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Color quality evaluation is key to judging map quality, which can improve data visualization and communication. However, most existing methods for evaluating map colors are tedious and subjective manual methods. In this paper, we study sequential color schemes, a widely used map color type and propose a learning-based approach for evaluating the color quality. The approach consists of two steps. First, we extract and characterize the cartographic factors for determining the quality of sequential color schemes, such as color order, color match, color harmony, color discrimination and color uniformity. Second, we present a model to predict the color quality based on AdaBoost, a type of ensemble learning algorithm with excellent classification performance and use these factors as input data. We conduct a case study based on 781 samples and train the AdaBoost-based model to predict the quality of sequential color schemes. To evaluate the model’s performance, we calculated the area under the receiver operating characteristic (ROC) curve (AUC). The AUC values are 0.983 and 0.977 on the training data and testing data, respectively. These results indicate that the proposed approach can be used to automatically evaluate the quality of sequential color schemes for maps, which helps mapmakers select good colors.
Taisheng Chen; Menglin Chen; A-Xing Zhu; Weixing Jiang. A learning-based approach to automatically evaluate the quality of sequential color schemes for maps. Cartography and Geographic Information Science 2021, 1 -16.
AMA StyleTaisheng Chen, Menglin Chen, A-Xing Zhu, Weixing Jiang. A learning-based approach to automatically evaluate the quality of sequential color schemes for maps. Cartography and Geographic Information Science. 2021; ():1-16.
Chicago/Turabian StyleTaisheng Chen; Menglin Chen; A-Xing Zhu; Weixing Jiang. 2021. "A learning-based approach to automatically evaluate the quality of sequential color schemes for maps." Cartography and Geographic Information Science , no. : 1-16.
To allow extraction of irrigation signals from satellite-derived data on soil moisture, this study describes the development of an irrigation signal extraction method that takes into account multiple environmental factors in irrigation. Firstly, the fuzzy membership functions of irrigation relating to multiple environmental factors are constructed. Then, a model is built based on the fuzzy membership functions by using operation rules of fuzzy sets, which is used to infer the relevant degree of irrigation to nonirrigation. Finally, the irrigation signals in satellite-based soil moisture data are recognized according to the relevant degree. Taking Henan Province in the North China Plain as the study area, the proposed method is used to extract irrigation signals from the SMAP Level 3 Passive Soil Moisture Product. Extracted irrigation signals from two SMAP grids are validated using daily in situ soil moisture and precipitation data, with the results showing correct identification of most of the irrigation signals. By grading the membership degree of the extracted irrigation signals, irrigation frequency maps for the 2016–2017 winter crop growth season and the 2017 summer crop growth season are obtained for Henan Province. Compared to the irrigation frequency maps with data on the annual precipitation and the annual potential evapotranspiration, the irrigation frequency maps show a spatial pattern opposite that of the annual precipitation and a spatial pattern similar to that of the annual potential evapotranspiration. It is common sense that areas with low precipitation and high evapotranspiration need more irrigation frequency and irrigation water. Thus, the spatial patterns of irrigation frequency maps are reasonable in a sense. However, it should be noted that the observed irrigation data used in the qualitative assessments are rendered less convincing by the SMAP product’s coarse resolution. Quantitative validation of extracted irrigation signals remains a significant challenge, and small-scale irrigation cannot be captured by coarse-resolution satellite-based soil moisture products. Thus, a high-resolution soil moisture product should be used to extract irrigation signals in future.
Liming Zhu; A-Xing Zhu. Extraction of Irrigation Signals by Using SMAP Soil Moisture Data. Remote Sensing 2021, 13, 2142 .
AMA StyleLiming Zhu, A-Xing Zhu. Extraction of Irrigation Signals by Using SMAP Soil Moisture Data. Remote Sensing. 2021; 13 (11):2142.
Chicago/Turabian StyleLiming Zhu; A-Xing Zhu. 2021. "Extraction of Irrigation Signals by Using SMAP Soil Moisture Data." Remote Sensing 13, no. 11: 2142.
Due to the wide availability of remote sensing data from different sensors and platforms, soil salinity inversion based on the fusion of multisource remote sensing data is becoming a reality. However, existing fusion methods mainly use the average relationship from samples, which does not consider the differences in the relationships among samples. In this paper, a differentiated fusion method for determining satellite and ground spectral variables of soil salinity according to the differences among samples is proposed to increase the regional inversion precision. Nonnegative matrix factorization was employed to decompose soil salinity spectral variables from the Sentinel-2A Multi-Spectral Instrument (MSI) image and the simulated spectra on ground spectra. Then, the base spectra matrix of soil salinity was from the simulated data, and the weight coefficient matrix was obtained from the Sentinel-2A MSI data as the differentiated correction coefficients. By multiplying the base matrix and weight matrix, the spectral variables were reconstructed. The results indicate that this differentiated fusion method can not only enhance the correlation between soil salinity and Sentinel-2A MSI data but also improve the precision of regional soil salinity inversion models. For the differentiated fused model, the validation R2, RMSE, and RPD were 0.71, 7.02 g/kg, and 1.49, respectively; compared with the unfused model, the validation R2 increased by 0.09, the RMSE decreased by 0.80 g/kg, and the RPD increased by 0.18. Furthermore, the differentiated fused model performed better than the average-ratio adjusted model. These findings have practical implications for the use of multisource optical remote sensing data for regional soil salinity mapping and analysis.
