M. Pilar Martín

Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies

This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology (COST) Action ESO903—“Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe” that provided a complete view on spectral sampling activities carried out within the different research teams in European countries. The results have highlighted that a wide variety of optical sensors are in use at flux sites across Europe, and responses further demonstrated that users were not always fully aware of the key issues underpinning repeatability and the reproducibility of their spectral measurements. The key findings of this survey point towards the need for greater awareness of the need for standardisation and development of a common protocol of optical sampling at the European EC sites.

Fire detection and fire growth monitoring using satellite data

The objective of this chapter is to review and discuss the use of near real-time satellite data for fire detection and fire growth monitoring, focusing on NOAA-AVHRR images. Capabilities and limitations of these images, as well as existing fire detection algorithms, are presented. Discussion on the potentials of future remote sensing systems for real-time fire detection concludes the chapter.

Human Factors of Fire Occurrence in the Mediterranean

The Mediterranean region accounts the larger proportion of human caused fires in the world (95%) followed by South Asia (90%), South America (85%) and Northeast Asia (80%) (FAO 2007). Socio-economic changes which are occurring in Europe along with global warming result in an augment of fire risk. Systematic and reliable information on fire causes is necessary in order to improve wildland fire management. However, collection of information on forest fire causes and motivations is still quite restricted in most countries around the world. The unknown cause is still too frequent in many wildfire statistics. A promising technique to overcome this shortcoming is the Delphi technique which uses a panel of carefully selected experts to improve the knowledge on fire motivations in a specific area. Understanding more about why people start fires would help to reduce the impacts of deliberate fire lighting. Spatial and temporal analysis of wildland fire occurrence data and the interaction with explanatory geographical variables is a critical part of fire management activities. Geographic Information Systems (GIS) are appropriate tools to create, transform, combine and integrate variables related to fire risk in order to find geographical and analytical relationships which help to discriminate areas where risk factors are most severe in order to adopt the appropriate preventive actions.

Regional-scale burnt area mapping in Southern Europe using NOAA-AVHRR 1 km data

A brief review of studies dealing with burnt area mapping using coarse spatial resolution satellite imagery is presented, followed by an analysis of areas burnt in Iberia during the 1991 and 1994 fire seasons, two of the worst on record in Portugal and Spain, respectively. In order to detect and map burnt areas, new multitemporal image compositing algorithms were developed. Burnt area mapping for the 1991 fire season relied on the Global Environment Monitoring Index (GEMI), albedo, and surface temperature. A rule-based classifier was induced from training data, using the classification and regression trees (CART) algorithm. Fire size estimates compared well to those derived with Landsat TM imagery, but appear unreliable for burns smaller than about 2000 ha. The 1994 fire season data were analysed with a two-phase procedure. First, a new spectral index was specifically designed for burnt area detection. Images of this index were thresholded to detect clearly burnt pixels and to avoid commission errors. Secondly, a distance-based multicriteria analysis technique was applied, combining spectral similarity and spatial contiguity criteria, to map the burns. The method detected over 80% of all large fires, and proved especially effective at mapping burns larger than 1000 ha.

Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling

Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC (Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean “dehesa” ecosystem) projects LST retrieved from Landsat data is required to integrate ground-based observations of energy, water, and carbon fluxes with multi-scale remotely-sensed data and assess water and carbon balance in ecologically fragile heterogeneous ecosystem of Mediterranean wooded grassland (dehesa). Thus, three methods based on the Radiative Transfer Equation were used to extract LST from a series of 2009–2011 Landsat-5 TM images to assess the applicability for temperature input generation to a Landsat-MODIS LST integration. When compared to surface temperatures simulated using MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) with atmospheric profiles inputs (LSTref), values from Single-Channel (SC) algorithm are the closest (root-mean-square deviation (RMSD) = 0.50 °C); procedure based on the online Radiative Transfer Equation Atmospheric Correction Parameters Calculator (RTE-ACPC) shows RMSD = 0.85 °C; Mono-Window algorithm (MW) presents the highest RMSD (2.34 °C) with systematical LST underestimation (bias = 1.81 °C). Differences between Landsat-retrieved LST and MODIS LST are in the range of 2 to 4 °C and can be explained mainly by differences in observation geometry, emissivity, and time mismatch between Landsat and MODIS overpasses. There is a seasonal bias in Landsat-MODIS LST differences due to greater variations in surface emissivity and thermal contrasts between landcover components.

Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard

Implementation of precision viticulture techniques requires the use of emerging sensing technologies to assess the vineyard spatial variability. This work shows the capability of multispectral imagery acquired from a remotely piloted aerial system (RPAS), and the derived spectral indices to assess the vegetative, productive, and berry composition spatial variability within a vineyard (Vitis vinifera L.). Multi-spectral imagery of 17 cm spatial resolution was acquired using a RPAS. Classical vegetation spectral indices and two newly defined normalised indices, NVI1 = (R802 − R531)/(R802 + R531) and NVI2 = (R802 − R570)/(R802 + R570), were computed. Their spatial distribution and relationships with grapevine vegetative, yield, and berry composition parameters were studied. Most of the spectral indices and field data varied spatially within the vineyard, as showed through the variogram parameters. While the correlations were significant but moderate among the spectral indices and the field variables, the kappa index showed that the spatial pattern of the spectral indices agreed with that of the vegetative variables (0.38-0.70) and mean cluster weight (0.40). These results proved the utility of the

Nonlinear Response in a Field Portable Spectroradiometer: Characterization and Effects on Output Reflectance

We report the characterization and correction of nonlinear responses of a commercial field portable spectroradiometer intended to be used to monitor vegetation physiology. Calibration of photoresponse allowed the successful correction of spectral data and the modeling of biases in reflectance at different levels of the dynamic range. Finally, the impact of nonlinearities on a spectral estimator of photosynthetic status, the photochemical reflectance index (PRI) is discussed. Significance of the biases proved that, although nonlinearity can potentially affect reflectance along most of the dynamic range of the instrument, experimental uncertainties can limit its impact. Nonlinearity biased PRI by affecting the reference band of the index and suggested unreal changes on plant physiology. Results show that nonlinearity could be a significant problem in field spectroscopy, especially in the case of spectroradiometers integrated in unattended systems to monitor vegetation responses to radiation. An automatic adjustment of integration time to reach only a certain level of the dynamic range may reduce nonlinearity effects, though may not always avoid them. We conclude that linearity characterization is necessary to understand impacts and correct potential biases.

Characterizing integration time and gray-level-related nonlinearities in a NMOS sensor

We report a nonlinearity effect related to the integration time in a double-beam spectroradiometer equipped with two negative-module metal-oxide semiconductor (NMOS) sensors. This effect can be explained by the addition of photoelectrons produced by the radiant flux on the sensors during the readout phase to the photoelectrons produced during the measurement phase. A new method is proposed to characterize and correct both gray-level and integration-time-related nonlinearities in NMOS sensors. This method is experimentally simple and outperforms other commonly used correction procedures.

Prototyping an artificial neural network for burned area mapping on a regional scale in Mediterranean areas using MODIS images

Abstract Each year thousands of ha of forest land are affected by forest fires in Southern European countries such as Spain. Burned area maps are a valuable instrument for designing prevention and recovery policies. Remote sensing has increasingly become the most widely used tool for this purpose on regional and global scales, where a large variety of techniques and data has been applied. This paper proposes a semiautomatic method for burned area mapping on a regional scale in Mediterranean areas (the Iberian Peninsula has been used as a study case). A Multi-layer Perceptron Network (MLPN) has been designed and applied to MODIS/Terra Surface Reflectance Daily L2G Global 500m SIN Grid multitemporal composite monthly images. The compositing criterion was based on maximum surface temperature. The research covered a six year period (2001–2006) from June to September, when most of the forest fires occur. The resulting burned area maps have been validated using official fire perimeters and compared with MODIS Collection 5 Burned Area Product (MCD45A1). The MLPN shown as an effective method, with a commission error of 29.1%, in the classification of the burned areas, while the omission error was of 14.9%. The results were compared with the MCD45A1 product, which had a slightly higher commission error (30.2%) and a considerably higher omission error (26.2%), indicating a high underestimation of the burned area.

Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms

The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment.