The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-4/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W1-2022, 111–118, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-111-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W1-2022, 111–118, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-111-2022
 
05 Aug 2022
05 Aug 2022

THE USE OF OPEN SOURCE SOFTWARE FOR MONITORING BEE DIVERSITY IN NATURAL SYSTEMS: THE BEEMS PROJECT

P. Dabove and V. Di Pietra P. Dabove and V. Di Pietra
  • Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy

Keywords: Photogrammetry, supervised classification, segmentation, bees monitoring, natural systems

Abstract. This work wants to highlight the results obtained during the BEEMS (Monitoring Bee Diversity in Natural System) project, which the main goal was to answer the following question: Which biotic and abiotic indicators of floral and nesting resources best reflect the diversity of bee species and community composition in the Israeli natural environment? The research was oriented towards the cost-effectiveness analysis of new aerial geomatics techniques and classical ground-based methods for collecting the indicators described above, based only on open-source software for data analysis. Two complementary study systems in central Israel have been considered: the Alexander Stream National Park, an area undergoing an ecological restoration project in a sandy ecosystem, and the Judean foothills area, to the South of Tel Aviv. In each study system, different surveys of bees, flowers, nesting substrates and soil, using classical field measurement methods have been conducted. Simultaneously, an integrated aero photogrammetric survey, acquiring different spectral responses of the land surface by means of Uncrewed Aerial Vehicle (UAV) imaging systems have been performed. The multispectral sensors have provided surface spectral response out of the visible spectrum, while the photogrammetric reconstruction has provided three-dimensional information. Thanks to Artificial Intelligence algorithms and the richness of the data acquired, a methodology for Land Cover Classification has been developed. The results obtained by ground surveys and advanced geomatics tools have been compared and overlapped. The results are promising and show a good fit between the two approaches, and high performance of the geomatics tools in providing valuable ecological data.