Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effect...Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.展开更多
Soil phosphorus (P) plays a vital role in both ecological and agricultural ecosystems, where total P (TP) in soil serves as a crucial indicator of soil fertility and quality. Most of the studies covered in the literat...Soil phosphorus (P) plays a vital role in both ecological and agricultural ecosystems, where total P (TP) in soil serves as a crucial indicator of soil fertility and quality. Most of the studies covered in the literature employ a single or narrow range of soil databases, which largely overlooks the impact of utilizing multiple mapping scales in estimating soil TP, especially in hilly topographies. In this study, Fujian Province, a subtropical hilly region along China’s southeast coast covered by a complex topographic environment, was taken as a case study. The influence of the mapping scale on soil TP storage (TPS)estimation was analyzed using six digital soil databases that were derived from 3 082 unique soil profiles at different mapping scales, i.e., 1:50 000 (S5),1:200 000 (S20), 1:500 000 (S50), 1:1 000 000 (S100), 1:4 000 000 (S400), and 1:10 000 000 (S1000). The regional TPS in the surface soil (0–20 cm) based on the S5, S20, S50, S100, S400, and S1000 soil maps was 20.72, 22.17, 23.06, 23.05, 22.04, and 23.48 Tg, respectively, and the corresponding TPS at0–100 cm soil depth was 80.98, 80.71, 85.00, 84.03, 82.96, and 86.72 Tg, respectively. By comparing soil TPS in the S20 to S1000 maps to that in the S5map, the relative deviations were 6.37%–13.32%for 0–20 cm and 0.33%–7.09%for 0–100 cm. Moreover, since the S20 map had the lowest relative deviation among different mapping scales as compared to S5, it could provide additional soil information and a richer soil environment than other smaller mapping scales. Our results also revealed that many uncertainties in soil TPS estimation originated from the lack of detailed soil information, i.e., representation and spatial variations among different soil types. From the time and labor perspectives, our work provides useful guidelines to identify the appropriate mapping scale for estimating regional soil TPS in areas like Fujian Province in subtropical China or other places with similar complex topographies. Moreover, it is of tremendous importance to accurately estimate soil TPS to ensure ecosystem stability and sustainable agricultural development, especially for regional decision-making and management of phosphate fertilizer application amounts.展开更多
The alpine terrestrials of the Maloti-Drakensberg in southern Africa play crucial roles in ecosystem functions and livelihoods,yet they face escalating degradation from various factors including overgrazing and climat...The alpine terrestrials of the Maloti-Drakensberg in southern Africa play crucial roles in ecosystem functions and livelihoods,yet they face escalating degradation from various factors including overgrazing and climate change.This study employs advanced Digital Soil Mapping(DSM)techniques coupled with remote sensing to map and assess wetland coverage and degradation in the northern Maloti-Drakensberg.The model achieved high accuracies of 96%and 92%for training and validation data,respectively,with Kappa statistics of 0.91 and 0.83,marking a pioneering automated attempt at wetland mapping in this region.Terrain attributes such as terrain wetness index(TWI)and valley depth(VD)exhibit significant positive correlations with wetland coverage and erosion gully density,Channel Network Depth and slope were negative correlated.Gully density analysis revealed terrain attributes as dominant factors driving degradation,highlighting the need to consider catchment-specific susceptibility to erosion.This challenge traditional assumptions which mainly attribute wetland degradation to external forces such as livestock overgrazing,ice rate activity and climate change.The sensitivity map produced could serve as a basis for Integrated Catchment Management(ICM)projects,facilitating tailored conservation strategies.Future research should expand on this work to include other highland areas,explore additional covariates,and categorize wetlands based on hydroperiod and sensitivity to degradation.This comprehensive study underscores the potential of DSM and remote sensing in accurately assessing and managing wetland ecosystems,crucial for sustainable resource management in alpine regions.展开更多
The geolocation of ground targets by airborne image sensors is an important task for unmanned aerial vehicles or surveillance aircraft.This paper proposes an Iterative Geolocation based on Cross-view Image Registratio...The geolocation of ground targets by airborne image sensors is an important task for unmanned aerial vehicles or surveillance aircraft.This paper proposes an Iterative Geolocation based on Cross-view Image Registration(IGCIR)that can provide real-time target location results with high precision.The proposed method has two key features.First,a cross-view image registration process is introduced,including a projective transformation and a two-stage multi-sensor registration.This process utilizes both gradient information and phase information of cross-view images.This allows the registration process to reach a good balance between matching precision and computational efficiency.By matching the airborne camera view to the preloaded digital map,the geolocation accuracy can reach the accuracy level of the digital map for any ground target appearing in the airborne camera view.Second,the proposed method uses the registration results to perform an iteration process,which compensates for the bias of the strap-down initial navigation module online.Although it is challenging to provide cross-view registration results with high frequency,such an iteration process allows the method to generate real-time,highly accurate location results.The effectiveness of the proposed IGCIR method is verified by a series of flying-test experiments.The results show that the location accuracy of the method can reach 4.18 m(at 10 km standoff distance).展开更多
