Prospective Cohort Study

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

/pdf/soilgrids250m-global-gridded-soil-information-based-on-2xwvokp8f3.pdf

SoilGrids250m: Global gridded soil information based on machine learning

[i] An inventory of air pollutant emissions in Asia in the year 2000 is developed to support atmospheric modeling and analysis of observations taken during the TRACE-P experiment funded by the National Aeronautics and Space Administration (NASA) and the ACE-Asia experiment funded by the National Science Foundation (NSF) and the National Oceanic and Atmospheric Administration (NOAA). Emissions are estimated for all major anthropogenic sources, including biomass burning, in 64 regions of Asia. We estimate total Asian emissions as follows: 34.3 Tg SO 2 , 26.8 Tg NO x , 9870 Tg CO 2 , 279 Tg CO, 107 Tg CH 4 , 52.2 Tg NMVOC, 2.54 Tg black carbon (BC), 10.4 Tg organic carbon (OC), and 27.5 Tg NH 3 . In addition, NMVOC are speciated into 19 subcategories according to functional groups and reactivity. Thus we are able to identify the major source regions and types for many of the significant gaseous and particle emissions that influence pollutant concentrations in the vicinity of the TRACE-P and ACE-Asia field measurements. Emissions in China dominate the signature of pollutant concentrations in this region, so special emphasis has been placed on the development of emission estimates for China. China's emissions are determined to be as follows: 20.4 Tg SO 2 , 11.4 Tg NO x , 3820 Tg CO 2 , 116 Tg CO, 38.4 Tg CH 4 , 17.4 Tg NMVOC, 1.05 Tg BC, 3.4 Tg OC, and 13.6 Tg NH 3 . Emissions are gridded at a variety of spatial resolutions from 1° × 1° to 30 s x 30 s, using the exact locations of large point sources and surrogate GIS distributions of urban and rural population, road networks, landcover, ship lanes, etc. The gridded emission estimates have been used as inputs to atmospheric simulation models and have proven to be generally robust in comparison with field observations, though there is reason to think that emissions of CO and possibly BC may be underestimated. Monthly emission estimates for China are developed for each species to aid TRACE-P and ACE-Asia data interpretation. During the observation period of March/ April, emissions are roughly at their average values (one twelfth of annual). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of ±16% for SO 2 to a high of ±450% for OC.

An inventory of gaseous and primary aerosol emissions in Asia in the year 2000 : NASA global tropospheric experiment transport and chemical evolution over the pacific (TRACE-P): Measurements and analysis (TRACEP1)

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Remote Sensing And Image Interpretation

Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Least squares method is one of the effective and widely used data process tool in statistical based data analysis.
Automated map generalization is an intelligent process in nature, which involves mining the knowledge from the original
data sets to form the generalized features that are the abstracted representation of the original features in multiple and
different scale levels. In this paper, we present an integrated methodology for the automated generalization of settlement
areas based on least squares adjustment. Taking the original data sets of the features as observational values, the
parameters representing the generalized features are computed according to the least squares adjustment model. The
four-level hierarchical model for settlement area generalization is first proposed. The rules and constraints conditions in
settlement area generalization are then described, and the least squares adjustment model is derived in settlement areas
generalization. The results of practical tests demonstrate that the validity and feasibility of the proposed model for the
simplification of settlement areas in geographic information system.

A least-squares-adjustment-based method for automated map generalization of settlement areas in GIS

. This paper analyzes the precision and deviation of elevations acquired from Envisat and The Ice, Cloud and Land Elevation Satellite (ICESat) over typical ice gaining and losing regions, i.e. Lambert-Amery System (LAS) in east Antarctica, and Amundsen Sea Sector (ASS) in west Antarctica, during the same period from 2003 to 2008. We used GLA12 dataset of ICESat and Level 2 data of Envisat. Data preprocessing includes data filtering, projection transformation and track classification. Meanwhile, the slope correction is applied to Envisat data and saturation correction for ICESat data. Then the crossover analysis was used to obtain the crossing points of the ICESat tracks, Envisat tracks and ICESat-Envisat tracks separately. The two tracks we chose for cross-over analysis should be in the same campaign for ICESat (within 33 days) or the same cycle for Envisat (within 35 days).The standard deviation of a set of elevation residuals at time-coincident crossovers is calculated as the precision of each satellite while the mean value is calculated as the deviation of ICESat-Envisat. Generally, the ICESat laser altimeter gets a better precision than the Envisat radar altimeter. For Amundsen Sea Sector, the ICESat precision is found to vary from 8.9 cm to 17 cm and the Envisat precision varies from 0.81 m to 1.57 m. For LAS area, the ICESat precision is found to vary from 6.7 cm to 14.3 cm and the Envisat precision varies from 0.46 m to 0.81 m. Comparison result between Envisat and ICESat elevations shows a mean difference of 0.43 ±7.14 m for Amundsen Sea Sector and 0.53 ± 1.23 m over LAS.

