Characterizing Soil Organic Carbon Content in Forests at National Scale using Reflectance Spectroscopy

2020 ◽  
Author(s):  
Asa Gholizadeh ◽  
Raphael Viscarra Rossel ◽  
Mohammadmehdi Saberioon ◽  
Lubos Boruvka ◽  
Lenka Pavlu
Keyword(s):  
Czech Republic ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Reflectance Spectroscopy ◽  
Ecosystem Functions ◽  
Support Vector ◽  
Organic Carbon Content ◽  
Soil Horizons ◽  
The Czech Republic ◽  
Organic Horizons

<p>Any strategy to change Carbon (C) pool would have a substantial effect on functionality of numerous ecosystem functions, detachment of Soil Organic Carbon (SOC), atmospheric carbon dioxide (CO<sub>2</sub>) concentration, and climate change mitigation. As the largest amount of the world’s C is stored in forests soils, the importance of forest SOC management is highlighted. Total SOC in forest varies not only laterally but also vertically with depth; however, the SOC storage of lower soil horizons have not been investigated enough despite their potential to frame our understanding of soil functioning. Visible–Near Infrared (vis–NIR) reflectance spectroscopy enables rapid examinations of the horizontal distribution of forest SOC, overcoming limitations of traditional soil assessment. This study aims to evaluate the potential of vis–NIR spectroscopy for characterizing the SOC contents of organic and mineral horizons in forests. We investigated 1080 forested sites across the Czech Republic at five individual soil layers, representing the Litter (L), Fragmented (F), and Humus (H) organic horizons, and the A<sub>1</sub> (depth of 2–10 cm) and A<sub>2</sub> (depth of 10–40 cm) mineral horizons (total 5400 samples). We then used Support Vector Machine (SVM) to model the SOC contents of (i) the profile (all organic and mineral horizons together), (ii) the combined organic horizons, (iii) the combined mineral horizons, and (iv) each individual horizon separately. The models were validated using 10-repeated 10-fold cross validation. Results showed that there was at least more than seven times as much SOC in the combined organic horizons compared to the combined mineral horizons with more variation in deeper layers. All individual horizons’ SOC was successfully predicted with low error and R<sup>2</sup> values higher than 0.63; however, the prediction accuracy of F and A<sub>1</sub> was greater compared to others (R<sup>2</sup> > 0.70 and very low-biased spatial estimates). We have shown that modelling of SOC with vis–NIR spectra in different soil horizons of highly heterogeneous forests of the Czech Republic is practical.</p>

scholarly journals National-Scale Forest Soil Carbon Characterizing Using Reflectance Spectroscopy

2020 ◽  
Author(s):  
Asa Gholizadeh ◽  
Raphael A. Viscarra Rossel ◽  
Mohammadmehdi Saberioon ◽  
Josef Kratina ◽  
Lubos Boruvka ◽  
...  
Keyword(s):  
Czech Republic ◽  
Forest Soil ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Ecosystem Functions ◽  
Support Vector ◽  
Soil Horizons ◽  
National Scale ◽  
The Czech Republic ◽  
Organic Horizons

Any strategy to change the Carbon (C) pool has a substantial effect on the functionality of numerous ecosystem functions, the detachment of Soil Organic Carbon (SOC), the atmospheric carbon dioxide (CO2) concentration, and climate change mitigation. As the largest amount of the world's C is stored in forests soils, the importance of forest SOC management is highlighted. The total SOC in a forest varies not only laterally, but also vertically (i.e., with depth). However, the SOC storage of different forest soil horizons has not been investigated in a national scale thoroughly, despite their potential to frame our understanding of soil function. Visible--Near Infrared (vis--NIR) reflectance spectroscopy enables rapid examination of the horizontal distribution of forest SOC, overcoming the limitations of traditional soil assessment methods. This study aims to evaluate the potential of vis--NIR spectroscopy in characterizing and predicting the SOC content of organic and mineral horizons in forests. We investigate 1080 forested sites across the Czech Republic at five individual soil layers, representing the Litter (L), Fragmented (F), and Humus (H) organic horizons, as well as the A1 (depth of 2--10 cm) and A2 (depth of 10--40 cm) mineral horizons (for a total of 5400 samples). We, then, use Support Vector Machines (SVMs) to classify the soil horizons based on their spectra and also to predict the SOC content of (i) the profile (all organic and mineral horizons together), (ii) the combined organic horizons, (iii) the combined mineral horizons, and (iv) each individual horizon separately. The models are validated using 10-repeated 10-fold cross validation. The results show that there is at least more than seven times as much SOC in the combined organic horizons, compared to the combined mineral horizons, with more variation in the deeper layers. The SVM with radial based kernel is a reliable classifier for classification of soil horizons, with Correct Classification Rate (CCR) of 70% and Kappa coefficient of 0.63. All individual horizon SOCs are successfully predicted with low error and with R2 values higher than 0.63. However, the prediction accuracies of the F and A1 models are greater, compared to others (R2~0.70 and very low-biased spatial estimates). We conclude that the modelling of SOC with vis--NIR spectra in different soil horizons of highly heterogeneous forests in the Czech Republic is practical. This study provides an example of how general pedological knowledge can be used to define depth functions of SOC for forested sites.

