Assessment of hail damages in maize using remote sensing and comparison with an insurance assessment: A case study in Lombardy
Articolo
Data di Pubblicazione:
2022
Citazione:
Assessment of hail damages in maize using remote sensing and comparison with an insurance assessment: A case study in Lombardy / C. Schillaci, F. Inverardi, M. Leonardo Battaglia, A. Perego, W. Thomason, M. Acutis. - In: ITALIAN JOURNAL OF AGRONOMY. - ISSN 2039-6805. - 17:4(2022 Dec), pp. 2126.1-2126.13. [10.4081/ija.2022.2126]
Abstract:
Studies have shown that the quantification of hail damage is
generally inaccurate and is influenced by the experience of the
field surveyors/technicians. To overcome this problem, the vegetation
indices retrieved by remote sensing, can be used to get
information about the hail damage. The aim of this work is the
detection of medium-low damages (i.e., between 10 and 30% of
the gross saleable production) using the much-used normalized
difference vegetation index (NDVI) in comparison with alternative
vegetation indices (i.e., ARVI, MCARI, SAVI, MSAVI,
MSAVI2) and their change from pre-event to post-event in five
hailstorms in Lombardy in 2018. Seventy-four overlapping scenes
(10% cloud cover) were collected from the Sentinel-2 in the
spring-summer period of 2018 in the Brescia district (Lombardy).
An unsupervised classification was carried out to automatically
identify the maize fields (grain and silage), testing the change
detection approach by searching for damage by hail and strong
wind in the Lombardy plain of Brescia. A database of 125 field
surveys (average size 4 Ha) after the hailstorm collected from the
insurance service allowed for the selection of the dates on which
the event occurred and provided a proxy of the extent of the damage
(in % of the decrease of the yield). Hail and strong wind damages
ranged from 5 to 70%, and they were used for comparison
with the satellite image change detection. The differences in the
vegetation indices obtained by Sentinel 2 before and after the hailstorm
and the insurance assessments of damage after the events
were compared to assess the degree of concordance. The modified
soil-adjusted vegetation index outperformed other vegetation
indices in detecting hail-related damages with the highest accuracy
(73.3%). On the other hand, the NDVI resulted in scarce performance
ranking last of the six indices, with an accuracy of
65.3%. Future research will evaluate how much uncertainty can be
found in the method’s limitations with vegetation indices derived
from satellites, how much is due to errors in estimating damage to
the ground, and how much is due to other causes.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
hail damages, maize, remote sensing;
Elenco autori:
C. Schillaci, F. Inverardi, M. Leonardo Battaglia, A. Perego, W. Thomason, M. Acutis
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