Coastal wetland vegetation is a vital component that plays an important role in environmental protection and the maintenance of the ecological balance. As such, the efficient classification of coastal wetland vegetation types is key to the preservation of wetlands. Based on its detailed spatial information, high spatial resolution imagery constitutes an important tool for extracting suitable texture features for improving the accuracy of classification. In this paper, a texture feature, Completed Local Binary Patterns (CLBP), which is highly suitable for face recognition, is presented and applied to vegetation classification using high spatial resolution Pléiades satellite imagery in the central zone of Yancheng National Natural Reservation (YNNR) in Jiangsu, China. To demonstrate the potential of CLBP texture features, Grey Level Co-occurrence Matrix (GLCM) texture features were used to compare the classification. Using spectral data alone and spectral data combined with texture features, the image was classified using a Support Vector Machine (SVM) based on vegetation types. The results show that CLBP and GLCM texture features yielded an accuracy 6.50% higher than that gained when using only spectral information for vegetation classification. However, CLBP showed greater improvement in terms of classification accuracy than GLCM for Spartina alterniflora. Furthermore, for the CLBP features, CLBP_magnitude (CLBP_m) was more effective than CLBP_sign (CLBP_s), CLBP_center (CLBP_c), and CLBP_s/m or CLBP_s/m/c. These findings suggest that the CLBP approach offers potential for vegetation classification in high spatial resolution images.

This study examined the relationship between the leaf reflectance of different seasons and the concentration of heavy metal elements in leaves, such as Co, Cu, Mo, and Ni in a post-mining area. The reflectance spectra and leaf samples of three typical plants were measured and collected in a whole growth cycle (June, July, August, and September). The Red Edge Position (REP), Readjustment Normalized Difference Vegetation Index (RE-NDVI), and Photochemical Reflectance Index (PRI) were extracted and used to explore its relation with the heavy metals concentrations in leaves between different seasons. The results show that all three Vegetation Indices (VIs) were insensitive indicators for monitoring the metal effects of vegetation in different seasons, which showed similar trends. Based on this, the Continuum Removal Indices (CRIs) were proposed from the continuum removed approach and extended for detecting the effects of heavy metal pollution over a full growth cycle. The relationship between the metal concentrations and CRIs of different plants was respectively analyzed by Stepwise Multiple Linear Regression (SMLR) and Partial Least Squares Regression (PLSR). It is found that a significant correlation exists between the band depth and the concentration of Cu and Ni based on the White birch data sets using the PLSR, resulting in a small deviation from the established relationships. Compared with VIs, the approach of coupling CRIs and multiple regressions was effective for improving the estimation accuracy. The presented study provides a detection model of leaf heavy metals that can be adapted to different growing cycles, even an arbitrary growing cycle.

On 15 May 2021, the Zhurong rover of China’s first Mars mission, Tianwen-1 (TW-1), successfully landed in southern Utopia Planitia on Mars. Various landforms were present in the landing area, and this area recorded a complex geological history. Cones are one of the typical landforms in the landing area and Utopia Planitia, and they have a great significance to the local geological processes due to the diversity of their origins. Using High-Resolution Imaging Camera (HiRIC) images collected by the TW-1 orbiter, we identified a total of 272 well-preserved circular cones in the landing area. Detailed surveys of their spatial distribution, morphological characteristics, and morphometric parameters were conducted. A preliminary analysis of the surface characteristics of these cones also provides additional information to strengthen our understanding of them. The results of the high-resolution topographic analysis show that the cone heights are in the range of 10.5–90.8 m and their basal diameters range from 178.9–1206.6 m. We compared the morphometric parameters of the cones in the landing area with terrestrial and Martian analogous features and found that our measured cones are consistent with the ranges of mud volcanoes and also a small subset of igneous origin cones. However, the result of spatial analysis is more favorable to mud volcanoes, and the lower thermal inertia of the cones in the landing area compared to their surrounding materials is also a typical characteristic of mud volcanoes. Based on current evidence and analysis, we favor interpreting the cones in the TW-1 landing area as mud volcanoes.

The main objective of this study is to develop a Hapke photometric model that is suited for Chang’E-1 (CE-1) Interference Imaging Spectrometer (IIM) data. We first divided the moon into three areas including ‘maria’, ‘new highland’ and old ‘highland’ with similar photometry characteristic based on the Hapke parameters of the moon derived from Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) multispectral data. Then, we selected the sample data in the ‘maria’ area and obtained a new set of Hapke model’s parameters that can best fit these data. Result shows that photometric correction using Hapke model with these new derived parameters can eliminate the effect of variations in viewing and luminating geometry, especially ‘opposition surge’, more efficiently than the empirical model. The corrected mosaic shows no significant artifacts along the tile boundaries and more detailed information of the image can be exhibited due to a better correction of ‘opposition surge’ at small phase angle (g < 15°).

As one of the rapidly-developing mega cities in China, Hangzhou has experienced great land use change during the past three decades. By analyzing land use change in designated period, it is beneficial to understand urbanization process in Hangzhou, and undertake further urban management and urban planning. In this study, the land use change from 1990 to 2017 in Hangzhou urban area was detected by a method of supervised classification with Landsat TM images from 1990, 1997, 2004, 2010 and 2017, and analyzed by a Markov matrix. The results show that from 1990 to 2017, a great deal of rural areas transformed into built up areas in the Hangzhou urban area. Consequently, the urban area of Hangzhou increased eight times over the period from 1990 to 2017. This may imply that such a change should be directly related to the Chinese government policy, of which the main factor is rapidly-developing urbanization in China, such as in Hangzhou. Thus, it is believed that China’s land use change is going to be small in the following decades. This may indicate that China’s urban construction is slowing down, while its urban planning is being shifted from construction to management.