Modeling the spatial distribution of above-ground carbon in Mexican coniferous forests using remote sensing and a geostatistical approach

Galeana-Pizaña, J. Mauricio; López‐Caloca, Alejandra A.; López-Quiroz, Penélope; Silván-Cárdenas, José Luis; Couturier, Stéphane

doi:10.1016/j.jag.2014.02.005

Cited by 26 publications

(24 citation statements)

References 38 publications

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“…Remote detection of forest canopies is complex due to the size, shape, and dielectric properties of its scatter elements (leaves, branches, and stems) (Galeana-Pizaña et al 2014). The spatial diversity of forest canopies makes the relationship between forest parameters and remote sensing data a major challenge, although several studies have already demonstrated correlation between spectral data and forest characteristics of interest (Stojanova et al 2010, Viana et al 2012, Castillo-Santiago et al 2013, Fayad et al 2016, Gao et al 2016.…”

Section: Discussionmentioning

confidence: 99%

Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods

Reis

Carvalho

Mello

et al. 2018

N.Z. j. of For. Sci.

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Background: In fast-growing forests such as Eucalyptus plantations, the correct determination of stand productivity is essential to aid decision making processes and ensure the efficiency of the wood supply chain. In the past decade, advances in remote sensing and computational methods have yielded new tools, techniques, and technologies that have led to improvements in forest management and forest productivity assessments. Our aim was to estimate and map the basal area and volume of Eucalyptus stands through the integration of forest inventory, remote sensing, parametric, and nonparametric methods of spatial prediction. Methods: This study was conducted in 20 5-year-old clonal stands (362 ha) of Eucalyptus urophylla S.T.Blake x Eucalyptus camaldulensis Dehnh. The stands are located in the northwest region of Minas Gerais state, Brazil. Basal area and volume data were obtained from forest inventory operations carried out in the field. Spectral data were collected from a Landsat 5 TM satellite image, composed of spectral bands and vegetation indices. Multiple linear regression (MLR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) methods were used for basal area and volume estimation. Using ordinary kriging, we spatialised the residuals generated by the spatial prediction methods for the correction of trends in the estimates and more detailing of the spatial behaviour of basal area and volume. Results: The ND54 index was the spectral variable that had the best correlation values with basal area (r = − 0.91) and volume (r = − 0.52) and was also the variable that most contributed to basal area and volume estimates by the MLR and RF methods. The RF algorithm presented smaller basal area and volume errors when compared to other machine learning algorithms and MLR. The addition of residual kriging in spatial prediction methods did not necessarily result in relative improvements in the estimations of these methods. Conclusions: Random forest was the best method of spatial prediction and mapping of basal area and volume in the study area. The combination of spatial prediction methods with residual kriging did not result in relative improvement of spatial prediction accuracy of basal area and volume in all methods assessed in this study, and there is not always a spatial dependency structure in the residuals of a spatial prediction method. The approaches used in this study provide a framework for integrating field and multispectral data, highlighting methods that greatly improve spatial prediction of basal area and volume estimation in Eucalyptus stands. This has potential to support fast growth plantation monitoring, offering options for a robust analysis of high-dimensional data.

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Section: Discussionmentioning

confidence: 99%

Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods

Reis

Carvalho

Mello

et al. 2018

N.Z. j. of For. Sci.

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“…Furthermore, HJ-CCD images and derived VIs were used in this study, and even though satisfactory results were obtained, the comparatively narrow spectral range still limited the more powerful VIs (e.g., reduced simple ratio (RSR) and cellulose absorption index (CAI)) used. In future studies, more powerful predictive variables derived from optical remote sensing datasets with broader spectral range (e.g., Landsat, Sentinel-2), light detection and ranging (LIDAR) [76] and synthetic aperture radar (SAR) [24] can be used, providing more information to train the dataset, which may lead to more accurate results. The study models were tested to determine their sensitivity to additional input variables (VIs); however, the impact of irrelevant information on model prediction was not tested in this study, and certain irrelevant and interference predictors can be introduced into the input variables to test their effect in future modeling.…”

Section: Model Comparison and Study Limitationsmentioning

confidence: 99%

“…In addition, geostatistical prediction methods, including ordinary kriging (OK) [20], kriging with external trend [21,22], and regression kriging (RK) [23,24], which model the data structure of spatial autocorrelation and incorporate this information in the response variables for unsampled locations, have also been used to map environmental variables [25][26][27]. Remote sensing images are widely used as auxiliary data.…”

Section: Introductionmentioning

confidence: 99%

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Wang

Tang

et al. 2016

Remote Sensing

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Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes of northern China. Here, we compare the predictive power of regression approaches and hybrid geostatistical methods using Chinese Huanjing (HJ) satellite charge coupled device (CCD) data. The regression methods evaluated include partial least squares regression (PLSR), artificial neural networks (ANNs) and random forests (RFs). The two hybrid geostatistical methods were regression kriging (RK) and random forests residuals kriging (RFRK). The predictions were validated for different grassland types and different growing stages, and their performances were also examined by adding several groups of vegetation indices (VIs). The two hybrid geostatistical models (RK and RFRK) yielded the most accurate predictions (root mean squared error (RMSE) = 0.21 m 2 /m 2 and 0.23 m 2 /m 2 for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m 2 /m 2 ), which was the most accurate among the regression models. These three models also exhibited the best temporal performance across the duration of the growing season. The PLSR and ANN models were less accurate (RMSE = 0.33 m 2 /m 2 and 0.35 m 2 /m 2 for ANN and PLSR, respectively), and the PLSR model performed the worst (exhibiting varied temporal performance and unreliable prediction accuracy that was susceptible to ground conditions). By adding VIs to the predictor variables, the predictions of the PLSR and ANN models were obviously improved (RMSE improved from 0.35 m 2 /m 2 to 0.28 m 2 /m 2 for PLSR and from 0.33 m 2 /m 2 to 0.28 m 2 /m 2 for ANN); the RF and RFRK models did not generate more accurate predictions and the performance of the RK model declined (RMSE decreased from 0.21 m 2 /m 2 to 0.32 m 2 /m 2 ).

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“…This model was chosen because it demonstrated good performance in previous studies in forestry, such as that of Galeana-Pizana et al (2014).…”

Section: Variographic Studymentioning

confidence: 99%

Hypsometric approaches to Eucalyptus experiments in Brazil

Scolforo¹,

Raimundo²,

Scolforo³

et al. 2015

Afr. J. Agric. Res.

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The objective of this study was to determine the best method to estimate tree height by different hypsometric approaches. This study is a provenance trial with Eucalyptus spp. in a 35-year-old pioneering plantation in the municipality of Lavras, Brazil. The census was taken by measuring the diameter at 1.30 m above ground and total height. All x and y coordinates of the individual trees were obtained and these trees were classified according to their stem quality. The ordinary kriging, cokriging and ordinary kriging of the residuals generated from the regression models were fitted by an exponential model. The Curtis model was selected for calculating the regression. The best method to estimate the height-diameter relationship was based on the statistical and graphical criteria. The spatial prediction model did not adequately represent the height-diameter relationship. The regression technique was more precise and accurate by inserting variables that helped capture different development standards of the trees. The regression-kriging produced more accurate total height estimates. Inclusion of the stochastic effect in the general spatial behavior of the total height variable helped to capture specific details about the stand. Therefore, this method is recommended for experimental areas.

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Modeling the spatial distribution of above-ground carbon in Mexican coniferous forests using remote sensing and a geostatistical approach

Cited by 26 publications

References 38 publications

Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods

Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Hypsometric approaches to Eucalyptus experiments in Brazil

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