DIGITAL SOIL MAPPING IN A MOUNTAINOUS AREA WITH MIXED LAND USE (HUMOR CATCHMENT - EASTERN CARPATHIANS, ROMANIA) USING SOIL-LANDSCAPE SYSTEMS, FUZZY LOGIC AND ENVIRONMENTAL COVARIATES

2019 ◽  
Vol 18 (2) ◽  
pp. 479-489 ◽  
Author(s):  
Mihai Niculita ◽  
Mihai-Gabriel Balan ◽  
Adrian Andrei ◽  
Eugen Rusu
Keyword(s):  
Land Use ◽  
Fuzzy Logic ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Mountainous Area ◽  
Environmental Covariates ◽  
Eastern Carpathians ◽  
Soil Landscape ◽  
Mixed Land Use

scholarly journals Digital Soil Mapping Using Machine Learning Algorithms in a Tropical Mountainous Area

2018 ◽  
Vol 42 (0) ◽  
Author(s):  
Martin Meier ◽  
Eliana de Souza ◽  
Marcio Rocha Francelino ◽  
Elpídio Inácio Fernandes Filho ◽  
Carlos Ernesto Gonçalves Reynaud Schaefer
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Soil Mapping ◽  
Machine Learning Algorithms ◽  
Digital Soil Mapping ◽  
Mountainous Area

Digital soil mapping of a coastal acid sulfate soil landscape

Soil Research ◽  
2014 ◽  
Vol 52 (4) ◽  
pp. 327 ◽  
Author(s):  
Jingyi Huang ◽  
Terence Nhan ◽  
Vanessa N. L. Wong ◽  
Scott G. Johnston ◽  
R. Murray Lark ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Traditional Approach ◽  
Management Strategies ◽  
Error Variance ◽  
Optimal Number ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Acid Sulfate ◽  
Soil Landscape ◽  
Number Of Classes

Coastal floodplains are commonly underlain by sulfidic sediments and coastal acid sulfate soils (CASS). Oxidation of sulfidic sediments leads to increases in acidity and mobilisation of trace metals, resulting in an increase in the concentrations of conducting ions in sediment and pore water. The distribution of these sediments on floodplains is highly heterogeneous. Accurately identifying the distribution of CASS is essential for developing targeted management strategies. One approach is the use of digital soil mapping (DSM) using ancillary information. Proximal sensing instruments such as an EM38 can provide data on the spatial distribution of soil salinity, which is associated with CASS, and can be complemented by digital elevation models (DEM). We used EM38 measurements of the apparent soil electrical conductivity (ECa) in the horizontal and vertical modes in combination with a high resolution DEM to delineate the spatial distribution of CASS. We used a fuzzy k-means algorithm to cluster the data. The fuzziness exponent, number of classes (k) and distance metric (i.e. Euclidean, Mahalanobis and diagonal) were varied to determine a set of parameters to identify CASS. The mean-squared prediction error variance of the class mean of various soil properties (e.g. EC1:5 and pH) was used to identify which of these metrics was suitable for further analysis (i.e. Mahalanobis) and also determine the optimal number of classes (i.e. k = 4). The final map is consistent with previously defined soil–landscape units generated using traditional soil profile description, classification and mapping. The DSM approach is amenable for evaluation on a larger scale and in order to refine CASS boundaries previously mapped using the traditional approach or to identify CASS areas that remain unmapped.

Digital soil mapping based on wavelet decomposed components of environmental covariates

Geoderma ◽  
2017 ◽  
Vol 303 ◽  
pp. 118-132 ◽  
Author(s):  
Xiao-Lin Sun ◽  
Hui-Li Wang ◽  
Yu-Guo Zhao ◽  
Chaosheng Zhang ◽  
Gan-Lin Zhang
Keyword(s):  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Environmental Covariates

scholarly journals DIGITAL SOIL MAPPING USING GEOMORPHOMETRIC ANALYSIS AND CASE-BASED FUZZY LOGIC APPROACH

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 863-866
Author(s):  
A. Rahmani ◽  
F. Sarmadian ◽  
S. R. Mousavi ◽  
S. E. Khamoshi
Keyword(s):  
Fuzzy Logic ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Kappa Index ◽  
Valley Bottom ◽  
Error Matrix ◽  
Soil Map ◽  
Expert’S Opinion ◽  
Fuzzy Logic Approach ◽  
Landscape Units

