Urban heat islands (UHIs) created through urbanization can have negative impacts on the lives of people living in cities. They may also vary spatially and temporally over a city. There is, thus, a need for greater understanding of these patterns and their causes. While previous UHI studies focused on only a few cities and/or several explanatory variables, this research provides a comprehensive and comparative characterization of the diurnal and seasonal variation in surface UHI intensities (SUHIIs) across 67 major Chinese cities. The factors associated with the SUHII were assessed by considering a variety of related social, economic and natural factors using a regression tree model. Obvious seasonal variation was observed for the daytime SUHII, and the diurnal variation in SUHII variedseasonally across China. Interestingly, the SUHII varied significantly in character between northern and southern China. Southern China experienced more intense daytime SUHIIs, while the opposite was true for nighttime SUHIIs. Vegetation had the greatest effect in the day time in northern China. In southern China, annual electricity consumption and the number of public buses were found to be important. These results have important theoretical significance and may be of use to mitigate UHI effects.

Landsat 8, the most recently launched satellite of the series, promises to maintain the continuity of Landsat 7. However, in addition to subtle differences in sensor characteristics and vegetation index (VI) generation algorithms, VIs respond differently to the seasonality of the various types of vegetation cover. The purpose of this study was to elucidate the effects of these variations on VIs between Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+). Ground spectral data for vegetation were used to simulate the Landsat at-senor broadband reflectance, with consideration of sensor band-pass differences. Three band-geometric VIs (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI)) and two band-transformation VIs (Vegetation Index based on the Universal Pattern Decomposition method (VIUPD), Tasseled Cap Transformation Greenness (TCG)) were tested to evaluate the performance of various VI generation algorithms in relation to multi-sensor continuity. Six vegetation types were included to evaluate the continuity in different vegetation types. Four pairs of data during four seasons were selected to evaluate continuity with respect to seasonal variation. The simulated data showed that OLI largely inherits the band-pass characteristics of ETM+. Overall, the continuity of band-transformation derived VIs was higher than band-geometry derived VIs. VI continuity was higher in the three forest types and the shrubs in the relatively rapid growth periods of summer and autumn, but lower for the other two non-forest types (grassland and crops) during the same periods.

Climate change has had a huge impact on Southwest China, where extreme weather events have taken on an increasing trend. The region lies in the transition zone of three monsoons (East Asian, Indian, and Tibetan Plateau). We analyzed long-term trends of drought and flood disasters using sequential data on China’s drought and flood incidence over 500 years (1501–2000), as well as precipitation records from 45 weather stations during 1961–2010. Results show that both types of disaster became more frequent. During 1501–2000, drought frequency rose from 2.12 to 4.15 per century to 9.03 and flood frequency from 2.06 to 5.09 per century to 11.94. For the 10 decades during 1900–2000, droughts exhibited a significant increasing trend (R2 = 0.4491), while floods showed a slight increasing trend (R2 = 0.0346). For extreme floods and droughts in 1961–2010, precipitation records revealed that most (2/3) areas in the region had increasing drought or flood frequencies. Generally, both frequency and intensity of these eather hazards conspicuously increased in Southwest China during 1501–2000.

Abstract—Sunlit vegetation and shaded vegetation are inseparable parts for most remotely sensed images, and the presence of shadows affects high spatial resolution remote sensing and multiangle remote sensing data. Shadows can lead to either a reduction in or a total loss of information in an image. This can potentially lead to the corruption of biophysical parameters derived from pixel values, such as vegetation indexes (VIs). VIs are widely used in remote sensing inversion applications. If the effects of shadows are not properly accounted for, retrieval may be uncertain when using a VI to calculate vegetation parameters. One of the major reasons that the effects of shadows are easy to be ignored in remote sensing is the spatial resolution of the measurement. High spatial and spectral resolutions are typically difficult to achieve simultaneously, and images that have one tend to not have the other. A ground-based imaging spectrometer brings a turning point to solve this problem as it can obtain both high spatial and high spectral resolutions to obtain feature and shadow images simultaneously. The resolution of the system used here was 1 mm at a height of 1 m, and the spectral resolution was better than 5 nm. For each pixel, the spectral curve of the image was almost a pure-pixel spectral curve, which allowed the differentiation of sunlit and shaded areas. To investigate the effects of shadows on different indexes, 14 hyperspectral VIs were calculated.Moreover, the vegetation fractional coverage calculated using the same 14 VIs was compared. The results show that shadows affect not only each narrowband of a VI but also vegetation parameters.

