Open digital mapping as a cost-effective method for mapping peat thickness and assessing the carbon stock of tropical peatlands

Rudiyanto, .; Minasny, Budiman; Setiawan, Budi Indra; Saptomo, Satyanto Krido; McBratney, Alex B.

doi:10.1016/j.geoderma.2017.10.018

Cited by 114 publications

(73 citation statements)

References 58 publications

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“…Despite the urgent need for a precise quantification of peat deposits from the local to the regional scale, the number of studies dealing with this topic is extremely limited. Remote sensing (RS) has been employed for this task, making use of satellite and airborne laser instruments and radar sensors (e.g., Ballhorn et al, 2011), as well as a combination of these techniques with multispectral sensors (Draper et al, 2014;Rudiyanto et al,…”

Section: Introductionmentioning

confidence: 99%

Peatland Volume Mapping Over Resistive Substrates With Airborne Electromagnetic Technology

Silvestri

Christensen

Lysdahl

et al. 2019

Geophysical Research Letters

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Despite the importance of peatlands as carbon reservoirs, a reliable methodology for the detection of peat volumes at regional scale is still missing. In this study we explore for the first time the use of airborne electromagnetic (AEM) to detect and quantify peat thickness and extension of two bogs located in Norway, where peat lays over resistive bedrock. Our results show that when calibrated using a small amount of field measurements, AEM can successfully detect peat volume even in less ideal conditions, that is, relatively resistive peat over resistive substrata. We expect the performance of AEM to increase significantly in presence of a conductive substratum without need of calibration with field data. The organic carbon content retrieved from field surveys and laboratory analyses combined with the 3‐D model of the peat extracted from AEM allowed us to quantify the total organic carbon of the selected bogs, hence assessing the carbon pool.

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Section: Introductionmentioning

confidence: 99%

Peatland Volume Mapping Over Resistive Substrates With Airborne Electromagnetic Technology

Silvestri

Christensen

Lysdahl

et al. 2019

Geophysical Research Letters

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“…Sidorova & Krasilnikov (2008) used an indicator kriging approach to study the spatial variation of the thickness of O, A, E and B horizons at three sites in southern and central Karelia, Russia. In peatlands, the interest is in mapping peat thickness to delineate areas for conservation and calculating carbon stocks (Rudiyanto et al, 2018).…”

Section: Mapping the Thickness Of Soil Horizonsmentioning

confidence: 99%

Pedology and digital soil mapping (DSM)

Minasny

Malone

et al. 2019

European J Soil Science

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175

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Pedology focuses on understanding soil genesis in the field and includes soil classification and mapping. Digital soil mapping (DSM) has evolved from traditional soil classification and mapping to the creation and population of spatial soil information systems by using field and laboratory observations coupled with environmental covariates. Pedological knowledge of soil distribution and processes can be useful for digital soil mapping. Conversely, digital soil mapping can bring new insights to pedogenesis, detailed information on vertical and lateral soil variation, and can generate research questions that were not considered in traditional pedology. This review highlights the relevance and synergy of pedology in soil spatial prediction through the expansion of pedological knowledge. We also discuss how DSM can support further advances in pedology through improved representation of spatial soil information. Some major findings of this review are as follows: (a) soil classes can be mapped accurately using DSM, (b) the occurrence and thickness of soil horizons, whole soil profiles and soil parent material can be predicted successfully with DSM techniques, (c) DSM can provide valuable information on pedogenic processes (e.g. addition, removal, transformation and translocation), (d) pedological knowledge can be incorporated into DSM, but DSM can also lead to the discovery of knowledge, and (e) there is the potential to use process‐based soil–landscape evolution modelling in DSM. Based on these findings, the combination of data‐driven and knowledge‐based methods promotes even greater interactions between pedology and DSM. Highlights Demonstrates relevance and synergy of pedology in soil spatial prediction, and links pedology and DSM. Indicates the successful application of DSM in mapping soil classes, profiles, pedological features and processes. Shows how DSM can help in forming new hypotheses and gaining new insights about soil and soil processes. Combination of data‐driven and knowledge‐based methods recommended to promote greater interactions between DSM and pedology.

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“…The soil was classified into two Orders: Entisols (43) and Aridisols (151). The former was mainly found in the mountains, hills and colluvial areas and the latter characterizing the predominantly alluvial fans, plateau, playas, flood and river alluvial plains.…”

Section: Soil Datamentioning

confidence: 99%

“…All trees are voted, with the tree receiving the most votes in the forest, selected as the final prediction of the given variable. Examples of recent applications of RF in DSM include those in citation [43]. Herein, we optimized two parameters (mtry and ntree), by iterating mtry values from 2 to 23 and ntree values from 100 to 10,000.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Digital Mapping of Soil Classes Using Ensemble of Models in Isfahan Region, Iran

et al. 2019

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Digital soil maps can be used to depict the ability of soil to fulfill certain functions. Digital maps offer reliable information that can be used in spatial planning programs. Several broad types of data mining approaches through Digital Soil Mapping (DSM) have been tested. The usual approach is to select a model that produces the best validation statistics. However, instead of choosing the best model, it is possible to combine all models realizing their strengths and weaknesses. We applied seven different techniques for the prediction of soil classes based on 194 sites located in Isfahan region. The mapping exercise aims to produce a soil class map that can be used for better understanding and management of soil resources. The models used in this study include Multinomial Logistic Regression (MnLR), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Bayesian Networks (BN), and Sparse Multinomial Logistic Regression (SMnLR). Two ensemble models based on majority votes (Ensemble.1) and MnLR (Ensemble.2) were implemented for integrating the optimal aspects of the individual techniques. The overall accuracy (OA), Cohen's kappa coefficient index (κ) and the area under the curve (AUC) were calculated based on 10-fold-cross validation with 100 repeats at four soil taxonomic levels. The Ensemble.2 model was able to achieve larger OA, κ coefficient and AUC compared to the best performing individual model (i.e., RF). Results of the ensemble model showed a decreasing trend in OA from Order (0.90) to Subgroup (0.53). This was also the case for the κ statistic, which was the largest for the Order (0.66) and smallest for the Subgroup (0.43). Same decrease was observed for AUC from Order (0.81) to Subgroup (0.67). The improvement in κ was substantial (43 to 60%) at all soil taxonomic levels, except the Order level. We conclude that the application of the ensemble model using the MnLR was optimal, as it provided a highly accurate prediction for all soil taxonomic levels over and above the individual models. It also used information from all models, and thus this method can be recommended for improved soil class modelling. Soil maps created by this DSM approach showed soils that are prone to degradation and need to be carefully managed and conserved to avoid further land degradation.

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Open digital mapping as a cost-effective method for mapping peat thickness and assessing the carbon stock of tropical peatlands

Cited by 114 publications

References 58 publications

Peatland Volume Mapping Over Resistive Substrates With Airborne Electromagnetic Technology

Peatland Volume Mapping Over Resistive Substrates With Airborne Electromagnetic Technology

Pedology and digital soil mapping (DSM)

Digital Mapping of Soil Classes Using Ensemble of Models in Isfahan Region, Iran

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