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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
- Source :
- Remote Sensing, Vol 8, Iss 7, p 533 (2016)
- Publication Year :
- 2016
- Publisher :
- MDPI AG, 2016.
-
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. Specifically, we show that (1) the accuracy assessment of MAD-MEX land cover maps is optimistically biased; (2) the ability of MAD-MEX to monitor land cover change, including deforestation and forest degradation; is poor and (3) the use of an entirely automatic classification approach, such as that followed by MAD-MEX, is highly problematic in the case of a large and heterogeneous country like Mexico. We discuss these limitations and call into question the ability of a land cover monitoring system, such as MAD-MEX, both to elaborate a national reference emissions level and to monitor future forest cover change, as part of a REDD+ MRV system. We provide some insights with the aim of improving the development of nation-wide land cover monitoring systems in Mexico and elsewhere.
- Subjects :
- Monitoring, Reporting and Verification (MRV)
010504 meteorology & atmospheric sciences
business.industry
Science
Environmental resource management
0211 other engineering and technologies
Monitoring system
02 engineering and technology
Land cover
01 natural sciences
Reduced Emissions from Deforestation and Degradation plus (REDD+)
Deforestation
Forest cover
General Earth and Planetary Sciences
Environmental science
land cover mapping
Forest degradation
business
Landsat
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
accuracy assessment
image classification
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 8
- Issue :
- 7
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....c72476268924381e3332c9c9a518bc73