<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Obtaining accurate forest cover information and dynamics of land occupation, through time, such as the spatial extent and pattern of disturbance and recovery is essential knowledge and assistance for forest managers and a crucial basis for the protection and conservation of current forest resources. Because most recent researches have focused on forest field survey and monitoring, a land classification containing information on forest cover dynamics is critically needed. Over the last decades, advances in remote sensing technology have enabled an accurate classification of different land covers from several sensors and remotely sensed data. We presently retained Tikjda forest (Djurdjura southerner, Algeria) as a case study to investigate the possibility of aerial photos classification and to analyze the historical dynamics of the area using a change detection analysis of multi-temporal data. To classify the study area’s main cover types, we used photographs collected over a period of 34 years (i.e., from 1983 to 2017). The results revealed that in 2017, Tikjda forest was composed of forest areas (24.1%), degraded areas (49.7%), and barren areas (26.2%). Throughout the investigated period, the analysis revealed a notable increase in barren areas (+9.8%), and degraded areas (+14.4%), While forest areas experienced a significant decrease (−24.2%). Moreover, the results confirm the potential of aerial photographs for an accurate classification of forests.</p>
</abstract>

Obtaining accurate forest cover information and dynamics of land occupation, through time, such as the spatial extent and pattern of disturbance and recovery is essential knowledge and assistance for forest managers and a crucial basis for the protection and conservation of current forest resources. Because most recent researches have focused on forest field survey and monitoring, a land classification containing information on forest cover dynamics is critically needed. Over the last decades, advances in remote sensing technology have enabled an accurate classification of different land covers from several sensors and remotely sensed data. We presently retained Tikjda forest (Djurdjura southerner, Algeria) as a case study to investigate the possibility of aerial photos classification and to analyze the historical dynamics of the area using a change detection analysis of multi-temporal data. To classify the study area’s main cover types, we used photographs collected over a period of 34 years (i.e., from 1983 to 2017). The results revealed that in 2017, Tikjda forest was composed of forest areas (24.1%), degraded areas (49.7%), and barren areas (26.2%). Throughout the investigated period, the analysis revealed a notable increase in barren areas (+9.8%), and degraded areas (+14.4%), While forest areas experienced a significant decrease (−24.2%). Moreover, the results confirm the potential of aerial photographs for an accurate classification of forests.

A Spatiotemporal Survey of Tikjda Forest Dynamics Over A 34-Year Period by Aerial Photographs

Ekológia (Bratislava)

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Obtaining accurate forest cover information and dynamics of land occupation, through time, such as the spatial extent and pattern of disturbance and recovery...

A Spatiotemporal Survey of Tikjda Forest Dynamics Over A 34-Year Period by Aerial Photographs

Publié en ligne: 30 juin 2023

Pages: 133 - 141

Reçu: 16 sept. 2022

Accepté: 06 mars 2023

DOI: https://doi.org/10.2478/eko-2023-0016

Mots clés
remote sensing, supervised classification, Djurdjura, Algeria

© 2023 Tassadit Dib et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Spatiotemporal Survey of Tikjda Forest Dynamics Over A 34-Year Period by Aerial Photographs

Publié en ligne: 30 juin 2023

Pages: 133 - 141

Reçu: 16 sept. 2022

Accepté: 06 mars 2023

DOI: https://doi.org/10.2478/eko-2023-0016

Mots clésremote sensing, supervised classification, Djurdjura, Algeria

© 2023 Tassadit Dib et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
remote sensing, supervised classification, Djurdjura, Algeria