Forest Cover Mapping in North-Central Mexico: A Comparison of Digital Image Processing Methods

Aguirre-Salado, Carlos Arturo; Garza, Eduardo Javier Treviño; Calderón, Óscar Alberto Aguirre; Jiménez-Pérez, Javier; Tagle, Marco Aurelio González; Miranda-Aragón, Liliana; Valdez-Lazalde, José René; Aguirre-Salado, Alejandro Ivan; Sánchez-Díaz, Guillermo

doi:10.2747/1548-1603.49.6.895

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…in southern Mexico. Other similar studies have been carried out in Mexico by Díaz-Franco et al [13], Mendoza-Ponce and Galicia [14], Búrquez et al [15], Aguirre-Salado et al [16], and Méndez-González et al [17]. Rojas-García et al [18] provide a comprehensive database with the above cited and other allometric equations applicable to a large number of tree and shrub species in Mexico.…”

Section: Introductionmentioning

confidence: 91%

Allometric Equations for Estimating Biomass and Carbon Stocks in the Temperate Forests of North-Western Mexico

Vargas-Larreta

López-Sánchez

Corral-Rivas

et al. 2017

Forests

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This paper presents new equations for estimating above-ground biomass (AGB) and biomass components of seventeen forest species in the temperate forests of northwestern Mexico. A data set corresponding to 1336 destructively sampled oak and pine trees was used to fit the models. The generalized method of moments was used to simultaneously fit systems of equations for biomass components and AGB, to ensure additivity. In addition, the carbon content of each tree component was calculated by the dry combustion method, in a TOC analyser. The results of cross-validation indicated that the fitted equations accounted for on average 91%, 82%, 83% and 76% of the observed variance in stem wood and stem bark, branch and foliage biomass, respectively, whereas the total AGB equations explained on average 93% of the total observed variance in AGB. The inclusion of total height (h) or diameter at breast height 2 × total height (d 2 h) as a predictor in the d-only based equations systems slightly improved estimates for stem wood, stem bark and total above-ground biomass, and greatly improved the estimates produced by the branch and foliage biomass equations. The predictive power of the proposed equations is higher than that of existing models for the study area. The fitted equations were used to estimate stand level AGB stocks from data on growing stock in 429 permanent sampling plots. Three machine-learning techniques were used to model the estimated stand level AGB and carbon contents; the selected models were used to map the AGB and carbon distributions in the study area, for which mean values of respectively 129.84 Mg ha −1 and 63.80 Mg ha −1 were obtained.

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Section: Introductionmentioning

confidence: 91%

Allometric Equations for Estimating Biomass and Carbon Stocks in the Temperate Forests of North-Western Mexico

Vargas-Larreta

López-Sánchez

Corral-Rivas

et al. 2017

Forests

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“…Remote-sensing data have unique characteristics (e.g. radiometric, spectral, spatial and temporal resolutions for optical sensor data and polarization options for radar data) to represent land-surface features, and have become primary data source for land cover or vegetation classification at various scales (Lu and Weng 2007;Aguirre-Salado et al 2012;Johnston, Henry, and Gorchov 2012;Gong et al 2013). Scientists have made great efforts using remotely sensed data for mapping stages of successional vegetation in the past two decades (Foody and Curran 1994;Moran et al 1994;Palubinskas et al 1995;Foody et al 1996;Kimes et al 1999;Salas et al 2002;Castro, Sanchez-Azofeifa, and Rivard 2003;Vieira et al 2003;Aguilar 2005;Kennaway and Helmer 2007;Galvão et al 2009;Mello and Alves 2011;Li, Lu, Moran, Dutra et al 2012;Lu et al 2012).…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon

Moran

et al. 2014

GIScience & Remote Sensing

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Research on separation of successional stages has been an active topic for the past two decades because successional vegetation plays an important role in the carbon budget and restoration of soil fertility in the Brazilian Amazon. This article examines classification of successional stages by conducting a comparative analysis of classification algorithms (maximum likelihood classifier -MLC, artificial neural network -ANN, K-nearest neighbour -KNN, support vector machine -SVM, classification tree analysis -CTA, and object-based classification -OBC) on varying remote-sensing data-sets (Landsat and ALOS PALSAR). Through this research we obtained the following four major conclusions: (1) Landsat data provide higher classification accuracy than ALOS PALSAR data, and individual PALSAR data cannot effectively separate successional stages; (2) Fusion of Landsat and PALSAR data provides better classification than individual sensor data; (3) Depending on the data-set, the best classification algorithm varies, MLC and CTA are recommended for Landsat or fusion images; and KNN is recommended for the combination of Landsat and PALSAR data as extra bands; (4) the MLC based on fusion images is recommended for vegetation classification in the moist tropical region when sufficiently representative training samples are available.

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“…When used spatially, this technique can be applied to highlight spectral features on remotely sensed data, e.g., in a single-date satellite scene, PCA can fuse spectral bands into new principal components that capture biophysical features such as chlorophyll (greenness) and moisture (wetness) [30]. Researchers have created RGB-composite imagery with the three first principal components to emphasize the spectral characteristics of natural cover [31]; others have used PCA as a noise-detection technique [32], and moreover, others have used it as a denoising technique [33]. When used temporally, this technique can be employed in time-series data for analyzing temporal patterns of a single variable, e.g., NDVI.…”

Section: Introductionmentioning

confidence: 99%

Some Geospatial Insights on Orange Grove Site Selection in a Portion of the Northern Citrus Belt of Mexico

Díaz-Rivera,

Aguirre-Salado,

Miranda-Aragón

et al. 2024

AgriEngineering

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This study aimed to delineate the most suitable areas for sustainable citrus production by integrating multi-criteria decision analysis, time-series remote sensing, and principal component analysis in a portion of the northern citrus belt of Mexico, particularly in the Rioverde Valley. Fourteen specific factors were grouped into four main factors, i.e., topography, soil, climate, and proximity to water sources, to carry out a multi-criteria decision analysis for classifying production areas according to suitability levels. To explore the effect of precipitation on land suitability for citrus production, we analyzed the historical record of annual precipitation estimated by processing 20-year NDVI daily data. The multi-criteria model was run for every precipitation year. The final map of land suitability was obtained by using the first component after principal component analysis on annual land suitability maps. The results indicate that approximately 30% of the study area is suitable for growing orange groves, with specific areas designated as suitable based on both mean annual precipitation (MAP) and principal component analysis (PCA) criteria, resulting in 84,415.7 ha and 95,485.5 ha of suitable land, respectively. The study highlighted the importance of remotely sensed data-based time-series precipitation in predicting potential land suitability for growing orange groves in semiarid lands. Our results may support decision-making processes for the effective land management of orange groves in the Mexico’s Rioverde region.

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Forest Cover Mapping in North-Central Mexico: A Comparison of Digital Image Processing Methods

Cited by 8 publications

References 40 publications

Allometric Equations for Estimating Biomass and Carbon Stocks in the Temperate Forests of North-Western Mexico

Allometric Equations for Estimating Biomass and Carbon Stocks in the Temperate Forests of North-Western Mexico

A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon

Some Geospatial Insights on Orange Grove Site Selection in a Portion of the Northern Citrus Belt of Mexico

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