WebGIS-based Natural Disaster Information System of China

We have constructed China natural disaster information system utilizing open source WebGIS framework. Through comparisons and researches on different WebGIS system structures, implementation patterns, spatial database management patterns and several popular software, open source WebGIS platform MapServer, PostgreSQL+PostGIS (spatial database and spatial data engine), client-side browser framework openLayer were used. This system have basic function of web information publishing, spatial data storage and management, and it collects natural disaster information that have been publishing on the internet through grabing internet information, and based on the analysis to the text information, different kinds of natural disaster information databases were constructed. Additionally, locations of the disaster occurred were combined to provide map services, and the system is able to meet multi-user access issues, and have functions of thematic mapping, query and statistics, charting and output, et.al

GeoDjango-Framwork-based Population and Social Economic Information System For China

We have constructed China population and social economic information system based on open source geographic Web framework GeoDjango. By comparing different WebGIS system structures, implementation patterns, spatial database management patterns and mainstream popular open source APIs, open source WebGIS framework GeoDjango, MVT pattern, Python and MySQL database, client-side browser framework openLayer were implemented. The database system utilized a Top-Down design method — DATAID-D to establish distributed database, and construct a uniform encoding standard, through normalized processes of the statistic yearbook, we constructed population and social economic database. This system has basic functions of web information publishing, spatial data storage, distributed management and spatial query. Additionally, the system is able to meet multi-user access issues, and have functions of population and economy data visualization, thematic mapping, query and statistics, charting and output, et.al

Evaluation of Satellite-Based Soil Moisture Products over Four Different Continental In-Situ Measurements

Global, near-real-time satellite-based soil moisture (SM) datasets have been developed over recent decades. However, there has been a lack of comparison among different passing times, retrieving algorithms, and sensors between SM products over various regions. In this study, we assessed seven types of SM products (AMSR_A, AMSR_D, ECV_A, ECV_C, ECV_P, SMOS_A, and SMOS_D) over four different continental in-situ networks in North America, the Tibetan Plateau, Western Europe, and Southeastern Australia. Bias, R, root mean square error (RMSE), unbiased root mean square difference (ubRMSD), anomalies, and anomalies R were calculated to explore the agreement between satellite-based SM and in-situ measurements. Taylor diagrams were drawn for an inter-comparison. The results showed that (1) ECV_C was superior both in characterizing the SM temporal variation tendency and absolute value, while ECV_A produced numerous abnormal values over all validation regions. ECV_P was able to basically express the SM variation tendency, except for a few overestimations and underestimations. (2) The ascending data (AMSR_A, SMOS_A) generally outperformed the corresponding descending data (AMSR_D, SMOS_D). (3) AMSR exceeded SMOS in terms of the coefficient of correlation. (4) The validation result of SMOS_D over the NAN and OZN networks was unsatisfactory, with a rather poor correlation for both original data and anomalies

Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China

Although numerous satellite-based soil moisture (SM) products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machine learning algorithms, including classification and regression trees (CART), K-nearest neighbors (KNN), Bayesian (BAYE), and random forests (RF), were implemented to downscale the monthly European Space Agency Climate Change Initiative (ESA CCI) SM product from 25-km to 1-km spatial resolution. During the regression, the land surface temperature (including daytime temperature, nighttime temperature, and diurnal fluctuation temperature), normalized difference vegetation index, surface reflections (red band, blue band, NIR band and MIR band), and digital elevation model were taken as explanatory variables to produce fine spatial resolution SM. We chose Northeast China as the study area and acquired corresponding SM data from 2003 to 2012 in unfrozen seasons. The reconstructed SM datasets were validated against in-situ measurements. The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation. Additionally, the RF was less affected by parameters, which revealed its robustness. Both CART and KNN ranked second. Compared to KNN, CART had a relatively close correlation with the validation data, but KNN showed preferable precision. Moreover, BAYE ranked last with significantly abnormal regression values

Mapping Urban Areas with Integration of DMSP/OLS Nighttime Light and MODIS Data Using Machine Learning Techniques

