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.
The regular monitoring of the evapotranspiration rates and their links with vegetation conditions and soil moisture may support management and hydrological planning leading to reduce the economic and environmental vulnerability of complex water-controlled Mediterranean ecosystems. In this work, the monitoring of water use over a basin with a predominant oak savanna (known in Spain as dehesa) was conducted for two years, 2013 and 2014, monitoring ET at both fine spatial and temporal resolution in different seasons.
A global 5 km daily ET product, developed with the ALEXI model and MODIS day-night temperature difference, was used as starting point. Flux estimations with higher spatial resolutions were obtained with the associated flux disaggregation scheme, DisALEXI, using surface temperature data from the polar orbiting satellites MODIS (1 Km, daily) and Landsat 7/8 (60-120m and sharpened to 30m, 16 days) and the previously estimated coarse resolution fluxes. The results achieved supported the ability of this scheme to accurately estimate daytime-integrated energy fluxes over this system, using input data with different spatio-temporal resolution and without the need for ground observations. Daily ET series at 30 m spatial resolution, generated using STARFM fusion technique, has provided a significant improvement in spatial heterogeneity assessment of the ET series, with RMSE values of 0.56 and 0.68 mm/day for each year, representing an enhancement with respect to interpolated Landsat series. In summary, this approach was demostrated to be robust and operative to map ET at watershed scale with a suitable spatial and temporal resolution for applications over the dehesa ecosystem.
Spatio-temporal changes in vegetation at the basin scale are difficult to characterize, and remote sensing is a major
source of data for this purpose. These sensors may provide distributed series of spectral properties of the vegetation with
different spatial and temporal resolutions, but they do not always satisfy the requirements of some of the applications.
These limitations can be overcome with the use of image integration techniques, which allow for the combination of
sensors with different characteristics. This work presents the monitoring of the vegetation cover in the Guadalfeo River
Basin (Spain), with a view to its hydrological modeling, by using Landsat-TM and MODIS data, analyzing the
implications of the scale differences in an heterogeneous area. A preliminary study is carried out into the deviations of
NDVI and ground cover fraction (fv) between the concurrent data of both sensors. Thereafter, the STARFM integration
algorithm is applied and evaluated to obtain synthetic NDVI images at the spatial resolution of Landsat-TM data with
MODIS time steps. The comparison between Landsat-TM and MODIS parameters revealed deviations on average
between 2-5% for NDVI and 3-5% for fv. No direct relationship was found between these deviations and basin
topography. However, higher deviations corresponded with the vegetation types with higher ground cover fractions and
heterogeneous landuses (fv relative deviations of 10% and 6% for conifers and quercus-scrub, respectively) The
STARFM algorithm improved the NDVI estimations when compared to the previous Landsat-TM date, with reductions
in the average NDVI differences of around 0.02 on average for the six simulated dates, with the accuracy of the
predictions depending on data input for the model and vegetation cover types.
Evapotranspiration (ET) is a critical variable in hydrological processes and an accurate estimation of the rate of
evapotranspiration is required if we wish to apply integrated management procedures to water resources. This study
offers new insights into remote sensing-based models that estimate ET at basin scale, evaluating the combination of a
surface energy balance based on thermal remote sensing and the use of the crop coefficient (Kc), a simple operational method that is widely used in irrigated agriculture. The study area is the Guadalfeo river basin in southern Spain, a large watershed with major topographical and landscape contrasts. Reference evapotranspiration (ETo) surfaces were generated by applying the FAO56-PM [1] equation, and real ET surfaces were estimated following a two-source energy balance model [2] [3]. Crop and vegetation coefficients were obtained as the ratio between ET and ETo. Kc maps were analysed in terms of vegetation type and development. The resulting coefficients generally ranged between 0.1 and 1.5, and could be directly related to vegetation ground cover for the main vegetation types, including natural vegetation and crops, with the determination coefficient (r2) lying between 0.77 and 0.97 in both humid and dry seasons. Relationships based on these coefficients are proposed as a simple proxy to monitor the water use of the basin on a regular basis by means of optical remote sensors alone, providing data with higher frequency and spatial resolution than can be obtained by thermal measurements; data that could complement thermal sensors whenever these were available.
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