest ranged from 18.2 to 28.7% and cropland from 40.5 to 53.7%. These significant differences are concerns for both remote-sensing scientists and decision-makers in Malawi.

Land use/cover mapping with quad-polarization radar and derived texture measures near Wad Madani, Sudan.

This study examines the relative utility of quad-polarization spaceborne radar and derived texture measures for classification of specific land cover categories at a site in east-central Sudan near the city of Wad Madani. Japanese Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) quad-polarization spaceborne radar data at 12.5 m spatial resolution were obtained for this study. Measures of variance texture were applied to the original PALSAR data over varied window sizes. Transformed divergence (TD) measures of separability were calculated in order to evaluate the best bands from the original and texture measures for classification. Results show that quad-polarization radar data and derived texture measures have high separability between different land cover classes, and therefore hold potential to attain high levels of classification accuracy. Specifically, when used individually the cross-polarization bands showed the highest separability, but when used in combination some mix of cross- and like-polarization bands had the highest separability.

Fusion of Radar and Optical Data for Land Cover Mapping

Abstract This study examined the complementarity of radar and optical data for feature identification. Spaceborne radar and Landsat Thematic Mapper (TM ) multispectral data sets were assessed independently and in combination to classify a site near Wad Medani, Sudan. Radar processing procedures included speckle reduction, texture extraction and post‐processing smoothing. Relative accuracy of the resultant classifications was established by comparison to ground truth information derived from field visitation. Neither speckle filtering nor post‐classification smoothing were improvements over the poor results obtained with the unfiltered, original radar data. Texture measures were significant improvements over the original data (20 percent overall accuracy increase) and several, but not all, individual classes had excellent results. Landsat TM had good overall results (80 percent correct) but considerable spectral confusion between urban and bare soil. Combination of radar with Landsat TM greatly improved results, achieving near perfect classification of all individual classes. The systematic strategy of this study, determination of the best individual method before introducing the next procedure, was effective in managing a complex set of analysis possibilities.