https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/1513/2017/isprs-archives-XLII-2-W7-1513-2017.pdf

Precision and deviation comparison between icesat and envisat in typical ice gaining and losing regions of antarctica

Along with a wide spread application of high resolution satellite imagery (HRSI) in urban mapping and change detection, the demand is increasing for higher accuracy metric products from HRSI. However, due to the inherent sensor orientation bias, the rational polynomial coefficients (RPCs) provided by the vendors can not produce accurate coordinates in ground point determination. Based on the QuickBird stereo HRSI, this paper has presented the performance of two schemes for bias elimination, including RPCs modification and RPCs regeneration. The experimental results show that, for the testing area, modified RPCs yields low positioning accuracy of 2 - 3 meters in planimetry and 3 meters in elevation since it is responsible for correcting only the shift bias. However, with minimal 2 control points, regenerated RPCs with shift and drift bias compensated produces accuracy of about 0.6 meters in planimetry and 1 meter in elevation. If more control points are available, RPCs regeneration with affine model bias compensation is more recommended. The bias-corrected RPCs provide a steady accuracy of half meter in planimetry. Furthermore, the bias-eliminated RPCs facilitate bias-free application. They can be used as replacements of the originals producing high accuracy in photogrammetric system for further processing such as ortho-rectification and DEM generation to provide cost advantage.

Bias-corrected RPCs for QuickBird stereo satellite imagery: A case study in Shanghai

As a complex non-linear and dynamic process,full understanding of the urban morphology and evolution mechanism requires modelling.Due to its abilities of simulating and predicting a complex system,cellular automata (CA) have been increasingly used to capture the nature of urban evolution since the pioneering work of Tobler.More recently,intelligence methods were widely adopted to optimize geographical CA models.The similarity between the nature of self-organization of particle swarm optimizers (PSO) and the "bottom-up" approach of cellular models makes it particularly suitable for optimizing transition rules.Based on automatically searching the minimum differences between the simulation results produced by a CA model based on conventional logistic regression method and the actual pattern of urban morphology,this research integrates the PSO method and a CA model to stochastically optimize combination of parameters of CA rules and construct the PSO based CA model for urban expansion and evolution modelling.Based on Matlab,Visual Studio.Net and GIS,the PSO algorithm and the PSO-CA model were successfully implemented.By using the 17 years (from 1989 to 2006) historical remotely sensed images,the PSO-CA model was calibrated to simulate the urban expansion of Jiading District,Shanghai Municipality.Besides,the urban pattern of Jiading District at 2010 was projected with the PSO-CA model.Evaluated with a confusion matrix,the simulation results of the PSO-CA model obtained accuracies of 87.42% for the non-urban category,76.51% for the urban category,82.35% for overall,and 64.31% for the Kappa coefficient,which outperforms the logistic regression based CA model,with accuracies of 84.36% for the non-urban category,71.58% for the urban category,78.42% for overall,and 56.32% for the Kappa coefficient.This research have demonstrated that conventional transition rules were substantially improved by the PSO technique,which also can optimize a wide range of traditional CA models for urban evolution modelling.

A particle swarm intelligence based cellular model for urban morphology evolution modelling : A case study in Jiading District of Shanghai

The invention relates to a multispectral remote sensing image unsupervised change detection method based on information expansion. The method comprises the following steps: 1) waveband spectral information expansion generated based on a nonlinear waveband: performing waveband spectral information expansion on any two original wavebands of multispectral images of a previous time phase and a next time phase respectively to obtain a newly-expanded nonlinear artificial spectral waveband; 2) wave band spatial information expansion based on multi-scale morphological reconstruction: respectively carrying out spatial reconstruction operation on the original wave band and the nonlinear artificial spectrum wave band to obtain a spatial expansion wave band feature set, and carrying out differential operation to obtain a differential wave band set; 3) Unsupervised multi-class change detection. Compared with the prior art, the method has the advantages that limited original wave band expansion andspatial information reconstruction are achieved, and automatic and unsupervised detection of multi-class changes is achieved.

Xiaohua Tong

Papers

A least-squares-adjustment-based method for automated map generalization of settlement areas in GIS

Precision and deviation comparison between icesat and envisat in typical ice gaining and losing regions of antarctica

Bias-corrected RPCs for QuickBird stereo satellite imagery: A case study in Shanghai

A particle swarm intelligence based cellular model for urban morphology evolution modelling : A case study in Jiading District of Shanghai

A multispectral remote sensing image unsupervised change detection method based on information expansion