SDG 15.3.1 indicator at local scale for monitoring land degradation in protected areas

• DOI: 10.5194/egusphere-egu24-10906
• Publication Date: 2024-03-08T20:55:33Z
• Authors: Cristina Tarantino; Mariella Aquilino; Saverio Vicario; Rocco Labadessa; Vito Emanuele Cam...; Christos Georgiadis; Marcello Vitale; Francesca Assennato; Paolo Mazzetti
• Keywords: Degradation

In the framework of NewLife4Drylands LIFE Preparatory project (LIFE20 PRE/IT/000007, 2021-2024) estimation SDG 15.3.1 indicator [1], adopted in UNCCD’s Good Practice Guidance [2], was applied for evaluating Land Degradation (LD) different Mediterranean Protected Areas (PA). To effectively support PAs managers, joint effort made evaluation at local scale by using satellite Remote Sensing data computation three main sub-indicators as trend Cover (LC), Primary Production (PP) and Soil Organic Carbon (SOC) stock. Where feasible, were not sourced from open-access global/European databases due to their lack accuracy site [3]. LD estimated only whole PA but also specific LC classes interest, considering additional related pressures threats affecting class. This study focuses on dryland Alta Murgia (IT9120007) PA, southern Italy, wetland Nestos River Delta (GR1150001) Greece. For site, featuring semi-natural dry grassland habitats community interest that are frequently subjected fire events during summer season, Burn Severity (BS) index included. BS trends measured assessing difference pre/post–fire Normalized Ratio (NBR) Landsat summer. Baseline 2004, coinciding with establishment a National Park within compared 2018 validating field availability. hosts largest natural riparian forest Greece is hydrological cycle modifications, involving water scarcity both inappropriate river management climate change, turn hampering transport nutrient-rich sediments enrichment soils being risk aridification. Within this framework, Hydroperiod Salinity indices considered impacts aquatic vegetation LC. 2017, after conditions 2016-2017, 2021 Both Nestos, mappings obtained data-driven pixel-based approach Landsat/Sentinel-2, respectively, multi-seasonal imagery multi-class Support Vector Machine (SVM) classifier trained in-field campaigns historical orthophotos interpretation. Time series MSAVI (which replaced standard NDVI its soil correction benefits [4]) PPI Sentinel-2 Copernicus services, used track PP trends. Lastly, SOC stock trends, open-source Trends.Earth QGIS plugin [5], incorporating customized global SoilGrids product, supplement limitations. According specification, computed integrating all according principle “one out, out” obtaining 3-classes output mapping (Degradation, Improvement, Stable). The findings can monitoring LD, guiding protective measures aligned Agenda 2030 Sustainable Development. They, also, highlight importance integration UNCCD methodology. References [1] https://unstats.un.org/sdgs/metadata/files/Metadata-15-03-01.pdf [2]https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land [3] https://doi.org/10.3390/rs13020277 [4] https://doi.org/10.3390/rs12010083 [5] http://trends.earth/docs/en