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- Classification of landforms in Southern Portugal (Ria Formosa Basin)Publication . Granja-Martins, Fernando M.; Fernandez, Helena; MGP Isidoro, Jorge; Jordan, Antonio; Zavala, LorenaA Geographic Information Systems-based tool is used for macro-landform classification following the Hammond procedure, based upon a Digital Terrain Model (DTM) created from ordinary Kriging. Gentle slopes, surface curvature, highlands and lowlands areas are derived from the DTM. Combining this information allows the classification of terrain units (landforms). The procedure is applied to the Ria Formosa basin (Southern Portugal), with five different terrain types classified (plains, tablelands, plains with hills, open hills and hills).
- Soil erosion, Serra de Grandola (Portugal)Publication . Fernandez, Helena; Granja-Martins, Fernando M.; MGP Isidoro, Jorge; Zavala, Lorena; Jordan, AntonioSoil erosion has long been the subject of attention for environmental management researchers because it implies the loss of a key natural resource for sustaining life. Several methodologies for soil erosion assessment have been developed; many of these are supported by Geographic Information Systems. This study aims to classify the susceptibility of rainfall-induced erosion at the Serra de Grandola (Portugal), based on the Priority Actions Programme/Regional Activity Centre guidelines for mapping soil erosion on the Mediterranean coast. Results show a low-to-moderate susceptibility to rainfall-induced erosion in the lowlands, becoming moderate to high in the highlands of the Serra de Grandola.
- Comparison of ratioing and RCNA methods in the detection of flooded areas using Sentinel 2 Imagery (case study: Tulun, Russia)Publication . Fernandez, Helena Maria; Granja-Martins, Fernando M.; Dziuba, Olga; Pereira, David A. B.; Isidoro, Jorge M. G. P.Climate change and natural disasters caused by hydrological, meteorological, and climatic phenomena have a significant impact on cities. Russia, a continental country with a vast territory of complex geographic–ecological environments and highly variable climatic conditions, is subject to substantial and frequent natural disasters. On 29 June 2019, an extreme precipitation event occurred in the city of Tulun in the Irkutsk oblast, Russian Federation, which caused flooding due to the increase in the water level of the Iya River that passes through the city, leaving many infrastructures destroyed and thousands of people affected. This study aims to determine the flooded areas in the city of Tulun based on two change detection methods: Radiometric Rotation Controlled by No-change Axis (RCNA) and Ratioing, using Sentinel 2 images obtained before the event (19 June 2019) and during the flood peak (29 June 2019). The results obtained by the two methodologies were compared through cross-classification, and a 98% similarity was found in the classification of the areas. The study was validated based on photointerpretation of Google Earth images. The methodology presented proved to be useful for the automatic precession of flooded areas in a straightforward, but rigorous, manner. This allows stakeholders to efficiently manage areas that are buffeted by flooding episodes.
- An assessment of forest fires and CO2 gross primary production from 1991 to 2019 in Mação (Portugal)Publication . Fernandez, Helena Maria; Granja-Martins, Fernando M.; Pedras, Celestina M. G.; Fernandes, Patrícia; Isidoro, JorgeForest-fire rates have increased in Southern European landscapes. These fires damage forest ecosystems and alter their development. During the last few decades, an increase in fast-growing and highly fuel-bearing plant species such as bush, Eucalyptus globulus Labill., and Pinus pinaster Ait. has been observable in the interior of Portugal. This study aims to verify this assumption by the quantification of the biomass carbon sink in the forests of the Mação municipality. Maps of fire severity and forest biomass evolution after a wildfire event were produced for the period of 1991 to 2019. To quantify carbon retention in this region, this evolution was correlated with gross primary production (GPP) on the basis of satellite imagery from Landsat 5, Landsat 8, and MODIS MYD17A2H. Results show that wildfires in Mação increased in area and severity with each passing decade due to the large accumulation of biomass promoted by the abandonment of rural areas. Before the large fires of 2003, 2017, and 2019, carbon rates reached a daily maximum of 5.4, 5.3, and 4.7 gC/m2/day, respectively, showing a trend of forest-biomass accumulation in the Mação municipality.
- Mapping rainfall aggressiveness from physiographical data: application to the Grândola Mountain Range (Alentejo, Portugal)Publication . Fernandez, Helena Maria; Granja-Martins, Fernando M.; Isidoro, Jorge M. G. P.The South of the Iberian Peninsula is subject to long periods of drought followed by heavy rain events over shallow soils, promoting soil loss. The Modified Fournier Index (MFI) is a good indicator of this process; however, MFI is sometimes difficult to assess due to the scarcity of rainfall data. This study proposes a methodology using MFI and supported by a geographic information system (GIS) and geostatistics to map rainfall aggressiveness with scarce spatial rainfall data, where physiographic variables are used to overcome the lack of rainfall data. The Grândola Mountain Range in the Alentejo region, Portugal, is presented as a case study. This area is a CORINE biotope, currently under application to the Natura 2000 network, and should be considered as a priority for the conservation of the environment. The model allowed us to create a map of rainfall aggressiveness, classified according to CORINE-CEC, found to be Moderate in the mountains and Low in the coastal area of the Grândola Mountain Range. This cartography is an important tool for local and national stakeholders and authorities with responsibilities in planning and protection of the territory. The methodology can be used in regions with scarce spatial rainfall data to assess areas susceptible to rainfall-induced soil erosion.