Hongyan Chen; Ying Ma; Axing Zhu; Zhuoran Wang; Gengxing Zhao; Yanan Wei. Soil salinity inversion based on differentiated fusion of satellite image and ground spectra. International Journal of Applied Earth Observation and Geoinformation 2021, 101, 102360 .
AMA StyleHongyan Chen, Ying Ma, Axing Zhu, Zhuoran Wang, Gengxing Zhao, Yanan Wei. Soil salinity inversion based on differentiated fusion of satellite image and ground spectra. International Journal of Applied Earth Observation and Geoinformation. 2021; 101 ():102360.
Chicago/Turabian StyleHongyan Chen; Ying Ma; Axing Zhu; Zhuoran Wang; Gengxing Zhao; Yanan Wei. 2021. "Soil salinity inversion based on differentiated fusion of satellite image and ground spectra." International Journal of Applied Earth Observation and Geoinformation 101, no. : 102360.
Rural revitalization is a global problem. The measures should be adjusted to local conditions to make targeted efforts. Natural and socioeconomic resource factors should be considered in rural revitalization. Therefore, this study focuses on the dike–pond system, which is an important traditional agricultural cultural heritage in the Pearl River Delta of China, to illustrate the importance of identifying the utilization mode of a certain land-use type in village integrated with socioeconomic factors to promote rural revitalization. The study used principal component analysis (PCA) and the variance inflation factor (VIF) to identify the main factors influencing the land-use modes of the dike–pond systems, systematic cluster analysis to identify the modes, and interpretive structural modeling to clarify the influence relationships and structures of the factors. We found that the seven modes reflected the different characteristics, organizational structures, and interaction relationships of the factors. There were significant differences in the ecological processes between the seven modes. More detailed village planning should be performed. Strengthening the economic affordability of the operator should be regarded as important in policy guidance and support measures. Agricultural support measures need to be adjusted to different land-use type systems, and localized resources should be revitalized by the theory of “human–earth–sphere”.
Haicong Li; Lu Wang; Jianzhou Gong; A-Xing Zhu; Yueming Hu. Land-Use Modes of the Dike–Pond System in the Pearl River Delta of China and Implications for Rural Revitalization. Land 2021, 10, 455 .
AMA StyleHaicong Li, Lu Wang, Jianzhou Gong, A-Xing Zhu, Yueming Hu. Land-Use Modes of the Dike–Pond System in the Pearl River Delta of China and Implications for Rural Revitalization. Land. 2021; 10 (5):455.
Chicago/Turabian StyleHaicong Li; Lu Wang; Jianzhou Gong; A-Xing Zhu; Yueming Hu. 2021. "Land-Use Modes of the Dike–Pond System in the Pearl River Delta of China and Implications for Rural Revitalization." Land 10, no. 5: 455.
Two main approaches are used in mapping rice paddy distribution from remote sensing images: phenological methods or machine learning methods. The phenological methods can map rice paddy distribution in a simple way but with limited accuracy. Machine learning, particularly deep learning, methods that learn the spectral signatures can achieve higher accuracy yet require a large number of field samples. This paper proposed a pheno-deep method to couple the simplicity of the phenological methods and the learning ability of the deep learning methods for mapping rice paddy at high accuracy without the need of field samples. The phenological method was first used to initially delineate the rice paddy for the purpose of creating training samples. These samples were then used to train the deep learning model. The trained deep learning model was applied to map the spatial distribution of rice paddy. The effectiveness of the pheno-deep method was evaluated in Jin’an District, Lu’an City, Anhui Province, China. Results show that the pheno-deep method achieved a high performance with the overall accuracy, the precision, the recall, and AUC (area under curve) being 88.8%, 87.2%, 91.1%, and 94.4%, respectively. The pheno-deep method achieved a much better performance than the phenological alone method and can overcome the noises in the training samples from the phenological method. The overall accuracy of the pheno-deep method is only 2.4% lower than that of the deep learning alone method trained with field samples and this difference is not statistically significant. In addition, the pheno-deep method requires no field sampling, which would be a noteworthy advantage for situations when large training samples are difficult to obtain. This study shows that by combining knowledge-based methods with data-driven methods, it is possible to achieve high mapping accuracy of geographic variables using remote sensing even with little field sampling efforts.
A-Xing Zhu; Fang-He Zhao; Hao-Bo Pan; Jun-Zhi Liu. Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics. Remote Sensing 2021, 13, 1360 .
AMA StyleA-Xing Zhu, Fang-He Zhao, Hao-Bo Pan, Jun-Zhi Liu. Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics. Remote Sensing. 2021; 13 (7):1360.
Chicago/Turabian StyleA-Xing Zhu; Fang-He Zhao; Hao-Bo Pan; Jun-Zhi Liu. 2021. "Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics." Remote Sensing 13, no. 7: 1360.
The Soil Land Inference Model (SoLIM) was primarily proposed by Zhu et al. (Zhu A X, Band L, Vertessy R, Dutton B. 1997. Derivation of soil properties using a soil land inference model (SoLIM). Soil Sci Soc Am J. 61: 523–533.) and was based on the Third Law of Geography. Based on the assumption that the soil property value at a location of interest will be more similar to that of a given soil sample when the environmental condition at the location of interest is more similar to that at the location from which the sample was taken, SoLIM estimates the soil property value of the location of interest using the soil property values of known samples weighted by the similarity between those samples and the location of interest in terms of an attribute domain of environmental conditions. However, the current SoLIM method ignores information about the spatial distances between the location of interest and those of the sample. In this study, we proposed a new method of soil property mapping, SoLIM-IDW, which incorporates spatial distance information into the SoLIM method by means of inverse distance weighting (IDW). The proposed method is based on the assumption that the soil property value at a location of interest will be more similar to that of a known sample both when the environmental conditions are more similar and when the distance between the location of interest and the sample location is shorter. Our evaluation experiments on A-horizon soil organic matter mapping in two study areas with independent evaluation samples showed that the proposed SoLIM-IDW method can obtain lower prediction errors than the original SoLIM method, multiple linear regression, geographically weighted regression, and regression-kriging with the same modeling points. Future work mainly includes the determination of optimal power parameter values and the appropriate setting of the parameter under different application contexts.
Chengzhi Qin; Yiming An; Peng Liang; Axing Zhu; Lin Yang. Soil property mapping by combining spatial distance information into the Soil Land Inference Model (SoLIM). Pedosphere 2021, 31, 638 -644.
AMA StyleChengzhi Qin, Yiming An, Peng Liang, Axing Zhu, Lin Yang. Soil property mapping by combining spatial distance information into the Soil Land Inference Model (SoLIM). Pedosphere. 2021; 31 (4):638-644.
Chicago/Turabian StyleChengzhi Qin; Yiming An; Peng Liang; Axing Zhu; Lin Yang. 2021. "Soil property mapping by combining spatial distance information into the Soil Land Inference Model (SoLIM)." Pedosphere 31, no. 4: 638-644.
Cellular Automata (CA) models have become the most commonly used tool for simulating urban expansion. To improve the accuracy of CA models, various driving factors like spatial proximity and neighbourhood effects have been explored in previous studies, but the inclusion of these factors does not address the directional differences in urban expansion. To address this issue, this study develops a method to measure urban spatial anisotropy (SA) with respect to 18 variables at both the global and local scales, and integrates all these SA variables into a logistic regression-based CA model. The revised CA model is evaluated with a case study for Huizhou, China. The case study shows that the simulation results for the CA model with SA exhibit 89% overall accuracy; compared to CA models that do not consider SA, the revised CA model can improve precision by 5% on newly developed cells. The consideration of SA in CA models proves promising in improving the accuracy of urban expansion simulations.
Jinqu Zhang; Yu Ling; A-Xing Zhu; Hongyun Zeng; Jia Song; Yunqiang Zhu; Lang Qian. Incorporation of spatial anisotropy in urban expansion modelling with cellular automata. International Journal of Geographical Information Science 2020, 1 -28.
AMA StyleJinqu Zhang, Yu Ling, A-Xing Zhu, Hongyun Zeng, Jia Song, Yunqiang Zhu, Lang Qian. Incorporation of spatial anisotropy in urban expansion modelling with cellular automata. International Journal of Geographical Information Science. 2020; ():1-28.
Chicago/Turabian StyleJinqu Zhang; Yu Ling; A-Xing Zhu; Hongyun Zeng; Jia Song; Yunqiang Zhu; Lang Qian. 2020. "Incorporation of spatial anisotropy in urban expansion modelling with cellular automata." International Journal of Geographical Information Science , no. : 1-28.
Projection transformation is an important part of geographic analysis in geographic information systems, which are particularly common for vector geographic data. However, achieving resistance to projection transformation attacks on watermarking for vector geographic data is still a challenging task. We proposed a digital watermarking against projection transformation based on feature invariants for vector geographic data in this paper. Firstly, the features of projection transformation are analyzed, and the number of vertices, the storage order, and the storage direction of two adjacent objects are designed and used as the feature invariant to projection transformation. Then, the watermark index is calculated by the number of vertices of two adjacent objects, and the embedding rule is determined by the storage direction of two adjacent objects. Finally, the proposed scheme performs blind detection through the storage direction of adjacent features. Experimental results demonstrate that the method can effectively resist arbitrary projection transformation, which indicates the superior performance of the proposed method in comparison to the previous methods.
