R script to: Characterise and map the underlying factors (human or climatic) in land productivity changes over a period, in order to assess land degradation.

This repository includes the R script to characterise and map the underlying factors (human or climatic) in land productivity changes over a period, in order to assess land degradation. The methodology is associated to the following article: Montfort, F., Bégué, A., Leroux, L., Blanc, L., Gond, V., Cambule, A.H., Remane, I.A.D., Grinand, C., 2020. From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions. Land Degrad Dev.; 32: 49– 65. doi.org Description: The methodology is based on remote sensing methodology. Land productivity change were first analyzed using MODIS NDVI time-series (2000–2016), and a two-step framework was then used to understand the main factors of these productivity changes, using climate times series (CHIRPS data for rainfall and CRU data for temperature), Land Use and Land Cover Change (LULCC) maps (Laurel project LULCC map), and ground knowledge.