Mapping urban areas at global and regional scales is an urgent and crucial task for detecting urbanization and human activities throughout the world and is useful for discerning the influence of urban expansion upon the ecosystem and the surrounding environment. DMSP-OLS stable nighttime lights have provided an effective way to monitor human activities on a global scale. Threshold-based algorithms have been widely used for extracting urban areas and estimating urban expansion, but the accuracy can decrease because of the empirical and subjective selection of threshold values. This paper proposes an approach for extracting urban areas with the integration of DMSP-OLS stable nighttime lights and MODIS data utilizing training sample datasets selected from DMSP-OLS and MODIS NDVI based on several simple strategies. Four classification algorithms were implemented for comparison: the classification and regression tree (CART), k-nearest-neighbors (k-NN), support vector machine (SVM), and random forests (RF). A case study was carried out on the eastern part of China, covering 99 cities and 1,027,700 km2. The classification results were validated using an independent land cover dataset, and then compared with an existing contextual classification method. The results showed that the new method can achieve results with comparable accuracies, and is easier to implement and less sensitive to the initial thresholds than the contextual method. Among the four classifiers implemented, RF achieved the most stable results and the highest average Kappa. Meanwhile CART produced highly overestimated results compared to the other three classifiers. Although k-NN and SVM tended to produce similar accuracy, less-bright areas around the urban cores seemed to be ignored when using SVM, which led to the underestimation of urban areas. Furthermore, quantity assessment showed that the results produced by k-NN, SVM, and RFs exhibited better agreement in larger cities and low consistency in small cities

Spatial Structure Evolution and Economic Benefits of Rapidly Expanding the High-Speed Rail Network in Developing Regions: A Case Study in Western China

High-speed rail (HSR) is an important form of transportation that affects the economic development of the regional spatial structure. However, there is less discussion about the impact of economically underdeveloped regions and the rapid construction of HSR on the region. This study uses a spatial econometric model to explore whether a rapidly formed high-speed rail network with changes in the network structure can bring economic effects based on the spatio-temporal panel data on high-speed rail construction and economic development in western China from 2015 to 2020. First, data of the daily departures between high-speed rail cities were used to analyze the western high-speed rail network’s spatial and temporal evolution characteristics. Second, we analyzed the changes in the centrality, external and internal connectivity, and transfer potential of the economic gap of the western HSR network. Finally, we analyzed the different economic effects of the HSR network structure by combining the Cobb–Douglas production function with the spatial econometric model. The conclusions are as follows: (1) The HSR network in western China is dense at the intra-provincial HSR network; then it expands along the cross-provincial region; and is gradually embedded in the national HSR network, forming a figure-8-shaped spatial structure. (2) In the rapid expansion and densification of the HSR network in western China, connectivity takes precedence, and dominance and control are then increased. The external connectivity of the western HSR city network develops first and shows fluctuating growth, while the internal connectivity improves relatively slowly. (3) The connectivity, convenience of transit, transshipment capacity, and internal and external connection structure of the HSR network all contribute to the economic development of western cities. The transfer potential of economic gaps is detrimental to their economic development but has a positive effect on adjacent cities

A Spatial Downscaling Algorithm for Satellite-Based Precipitation over the Tibetan Plateau Based on NDVI, DEM, and Land Surface Temperature

Precipitation is an important controlling parameter for land surface processes, and is crucial to ecological, environmental, and hydrological modeling. In this study, we propose a spatial downscaling approach based on precipitation–land surface characteristics. Land surface temperature features were introduced as new variables in addition to the Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) to improve the spatial downscaling algorithm. Two machine learning algorithms, Random Forests (RF) and support vector machine (SVM), were implemented to downscale the yearly Tropical Rainfall Measuring Mission 3B43 V7 (TRMM 3B43 V7) precipitation data from 25 km to 1 km over the Tibetan Plateau area, and the downscaled results were validated on the basis of observations from meteorological stations and comparisons with previous downscaling algorithms. According to the validation results, the RF and SVM-based models produced higher accuracy than the exponential regression (ER) model and multiple linear regression (MLR) model. The downscaled results also had higher accuracy than the original TRMM 3B43 V7 dataset. Moreover, models including land surface temperature variables (LSTs) performed better than those without LSTs, indicating the significance of considering precipitation–land surface temperature when downscaling TRMM 3B43 V7 precipitation data. The RF model with only NDVI and DEM produced much worse accuracy than the SVM model with the same variables. This indicates that the Random Forests algorithm is more sensitive to LSTs than the SVM when downscaling yearly TRMM 3B43 V7 precipitation data over Tibetan Plateau. Moreover, the precipitation–LSTs relationship is more instantaneous, making it more likely to downscale precipitation at a monthly or weekly temporal scale