Qifei Zhou; Na Ren; Changqing Zhu; A-Xing Zhu. Blind Digital Watermarking Algorithm against Projection Transformation for Vector Geographic Data. ISPRS International Journal of Geo-Information 2020, 9, 692 .
AMA StyleQifei Zhou, Na Ren, Changqing Zhu, A-Xing Zhu. Blind Digital Watermarking Algorithm against Projection Transformation for Vector Geographic Data. ISPRS International Journal of Geo-Information. 2020; 9 (11):692.
Chicago/Turabian StyleQifei Zhou; Na Ren; Changqing Zhu; A-Xing Zhu. 2020. "Blind Digital Watermarking Algorithm against Projection Transformation for Vector Geographic Data." ISPRS International Journal of Geo-Information 9, no. 11: 692.
Soil organic carbon (SOC) leads to a significant impact on global carbon (C) cycling and soil quality. Variations in SOC are controlled by vegetation, geomorphic, geological and climatic factors, but the dominant environmental differs. In the Qinghai -Tibet Plateau, which contains large amount of low-latitude permafrost, the impact of environmental factors for the variations of SOC may be different due to the unique and complicated geographical condition. In this study, the two-dimension empirical mode decomposition (2D-EMD) is applied to examine the variations of SOC at different scales and locations, and the correlations between SOC and environmental factors are explained. The spatial distribution of SOC in Tibet was decomposed into three intrinsic mode functions (IMFs) under different scales, with spatial variation scales of approximately 7 km, 109 km and 338 km, which represented the small, medium and large scale, respectively. The remaining residual represented the variation trend of SOC across Tibet. The correlations between SOC and environmental factors (elevation, radiation, evapotranspiration and temperature) are distinguished by the physiographic zone at small and medium scales. Temperature is weekly or nonsignificantly correlated to SOC in cold-dry western Tibet at large scale. Normalized difference vegetation index (NDVI) and precipitation influenced SOC mainly at small scales, while the effects of precipitation and evapotranspiration on the distribution of SOC were due to geomorphology and type of permafrost. The combined effect of climate on SOC was larger than other factors at large scale while factors refer to DEM, evapotranspiration, water erosion and NDVI accounted for more contribution at small scale. The results indicated that the environmental factors influence SOC under a combination of scale and location effect. These findings are of great significance for future studies in SOC dynamic modelling under the influence of natural changes and human activities.
Yin Zhou; Songchao Chen; A-Xing Zhu; Bifeng Hu; Zhou Shi; Yan Li. Revealing the scale- and location-specific controlling factors of soil organic carbon in Tibet. Geoderma 2020, 382, 114713 .
AMA StyleYin Zhou, Songchao Chen, A-Xing Zhu, Bifeng Hu, Zhou Shi, Yan Li. Revealing the scale- and location-specific controlling factors of soil organic carbon in Tibet. Geoderma. 2020; 382 ():114713.
Chicago/Turabian StyleYin Zhou; Songchao Chen; A-Xing Zhu; Bifeng Hu; Zhou Shi; Yan Li. 2020. "Revealing the scale- and location-specific controlling factors of soil organic carbon in Tibet." Geoderma 382, no. : 114713.
With the increasing demand for copyright protection of high-precision and sensitive vector data in Geographical Information System (GIS), research on lossless watermarking has attracted more and more attention. In this paper, a new lossless watermarking method based on line pairs is proposed. Firstly, the points and polygons are unified into the polylines, and then every two adjacent polylines are combined into a line pair. Besides, the storage direction and the interior angle of the line pairs are analyzed. Secondly, the watermark bit is determined by the interior angle of each line pair. Then, the watermark information is embedded by judging whether the storage direction of each line pair and the watermark bit is the same. Finally, experimental results verify that the proposed method works effectively for the data of the points, polylines and polygons, and has good invisibility without damaging the vector data. In addition, compared with the existing algorithm, the proposed method achieves higher robustness against geometric attacks, such as translating, rotating, and scaling the vector data.
Na Ren; Qifei Zhou; Changqing Zhu; A-Xing Zhu; Weitong Chen. A Lossless Watermarking Algorithm Based on Line Pairs for Vector Data. IEEE Access 2020, 8, 156727 -156739.
AMA StyleNa Ren, Qifei Zhou, Changqing Zhu, A-Xing Zhu, Weitong Chen. A Lossless Watermarking Algorithm Based on Line Pairs for Vector Data. IEEE Access. 2020; 8 (99):156727-156739.
Chicago/Turabian StyleNa Ren; Qifei Zhou; Changqing Zhu; A-Xing Zhu; Weitong Chen. 2020. "A Lossless Watermarking Algorithm Based on Line Pairs for Vector Data." IEEE Access 8, no. 99: 156727-156739.
