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The objective of this work was to generate a series of equations to describe the voltinism of Lobesia botrana in the quarantine area of the main winemaking area of Argentina, Mendoza. To do this we considered an average climate scenario and extrapolatedthese equations to other winegrowing areas at risk of being invaded. A grid of 4 km2was used to generate statistics on L. botrana captures and the mean temperature accumulation for the pixel. Four sets of logistic regression were constructed using the percentage of accumulated trap catches/grid/week and the degree-day accumulation above7°C, from 1st July. By means of a habitat model, an extrapolation of the phenologicalmodel generated to other Argentine winemaking areas was evaluated. According to ourresults, it can be expected that 50% of male adult emergence for the first flight occurs at248.79 ± 4 degree-days (DD), in the second flight at 860.18 ± 4.1 DD, while in the thirdand the fourth flights, 1671.34 ± 5.8 DD and 2335.64 ± 4.3 DD, respectively. Subsequentclimatic comparison determined that climatic conditions of uncolonized areas of Cuyo Region have a similar suitability index to the quarantine area used to adjust the phenologicalmodel. The upper valley of Río Negro and Neuquén are environmentally similar. Valleys ofthe northwestern region of Argentina showed lower average suitability index and greatervariability among SI estimated by the algorithm considered. The combination of two models for the estimation of adult emergence time and potential distribution, can provide greater certainties in decision-making and risk assessment of invasive species. Fil: Heit, Guillermo Eugenio. Ministerio de Agricultura, Ganadería, Pesca y Alimento. Servicio Nacional de Sanidad y Calidad Agroalimentaria; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina Fil: Sione, Walter Fabian. Universidad Autónoma de Entre Ríos; Argentina Fil: Aceñolaza, Pablo Gilberto. Universidad Nacional de Entre Ríos; Argentina. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentina

The transmission of leptospirosis is conditioned by climatic variables. In northeastern Argentina leptospirosis outbreaks occur mainly in coincidence with periods of abundant precipitation and high hydrometric level. A Susceptible-Infectious-Recovered Epidemiological Model (SIR) is proposed, which incorporates hydroclimatic variables for the three most populated cities in the area (Santa Fe, Paraná and Rosario), during the 2009 – 2018 period. Results obtained by solving the proposed SIR model for the 2010 outbreaks are in good agreement with the actual data, capturing the dynamics of the leptospirosis outbreak wave. However, the model does not perform very well when isolated cases appear outside the outbreak periods, probably due to non-climatic factors not explicitly considered in the present version of the model. Nevertheless, the dynamic modeling of infectious diseases considering hydroclimatic variables constitutes a climatic service for the public health system, not yet available in Argentina.

El objetivo de este trabajo fue comparar el desempeño de los criterios de clasificación nítidos y difusos en la construcción de modelos deductivos de la distribución potencial de insectos exóticos. Considerando criterios de clasificación binaria nítida y difusa, de capas ráster de temperatura máxima, media y mínima diaria, se generó un índice de riesgo bioclimático relativo, considerando el número de días con condiciones óptimas para el desarrollo de Bactrocera oleae (Gmelin) (Diptera: Tephritidae) y Cerotoma arcuatus (Olivier) (Coleoptera: Chrysomelidae). Se realizaron análisis de sensibilidad de los modelos. Los modelos deductivos de distribución potencial de especies realizados mediante clasificación difusa, serían más robustos y menos restrictivos en la determinación de áreas de riesgo fitosanitario potencial que aquellos realizados con criterios de clasificación nítidos. Estos últimos serían más sensibles y tendrían mayor capacidad de discriminar áreas con diferentes perfiles de riesgo ambiental. The objective of this paper was to evaluate the performance of crisp and fuzzy classification criteria in the construction of deductive potential distribution models of exotic insects. As case studies, Bactrocera oleae (Gmelin) (Diptera: Tephritidae) and Cerotoma arcuatus (Olivier) (Coleoptera: Chrysomelidae) were selected. Considering crisp and fuzzy classification for raster layers of maximum, average and minimum daily temperature, a relative bioclimatic risk index was generated. The number of days with optimal conditions for pests’ development was considered. Sensitivity analyses of both models were performed. Considering each case evaluated and the variables used, deductive pest distribution models made by fuzzy classification was more robust and less conservative in the determination of potential phytosanitary risk areas than those made with crisp classification criteria. This last case was more sensitive and would have a greater capacity to discriminate areas with different environmental risk profiles. Fil: Heit, Guillermo Eugenio. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Vegetal. Cátedra de Zoología Agrícola; Argentina Fil: Sione, Walter Fabian. Universidad Autónoma de Entre Rí­os. Facultad de Ciencia y Tecnología. Centro Regional de Geomática; Argentina. Universidad Nacional de Luján; Argentina Fil: Claps, Lucia Elena. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto Superior de Entomología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Aceñolaza, Pablo Gilberto. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentina

El Huanglongbing (HLB), es la enfermedad de los cítricos más destructiva a nivel mundial, que en Sudamérica presenta una distribución restringida a algunos estados de Brasil. El objetivo del trabajo fue estimar los efectos del cambio climático en la distribución geográfica potencial de HLB en Sudamérica, mediante una técnica basada en el Análisis de Componentes Principales, que predice la aptitud ambiental de una especie a partir de la distancia euclidiana desde cualquier punto geográfico de Sudamérica hacia los sitios dónde HLB se ha establecido en Sudamérica. Para estimar los cambios en la distribución potencial de HLB ante un escenario de cambio climático, se consideró la Vía de Concentración Representativa 2,6, propuesta por el Quinto Informe de Evaluación del Intergovernmental Panel on Climate Change, generada por el modelo HadGEM2-ES, para las décadas de 2050 y 2070. A posteriori, se calculó el estadístico kappa de las capas resultantes contra una matriz de sitios con presencia de HLB en Brasil. Se utilizaron los softwares QGIS 2.12, IDRISI Selva e Infostat. El modelo estimó que los sitios con un elevado riesgo de establecimiento se distribuirían en el sur de Brasil, sudeste de Paraguay, región Noreste y zona oriental de la región Noroeste de Argentina y el centro-sur de Bolivia. Según las estimaciones del IPCC5, los efectos del cambio climático determinarían que a futuro, las áreas de elevado riesgo climático para el establecimiento de HLB, se desplazarían hacia el sudoeste de las áreas consideradas de mayor riesgo climático en la actualidad.

Analyzing social-ecological systems should include both temporal and spatial dimensions as components of their complexity. Four conditionalities integrate the response of the territory to external and internal impacts: biophysical determinants, social structures, economic forces, and political reconfigurations. The causality behind the dynamics of systemic elements represents multiple relationships causing transformations in the structure and identity of the system.

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land (e.g. ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour l?Observationde la Terre (SPOT) are available to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km² in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the Normalized Burned Ratio Index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas) , and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-areareference data was used for validation purposes. Additionally, the performance of theadaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area ( MCD45A1) , the active fire algorithm ( MOD14) ; and the L3JRC SPOT VEGETATION 1km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R² = 0.01-0.28, while our algorithm performed showed a stronger correlation coefficient (R² =0.96) . Our findings confirm prior research calling for caution when using the global fire products locally or regionally. Fil: Hardtke, Leonardo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina Fil: Blanco, Paula Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina Fil: del Valle, Hector Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina Fil: Metternicht, Graciela. University of New South Wales; Australia Fil: Sione, Walter Fabian. Universidad Autónoma de Entre Ríos; Argentina. Universidad Nacional de Luján; Argentina

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Standard satellite burned area and active fire products derived from the 500-m MODIS and SPOT are avail - able to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applica - tions. Consequently, we propose a novel algorithm for automated identification and mapping of burned areas at regional scale in semi-arid shrublands. The algorithm uses a set of the Normalized Burned Ratio Index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. The correlation between the size of burnt areas detected by the global fire products and independently-derived Landsat reference data ranged from R2 = 0.01 - 0.28, while our algorithm performed showed a stronger correlation coefficient (R2 = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

Most vector-borne diseases exhibit a distinct seasonal pattern, which clearly suggests that they are weather sensitive. Rainfall, temperature, and other climate variables affect in many ways both the vectors and the pathogens they transmit. Likewise, climate can be determinant in outbreaks incidence. A growing number of studies have provided evidence indicating the effects of climate variability on vector-borne diseases. However, oftentimes, the different diseases and regions are not uniformly represented, scarcity or lack of publications in some countries is common. The objectives of this work were to analyze the distribution and abundance of publications on vector-borne diseases associated with climate variability in South America, identify those works that conducted a geographic analysis and detect the countries where outbreaks occurred and the climate variables with which they were associated. A systematic review of the literature published on vector-borne diseases linked to climate variability in South America was conducted, identifying, evaluating and summarizing scientific papers. The distribution of the study areas and disease type in the publications were represented on maps. Dengue and leishmaniasis were the most studied and widely represented diseases in South America. The country with the largest number of published papers and presence of all disease types was Brazil. Outbreaks of disease were related to different climate variables. Most diseases from the publications under study occurred in equatorial and tropical climates. The disease represented by the largest number of different types of climates was dengue. The technique used in this work allowed us to determine the status of knowledge of the main diseases associated with climate variability in South America. This methodology could be improved in the future by incorporating other bibliographic sources as well as other diseases related to climate variability. Fil: López, María S.. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina Fil: Muller, Gabriela Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina Fil: Sione, Walter Fabian. Universidad Autónoma de Entre Rí­os. Facultad de Ciencia y Tecnología. Centro Regional de Geomática; Argentina