A Regionalized Study on the Spatial-Temporal Changes of Grassland Cover in the Three-River Headwaters Region from 2000 to 2016

The Three-River Headwaters Region (TRHR) is located in the interior of the Qinghai-Tibetan Plateau, which is a typical research area in East Asia and is of fragile environment. This paper studied the characteristics of grassland cover changes in the TRHR between 2000 and 2016 using methods of area division (AD) based on natural conditions and tabulate area (TA) dependent on Moderate-resolution Imaging Spectroradiometer (MODIS) 44B product. Further investigations were conducted on some of the typical areas to determine the characteristics of the changes and discuss the driving factors behind these changes. Classification and Regression Trees (CART), Random Forest (RF), Bayesian (BAYE), and Support Vector Machine (SVM) Machine Learning (ML) methods were employed to evaluate the correlation between grassland cover changes and corresponding variables. The overall trend for grassland cover in the TRHR towards recovery that rose 0.91% during the 17-year study period. The results showed that: (1) The change in grassland cover was more divisive in similar elevation and temperature conditions when the precipitation was stronger. The higher the temperature was, the more significant the rise of grassland cover was in comparable elevation and precipitation conditions. (2) There was a distinct decline and high change standard deviation of grassland cover in some divided areas, and strong correlations were found between grassland cover change and aspect, slope, or elevation in these areas. (3) The study methods of AD and TA achieved enhancing performance in interpretation of grassland cover changes in the broad and high elevation variation areas. (4) RF and CART methods showed higher stability and accuracy in application of grassland cover change study in TRHR among the four ML methods utilized in this study

A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China

Environmental monitoring of Earth from space has provided invaluable information for understanding land–atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation–land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day–night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI), the Digital Elevation Model (DEM), and geolocation (longitude, latitude). Four machine learning regression algorithms, the classification and regression tree (CART), the k-nearest neighbors (k-NN), the support vector machine (SVM), and random forests (RF), were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North China for the purpose of comparison of algorithm performance. The downscaled results were validated based on observations from meteorological stations and were also compared to a previous downscaling algorithm. According to the validation results, the RF-based model produced the results with the highest accuracy. It was followed by SVM, CART, and k-NN, but the accuracy of the downscaled results using SVM relied greatly on residual correction. The downscaled results were well correlated with the observations during the year, but the accuracies were relatively lower in July to September. Downscaling errors increase as monthly total precipitation increases, but the RF model was less affected by this proportional effect between errors and observation compared with the other algorithms. The variable importances of the land surface temperature (LST) feature variables were higher than those of NDVI, which indicates the significance of considering the precipitation–land surface temperature relationship when downscaling TRMM 3B43 V7 precipitation data

Spatial Structure Evolution and Economic Benefits of Rapidly Expanding the High-Speed Rail Network in Developing Regions: A Case Study in Western China

High-speed rail (HSR) is an important form of transportation that affects the economic development of the regional spatial structure. However, there is less discussion about the impact of economically underdeveloped regions and the rapid construction of HSR on the region. This study uses a spatial econometric model to explore whether a rapidly formed high-speed rail network with changes in the network structure can bring economic effects based on the spatio-temporal panel data on high-speed rail construction and economic development in western China from 2015 to 2020. First, data of the daily departures between high-speed rail cities were used to analyze the western high-speed rail network’s spatial and temporal evolution characteristics. Second, we analyzed the changes in the centrality, external and internal connectivity, and transfer potential of the economic gap of the western HSR network. Finally, we analyzed the different economic effects of the HSR network structure by combining the Cobb–Douglas production function with the spatial econometric model. The conclusions are as follows: (1) The HSR network in western China is dense at the intra-provincial HSR network; then it expands along the cross-provincial region; and is gradually embedded in the national HSR network, forming a figure-8-shaped spatial structure. (2) In the rapid expansion and densification of the HSR network in western China, connectivity takes precedence, and dominance and control are then increased. The external connectivity of the western HSR city network develops first and shows fluctuating growth, while the internal connectivity improves relatively slowly. (3) The connectivity, convenience of transit, transshipment capacity, and internal and external connection structure of the HSR network all contribute to the economic development of western cities. The transfer potential of economic gaps is detrimental to their economic development but has a positive effect on adjacent cities