Plague is a natural infectious disease which both people and mammals can contract once bitten by infected parasitic fleas. Flea index is an important outbreak indication. Outbreaks are more likely to occur for areas where flea index values are high. Thus, Prediction of spatial variation in flea index is vital to the prevention and control of the related plague. Existing methods of spatial prediction techniques require extensive field data to develop the needed models and limited for areas with insufficient field data. This paper presents an approach based on environmental similarity. The basic idea is that the more similar the environment of a location to locations at which the flea index is high the more likely that the flea index at this location is high as well. A method based on this idea was developed to predict spatial distribution of flea index of plague based on environmental similarity. A case study of mapping the flea index in Mongolian gerbil was conducted to evaluate the effectiveness of this idea and the environmental similarity method. The result showed that compared with multiple linear regression (MLR), the environmental similarity method produced a flea index map of higher accuracy (root mean squared error (RMSE) of 6.6493 and mean absolute error (MAE) 1.2713 than that from MLR (7.293 RMSE and MAE for MLR is not 1.2713 but 2.928). In addition, the spatial distribution of prediction uncertainty estimated from the environmental similarity method can be used as an indicator of spatial distribution of prediction accuracy.
Haiwen Du; A-Xing Zhu; Yong Wang. Spatial prediction of flea index of transmitting plague based on environmental similarity. Annals of GIS 2020, 26, 227 -236.
AMA StyleHaiwen Du, A-Xing Zhu, Yong Wang. Spatial prediction of flea index of transmitting plague based on environmental similarity. Annals of GIS. 2020; 26 (3):227-236.
Chicago/Turabian StyleHaiwen Du; A-Xing Zhu; Yong Wang. 2020. "Spatial prediction of flea index of transmitting plague based on environmental similarity." Annals of GIS 26, no. 3: 227-236.
Digital soil mapping (DSM) is currently the primary framework for predicting the spatial variation of soil information (soil type or soil properties). Random forests and similarity-based methods have been used widely in DSM. However, the accuracy of the similarity-based approach is limited, and the performance of random forests is affected by the quality of the feature set. The objective of this study was to present a method for soil mapping by integrating the similarity-based approach and the random forests method. The Heshan area (Heilongjiang province, China) was selected as the case study for mapping soil subgroups. The results of the regular validation samples showed that the overall accuracy of the integrated method (71.79%) is higher than that of a similarity-based approach (58.97%) and random forests (66.67%). The results of the 5-fold cross-validation showed that the overall accuracy of the integrated method, similarity-based approach, and random forests range from 55% to 72.73%, 43.48% to 69.57%, and 54.17% to 70.83%, with an average accuracy of 66.61%, 57.39%, and 59.62%, respectively. These results suggest that the proposed method can produce a high-quality covariate set and achieve a better performance than either the random forests or similarity-based approach alone.
Desheng Wang; A-Xing Zhu. Soil Mapping Based on the Integration of the Similarity-Based Approach and Random Forests. Land 2020, 9, 174 .
AMA StyleDesheng Wang, A-Xing Zhu. Soil Mapping Based on the Integration of the Similarity-Based Approach and Random Forests. Land. 2020; 9 (6):174.
Chicago/Turabian StyleDesheng Wang; A-Xing Zhu. 2020. "Soil Mapping Based on the Integration of the Similarity-Based Approach and Random Forests." Land 9, no. 6: 174.
The soil spectral dynamic feedback captured from high temporal resolution remote sensing data, such as MODIS, during the soil drying process after a rainfall could assist with digital soil mapping. However, this method is ineffective in utilizing the images with a relatively high spatial resolution. There are an insufficient number of images in the soil drying process since those high spatial resolution images tend to have a low temporal resolution. This study is aimed at generating soil spectral dynamic feedback by integrating the feedback captured from the images with a high spatial resolution during the process of multiple drying after different rainfall events. The Landsat 8 data with a temporal resolution of 16 day was exemplified. Each single spectral feedback obtained from Landsat 8 was first adjusted to eliminate the impact of different rainfall magnitudes. Then, the soil spectral dynamic feedback was reorganized and generated based on the adjusted feedback. Finally, the soil spectral dynamic feedback generated based on Landsat 8 was used for mapping topsoil texture and compared with the mapping results based on the MODIS data and the fusion data of MODIS and Landsat 8. As revealed by the results, not only could the generated soil spectral dynamic feedback based on Landsat 8 data improve the details of the spatial distribution of soil texture, but it also enhances the accuracy of mapping. The mapping accuracy based on Landsat 8 data is higher than that based on the MODIS data and fusion data. The improvements of accuracy are more obvious in the areas with more complex surface conditions. This study widens the scope of application for soil spectral dynamic feedback and provides support for large-scale and high-precision digital soil mapping.
Canying Zeng; Lin Yang; A-Xing Zhu. The Generation of Soil Spectral Dynamic Feedback Using Landsat 8 Data for Digital Soil Mapping. Remote Sensing 2020, 12, 1691 .
