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Perceptual complexity of soil-landscape maps: a user evaluation of color organization in legend designs using eye tracking 被引量:2
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作者 Arzu Coltekin Alzbeta Brychtova +3 位作者 Amy L.Griffin Anthony C.Robinson Mark Imhof Chris Pettit 《International Journal of Digital Earth》 SCIE EI 2017年第6期560-581,共22页
We compared the ability of two legend designs on a soil-landscape map to efficiently and effectively support map reading tasks with the goal of better understanding how the design choices affect user performance.Devel... We compared the ability of two legend designs on a soil-landscape map to efficiently and effectively support map reading tasks with the goal of better understanding how the design choices affect user performance.Developing such knowledge is essential to design effective interfaces for digital earth systems.One of the two legends contained an alphabetical ordering of categories,while the other used a perceptual grouping based on the Munsell color space.We tested the two legends for 4 tasks with 20 experts(in geography-related domains).We analyzed traditional usability metrics and participants’eye movements to identify the possible reasons behind their success and failure in the experimental tasks.Surprisingly,an overwhelming majority of the participants failed to arrive at the correct responses for two of the four tasks,irrespective of the legend design.Furthermore,participants’prior knowledge of soils and map interpretation abilities led to interesting performance differences between the two legend types.We discuss how participant background might have played a role in performance and why some tasks were particularly hard to solve despite participants’relatively high levels of experience in map reading.Based on our observations,we caution soil cartographers to be aware of the perceptual complexity of soil-landscape maps. 展开更多
关键词 soil-landscape maps legend design visual complexity COLOR empirical study eye tracking
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Recent progress and future prospect of digital soil mapping: A review 被引量:15
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作者 ZHANG Gan-lin LIU Feng SONG Xiao-dong 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2017年第12期2871-2885,共15页
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
关键词 digital soil mapping soil-landscape model predictive models soil functions spatial variation
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Relationships between Soil Depth and Terrain Attributes in a Semi Arid Hilly Region in Western Iran 被引量:7
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作者 Abdolmohammad MEHNATKESH Shamsollah AYOUBI +1 位作者 Ahmad JALALIAN Kanwar L.SAHRAWAT 《Journal of Mountain Science》 SCIE CSCD 2013年第1期163-172,共10页
Soil depth generally varies in mountainous regions in rather complex ways.Conventional soil survey methods for evaluating the soil depth in mountainous and hilly regions require a lot of time,effort and consequently r... Soil depth generally varies in mountainous regions in rather complex ways.Conventional soil survey methods for evaluating the soil depth in mountainous and hilly regions require a lot of time,effort and consequently relatively large budget to perform.This study was conducted to explore the relationships between soil depth and topographic attributes in a hilly region in western Iran.For this,one hundred sampling points were selected using randomly stratified methodology,and considering all geomorphic surfaces including summit,shoulder,backslope,footslope and toeslope;and soil depth was actually measured.Eleven primary and secondary topographic attributes were derived from the digital elevation model(DEM) at the study area.The result of multiple linear regression indicated that slope,wetness index,catchment area and sediment transport index,which were included in the model,could explain about 76 % of total variability in soil depth at the selected site.This proposed approach may be applicable to other hilly regions in the semi-arid areas at a larger scale. 展开更多
关键词 Soil depth prediction Topographic attributes Digital elevation model soil-landscape model
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The Effect of the Geomorphologic Type as Surrogate to the Time Factor on Digital Soil Mapping 被引量:1
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作者 Huixia Chai Sheng Rao +3 位作者 Reibo Wang Jing Liu Qi Huang Xuejie Mou 《Open Journal of Soil Science》 2015年第6期123-134,共12页
