Método basado en teledetección para estimar la emisión de gases efecto invernadero por quema de biomasa

La quema de biomasa es una fuente importante de gases efecto invernadero en países en vías de desarrollo. En Colombia, el cambio de uso del suelo, la silvicultura y el sector agropecuario superan el 50% de las emisiones totales de efecto invernadero.El fuego se utiliza con frecuencia como un mecanismo para cambiar el uso del suelo. Los Llanos orientales y la Amazonía colombiana están sometidos todos los años a la quema de biomasa, especialmente entre enero y marzo. Los estudios en la distribución espacial y temporal de las emisiones son importantes de cara a los informes en el ámbito nacional. Este artículo de revisión describe el método para hacer estas estimaciones utilizando teledetección y algunos de los resultados disponibles para Colombia.ABSTRACTBiomass burning is a major source of greenhouse gas emissions in developing countries. In Colombia, land use change, forestry, and agriculture are responsible for more than 50% of the total greenhouse gas emissions. Fire is commonly used as a mechanism for land use change. In Colombia the Llanos Orientales and the Amazonia are subject to biomass burning every year during the dry season, specially from January to March. Studies of the spatial and temporal distribution of emissions are required for emissions report at a national level. The goal of this state of the art article is to describe a method to estimate emissions with a remote sensing approach and to present some of the variables already measured in Colombia.Key words: emissions, remote sensing, biomass, burned area.

Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod

The aims of this study were to assess the dynamics of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI(1) and NDWI(2)) and Shortwave Angle Slope Index (SASI) in relation to rice agricultural practices and hydroperiod, and (2) to assess the capability for these indices to detect phenometrics in rice under different flooding regimes

Dynamic relationships between gross primary production and energy partitioning in three different ecosystems based on eddy covariance time series analysis

Ecosystems are responsible for strong feedback processes that affect climate. The mechanisms and consequences of this feedback are uncertain and must be studied to evaluate their influence on global climate change. The main objective of this study is to assess the gross primary production (GPP) dynamics and the energy partitioning patterns in three different European forest ecosystems through time series analysis. The forest types are an Evergreen Needleleaf Forest in Finland (ENF_FI), a Deciduous Broadleaf Forest in Denmark (DBF_DK), and a Mediterranean Savanna Forest in Spain (SAV_SP). Buys-Ballot tables were used to study the intra-annual variability of meteorological data, energy fluxes, and GPP, whereas the autocorrelation function was used to assess the inter-annual dynamics. Finally, the causality of GPP and energy fluxes was studied with Granger causality tests. The autocorrelation function of the GPP, meteorological variables, and energy fluxes revealed that the Mediterranean ecosystem is more irregular and shows lower memory in the long term than in the short term. On the other hand, the Granger causality tests showed that the vegetation feedback to the atmosphere was more noticeable in the ENF_FI and the DBF_DK in the short term, influencing latent and sensible heat fluxes. In conclusion, the impact of the vegetation on the atmosphere influences the energy partitioning in a different way depending on the vegetation type, which makes the study of the vegetation dynamics essential at the local scale to parameterize these processes with more detail and build improved global models

Vegetation water use based on a thermal and optical remote sensing model in the mediterranean region of Doñana

Terrestrial evapotranspiration (ET) is a central process in the climate system, is a major component in the terrestrial water budget, and is responsible for the distribution of water and energy on land surfaces especially in arid and semiarid areas. In order to inform water management decisions especially in scarce water environments, it is important to assess ET vegetation use by differentiating irrigated socio-economic areas and natural ecosystems. The global remote sensing ET product MOD16 has proven to underestimate ET in semiarid regions where ET is very sensitive to soil moisture. The objective of this research was to test whether a modified version of the remote sensing ET model PT-JPL, proven to perform well in drylands at Eddy Covariance flux sites using the land surface temperature as a proxy to the surface moisture status (PT-JPL-thermal), could be up-scaled at regional levels introducing also a new formulation for net radiation from various MODIS products. We applied three methods to track the spatial and temporal characteristics of ET in the World Heritage UNESCO Doñana region: (i) a locally calibrated hydrological model (WATEN), (ii) the PT-JPL-thermal, and (iii) the global remote sensing ET product MOD16. The PT-JPL-thermal showed strong agreement with the WATEN ET in-situ calibrated estimates (ρ = 0.78, ρ1month-lag = 0.94) even though the MOD16 product did not (ρ = 0.48). The PT-JPL-thermal approach has proven to be a robust remote sensing model for detecting ET at a regional level in Mediterranean environments and it requires only air temperature and incoming solar radiation from climatic databases apart from freely available satellite products

