scholarly journals Comparing three approaches of spatial disaggregation of legacy soil maps based on DSMART algorithm

2019 ◽  
Author(s):  
Yosra Ellili ◽  
Brendan Philip Malone ◽  
Didier Michot ◽  
Budiman Minasny ◽  
Sébastien Vincent ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Soil Type ◽  
Expert Knowledge ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Soil Profiles ◽  
Soil Maps ◽  
Soil Map ◽  
Soil Landscape ◽  
Spatial Disaggregation

Abstract. Enhancing the spatial resolution of pedological information is a great challenge in the field of Digital Soil Mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially available at coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining Soil Map Units (SMU), where each SMU can include one or several Soil Type Units (STU) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at finer spatial resolution by machine learning algorithms using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithm (DSMART with extra soil profiles, DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at 50 m resolution was assessed over a large study area (6775 km2) using an external validation based on independent 135 soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1 : 25 000 soil maps. Pairwise comparisons were also performed, using Shannon entropy measure, to spatially locate differences between disaggregated maps. The main results show that adding soil landscape relationships in the disaggregation process enhances the performance of prediction of soil type distribution. Considering the three most probable STU and using 135 independent soil profiles, the overall accuracy measures are: 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost twofold higher when validated using 3 × 3 windows. They achieved 28.5 % for DSMART with soil landscape relationships, 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships as well as extra soil observations constraints the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial to obtain consistent soil maps with clear internal disaggregation of SMU across the landscape.

scholarly journals Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm

SOIL ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 371-388 ◽  
Author(s):  
Yosra Ellili-Bargaoui ◽  
Brendan Philip Malone ◽  
Didier Michot ◽  
Budiman Minasny ◽  
Sébastien Vincent ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Soil Type ◽  
Expert Knowledge ◽  
Classification Trees ◽  
Machine Learning Algorithms ◽  
Soil Profiles ◽  
Soil Maps ◽  
Soil Map ◽  
Soil Landscape ◽  
Spatial Disaggregation

Abstract. Enhancing the spatial resolution of pedological information is a great challenge in the field of digital soil mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially and are available at a coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining soil map units (SMUs), where each SMU can include one or several soil type units (STUs) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at a finer spatial resolution by machine-learning algorithms, using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of the spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithms (DSMART with extra soil profiles; DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at a 50 m resolution was assessed over a large study area (6775 km2) using an external validation based on 135 independent soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1:25 000 soil maps. Pairwise comparisons were also performed, using the Shannon entropy measure, to spatially locate the differences between disaggregated maps. The main results show that adding soil landscape relationships to the disaggregation process enhances the performance of the prediction of soil type distribution. Considering the three most probable STUs and using 135 independent soil profiles, the overall accuracy measures (the percentage of soil profiles where predictions meet observations) are 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost 2 times higher when validated using 3×3 windows. They achieved 28.5 % for DSMART with soil landscape relationships and 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships and extra soil observations constraints allow the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial for obtaining consistent soil maps with a clear internal disaggregation of SMUs across the landscape.

Consequences of soil map unit uncertainty on environmental risk assessment

Soil Research ◽  
2005 ◽  
Vol 43 (2) ◽  
pp. 119 ◽  
Author(s):  
T. H. Webb ◽  
L. R. Lilburne
Keyword(s):  
Soil Properties ◽  
Environmental Risk ◽  
Soil Type ◽  
Expert Knowledge ◽  
Regional Scale ◽  
Soil Survey ◽  
Soil Maps ◽  
Considerable Uncertainty ◽  
Soil Map ◽  
Wide Range

Soil maps are being applied in modelling of environmental risk at a regional scale. Analysis of soil maps shows that there can be considerable uncertainty in map unit composition with consequent spatial variability in soil properties within map units. Uncertainty of map unit composition arises from a number of factors including map labels that do not reflect the mix of soil types present and imprecise location of map unit boundaries. Usually little, if any, information is supplied as to the extent and magnitude of these uncertainties. In this paper we develop datasets to quantify soil property variability for map units on the Canterbury Plains and use expert knowledge to quantify uncertainty in map unit composition for a small case-study area. We then apply Monte Carlo sampling to the estimated range of soil properties to input soil data into the GLEAMS simulation model to assess the effect of soil variability on nitrate leaching. The simulation results show that there is considerable uncertainty in leaching from data derived from current soil map units. Even simple map units (map units characterised by a single soil type) have a wide range of potential leaching related to variation in soil profiles within the soil type. When map-unit inclusions were accounted for, variation in leaching risk can increase by 10–30%. Compound map units encompass the range of variability of the named component soils and there is a smaller increase in leaching variability in these units (compared with simple map units) when inclusions are added to the analysis. We discuss the implications of this study to the use of current soil survey data and make recommendations for future soil survey practice. In particular, greater effort is needed on description of the composition and reliability of soil map units.

Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships

Geoderma ◽  
2018 ◽  
Vol 311 ◽  
pp. 130-142 ◽  
Author(s):  
Sébastien Vincent ◽  
Blandine Lemercier ◽  
Lionel Berthier ◽  
Christian Walter
Keyword(s):  
Regional Scale ◽  
Soil Map ◽  
Soil Landscape ◽  
Spatial Disaggregation

scholarly journals Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches

2021 ◽  
Vol 13 (18) ◽  
pp. 3777
Author(s):  
He Zhang ◽  
Marijn Bauters ◽  
Pascal Boeckx ◽  
Kristof Van Oost
Keyword(s):  
Machine Learning ◽  
Tropical Forests ◽  
Expert Knowledge ◽  
External Validation ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Congo Basin ◽  
Supervised Machine Learning

Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests.

scholarly journals Individualization of soil classes by disaggregation of physiographic map polygons

2019 ◽  
Vol 54 ◽  
Author(s):  
José Janderson Ferreira Costa ◽  
Elvio Giasson ◽  
Elisângela Benedet da Silva ◽  
Alcinei Ribeiro Campos ◽  
Israel Rosa Machado ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Soil Profiles ◽  
Soil Maps ◽  
Physiographic Unit ◽  
Tree Model ◽  
Soil Map ◽  
Terrain Model ◽  
Digital Terrain ◽  
Cross Tabulation ◽  
Geomorphometric Parameters

Abstract: The objective of this work was to disaggregate the polygons of physiographic map units in order to individualize the soil classes in each one, representing them as simple soil map units and generating a more detailed soil map than the original one, making these data more useful for future reference. A physiographic map, on a 1:25,000 scale, of the Tarumãzinho watershed, located in the municipality of Águas Frias, in the state of Santa Catarina, Brazil, was used. For disaggregation, three geomorphometric parameters were applied: slope and landforms, both derived from the digital terrain model; and an elevation map. The boundaries of the physiographic units and the elevation, slope, and landform maps were subjected to cross tabulation to identify the existing combinations between the soil classes of each physiographic unit. Based on these combinations, rules were established to select typical areas of occurrence of each soil type in order to train a decision tree model to predict the occurrence of soil classes. The model was trained using the Weka software and was validated with a set of georeferenced soil profiles. Disaggregation enables the individualization and spatialization of soil classes and is useful in producing detailed soil maps.

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

scholarly journals Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Shijun Yang ◽  
Bin Wang ◽  
Xiong Han
Keyword(s):  
Antiepileptic Drugs ◽  
Statistical Models ◽  
Prediction Models ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Treatment Withdrawal ◽  
Patient Treatment ◽  
Recent Developments ◽  
Progression Of Disease ◽  
Patients With Epilepsy

AbstractAlthough antiepileptic drugs (AEDs) are the most effective treatment for epilepsy, 30–40% of patients with epilepsy would develop drug-refractory epilepsy. An accurate, preliminary prediction of the efficacy of AEDs has great clinical significance for patient treatment and prognosis. Some studies have developed statistical models and machine-learning algorithms (MLAs) to predict the efficacy of AEDs treatment and the progression of disease after treatment withdrawal, in order to provide assistance for making clinical decisions in the aim of precise, personalized treatment. The field of prediction models with statistical models and MLAs is attracting growing interest and is developing rapidly. What’s more, more and more studies focus on the external validation of the existing model. In this review, we will give a brief overview of recent developments in this discipline.

scholarly journals A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

2021 ◽  
Vol 13 (11) ◽  
pp. 2040
Author(s):  
Xin Yan ◽  
Hua Chen ◽  
Bingru Tian ◽  
Sheng Sheng ◽  
Jinxing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Daily Precipitation ◽  
Machine Learning Algorithms ◽  
Precipitation Data ◽  
Large Area ◽  
Tree Model ◽  
Daily Precipitation Data ◽  
Precipitation Estimates

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.

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