The significance of street-side greenery monitoring is increasing for precise urban planning and environmental management, especially in rapid sprawling urban cities, while there have been few studies focusing on urban greenery estimation using new profile image system. In this study, Baidu Street View (BSV) images, which were taken by Baidu vehicles and had view angles similar to those of pedestrians on the street, were selected for calculating the magnitude of street profile greenery. From 278 randomly selected street sample sites in Tai’an city of China, 3336 images were acquired via the Baidu Application Programming Interface (API). A Baidu Green View Index (BGVI) was proposed to quantitatively describe the street-side profile greenery. The results demonstrated that green vegetation can be distinguished efficiently from BSV images. The BGVI varied in the different portions of the study area, and it can be used to grade street system by considering pedestrians’ visualized greenery. Though BGVI had a significant correlation with the overlooking green canopy coverage, in some street sample sites it can delineate different scenarios. BGVI can be regarded as complementary information to urban planning and management.

The establishment of a comprehensive framework to identify village development types is crucial to formulate plans for rural development and promote rural revitalization. This study proposed a natural–socioeconomic framework to identify the types of villages based on field survey, statistical data, and multi-source remote sensing images. The framework was constructed by combining the two-dimensional natural suitability/restriction evaluation and the four-dimensional socioeconomic development level evaluation. Then, the modified multiplication-weighted summation method and the coupling coordination degree algorithm were employed to identify the villages’ development types. A total of 774 villages of the Laiyang County, eastern China were used as the study areas to examine the framework. The results demonstrated the following. (1) There were 243,318 and 151 villages with high, moderate, low natural suitability, and 62 villages with natural restrictions; and 158,366 and 250 villages with high, moderate, and low economic development level, respectively. The distribution characteristic of natural evaluation was “high in the southwest and low in the northeast”, and the socioeconomic development level was generally centered on the urban area, which presented a “high–medium–low” circle-layer distribution structure. (2) There were 247 villages with high-level coupling coordination, 464 villages with intermediate coupling coordination, 1 village with low-level coupling coordination, and 62 villages with disordered coupling. (3) Based on the coupling coordination evaluation results, villages in the study area were grouped into five types: urbanization development (31%), construction development (16%), agglomeration linkage development (27%), decrease and improvement development (18%), and relocation and integration development (8%). The framework of villages’ development types identification established in this study can enrich the theory of rural geography, and the applied research results can provide a basis for rural revitalization and development planning.

This study aimed to assess the relationship between the landscape patterns and non-point source (NPS) pollution distribution in Qixia County, China. The sub-basin classification was conducted based on a digital elevation model and Landsat8 satellite images. Water samples were collected from each sub-basin, andtheir water quality during the wet and dry seasons was estimated. The correlation between the landscape indices and water pollution indicators was determined by Pearson analysis. The location-weighted landscape contrast index (LWLCI) was calculated based on the “source-sink” theory. Qixia was further divided into five sections based on the LWLCI score to illustrate the potential risk of NPS pollution. The results showed that the water quality in Qixia County was generally good. Cultivated land, orchards, construction areas, and unused land were positively correlated with the water pollution index and weredesignated as the “source” landscape categories, while forests, grasslands, and water bodies, which were negatively correlated with water pollution, were the “sink” landscapes; the LWCI was high in 36.94% of the study area. In these areas, measures such as increasing vegetation buffer zones are necessary to decrease the sediment and nutrient loads carried by precipitation.

Precipitation data from nine meteorological stations in arid oases of Hexi Corridor, northwest China during 1970–2012 were analyzed to detect trends in precipitation and Standardized Precipitation Index (SPI) at multiple time scales using linear regression, Mann–Kendall and Spearman’s Rho tests. The results found that annual precipitation in the observed stations was rare and fell into the arid region category according to the aridity index analysis. The monthly analysis of precipitation found that three stations showed significant increasing trends in different months, while on the annual level, only Yongchang station had a significant increasing trend. The analysis of SPI-12 found three main drought intervals, i.e., 1984–1987, 1991–1992 and 2008–2011, and an extremely dry year among the stations was recorded in 1986; the southeast and middle portions of the study area are expected to have more precipitation and less dry conditions.

The importance of accurately monitoring rangeland degradation dynamics over decades is increasing in Linxia rangeland, the birthplace of the Yellow River in China. Since 2000, the Chinese government has implemented the “Grain for Green” program and enforced a grazing ban in Gansu province, one of the most degraded provinces, to mitigate the problem of rangeland degradation. The effects of these policies are controversial and have become a topic of public concern. In this study, a grading system was established for the estimation of Linxia rangeland degradation. Degrees of rangeland degradation were interpreted and the spatio-temporal dynamics of the degraded rangeland through several study periods were mapped and monitored using the Linear Spectral Mixture Analysis method on Landsat Thematic Mapper (TM)/ETM+ (Enhanced Thematic Mapper Plus) images for the years of 1996, 2001, 2006, and 2011. The results demonstrated that the time around the year 2001 appeared to be a turning point of the rangeland degradation reversion course, as the rangeland degradation reversed significantly since 2001. From 1996 to 2001, the total degraded area in Linxia rangeland increased from 2922.01 km² to 3048.48 km² (+4.33%), and decreased by 4.54% to 2909.97 km² in 2011; the non-degraded rangeland gradually increased from 602.74 km² to 710.01 km², an increase of 17.80%. Degraded rangeland vegetation was restored significantly during 2001–2011: the area of slightly degraded rangeland increased by 3.71% and 3.83% annually during 2001–2006 and 2006–2011 intervals, respectively, and the area of moderately and severely degraded rangeland decreased annually by 4.77% and 2.41% from 2001 to 2006, and 4.58% and 0.81% during 2006–2011, respectively. These results indicated that the “Grain for Green” program and grazing ban policy, together with other ecological impacting factors, helped reverse the rangeland degradation and promote the rehabilitation of rangeland vegetation.