The integration of Global Navigation Satellite System(GNSS)technology into railway train control systems is a crucial step toward achieving the vision of a digital railway.Traditional train control systems undergo ext...The integration of Global Navigation Satellite System(GNSS)technology into railway train control systems is a crucial step toward achieving the vision of a digital railway.Traditional train control systems undergo extensive in-house tests and prolonged field tests for certification and approval before operational deployment,leading to high costs,delays,and operational disruptions.This paper introduces a GNSS-based train control localization framework which eliminates the need for on-site testing by leveraging train movement dynamics and 3D environment modeling to create a zero on-site testing platform.The proposed framework simulates train movement and the surrounding 3D environment using collected railway line location data and environmental attributes to generate realistic multipath signals and obscuration effects.This approach enables comprehensive laboratory-based case studies for train localization,reducing the huge amount test of needed for physical field trials.The framework is established in house,using the data collected at the Test Base of China Academy of Railway Sciences(Circular Railway).Results from the open area and cutting environment tests demonstrate high localization accuracy repeatability within the simulated environment,validating the feasibility and effectiveness of zero on-site testing for GNSS-based train control systems.This research highlights the potential of GNSS simulation platforms in enhancing cost efficiency,operational safety,and accuracy for future digital railways.展开更多
It is of great importance to obtain precise trace data,as traces are frequently the sole visible and measurable parameter in most outcrops.The manual recognition and detection of traces on high-resolution three-dimens...It is of great importance to obtain precise trace data,as traces are frequently the sole visible and measurable parameter in most outcrops.The manual recognition and detection of traces on high-resolution three-dimensional(3D)models are relatively straightforward but time-consuming.One potential solution to enhance this process is to use machine learning algorithms to detect the 3D traces.In this study,a unique pixel-wise texture mapper algorithm generates a dense point cloud representation of an outcrop with the precise resolution of the original textured 3D model.A virtual digital image rendering was then employed to capture virtual images of selected regions.This technique helps to overcome limitations caused by the surface morphology of the rock mass,such as restricted access,lighting conditions,and shading effects.After AI-powered trace detection on two-dimensional(2D)images,a 3D data structuring technique was applied to the selected trace pixels.In the 3D data structuring,the trace data were structured through 2D thinning,3D reprojection,clustering,segmentation,and segment linking.Finally,the linked segments were exported as 3D polylines,with each polyline in the output corresponding to a trace.The efficacy of the proposed method was assessed using a 3D model of a real-world case study,which was used to compare the results of artificial intelligence(AI)-aided and human intelligence trace detection.Rosette diagrams,which visualize the distribution of trace orientations,confirmed the high similarity between the automatically and manually generated trace maps.In conclusion,the proposed semi-automatic method was easy to use,fast,and accurate in detecting the dominant jointing system of the rock mass.展开更多
To deal with the global and regional issues including food security, climate change, land degradation, biodiversity loss, water resource management, and ecosystem health, detailed accurate spatial soil information is ...To deal with the global and regional issues including food security, climate change, land degradation, biodiversity loss, water resource management, and ecosystem health, detailed accurate spatial soil information is urgently needed. This drives the worldwide development of digital soil mapping. In recent years, significant progresses have been made in different aspects of digital soil mapping. The main purpose of this paper is to provide a review for the major progresses of digital soil mapping in the last decade. First, we briefly described the rise of digital soil mapping and outlined important milestones and their influence, and main paradigms in digital soil mapping. Then, we reviewed the progresses in legacy soil data, environmental covariates, soil sampling, predictive models and the applications of digital soil mapping products. Finally, we summarized the main trends and future prospect as revealed by studies up to now. We concluded that although the digital soil mapping is now moving towards mature to meet various demands of soil information, challenges including new theories, methodologies and applications of digital soil mapping, especially for highly heterogeneous and human-affected environments, still exist and need to be addressed in the future.展开更多
The use of landscape covariates to variability of soil properties in similar estimate soil properties is not suitable topographic and vegetation conditions. for the areas of low relief due to the high A new method wa...The use of landscape covariates to variability of soil properties in similar estimate soil properties is not suitable topographic and vegetation conditions. for the areas of low relief due to the high A new method was implemented to map regional soil texture （in terms of sand, silt and clay contents） by hypothesizing that the change in the land surface diurnal temperature difference （DTD） is related to soil texture in case of a relatively homogeneous rainfall input. To examine this hypothesis, the DTDs from moderate resolution imagine spectroradiometer （MODIS） during a selected time period, i.e., after a heavy rainfall between autumn harvest and autumn sowing, were classified using fuzzy-c-means （FCM） clustering. Six classes were generated, and for each class, the sand （〉 0.05 mm）, silt （0.002-0.05 mm） and clay （〈 0.002 mm） contents at the location of maximum membership value were considered as the typical values of that class. A weighted average model was then used to digitally map soil texture. The results showed that the predicted map quite accurately reflected the regional soil variation. A validation dataset produced estimates of error for the predicted maps of sand, silt and clay contents at root mean of squared error values of 8.4%, 7.8% and 2.3%, respectively, which is satisfactory in a practical context. This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.展开更多
Selecting a proper set of covariates is one of the most important factors that influence the accuracy of digital soil mapping(DSM).The statistical or machine learning methods for selecting DSM covariates are not avail...Selecting a proper set of covariates is one of the most important factors that influence the accuracy of digital soil mapping(DSM).The statistical or machine learning methods for selecting DSM covariates are not available for those situations with limited samples.To solve the problem,this paper proposed a case-based method which could formalize the covariate selection knowledge contained in practical DSM applications.The proposed method trained Random Forest(RF)classifiers with DSM cases extracted from the practical DSM applications and then used the trained classifiers to determine whether each one potential covariate should be used in a new DSM application.In this study,we took topographic covariates as examples of covariates and extracted 191 DSM cases from 56 peer-reviewed journal articles to evaluate the performance of the proposed case-based method by Leave-One-Out cross validation.Compared with a novices’commonly-used way of selecting DSM covariates,the proposed case-based method improved more than 30%accuracy according to three quantitative evaluation indices(i.e.,recall,precision,and F1-score).The proposed method could be also applied to selecting the proper set of covariates for other similar geographical modeling domains,such as landslide susceptibility mapping,and species distribution modeling.展开更多
Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil...Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe(Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer(MODIS), Normalized Difference Vegetation Indices(NDVIs) and land surface reflectance data from Landsat Thematic Mapper(TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient(CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area(with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.展开更多
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...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, So LIM-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.展开更多
Due to the almost homogeneous topography in low relief areas, it is usually difficult to make accurate predictions of soil properties using topographic covariates. In this study, we examined how time series of field s...Due to the almost homogeneous topography in low relief areas, it is usually difficult to make accurate predictions of soil properties using topographic covariates. In this study, we examined how time series of field soil moisture observations can be used to estimate soil texture in an oasis agricultural area with low relief in the semi-arid region of northwest China. Time series of field-observed soil moisture variations were recorded for 132 h beginning at the end of an irrigation event during which the surface soil was saturated.Spatial correlation between two time-adjacent soil moisture conditions was used to select the factors for fuzzy c-means clustering. In each of the ten generated clusters, soil texture of the soil sample with the maximum fuzzy membership value was taken as the cluster centroid. Finally, a linearly weighted average was used to predict soil texture from the centroids. The results showed that soil moisture increased with the increase of clay and silt contents, but decreased with the increase of sand content. The spatial patterns of soil moisture changed during the entire soil drying phase. We assumed that these changes were mainly caused by spatial heterogeneity of soil texture. A total of 64 independent samples were used to evaluate the prediction accuracy. The root mean square error(RMSE)values of clay, silt and sand were 1.63, 2.81 and 3.71, respectively. The mean relative error(RE) values were 9.57% for clay, 3.77% for silt and 12.83% for sand. It could be concluded that the method used in this study was effective for soil texture mapping in the low-relief oasis agricultural area and could be applicable in other similar irrigation agricultural areas.展开更多
Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) a...Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.展开更多
Detailed knowledge about the estimates and spatial patterns of soil organic carbon(SOC) and total nitrogen(TN) stocks is fundamental for sustainable land management and climate change mitigation.This study aimed at:(1...Detailed knowledge about the estimates and spatial patterns of soil organic carbon(SOC) and total nitrogen(TN) stocks is fundamental for sustainable land management and climate change mitigation.This study aimed at:(1) mapping the spatial patterns,and(2) quantifying SOC and TN stocks to 30 cm depth in the Eastern Mau Forest Reserve using field,remote sensing,geographical information systems(GIS),and statistical modelling approaches.This is a critical ecosystem offering essential services,but its sustainability is threatened by deforestation and degradation.Results revealed that elevation,silt content,TN concentration,and Landsat 8 Operational Land Imager band 11 explained 72% of the variability in SOC stocks,while the same factors(except silt content) explained 71% of the variability in TN stocks.The results further showed that soil properties,particularly TN and SOC concentrations,were more important than that other environmental factors in controlling the observed patterns of SOC and TN stocks,respectively.Forests stored the highest amounts of SOC and TN(3.78 Tg C and 0.38 Tg N) followed by croplands(2.46 Tg C and 0.25 Tg N) and grasslands(0.57 Tg C and 0.06 Tg N).Overall,the Eastern Mau Forest Reserve stored approximately 6.81 Tg C and 0.69 Tg N.The highest estimates of SOC and TN stocks(hotspots) occurred on the western and northwestern parts where forests dominated,while the lowest estimates(coldspots) occurred on the eastern side where croplands had been established.Therefore,the hotspots need policies that promote conservation,while the coldspots need those that support accumulation of SOC and TN stocks.展开更多
Soil organic carbon （SOC） pool has the potential to mitigate or enhance climate change by either acting as a sink, or a source of atmospheric carbon dioxide （CO2） and also plays a fundamental role in the health an...Soil organic carbon （SOC） pool has the potential to mitigate or enhance climate change by either acting as a sink, or a source of atmospheric carbon dioxide （CO2） and also plays a fundamental role in the health and proper functioning of soils to sustain life on Earth. As such, the objective of this study was to investigate the applicability of a novel evolutionary genetic optimization-based adaptive neuro-fuzzy inference system （ANFIS-EG） in predicting and mapping the spatial patterns of SOC stocks in the Eastern Mau Forest Reserve, Kenya. Field measurements and auxiliary data reflecting the soil-forming factors were used to design an ANFIS-EG model, which was then implemented to predict and map the areal differentiation of SOC stocks in the Eastern Mau Forest Reserve. This was achieved with a reasonable level of uncertainty （i.e., root mean square error of 15.07 Mg C ha-l）, hence demonstrating the applicability of the ANFIS-EG in SOC mapping studies. There is potential for improving the model performance, as indicated by the current ratio of performance to deviation （1.6）. The mapping also revealed marginally higher SOC stocks in the forested ecosystems （i.e., an average of 109.78 M C ha-1） than in the aro-ecosvstems （i.e., an average of 95.9 Mg C ha-l）.展开更多
