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

Abstract

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.

Comment on Gebhardt et al. MAD-MEX: Automatic Wall-to-Wall Land Cover Monitoring for the Mexican REDD-MRV Program Using All Landsat Data. Remote Sens. 2014, 6, 3923–3943

“…In addition, because the MAD-MEX classification scheme does not take into account the degradation level of forest covers, this monitoring system is not seen to be appropriate to assess either the area or the rate of forest degradation, nor can it be used to monitor degradation in Mexico over time. Although we acknowledge the considerable difficulty of degradation detection using available remote sensing historical datasets, this key limitation was not discussed by Gebhardt et al [1]. Indeed, detecting degradation is one of the most pressing problems as regards building the necessary baselines and MRV systems for REDD+ [14,15].…”

“…Gebhardt et al [1] claim that the MAD-MEX accuracy assessment procedure follows the protocol described by Olofsson et al [2] using a random area-weighted stratified sampling. However, they selected validation data from a reference dataset, which is not randomly distributed within the different categories of the MAD-MEX maps.…”

“…Version 4.2 of MAD-MEX, which was produced after the publication of Gebhardt et al [1], is based on a classification scheme of 32 categories, including temperate and tropical forest with secondary vegetation. These maps present an improvement of temporal congruency, but have also a large amount of "no data" area and still present a high level of error, which brings into doubt the computed rates of change (see Table 7).…”

“…Gebhardt et al [1] published an article in remote sensing that describes the Monitoring Activity Data for the Mexican REDD+ program (MAD-MEX), an automatic wall-to-wall land cover monitoring system for the Mexican Deforestation and Degradation plus (REDD+) Monitoring, Reporting and Verification (MRV) program. To create their seven land cover maps (for years 1993,1995,2000,2002,2005,2008) at a scale 1:100,000, the authors utilized existing land use/land cover cartography from the National Institute of Statistics and Geography (INEGI, the Mexican mapping agency) and Landsat imagery, while they used existing field inventory data for classification accuracy assessment.…”

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Abstract: Gebhardt et al (2014) presented the Monitoring Activity Data for the Mexican REDD+ program (MAD-MEX), an automatic nation-wide land cover monitoring system for the Mexican REDD+ MRV. Though MAD-MEX represents a valuable first effort toward establishing a national reference emissions level for the implementation of REDD+ in Mexico, in this paper, we argue that this land cover system has important limitations that may prevent it from becoming operational for REDD+ MRV.

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