Modeling Land-Cover Types Using Multiple Endmember Spectral Mixture Analysis in a Desert City

Spectral mixture analysis is probably the most commonly used approach among sub-pixel analysis techniques. This method models pixel spectra as a linear combination of spectral signatures from two or more ground components. However, spectral mixture analysis does not account for the absence of one of the surface features or spectral variation within pure materials since it utilizes an invariable set of surface features. Multiple endmember spectral mixture analysis (MESMA), which addresses these issues by allowing endmembers to vary on a per pixel basis, was employed in this study to model Landsat ETM+ reflectance in the Phoenix metropolitan area. Image endmember spectra of vegetation, soils, and impervious surfaces were collected with the use of a fine resolution Quickbird image and the pixel purity index. This study employed 204 (=3x17x4) total four-endmember models for the urban subset and 96 (=6x6x2x4) total five-endmember models for the non-urban subset to identify fractions of soil, impervious surface, vegetation, and shade. The Pearson correlation between the fraction outputs from MESMA and reference data from Quickbird 60 cm resolution data for soil, impervious, and vegetation were 0.8030, 0.8632, and 0.8496 respectively. Results from this study suggest that the MESMA approach is effective in mapping urban land covers in desert cities at sub- pixel level.

Patterns and Rates of Land Use Land Cover Change: A Case Study of Ambos Nogales (Arizona and Sonora), 1985-2004

Abstract The continued expansion of the paired U.S.–Mexican border cities of Ambos Nogales presents many environmental management and urban planning challenges. This study focuses on a comparative study of spatial patterns and rates of land-use and land-cover change, in relation to land degradation, deforestation, and urban growth over different time periods. Based on historical data the study suggests that both cities have experi- enced high land degradation; however, land on the Arizona-side has been more stable and less degraded. However, there were more severely degraded areas found in Nogales, Arizona, than in Nogales, Sonora. The delineation of land use change and the severity of land degradation provide important information to planners about areas that should be targeted for development and other areas that require restoration to natural settings. Keywords: Nogales, land use land cover, urban growth, land degradation Resumen La expansión continua de las ciudades gemelas de Ambos Nogales (USA-Mexico) presenta muchos desafíos de planificación urbana y manejo ambiental. Este estudio se concentra en un análisis comparativo de los patrones espaciales y velocidad de cambios de uso del suelo con relación a la degradación del terreno, deforestación, y crecimiento urbano durante distintos períodos de tiempo. Basado en datos históricos el estudio sugiere que ambas ciudades han experimentado alta degradación de tierra, pero la tierra en el lado de Arizona ha sido más estable y menos degradada. Sin embargo, en Nogales Arizona, habían áreas severamente más degradadas que en Nogales, Sonora. Palabras clave: Nogales, crecimiento urbano, uso de suelo, degradación de tierra

Combined Effects of Impervious Surface and Vegetation Cover on Air Temperature Variations in a Rapidly Expanding Desert City

The goal of this study is to improve our understanding of the interac- tive function of impervious and vegetation covers at different levels of the local and intra-urban spatial scales in relation to air temperatures in an urban environment. A multiple regression model was developed using impervious and vegetation frac- tions at different scales to predict maximum air temperature for the entire Phoenix metropolitan area in Arizona, USA. This study demonstrates that a small amount of impervious cover in a desert city can still increase maximum air temperature despite abundant vegetation cover.

Multi-Resolution Decomposition in Relation to Characteristic Scales and Local Window Sizes Using an Operational Wavelet Algorithm

Data from an IKONOS image acquired over Dallas was used to demonstrate the use of an operational wavelet-based algorithm to examine the performance of different texture measures and window sizes at various resolutions in connection to characteristic scales. It was found that a 63x63 window was the optimal window size, and energy measure produced the highest accuracy. Results from this study suggest that the choice of window size in wavelet-based classification affects the accuracy. Larger window sizes significantly improve the overall accuracy when using homogeneous samples. In the real-world situation, a larger window may not necessarily produce higher accuracy since a larger window tends to cover more land-use and land-cover classes and therefore may miss smaller regions of classes that could lead to poorer accuracy. On the other hand, a smaller window tends to be incomplete in its coverage of texture features that represent a complex class. The classification accuracy can be improved by using more combinations of sub-images at different scales. However, smaller sub-images at the last two levels may lower the classification accuracy.  The characteristic scale of the most complex feature among all selected classes could be the optimal local window size necessary to achieve the highest accuracy.