AMA StyleCanying Zeng, Lin Yang, A-Xing Zhu. The Generation of Soil Spectral Dynamic Feedback Using Landsat 8 Data for Digital Soil Mapping. Remote Sensing. 2020; 12 (10):1691.
Chicago/Turabian StyleCanying Zeng; Lin Yang; A-Xing Zhu. 2020. "The Generation of Soil Spectral Dynamic Feedback Using Landsat 8 Data for Digital Soil Mapping." Remote Sensing 12, no. 10: 1691.
Existing GIS software mainly target at expert users and do not sufficiently integrate resources for efficient computing. They are difficult for non-experts to use and are often slow in completing the complicated geographic analysis. To address these problems, future generation of GIS software must be ‘easy’. By ‘easy’ we mean ‘easy to use’ and ‘easy to compute’. ‘Easy to use’ means that software system should be goal-oriented, rather than the currently procedure-oriented doctrine. The goal-oriented will relieve users, particularly novice users, the burden of knowing the exact commands and their sequences to perform for achieving the goal they want to achieve. ‘Easy to compute’ means that implementation of GIS analytical functionality should be able to utilize the high-performance computing infrastructures for complicated geographic analysis. Two case studies, one in digital soil mapping and the other in digital terrain analysis, are presented to illustrate the meaning of ‘easy’. We believe that the future generations of GIS platforms should be goal-driven, intelligent, high-performance computing enabled, easily accessible, and participatory. It allows anyone to participate in geo-computation at anywhere and anytime.
A-Xing Zhu; Fang-He Zhao; Peng Liang; Cheng-Zhi Qin. Next generation of GIS: must be easy. Annals of GIS 2020, 27, 71 -86.
AMA StyleA-Xing Zhu, Fang-He Zhao, Peng Liang, Cheng-Zhi Qin. Next generation of GIS: must be easy. Annals of GIS. 2020; 27 (1):71-86.
Chicago/Turabian StyleA-Xing Zhu; Fang-He Zhao; Peng Liang; Cheng-Zhi Qin. 2020. "Next generation of GIS: must be easy." Annals of GIS 27, no. 1: 71-86.
Observations of soil at georeferenced sample points are the indispensable input to digital soil mapping (DSM) models that relate soil properties and types to the soil-forming environment. Many existing DSM methods require soil samples to be collected following a pre-defined sampling pattern. This requirement is often not met due to various operational reasons. The individual sample-based predictive soil mapping (iPSM) method has been developed to avoid this requirement. However, the prediction accuracy of iPSM depends heavily on the reliability of the known sample points used in prediction. Unreliable sample points are those with unreliable soil-environment relationship: the target soil property value and environmental covariate data are not correctly paired at the sample point location. Such unreliable sample points will lead to poorly constructed models and low prediction accuracy. This paper presents a new method to estimate the reliability of soil-environment relationship at each sample location and to evaluate the trustworthiness of prediction at each unvisited location. Under the assumption that sample points with similar environmental conditions have similar soil property values, the method first identifies the sample points that are environmentally similar to the sample point to be evaluated, then uses their agreement on the targeted soil property to evaluate the reliability of soil-environment relationship at the sample point. The effectiveness of the method was assessed in a case study located in Anhui Province in China to map soil organic matter content (SOM, %) in the topsoil. When the reliability threshold increased from 0.5 to 0.8, more unreliable sample points are excluded from prediction: 41% of total sample points were excluded when the threshold was set to 0.5, and 88% were excluded when the threshold was set to 0.8. As a result, less unknown area could be predicted – only nine validation points were predictable with the 0.8 reliability threshold. However, the prediction accuracy was improved: the root mean squared error, RMSE, decreased from 1.37% to 0.63%, and R2 increased from 0.21 to 0.98. Prediction trustworthiness at each unvisited location was also produced along with prediction accuracy, which was negatively related to the absolute prediction residuals. This study shows that the reliability of individual sample points is an important determinant of the prediction accuracy of the iPSM method. When applying iPSM method, an optimal trade-off between prediction accuracy and completeness of the predicted map needs to be found by adjusting the reliability threshold on the sample points used in prediction.
Jing Liu; A-Xing Zhu; David Rossiter; Fei Du; James Burt. A trustworthiness indicator to select sample points for the individual predictive soil mapping method (iPSM). Geoderma 2020, 373, 114440 .
AMA StyleJing Liu, A-Xing Zhu, David Rossiter, Fei Du, James Burt. A trustworthiness indicator to select sample points for the individual predictive soil mapping method (iPSM). Geoderma. 2020; 373 ():114440.
Chicago/Turabian StyleJing Liu; A-Xing Zhu; David Rossiter; Fei Du; James Burt. 2020. "A trustworthiness indicator to select sample points for the individual predictive soil mapping method (iPSM)." Geoderma 373, no. : 114440.
