This study analyzed the time series curves of enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and land surface water index (LSWI) of paddy rice areas in Hunan Province based on MODIS data. Single and double-season paddy rice was distinguished with the classification and regression tree (CART) decision tree method. The inflection method and the dynamic threshold method were applied to retrieve the growth periods of double-season paddy rice. The result shows that double-season paddy rice of Hunan Province was mainly distributed in the Dongting Lake area, the plain area surrounding the main stream and tributaries of the section between Hengyang and Zhuzhou of the Xiangjiang River, and the panhandle between the Yangming Mountains and the Nanling Mountains in Yongzhou and Chenzhou. Single-season paddy rice was mainly distributed on the periphery of the zones planted with double-season paddy rice and the valleys in Xiangxi and Huaihua. The growth periods of double-season paddy rice planted in the southern part of the Dongting Lake area and the hilly areas in southern Hunan are earlier than other regions. The distribution of single/double-season paddy rice and their growth periods in the most part of Hunan Province were spatially un-contiguous and this pattern is relatively consistent across space. These findings can provide support for future study of the relationship between natural disasters that affect paddy rice and the risk of climate change in Hunan Province.

Plant phenology is one of the most salient and sensitive indicators of terrestrial ecosystem's response to climate change. Understanding its spatiotemporal change is significantly important for understanding both land surface processes and carbon cycle and predicting changes in the terrestrial ecosystem. MODIS MOD09A1, with the spatial resolution of 500 m × 500 m and at an 8-day temporal interval, was used in this study to investigate the change in forest phenology in the Qinling zone of central China in 2001?2010. First, we used the day of year (DOY) of MOD09A1 to improve the temporal precision of EVI; we then combined the maximum ratio and the threshold method for phenology data extraction [start of growth season (SOG), end of growth season (EOG), and length of growth season (LOG)] in the Qinling zone. Results of this study show that: Accompanying the deterioration in heat and water conditions from low altitude to high altitude and southeast to northwest, SOG delayed, EOG advanced, and LOG shortened gradually. SOG and EOG mainly occurred on the 81th?120th and 270th?311th days respectively. LOG was mainly between 150 and 230 days. The phenology of forest in Qinling zone is closely related to altitude, with every 100 m rising in altitude, SOG, EOG, and LOG gradualy delayed 2 days, advanced 1.9 days, and shortened 3.9 days, respectively. From 2001 to 2010, early SOG, late EOG, and extended LOG mainly occurred in medium altitude. SOG, EOG, and LOG gradually delayed, advanced, and shortened respectively in some areas that are lowered than 1,000 m above sea level. Interannual changes at high altitude were more complicated than that at low altitude, and SOG advanced, EOG advanced, and LOG shortened above 2000 m. The reasons for these changes remain unclear. The findings quantified the differences of forest phenology with the change in elevation and revealed the spatiotemporal variations in forest phenology from 2001 to 2010. This article provides a reference for the evaluation and protection of ecological environment in the Qinling zone. In future study, reasons for the above mentioned differences in forest phenology need to be explored.

Extraction of active fault location and active fault surface deformation features is essential for the study of active fault systems, and a large number of studies have been carried out on fault extraction based on Digital Elevation Model (DEM). This article summarizes the active fault extraction methods using DEM of lower than 30 m resolution and very-high resolution DEM, such as Light Detection and Ranging (LiDAR) DEM and Structure from Motion (SfM) DEM. The fault extraction methods can be divided mainly into three categories: geomorphic feature interpretation, image interpretation and multiple interpretation. Geomorphic feature interpretation is based on GIS spatial analyses. Image interpretation identifies faults by examining linear variation of surface deformation through image processing algorithms. Multiple interpretation combines the above two methods with remote sensing image processing. Meanwhile, this article reviews the most recent progress in the extraction of surface deformation features using DEM, and enumerates the extraction of fault scarp and deformed drainage characteristics. With the progress in high-resolution DEM, DEM and its spatial analysis techniques have become a conventional geoscience research method. The integration of this method with field research, remote sensing, and dating techniques can provide a strong technical support to quantitative study in fault research.

This article takes the general principles and application values of the discrete choice model system as a departure point and summarizes the classical model forms with respect to their basic theories and typical applications. Important latest developments are also introduced. Multinomial logit (MNL) model is the basis of the discrete choice model system, with the advantages of simplicity, reliability, and easy implementation. However, it also has some inherent theoretic defects, which led to the need for more refined models. Nested logit model is usually used to deal with problems of correlation among alternatives, no-choice alternative, and data enrichment. Its more general form is the generalized extreme value (GEV) model system; mixed logit model is suitable for handling random preference and some kinds of correlation problems, such as correlation among alternatives, panel data, random coefficients, and data for enrichment. A similar model form named latent class model is also widely used. Multinomial probit (MNP) model is highly flexible. However, its application is limited due to the complexity of model specification and very high computation demands. With regard to the new development of discrete choice model system, four important areas are introduced. These include complex new models derived from the combination of classical models; models suitable for dealing with revealed preference/stated preference (RP/SP), ordered, ranked, and multiple choice data; models based on bounded rationality choice which is more close to reality; and models considering the spatiotemporal background of choice.