Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city

Information on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land cover types in a large Latin-American city, which is well known for its mushrooming unplanned and informal settlements. After carefully considering the landscape complexity there, we designed a data fusion method combining multispectral imagery and non-spectral data for urban and land mapping. Specifically, we acquired a cloud-free Landsat-8 image and two non-spectral datasets, i.e., digital elevation models and road networks. Then, we implemented a set of experiments with different inputs to evaluate their merits in thematic mapping through a supervised protocol. We found that the map generated with the multispectral data alone had an overall accuracy of 73.3% but combining multispectral imagery and non-spectral data yielded a land cover map with 90.7% overall accuracy. Interestingly, the thermal infrared information helped substantially improve both the overall and categorical accuracies, particularly for the two urban classes. The two types of non-spectral data were critical in resolving several spectrally confused categories, thus considerably increasing the mapping accuracy. However, the panchromatic band with higher spatial resolution and its derived textural measurement only generated a marginal accuracy improvement. The novelties of our work are with the successful separation between the two major types of urban settlements in a complex environment using a carefully designed data fusion approach and the insight into the relative merits of the thermal infrared information and non-spectral data in helping resolve the issue of class ambiguity. These findings should be valuable in deriving accurate urban settlement information which can further advance the research on socio-ecological dynamics and urban sustainability

Sensing the multi-scale landscape functions heterogeneity by big geodata from parcel to urban agglomerations-a case of the Greater Bay Area, China

ABSTRACTMulti-scale landscape functions play a critical role in revealing intricate functional structures within large regions. However, previous studies on landscape functions have predominantly focused on a single macro or micro scale, impeding a holistic multi-scale understanding of the spatial distribution and heterogeneity of landscape functions. To address this gap, this study proposes a framework leveraging the power of big geodata to mine multi-scale landscape functions from parcel to entire urban agglomerations, as well as non-administrative divisions. Our study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. Firstly, we integrated multi-source big geodata to derive parcel-scale landscape functions. Subsequently, we employed the Normalized Revealed Comparative Advantage index to derive landscape functions at broader scales, including towns, counties and cities. The effectiveness of our approach is validated through in-field investigations and comparisons with established policy planning positions. The outcomes not only offer distinctive planning insights at various scales but also highlight the versatility of big geodata in extracting landscape functions across scales. This study demonstrates that big geodata is adept at uncovering multi-scale landscape functions irrespective of administrative boundaries, providing valuable insights for fostering multi-scale regional coordinated development

Polymorphisms of the FAS and FASL genes and risk of breast cancer

FAS and its ligand FASL are crucial in apoptotic cell death. Loss of FAS and gain of aberrant FASL expression are common features of malignant transformation. This study was designed to investigate whether the functional polymorphisms of FAS -1377G/A (rs2234767) and FASL -844T/C (rs763110) affect the risk of developing breast cancer. Genotypes were analyzed by a polymerase chain reaction-restriction fragment length polymorphism assay in 436 breast cancer patients and 496 healthy controls. In this study, as compared to the wild-type homozygote and heterozygote, the distribution of the FAS -1377GG, GA and AA genotypes among breast cancer patients were significantly different from those among healthy controls (P=0.011), with the AA genotype being more prevalent among patients than the controls (P=0.003). Similarly, the frequencies of the FASL -844TT, TC and CC genotypes also significantly differed among breast cancer patients and healthy controls (P<0.001), with the CC genotype being significantly over-represented in breast cancer patients compared with the controls (P<0.001). In the unconditional logistic regression model following adjustment for age, the subjects carrying the FAS -1377AA genotype had a 1.75-fold increased risk [95% confidence interval (CI), 1.13–2.69] for development of breast cancer compared with patients carrying the GG genotype. Similarly, in the recessive model, the FASL -844CC genotype significantly increased the risk of breast cancer with an odds ratio (OR) of 1.92 (95% CI 1.46–2.54) compared with the TT or TT + TC genotypes. Our results suggest that functional polymorphisms in the death pathway genes FAS and FASL significantly contribute to the occurrence of breast cancer