Toxic trace elements in farmland soils are potential threats to human health. In this study, we collected soil samples from the farmlands of southern Guangzhou. We used a sequential indicator simulation (SIS) to deal with the problem of skewed distribution in the sample data. We assessed the human health risks, as well as the uncertainties, of five toxic trace elements: arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), and mercury (Hg). The results were as follows: (1) The risk indices of two trace elements (Cd and Hg) were less than the standard threshold, which means that there was no human health risk due to Cd and Hg in the study area. However, the maximum risk indices of As, Cr, and Pb exceeded the standard threshold. In particular, the maximum risk index of Pb was twice the standard threshold; (2) The risk probabilities of As and Cr were less than 25% in most areas, and only a few parcels of farmland have a 100% risk probability. The risk map of Pb was used to identify contiguous areas of high-risk probability (i.e., 75%–100%) in the center of the study area. (3) E-type estimation by the SIS method overestimates the risk when the number of samples with concentrations above the threshold have a large proportion of total samples. Our conclusions are as follows: (1) The simulation results show that areas with high-risk indices were concentrated in the Panyu District, which is close to the Pearl River and the core urban area of Guangzhou; (2) Except for Pb, these trace elements are not likely to pose health risks in southern Guangzhou; (3) This study considers the risk probability found with the SIS method to be more reliable for visualizing regional risk.
Hao Yang; Yingqiang Song; A-Xing Zhu; Yueming Hu; Bo Li. An Uncertainty Assessment of Human Health Risk for Toxic Trace Elements Using a Sequential Indicator Simulation in Farmland Soils. Sustainability 2020, 12, 3852 .
AMA StyleHao Yang, Yingqiang Song, A-Xing Zhu, Yueming Hu, Bo Li. An Uncertainty Assessment of Human Health Risk for Toxic Trace Elements Using a Sequential Indicator Simulation in Farmland Soils. Sustainability. 2020; 12 (9):3852.
Chicago/Turabian StyleHao Yang; Yingqiang Song; A-Xing Zhu; Yueming Hu; Bo Li. 2020. "An Uncertainty Assessment of Human Health Risk for Toxic Trace Elements Using a Sequential Indicator Simulation in Farmland Soils." Sustainability 12, no. 9: 3852.
Predictive mapping of environment is an important means for environment assessment and management. The selection of predictor variables (or environmental covariates) is the first and key step in predictive mapping. A number of machine learning and statistical models have been developed to select what and how many environmental covariates in a wide range of predictive mapping. Nevertheless, those models require a large amount of field data for model training and calibration, which can be problematic in applying to the areas with no or very limited field data available. To overcome the shortcoming, this paper proposes the most similar case method for selecting environmental covariates for predictive mapping. First, we describe the basic idea and the development procedures of the most similar case method; second, as an experimental test, we employ the proposed method to select the topographic covariates for inputting to the predictive soil mapping; third, we evaluate the effectiveness of the proposed method in the designed experiment using the leave-one-out cross-validation method. In total, 191 evaluation cases are included in the experimental case base and the test results show that 58.7% of the topographic covariates originally used in each evaluation case are correctly selected by the proposed method, which suggests that the proposed most-similar-case method perform reasonably well even with a relatively limited size of the case base. The future work should include the selection of other types of environmental covariates (e.g., climate, organism, etc.) and the development of an automatic method to extract the existing application cases from literature.
Peng Liang; Cheng-Zhi Qin; A-Xing Zhu; Tong-Xin Zhu; Nai-Qing Fan; Zhi-Wei Hou. Using the most similar case method to automatically select environmental covariates for predictive mapping. Earth Science Informatics 2020, 13, 719 -728.
AMA StylePeng Liang, Cheng-Zhi Qin, A-Xing Zhu, Tong-Xin Zhu, Nai-Qing Fan, Zhi-Wei Hou. Using the most similar case method to automatically select environmental covariates for predictive mapping. Earth Science Informatics. 2020; 13 (3):719-728.
Chicago/Turabian StylePeng Liang; Cheng-Zhi Qin; A-Xing Zhu; Tong-Xin Zhu; Nai-Qing Fan; Zhi-Wei Hou. 2020. "Using the most similar case method to automatically select environmental covariates for predictive mapping." Earth Science Informatics 13, no. 3: 719-728.
The land surface dynamic feedback (LSDF) information captured by time-series remote sensing data during the soil-drying process after a rainfall event provides effective covariates for digital soil mapping over low-relief areas. However, current methods used to capture LSDF require a uniform rainfall magnitude in the geographic space; a condition that is not often met for large areas. Here, we propose a LSDF construction method considering the spatial heterogeneity of rainfall magnitudes by adjusting the evaporation variables in the LSDF. For this, the relationships between evaporation and rainfall magnitudes were first established. The LSDFs from various locations for rainfall events with different magnitudes were then adjusted based on these relationships. Using a case study, the adjusted LSDFs after two rainfall events were then used to predict soil texture over a low-relief area. The results showed that the cubic polynomial model performed best when constructing the relationship between evaporation adjustment and rainfall magnitude, giving the highest R2 value and a low Akaike information criterion. Adjustment to the LSDF decreases with increasing rainfall and the rate of change in the adjustment also decreases with increasing rainfall. For both rainfall events, prediction accuracies with the adjusted LSDFs were higher than those based on the original LSDFs. Furthermore, the greater the adjustment, the greater the improvement in the accuracy. We conclude that the proposed construction method for LSDF, accounting for the spatial heterogeneity of rainfall magnitudes, offers improved predictive power for digital soil mapping over large areas.
