Monitoring migrations by sea and analysis of refugee camps’ environmental impact with remote sensing

Migracije so stalnica človekovega obstoja. V zadnjih letih so se v Evropi drastično povečale migracije prebežnikov čez Sredozemsko morje. Njihova pot se pogosto zaključi v begunskih taboriščih, kjer so nastanjeni v slabih razmerah. V nalogi se z uporabo daljinsko zaznanih podatkov navezujemo na širšo tematiko migracij. Osredotočamo se na zaznavanje plovil prebežnikov po morju in na opazovanje vplivov, ki jih lahko povečane migracije povzročijo na okolje v okolici begunskih centrov. Za pridobitev podatkov o gibanju prebežnikov na morju raziskujemo možnosti uporabe raznovrstnih (zelo) visoko ločljivih optičnih satelitskih posnetkov. V ta namen smo razvili metodo za samodejno zaznavanje plovil, ki je v grobem sestavljena iz štirih zaporednih korakov: ločevanje kopnega in morja, določanje kandidatov za plovila, ločevanje plovil od neplovil in klasifikacija plovil. Rezultati kažejo, da z omenjenim algoritmom zaznavamo plovila najbolj natančno s posnetkov z ugodnejšimi vremenskimi razmerami. Večji problem pri zaznavi predstavljajo lažni pozitivi kot nezaznana plovila. Za odkrivanje sprememb površine taborišč sprememb smo uporabili analizo časovnih vrst BFAST (Breaks For Additive Season and Trend) Monitor, s katero spremljamo motnje v časovnih vrstah na podlagi modela stabilnega zgodovinskega vedenja. Analizo smo naredili na posnetkih Sentinel-2 na območjih begunskih taborišč na sredozemskih otokih, ki se že dlje časa srečujejo s pritokom migracij. Opazovane so bile negativno zaznane spremembe NDVI (normirani diferencialni vegetacijski indeks) v letu 2019. Ugotovili smo, da so podatki Sentinel-2 primerni za analizo časovnih vrst zaradi njihove goste časovne vrste. Ocena verjetnih vplivov v okolici begunskih taborišč je določena na podlagi treh verjetnostnih razredov glede na velikost negativne magnitude zaznanih sprememb.Migrations have been a feature of human existence for centuries. The migrations of migrants who risk their lives to reach Europe via the Mediterranean have increased dramatically in recent times. Their journey often ends in refugee camps where they are housed in poor conditions. In this dissertation we use remote sensing data in the context of the broader issue of migration. We focus on the detection of migrant vessels at sea and the environmental impact that increased migration can have around refugee centers. In order to obtain data on the movement of migrants at sea, we investigate the possibilities of using a variety of (very) high-resolution optical satellite images. We have developed a method for automatic vessel detection consisting of four consecutive steps: sea-land separation, candidate detection, vessel discrimination and vessel classification. The results show that the developed algorithm more accurately detects vessels from images with more favourable weather conditions. False positives are a greater problem in detection than undetected vessels. To detect changes in the vicinity of the refugee camps, we used the time series analysis BFAST (Breaks For Additive Season and Trend) Monitor, which monitors disturbances in time series based on a model for stable historical behaviour. The analysis was applied on Sentinel-2 images in areas of refugee camps on the Mediterranean islands that have been experiencing long term influxes of migrants. We observed negatively detected changes in the NDVI (normalised difference vegetation index) in 2019. Sentinel-2 data proved to be suitable for time series analysis due to their dense time series. The assessment of potential environmental impacts in the vicinity of refugee camps are further determined on the basis of probability classes defined according to the negative magnitude of the observed changes

Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase