Direct applications of remote sensing thermal infrared (TIR) data in landscape ecological research are rare due to limitations
in the sensors, calibration, and difficulty in interpretation. Currently there is a general lack of methodology for examining
the relationship between land surface temperatures (LST) derived from TIR data and landscape patterns extracted from optical
sensors. A separation of landscapes into values directly related to their scale and signature is a key step. In this study,
a Landsat ETM+ image of Indianapolis, Unites States, acquired on June 22, 2000, was spectrally unmixed (using spectral mixture
analysis, SMA) into fraction endmembers of green vegetation, soil, high albedo, and low albedo. Impervious surface was then
computed from the high and low albedo images. A hybrid classification procedure was developed to classify the fraction images
into seven land use and land cover (LULC) classes. Using the fractional images, the landscape composition and pattern were
examined. Next, pixel-based LST measurements were correlated with the landscape fractional components to investigate LULC
based relationships between LST and impervious surface and green vegetation fractions. An examination of the relationship
between the LULC and LST maps with landscape metrics was finally conducted to deepen understanding of their interactions.
Results indicate that SMA-derived fraction images were effective for quantifying the urban morphology and for providing reliable
measurements of biophysical variables. LST was found to be positively correlated with impervious surface fraction but negatively
correlated with green vegetation fraction. Each temperature zone was associated with a dominant LULC category. Further research
should be directed to the theoretical and applied implications of describing such relationships between LULC patterns and
urban thermal conditions.
As China’s economy is rapidly changing from a planned to a capitalist economy, many families find themselves financially struggling. In some cases, conflicting values and attitudes may contribute to mental health challenges such as depression that would lead to further feelings of helplessness and immobilization. Using a random sample of 1006 low-income households from Pudong District of Shanghai, China, this study aims to examine the relationships between household assets, beliefs about government as the primary way to improve economic circumstances and self-reported depressive symptoms. In addition, this study investigates the mediation effects of beliefs that government is the best change agent for improved life circumstances on the relationship between household assets and depression. We found those who indicated that government was the main means for attaining a better life had significantly higher depression levels whereas higher numbers of household assets were associated with lower depression levels. We also found that viewing government as the most important change agent only partially mediated the relationship between household assets and depression (p?<?.001). Findings from this study support anti-poverty policies and social work related practice initiatives aimed at assisting low income families in China, in particular the need to address psychological as well as economic needs.
Many research papers calculate corporate social performance (CSP) with the net score method, i.e., by subtracting the number of concerns from the number of strengths. Although widely adopted, this method implies, perhaps mistakenly, that each indicator is of equal importance and that however serious the social misconduct a firm may have engaged in, it can be completely offset by some positive social action. The method also implies that a given firm that has done both a lot of harm and a lot of good will have CSP similar to that of another firm that has done little harm and little good. In this study, however, we question the appropriateness of the net score method in terms of its ability to truly reflect CSP and truly identify the real effects of CSP on various characteristics. We therefore propose a data envelopment analysis-based methodology that adopts the assurance region approach for evaluating CSP, through which various CSP indicators are converted into a single composite measure of CSP. Our findings show that our proposed methodology consistently performs better than the net score method in evaluating CSP.