　Ｔｈｅｒｅ　ａｒｅ　ｔｗｏ　ｍａｊｏｒ　ｐｒｏｂｌｅｍｓ　ｏｆ　ｓｏｐｈｉｓｔｉｃａｔｅｄ　ｖｅｇｅｔａｔｉｏｎ　ｃｌａｓｓｉｆｉｃａｔｉｏｎ（ＳＶＣ）ｕｓｉｎｇ　ｈｙｐｅｒｓｐｅｃｔｒａｌ ｉｍａｇｅ．Ｃｌａｓｓｉｆｉｃａｔｉｏｎ　ｒｅｓｕｌｔｓ　ｕｓｉｎｇ　ｏｎｌｙ　ｓｐｅｃｔｒａｌ　ｉｎｆｏｒｍａｔｉｏｎ　ｃａｎ　ｈａｒｄｌｙ　ｍｅｅｔ　ｔｈｅ　ａｐｐｌｉｃａｔｉｏｎ　ｒｅｑｕｉｒｅｍｅｎｔｓ　ｗｉｔｈ ｔｈｅ　ｎｅｅｄｅｄ　ｖｅｇｅｔａｔｉｏｎ　ｔｙｐｅ　ｂｅｃｏｍｉｎｇ　ｍｏｒｅ　ｓｏｐｈｉｓｔｉｃａｔｅｄ．Ａｎｄ　ａｐｐｌｉｃａｔｉｏｎｓ　ｏｆ　ｃｌａｓｓｉｆｉｃａｔｉｏｎ　ｉｍａｇｅ　ａｒｅ　ａｌｓｏ　ｌｉｍｉｔｅｄ 　ｄｕｅ　ｔｏ　ｓａｌｔ　ａｎｄ　ｐｅｐｐｅｒ　ｎｏｉｓｅ．Ｔｈｅｒｅｆｏｒｅ　ｔｈｅ　ＳＶＣ　ｓｔｒａｔｅｇｙ　ｂａｓｅｄ　ｏｎ　ｃｏｎｓｔｒｕｃｔｉｏｎ　ａｎｄ　ｏｐｔｉｍｉｚａｔｉｏｎ　ｏｆ　ｖｅｇｅｔａｔｉｏｎ 　ｆｅａｔｕｒｅ　ｂａｎｄ　ｓｅｔ（ＦＢＳ）ｉｓ　ｐｒｏｐｏｓｅｄ．Ｂｅｓｉｄｅｓ　ｓｐｅｃｔｒａｌ　ａｎｄ　ｔｅｘｔｕｒｅ　ｆｅａｔｕｒｅｓ　ｏｆ　ｏｒｉｇｉｎａｌ　ｉｍａｇｅ，３０ｓｐｅｃｔｒａｌ　ｉｎｄｉｃｅｓ 　ｗｈｉｃｈ　ａｒｅ　ｓｅｎｓｉｔｉｖｅ　ｔｏ　ｂｉｏｌｏｇｉｃａｌ　ｐａｒａｍｅｔｅｒｓ　ｏｆ　ｖｅｇｅｔａｔｉｏｎ　ａｒｅ　ａｄｄｅｄ　ｉｎｔｏ　ＦＢＳ　ｉｎ　ｏｒｄｅｒ　ｔｏ　ｉｍｐｒｏｖｅ　ｔｈｅ　ｓｅｐａｒａｂｉｌｉｔｙ 　ｂｅｔｗｅｅｎ　ｄｉｆｆｅｒｅｎｔ　ｋｉｎｄｓ　ｏｆ　ｖｅｇｅｔａｔｉｏｎ．Ａｎｄ　ｔｏ　ａｃｈｉｅｖｅ　ｔｈｅ　ｓａｍｅ　ｇｏａｌ　ａ　ｓｐｅｃｔｒａｌ－ｄｉｍｅｎｓｉｏｎ　ｏｐｔｉｍｉｚａｔｉｏｎ ａｌｇｏｒｉｔｈｍ　ｏｆ　ＦＢＳ　ｂａｓｅｄ　ｏｎ　ｃｌａｓｓ－ｐａｉｒ　ｓｅｐａｒａｂｉｌｉｔｙ（ＣＰＳ）ｉｓ　ａｌｓｏ　ｐｒｏｐｏｓｅｄ．Ａ　ｓｐａｔｉａｌ－ｄｉｍｅｎｓｉｏｎ　ｏｐｔｉｍｉｚａｔｉｏｎ　ａｌｇｏｒｉｔｈｍ 　ｏｆ　ＦＢＳ　ｂａｓｅｄ　ｏｎ　ｎｅｉｇｈｂｏｒｈｏｏｄ　ｐｉｘｅｌｓ’ｓｐｅｃｔｒａｌ　ａｎｇｌｅ　ｄｉｓｔａｎｃｅ（ＮＰＳＡＤ）ｉｓ　ｐｒｏｐｏｓｅｄ　ｓｏ　ｔｈａｔ　ｄｅｔａｉｌｅｄ ｉｎｆｏｒｍａｔｉｏｎ　ｃａｎ　ｂｅ　ｋｅｐｔ　ｄｕｒｉｎｇ　ｔｈｅ　ｉｍａｇｅ　ｓｍｏｏｔｈｉｎｇ　ｐｒｏｃｅｓｓ．Ｔｈｅ　ｒｅｓｕｌｔｓ　ｏｆ　ＳＶＣ　ｅｘｐｅｒｉｍｅｎｔｓ　ｂａｓｅｄ　ｏｎ　ａｉｒｂｏｒｎｅ 　ｈｙｐｅｒｓｐｅｃｔｒａｌ　ｉｍａｇｅ　ｓｈｏｗ　ｔｈａｔ　ｔｈｅ　ｐｒｏｐｏｓｅｄ　ｍｅｔｈｏｄ　ｃａｎ　ｓｉｇｎｉｆｉｃａｎｔｌｙ　ｉｍｐｒｏｖｅ　ｔｈｅ　ａｃｃｕｒａｃｙ　ｏｆ　ＳＶＣ　ｓｏ ｔｈａｔ　ｓｏｍｅ　ｗｉｄｅｓｐｒｅａｄ　ａｐｐｌｉｃａｔｉｏｎ　ｐｒｏｓｐｅｃｔｓ　ｌｉｋｅ　ｉｄｅｎｔｉｆｉｃａｔｉｏｎ　ｏｆ　ｃｒｏｐ　ｓｐｅｃｉｅｓ，ｍｏｎｉｔｏｒｉｎｇ　ｏｆ　ｉｎｖａｓｉｖｅ　ｓｐｅｃｉｅｓ ａｎｄ　ｐｒｅｃｉｓｉｏｎ　ａｇｒｉｃｕｌｔｕｒｅ　ａｒｅ　ｅｘｐｅｃｔａｂｌｅ．

