Vegetation type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity

• Autores: Peri, Pablo Luis; Gaitan, Juan José; Diaz, Boris Gaston; Almonacid, Leandro; Morales, Cristian Gabriel; Ferrer, Francisco; Lasagno, Romina Gisele; Rodríguez‑Souilla, Julián; Martínez Pastur, Guillermo José
• Año de publicación: 2024
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Sustainability 16 : e2025 (2024)
• Materia: Rangelands; Livestock; Carbon; Ecosystem Services; Vegetation; Resource Conservation; Soil Organic Carbon; Forests; Grasslands; Scrublands; Tierras de pastos; Ganado; Biodiversidad; Carbono; Servicios de los Ecosistemas; Vegetación; Conservación de los Recursos; Carbono Orgánico del Suelo; Nothofagus Pumilio; Bosque; Pastizales; Tierras de Matorral; Santa Cruz (Argentina); Biodiversity; Carbon Balance; Provisioning Ecosystem Services; Ecosystem Functions; Spatial Patterns; Land Use Cover; Steppe Grasslands; Mata Negra Matorral Thicket; Bare Soil; Nothofagus Pumilio Forest; Nothofagus Antarctica Forest; PEBANPA Network; Balance de Carbono; Servicios Ecosistémicos de Provisión; Funciones del Ecosistema; Patrones Espaciales; Cobertura del Uso de la Tierra; Matorral Mata Negra; Suelo Desnudo; Bosques de Nothofagus Pumilio; Bosque de Nothofagus Antarctica; Red PEBANPA; Región Patagónica
• Institución: Instituto Nacional de Tecnología Agropecuaria

Vegetation Type (VT) mapping using Optical Earth observation data is essential for the management and conservation of natural resources, as well as for the evaluation of the supply of provisioning ecosystem services (ESs), the maintenance of ecosystem functions, and the conservation of biodiversity in anthropized environments. The main objective of the present work was to determine the spatial patterns of VTs related to climatic, topographic, and spectral variables across Santa Cruz province (Southern Patagonia, Argentina) in order to improve our understanding of land use cover at the regional scale. Also, we examined the spatial relationship between VTs and potential biodiversity (PB), ESs, and soil organic content (SOC) across our study region. We sampled 59,285 sites sorted into 19 major categories of land cover with a reliable discrimination level from field measurements. We selected 31 potential predictive environmental dataset covariates, which represent key factors for the spatial distribution of land cover such as climate (four), topography (three), and spectral (24) factors. All covariate maps were generated or uploaded to the Google Earth Engine cloud-based computing platform for subsequent modeling. A total of 270,292 sampling points were used for validation of the obtained classification map. The main land cover area estimates extracted from the map at the regional level identified about 142,085 km2 of grasslands (representing 58.1% of the total area), 38,355 km2 of Mata Negra Matorral thicket (15.7%), and about 25,189 km2 of bare soil (10.3%). From validation, the Overall Accuracy and the Kappa coefficient values for the classification map were 90.40% and 0.87, respectively. Pure and mixed forests presented the maximum SOC (11.3–11.8 kg m−2), followed by peatlands (10.6 kg m−2) and deciduous Nothofagus forests (10.5 kg m−2). The potential biodiversity was higher in some shrublands (64.1% in Mata Verde shrublands and 63.7% in mixed shrublands) and was comparable to those values found for open deciduous forests (Nothofagus antarctica forest with 60.4%). The provision of ESs presented maximum values at pure evergreen forests (56.7%) and minimum values at some shrubland types (Mata Negra Matorral thicket and mixed shrubland) and steppe grasslands (29.7–30.9%). This study has provided an accurate land cover and VT map that provides crucial information for ecological studies, biodiversity conservation, vegetation management and restoration, and regional strategic decision-making.
EEA Santa Cruz
Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.
Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral (UNPA); Argentina.
Fil: Gaitan, Juan José. Universidad Nacional de Luján; Argentina.
Fil: Gaitan, Juan José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Diaz, Boris Gaston. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.
Fil: Almonacid, Leandro. Municipalidad de Río Gallegos. CONVENIO INTA. Santa Cruz; Argentina.
Fil: Almonacid, Leandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.
Fil: Morales, Cristian Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.
Fil: Ferrer, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Ferrer, Francisco. Universidad Nacional de la Patagonia Austral (UNPA). Departamento de Recursos Naturales; Argentina.
Fil: Ferrer, Francisco. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.
Fil: Lasagno, Romina Gisele. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.
Fil: Rodríguez‑Souilla, Julián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina.
Fil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina.