@phdthesis{braden_quantifying_2023,
	title = {Quantifying uncertainty of forest extent estimates in {Mexico} by comparing satellite-derived land and tree cover products},
	url = {https://udspace.udel.edu/handle/19716/32760},
	abstract = {Information about forest extent and tree cover is needed to evaluate the status of natural resources, conservation practices and environmental policies. The challenge is that different forest definitions, remote sensing products, and data availability can lead to discrepancies in reporting forest area and ultimately forest carbon estimates. Here, I compared forest extent estimates from 7 regional and global land or tree cover products at 30 m resolution across mainland Mexico. Comparison results presented significant uncertainty in forest extent estimates for Mexico, ranging from 387,607 km2 to 675,239 km2 depending on which satellite-derived product and forest definition is utilized. Next, I compared these satellite-derived products with two independent forest inventory datasets at the national scale (n=21,167) and at the local scale (n=486). The highest agreement between satellite-derived products and forest inventory data is within the tropical moist forest, and the least agreement is within the subtropical steppe ecozones. I further developed a hybrid uncertainty product by combining the 7 forest extents to calculate forest likelihood. I identified a forest area of 340,661 km2 that has low agreement among satellite-derived products. The tropical dry forest and subtropical mountain system represented the two ecozones with the greatest amount of disagreement among satellite-derived products. These findings identify uncertainty surrounding forest extent estimates across ecozones in Mexico where additional ground data and research is needed.},
	language = {en},
	urldate = {2023-06-02},
	school = {University of Delaware},
	author = {Braden, Dustin S.},
	year = {2023},
	keywords = {NALCMS},
}

{"_id":"fCXDEY9icxPM45pwh","bibbaseid":"braden-quantifyinguncertaintyofforestextentestimatesinmexicobycomparingsatellitederivedlandandtreecoverproducts-2023","author_short":["Braden, D. S."],"bibdata":{"bibtype":"phdthesis","type":"phdthesis","title":"Quantifying uncertainty of forest extent estimates in Mexico by comparing satellite-derived land and tree cover products","url":"https://udspace.udel.edu/handle/19716/32760","abstract":"Information about forest extent and tree cover is needed to evaluate the status of natural resources, conservation practices and environmental policies. The challenge is that different forest definitions, remote sensing products, and data availability can lead to discrepancies in reporting forest area and ultimately forest carbon estimates. Here, I compared forest extent estimates from 7 regional and global land or tree cover products at 30 m resolution across mainland Mexico. Comparison results presented significant uncertainty in forest extent estimates for Mexico, ranging from 387,607 km2 to 675,239 km2 depending on which satellite-derived product and forest definition is utilized. Next, I compared these satellite-derived products with two independent forest inventory datasets at the national scale (n=21,167) and at the local scale (n=486). The highest agreement between satellite-derived products and forest inventory data is within the tropical moist forest, and the least agreement is within the subtropical steppe ecozones. I further developed a hybrid uncertainty product by combining the 7 forest extents to calculate forest likelihood. I identified a forest area of 340,661 km2 that has low agreement among satellite-derived products. The tropical dry forest and subtropical mountain system represented the two ecozones with the greatest amount of disagreement among satellite-derived products. These findings identify uncertainty surrounding forest extent estimates across ecozones in Mexico where additional ground data and research is needed.","language":"en","urldate":"2023-06-02","school":"University of Delaware","author":[{"propositions":[],"lastnames":["Braden"],"firstnames":["Dustin","S."],"suffixes":[]}],"year":"2023","keywords":"NALCMS","bibtex":"@phdthesis{braden_quantifying_2023,\n\ttitle = {Quantifying uncertainty of forest extent estimates in {Mexico} by comparing satellite-derived land and tree cover products},\n\turl = {https://udspace.udel.edu/handle/19716/32760},\n\tabstract = {Information about forest extent and tree cover is needed to evaluate the status of natural resources, conservation practices and environmental policies. The challenge is that different forest definitions, remote sensing products, and data availability can lead to discrepancies in reporting forest area and ultimately forest carbon estimates. Here, I compared forest extent estimates from 7 regional and global land or tree cover products at 30 m resolution across mainland Mexico. Comparison results presented significant uncertainty in forest extent estimates for Mexico, ranging from 387,607 km2 to 675,239 km2 depending on which satellite-derived product and forest definition is utilized. Next, I compared these satellite-derived products with two independent forest inventory datasets at the national scale (n=21,167) and at the local scale (n=486). The highest agreement between satellite-derived products and forest inventory data is within the tropical moist forest, and the least agreement is within the subtropical steppe ecozones. I further developed a hybrid uncertainty product by combining the 7 forest extents to calculate forest likelihood. I identified a forest area of 340,661 km2 that has low agreement among satellite-derived products. The tropical dry forest and subtropical mountain system represented the two ecozones with the greatest amount of disagreement among satellite-derived products. These findings identify uncertainty surrounding forest extent estimates across ecozones in Mexico where additional ground data and research is needed.},\n\tlanguage = {en},\n\turldate = {2023-06-02},\n\tschool = {University of Delaware},\n\tauthor = {Braden, Dustin S.},\n\tyear = {2023},\n\tkeywords = {NALCMS},\n}\n\n\n\n\n\n\n\n","author_short":["Braden, D. S."],"key":"braden_quantifying_2023","id":"braden_quantifying_2023","bibbaseid":"braden-quantifyinguncertaintyofforestextentestimatesinmexicobycomparingsatellitederivedlandandtreecoverproducts-2023","role":"author","urls":{"Paper":"https://udspace.udel.edu/handle/19716/32760"},"keyword":["NALCMS"],"metadata":{"authorlinks":{}}},"bibtype":"phdthesis","biburl":"https://bibbase.org/zotero/NAAtlas2024","dataSources":["qLjf8q88GSLZ5dAmC"],"keywords":["nalcms"],"search_terms":["quantifying","uncertainty","forest","extent","estimates","mexico","comparing","satellite","derived","land","tree","cover","products","braden"],"title":"Quantifying uncertainty of forest extent estimates in Mexico by comparing satellite-derived land and tree cover products","year":2023}