Changes in natural forest cover in tropical areas have attracted international attention. Rubber and pulp plantations threaten Hainan Island’s natural tropical forests. Remote sensing provides a crucial tool for understanding how forests change in response to forest protection strategies and economic development. China’s government has adopted protective measures designed to balance forest protection and economic development on Hainan; however, the effect of both management and economic development on natural tropical forest remains unclear. To identify changes in forest types, object-oriented decision-tree identification techniques were developed using Landsat TM images to identify causes of forest change. GIS techniques allowed analysis of the forest’s spatial shift using elevation, slope, transportation corridors, natural reserves, and farmlands linked to three different periods of forestry policy and economic development from 1988 to 2008. The analysis shows: (1) Total tropical forest area increased from 1988 to 2008, while natural tropical forest area increased slightly from 1988 to 1998, but decreased significantly from 1998 to 2008, despite implementation of the "Natural Forest Protection Project." Meanwhile, economic forests, mainly rubber and pulp plantations, expanded from 1988 to 2008. (2) Spatial changes occurred. Natural tropical forest shifted from the lower piedmont to higher mountaintops, and economic forests shifted to higher elevations under the complex effects of multiple factors. (3) The observed changes in forest cover could be related to protective measures and economic development, with economic development seemingly having the strongest influence on the condition of the forests. Elevation, slope, transportation corridors, and farmlands also affected the shift of tropical forests.

Hyperspectral (HS) remote sensing has an important role in a wide variety of fields. However, its rapid progress has been constrained due to the narrow swath of HS images. This paper proposes a spectral resolution enhancement method (SREM) for remotely sensed multispectral (MS) image, to generate wide swath HS images using auxiliary multi/hyper-spectral data. Firstly, a set number of spectra of different materials are extracted from both the MS and HS data. Secondly, the approach makes use of the linear relationships between multi and hyper-spectra of specific materials to generate a set of transformation matrices. Then, a spectral angle weighted minimum distance (SAWMD) matching method is used to select a suitable matrix to create HS vectors from the original MS image, pixel by pixel. The final result image data has the same spectral resolution as the original HS data that used and the spatial resolution and swath were also the same as for the original MS data. The derived transformation matrices can also be used to generate multitemporal HS data from MS data for different periods. The approach was tested with three image datasets, and the spectra-enhanced and real HS data were compared by visual interpretation, statistical analysis, and classification to evaluate the performance. The experimental results demonstrated that SREM produces good image data, which will not only greatly improve the range of applications for HS data but also encourage more utilization of MS data.

In spectral analysis, diagnostic absorption features can indicate the existence of specific materials. Absorption parameters such as absorption center, absorption width, and absorption depth can be used in not only identification and quantitative analysis of minerals, but also in retrieval of surface physical properties. Continuum removal (CR) is commonly used to extract absorption features. However, for a band range containing more than one absorption contribution factors, the feature extracted by CR could be a result of comprehensive effect of different factors. In this paper, a new spectral feature extraction method named reference spectral background removal (RSBR) is proposed. Given the reference spectral background, RSBR can eliminate the influence of unwanted contribution factor, and extract the absorption feature of target contribution factor. Using RSBR, the basic absorption feature parameters including the absorption center, absorption width, and absorption depth are extracted. The results are compared with those obtained from the CR. It is shown that RSBR can effectively extract pure absorption features of target material, while more accurate absorption parameters can also be achieved.