In the era of big data, advances in relevant technologies are profoundly impacting the field of real estate appraisal. Many scholars regard the integration of big data technology as an inevitable future trend in the real estate appraisal industry. In this paper, we summarize 124 studies investigating the use of big data technology to optimize real estate appraisal through the hedonic price model (HPM). We also list a variety of big data resources and key methods widely used in the real estate appraisal field. On this basis, the development of real estate appraisal moving forward is analyzed. The results obtained in the current studies are as follows: First, the big data resources currently applied to real estate appraisal include more than a dozen big data types from three data sources; the internet, remote sensing, and the Internet of things (IoT). Additionally, it was determined that web crawler technology represents the most important data acquisition method. Second, methods such as data pre-processing, spatial modeling, Geographic information system (GIS) spatial analysis, and the evolving machine learning methods with higher valuation accuracy were successfully introduced into the HPM due to the features of real estate big data. Finally, although the application of big data has greatly expanded the amount of available data and feature dimensions, this has caused a new problem: uneven data quality. Uneven data quality can reduce the accuracy of appraisal results, and, to date, insufficient attention has been paid to this issue. Future research should pay greater attention to the data integration of multi-source big data and absorb the applications developed in other disciplines. It is also important to combine various methods to form a new united evaluation model based on taking advantage of, and avoiding shortcomings to compensate for, the mechanism defects of a single model.

Rapid urbanization and drastic land-use change have led to landscape fragmentation and ecological environment deterioration in the regions along the Grand Canal. Building an ecological network is an important means to improve the connectivity of habitat patches and carry out ecological protection and restoration of territorial space, which is of great significance to ensure regional biodiversity and ecological security. In this article, we took the Huaiyang Section of the Grand Canal (Huaiyang Canal) as the study area, used the ecosystem service assessment model, morphological spatial pattern analysis (MSPA), and the landscape connectivity evaluation method to identify ecological sources, then used the minimum cumulative resistance (MCR) model and the gravity model to extract and grade ecological corridors. Based on these, the ecological network was constructed by combining the identification method of ecological nodes and ecological breakpoints. The aim of this was to provide a reference for the ecological space optimization of Huaiyang Canal and even the entire Grand Canal, the formulation of an ecological protection plan, and the implementation of territorial space ecological restoration. The results showed that the spatial distribution of the water conservation service, soil conservation service, carbon sequestration service, and biodiversity conservation service were significantly different, and the level of ecosystem services showed a trend of continuous degradation from 1990 to 2018. There were 12 ecological source patches comprehensively identified by multiple methods, with a total area of 2007.06 km². In terms of spatial distribution, large ecological source patches were mainly distributed in the central and western areas adjacent to the Grand Canal, while small ecological source patches were scattered in the eastern and southern border regions of the study area. The total length of ecological corridors was 373.84 km, of which the number of the primary ecological corridor, secondary ecological corridor, and tertiary ecological corridor were 9, 7, and 7, respectively, and the suitable width of the ecological corridor was 200–400 m. After optimization, the proposed ecological network was composed of 3 key ecological source patches, 9 important ecological source patches, 23 terrestrial corridors, 10 aquatic corridors, and 18 ecological nodes. Twenty-nine ecological breakpoints were key areas requiring ecological restoration. The overlap rate of the integrated ecosystem service change area and land-use change area was 99%, indicating that land-use change has a significant impact on regional ecosystem services. This study is of great significance for carrying out the ecological protection and restoration of the Huaiyang Canal and adjusting local land-use policies. It also provides a typical case demonstration for identifying an ecological network and formulating ecological restoration planning for other sections of the Grand Canal and cities along the canal.

The tense relationship between the supply and demand of land resources and the past spatial expansion of urban development in Beijing have brought many urban problems. Mixed land use is considered to be able to solve these urban problems as well as promote sustainable urban development. In this context, this study uses multi-source big data such as POI, OpenStreetMap and web crawler data to construct current land-use data of the area within the sixth ring road of Beijing, and then uses the entropy index and type number index to analyze the spatial distribution and aggregation characteristics of the mixed land-use level. Finally, a multi-scale geographically weighted regression is applied to explore the impact of the block and life circle scale mixed land use on housing prices. The results show that: (1) the accuracy of land use data obtained by using multi-source big data is high, and the consistency with the real land use situation is as high as 82.67%. (2) the mixed land use level in the study area is higher in the urban center and lower in the periphery of the city. However, it does not show the spatial distribution characteristics gradually decreasing with the increase of the distance from the urban center but shows that the area from the third to the fifth ring road is the highest. (3) the impact of block scale and life circle scale mixed land use on housing price is different. The type number index has a negative effect on the housing price in block scale mixed land use, while the entropy index has a positive effect on the housing price in life circle scale mixed land use. Based on the existing “bottom-up” individual-dominant development mode, the government of Beijing should issue relevant policies and documents to give “top-down” control and guidance in the future, so as to promote the maximization of the benefits of mixed land use. Furthermore, in the practice of mixed land use in Beijing, land use types should be reduced at the block scale and the area of different land use types should be balanced at the life circle scale.

