Search

Accurate estimates of forest inventory parameters are essential to assess the potential hazards of wildfire and obtain above-ground biomass and carbon sequestration data that help develop strategies for the sustainable management of forests. This study aims to assess the accuracy of estimation of forest inventory parameters, such as diameter at breast height (DBH) and tree height, obtained using a Terrestrial Laser Scanner (TLS) in a Mediterranean coniferous stand in western Greece. DBH values measured in the field were compared with those derived from a TLS using the Computree algorithm for automatic DBH detection, and resulted in a coefficient of determination ($$R^{2})$$ R2) that ranged from 0.75 to 0.96 at the plot level. The average $$R^{2}$$ R2 and RMSE values of 0.80 and 1.07 m, respectively, were obtained when comparing the tree heights recorded by TLS and field data. Finally, the feasibility of TLS to estimate total dry biomass was investigated by comparing the TLS-derived total dry biomass values with those derived from field estimates using an allometric equation. The average estimate of biomass per hectare according to the TLS inventory data was 373.17 Mg/ha while that from field observations was 366.82 Mg/ha. The results confirm that TLS can provide non-destructive, high-resolution and precise determination of forest inventory parameters. The outcomes of this research will help researchers to better comprehend deviations in the accuracy of forest inventory variable retrieval resulting from the variation in the processing parameters supplied and additionally boost decision-making in forest management. Einsatz von terrestrischem Laserscanning zur Bestimmung von Forstinventurparametern in einem mediterranen Kiefernwald im westlichen Griechenland.Eine genaue Abschätzung von Parametern der Waldinventur ist wichtig, um potenzielle Waldbrandgefahr zu erkennen oder Daten zur oberirdischen Biomasse und Kohlenstoffbindung zu erhalten. Diese sind wiederum bei der strategischen Planung für nachhaltiges Waldmanagement von Nutzen. Ziel dieser Arbeit ist die Bewertung der Genauigkeit von aus terrestrischem Laserscanning (TLS) abgeleiteten Waldparametern Brusthöhendurchmesser (DBH) und Baumhöhe. Dazu wurde eine Studie in einem Koniferenschlag im Mittelmeerraum, Westgriechenland, durchgeführt. Zur automatisierten Ableitung des TLS-basierten DBH wurde der Computree-Algorithmus verwendet. Im statistischen Vergleich mit Feldmessungen des DBH wurden auf der Plot-Ebene Bestimmtheitsmaße zwischen 0,75 und 0,96 erreicht. Bei der Baumhöhenabschätzung mittels TLS wurden im Vergleich mit den Aufnahmen auf der Plot-Ebene Bestimmtheitsmaße von 0,8 und mittlere quadratische Abweichungen von 1,07 m ermittelt. Abschließend wurde untersucht, inwieweit sich Ableitungen der gesamten trockenen Biomasse eine allometrische Gleichung unterscheiden, wenn sie auf TLS-Ergebnisse oder Bodenmessungen angewendet werden. Es wurde mit 373,17 Mg/ha für TLS und 366,82 Mg/ha für Bodenmessungen nur eine geringe Abweichung zwischen den beiden Eingangsdaten zur Bestimmung der Biomasse festgestellt. Die Ergebnisse unterstreichen die Eignung von TLS als Methode zur nicht-destruktiven, hoch-aufgelösten und genauen Bestimmung von Parametern der Waldinventur. Die Ergebnisse können zukünftigen Forschungsarbeiten zu einem besseren Verständnis von Abweichungen in der Genauigkeit bei der Nutzung von Eingabeparametern unterschiedlicher Herkunft dienen und Entscheidungsfindungen im Waldmanagement unterstützen.

In July 1983, a large wildfire occurred on the island of Karpathos in Greece. However, only a general sketch of the burn perimeter was available and this lacked detailed spatial information, particularly for unburned patches within the fire scar perimeter. A study was undertaken to correctly map the area burned using USGS-archived Landsat data by applying several digital image processing techniques. This paper summarizes and discusses the main findings of that study and provides some general recommendations on the use of remote sensing and archived Landsat data for reconstructing fire history. Remote sensing along with geographic information systems can provide an excellent framework for fast, reliable data capture, measurement, and synthesis, all of which are essential for thorough eco-environmental analysis. Satellite data of multiple types offer an unlimited source of information due to their rich spectral and spatial information content. Satellite mapping of burned areas is considered a standard technique in creating maps of fire scars at multiple scales as a function of the satellite sensor's geometric resolution.

This paper describes the results of a geo-statistical analysis carried out at the provincial level in Southern Europe to model wildfire occurrence from socio-economic and demographic indicators together with land cover and agricultural statistics. We applied a classical ordinary least squares (OLS) linear regression together with a geographically weighted regression (GWR) to explain long-term wild-fire occurrence patterns (mean annual density of >1 ha fires). The explanatory power of the OLS model increased from 52% to 78% as a result of the non-constant relationships between fire occurrence and the underlying explanatory variables throughout the Mediterranean Basin. The global model we developed (i.e., OLS regression) was not sufficient to fully describe the underlying causal factors in wildfire occurrence modeling. Indeed, local approaches (i.e., GWR) can complement the global model in overcoming the problem of non-stationarity or missing variables. Our results confirm the importance of agrarian activities, land abandonment, and development processes as underlying factors of fire occurrence. The identification of regions with spatially varying relationships can contribute to the better understanding of the fire problem, especially over large geographic areas, while at the same time recognizing its local character. This can be very important for fire management and policy.