Land degradation leads to ecosystem degradation, reducing ecosystem functioning and depleting ecosystems' resilience. The majority of factors linked to land degradation are closely related with the depletion of below- and above-ground stocks of organic carbon. Organic carbon stock is important for climate change mitigation and for restoring soil functions such as those crucial to support food security. In this study, we mapped carbon stocks to infer land degradation in a small area in the Ethiopian Great Rift Valley. The study aimed to assess carbon stock status and to identify limitations and advantages of using global data in mapping at local scale relative to using local data. Two different datasets were developed; i) a “global dataset” characterised by data from datasets with global coverage data, and ii) a “hybrid dataset” that coupled data from global datasets, soil data derived from a local survey, and land cover data derived from a supervised classification of satellite images. The results showed that i) global datasets introduced inaccuracy that must be taken into account for advocating interventions at a local scale, and ii) global datasets could be used at a small catchment level for decision-making, if a simple rank of values is sufficient, but they might provide an optimistic picture of land degradation because they overestimate stocks.

The advantages and limitations of global datasets to assess carbon stocks as proxy for land degradation in an Ethiopian case study

Peressotti A.;
2021-01-01

Abstract

Land degradation leads to ecosystem degradation, reducing ecosystem functioning and depleting ecosystems' resilience. The majority of factors linked to land degradation are closely related with the depletion of below- and above-ground stocks of organic carbon. Organic carbon stock is important for climate change mitigation and for restoring soil functions such as those crucial to support food security. In this study, we mapped carbon stocks to infer land degradation in a small area in the Ethiopian Great Rift Valley. The study aimed to assess carbon stock status and to identify limitations and advantages of using global data in mapping at local scale relative to using local data. Two different datasets were developed; i) a “global dataset” characterised by data from datasets with global coverage data, and ii) a “hybrid dataset” that coupled data from global datasets, soil data derived from a local survey, and land cover data derived from a supervised classification of satellite images. The results showed that i) global datasets introduced inaccuracy that must be taken into account for advocating interventions at a local scale, and ii) global datasets could be used at a small catchment level for decision-making, if a simple rank of values is sufficient, but they might provide an optimistic picture of land degradation because they overestimate stocks.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/1205769
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