scholarly journals National spectral data and learning algorithms for potentially toxic elements modelling in forest soil horizons

2020 ◽  
Author(s):  
Asa Gholizadeh ◽  
Mohammadmehdi Saberioon ◽  
Eyal Ben Dor ◽  
Raphael A. Viscarra Rossel ◽  
Lubos Boruvka
Keyword(s):  
Czech Republic ◽  
Spectral Data ◽  
Forest Soils ◽  
Toxic Elements ◽  
Potentially Toxic Elements ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
The Czech Republic ◽  
Organic Horizons

Forest ecosystems are among the main parts of the biosphere; however, they have been endangered from the significant elevation and harmful effects of air and soil pollutants, including potentially toxic elements (PTEs). The concentration of PTEs in forest soils varies not only laterally but also vertically with depth. Forest surface organic horizons are of particular interest in forest ecosystem monitoring due to their role as stable adsorbents of the deposited atmospheric substances. Therefore, the main purpose of this study was to conduct rapid examinations of forest soils PTEs (Cr, Cu, Pb, Zn, and Al), testing the capability of VIS--NIR spectroscopy coupled with machine learning (ML) techniques (partial least square regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and fully connected neural network (FNN), a deep learning (DL) approach, in forest organic horizons. One-thousand-and-eighty forested sites across the Czech Republic at two soil layers, defining the fragmented (F) and humus (H) organic horizons, were investigated (total 2160 samples). PTEs as well as total Fe and SOC, as auxiliary data, were conventionally and spectrally determined and modelled in the combined organic horizons (F + H) and in each individual horizon using the ML and DL algorithms. Results indicated that the concentration of all PTEs was higher in the horizon H compared to the F horizon. Although the spectral reflectance of samples tended to decrease with increased PTEs concentration. Strongly significant positive correlations between all PTEs and total Fe in all horizons were obtained, which were higher in the H and F + H horizons than the F horizon. The highest correlations of PTEs with the spectra were at 460--590~nm, which is mostly linked to the presence of Fe-oxide. These results show the importance of Fe for spectral prediction of PTEs. Cr and Al were the most accurately predicted elements, regardless of the applied learning technique. SVMR provided the best results in assessing the H horizon (e.g., R\(^2\) = 0.88 and root mean square error (RMSE) = 3.01~mg/kg, and R\(^2\) = 0.82 and RMSE = 1682.25~mg/kg for Cr and Al, respectively); however, FNN predicted the combined F + H horizons the best (R\(^2\) = 0.89 and RMSE = 2.95~mg/kg, and R\(^2\) = 0.86 and RMSE = 1593.64~mg/kg for Cr and Al, respectively) due to the larger number of samples. In the F horizon, almost no parameters were predicted adequately. This study shows that given the availability of larger sample sizes, FNN can be a more promising technique compared to ML methods for assessment of Cr and Al concentration based on national spectral data in the forests of the Czech Republic.