Soil type maps at the scale of I Z 1 000 000 are used extensively to provide soil spatial distribution information for soil erosion assessment and watershed management models in China. However, the soil property maps ...Soil type maps at the scale of I Z 1 000 000 are used extensively to provide soil spatial distribution information for soil erosion assessment and watershed management models in China. However, the soil property maps produced through conventional direct linking method usually suffer low accuracy as well as the lack of spatial details within a soil type polygon. This paper presents an effective method to produce detailed soil property map based on representative samples which were extracted from each polygon on the 1 ： 1 000 000 soil type map. The representative sample of each polygon is defined as the location that can represent the largest area within the polygon. The representativeness of a candidate sample is determined by calculating the soil-forming environment condition similarities between the sample and other locations. Once the representative sample of each polygon has been chosen, the property values of the existing typical samples are assigned to the corresponding representative samples with the same soil type. Finally, based on these representative samples, the detailed soil property map could be produced by using existing digital soil mapping methods. The case study in XuanCheng City, Anhui Province of China, demonstrated the proposed method could produce soil property map at a higher level of spatial details and accuracy： 1） The soil organic matter （SOM） map produced based on the representative samples can not only depict the detailed spatial distribution of SOM within a soil type polygon but also largely reduce the abrupt change of soil property at the boundaries of two adjacent polygons. 2） The Root Mean Squared Error （RMSE） of the SOM map based on the representative samples is 1.61, and it is 1.37 for the SOM map produced by using conventional direct linking method. Therefore, the proposed method is an effective approach to produce spatial detailed soil property map with higher accuracy for environment simulation models.展开更多
Antarctic surveying, mapping and remote sensing is one of the important aspects of the Chinese Antarctic geoscience research program that stretch back over 25 years, since the first Chinese National Antarctic Research...Antarctic surveying, mapping and remote sensing is one of the important aspects of the Chinese Antarctic geoscience research program that stretch back over 25 years, since the first Chinese National Antarctic Research Expedition （CHINARE） in 1984. During the 1980＇s, the geodetic datum, height system and absolute gravity datum were established at the Great Wall and Zhongshan Stations. Significant contributions have been made by the construction of the Chinese Great Wall, Zhongshan and Kunlun Stations in Antarctica. Geodetic control and gravity networks were established in the King George Islands, Grove Moun- tains and Dome Argus. An area of more than 200 000 km2 has been mapped using satellite image data, aerial photogrammetry and in situ data. Permanent GPS stations and tide gauges have been established at both the Great Wall and Zhongshan Stations. Studies involving plate motion, precise satellite orbit determination, the gravity field, sea level change, and various GPS applications for atmospheric studies have been carried out. Based on remote sensing techniques, studies have been undertaken on ice sheet and glacier movements, the distributions of blue ice and ice crevasses, and ice mass balance. Polar digital and visual mapping tech- niques have been introduced, and a polar survey space database has been built. The Chinese polar scientific expedition manage- ment information system and Chinese PANDA plan display platform were developed, which provides technical support for Chi- nese polar management. Finally, this paper examines prospects for future Chinese Antarctic surveying, mapping and remote sens- ing.展开更多
Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land managem...Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land management,agricultural activities,water quality,and sustainable development.The remote sensing images taken by the synthetic aperture radar(SAR)Sentinel-1 and the multispectral satellite Sentinel-2 with high resolution and short revisit period have the potential to monitor the spatial distribution of soil attribute information on a large area;however,there are limited studies on the combination of Sentinel-1 and Sentinel-2 for digital mapping of soil salinization.Therefore,in this study,we used topography indices derived from digital elevation model(DEM),SAR indices generated by Sentinel-1,and vegetation indices generated by Sentinel-2 to map soil salinization in the Ogan-Kuqa River Oasis located in the central and northern Tarim Basin in Xinjiang of China,and evaluated the potential of multi-source sensors to predict soil salinity.Using the soil electrical conductivity(EC)values of 70 ground sampling sites as the target variable and the optimal environmental factors as the predictive variable,we constructed three soil salinity inversion models based on classification and regression tree(CART),random forest(RF),and extreme gradient boosting(XGBoost).Then,we evaluated the prediction ability of different models through the five-fold cross validation.The prediction accuracy of XGBoost model is better than those of CART and RF,and soil salinity predicted by the three models has similar spatial distribution characteristics.Compared with the combination of topography indices and vegetation indices,the addition of SAR indices effectively improves the prediction accuracy of the model.In general,the method of soil salinity prediction based on multi-source sensor combination is better than that based on a single sensor.In addition,SAR indices,vegetation indices,and topography indices are all effective variables for soil salinity prediction.Weighted Difference Vegetation Index(WDVI)is designated as the most important variable in these variables,followed by DEM.The results showed that the high-resolution radar Sentinel-1 and multispectral Sentinel-2 have the potential to develop soil salinity prediction model.展开更多
Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon(SOC) conce...Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon(SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types(square and circular) and 6 representative algorithms(Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods(i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of firstand second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30% and 9.68%.The SOC maps resulting from the higher-order algorithms(Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.展开更多
Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatia...Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.展开更多
基金the National Natural Science Foundation of China(U1901601)the National Key Research and Development Program of China(2022YFB3903503)。
文摘Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.
