Recent reports stress the vulnerability of forest ecosystems in the European Union (EU), especially in the south. Cyprus is an island in the south of EU and the eastern of the Mediterranean Sea. While Cyprus’ vulnerability is stressed, Cyprus was included in the worst-performing countries regarding EU carbon emission’s targets of 2020. For mitigating climate change, Cyprus could benefit for tailored education and improved policy making. This study analyses the perceptions of the Cypriot residents about climate change and forest degradation aiming (1) to gain a better understanding of whether Cypriot residents understand its importance, (2) to understand if the general public is able to observe the changes noted in the literature, (3) to understand how perceptions are differentiated across different demographic categories, and (4) to derive correlations between demographic data and perceptions. This is a quantitative study; a questionnaire was used as a tool and the responses received were 416. It was highlighted that 65.62% of the participants stated that they noticed moderate to very much degradation of Cypriot coniferous forests. A potential degradation reason was written down by 150 people, of whom 31.33% referred to tree die-back, while many stated decreased soil moisture and difficulty in regeneration. All these reasons of degradation were either stated or suspected in the literature. Additionally, the demographic analysis showed that there may be an association between employability and beliefs/observations about climate change. The results of the research could be used for tailored education, further research, and promoting environmentally friendly policies. This will support Cyprus and other countries in reaching their Green Deal targets and, consequently, mitigate the severe effects of climate change.

<p>Forests are globally an important environmental and ecological resource since they retrain water through their routes and therefore limit flooding events and soil erosion from moderate rainfall. They also act as carbon sinks, provide food, clean water and natural habitat for humans and other species, including threatened ones. Recent reports stressed the vulnerability of EU forest ecosystem to climate change impacts (EEA, 2012) (IPPC, et al., 2014). Climate change is a significant factor in the increasing forest fires and tree species being unable to adapt to the severity and frequency of drought during the summer period. Consequently, the possibility of increased insect pests and tree diseases is high as trees have been weakened by the extreme weather conditions. In Cyprus, there are two types of pine trees that exists on Troodos mountains, Pinus Nigra and Pinus Brutia, that may have been influenced by the reduced snowfall and extended summer droughts during the last decades.</p><p> </p><p>The overarching aim of this project is to research the impact of Land Surface Temperature on Cypriot forests on Troodos mountains by analysing time-series of radar and thermal satellite data. Impacts may include forest decline that does not relate to fire events, decreased forest density and alternations to timing of forest blooming initiation, duration and termination. Radar systems emitted pulses that can penetrate forest canopy due to the size of its wavelength and, therefore, collect information between tree branches without being affected by clouds. This presentation will focus on radar analysis conducted; testing of various methods, and how the processing pipeline has been automated.</p><p> </p><p>The project ‘ASTARTE’ (EXCELLENCE/0918/0341) is co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Innovation Foundation.</p>

<div> <p><span data-contrast="auto">Landslides constitute a significant geohazard causing human losses and significantly affecting the economy worldwide. Earth Observation and the exploitation of the freely available Copernicus datasets, such as the Sentinel-1 and Sentinel-2 satellite images, can assist in the systematic monitoring of landslides overcoming the restrictions arising from in situ measurements. This study shows how the Google Earth Engine (GEE) platform can be utilised for the rapid mapping of landslides and effectively integrate both passive and active satellite data to enhance the results’ reliability. The GEE is a cloud computing platform designed to store and process huge datasets for scientific analysis and visualization of geospatial datasets where open-source images are acquired by several satellites.</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":0,"335559740":240}"> </span></p> </div> <div> <p><span data-contrast="auto">For this study, Ground Range Detection (GRD) Sentinel-1 and multispectral Sentinel-2 satellite data were utilised for a time period between 2016 and 2021. Multitemporal SAR change detection was conducted to identify potential landslides using GRD Sentinel-1 satellite images. Moreover, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Moisture Index (SMI) and Bare Soil Index (BSI) indices were used for the multispectral data. Multi-temporal image composites were created for the two periods. Furthermore, for all image collections, the calculated spectral indices were added as new bands to all images, and the maximum value for each pixel of the vegetation indices was taken. Following, the difference image for each spectral index was created based on two methods, i.e., the first method was based on subtracting the two time periods, and the second one on subtracting each year from the total average for the time period from 2016 until 2021. The possible events were then masked using the thresholding technique based on the trial-and-error procedure where the analyst adjusts manually the thresholds and evaluates the resulting image until satisfied. Based on the results derived from the abovementioned processing, the use of the second method, i.e., subtracting each year from the average, based on the NDVI spectral index provides better results. The proposed methodology was tested in Paphos city in Cyprus because of the occurrence of numerous landslide events in this area, based on the landslide inventory provided by the Geological Survey Department of the Ministry of Agriculture, Rural Development and Environment. The results of this study were validated using high-resolution images from Google Earth in combination with the data from the Geological Survey department.</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":0,"335559740":240}"> </span></p> <div> <p><strong><span data-contrast="none">Acknowledgements</span></strong><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":6,"335551620":6,"335559738":0,"335559739":0,"335559740":240}"> </span></p> </div> <div> <p><span data-contrast="none">The authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The 'EXCELSIOR' project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology. The authors would also like to thank the Geological Survey Department of the Ministry of Agriculture, Rural Development and Environment for the provision of the landslide inventory.</span></p> </div> </div>

<p>Climate change can be described as the dominant factor all these decades concerning changes in forest phenology while, at the same time, temperature affects the development time (Barrett & Brown, 2021; X.Zhou et al., 2020; Suepa et al., 2016). Satellite image-time series data have proven their value regarding forest health and forest phenology observation. Monitoring continuous plant phenology is critical for the ecosystem at a regional and global level since the high sensitivity of vegetation life cycle to climate change; the so-called phenophases are essential biological indicators to comprehend how climate change has impacted these ecosystems and how this will change the ensuing years. (Buitenwerf, Rose, and Higgins 2015; Johansson et al. 2015).  </p> <p><span data-preserver-spaces="true">This study conducts a time-series analysis using the breaks for additive season and trend (BFAST) time-series decomposition algorithm, to detect possible abrupt changes in forest seasonality and the impacts of extreme climatic events on forest health, examining Sentinel-1 and Sentinel-2 data for the period 2017-2021. The backscatter coefficient from Sentinel-1, Normalised Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and Green Chlorophyll Index (GCI) were created by Sentinel-2 and assessed to find possible correlations between them. All the satellite time-series data derived through the Google Earth Engine platform.</span></p> <p><span data-preserver-spaces="true">The study area is the Paphos Forest, managed by the Department of Forest which could be described as a representative Mediterranean forest; thus, it is vital to monitor it because Mediterranean forests are expected to experience the first climate change in Europe. More specifically, the study focus on the Nortwest, West and Southwest areas of the Paphos Forest since the SAR images are from Ascending orbit. Moreover, Paphos forest has unspoiled vegetation, and a highly reduced number of forest wildfires have occurred in recent years, favouring the reliability of the research's results. </span></p> <p> </p> <p> </p> <p><strong><span data-preserver-spaces="true">Acknowledgements</span></strong></p> <p><span data-preserver-spaces="true">The authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The 'EXCELSIOR' project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.</span></p>

Observing phenological changes are important for evaluating the natural regeneration process of forests, especially in Mediterranean areas where the regeneration of coniferous forests depends on seeds and the changes in blossoming time are influenced by climate change. The high temporal resolution of Sentinel-1 data allows the time series analysis of synthetic aperture radar (SAR) data, but it is still unknown how these data could be utilised for better understanding forest phenology and climate-related alternations. This study investigates the phenological cycle of Paphos forest, Cyprus using SAR data from 1992 to 2021, acquired by ERS-1/2, Envisat and Sentinel-1. An average phenological diagram was created for each space mission and a more detailed analysis was performed from October 2014 to November 2021, using the higher temporal resolution of Sentinel-1 data. Meteorological data were used to better understand the drivers of blooming alternations. Using the interquartile range (IQR), outliers were detected and replaced using the Kalman filter imputation. Forecasting trend lines were used to estimate the amplitude of the summer peaks and the annual mean. The observation of the average phenology from each satellite mission showed that there were two main blooming peaks each year: the winter and the summer peak. We argue that the winter peak relates to increased foliage, water content and/or increased soil moisture. The winter peak was followed by a fall in February reaching the lower point around March, due to the act of pine processionary (Thaumetopoea pityocampa). The summer peak should relate to the annual regeneration of needles and the drop of the old ones. A delay in the summer peak—in August 2018—was associated with increased high temperatures in May 2018. Simultaneously, the appearance of one peak instead of two in the σVH time series during the period November 2014–October 2015 may be linked to a reduced act of the pine processionary associated with low November temperatures. Furthermore, there was an outlier in February 2015 with very low backscattering coefficients and it was associated with a drought year. Finally, predicting the amplitude of July 2020 returned high relevant Root Mean Square Error (rRMSE). Seven years of time series data are limiting for predicting using trend lines and many parameters need to be taken into consideration, including the increased rainfall between November 2018 and March 2020.