Many environmental variables are frequently used to predict values of soil in locations where they are not measured. Digital soil mapping (DSM) has a long-standing convention to describe soils as a function of climate... Many environmental variables are frequently used to predict values of soil in locations where they are not measured. Digital soil mapping (DSM) has a long-standing convention to describe soils as a function of climate, organisms, topography, parent material, time and space. It is obvious that terrain, climate, parent material and organisms are used frequently in the prediction of soil properties while time and space factors are rarely used. Time is the indirect factor for the formation and development of soil. Moreover, it is very useful to explicit and implicit estimates of soil age for DSM. However, it is often difficult to obtain time factor. In the absence of explicit soil age data, geomorphologic data are commonly related to soil relative age. Consequently, this study adopts the geomorphologic types (genesis type of geomorphology) as surrogate to the time factor and analyzes its effect on DSM. To examine this idea, we selected the Ili region of northwestern China as the study area. This paper uses geomorphologic data from a new digital geomorphology map as the implicit soil age in predictive soil mapping. For this study, Soil-landscape inference model (SoLIM) was used to predict soil properties based on the individual representation of each sample. This model applies the terrain (topography), climate, parent material (geology) and time (geomorphologic type) to predict soil values in the study area where they are not measured. And the independent sample validation method was used to estimate the precision of results. The validation result shows that the use of geomorphologic data as surrogate to the time factor in the individual representation leads to a considerable and significant increase in the accuracy of results. In other words, implicit estimates of soil age by genesis type of geomorphology are very useful for DSM. This increase was due to the high purity of the geomorphologic data. This means that the geomorphologic variable, if used, can improve the quality of DSM. Predicted value through the proposed approach comes closer to the real value. 展开更多
关键词 Geomorphologic Type ENVIRONMENTAL VARIABLES Digital SOIL Mapping soil-landscape INFERENCE Model (SoLIM) INDIVIDUAL Validation
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Predicting soil depth in a large and complex area using machine learning and environmental correlations
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作者 LIU Feng YANG Fei +2 位作者 ZHAO Yu-guo ZHANG Gan-lin LI De-cheng 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2022年第8期2422-2434,共13页
Soil depth is critical for eco-hydrological modeling,carbon storage calculation and land evaluation.However,its spatial variation is poorly understood and rarely mapped.With a limited number of sparse samples,how to p... Soil depth is critical for eco-hydrological modeling,carbon storage calculation and land evaluation.However,its spatial variation is poorly understood and rarely mapped.With a limited number of sparse samples,how to predict soil depth in a large area of complex landscapes is still an issue.This study constructed an ensemble machine learning model,i.e.,quantile regression forest,to quantify the relationship between soil depth and environmental conditions.The model was then combined with a rich set of environmental covariates to predict spatial variation of soil depth and straightforwardly estimate the associated predictive uncertainty in the 140000 km^(2)Heihe River basin of northwestern China.A total of 275 soil depth observation points and 26 covariates were used.The results showed a model predictive accuracy with coefficient of determination(R)of 0.587 and root mean square error(RMSE)of 2.98 cm(square root scale),i.e.,almost 60% of soil depth variation explained.The resulting soil depth map clearly exhibited regional patterns as well as local details.Relatively deep soils occurred in low lying landscape positions such as valley bottoms and plains while shallow soils occurred in high and steep landscape positions such as hillslopes,ridges and terraces.The oases had much deeper soils than outside semi-desert areas,the middle of an alluvial plain had deeper soils than its margins,and the middle of a lacustrine plain had shallower soils than its margins.Large predictive uncertainty mainly occurred in areas with a lack of soil survey points.Both pedogenic and geomorphic processes contributed to the shaping of soil depth pattern of this basin but the latter was dominant.This findings may be applicable to other similar basins in cold and arid regions around the world. 展开更多
关键词 digital soil mapping spatial variation UNCERTAINTY machine learning soil-landscape model soil depth
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Mapping high resolution National Soil Information Grids of China 被引量:37
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作者 Feng Liu Huayong Wu +6 位作者 Yuguo Zhao Decheng Li Jin-Ling Yang Xiaodong Song Zhou Shi A-Xing Zhu Gan-Lin Zhang 《Science Bulletin》 SCIE EI CSCD 2022年第3期328-340,共13页
Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires hig... Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties(pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate(Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development. 展开更多
关键词 Predictive soil mapping soil-landscape model Machine learning Depth function Large and complex areas Soil spatial variation
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