基金supported by the National Natural Science Foundation of China(Nos.41971050 and 42207271)the Provincial Natural Science Foundation of Fujian,China(No.2022J05036)the Open Project Program of the State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry,Institute of Atmospheric Physics,Chinese Academy of Sciences(No.LAPC-KF-2022-08)。
文摘Soil phosphorus (P) plays a vital role in both ecological and agricultural ecosystems, where total P (TP) in soil serves as a crucial indicator of soil fertility and quality. Most of the studies covered in the literature employ a single or narrow range of soil databases, which largely overlooks the impact of utilizing multiple mapping scales in estimating soil TP, especially in hilly topographies. In this study, Fujian Province, a subtropical hilly region along China’s southeast coast covered by a complex topographic environment, was taken as a case study. The influence of the mapping scale on soil TP storage (TPS)estimation was analyzed using six digital soil databases that were derived from 3 082 unique soil profiles at different mapping scales, i.e., 1:50 000 (S5),1:200 000 (S20), 1:500 000 (S50), 1:1 000 000 (S100), 1:4 000 000 (S400), and 1:10 000 000 (S1000). The regional TPS in the surface soil (0–20 cm) based on the S5, S20, S50, S100, S400, and S1000 soil maps was 20.72, 22.17, 23.06, 23.05, 22.04, and 23.48 Tg, respectively, and the corresponding TPS at0–100 cm soil depth was 80.98, 80.71, 85.00, 84.03, 82.96, and 86.72 Tg, respectively. By comparing soil TPS in the S20 to S1000 maps to that in the S5map, the relative deviations were 6.37%–13.32%for 0–20 cm and 0.33%–7.09%for 0–100 cm. Moreover, since the S20 map had the lowest relative deviation among different mapping scales as compared to S5, it could provide additional soil information and a richer soil environment than other smaller mapping scales. Our results also revealed that many uncertainties in soil TPS estimation originated from the lack of detailed soil information, i.e., representation and spatial variations among different soil types. From the time and labor perspectives, our work provides useful guidelines to identify the appropriate mapping scale for estimating regional soil TPS in areas like Fujian Province in subtropical China or other places with similar complex topographies. Moreover, it is of tremendous importance to accurately estimate soil TPS to ensure ecosystem stability and sustainable agricultural development, especially for regional decision-making and management of phosphate fertilizer application amounts.
基金The Afromontane Research Unit of the University of the Free State partially funded this project.
文摘The alpine terrestrials of the Maloti-Drakensberg in southern Africa play crucial roles in ecosystem functions and livelihoods,yet they face escalating degradation from various factors including overgrazing and climate change.This study employs advanced Digital Soil Mapping(DSM)techniques coupled with remote sensing to map and assess wetland coverage and degradation in the northern Maloti-Drakensberg.The model achieved high accuracies of 96%and 92%for training and validation data,respectively,with Kappa statistics of 0.91 and 0.83,marking a pioneering automated attempt at wetland mapping in this region.Terrain attributes such as terrain wetness index(TWI)and valley depth(VD)exhibit significant positive correlations with wetland coverage and erosion gully density,Channel Network Depth and slope were negative correlated.Gully density analysis revealed terrain attributes as dominant factors driving degradation,highlighting the need to consider catchment-specific susceptibility to erosion.This challenge traditional assumptions which mainly attribute wetland degradation to external forces such as livestock overgrazing,ice rate activity and climate change.The sensitivity map produced could serve as a basis for Integrated Catchment Management(ICM)projects,facilitating tailored conservation strategies.Future research should expand on this work to include other highland areas,explore additional covariates,and categorize wetlands based on hydroperiod and sensitivity to degradation.This comprehensive study underscores the potential of DSM and remote sensing in accurately assessing and managing wetland ecosystems,crucial for sustainable resource management in alpine regions.
基金supported by the National Level Project of China(No.52-L0D01-0613-20/22)。
文摘The geolocation of ground targets by airborne image sensors is an important task for unmanned aerial vehicles or surveillance aircraft.This paper proposes an Iterative Geolocation based on Cross-view Image Registration(IGCIR)that can provide real-time target location results with high precision.The proposed method has two key features.First,a cross-view image registration process is introduced,including a projective transformation and a two-stage multi-sensor registration.This process utilizes both gradient information and phase information of cross-view images.This allows the registration process to reach a good balance between matching precision and computational efficiency.By matching the airborne camera view to the preloaded digital map,the geolocation accuracy can reach the accuracy level of the digital map for any ground target appearing in the airborne camera view.Second,the proposed method uses the registration results to perform an iteration process,which compensates for the bias of the strap-down initial navigation module online.Although it is challenging to provide cross-view registration results with high frequency,such an iteration process allows the method to generate real-time,highly accurate location results.The effectiveness of the proposed IGCIR method is verified by a series of flying-test experiments.The results show that the location accuracy of the method can reach 4.18 m(at 10 km standoff distance).
基金supported by the National Natural Science Foundation of China(62027809,U2268206,T2222015,U2468202).
文摘The integration of Global Navigation Satellite System(GNSS)technology into railway train control systems is a crucial step toward achieving the vision of a digital railway.Traditional train control systems undergo extensive in-house tests and prolonged field tests for certification and approval before operational deployment,leading to high costs,delays,and operational disruptions.This paper introduces a GNSS-based train control localization framework which eliminates the need for on-site testing by leveraging train movement dynamics and 3D environment modeling to create a zero on-site testing platform.The proposed framework simulates train movement and the surrounding 3D environment using collected railway line location data and environmental attributes to generate realistic multipath signals and obscuration effects.This approach enables comprehensive laboratory-based case studies for train localization,reducing the huge amount test of needed for physical field trials.The framework is established in house,using the data collected at the Test Base of China Academy of Railway Sciences(Circular Railway).Results from the open area and cutting environment tests demonstrate high localization accuracy repeatability within the simulated environment,validating the feasibility and effectiveness of zero on-site testing for GNSS-based train control systems.This research highlights the potential of GNSS simulation platforms in enhancing cost efficiency,operational safety,and accuracy for future digital railways.
