A step-wise land-cover classification of the tropical forests of the Southern Yucatán, Mexico

Schmook, Birgit; Dickson, Rebecca; Sangermano, Florencia; Vadjunec, Jacqueline M.; Eastman, J. Ronald; Rogan, John

doi:10.1080/01431160903527413

Cited by 27 publications

(10 citation statements)

References 37 publications

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“…The state and change of the land cover in Mexico has been characterized by several regional studies utilizing Landsat data and applying different class schemes, e.g., in Southern Mexico [2], eastern Coastal Mexico [3], Northwestern coastal Mexico [4], Southern Yucatan [5], the states of Campeche [6], or Chihuahua [7]. Only a few studies attempted mapping at the national level.…”

Section: "At Present Redd Pilot Projects Are Sprouting Up In Communimentioning

confidence: 99%

MAD-MEX: Automatic Wall-to-Wall Land Cover Monitoring for the Mexican REDD-MRV Program Using All Landsat Data

Gebhardt

Wehrmann

Ruiz³

et al. 2014

Remote Sensing

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3924 manner by automatic image classification. This paper describes the operational land cover monitoring system for Mexico. It utilizes national-scale cartographic reference data, all available Landsat satellite imagery, and field inventory data for validation. Seven annual national land cover maps between 1993 and 2008 were produced. The classification scheme defined 9 and 12 classes at two hierarchical levels. Overall accuracies achieved were up to 76%. Tropical and temperate forest was classified with accuracy up to 78% and 82%, respectively. Although specifically designed for the needs of Mexico, the general process is suitable for other participating countries in the REDD+ program to comply with guidelines on standardization and transparency of methods and to assure comparability. However, reporting of change is ill-advised based on the annual land cover products and a combination of annual land cover and change detection algorithms is suggested.

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Section: "At Present Redd Pilot Projects Are Sprouting Up In Communimentioning

confidence: 99%

MAD-MEX: Automatic Wall-to-Wall Land Cover Monitoring for the Mexican REDD-MRV Program Using All Landsat Data

Gebhardt

Wehrmann

Ruiz³

et al. 2014

Remote Sensing

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“…In Mexico, investigations of land cover and change have primarily operated at local scales, encompassing a single city and environs (López et al 2001;Torres-Vera, ProlLedesma, and Garcia-Lopez 2009), a state or substate region (Velázquez et al 2003;Schmook et al 2011), or assessments of the whole country (Lunetta et al 2002;Mas et al 2004). Between these scales lie regional ecosystems and patterns of use that transcend political borders, including mountain ranges, watersheds, coastal floodplains, national parks, and biosphere reserves (Wessels et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Quantifying uncertainty and confusion in land change analyses: a case study from central Mexico using MODIS data

Christman

Rogan

Eastman

et al. 2015

GIScience & Remote Sensing

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Land cover classifications of coarse-resolution data can aid the identification and quantification of natural variability and anthropogenic change at regional scales, but true landscape change can be distorted by misrepresentation of map classes. The Lerma-Chapala-Santiago (LCS) is biophysically diverse and heavily modified by urbanization and agricultural expansion. Land cover maps classified with a Mahalanobis distance algorithm and possibilistic metrics of class membership were used to quantify uncertainty (potential error in class assignment) and change confusion (potential error in land change identification). While land change analysis suggests that 33% of the landscape underwent a change in class, led by changes from or to the mosaic class (~19% of landscape), classification uncertainty values for 2001 and 2007 were 0.59 and 0.62, respectively, with highest uncertainty among bare soil classes, and an average confusion index value of 0.65, with pixels experiencing change at 0.67 and pixels experiencing persistence at 0.61 on average. These results indicate that uncertainty and potential error in land cover classifications estimates may inhibit accurate assessments of land change. Estimates of land change may be refined using these metrics to more confidently identify true landscape change and to find classes and locations that are contributing to errors in land change assessments.

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“…Images were also corrected for atmospheric and radiometric errors in ENVI 4.5. A root mean square error less than 3 meters in each case ensured that changes between the two dates resulted from actual land-use change, and not from uneven image registration [61,63,64].…”

Section: Quantitative Methodsmentioning

confidence: 99%

The Politics of Land Use in the Korup National Park

Siewe

Vadjunec

Caniglia

2017

Land

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Abstract:Recently, the call to combine land change science (LCS) and political ecology (PE) in the study of human-environment interactions has been widely discussed by scientists from both subfields of geography. In this paper, we use a hybrid ecology framework to examine the effects of conservation policies on the environment and the livelihood of the people of the Korup National Park (KNP). Using techniques in both PE and LCS, our results show that conservation policies, politics, and population are the primary drivers of environmental change in the KNP. We conclude by arguing that a deeper understanding can be garnered by combining LCS and PE approaches to analyze and contribute to the people and parks debate.

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A step-wise land-cover classification of the tropical forests of the Southern Yucatán, Mexico

Cited by 27 publications

References 37 publications

MAD-MEX: Automatic Wall-to-Wall Land Cover Monitoring for the Mexican REDD-MRV Program Using All Landsat Data

MAD-MEX: Automatic Wall-to-Wall Land Cover Monitoring for the Mexican REDD-MRV Program Using All Landsat Data

Quantifying uncertainty and confusion in land change analyses: a case study from central Mexico using MODIS data

The Politics of Land Use in the Korup National Park

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