Land degradation assessment in the Argentinean Puna: Comparing expert knowledge with satellite-derived information - García, César Luis; Teich, Ingrid; Gonzalez Roglich, Mariano; Kindgard, Adolfo Federico; Ravelo, Andrés Carlos; Liniger, Hanspeter (Elsevier, 2018)
The Puna region, located in NW Argentina, is a dry highland with many endemic species and significant traditional cultural heritage. The Puna was a pilot region for the Land Degradation Assessment in Drylands project (LADA-FAO) which aimed at assessing desertification status in different land use systems (LUS). The results of these assessments are used for reporting to the United Nations Convention to Combat Desertification (UNCCD) and for Land Degradation Neutrality (LDN) monitoring. The assessment was performed using an expert knowledge questionnaire following the World Overview of Conservation Approaches and Technologies initiative methodology (LADA-WOCAT), on LUS map units obtained by a participatory mapping methodology. In this article we compare the inferences on land degradation status and its temporal trends derived from LADA-WOCAT method with those obtained from remotely sensed data. Our aim is to understand similarities and differences in the assessments in order to provide recommendations and suggestions on improved LDN assessment methods, reporting and monitoring. Our results suggest that the LADA-WOCAT participatory mapping successfully delineated LUSs with different phenological characteristics over an extended area. However, the trend and the degradation processes described by experts at the LUS scale did not agree with the ones derived from satellite data. Results obtained at pixel scale help in identifying areas undergoing potential human induced degradation processes and specific areas of agreement between methods. The spatial scale of land degradation assessments obtained by either satellite or expert knowledge may impact the accuracy of the final results. A methodology integrating satellite and expert opinion data at an appropriate scale needs to coalesce to make the most of both data sources and gain accuracy in land degradation and LDN assessments.