基金supported by grants from the Human Resources Development program (Grant No.20204010600250)the Training Program of CCUS for the Green Growth (Grant No.20214000000500)by the Korea Institute of Energy Technology Evaluation and Planning (KETEP)funded by the Ministry of Trade,Industry,and Energy of the Korean Government (MOTIE).
文摘It is of great importance to obtain precise trace data,as traces are frequently the sole visible and measurable parameter in most outcrops.The manual recognition and detection of traces on high-resolution three-dimensional(3D)models are relatively straightforward but time-consuming.One potential solution to enhance this process is to use machine learning algorithms to detect the 3D traces.In this study,a unique pixel-wise texture mapper algorithm generates a dense point cloud representation of an outcrop with the precise resolution of the original textured 3D model.A virtual digital image rendering was then employed to capture virtual images of selected regions.This technique helps to overcome limitations caused by the surface morphology of the rock mass,such as restricted access,lighting conditions,and shading effects.After AI-powered trace detection on two-dimensional(2D)images,a 3D data structuring technique was applied to the selected trace pixels.In the 3D data structuring,the trace data were structured through 2D thinning,3D reprojection,clustering,segmentation,and segment linking.Finally,the linked segments were exported as 3D polylines,with each polyline in the output corresponding to a trace.The efficacy of the proposed method was assessed using a 3D model of a real-world case study,which was used to compare the results of artificial intelligence(AI)-aided and human intelligence trace detection.Rosette diagrams,which visualize the distribution of trace orientations,confirmed the high similarity between the automatically and manually generated trace maps.In conclusion,the proposed semi-automatic method was easy to use,fast,and accurate in detecting the dominant jointing system of the rock mass.
基金supported by the National Natural Science Foundation of China (91325301, 41571130051)
文摘To deal with the global and regional issues including food security, climate change, land degradation, biodiversity loss, water resource management, and ecosystem health, detailed accurate spatial soil information is urgently needed. This drives the worldwide development of digital soil mapping. In recent years, significant progresses have been made in different aspects of digital soil mapping. The main purpose of this paper is to provide a review for the major progresses of digital soil mapping in the last decade. First, we briefly described the rise of digital soil mapping and outlined important milestones and their influence, and main paradigms in digital soil mapping. Then, we reviewed the progresses in legacy soil data, environmental covariates, soil sampling, predictive models and the applications of digital soil mapping products. Finally, we summarized the main trends and future prospect as revealed by studies up to now. We concluded that although the digital soil mapping is now moving towards mature to meet various demands of soil information, challenges including new theories, methodologies and applications of digital soil mapping, especially for highly heterogeneous and human-affected environments, still exist and need to be addressed in the future.
基金Supported by the Basic Research Program of Jiangsu Province,China (No. BK2008058)the Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX2-YW-409)
文摘The use of landscape covariates to variability of soil properties in similar estimate soil properties is not suitable topographic and vegetation conditions. for the areas of low relief due to the high A new method was implemented to map regional soil texture （in terms of sand, silt and clay contents） by hypothesizing that the change in the land surface diurnal temperature difference （DTD） is related to soil texture in case of a relatively homogeneous rainfall input. To examine this hypothesis, the DTDs from moderate resolution imagine spectroradiometer （MODIS） during a selected time period, i.e., after a heavy rainfall between autumn harvest and autumn sowing, were classified using fuzzy-c-means （FCM） clustering. Six classes were generated, and for each class, the sand （〉 0.05 mm）, silt （0.002-0.05 mm） and clay （〈 0.002 mm） contents at the location of maximum membership value were considered as the typical values of that class. A weighted average model was then used to digitally map soil texture. The results showed that the predicted map quite accurately reflected the regional soil variation. A validation dataset produced estimates of error for the predicted maps of sand, silt and clay contents at root mean of squared error values of 8.4%, 7.8% and 2.3%, respectively, which is satisfactory in a practical context. This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.
基金supported by grants from the National Natural Science Foundation of China(41431177 and 41871300)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),China+4 种基金the Innovation Project of State Key Laboratory of Resources and Environmental Information System(LREIS),China(O88RA20CYA)the Outstanding Innovation Team in Colleges and Universities in Jiangsu Province,ChinaSupports to A-Xing Zhu through the Vilas Associate Awardthe Hammel Faculty Fellow Awardthe Manasse Chair Professorship from the University of Wisconsin-Madison。
文摘Selecting a proper set of covariates is one of the most important factors that influence the accuracy of digital soil mapping(DSM).The statistical or machine learning methods for selecting DSM covariates are not available for those situations with limited samples.To solve the problem,this paper proposed a case-based method which could formalize the covariate selection knowledge contained in practical DSM applications.The proposed method trained Random Forest(RF)classifiers with DSM cases extracted from the practical DSM applications and then used the trained classifiers to determine whether each one potential covariate should be used in a new DSM application.In this study,we took topographic covariates as examples of covariates and extracted 191 DSM cases from 56 peer-reviewed journal articles to evaluate the performance of the proposed case-based method by Leave-One-Out cross validation.Compared with a novices’commonly-used way of selecting DSM covariates,the proposed case-based method improved more than 30%accuracy according to three quantitative evaluation indices(i.e.,recall,precision,and F1-score).The proposed method could be also applied to selecting the proper set of covariates for other similar geographical modeling domains,such as landslide susceptibility mapping,and species distribution modeling.
