The intensity of flowering of the holm oak trees is important for the annual phenological monitoring and as a predictive index of final acorn production. Their male flowers present in long catkins of intense yellow color and the estimation of their abundance in the field is a time-consuming task that becomes unfeasible at a large scale. In this work, a methodology to estimate the intensity of flowering of oak trees using RGB (Red Green Blue) images, provided by an unmanned aerial vehicle, was tested. During the spring of 2019, three aerial zenith images of 3 cm spatial resolution were taken in three selected dehesa sites, together with simultaneous ground digital photographs per tree (50 at each site). The intensity of flowering was visually estimated using the ground digital photographs in three categories, ranging from 1 (little or no flowering) to 3 (high flowering). A simple flowering intensity index, based on the closeness to pure yellow within a Cartesian RGB space, was developed to check the relationship between the drone images and the visually analyzed photographs. The results showed that those trees with lower flowering intensity were grouped in higher yellow distances and the high flowering intensity trees in the lower ones. As a result, it can be concluded that this index was able to identify qualitatively the flowering intensity of holm oaks at the farm level and could be useful for future phenological or productivity applications.
KEYWORDS: Vegetation, Remote sensing, Satellites, Biological research, Agriculture, Data modeling, Solar radiation models, Systems modeling, Solar radiation, Sensors
Cover crop in olive orchards is an increasingly applied soil and water conservation strategy, supported by European policies due to its multiple environmental benefits. To quantify these benefits, supervise and encourage the adoption of this practice, robust and affordable monitoring indicators of the cover crop dynamic and its biomass are required. This work represents the first attempt to estimate the biomass produced by olive grove cover crops based on remotely sensed data and an adaptation of the Monteith efficiencies approach. Ten olive tree fields were selected, distributed in three zones of Southern Spain. They comprised a high environmental variability and differed in the herbaceous layer management: cover crop in strips; non-tillage without strips (full coverage); and conventional tillage. An adaptation of the LUE (Light Use Efficiency)- model was applied to estimate Net Primary Production (NPP) using meteorological and Sentinel-2 data and subtracting the contribution of the wooded vegetation from the ground spectral response. The results showed an uneven adjustment in different fields. RMSD was equal to 650 kg ha-1, with an MBD of -17 kg ha-1, indicating a moderately high error (around 39%) but not too much bias. This error suggests that the model requires further refining, including the adjustment of model parameters to better represent this agrosystem. However, the evolution of biomass accumulation throughout the cover crop growing season and the behaviour of the daily biomass production provided interesting keys about the cover crops’ phenology and management, supporting the discrimination between management practices.
This work shows the sensitivity of NDVI as an indicator of the global hydrological regime of the year. The annual
water balance in the area was simulated through a physically-based distributed hydrological model previously calibrated
and validated in the area from 2001 till 2010. NDVI was obtained from Landsat TM at the end of the dry season in 1000
points randomly distributed over a pine cover in a mountainous Mediterranean area. The influence of different
hydrological processes related to the water balance in the soil on the NDVI values was analyzed through Pearson
correlation matrices and Principal Components Analyses. Results showed that the NDVI was particularly sensitive to the
regime of annual variables related to the snow layer dynamics, especially to snowmelt. These relationships were
quantified, with the best fit being obtained between NDVI and the dimensionless index snowmelt divided by
precipitation (R2 around 0.7). The adjustments obtained could, in the future, constitute a tool for the estimation of hydrological variables from satellite data in data-poor situations conditioned by the commonly steep slopes and difficult
access in mountainous areas.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.