National-scale spectroscopic assessment of soil organic carbon in forests of the Czech Republic

Geoderma ◽  
2021 ◽  
Vol 385 ◽  
pp. 114832
Author(s):  
Asa Gholizadeh ◽  
Raphael A. Viscarra Rossel ◽  
Mohammadmehdi Saberioon ◽  
Luboš Borůvka ◽  
Josef Kratina ◽  
...  
Keyword(s):  
Czech Republic ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
National Scale ◽  
The Czech Republic

scholarly journals Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic

2020 ◽  
Vol 274 ◽  
pp. 111206
Author(s):  
Juraj Balkovič ◽  
Mikuláš Madaras ◽  
Rastislav Skalský ◽  
Christian Folberth ◽  
Michaela Smatanová ◽  
...  
Keyword(s):  
Czech Republic ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Test Case ◽  
The Czech Republic ◽  
Carbon Modelling ◽  
Carbon Change

scholarly journals Organic carbon stock in some forest soils in Serbia

2012 ◽  
pp. 81-98
Author(s):  
Ratko Kadovic ◽  
Snezana Belanovic ◽  
Milan Knezevic ◽  
Milorad Danilovic ◽  
Olivera Kosanin ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Stock ◽  
Chemical Properties ◽  
National Forest Inventory ◽  
Organic Carbon Content ◽  
Mean Values ◽  
Average Value ◽  
Organic Horizons ◽  
Organic Carbon Stock

The content of organic carbon (C) was researched in topsoil layers (0-20 cm) in the most represented soils of forest ecosystems in central Serbia: eutric ranker, eutric cambisol and dystric cambisol. The soils were sampled during 2003, 2004 and 2010. Laboratory analyses included the soil physical and chemical properties necessary for the quantification of the soil organic carbon in organic and mineral layers. Mean values of the soil organic carbon (SOC) stores in organic horizons of the study soils varied between: 1.01?0.4 kg(C).m-2 (dystric cambisol), 0.90?0.41 kg(C).m-2 (eutric ranker) and 0.94?0.36 kg(C).m-2 (eutric cambisol). Average values of organic carbon in mineral layers (0-20 cm) ranged between: 3.83?1.70 kg(C).m-2 (dystric cambisol), 6.26?3.41 kg(C).m-2 (eutric ranker) and 4.36?1.91 kg(C).m-2 (eutric cambisol). The average value of total organic carbon stock in the study soils (both organic and mineral layers) was 5.77 kg(C).m-2. This paper addresses the methodological aspects of regional estimation of soil organic carbon content as the potential to be applied in the National Forest Inventory Program.

Space-time monitoring of soil organic carbon content across a semi-arid region of Australia

2021 ◽  
Vol 24 ◽  
pp. e00367
Author(s):  
Patrick Filippi ◽  
Stephen R. Cattle ◽  
Matthew J. Pringle ◽  
Thomas F.A. Bishop
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Arid Region ◽  
Space Time ◽  
Organic Carbon Content ◽  
Soil Organic Carbon Content ◽  
Semi Arid Region ◽  
Semi Arid

scholarly journals Influence of Slope Gradient and Aspect on Soil Organic Carbon Content in the Region of Niš, Serbia

2021 ◽  
Vol 13 (15) ◽  
pp. 8332
Author(s):  
Snežana Jakšić ◽  
Jordana Ninkov ◽  
Stanko Milić ◽  
Jovica Vasin ◽  
Milorad Živanov ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Slope Aspect ◽  
Available Phosphorus ◽  
Organic Carbon Content ◽  
Slope Gradient ◽  
Phosphorus And Potassium ◽  
Uncultivated Land

Topography-induced microclimate differences determine the local spatial variation of soil characteristics as topographic factors may play the most essential role in changing the climatic pattern. The aim of this study was to investigate the spatial distribution of soil organic carbon (SOC) with respect to the slope gradient and aspect, and to quantify their influence on SOC within different land use/cover classes. The study area is the Region of Niš in Serbia, which is characterized by complex topography with large variability in the spatial distribution of SOC. Soil samples at 0–30 cm and 30–60 cm were collected from different slope gradients and aspects in each of the three land use/cover classes. The results showed that the slope aspect significantly influenced the spatial distribution of SOC in the forest and vineyard soils, where N- and NW-facing soils had the highest level of organic carbon in the topsoil. There were no similar patterns in the uncultivated land. No significant differences were found in the subsoil. Organic carbon content was higher in the topsoil, regardless of the slope of the terrain. The mean SOC content in forest land decreased with increasing slope, but the difference was not statistically significant. In vineyards and uncultivated land, the SOC content was not predominantly determined by the slope gradient. No significant variations across slope gradients were found for all observed soil properties, except for available phosphorus and potassium. A positive correlation was observed between SOC and total nitrogen, clay, silt, and available phosphorus and potassium, while a negative correlation with coarse sand was detected. The slope aspect in relation to different land use/cover classes could provide an important reference for land management strategies in light of sustainable development.

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