Model design to predict forest fire risk in Navarra (Spain) using time series analysis

Understand and predict how forest fire potential changes over time are essential for prioritizing forest management activities and reducing damage. Nowadays we lack the capacity to predict future forest fire trends in response to climate change. The main goal of this research is to build an empirical model to describe, estimate and forecast the forest fires dynamics using the improved Fire Potential Index (FPI) (Huesca et al., 2007) as indicator of fire

Dynamic relationships between gross primary production and energy partitioning in three different ecosystems based on eddy covariance time series analysis

Ecosystems are responsible for strong feedback processes that affect climate. The mechanisms and consequences of this feedback are uncertain and must be studied to evaluate their influence on global climate change. The main objective of this study is to assess the gross primary production (GPP) dynamics and the energy partitioning patterns in three different European forest ecosystems through time series analysis. The forest types are an Evergreen Needleleaf Forest in Finland (ENF_FI), a Deciduous Broadleaf Forest in Denmark (DBF_DK), and a Mediterranean Savanna Forest in Spain (SAV_SP). Buys-Ballot tables were used to study the intra-annual variability of meteorological data, energy fluxes, and GPP, whereas the autocorrelation function was used to assess the inter-annual dynamics. Finally, the causality of GPP and energy fluxes was studied with Granger causality tests. The autocorrelation function of the GPP, meteorological variables, and energy fluxes revealed that the Mediterranean ecosystem is more irregular and shows lower memory in the long term than in the short term. On the other hand, the Granger causality tests showed that the vegetation feedback to the atmosphere was more noticeable in the ENF_FI and the DBF_DK in the short term, influencing latent and sensible heat fluxes. In conclusion, the impact of the vegetation on the atmosphere influences the energy partitioning in a different way depending on the vegetation type, which makes the study of the vegetation dynamics essential at the local scale to parameterize these processes with more detail and build improved global models.14 página

Aplicación de índices de vegetación para evaluar la falta de producción de pastos y montaneras en dehesas.

El ganado porcino ibérico aprovecha los recursos naturales de la dehesa mediante montanera, principalmente la bellota y los pastos existentes. La línea 133 de los seguros agrarios españoles recoge el seguro de compensación por pérdida de pastos, solo para bovino reproductor y de lidia, ovino, caprino y equino, no incluyen los cerdos en montanera. Emplea un Índice de Vegetación de la Diferencia Normalizada (NDVI) medido por satélite sobre pastos desarbolados. El objetivo es comprobar si se puede utilizar un índice de vegetación para estimar la producción de pasto y bellota. Se han tomado datos del aforo de montaneras desde 1999 al 2005, y del pasto en dehesas de Salamanca (Vitigudino), Cáceres (Trujillo) y Córdoba (Pozoblanco) durante 2010 al 2012. Con los datos de 2010 y 2011 se estableció una función de producción del pasto fresco en función del NDVI, mostrando un coeficiente de correlación de 0,975, altamente significativa. Los datos obtenidos en 2012 se utilizaron para validar la función de producción de pasto fresco. La comparación entre los valores observados y simulados para 2012 ha mostrado un coeficiente de correlación de 0,734. Como conclusión, el NDVI puede ser un buen estimador de la cantidad de pasto fresco en dehesas españolas

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

Characterization of soil erosion indicators using hyperspectral data from a Mediterranean rainfed cultivated region

The determination of surface soil properties is an important application of remotely sensed hyperspectral imagery. Moreover, different soil properties can be associated with erosion processes, with significant implications for land management and agricultural uses. This study integrates hyperspectral data supported by morphological and physico-chemical ground data to identify and map soil properties that can be used to assess soil erosion and accumulation. These properties characterize different soil horizons that emerge at the surface as a consequence of the intensity of the erosion processes, or the result of accumulation conditions. This study includes: 1) field and laboratory characterization of the main soil types in the study area; 2) identification and definition of indicators of soil erosion and accumulation stages (SEAS); 3) compilation of the site-specific MEDiterranean Soil Erosion Stages (MEDSES) spectral library of soil surface characteristics using field spectroscopy; 4) using hyperspectral airborne data to determine a set of endmembers for different SEAS and introducing these into the support vector machine (SVM) classifier to obtain their spatial distribution; and 5) evaluation of the accuracy of the classification applying a field validation protocol. The study region is located within an agricultural region in Central Spain, representative of Mediterranean agricultural uses dominated by a gently sloping relief, and characterized by soils with contrasting horizons. Results show that the proposed method is successful in mapping different SEAS that indicate preservation, partial loss, or complete loss of fertile soils, as well as down-slope accumulation of different soil materials