Forest is one of the most important surface coverage types. Monitoring its dynamics is of great significance in global ecological environment monitoring and global carbon circulation research. Forest monitoring based on Landsat time-series stacks is a research hotspot, and continuous change detection is a novel approach to real-time change detection. Here, we present an approach, continuous change detection and classification-spectral trajectory breakpoint recognition, running on Google Earth Engine (GEE) for monitoring forest disturbance and forest long-term trends. We used this approach to monitor forest disturbance and the change in forest cover rate from 1987 to 2020 in Nanning City, China. The high-resolution Google Earth images are collected for the validation of forest disturbance. The classification accuracy of forest, non-forest, and water maps by using the optima classification features was 95.16%. For disturbance detection, the accuracy of our map was 86.4%, significantly higher than 60% of the global forest change product. Our approach can successfully generate high-accuracy classification maps at any time and detect the forest disturbance time on a monthly scale, accurately capturing the thinning cycle of plantations, which earlier studies failed to estimate. All the research work is integrated into GEE to promote the use of the approach on a global scale.

It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we developed a function-based model, the CCTM (Continuous Change Tracker Model) model, that can achieve high precision in time series reconstruction by tracking the time series variation rate. The goal of this paper is to provide a new solution for high-precision time series reconstruction and related applications. To test the reconstruction effects, the model was applied to four types of datasets: normalized difference vegetation index (NDVI), gross primary productivity (GPP), leaf area index (LAI), and MODIS surface reflectance (MSR). Several new observations are as follows. First, the CCTM model is well explained and based on the second-order derivative theorem, which divides the yearly time series into four variation types including uniform variations, decelerated variations, accelerated variations, and short-periodical variations, and each variation type is represented by a designed function. Second, the CCTM model provides much better reconstruction results than the Harmonic model on the NDVI, GPP, MSR, and LAI datasets for the seasonal segment reconstruction. The combined use of the Savitzky–Golay filter and the CCTM model is better than the combinations of the Savitzky–Golay filter with other models. Third, the Harmonic model has the best trend-fitting ability on the yearly time series dataset, with the highest R-Square and the lowest RMSE among the four function fitting models. However, with seasonal piecewise fitting, the four models all achieved high accuracy, and the CCTM performs the best. Fourth, the CCTM model should also be applied to time series image compression, two compression patterns with 24 coefficients and 6 coefficients respectively are proposed. The daily MSR dataset can achieve a compression ratio of 15 by using the 6-coefficients method. Finally, the CCTM model also has the potential to be applied to change detection, trend analysis, and phenology and seasonal characteristics extractions.

Since the implementation of the Natural Forest Resources Protection Project (NFRP) and the Grain for Green Program (GFGP), two key ecological projects related to forestry, the vegetation cover in Chongqing, has improved significantly. Existing studies have revealed the effects of climate change and human activity on vegetation cover in arid regions. However, more studies are needed to reveal the influence of drivers on vegetation cover in mild and humid areas, to quantify the relative contribution of drivers and to analyze the overall land use characteristics in different regions. In this study, we used Theil–Sen slope analysis and the Mann–Kendall test to investigate the spatial and temporal changes in vegetation cover in Chongqing. Further, we used Pearson correlation analysis to analyze the correlations between vegetation cover and drivers, quantitatively analyzing the relative contributions of these drivers. Complex network model analysis was used for different regions to obtain their land-use system characteristics, and the Hurst index was adopted to predict future vegetation-cover changes. The results of this study showed that the average vegetation cover in Chongqing increased significantly from 2000 to 2020, and the overall greening trend was most obvious in winter. Precipitation and temperature influenced the vegetation cover of Chongqing city to a certain extent, and the positive correlation between vegetation cover and precipitation was more significant than that with temperature. In terms of the precipitation factor, the areas with significant positive correlations were mainly concentrated in the central and southern parts of Chongqing, which could be related to the higher precipitation in the southern part of the city. Under the combined influence of climate change and human activity, vegetation cover increased in 71.95% of the total area. Human activity had a relative contribution of 70.39% and 69.14% in the areas where vegetation cover decreased and increased, respectively. The analysis results of the complex network model showed that woodlands and grasslands contributed more to areas where the vegetation cover exhibited an increasing trend. In the future, it is estimated that 72.92% of the vegetation cover in Chongqing will exhibit a degradation trend. This study helps us further understand vegetation-cover changes in mild and humid areas, providing new research directions for informing forestry-related policies.