基金Under the auspices of Special Fund for Ocean Public Welfare Profession Scientific Research(No.201105020)National Natural Science Foundation of China(No.41471178,41023010,41431177)National Key Technology Innovation Project for Water Pollution Control and Remediation(No.2013ZX07103006)
文摘Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe(Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer(MODIS), Normalized Difference Vegetation Indices(NDVIs) and land surface reflectance data from Landsat Thematic Mapper(TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient(CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area(with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.
基金funded by the National Natural Science Foundation of China (Nos.41871300,41422109,and 41431177)the National Basic Research Program of China (No.2015CB954102)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions,China (No.164320H116)the Outstanding Innovation Team in Colleges and Universities in Jiangsu Province,China the support from the Innovation Project of State Key Laboratory of Resources and Environmental Information System of China (No.O88RA20CYA)。
文摘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, So LIM-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.
基金supported by the National Natural Science Foundation of China(Nos.41130530,91325301,41201207 and 41571212)the Project of Frontier Fields during the Thirteenth Five-Year Plan Period of the Institute of Soil Science,Chinese Academy of Sciences(ISSASIP1622)
文摘Due to the almost homogeneous topography in low relief areas, it is usually difficult to make accurate predictions of soil properties using topographic covariates. In this study, we examined how time series of field soil moisture observations can be used to estimate soil texture in an oasis agricultural area with low relief in the semi-arid region of northwest China. Time series of field-observed soil moisture variations were recorded for 132 h beginning at the end of an irrigation event during which the surface soil was saturated.Spatial correlation between two time-adjacent soil moisture conditions was used to select the factors for fuzzy c-means clustering. In each of the ten generated clusters, soil texture of the soil sample with the maximum fuzzy membership value was taken as the cluster centroid. Finally, a linearly weighted average was used to predict soil texture from the centroids. The results showed that soil moisture increased with the increase of clay and silt contents, but decreased with the increase of sand content. The spatial patterns of soil moisture changed during the entire soil drying phase. We assumed that these changes were mainly caused by spatial heterogeneity of soil texture. A total of 64 independent samples were used to evaluate the prediction accuracy. The root mean square error(RMSE)values of clay, silt and sand were 1.63, 2.81 and 3.71, respectively. The mean relative error(RE) values were 9.57% for clay, 3.77% for silt and 12.83% for sand. It could be concluded that the method used in this study was effective for soil texture mapping in the low-relief oasis agricultural area and could be applicable in other similar irrigation agricultural areas.
基金Under the auspices of Priority Academic Program Development of Jiangsu Higher Education Institutions,National Natural Science Foundation of China(No.41271438,41471316,41401440,41671389)
文摘Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.
文摘Detailed knowledge about the estimates and spatial patterns of soil organic carbon(SOC) and total nitrogen(TN) stocks is fundamental for sustainable land management and climate change mitigation.This study aimed at:(1) mapping the spatial patterns,and(2) quantifying SOC and TN stocks to 30 cm depth in the Eastern Mau Forest Reserve using field,remote sensing,geographical information systems(GIS),and statistical modelling approaches.This is a critical ecosystem offering essential services,but its sustainability is threatened by deforestation and degradation.Results revealed that elevation,silt content,TN concentration,and Landsat 8 Operational Land Imager band 11 explained 72% of the variability in SOC stocks,while the same factors(except silt content) explained 71% of the variability in TN stocks.The results further showed that soil properties,particularly TN and SOC concentrations,were more important than that other environmental factors in controlling the observed patterns of SOC and TN stocks,respectively.Forests stored the highest amounts of SOC and TN(3.78 Tg C and 0.38 Tg N) followed by croplands(2.46 Tg C and 0.25 Tg N) and grasslands(0.57 Tg C and 0.06 Tg N).Overall,the Eastern Mau Forest Reserve stored approximately 6.81 Tg C and 0.69 Tg N.The highest estimates of SOC and TN stocks(hotspots) occurred on the western and northwestern parts where forests dominated,while the lowest estimates(coldspots) occurred on the eastern side where croplands had been established.Therefore,the hotspots need policies that promote conservation,while the coldspots need those that support accumulation of SOC and TN stocks.
文摘Soil organic carbon （SOC） pool has the potential to mitigate or enhance climate change by either acting as a sink, or a source of atmospheric carbon dioxide （CO2） and also plays a fundamental role in the health and proper functioning of soils to sustain life on Earth. As such, the objective of this study was to investigate the applicability of a novel evolutionary genetic optimization-based adaptive neuro-fuzzy inference system （ANFIS-EG） in predicting and mapping the spatial patterns of SOC stocks in the Eastern Mau Forest Reserve, Kenya. Field measurements and auxiliary data reflecting the soil-forming factors were used to design an ANFIS-EG model, which was then implemented to predict and map the areal differentiation of SOC stocks in the Eastern Mau Forest Reserve. This was achieved with a reasonable level of uncertainty （i.e., root mean square error of 15.07 Mg C ha-l）, hence demonstrating the applicability of the ANFIS-EG in SOC mapping studies. There is potential for improving the model performance, as indicated by the current ratio of performance to deviation （1.6）. The mapping also revealed marginally higher SOC stocks in the forested ecosystems （i.e., an average of 109.78 M C ha-1） than in the aro-ecosvstems （i.e., an average of 95.9 Mg C ha-l）.
