The contribution of remote sensing for the development of a Green-Holistic IoT Platform for Forest Management and Monitoring: Reforestation and Deforestation Modules
Journal
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Date Issued
July 31, 2025
DOI
10.5194/isprs-archives-XLVIII-G-2025-1215-2025
Abstract
The Green-HIT project focuses on effective and efficient forest monitoring and management, which holds the promise for climate
change mitigation, ecosystem conservation, and biodiversity loss reduction. This project is funded by the Cyprus Research &
Innovation Foundation (CODEVELOP-GT/0322) and is currently being implemented in Cyprus. Cyprus is located in the Eastern
Mediterranean, an area frequently affected by various incidents that impact the preservation of forests (for example, forest fires, illegal
logging, hunting, trespassing, and other activities that are damaging to biodiversity), especially during the summer season. Specifically
for forest fires, several factors contribute to the increased risk of fire, such as prolonged drought, hot summers, strong winds, steep
forest slopes, and flammable vegetation. Early warning and direct management facilities are paramount to efficiently tackling such
disastrous events. To this end, the Green-HIT project aims to develop a holistic IoT platform for supporting productivity,
competitiveness, and growth of the economy and the promotion of digital and green technology via forest management and monitoring
in a post-pandemic world by (a) offering support for prevention, detection and reaction to forest fires, (b) providing afforestation and/or
reforestation recommendations, (c) protecting forests from illegal logging and hunting, (d) monitoring forests and forest areas, and (e)
offering forest mapping and inventory facilities by collecting, combining and analyzing field and remotely sensed data. This study will
present the deforestation and reforestation module of the Green-HIT platform, which aims to identify and suggest (to relevant
authorities), possible areas for reforestation. This module was developed using remote sensing data. Specifically, a change detection
technique using the Euclidean distance was used for the identification of deforested areas achieving an Overal Accuracy equal to 67.7
%. Also, for the reforestation module, a multicriteria analysis was applied using several parameters like dNBR, land cover, fire history,
soil erosion, etc., using the Google Earth Engine platform. For the purposes of this study, the Argaka fire event was selected to evaluate
the accuracy of the developed model.
change mitigation, ecosystem conservation, and biodiversity loss reduction. This project is funded by the Cyprus Research &
Innovation Foundation (CODEVELOP-GT/0322) and is currently being implemented in Cyprus. Cyprus is located in the Eastern
Mediterranean, an area frequently affected by various incidents that impact the preservation of forests (for example, forest fires, illegal
logging, hunting, trespassing, and other activities that are damaging to biodiversity), especially during the summer season. Specifically
for forest fires, several factors contribute to the increased risk of fire, such as prolonged drought, hot summers, strong winds, steep
forest slopes, and flammable vegetation. Early warning and direct management facilities are paramount to efficiently tackling such
disastrous events. To this end, the Green-HIT project aims to develop a holistic IoT platform for supporting productivity,
competitiveness, and growth of the economy and the promotion of digital and green technology via forest management and monitoring
in a post-pandemic world by (a) offering support for prevention, detection and reaction to forest fires, (b) providing afforestation and/or
reforestation recommendations, (c) protecting forests from illegal logging and hunting, (d) monitoring forests and forest areas, and (e)
offering forest mapping and inventory facilities by collecting, combining and analyzing field and remotely sensed data. This study will
present the deforestation and reforestation module of the Green-HIT platform, which aims to identify and suggest (to relevant
authorities), possible areas for reforestation. This module was developed using remote sensing data. Specifically, a change detection
technique using the Euclidean distance was used for the identification of deforested areas achieving an Overal Accuracy equal to 67.7
%. Also, for the reforestation module, a multicriteria analysis was applied using several parameters like dNBR, land cover, fire history,
soil erosion, etc., using the Google Earth Engine platform. For the purposes of this study, the Argaka fire event was selected to evaluate
the accuracy of the developed model.
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