Abstract. In low relief region such as plains, applied digital soil mapping has a controvertible issue, therefore, this study was aimed to digital mapping of soil classes at family levels by appropriate Geomorphometric variables along with fuzzy logic with area of 16,600 hectares in Qazvin Plain. Based on the geomorphologic map, the plain and pen plain are dominant landscape units. In this regards, 61 soil profiles were dogged. According to the expert’s opinion, covariates including diffuse insolation, standardized height, catchment area, valley depth and multiresolution valley bottom flatness (MrVBF) had the most important in order to generating soil map. Also, 19 fuzzy soil class maps were generated through using sample-based in ArcSIE software. Validation were carried out using achieved overall accuracy (OA) and Kappa index through error matrix. Subsequently, both ignorance and exaggerating uncertainty of hardened soil map were also done. The results showed that 19 soil families class were found. Accordingly, OA and the Kappa index were 54% and 46% respectively. The uncertainty of ignorance and exaggeration were obtained from 0 to 0.64 and 0 to 1, respectively. Moreover, the results indicated that exaggerated uncertainty was the highest in the northern and the lowest in the southern regions. Generally, applied geomorphometric parameters had the specific importance in the low relief areas for mapping of soils that have not been assessed properly so far.

Digital soil mapping: the challenge to obtain the best soil dataset and create a precise environmental model to support land use management at a national level (Ecuador).

2020 ◽  
Author(s):  
Daphne Armas ◽  
Mário Guevara ◽  
Fernando Bezares ◽  
Rodrigo Vargas ◽  
Pilar Durante ◽  
...  
Keyword(s):  
Land Use ◽  
Information System ◽  
Soil Properties ◽  
Soil Profile ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Land Use Management ◽  
National Scale ◽  
Global Soil ◽  
Soil Information

<p>One of the biggest challenges for digital soil mapping is the limited of field soil information (e.g., soil profile descriptions, soil sample analysis) for representing soil variability across scales. Global initiatives such as the Global Soil Partnership (GSP) and the development of a <strong>Global Soil Information System</strong> (GloSIS), World Soil Information Service (WoSis) or SoilGrids250m for global pedometric mapping highlight new opportunities but the crescent need of new and better soil datasets across the world. Soil datasets are increasingly required for the development of soil monitoring baselines, soil protection and sustainable land use strategies, and to better understand the response of soils to global environmental change.  However, soil surveys are a very challenging task due to their high acquisition costs such data and operational complexity. The use of legacy soil data can reduce these sampling efforts.</p><p>The main objective of this research was the rescue, synthesis and harmonization of legacy soil profile information collected between 2009 and 2015 for different purposes (e.g., soil or natural resources inventory) across Ecuador. This project will support the creation of a soil information system at the national scale following international standards for archiving and sharing soil information (e.g., GPS or the GlobalSoilMap.net project). This new information could be useful to increase the accuracy of current digital soil information across the country and the future development of digital soil properties maps.</p><p>We provided an integrated framework combining multiple data analytic tools (e.g., python libraries, pandas, openpyxl or pdftools) for the automatic conversion of text in paper format (e.g., pdf, jpg) legacy soil information, as much the qualitative soil description as analytical data,  to usable digital soil mapping inputs (e.g., spatial datasets) across Ecuador. For the conversion, we used text data mining techniques to automatically extract the information. We based on regular expressions using consecutive sequences algorithms of common patterns not only to search for terms, but also relationships between terms. Following this approach, we rescued information of 13.696 profiles in .pdf, .jpg format and compiled a database consisting of 10 soil-related variables.</p><p>The new database includes historical soil information that automatically converted a generic tabular database form (e.g., .csv) information.</p><p>As a result, we substantially improved the representation of soil information in Ecuador that can be used to support current soil information initiatives such as the WoSis, Batjes et al. 2019, with only 94 pedons available for Ecuador, the Latin American Soil Information System (SISLAC, http://54.229.242.119/sislac/es),  and the United Nations goals  towards increasing soil carbon sequestration areas or decreasing land desertification trends.  In our database there are almost 13.696 soil profiles at the national scale, with soil-related (e.g., depth, organic carbon, salinity, texture) with positive implications for digital soil properties mapping. </p><p>With this work we increased opportunities for digital soil mapping across Ecuador. This contribution could be used to generate spatial indicators of land degradation at a national scale (e.g., salinity, erosion).</p><p>This dataset could support new knowledge for more accurate environmental modelling and to support land use management decisions at the national scale.</p><p> </p>

