Assessment of spatial hybrid methods for predicting soil organic matter using DEM derivatives and soil parameters

Tziachris, Panagiotis; Aschonitis, Vassilis; Chatzistathis, Theocharis; Papadopoulou, Maria

doi:10.1016/j.catena.2018.11.010

Cited by 98 publications

(64 citation statements)

References 26 publications

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“…In addition, the semivariogram results (Table 5) indicated that the 1992 RFOK model had a high RI because of the strong spatial autocorrelation of the residuals. Studies using similar residual interpolation methods to predict soil organic matter have shown that the accuracy was substantially improved when the nugget/sill values were lower than 0.6 (Guo et al 2015;Tziachris et al 2019). The results of our study con rmed these ndings.…”

Section: Model Improvement and Map Accuracysupporting

confidence: 83%

“…Fox et al (2020) proposed the random forest regression kriging (RFRK) model to improve the two techniques by comparing spatial regression and the RF. The prediction accuracy of RF model combining with kriging outperformed RF for predicting the spatial distribution of soil attributes and pollutant concentrations (Guo et al 2015;Tziachris et al 2019). Several studies have focused on AGB prediction using remote sensing data from different sources based on RF coupled with ordinary kriging (OK) (RFOK); this method combines RF predictions and residual estimations by OK (Chen et al 2019;Silveira et al 2019a).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

Shen

Wang³

et al. 2020

Preprint

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Background: Aboveground biomass (AGB) is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans. Methods: Here, we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong province of China. We used Landsat time-series observations, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data, and National Forest Inventory (NFI) plot measurements, to generate the forest AGB maps at three time points (1992, 2002, and 2010) showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong, China. Results: The proposed model provided excellent performance for mapping AGB using spectral, textural, and topographical variables, and the radar backscatter coefficients. The root mean square error of the plot-level AGB validation was between 15.62 and 53.78 (t/ha), the mean absolute error ranged from 6.54 to 32.32 t/ha, and the relative improvement over the random forest algorithm was between 3.8% and 17.7%. The highest coefficient of determination (0.81) and the lowest mean absolute error (6.54 t/ha) were observed in the 1992 AGB map. The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010. By adding elevation as a covariable, the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals, because co-kriging resulted in better interpolation results in the valleys and plains of the study area. Conclusions: Validation of the three AGB maps with an independent dataset indicated that the random forest/co-kriging performed best for AGB prediction, followed by random forest coupled with ordinary kriging (random forest/ordinary kriging), and the random forest model. The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography. The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.

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Section: Model Improvement and Map Accuracysupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

Shen

Wang³

et al. 2020

Preprint

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“…These methods tried to combine the advantages of both worlds, deterministic and stochastic, achieving improved results. Currently, more innovative implementations of the abovementioned hybrid methods are increasingly used; they introduce machine learning (ML) as the deterministic part, along with kriging of the ML residuals as the stochastic part [8][9][10][11][12]. These methods are widely used in environmental sciences mainly for two reasons: their improved prediction accuracy and the elimination of most of the restrictions (e.g., statistical assumptions) that regression, kriging, and their variations (RK, KED, etc.)…”

Section: Introductionmentioning

confidence: 99%

Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction

Tziachris¹,

Aschonitis²,

Chatzistathis³

et al. 2020

IJGI

Self Cite

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In the current paper we assess different machine learning (ML) models and hybrid geostatistical methods in the prediction of soil pH using digital elevation model derivates (environmental covariates) and co-located soil parameters (soil covariates). The study was located in the area of Grevena, Greece, where 266 disturbed soil samples were collected from randomly selected locations and analyzed in the laboratory of the Soil and Water Resources Institute. The different models that were assessed were random forests (RF), random forests kriging (RFK), gradient boosting (GB), gradient boosting kriging (GBK), neural networks (NN), and neural networks kriging (NNK) and finally, multiple linear regression (MLR), ordinary kriging (OK), and regression kriging (RK) that although they are not ML models, they were used for comparison reasons. Both the GB and RF models presented the best results in the study, with NN a close second. The introduction of OK to the ML models’ residuals did not have a major impact. Classical geostatistical or hybrid geostatistical methods without ML (OK, MLR, and RK) exhibited worse prediction accuracy compared to the models that included ML. Furthermore, different implementations (methods and packages) of the same ML models were also assessed. Regarding RF and GB, the different implementations that were applied (ranger-ranger, randomForest-rf, xgboost-xgbTree, xgboost-xgbDART) led to similar results, whereas in NN, the differences between the implementations used (nnet-nnet and nnet-avNNet) were more distinct. Finally, ML models tuned through a random search optimization method were compared with the same ML models with their default values. The results showed that the predictions were improved by the optimization process only where the ML algorithms demanded a large number of hyperparameters that needed tuning and there was a significant difference between the default values and the optimized ones, like in the case of GB and NN, but not in RF. In general, the current study concluded that although RF and GB presented approximately the same prediction accuracy, RF had more consistent results, regardless of different packages, different hyperparameter selection methods, or even the inclusion of OK in the ML models’ residuals.

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“…Results demonstrated that SVRK improved the mapping accuracy by incorporating interpolation values of residuals to SVR models ( Figure 5 and Table 4). The value of the accuracy improvement of SVRK was smaller than that of RFK and ANNK for soil carbon prediction as reported [86][87][88]. It was resulted from the weaker autocorrelation of residuals from SVR compared with that of soil attributes, as well as the smaller sampling density.…”

Section: Svr Versus Svrkmentioning

confidence: 89%

Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging

Chen

Ren

Zhang

et al. 2020

Forests

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Quantifying stand volume through open-access satellite remote sensing data supports proper management of forest stand. Because of limitations on single sensor and support vector machine for regression (SVR) as well as benefits from hybrid models, this study innovatively builds a hybrid model as support vector machine for regression kriging (SVRK) to map stand volume of the Changbai Mountains mixed forests covering 171,450 ha area based on a small training dataset (n = 928). This SVRK model integrated SVR and its residuals interpolated by ordinary kriging. To determine the importance of multi-sensor predictors from ALOS and Sentinel series, the increase in root mean square error (RMSE) of SVR was calculated by removing the variable after the standardization. The SVRK model achieved accuracy with mean error, RMSE and correlation coefficient in -2.67%, 25.30% and 0.76, respectively, based on an independent dataset (n = 464). The SVRK improved the accuracy of 9% than SVR based on RMSE values. Topographic indices from L band InSAR, backscatters of L band SAR, and texture features of VV channel from C band SAR, as well as vegetation indices of the optical sensor were contributive to explain spatial variations of stand volume. This study concluded that SVRK was a promising approach for mapping stand volume in the heterogeneous temperate forests with limited samples.

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Assessment of spatial hybrid methods for predicting soil organic matter using DEM derivatives and soil parameters

Cited by 98 publications

References 26 publications

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction

Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging

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