Under the continuously changing conditions of the environment, the exploration of spatial variability of soil erosion at a sub-annual temporal resolution, as well as the identification of high-soil loss time periods and areas, are crucial for implementing mitigation and land management interventions. The main objective of this study was to estimate the monthly and seasonal soil loss rates by water-induced soil erosion in Greek island of Crete for two recent hydrologically contrasting years, 2016 (dry) and 2019 (wet), as a result of Revised Universal Soil Loss Equation (RUSLE) modeling. The impact of temporal variability of the two dynamic RUSLE factors, namely rainfall erosivity (R) and cover management (C), was explored by using rainfall and remotely sensed vegetation data time-series of high temporal resolution. Soil, topographical, and land use/cover data were exploited to represent the other three static RUSLE factors, namely soil erodibility (K), slope length and steepness (LS) and support practice (P). The estimated rates were mapped presenting the spatio-temporal distribution of soil loss for the study area on a both intra-annual and inter-annual basis. The identification of high-loss months/seasons and areas in the island was achieved by these maps. Autumn (about 35 t ha⁻¹) with October (about 61 t ha⁻¹) in 2016, and winter (about 96 t ha⁻¹) with February (146 t ha⁻¹) in 2019 presented the highest mean soil loss rates on a seasonal and monthly, respectively, basis. Summer (0.22–0.25 t ha⁻¹), with its including months, showed the lowest rates in both examined years. The intense monthly fluctuations of R-factor were found to be more influential on water-induced soil erosion than the more stabilized tendency of C-factor. In both years, olive groves in terms of agricultural land use and Chania prefecture in terms of administrative division, were detected as the most prone spatial units to erosion.

Soil erosion is a constant environmental threat for the entirety of Europe. Numerous studies have been published during the last years concerning assessing soil erosion utilising Remote Sensing (RS) and Geographic Information Systems (GIS). Such studies commonly employ empirical erosion models to estimate soil loss on various spatial scales. In this context, empirical models have been highlighted as major approaches to estimate soil loss on various spatial scales. Most of these models analyse environmental factors representing soil-erosion-influencing conditions such as the climate, topography, soil regime, and surface vegetation coverage. In this study, the Google Earth Engine (GEE) cloud computing platform and Sentinel-2 satellite imagery data have been combined to assess the vegetation-coverage-related factor known as cover management factor (C-factor) at a high spatial resolution (10 m) considering a total of 38 European countries. Based on the employment of the RS derivative of the Normalised Difference Vegetation Index (NDVI) for January and December 2019, a C-factor map was generated due to mean annual estimation. National values were then calculated in terms of different types of agricultural land cover classes. Furthermore, the European C-factor (C_EUROPE) values concerning the island of Crete (Greece) were compared with relevant values estimated for the island (C_CRETE) based on Sentinel-2 images being individually selected at a monthly time-step of 2019 to generate a series of 12 maps for the C-factor in Crete. Our results yielded identical C-factor values for the different approaches. The outcomes denote GEE’s high analytic and processing abilities to analyse massive quantities of data that can provide efficient digital products for soil-erosion-related studies.

We present and assess a method to estimate missing values in daily precipitation time series for the Mediterranean island of Crete. The method involves a quantile mapping methodology originally developed for the bias correction of climate models’ output. The overall methodology is based on a two-step procedure: (a) assessment of missing values from nearby stations and (b) adjustment of the biases in the probability density function of the filled values towards the existing data of the target. The methodology is assessed for its performance in filling-in the time series of a dense precipitation station network with large gaps on the island of Crete, Greece. The results indicate that quantile mapping can benefit the filled-in missing data statistics, as well as the wet day fraction. Conceptual limitations of the method are discussed, and correct methodology application guidance is provided.

The Samaria Gorge, a tourist destination in Crete, faces wildfire risk due to its dense forest, topography, number of visitors, and climate. As part of a H2020 project, the Gorge serves as a testing pilot for a new wildfire management platform that integrates technologies to improve fire detection, risk assessment, and resource allocation during a wildfire evacuation. A central component of the project is a multi-stakeholder network, which supports both governance and acceptance of solutions. To understand the drivers of network formation, we used a Social Network Analysis and Exponential Random Graphs Model approaches to identify the drivers of stakeholder collaboration. Our findings indicate that sectoral and wildfire management focus phase are factors driving connections, while the working in intersecting jurisdictions is not. The results highlight the challenges of multi-stakeholder collaboration, suggesting that policy frameworks and information systems need to develop specific mechanisms to encourage stakeholders to bridge collaborative gaps.

Cultural heritage is a non-renewable and irreplaceable resource, and using digital technologies, especially geoinformatics, has become a powerful means of preserving and promoting it. However, integrating digital heritage, which utilizes geoinformation, into sustainable development discussions remains an ongoing challenge. This paper examines geoinformatics technologies applied to critical domains of digital heritage, including documentation, representation, and dissemination. It also explores digital heritage’s threefold role in cultural sustainability: how it mediates society, economy, and environment, develops alongside these pillars, and how geoinformatics becomes embedded within digital heritage. The study employs literature reviews and case studies to investigate the integration of geoinformatics into digital cultural heritage, aiming to inform policy and practice in heritage conservation. As a multidisciplinary field, geoinformatics combines geospatial technologies and information science, such as Participatory Geographic Information Systems (PGIS), to manage, conserve, and promote cultural heritage. Despite these technologies’ potential, digital heritage faces challenges like data security, interoperability, cost, and accessibility, alongside issues such as application simplification and speed. Opening access to digital heritage and enhancing ease of understanding are also significant obstacles. Integrating geoinformatics into digital heritage is a milestone in cultural heritage conservation. This interdisciplinary approach strengthens documentation, spatial analysis, and public engagement, fostering greater involvement in protecting shared heritage. As geospatial technologies evolve, collaboration among stakeholders is vital to ensure that cultural heritage preservation aligns with the sustainable development of society, the economy, and the environment.