Integrating low-altitude drone based-imagery and OBIA for mapping and manage semi natural grassland habitats
Ventura, Daniele | Napoleone, Francesca | Cannucci, Silvia | Alleaume, Samuel | Valentini, Emiliana | Casoli, Edoardo | Burrascano, Sabina | Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome] (UNIROMA) | Università degli Studi di Siena = University of Siena (UNISI) | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Institute of Polar Sciences [Venezia-Mestre] (CNR-ISP) ; National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR)
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Показать больше [+] Меньше [-]Английский. Monitoring semi-natural grasslands is difficult and costly because they occur in highly dynamic and extremely complex habitat mosaics. We combined the use of a low-cost unmanned aerial vehicle (UAV) and Structure from Motion (SfM) photogrammetry to acquire high spatial resolution (∼1.5 cm pixel) RGB imagery. After image classification through Object-Based Image Analysis (OBIA), we accurately were able to distinguish three semi-natural grassland types, one of which is a habitat of conservation concern. The use of orthomosaics, digital elevation models (DEMs), and canopy height models (CHMs) yielded excellent overall classification accuracies (>89%) assessed through both remotely validated and ground-truthed points. We identified two layers of woody vegetation with a user's (UA) and producer's (PA) accuracies >73% and three grassland types: closed grassland (UA = 94%; PA = 97%), open grassland habitat (UA = 97%; PA = 93%) and open grasslands with soil erosion (UA = 96%; PA = 98%). The grassland types differed substantially in the cover of vegetation, rocks, stones, and bare soil measured in the field, as well as in the number and relative cover of the habitat diagnostic species. The proposed methodology is highly promising for mapping and monitoring semi-natural grassland of conservation concern in support of tailored management actions.
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