Estimating forest extent across Mexico. Braden, D., Mondal, P., Park, T., Rosa, J. A. A. d. l., Leal, M. I. A., Lara, R. A. C., Saucedo, R. M., Paz, F., Salas-Aguilar, V. M., Soriano-Luna, M. d. L. Á., & Vargas, R. Environmental Research Letters, 19(1):014083, January, 2024. Publisher: IOP Publishing![Estimating forest extent across Mexico [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Information on forest extent and tree cover is required to evaluate the status of natural resources, conservation practices, and environmental policies. The challenge is that different forest definitions, remote sensing-based (RSB) products, and data availability can lead to discrepancies in reporting total forest area. Consequently, errors in forest extent can be propagated into forest biomass and carbon estimates. Here, we present a simple approach to compare forest extent estimates from seven regional and global land or tree cover RSB products at 30 m resolution across Mexico. We found substantial differences in forest extent estimates for Mexico, ranging from 387 607 km2 to 675 239 km2. These differences were dependent on the RSB product and forest definition used. Next, we compared these RSB products with two independent forest inventory datasets at national (n = 26 220 plots) and local scales (n = 754 plots). The greatest accuracy among RSB products and forest inventory data was within the tropical moist forest (range 82%–95%), and the smallest was within the subtropical desert (range \textless10%–80%) and subtropical steppe ecological zones (range \textless10%–60%). We developed a forest extent agreement map by combining seven RSB products and identifying a consensus in their estimates. We found a forest area of 288 749 km2 with high forest extent agreement, and 340 661 km2 with medium forest extent agreement. The high-to-medium forest extent agreement of 629 410 km2 is comparable to the official national estimate of 656 920 km2. We found a high forest extent agreement across the Yucatan Peninsula and mountain areas in the Sierra Madre Oriental and Sierra Madre Occidental. The tropical dry forest and subtropical mountain system represent the two ecological zones with the highest areas of disagreement among RSB products. These findings show discrepancies in forest extent estimates across ecological zones in Mexico, where additional ground data and research are needed. Dataset available at https://doi.org/10.3334/ORNLDAAC/2320.
@article{braden_estimating_2024,
title = {Estimating forest extent across {Mexico}},
volume = {19},
issn = {1748-9326},
url = {https://dx.doi.org/10.1088/1748-9326/ad193e},
doi = {10.1088/1748-9326/ad193e},
abstract = {Information on forest extent and tree cover is required to evaluate the status of natural resources, conservation practices, and environmental policies. The challenge is that different forest definitions, remote sensing-based (RSB) products, and data availability can lead to discrepancies in reporting total forest area. Consequently, errors in forest extent can be propagated into forest biomass and carbon estimates. Here, we present a simple approach to compare forest extent estimates from seven regional and global land or tree cover RSB products at 30 m resolution across Mexico. We found substantial differences in forest extent estimates for Mexico, ranging from 387 607 km2 to 675 239 km2. These differences were dependent on the RSB product and forest definition used. Next, we compared these RSB products with two independent forest inventory datasets at national (n = 26 220 plots) and local scales (n = 754 plots). The greatest accuracy among RSB products and forest inventory data was within the tropical moist forest (range 82\%–95\%), and the smallest was within the subtropical desert (range {\textless}10\%–80\%) and subtropical steppe ecological zones (range {\textless}10\%–60\%). We developed a forest extent agreement map by combining seven RSB products and identifying a consensus in their estimates. We found a forest area of 288 749 km2 with high forest extent agreement, and 340 661 km2 with medium forest extent agreement. The high-to-medium forest extent agreement of 629 410 km2 is comparable to the official national estimate of 656 920 km2. We found a high forest extent agreement across the Yucatan Peninsula and mountain areas in the Sierra Madre Oriental and Sierra Madre Occidental. The tropical dry forest and subtropical mountain system represent the two ecological zones with the highest areas of disagreement among RSB products. These findings show discrepancies in forest extent estimates across ecological zones in Mexico, where additional ground data and research are needed. Dataset available at https://doi.org/10.3334/ORNLDAAC/2320.},
language = {en},
number = {1},
urldate = {2024-01-31},
journal = {Environmental Research Letters},
author = {Braden, Dustin and Mondal, Pinki and Park, Taejin and Rosa, José Armando Alanís de la and Leal, Metzli Ileana Aldrete and Lara, Rubi Angélica Cuenca and Saucedo, Rafael Mayorga and Paz, Fernando and Salas-Aguilar, Victor Manuel and Soriano-Luna, María de Los Ángeles and Vargas, Rodrigo},
month = jan,
year = {2024},
note = {Publisher: IOP Publishing},
keywords = {NALCMS},
pages = {014083},
}
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Here, we present a simple approach to compare forest extent estimates from seven regional and global land or tree cover RSB products at 30 m resolution across Mexico. We found substantial differences in forest extent estimates for Mexico, ranging from 387 607 km2 to 675 239 km2. These differences were dependent on the RSB product and forest definition used. Next, we compared these RSB products with two independent forest inventory datasets at national (n = 26 220 plots) and local scales (n = 754 plots). The greatest accuracy among RSB products and forest inventory data was within the tropical moist forest (range 82%–95%), and the smallest was within the subtropical desert (range \\textless10%–80%) and subtropical steppe ecological zones (range \\textless10%–60%). We developed a forest extent agreement map by combining seven RSB products and identifying a consensus in their estimates. 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