Proposing a Governance model for environmental crises

During August 2021, a wildfire outbreak in Evia, Greece's second largest island, resulted in a major environmental and economic crisis. Apart from biodiversity and habitat loss, the disaster triggered a financial crisis because it wiped out wood-productive forests and outdoor areas that attract visitors. This crisis highlighted the need for a new governance model in order to respond to environmental crises more effectively. The aim of this study was to investigate the acceptance and attitudes of relevant stakeholders towards establishing a Hub a proposed governance model responsible for monitoring and restoring the natural capital and biodiversity after environmental crises. Results based on quantitative data collected via questionnaires showed that most respondents were positive to the Hub and perceived that its main functions should be to recommend measures after environmental crises and to facilitate cooperation among involved stakeholders. Moreover, results pointed to preferred funding sources, stakeholder groups that should participate in the Hub and key performance indicators (KPIs) for monitoring Hub's performance. The applied methodology could guide the establishment of governance models both in the study area and other countries facing environmental crises

Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing

Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer (Emberiza citronella), which requires hedgerows associated with grassy margins. We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m2. The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability

On the Use of Unmanned Aerial Systems for Environmental Monitoring

[EN] Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.The present work has been funded by the COST Action CA16219 "HARMONIOUS-Harmonization of UAS techniques for agricultural and natural ecosystems monitoring". B. Toth acknowledges financial support by the Hungarian National Research, Development and Innovation Office (NRDI) under grant KH124765. J. Millerovd was supported by projects GA17-13998S and RVO67985939. Isabel and Jodo de Lima were supported by project HIRT (PTDC/ECM-HID/4259/2014-POCI-0145-FEDER016668).Manfreda, S.; Mccabe, MF.; Miller, PE.; Lucas, R.; Pajuelo Madrigal, V.; Mallinis, G.; Ben Dor, E.... (2018). On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sensing. 10(4):1-28. https://doi.org/10.3390/rs10040641S12810

Quantification and analysis of icebergs in a tidewater glacier fjord using an object-based approach

Tidewater glaciers are glaciers that terminate in, and calve icebergs into, the ocean. In addition to the influence that tidewater glaciers have on physical and chemical oceanography, floating icebergs serve as habitat for marine animals such as harbor seals (Phoca vitulina richardii). The availability and spatial distribution of glacier ice in the fjords is likely a key environmental variable that influences the abundance and distribution of selected marine mammals; however, the amount of ice and the fine-scale characteristics of ice in fjords have not been systematically quantified. Given the predicted changes in glacier habitat, there is a need for the development of methods that could be broadly applied to quantify changes in available ice habitat in tidewater glacier fjords. We present a case study to describe a novel method that uses object-based image analysis (OBIA) to classify floating glacier ice in a tidewater glacier fjord from high-resolution aerial digital imagery. Our objectives were to (i) develop workflows and rule sets to classify high spatial resolution airborne imagery of floating glacier ice; (ii) quantify the amount and fine-scale characteristics of floating glacier ice; (iii) and develop processes for automating the object-based analysis of floating glacier ice for large number of images from a representative survey day during June 2007 in Johns Hopkins Inlet (JHI), a tidewater glacier fjord in Glacier Bay National Park, southeastern Alaska. On 18 June 2007, JHI was comprised of brash ice ([Formula: see text] = 45.2%, SD = 41.5%), water ([Formula: see text] = 52.7%, SD = 42.3%), and icebergs ([Formula: see text] = 2.1%, SD = 1.4%). Average iceberg size per scene was 5.7 m2 (SD = 2.6 m2). We estimate the total area (± uncertainty) of iceberg habitat in the fjord to be 455,400 ± 123,000 m2. The method works well for classifying icebergs across scenes (classification accuracy of 75.6%); the largest classification errors occur in areas with densely-packed ice, low contrast between neighboring ice cover, or dark or sediment-covered ice, where icebergs may be misclassified as brash ice about 20% of the time. OBIA is a powerful image classification tool, and the method we present could be adapted and applied to other ice habitats, such as sea ice, to assess changes in ice characteristics and availability

Wildfire Risk Assessment in a Typical Mediterranean Wildland–Urban Interface of Greece

The purpose of this study was to assess spatial wildfire risk in a typical Mediterranean wildland–urban interface (WUI) in Greece and the potential effect of three different burning condition scenarios on the following four major wildfire risk components: burn probability, conditional flame length, fire size, and source–sink ratio. We applied the Minimum Travel Time fire simulation algorithm using the FlamMap and ArcFuels tools to characterize the potential response of the wildfire risk to a range of different burning scenarios. We created site-specific fuel models of the study area by measuring the field fuel parameters in representative natural fuel complexes, and we determined the spatial extent of the different fuel types and residential structures in the study area using photointerpretation procedures of large scale natural color orthophotographs. The results included simulated spatially explicit fire risk components along with wildfire risk exposure analysis and the expected net value change. Statistical significance differences in simulation outputs between the scenarios were obtained using Tukey’s significance test. The results of this study provide valuable information for decision support systems for short-term predictions of wildfire risk potential and inform wildland fire management of typical WUI areas in Greece. © 2014, Springer Science+Business Media New York

Assessing Wildfire Risk in Cultural Heritage Properties Using High Spatial and Temporal Resolution Satellite Imagery and Spatially Explicit Fire Simulations: The Case of Holy Mount Athos, Greece

Fire management implications and the design of conservation strategies on fire prone landscapes within the UNESCO World Heritage Properties require the application of wildfire risk assessment at landscape level. The objective of this study was to analyze the spatial variation of wildfire risk on Holy Mount Athos in Greece. Mt. Athos includes 20 monasteries and other structures that are threatened by increasing frequency of wildfires. Site-specific fuel models were created by measuring in the field several fuel parameters in representative natural fuel complexes, while the spatial extent of the fuel types was determined using a synergy of high-resolution imagery and high temporal information from medium spatial resolution imagery classified through object-based analysis and a machine learning classifier. The Minimum Travel Time (MTT) algorithm, as it is embedded in FlamMap software, was applied in order to evaluate Burn Probability (BP), Conditional Flame Length (CFL), Fire Size (FS), and Source-Sink Ratio (SSR). The results revealed low burn probabilities for the monasteries; however, nine out of the 20 monasteries have high fire potential in terms of fire intensity, which means that if an ignition occurs, an intense fire is expected. The outputs of this study may be used for decision-making for short-term predictions of wildfire risk at an operational level, contributing to fire suppression and management of UNESCO World Heritage Properties