Forest biodiversity is an essential indicator of the sustainability and functioning of forest ecosystems worldwide. Improvements in remote sensing data characteristics such as temporal, spectral, radiometric and spatial resolution, elevates the potential of satellite imagery for species diversity monitoring at various spatial and temporal scales. This study investigated the use of Sentinel-2 MSI and RapidEye imagery for estimating and mapping a-diversity in a protected forest area in Northern Greece. Additional objectives of the study included the comparative evaluation of the information content of the two sensors and the assessment of the optimum diversity index (S, H and D1) that could be related with the spectral and spatial information content of the images. Field data were collected during summer 2018, from 60 square plots within the Natura 2000 sites of the Northern Pindos National Park. Sentinel-2 and RapidEye satellite images were acquired over the same season and pre-processed for minimizing errors and variability due to atmosphere and topography. A robust machine learning algorithm was used to model the relationship between diversity indices and spectral and spatial features of the images. The results of the analysis demonstrated the potential of the remote sensing technology for monitoring and reporting biodiversity over forest protected areas.
Fire danger forecast constitutes one of the most important components of integrated fire management since it provides
crucial information for efficient pre-fire planning, alertness and timely response to a possible fire event. The aim of this
work is to develop an index that has the capability of predicting accurately fire danger on a mid-term basis. The
methodology that is currently under development is based on an innovative approach that employs dry fuel spatial
connectivity as well as biophysical and topological variables for the reliable prediction of fire danger. More specifically,
the estimation of the dry fuel connectivity is based on a previously proposed automated procedure implemented in R
software that uses Moderate Resolution Imaging Spectrometer (MODIS) time series data. Dry fuel connectivity estimates
are then combined with other ancillary data such as fuel type and proximity to roads in order to result in the generation of
the proposed mid-term fire danger index. The innovation of the proposed index—which will be evaluated by comparison
to historical fire data—lies in the fact that its calculation is almost solely affected by the availability of satellite data.
Finally, it should be noted that the index is developed within the framework of the National Observatory of Forest Fires
(NOFFi) project.
Efficient forest fire management is a key element for alleviating the catastrophic impacts of wildfires. Overall, the effective response to fire events necessitates adequate planning and preparedness before the start of the fire season, as well as quantifying the environmental impacts in case of wildfires. Moreover, the estimation of fire danger provides crucial information required for the optimal allocation and distribution of the available resources. The Greek National Observatory of Forest Fires (NOFFi)—established by the Greek Forestry Service in collaboration with the Laboratory of Forest Management and Remote Sensing of the Aristotle University of Thessaloniki and the International Balkan Center—aims to develop a series of modern products and services for supporting the efficient forest fire prevention management in Greece and the Balkan region, as well as to stimulate the development of transnational fire prevention and impacts mitigation policies. More specifically, NOFFi provides three main fire-related products and services: a) a remote sensing-based fuel type mapping methodology, b) a semi-automatic burned area mapping service, and c) a dynamically updatable fire danger index providing mid- to long-term predictions. The fuel type mapping methodology was developed and applied across the country, following an object-oriented approach and using Landsat 8 OLI satellite imagery. The results showcase the effectiveness of the generated methodology in obtaining highly accurate fuel type maps on a national level. The burned area mapping methodology was developed as a semi-automatic object-based classification process, carefully crafted to minimize user interaction and, hence, be easily applicable on a near real-time operational level as well as for mapping historical events. NOFFi’s products can be visualized through the interactive Fire Forest portal, which allows the involvement and awareness of the relevant stakeholders via the Public Participation GIS (PPGIS) tool.
Forest fires greatly influence the stability and functions of the forest ecosystems. The ever increasing need for accurate and detailed information regarding post-fire effects (burn severity) has led to several studies on the matter. In this study the combined use of Very High Resolution (VHR) satellite data (GeoEye), Objectbased image analysis (OBIA) and Composite Burn Index (CBI) measurements in estimating burn severity, at two different time points (2011 and 2012) is assessed. The accuracy of the produced maps was assessed and changes in burn severity between the two dates were detected using the post classification comparison approach. It was found that the produced burn severity map for 2011 was approximately 10% more accurate than that of 2012. This was mainly attributed to the increased heterogeneity of the study area in the second year, which led to an increased number of mixed class objects and consequently made it more difficult to spectrally discriminate between the severity classes. Following the post-classification analysis, the severity class changes were mainly attributed to the trees’ ability to survive severe fire damage and sprout new leaves. Moreover, the results of the study suggest that when classifying CBI-based burn severity using VHR imagery it would be preferable to use images captured soon after the fire.
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