Application of Vegetation Indices to Estimate Acorn Production at Iberian

The Iberian pig valued natural resources of the pasture when fattened in mountain. The variability of acorn production is not contained in any line of Spanish agricultural insurance. However, the production of arable pasture is covered by line insurance number 133 for loss of pasture compensation. This scenario is only contemplated for breeding cows and brave bulls, sheep, goats and horses, although pigs are not included. This insurance is established by monitoring ten-day composites Normalized Difference Vegetation Index (NDVI) measured by satellite over treeless pastures, using MODIS TERRA satellite. The aim of this work is to check if we can use a satellite vegetation index to estimate the production of acorns.

Innovative tools to address drought risk in grazing lands

Drought spells can impose severe impacts in most vulnerable farms. It is well known that uninsured exposure exacerbates income inequality in farming systems. However, high administrative costs of traditional insurance hinder small farmers? access to risk management tools. The existence of moral hazard and systemic risk prevents the implementation of traditional insurance programs to address drought risk in rural areas. Innovative technologies like satellite images are being used to derive vegetation index which are highly correlated with drought impacts. The implementation of this technology in agricultural insurance may help to overcome some of the limitations of traditional insurance. However, basis risk has been identified as one of the main problems that hinder the acceptance of index insurance. In this paper we focus on the analyses of basis risk under different contract options in the grazing lands of the Araucanía region. A vegetation index database is used to develop an actuarial insurance model and estimate risk premiums for moderate and severe drought coverage. Risk premium sharply increases with risk coverage. In contrast with previous findings in the literature, our results are not conclusive and show that lowering the coverage level does not necessarily imply a reduction in basis risk. Further analyses of the relation between contract design and basis risk is a promising area of research that may render an important social utility for most vulnerable farming systems.

Comparison of fractal dimensions based on segmented NDVI fields obtained from different remote sensors.

Satellite image data have become an important source of information for monitoring vegetation and mapping land cover at several scales. Beside this, the distribution and phenology of vegetation is largely associated with climate, terrain characteristics and human activity. Various vegetation indices have been developed for qualitative and quantitative assessment of vegetation using remote spectral measurements. In particular, sensors with spectral bands in the red (RED) and near-infrared (NIR) lend themselves well to vegetation monitoring and based on them [(NIR - RED) / (NIR + RED)] Normalized Difference Vegetation Index (NDVI) has been widespread used. Given that the characteristics of spectral bands in RED and NIR vary distinctly from sensor to sensor, NDVI values based on data from different instruments will not be directly comparable. The spatial resolution also varies significantly between sensors, as well as within a given scene in the case of wide-angle and oblique sensors. As a result, NDVI values will vary according to combinations of the heterogeneity and scale of terrestrial surfaces and pixel footprint sizes. Therefore, the question arises as to the impact of differences in spectral and spatial resolutions on vegetation indices like the NDVI. The aim of this study is to establish a comparison between two different sensors in their NDVI values at different spatial resolutions.

Automatic detection of crop rows in maize fields with high weeds pressure

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu’s method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

Comparison of NDVI fields obtained from different remote sensors

Satellite image data have become an important source of information for monitoring vegetation and mapping land cover at several scales. Beside this, the distribution and phenology of vegetation is largely associated with climate, terrain characteristics and human activity. Various vegetation indices have been developed for qualitative and quantitative assessment of vegetation using remote spectral measurements. In particular, sensors with spectral bands in the red (RED) and near-infrared (NIR) lend themselves well to vegetation monitoring and based on them [(NIR - RED) / (NIR + RED)] Normalized Difference Vegetation Index (NDVI) has been widespread used. Given that the characteristics of spectral bands in RED and NIR vary distinctly from sensor to sensor, NDVI values based on data from different instruments will not be directly comparable. The spatial resolution also varies significantly between sensors, as well as within a given scene in the case of wide-angle and oblique sensors. As a result, NDVI values will vary according to combinations of the heterogeneity and scale of terrestrial surfaces and pixel footprint sizes. Therefore, the question arises as to the impact of differences in spectral and spatial resolutions on vegetation indices like the NDVI and their interpretation as a drought index. During 2012 three locations (at Salamanca, Granada and Córdoba) were selected and a periodic pasture monitoring and botanic composition were achieved. Daily precipitation, temperature and monthly soil water content were measurement as well as fresh and dry pasture weight. At the same time, remote sensing images were capture by DEIMOS-1 and MODIS of the chosen places. DEIMOS-1 is based on the concept Microsat-100 from Surrey. It is conceived for obtaining Earth images with a good enough resolution to study the terrestrial vegetation cover (20x20 m), although with a great range of visual field (600 km) in order to obtain those images with high temporal resolution and at a reduced cost. By contranst, MODIS images present a much lower spatial resolution (500x500 m). The aim of this study is to establish a comparison between two different sensors in their NDVI values at different spatial resolutions. Acknowledgements. This work was partially supported by ENESA under project P10 0220C-823. Funding provided by Spanish Ministerio de Ciencia e Innovación (MICINN) through project no. MTM2009-14621 and i-MATH No. CSD2006-00032 is greatly appreciated.

Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes.

Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and play a significant role in the global cycles of carbon, nitrogen and water. The purpose of this study is to use field-based and satellite remote-sensing-based methods to assess leaf nitrogen pools in five diverse European agricultural landscapes located in Denmark, Scotland (United Kingdom), Poland, the Netherlands and Italy. REGFLEC (REGularized canopy reFLECtance) is an advanced image-based inverse canopy radiative transfer modelling system which has shown proficiency for regional mapping of leaf area index (LAI) and leaf chlorophyll (CHLl) using remote sensing data. In this study, high spatial resolution (10–20 m) remote sensing images acquired from the multispectral sensors aboard the SPOT (Satellite For Observation of Earth) satellites were used to assess the capability of REGFLEC for mapping spatial variations in LAI, CHLland the relation to leaf nitrogen (Nl) data in five diverse European agricultural landscapes. REGFLEC is based on physical laws and includes an automatic model parameterization scheme which makes the tool independent of field data for model calibration. In this study, REGFLEC performance was evaluated using LAI measurements and non-destructive measurements (using a SPAD meter) of leaf-scale CHLl and Nl concentrations in 93 fields representing crop- and grasslands of the five landscapes. Furthermore, empirical relationships between field measurements (LAI, CHLl and Nl and five spectral vegetation indices (the Normalized Difference Vegetation Index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green chlorophyll index) were used to assess field data coherence and to serve as a comparison basis for assessing REGFLEC model performance. The field measurements showed strong vertical CHLl gradient profiles in 26% of fields which affected REGFLEC performance as well as the relationships between spectral vegetation indices (SVIs) and field measurements. When the range of surface types increased, the REGFLEC results were in better agreement with field data than the empirical SVI regression models. Selecting only homogeneous canopies with uniform CHLl distributions as reference data for evaluation, REGFLEC was able to explain 69% of LAI observations (rmse = 0.76), 46% of measured canopy chlorophyll contents (rmse = 719 mg m−2) and 51% of measured canopy nitrogen contents (rmse = 2.7 g m−2). Better results were obtained for individual landscapes, except for Italy, where REGFLEC performed poorly due to a lack of dense vegetation canopies at the time of satellite recording. Presence of vegetation is needed to parameterize the REGFLEC model. Combining REGFLEC- and SVI-based model results to minimize errors for a "snap-shot" assessment of total leaf nitrogen pools in the five landscapes, results varied from 0.6 to 4.0 t km−2. Differences in leaf nitrogen pools between landscapes are attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. In order to facilitate a substantial assessment of variations in Nl pools and their relation to landscape based nitrogen and carbon cycling processes, time series of satellite data are needed. The upcoming Sentinel-2 satellite mission will provide new multiple narrowband data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing capabilities for mapping LAI, CHLl and Nl.

Monitorización de emisiones de CO2 en análogos naturales de almacenamientos geológicos a través de la vegetación mediante técnicas de teledetección multiespectral

