Spatial analysis of tree species before forest fires

Spain is included in the top five European countries with the highest number of wildfires. The occurrence and magnitude of forest fires involves aspects of a very diverse nature, from those of a socio-economic, climatic, or physiographic nature, to those concerning fuel or the availability and quantity of resources and means of extinction. The distribution of wildfires in Galicia is not random and that fire occurrence may depend on ownership conflicts also a spatial dependence between productive or non-productive area exists. Satellite data play a major role in providing knowledge about fires by delivering rapid information to map fire-damaged areas precisely and promptly. In addition, the availability of large-scale data and the high temporal resolution offered by the Sentinel-2 satellite enables to classify and determine the land cover changes with high accuracy. This study describes a methodology to detect burned areas and analyse the Land Cover and Land Use (LCLU) classes present in these areas during the period of 5 years (2016–2021) by Sentinel-2 images. The training areas were obtained by photointerpretation and the image classification was performed using the Random Forest algorithm which shows an overall accuracy range between 80–85%. The methodology concluded that Lobios and Muiños were the most affected municipalities by wildfires. Additionally, the spatial analysis determined that the Deciduous Forest mainly composed by Quercus sp. were the most affected in 2017 followed by Coniferous Forest mainly composed by Pinus sp.in 2016. Although, Scrub and Rock are the classes more affected for wildfire during 2016–2020 period.Universidade de Vigo | Ref. 00VI 131H 6410211Agencia Estatal de Investigación | Ref. PCI2020-120705-2Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/0

Modelling and evaluation of land use changes through satellite images in a multifunctional catchment: social, economic and environmental implications

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGFloods are recurrent phenomena with significant environmental and socio-economic impacts. The risk of flooding increases when land use changes. The objective of this research is to detect land cover changes via Sentinel-2 images in the Umia Basin (Galicia, NW Spain) in 2016–2021 and to analyse the associated flood risk. This study focuses on how forest use and nature-based solutions (NBS) can reduce the risk and hazard of flooding in cities and crops in the high-risk area. A flood simulation was performed with the land use obtained from Sentinel-2 (Observed) and three more simulations were performed changing the location of afforestation and NBS, i.e. “S-Upstream”, “S-Downstream” and “S-Total”. Finally, the environmental, economic and social impacts of the scenarios designed and estimated are analysed and discussed. Land cover change was successfully monitored with Sentinel-2 imagery. The catchment area showed noteworthy changes in land use, most notably for the category of trees, which covered 6700 ha in 2016 and 10,911 ha in 2021. However riparian vegetation decreased by almost 11%. For the flood hazard simulations, an average reduction in peak discharge was obtained for all three scenarios (9.3% for S-Up; 8.6% for S-Down and 13% for S-Total). From the economic perspective, all three scenarios show a positive net present value for the period studied. However, S-Down is the scenario with the lowest benefits (€15,476,487), while S-Up and S-Total show better values at €29,580,643 and €65,158,130 respectively. However, investment cost is much higher for the S-Total scenario, and upstream actions affect the whole catchment, so S-Up is the best decision. This study concludes that the information provided by satellites is a large-scale analysis tool for small heterogeneous plots that facilitates the comprehensive analysis of a territory. This information can be incorporated into flood analysis models, facilitating simulation through the use of NBS. It has been proven that the use of reforestation upstream only is almost as beneficial as reforestation in the entire catchment and is economically more viable. This confirms that the methodology used reduces flood hazard, despite the territorial complexity, facilitating decision making on the use of NBS.Universidade de Vig

Analysis of multitemporal Sentinel-2 images in the framework of the ESA Scientific Exploitation of Operational Missions

This paper focuses on the scientific preliminary results of the project “S2-4Sci Land and Water - Multitemporal Analysis” funded by the European Space Agency (ESA) in the framework of the Scientific Exploitation of Operational Missions (SEOM). The aim of the project is the development of advanced multitemporal methods tailored on the specific properties of S2 images. The Sentinel-2 (S2) constellation has a huge potential for multitemporal analysis, due to the increased geometrical resolution, the novel spectral capabilities, a swath width of 290Km and the short revisit time. Three main applications and methodological areas are investigated: i) land-cover maps updating, ii) land-cover change detection, and iii) time series analysis. The proposed approaches are briefly described and preliminary results obtained on S2 images are discussed

Discrimination and biomass estimation of co-existing C3 and C4 grass functional types over time : a view from space.

Doctor of Philosophy in Environmental Sciences. University of KwaZulu-Natal, Pietermaritzburg, 2018.The co-existence of C3 and C4 grass species significantly influence their spatio-temporal variations of biochemical cycling, productivity (i.e. biomass) and role in provision of ecosystem goods and services. Consequently, the discrimination of the two species is critical in understanding their spatial distribution and productivity. Such discrimination is particularly valuable for accounting for their socio-economic and environmental contributions, as well as decisions related to climate change mitigation. Due to the growing popularity of remotely sensed approaches, this study sought to discriminate the two grass species and determine their AGB using new generation sensors. Specifically, the potential of Landsat 8, Sentinel 2 and Worldview 2, with improved sensing characteristics were tested in achieving the above objectives. Generally, the results demonstrate the suitability of the adopted sensors in the discrimination and determination of C3 and C4 AGB using Discriminant Analysis and Sparse Partial Least Squares Regression models. Using multi-date Sentinel 2 data, the study established that winter period (May) was the most suitable for discriminating the two grass species. On the other hand, the winter fall (August) was found to be the least optimal period for the two grass species discrimination. The study also established that the amount of AGB for C3 and C4 were higher in winter and summer, respectively; a variability attributed to elevation and rainfall. The study concludes that Sentinel 2 dataset, although had weaker performance than Worldview 2; it offers a valuable opportunity in understanding the C3 and C4 spatial distribution within a landscape; hence useful in understanding both temporal and multi-temporal distribution of the two grass species. Successful seasonal characterization of C3 and C4 AGB allows for inferences on their contribution to forage availability and fire regimes; therefore, this contributes to the development of well-informed conservation strategies, which can lead to sustainable utilization of rangelands, especially in relation to the changing climate