Natura 2000 habitat mapping in Cyprus using high resolution orthophoto maps

Tzirkalli, Elli; Eliades, Elias; Chrysopolitou, Vasiliki; Hatziiordanou, Lena; Panagiotou, Nikos; Αγαπίου, Άθως; Antoniou, Andréas; Xenophontos, Marina; Panayiotou, Charalampos; Hadjicharalambous, Helena

doi:10.1117/12.2324301

Cited by 1 publication

(2 citation statements)

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“…Although the second study was newer (2018), focusing on multiple NATURA 2000 sites, it implemented only photointerpretation using orthophotos with some supplementary materials (i.e., Google Earth imagery, etc.) [45]. These two studies highlighted the need for more research on mapping NATURA 2000 habitats in Cyprus, underscoring the need for further studies to understand and preserve the unique biodiversity and habitats within these protected areas.…”

Section: Introductionmentioning

confidence: 98%

“…Another crucial factor for the study area is where the classifier is utilised. Diverse types of vegetation such as grasslands [35,36], shrubs [37,38], mangroves [39,40], wetlands [41,42], croplands [43], tundra [44] and forests [45] may require different remote sensing classification methods due to their unique characteristics. The challenges posed by data complexity and availability are crucial across these ecosystems.…”

Section: Introductionmentioning

confidence: 99%

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Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

Prodromou,

Theocharidis,

Gitas

et al. 2024

Remote Sensing

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Accurate mapping of forest habitats, especially in NATURA sites, is essential information for forest monitoring and sustainable management but also for habitat characterisation and ecosystem functioning. Remote sensing data and spatial modelling allow accurate mapping of the presence and distribution of tree species and habitats and are valuable tools for the long-term assessment of habitat status required by the European Commission. In order to serve the above, the present study aims to propose a methodology to accurately map the spatial distribution of forest habitats in three NATURA2000 sites of Cyprus by employing Sentinel-1 and Sentinel-2 data as well as topographic features using the Google Earth Engine (GEE). A pivotal aspect of the methodology identified was that the best band combination of the Random Forest (RF) classifier achieves the highest performance for mapping the dominant habitats in the three case studies. Specifically, in the Akamas region, eight habitat types have been mapped, in Paphos nine and six in Troodos. These habitat types are included in three of the nine habitat groups based on the EU’s Habitat Directive: the sclerophyllous scrub, rocky habitats and caves and forests. The results show that using the RF algorithm achieves the highest performance, especially using Dataset 6, which is based on S2 bands, spectral indices and topographical features, and Dataset 13, which includes S2, S1, spectral indices and topographical features. These datasets achieve an overall accuracy (OA) of approximately 91–94%. In contrast, Dataset 7, which includes only S1 bands and Dataset 9, which combines S1 bands and spectral indices, achieve the lowest performance with an OA of approximately 25–43%.

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Section: Introductionmentioning

confidence: 98%