Canying Zeng; Feng Qi; A-Xing Zhu; Feng Liu. Construction of land surface dynamic feedback for digital soil mapping considering the spatial heterogeneity of rainfall magnitude. CATENA 2020, 191, 104576 .
AMA StyleCanying Zeng, Feng Qi, A-Xing Zhu, Feng Liu. Construction of land surface dynamic feedback for digital soil mapping considering the spatial heterogeneity of rainfall magnitude. CATENA. 2020; 191 ():104576.
Chicago/Turabian StyleCanying Zeng; Feng Qi; A-Xing Zhu; Feng Liu. 2020. "Construction of land surface dynamic feedback for digital soil mapping considering the spatial heterogeneity of rainfall magnitude." CATENA 191, no. : 104576.
Environmental covariates are fundamental inputs of digital soil mapping (DSM) based on the soil–environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regions over an area, especially over a large area. Among the two main existing ways to deal with locations with invalid environmental covariate data in DSM, the location-skipping scheme does not predict these locations and, thus, completely ignores the potentially useful information provided by valid covariate values. The void-filling scheme may introduce errors when applying an interpolation algorithm to removing NoData environmental covariate values. In this study, we propose a new scheme called FilterNA that conducts DSM for each individual location with NoData value of a covariate by using the valid values of other covariates at the location. We design a new method (SoLIM-FilterNA) combining the FilterNA scheme with a DSM method, Soil Land Inference Model (SoLIM). Experiments to predict soil organic matter content in the topsoil layer in Anhui Province, China, under different test scenarios of NoData for environmental covariates were conducted to compare SoLIM-FilterNA with the SoLIM combined with the void-filling scheme, the original SoLIM with the location-skipping scheme, and random forest. The experimental results based on the independent evaluation samples show that, in general, SoLIM-FilterNA can produce the lowest errors with a more complete spatial coverage of the DSM result. Meanwhile, SoLIM-FilterNA can reasonably predict uncertainty by considering the uncertainty introduced by applying the FilterNA scheme.
Nai-Qing Fan; A-Xing Zhu; Cheng-Zhi Qin; Peng Liang. Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data. ISPRS International Journal of Geo-Information 2020, 9, 102 .
AMA StyleNai-Qing Fan, A-Xing Zhu, Cheng-Zhi Qin, Peng Liang. Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data. ISPRS International Journal of Geo-Information. 2020; 9 (2):102.
Chicago/Turabian StyleNai-Qing Fan; A-Xing Zhu; Cheng-Zhi Qin; Peng Liang. 2020. "Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data." ISPRS International Journal of Geo-Information 9, no. 2: 102.
With the development of urban science, researches on mining of urban big data have attracted more and more attention. One typical microcosm of urban big data is taxi trajectory data. Predicting the travel time between the two specified points accurately is great significance for applications, such as travel plan. However, the current approach just uses limited modality data or single model without considering their one-sidedness. This paper puts forward to one optimized method to estimate travel time, which is based on ensemble method with multi-modality urban big data, namely Travel Time Estimation-Ensemble (TTE-Ensemble). First, we extract the feature sub-vectors from the multi-modality data as the model input. Then we use the gradient boosting decision tree (GBDT) model to process the low dimensional simple features and adopt the deep neural network (DNN) model to handle high dimensional underlying features. Finally, the ensemble method was introduced to integrate the two model of GBDT and the DNN. Extensive experiments were conducted based on real datasets of origin-destination points in Chengdu and Shanghai, China. These experiments demonstrate the superiority of the TTE-Ensemble model.
Zhiqiang Zou; Haoyu Yang; A-Xing Zhu. Estimation of Travel Time Based on Ensemble Method With Multi-Modality Perspective Urban Big Data. IEEE Access 2020, 8, 24819 -24828.
AMA StyleZhiqiang Zou, Haoyu Yang, A-Xing Zhu. Estimation of Travel Time Based on Ensemble Method With Multi-Modality Perspective Urban Big Data. IEEE Access. 2020; 8 ():24819-24828.
Chicago/Turabian StyleZhiqiang Zou; Haoyu Yang; A-Xing Zhu. 2020. "Estimation of Travel Time Based on Ensemble Method With Multi-Modality Perspective Urban Big Data." IEEE Access 8, no. : 24819-24828.