The present work describes a transferability analysis for soil and water bioengineering techniques as an instrument for sustainable erosion control in Central and South America based on an empirical data base from the last decades. In total, 31 case studies in Mexico, Nicaragua, Guatemala, Colombia, Ecuador and Brazil generated a database from an area where soil and water bioengineering techniques are not commonly used. The Transferability Analysis is structured in seven steps: (1) Objectives of the procedure, (2) Impacts of the measure, (3) Identification of up-scaling/down-scaling needs (4) Identification of the main phases and its components, (5) Identification of the level of importance of the components, (6) Assessment of the components in the context of the Take-Up Site and (7) Conclusions. For the assessment of soil and water bioengineering via the Transferability Analysis, in step 4 the following main phases have been identified from the data base: (a) Planning Phase, (b) Construction Phase, (c) Use Phase, as well as (d) End of Life Phase of a construction. Within these categories, 14 components have been defined: (a) know-how of soil and water bioengineering techniques, local climate conditions, botany, hydraulics, pedology; (b) materials, qualified labor, equipment and mechanical instruments, economic resources; (c) monitoring, efficiency, sustainability, maintenance; (d) replicability. The following assessment of the components allowed to determine key barriers, as well as key support factors for the transfer of soil and water bioengineering. As a result, barriers appeared to be the components qualified labor, equipment/mechanical instruments, hydraulics, know-how in soil and water bioengineering and pedology. Neither barriers, nor supporting key factors resulted to be the components local climate conditions, economic resources and efficiency. Supporting key factors for the transfer were materials, monitoring, sustainability, maintenance and replicability. The most important key factor of success was assessed to be botany, as various plant species with important characteristics for soil and water bioengineering are available in Central and South America, able to compensate the constraints through barriers in certain cases.

In wildfire areas, earth observation data is used for the development of fire-severity maps or vegetation recovery to select post-fire measures for erosion control and revegetation. Appropriate vegetation indices for post-fire monitoring vary with vegetation type and climate zone. This study aimed to select the best vegetation indices for post-fire vegetation monitoring using remote sensing and classification methods for the temperate zone in southern Ecuador, as well as to analyze the vegetation’s development in different fire severity classes after a wildfire in September 2019. Random forest classification models were calculated using the fire severity classes (from the Relativized Burn Ratio—RBR) as a dependent variable and 23 multitemporal vegetation indices from 10 Sentinel-2 scenes as descriptive variables. The best vegetation indices to monitor post-fire vegetation recovery in the temperate Andes were found to be the Leaf Chlorophyll Content Index (LCCI) and the Normalized Difference Red-Edge and SWIR2 (NDRESWIR). In the first post-fire year, the vegetation had already recovered to a great extent due to vegetation types with a short life cycle (seasonal grass-species). Increasing index values correlated strongly with increasing fire severity class (fire severity class vs. median LCCI: 0.9997; fire severity class vs. median NDRESWIR: 0.9874). After one year, the vegetations’ vitality in low severity and moderate high severity appeared to be at pre-fire level.

Soil and water bioengineering (SWBE) measures in fire-prone areas are essential for erosion mitigation, revegetation, as well as protection of settlements against inundations and landslides. This study’s aim was to detect erosive areas at the basin scale for SWBE implementation in pre- and post-fire conditions based on a wildfire event in 2019 in southern Ecuador. The Revised Universal Soil Loss Equation (RUSLE) was used in combination with earth observation data to detect the fire-induced change in erosion behavior by adapting the cover management factor (C-factor). To understand the spatial accuracy of the predicted erosion-prone areas, high-resolution data from an Unmanned Aerial Vehicle (UAV) served for comparison and visual interpretation at the sub-basin level. As a result, the mean erosion at the basin was estimated to be 4.08 t ha⁻¹ yr⁻¹ in pre-fire conditions and 4.06 t ha⁻¹ yr⁻¹ in post-fire conditions. The decrease of 0.44% is due to the high autonomous vegetation recovery capacity of grassland in the first post-fire year. Extreme values increased by a factor of 4 in post-fire conditions, indicating the importance of post-fire erosion measures such as SWBE in vulnerable areas. The correct spatial location of highly erosive areas detected by the RUSLE was successfully verified by the UAV data. This confirms the effectivity of combining the RUSLE with very-high-resolution data in identifying areas of high erosion, suggesting potential scalability to other fire-prone regions.

Wildfires are an increasingly alarming phenomenon that affects forests and agro-ecosystems, generating several cascade effects among which soil erosion is one of the most deleterious. A robust body of data-based evidence on post-fire soil erosion and sediment yield at watershed scale is thus required, especially dealing with areas where wildfires are particularly frequent, such as the Mediterranean Basin. This study analyses the impact of the first rains after a large wildfire in terms of soil erosion and sediment yield at watershed scale in a Mediterranean area, the Pisan Mountains, Central Italy. Here about 1,000 ha of olive groves, maquis, maritime pine and chestnut forests burned. Fire severity was mapped by remote sensing and checked by a field survey. Sediment yield was assessed by sampling the earthy material deposited upstream a check dam at the outlet of the watershed. Finally, a hydrological model was developed in HEC-HMS environment for exploring the relationship between the erosion-deposition events observed in the watershed and the rainfall-induced hydrological processes. The first two post-fire rainy events relocated a high amount of sediments, mostly non-organic, perhaps already in the stream before the fire, while the subsequent four rains deposited materials rich in pyrogenic organic matter. Overall, the soil erosion caused by such six main post-fire rains – the larger of which had a return time of one year – was estimated to amount to 7.85 t ha ^-1 , corresponding to 42% of the watershed average annual potential erosion rate in normal conditions. This value is lower than expected and, overall, moderate if compared to other Mediterranean case studies, possibly because of the nature of soils in the studied watershed, i.e. shallow and quite stony, thus poor in fines prone to erosion.