基金Under the auspices of Program of International Science & Technology Cooperation,Ministry of Science and Technology of China(No.2010DFB24140)National Natural Science Foundation of China(No.41023010,41001298)National High Technology Research and Development Program of China(No.2011AA120305)
文摘Soil type maps at the scale of I Z 1 000 000 are used extensively to provide soil spatial distribution information for soil erosion assessment and watershed management models in China. However, the soil property maps produced through conventional direct linking method usually suffer low accuracy as well as the lack of spatial details within a soil type polygon. This paper presents an effective method to produce detailed soil property map based on representative samples which were extracted from each polygon on the 1 ： 1 000 000 soil type map. The representative sample of each polygon is defined as the location that can represent the largest area within the polygon. The representativeness of a candidate sample is determined by calculating the soil-forming environment condition similarities between the sample and other locations. Once the representative sample of each polygon has been chosen, the property values of the existing typical samples are assigned to the corresponding representative samples with the same soil type. Finally, based on these representative samples, the detailed soil property map could be produced by using existing digital soil mapping methods. The case study in XuanCheng City, Anhui Province of China, demonstrated the proposed method could produce soil property map at a higher level of spatial details and accuracy： 1） The soil organic matter （SOM） map produced based on the representative samples can not only depict the detailed spatial distribution of SOM within a soil type polygon but also largely reduce the abrupt change of soil property at the boundaries of two adjacent polygons. 2） The Root Mean Squared Error （RMSE） of the SOM map based on the representative samples is 1.61, and it is 1.37 for the SOM map produced by using conventional direct linking method. Therefore, the proposed method is an effective approach to produce spatial detailed soil property map with higher accuracy for environment simulation models.
基金supported by the National Administration of Surveying, Mapping and Geoinformation (Grant no.1469990324229)the National Natural Science Foundation of China (Grant nos.40806076, 41176172, 41176173)+2 种基金the National High Technology Research and Development Program of China (Grant no. 2008AA121702–5)the National Science and Technology Infrastructure Program of China (Grant no.2006BAB18B01)the Chinese Arctic and Antarctic Administration, SOA(Grant no. 20070206)
文摘Antarctic surveying, mapping and remote sensing is one of the important aspects of the Chinese Antarctic geoscience research program that stretch back over 25 years, since the first Chinese National Antarctic Research Expedition （CHINARE） in 1984. During the 1980＇s, the geodetic datum, height system and absolute gravity datum were established at the Great Wall and Zhongshan Stations. Significant contributions have been made by the construction of the Chinese Great Wall, Zhongshan and Kunlun Stations in Antarctica. Geodetic control and gravity networks were established in the King George Islands, Grove Moun- tains and Dome Argus. An area of more than 200 000 km2 has been mapped using satellite image data, aerial photogrammetry and in situ data. Permanent GPS stations and tide gauges have been established at both the Great Wall and Zhongshan Stations. Studies involving plate motion, precise satellite orbit determination, the gravity field, sea level change, and various GPS applications for atmospheric studies have been carried out. Based on remote sensing techniques, studies have been undertaken on ice sheet and glacier movements, the distributions of blue ice and ice crevasses, and ice mass balance. Polar digital and visual mapping tech- niques have been introduced, and a polar survey space database has been built. The Chinese polar scientific expedition manage- ment information system and Chinese PANDA plan display platform were developed, which provides technical support for Chi- nese polar management. Finally, this paper examines prospects for future Chinese Antarctic surveying, mapping and remote sens- ing.
基金This work was financially supported by the National Natural Science Foundation of China(41771470)the China Postdoctoral Science Foundation(2020M672776).
文摘Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land management,agricultural activities,water quality,and sustainable development.The remote sensing images taken by the synthetic aperture radar(SAR)Sentinel-1 and the multispectral satellite Sentinel-2 with high resolution and short revisit period have the potential to monitor the spatial distribution of soil attribute information on a large area;however,there are limited studies on the combination of Sentinel-1 and Sentinel-2 for digital mapping of soil salinization.Therefore,in this study,we used topography indices derived from digital elevation model(DEM),SAR indices generated by Sentinel-1,and vegetation indices generated by Sentinel-2 to map soil salinization in the Ogan-Kuqa River Oasis located in the central and northern Tarim Basin in Xinjiang of China,and evaluated the potential of multi-source sensors to predict soil salinity.Using the soil electrical conductivity(EC)values of 70 ground sampling sites as the target variable and the optimal environmental factors as the predictive variable,we constructed three soil salinity inversion models based on classification and regression tree(CART),random forest(RF),and extreme gradient boosting(XGBoost).Then,we evaluated the prediction ability of different models through the five-fold cross validation.The prediction accuracy of XGBoost model is better than those of CART and RF,and soil salinity predicted by the three models has similar spatial distribution characteristics.Compared with the combination of topography indices and vegetation indices,the addition of SAR indices effectively improves the prediction accuracy of the model.In general,the method of soil salinity prediction based on multi-source sensor combination is better than that based on a single sensor.In addition,SAR indices,vegetation indices,and topography indices are all effective variables for soil salinity prediction.Weighted Difference Vegetation Index(WDVI)is designated as the most important variable in these variables,followed by DEM.The results showed that the high-resolution radar Sentinel-1 and multispectral Sentinel-2 have the potential to develop soil salinity prediction model.
基金supported by the National Natural Science Foundation of China(Nos.91325301,41571130051,41401237,41571212,and 41371224)partly by the Jiangsu Provincial Science Foundation for Youths,China(No.BK20141053)
文摘Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon(SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types(square and circular) and 6 representative algorithms(Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods(i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of firstand second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30% and 9.68%.The SOC maps resulting from the higher-order algorithms(Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.
基金funded by the Natural Science and Engineering Research Council (NSERC) of Canada (No. RGPIN-2014-04100)
文摘Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.
