ESTIMATING CROP DENSITY FROM MULTI-SPECTRAL UAV IMAGERY IN MAIZE CROP

Stroppiana, D.; Pepe, M.; Boschetti, M.; Crema, A.; Candiani, G.; Giordan, D.; Baldo, M.; Allasia, P.; Monopoli, L.

In this study we exploit UAV data for estimating Fractional Vegetation Cover (FVC) of maize crop at the early stages of the growing season. UAV survey with a MicaSense RedEdge multispectral sensor was carried out on July 13th, 2017 over a maize field in Italy; simultaneous RGB in situ pictures were collected to build a reference dataset of FVC over 15 ESU (Elementary Sampling Units) distributed over the field under investigation. The approach proposed for classification of UAV data is based on local contrast enhancement techniques applied to a vegetation index (NDVI-Normalized Difference Vegetation Index) to capture signal from small plants at the early development stage. The output fc map is obtained over grid cells over 70 × 70 cm size. The approach proposed here, based on contextual analysis, reduced artefacts due to illumination conditions by better enhancing signal from vegetation compared to, for example, simple band combination such as vegetation index alone (e.g. NDVI). Validation accomplished by a point comparison between estimated (from UAV) and in situ measured FVC values provided R2 = 0.73 and RMSE = 6%.

Zitieren

Zitierform:

Stroppiana, D. / Pepe, M. / Boschetti, M. / et al: ESTIMATING CROP DENSITY FROM MULTI-SPECTRAL UAV IMAGERY IN MAIZE CROP. 2019. Copernicus Publications.

Rechte

Rechteinhaber: D. Stroppiana et al.

Nutzung und Vervielfältigung:

Export