The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.

Rapid urbanization has already caused many impacts, such as environmental degradation and imbalanced resource allocation. As the frontiers of urbanization, urban fringe areas (UFAs) present both urban and rural characteristics and undergo complex socio-economic structural changes. Accurately identifying the spatial extent of UFAs is highly significant because it contributes to understanding the pattern of urban spatial expansion and guides future urban planning. However, existing methods are strongly affected by subjective factors. To solve this problem, this study presents a new approach to identifying UFAs, with the Chengdu-Chongqing economic circle as the study area. The new method achieved an identification accuracy of 74.2%, effectively eliminated some noise points, and reduced the influence of subjective factors. From an applied perspective, this study employed the Geo-information Tupu and density-field-based hotspot detector to analyze the spatial pattern of educational resources. Overall, the results showed that hotspots of educational resources are concentrated in places with good transportation or near urban areas; and the generalized symmetric structure Tupu of hotspots is diverse. In addition, the results can reveal the hotspot formation mechanism and provide a reference for resource allocation.

China’s education has developed rapidly in recent years, but the issue of educational equality still exists. Currently, there are few studies on educational resources, and their spatial pattern and fairness remain unclear. Thus, this study selected the point of interest data and spatial analysis methods to depict the spatial pattern of educational resources (containing the number of teachers, students, facilities, etc.). Then, we evaluated the equity of educational resources (including the number of schools and school teachers) in terms of geographic and population distribution by combining statistical yearbook data with two indices (the index of dissimilarity and agglomeration degree) to promote healthy urban development. The results show the following. (1) Educational resources have a multicenter spatial structure of “dual cores and multiple sub-centers”. The Moran index reflects a weak positive spatial correlation between educational resources. (2) The index of dissimilarity is between 0.02 and 0.21, which shows that the allocation of resources is relatively balanced. Regarding internal units, obvious differences exist in the agglomeration degree and equilibrium of educational resources.

As a viable means to understand the associated deformation, performance study of deep excavations has been conducted worldwide in previous studies, a scenario of ground surface settlement characteristics among different sites has been proposed since 1960s. The objective is to study the characteristics during excavation in Beijing and fill in the blank on the scenario. Thirty excavation cases with good-quality construction and field observation data are selected to quantify the relationship between settlement and excavation, and to analyze the ground surface deformation behavior. The magnitude and location of maximum ground surface settlement ($\delta vm$), the apparent influence range, development process and pattern are depicted. This study demonstrates that: (1) Concave trough pattern of final ground surface settlement is detected, slight heave was observed and $\delta vm$ located at distances about 0.5H to 0.75H from the wall. (2) Spatial difference of ground surface settlement ($\delta v$) varies greatly, great depression appeared behind each wall and maximum settlements at midspans of both sides occurs with the lowest 1-2 struts removal, especially the long side with a $\Delta \delta v$ of $2.8 \pm 0.75$ mm. Movement was least of all by the corners. (3) Final settlement generally ranges from 5mm to -20mm, less than $\delta vm$. Influence zones reach to a distance about 2.5H to 3.1H outside the excavation.