Semi-supervised learning for increasing transferability of machine learning in digital soil mapping 

2021 ◽  
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Razieh Sheikhpour ◽  
Norair Toomanian ◽  
Thomas Scholten
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Transfer Learning ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Science ◽  
Target Area ◽  
Reference Area ◽  
Environmental Covariates ◽  
Soil Information

<p>The most critical aspect of application of digital soil mapping is its limited transferability. Modelling soil properties for regions where no or only sparse soil information is available is highly uncertain, when using the low-cost geo-spatial environmental covariates alone. To overcome this drawback, transfer learning has been introduced in different environmental sciences, including soil science. The general idea behind extrapolation of soil information with transfer learning in soil science is that the target area to transfer to is alike, e.g. in terms of soil-forming factors, and the same machine learning rules can be applied. Supervised machine learning, so far, has been used to transfer the soil information from the reference to the target areas with very similar environmental characteristics between both. Hence, it is unclear how machine learning can perform for other target regions with different environmental characteristics. Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data (reference area) with a large amount of unlabeled data (target area) during training. In this study, we explored if semi-supervised learning could improve the transferability of digital soil mapping relative to supervised learning methods. Soil data for two arid regions and associated environmental covariates were obtained. Semi-supervised learning and supervised learning models were trained based on the data in the reference area and then tested based on the data in the target area. The results of this study indicated the higher power of semi-supervised learning for transferring soil information from one area to another in comparison to the supervised learning method.   </p>

Global soil mapping with Neural Networks 

2021 ◽  
Author(s):  
Giulio Genova ◽  
Luis de Sousa ◽  
Tanja Mimmo ◽  
Luigi Borruso ◽  
Laura Poggio
Keyword(s):  
Neural Networks ◽  
Prediction Models ◽  
Traditional Approach ◽  
Contextual Information ◽  
Water Security ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Environmental Covariates ◽  
Complex Architectures ◽  
Global Soil

<p>High quality global soil maps are crucial to face several challenges such as reducing soil erosion, climate change adaptation and mitigation, ensuring food and water security, and biodiversity conservation planning. To obtain accurate and robust soil properties maps, research and development are necessary to identify the most appropriate prediction models and to develop efficient and robust workflows. A few recent studies used Artificial Neural Networks (ANN) in Digital Soil Mapping, in some cases improving the accuracy of the predicted maps compared to other methods like Random Forest (RF). In this study we tested different ANN architectures on a global top-soil dataset of ca. 110 000 samples, comparing the results for the different architectures with the more traditional approach of RF. The target variables considered are pH, Soil Organic Carbon, Sand, Silt, and Clay. We selected 40 environmental covariates from a pool of over 400 to represent the most important soil forming factors. We tried simpler architectures (single input – single target) using point observations for one target variable with corresponding raster cell values for spatially explicit environmental covariates. We also used more complex architectures (multi input - multi target) incorporating contextual information surrounding an observation (convolutional) and with multiple target variables. Preliminary results show that increasing the number of hidden layers in the neural network does not significantly influence the results, while changing the type of architecture can play a bigger role in the overall accuracy of the model. The overall prediction accuracy of the ANN was comparable with the RF model. We conclude that ANN are a promising, relatively new, approach for Global Digital Soil Mapping and that further research is needed to improve performance.</p>

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