Uno de los problemas más importantes a los que se enfrenta nuestra sociedad es el de la degradación del medioambiente por la emisión de gases de efecto invernadero. La captura de CO2 en los puntos de emisión y su enterramiento mediante inyección en reservorios geológicos profundos se plantea como una solución hasta que a medio o largo plazo pueda ser mitigada la actual dependencia de la quema de combustibles fósiles. Pero la estabilidad de esos reservorios debe ser monitorizada adecuadamente. En esta tesis se ha estudiado el problema de la detección de fugas de CO2 en un análogo natural de un emplazamiento de almacenamiento profundo a través del análisis de imágenes de satélite multiespectrales. El análogo utilizado ha sido la zona de Campo de Calatrava (Ciudad Real, España), donde, por efecto de la actividad volcánica remanente, aún se pueden encontrar numerosos puntos de emisión de CO2. Se han caracterizado los puntos de emisión de CO2 identificándose dos tipologías con características y manifestaciones claramente diferenciadas: puntos de emisión húmeda o hervideros, y puntos de emisión seca o fumarolas. Para el estudio se han utilizado índices de vegetación y su relación de éstos con los contenidos atmosféricos de CO2. Se han utilizado imágenes multiespectrales de los satélites QuickBird y WorldView‐2. Se ha realizado una preselección de doce índices de vegetación especialmente adecuados para la detección de puntos de emisión de CO2. Mediante análisis y comparación de imágenes de índices de vegetación sobre puntos de emisión conocidos se ha seleccionado los cinco índices con mayor sensibilidad frente al fenómeno. Atendiendo a los principales factores condicionantes de la aparición de nuevos puntos de emisión de CO2 se ha realizado sobre las imágenes de índices de vegetación una predicción de nuevos puntos de emisión. Entre los puntos candidato se han encontrado tres nuevos puntos de emisión de CO2 no descritos previamente en la bibliografía. ABSTRACT One of the most important issues facing our society is the degradation of the environment caused by the emission of greenhouse gases. Capturing CO2 emissions, injection and burial in deep geological reservoirs is presented as a solution until the medium or long term, when the problem of the current dependence on fossil fuels burning can be mitigated. But the stability of these reservoirs should be properly monitored. In this work we study the problem of detecting CO2 leakage in a natural analogue of a deep storage site through analysis of multispectral satellite imagery. The analogue used is in the Campo de Calatrava (Ciudad Real, Spain) where, due to the remaining volcanic activity, it can still be found numerous CO2 emission points. CO2 emission points have been characterized identifying two types having distinct characteristics and effects: wet emission points or hotbeds, and dry emission points or fumaroles. For this study it has been used vegetation indices and its relationship with atmospheric CO2 contents. It has been used multispectral images from QuickBird and WorldView‐2 satellites. It has been done a preselection of twelve vegetation indices especially suitable for the detection of CO2 emission points. Using analysis and comparison of vegetation index images on real emission points it has been selected the five indexes with greater sensitivity to this phenomenon. Based upon the main factors of the emergence of new CO2 emission points it has been made a prediction of new emission points over the vegetation index images. Among the candidate points it has been found three new CO2 emission points not previously described in the literature.

Evaluating the quality of riparian forest vegetation: The Riparian Forest Evaluation (RFV) index

Aim of study: This paper presents a novel index, the Riparian Forest Evaluation (RFV) index, for assessing the ecological condition of riparian forests. The status of riparian ecosystems has global importance due to the ecological and social benefits and services they provide. The initiation of the European Water Framework Directive (2000/60/CE) requires the assessment of the hydromorphological quality of natural channels. The Directive describes riparian forests as one of the fundamental components that determine the structure of riverine areas. The RFV index was developed to meet the aim of the Directive and to complement the existing methodologies for the evaluation of riparian forests. Area of study: The RFV index was applied to a wide range of streams and rivers (170 water bodies) inSpain. Materials and methods: The calculation of the RFV index is based on the assessment of both the spatial continuity of the forest (in its three core dimensions: longitudinal, transversal and vertical) and the regeneration capacity of the forest, in a sampling area related to the river hydromorphological pattern. This index enables an evaluation of the quality and degree of alteration of riparian forests. In addition, it helps to determine the scenarios that are necessary to improve the status of riparian forests and to develop processes for restoring their structure and composition. Main results: The results were compared with some previous tools for the assessment of riparian vegetation. The RFV index got the highest average scores in the basins of northernSpain, which suffer lower human influence. The forests in central and southern rivers got worse scores. The bigger differences with other tools were found in complex and partially altered streams and rivers. Research highlights: The study showed the index’s applicability under diverse hydromorphological and ecological conditions and the main advantages of its application. The utilization of the index allows a better understanding of the status of riparian forests, and enhances improvements in the conservation and management of riparian areas.

Implementation of a module for risk of ozone impacts assessment to vegetation in the integrated assessment modelling system for the Iberian peninsula. Evaluation for wheat and holm oak

A module to estimate risks of ozone damage to vegetation has been implemented in the Integrated Assessment Modelling system for the Iberian Peninsula. It was applied to compute three different indexes for wheat and Holm oak; daylight AOT40 (cumulative ozone concentration over 40 ppb), cumulative ozone exposure index according to the Directive 2008/50/EC (AOT40-D) and PODY (Phytotoxic Ozone Dose over a given threshold of Y nmol m−2 s−1). The use of these indexes led to remarkable differences in spatial patterns of relative ozone risks on vegetation. Ozone critical levels were exceeded in most of the modelling domain and soil moisture content was found to have a significant impact on the results. According to the outputs of the model, daylight AOT40 constitutes a more conservative index than the AOT40-D. Additionally, flux-based estimations indicate high risk areas in Portugal for both wheat and Holm oak that are not identified by AOT-based methods.