Role of Modern Immunotherapy in Gastrointestinal Malignancies: A Review of Current Clinical Progress

Gastrointestinal (GI) cancers are a group of highly aggressive malignancies with a huge disease burden worldwide. There is clearly a significant unmet need for new drugs and therapies to further improve the treatment outcomes of GI malignancies. Immunotherapy is a novel treatment strategy that is emerging as an effective and promising treatment option against several types of cancers. CTLA-4 and PD-1 are critical immune checkpoint molecules that negatively regulate T cell activation via distinct mechanisms. Immune checkpoint blockade with antibodies directed against these pathways has already shown clinical efficacy that has led to their FDA approval in the treatment of several solid tumors including melanoma, non-small cell lung cancer, renal cell carcinoma, urothelial carcinoma, and head and neck cancer. This review will summarize the current clinical progress of modern immunotherapy in the field of GI tumors, with a special focus on immune checkpoint blockade

Per-Pixel Versus Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery

In using traditional digital classification algorithms, a researcher typically encounters serious issues in identifying urban land cover classes employing high resolution data. A normal approach is to use spectral information alone and ignore spatial information and a group of pixels that need to be considered together as an object. We used QuickBird image data over a central region in the city of Phoenix, Arizona to examine if an object-based classifier can accurately identify urban classes. To demonstrate if spectral information alone is practical in urban classification, we used spectra of the selected classes from randomly selected points to examine if they can be effectively discriminated. The overall accuracy based on spectral information alone reached only about 63.33%. We employed five different classification procedures with the object-based paradigm that separates spatially and spectrally similar pixels at different scales. The classifiers to assign land covers to segmented objects used in the study include membership functions and the nearest neighbor classifier. The object-based classifier achieved a high overall accuracy (90.40%), whereas the most commonly used decision rule, namely maximum likelihood classifier, produced a lower overall accuracy (67.60%). This study demonstrates that the object-based classifier is a significantly better approach than the classical per- pixel classifiers. Further, this study reviews application of different parameters for segmentation and classification, combined use of composite and original bands, selection of different scale levels, and choice of classifiers. Strengths and weaknesses of the object-based prototype are presented and we provide suggestions to avoid or minimize uncertainties and limitations associated with the approach.

Urban vegetation mapping using sub-pixel analysis and expert system rules: A critical approach

Since the traditional hard classifier can label each pixel only with one class, urban vegetation (e.g. trees) can only be recorded as either present or absent. The subpixel analysis that can provide the relative abundance of surface materials within a pixel may be a potential solution to effectively identifying urban vegetation distribution. This study examines the effectiveness of a sub-pixel classifier with the use of expert system rules to estimate varying distributions of different vegetation types in urban areas. The Spearman's rank order correlation between the vegetation output and reference data for wild grass, man-made grass, riparian vegetation, tree, and agriculture were 0.791, 0.869, 0.628, 0.743, and 0.840 respectively. Results from this study demonstrated that the expert system rule using NDVI threshold procedure is reliable and the sub-pixel processor picked the signatures relatively well. This study reports a checklist of the sources of limitation in the application of sub-pixel approaches

Discriminant Analysis with Spatial Weights for Urban Land Cover Classification

Classifying urban area images is challenging because of the heterogeneous nature of the urban landscape resulting in mixed pixels and classes with highly variable spectral ranges. Approaches using ancillary data, such as knowledge based or expert systems, have shown to improve the classification accuracy in urban areas. Appropriate ancillary data, however, may not always be available. The goal of this study is to compare the results of the discriminant analysis statistical technique with discriminant analysis with spatial weights to classify urban land cover. Discriminant analysis is a statistical technique used to predict group membership for a target based on the linear combination of independent variables. Strict per pixel statistical analysis however does not consider the spatial dependencies among neighbouring pixels. Our study shows that approaches using ancillary data continue to outperform strict spectral classifiers but that using a spatial weight improved the results. Furthermore, results show that when the discriminant analysis technique works well then the spatially weighted approach performs better. However, when the discriminant analysis performs poorly, those poor results are magnified in the spatially weighted approach in the same study area. The study shows that for dominant classes, adding spatial weights improves the classification accuracy.

A Case Report of Metastatic Castration-Resistant Prostate Cancer Harboring a \u3ci\u3ePTEN\u3c/i\u3e Loss

The treatment landscape of metastatic castration-resistant prostate cancer (mCRPC) has dramatically improved over the last decade; however, patients with visceral metastases are still faced with poor outcomes. Phosphatase and tensin homolog (PTEN) loss is observed in 40%–60% of mCRPC patients and is also associated with a poor prognosis. Several PI3K/AKT/mTOR pathway inhibitors have been studied, with disappointing anti-tumor activity. Here, we present a case of a patient with heavily treated mCRPC who had a modest tumor response to concurrent carboplatin, abiraterone acetate/prednisone, and liver-directed radiation therapy. We discuss the potential rationale supporting the use of this combination therapy and its safety in mCRPC. While the underlying basic mechanism of our patient’s anti-tumor response remains uncertain, we suggest that further prospective studies are warranted to evaluate whether this combination therapy is effective in this population of patients with pre-treated mCRPC and PTEN loss