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This paper suggests how the United Nations Convention to Combat Desertification (UNCCD) community can progressively make use of a flexible framework of analytical approaches that have been recently developed by scientific research. This allows a standardized but flexible use of indicator sets adapted to specific objectives or desertification issues relevant for implementing the Convention. Science has made progress in understanding major issues and proximate causes of dryland degradation such that indicator sets can be accordingly selected from the wealth of existing and documented indicator systems. The selection and combination should be guided according to transparent criteria given by existing indicator frameworks adapted to desertification conceptual frameworks such as the Dryland Development Paradigm and can act as a pragmatic entry point for selecting area-and theme-specific sets of indicators from existing databases. Working on different dryland sub-types through a meaningful stratification is proposed to delimit and characterize affected areas beyond the national level. Such stratification could be achieved by combining existing land use information with additional biophysical and socio-economic data sets, allowing indicator-based monitoring and assessment to be embedded in a framework of specific dryland degradation issues and their impacts on key ecosystem services.

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Soil salinization affects crop production and food security. Mapping spatial distribution and severity of salinity is essential for agricultural management and development. This study was aimed to test the effectiveness of machine learning algorithms for soil salinity mapping taking the Mussaib area in Central Mesopotamia as an example.A combined dataset consisting of Landsat 5 Thematic Mapper (TM) and ALOS L-band radar data acquired at the same time was used for fulfilling the task. Relevant biophysical indicators were derived from the TM images, and the soil component was retrieved by removing the vegetation contribution from the L-band radar backscattering coefficients. Field-measured salinity at the three corner plots of triangles were averaged to represent the salinity of these triangular areas. These averaged plots were converted into raster by either direct rasterization or buffering-based rasterization into different cell size to create the training set (TS). One of the three triangle corners was randomly selected to constitute a validation set (VS). Using this TS, the support vector regression (SVR) and random forest regression (RFR) algorithms were then applied to the combined dataset for salinity prediction. Results revealed that RFR performed better than SVR with higher accuracy (93.4-94.2% vs. 85.2-89.4%) and less normalized root mean square error (NRMSE; 6.10-7.69% vs. 10.29-10.52%) when calibrated with both TS and VS.In comparison, prediction by multivariate linear regression (MLR) achieved in our previous study using the same datasets also showed less NRMSE than SVR. Hence, both RFR and MLR are recommended for soil salinity mapping. KEYWORDScombined optical-radar dataset, field sample rasterization, random forest regression, soil salinity prediction, support vector regression

Abstract. Soil indicators may be used for assessing both land suitability for restoration and the effectiveness of restoration strategies in restoring ecosystem functioning and services. In this review paper, several soil indicators, which can be used to assess the effectiveness of ecological restoration strategies in dryland ecosystems at different spatial and temporal scales, are discussed. The selected indicators represent the different viewpoints of pedology, ecology, hydrology, and land management. Two overall outcomes stem from the review. (i) The success of restoration projects relies on a proper understanding of their ecology, namely the relationships between soil, plants, hydrology, climate, and land management at different scales, which are particularly complex due to the heterogeneous pattern of ecosystems functioning in drylands.(ii) The selection of the most suitable soil indicators follows a clear identification of the different and sometimes competing ecosystem services that the project is aimed at restoring.

Biophysical restoration or rehabilitation measures of land have demonstrated to be effective in many scientific projects and small-scale environmental experiments. However circumstances such as poverty, weak policies, or inefficient scientific knowledge transmission can hinder the effective upscaling of land restoration and the long term maintenance of proven sustainable use of soil and water. This may be especially worrisome in lands with harsh environmental conditions. This review covers recent efforts in landscape restoration and rehabilitation with a functional perspective aiming to simultaneously achieve ecosystem sustainability, economic efficiency, and social wellbeing. Water management and rehabilitation of ecosystem services in croplands, rangelands, forests, and coastlands are reviewed. The joint analysis of such diverse ecosystems provides a wide perspective to determine: (i) multifaceted impacts on biophysical and socio-economic factors; and (ii) elements influencing effective upscaling of sustainable land management practices. One conclusion can be highlighted: voluntary adoption is based on different pillars, i.e. external material and economic support, and spread of success information at the local scale to demonstrate the multidimensional benefits of sustainable land management. For the successful upscaling of land management, more attention must be paid to the social system from the first involvement stage, up to the long term maintenance.

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Restoration efforts in the Mediterranean Basin have been changing from a silvicultural to an ecological restoration approach. Yet, to what extent the projects are guided by ecological restoration principles remains largely unknown. To analyse this issue, we built an on-line survey addressed to restoration practitioners. We analysed 36 restoration projects, mostly from drylands (86%). The projects used mainly soil from local sources. The need to comply with legislation was more important as a restoration motive for European Union (EU) than for non-EU countries, while public opinion and health had a greater importance in the latter. Non-EU countries relied more on non-native plant species than EU countries, thus deviating from ecological restoration guidelines. Nursery-grown plants used were mostly of local or regional provenance, whilst seeds were mostly of national provenance. Unexpected restoration results (e.g. inadequate biodiversity) were reported for 50% of the projects and restoration success was never evaluated in 22%. Long term evaluation (>6years) was only performed in 31% of cases, and based primarily on plant diversity and cover. The use of non-native species and species of exogenous provenances may: i) entail the loss of local genetic and functional trait diversity, critical to cope with drought, particularly under the predicted climate change scenarios, and ii) lead to unexpected competition with native species and/or negatively impact local biotic interactions. Absent or inappropriate monitoring may prevent the understanding of restoration trajectories, precluding adaptive management strategies, often crucial to create functional ecosystems able to provide ecosystem services. The overview of ecological restoration projects in the Mediterranean Basin revealed high variability among practices and highlighted the need for improved scientific assistance and information exchange, greater use of native species of local provenance, and more long-term monitoring and evaluation, including functional and ecosystem services' indicators, to improve and spread the practice of ecological restoration.