Modelling dasometric attributes of mixed and uneven-aged forests using Landsat-8 OLI spectral data in the Sierra Madre Occidental, Mexico

López-Sánchez, Carlos A.; García-Ramírez, Pedro J.; Resl, Richard; Hernández-Díaz, José Ciro; López-Serrano, Pablito Marcelo; Wehenkel, Christian

doi:10.3832/ifor1891-009

Cited by 8 publications

(12 citation statements)

References 36 publications

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“…In our study, the performance of SVR was better than RF. The results of the optimized SVR model in the current study (R 2 = 0.80, RMSE = 8.20 Mg ha −1 ), are higher than previous results with in the SMO with non-parametric algorithms such as regression trees [16], MP5 model trees [15] and MARS techniques [23], with R 2 in the range 0.5-0.7 and RMSE values of 20-38 Mg ha −1 (20-40% of the average value). They also improve the previous results with SVR in the area of study with a smaller dataset of 99 plots [21], with R 2 of 0.62 and RMSE of 27 Mg ha −1 , obtaining similar results for the case of RF (R 2 of 0.48 and RMSE of 31 Mgha −1 ).…”

Section: Discussioncontrasting

confidence: 75%

“…Whereas this approach is valuable and can be precise at a local scale, it is also expensive and time consuming, and inherently limited in geographic representativeness [10,11]. The natural variation in forest structure and biomass, combined with the enormous geographic extent and rate of forest loss and disturbance, makes field plots difficult to use (alone) for assessing forest biomass and carbon densities [12], particularly over large areas [13][14][15] such as Mexican forests [16]. The use of remote-sensing techniques, calibrated and validated with representative field sites of forest biomass monitoring, allows spatially representative maps of forest ecosystems' structure and productivity to be derived, over larger areas, and at lower costs [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…The forests of the Sierra Madre Occidental (SMO), NW Mexico, provide a good opportunity to compare methodologies for predicting and mapping biomass from spectral, textural, topographic and climatic variables, because of their variation in complex and diverse forest structure and their diverse physiographic and climatic conditions [16,21,23,35]. Furthermore, the SMO is also important because of the presence of some of the most important commercial species of pine and oak in Mexican ecosystems [23].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the SMO is also important because of the presence of some of the most important commercial species of pine and oak in Mexican ecosystems [23]. In particular, the state of Durango generates between 25% and 30% of national timber production, producing a total of 1.5 million m 3 of roundwood per year, and boasts forest reserves that are important sources of environmental services [16].…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

López-Serrano

Domínguez

Corral-Rivas

et al. 2019

Forests

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An accurate estimation of forests’ aboveground biomass (AGB) is required because of its relevance to the carbon cycle, and because of its economic and ecological importance. The selection of appropriate variables from satellite information and physical variables is important for precise AGB prediction mapping. Because of the complex relationships for AGB prediction, non-parametric machine-learning techniques represent potentially useful techniques for AGB estimation, but their use and comparison in forest remote-sensing applications is still relatively limited. The objective of the present study was to evaluate the performance of automatic learning techniques, support vector regression (SVR) and random forest (RF), to predict the observed AGB (from 318 permanent sampling plots) from the Landsat 8 Landsat 8 Operational Land Imager (OLI) sensor, spectral indexes, texture indexes and physical variables the Sierra Madre Occidental in Mexico. The result showed that the best SVR model explained 80% of the total variance (root mean square error (RMSE) = 8.20 Mg ha−1). The variables that best predicted AGB, in order of importance, were the bands that belong to the region of red and near and middle infrared, and the average temperature. The results show that the SVR technique has a good potential for the estimation of the AGB and that the selection of the model hyperparameters has important implications for optimizing the goodness of fit.

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Section: Discussioncontrasting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

López-Serrano

Domínguez

Corral-Rivas

et al. 2019

Forests

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“…Due to easy accessibility and affordability, a number of studies have employed Landsat images and found statistically significant correlations between remotely sensed data and dendrometric characteristics using ground plots ranging from 315 to 2500 m 2 (Dube and Mutanga 2015, López-Sánchez et al 2014, Zhang et al 2014, López-Serrano et al 2016.…”

Section: Dataset Integrationmentioning

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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Landsat time series analysis for temperate forest cover change detection in the Sierra Madre Occidental, Durango, Mexico

Novo-Fernández

Franks

Wehenkel

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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Modelling dasometric attributes of mixed and uneven-aged forests using Landsat-8 OLI spectral data in the Sierra Madre Occidental, Mexico

Cited by 8 publications

References 36 publications

Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

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

Landsat time series analysis for temperate forest cover change detection in the Sierra Madre Occidental, Durango, Mexico

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