Phase Two: Visioning and Goal-Setting
Overview
Within the LDN framework, the vision of no-net land degradation by 2030 informs national voluntary targets concerning no net loss of healthy and productive land. This represents a commitment to a minimum, but does not hinder more ambitious goals concerning net gain in land productivity. Lower targets are conversely also permissible if adequate justification is given. In relation to ILUP, visioning can be a top-down or a participatory process involving a wide variety of actors with stakes in land productivity. A progressive revision of visioning and goals is expected, based on competing interests among stakeholders.
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Tool |
Description |
Source |
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Restoration Opportunities Optimization Tool (ROOT) |
Optimization tool developed by IUCN that supports selection of areas for restoration of ecosystem services |
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LUP4LDN |
Participatory land use planning tool that integrates LDN and is applicable to all LUP phases |
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Collect Earth |
Open-source satellite image viewing and interpretation system providing land cover and land use reference data for classifying and monitoring land |
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SEPAL |
System for land monitoring w/ access to earth observation data, processing and analysis, suitable for local needs |
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WOCAT Earth Engine Apps |
Set of spatially-explicit tools that help identify priority areas for implementation of sustainable management practices and integration of indicators for monitoring and evaluation; specific to LDN |
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Evaluating Land Management Options (ELMO) |
Tool for assessing farmers’ preferences and perceptions of pros, cons, and tradeoffs between different land management practices (participatory stakeholder engagement tool) |
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CLUMondo model |
Tool for modelling future land use patterns based on LDN targets (incorporating counterbalancing mechanism) |
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Revised Universal Soil Loss Equation (RUSLE) |
Erosion models that require limited data input and are not region-specific |
Revised Universal Soil Loss Equation (RUSLE) - Welcome to RUSLE 1 and RUSLE 2 : USDA ARS |
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Pan European Soil Erosion Risk Assessment (PESERA) |
Similar to RUSLE, but potentially more accurate |
Pan European Soil Erosion Risk Assessment - PESERA - ESDAC - European Commission (europa.eu) |
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LAND System Cellular Automata model for Potential Effects (LANDSCAPE) |
Tool for quantitative modelling different strategies to reaching LDN (includes degradation and restoration modelling) |
SPI Objective 1 Technical Report_Advance Copy_Final_6May2022.pdf (unccd.int) - pp 114 / A CA-based land system change model: LANDSCAPE: International Journal of Geographical Information Science: Vol 31, No 9 (tandfonline.com) |
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Computable General Equilibrium of Land Use Change (CGELUC) |
Models detailed land demand and supply scenarios (non-spatial), can support spatially-explicit tools to provide context to LDN goal-setting/visioning |
SPI Objective 1 Technical Report_Advance Copy_Final_6May2022.pdf (unccd.int) - pp 114 / 20171229202000959983.pdf (pku.edu.cn) |
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Analytical Hierarchy Process tool |
Common multi-criteria analysis tool that uses pairwise comparisons to support identification of the best alternative and of relative importance of different criteria; useful for identifying most suitable land management options to satisfy stakeholder objectives |
SPI Objective 1 Technical Report_Advance Copy_Final_6May2022.pdf (unccd.int) - pp 114 / AHP Online System - AHP-OS (bpmsg.com) |
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Multi-Attribute Utility Theory |
Multi-criteria analysis tool that addresses complex decision problems and multi-stakeholder negotiations |
SPI Objective 1 Technical Report_Advance Copy_Final_6May2022.pdf (unccd.int) - pp 114-5 |
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Outranking tools |
Use pairwise comparison to rank alternative plans; criteria importance is defined subjectively by stakeholders while performance of alternatives is determined by objective measurement of attributes for each criterion |
SPI Objective 1 Technical Report_Advance Copy_Final_6May2022.pdf (unccd.int) - pp 115 |
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Hierarchical tools |
Suitability analysis evaluates where land uses occur and conflict with one another; followed by priority-ranked allocation of land use |
SPI Objective 1 Technical Report_Advance Copy_Final_6May2022.pdf (unccd.int) - pp 115 |
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Goal programming tool |
Find solution that conforms as much as possible with predefined optimal solution and furthest distance from worst case |
SPI Objective 1 Technical Report_Advance Copy_Final_6May2022.pdf (unccd.int) - pp 115-6 |
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Evolutionary algorithms |
Successive generations of algorithms develop refined land use plans via selection, crossover, mutation |
SPI Objective 1 Technical Report_Advance Copy_Final_6May2022.pdf (unccd.int) - pp 116 / Introduction to Evolutionary Algorithms | by Devin Soni | Towards Data Science |
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Global Agro-Ecological Zoning (GAEZ) |
Modelling framework that assesses natural resources for finding suitable agricultural land use options |
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Earth Observation for the SDGs (EO4SDGs) |
Presents information on the potential for EO to contribute to SDGs w/ example cases |
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World Atlas for Desertification |
2018 atlas depicting land degradation and other environmental trends globally, in relation to human activities (incl. land use) |