El objetivo de este trabajo fue analizar la distribución espacial y temporal de los incendios en la cuenca del Río Gualeguay y la relación de esa distribución con los tipos de cobertura del suelo. El área de estudio fue la cuenca del Río Gualeguay situada en la Provincia de Entre Ríos, Argentina. Los materiales fueron Imágenes Landsat TM y coberturas de focos de calor del Sistema de Gestión de Recursos para Manejo del Fuego - FIRMS, el software QGIS e Idrisi. La metodología articuló, en una primera etapa, clasificaciones no supervisadas, supervisada, segmentación y agrupamiento temático para obtener el mapa final de cobertura de las tierras. En la segunda etapa, la Carta de puntos de calor y el mapa de coberturas fue combinada para obtener una tabla de frecuencias y distribución anual de incendios por clases temáticas. Los resultados muestran que las clases con mayor incidencia de incendios fueron principalmente tres: "las áreas terrestres cultivadas y/o manejadas", "pajonal" y "arbórea terrestre abierto". Se recomienda el uso de esta información para el manejo del fuego, asignación de recursos y estudiar su utilidad para detección de desmontes.

Leptospirosis is considered the most globally widespread zoonotic illness; it has been classified as an emerging or reemerging infectious disease by the World Health Organization. Leptospirosis is a disease caused by a pathogenic spirochete of the genus Leptospira. The infection occurs by contacting with the urine of animal reservoirs or contaminated environments. Leptospirosis can be controlled by vaccines used mainly in animals, antibiotics given to exposed humans, and flood-affected people moved to a safe place. Northeastern Argentina accounts for the highest annual number of cases and deaths due to leptospirosis of the country. This interdisciplinary study aims to analyze the spatial and temporal distribution of leptospirosis, and assesses the hydroclimatic factors that give rise to the outbreaks in northeastern Argentina. The main goal is to detect the hydroclimatic indicators that can influence leptospirosis outbreaks occurrence in the northeastern Argentina. We perform a spatio-temporal analysis of the leptospirosis in the provinces of Santa Fe and Entre Ríos to distinguish the regions, years and seasons with the highest incidence of this disease. This study analyzes confirmed cases of leptospirosis between 2009 and 2018 years. Hydroclimatic indicators (monthly total precipitation, monthly river hydrometric level and Oceanic Niño Index) associated with outbreaks of leptospirosis vary in different spatial scales (provincial, departmental and cities). In the last outbreak of leptospirosis in 2015–2016 the number of cases was lower than expected. This could have been a consequence of increased prophylaxis in that flood event. Therefore, this is a variable that should be incorporated in future studies. Funding for this study was provided through PIO (CONICET-UNER), Argentina code 14620140100011CO and SECTeI, Argentina “Government of Santa Fe Province” code 2010-056-14.

Terrestrial environments surrounding aquatic resources are important and intensively used by semi-aquatic species. In the present work, terrestrial dispersal and nesting sites of the freshwater turtle Phrynops hilarii were analyzed in the floodplain of the Paraná River, using field data and variables obtained from remote sensing. A total of 112 turtles and 44 nests were recorded during road sampling for one year (covered a total of 786 km in 30 surveys). Individuals were at a mean distance of 171.45 m from water, with a negative correlation between number of turtles and distance from water bodies. No significant differences in distance of turtles from water were observed among seasons. Phrynops hilarii nested at a mean distance of 136.51 m from water, showing a negative correlation between number of nests and distance from water bodies. Mean elevation of nests relative to maximum level of water body nearest each record was 1.13 m. The correlation between number of nests and elevation of the nearest water body was positive and significant. The landscape surrounding wetlands is important for P. hilarii to complete the life cycle, as nesting is done in this environment. Our results show that the habitat selected for nesting and terrestrial dispersal was proportionally different from that available in the entire study area, with a higher proportion of wetlands, grasslands and forests. Fil: Lopez, Maria Soledad. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Sione, Walter Fabian. Universidad Autónoma de Entre Rí­os. Facultad de Ciencia y Tecnología. Centro Regional de Geomática; Argentina. Universidad Nacional de Luján; Argentina Fil: Leynaud, Gerardo Cristhian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Zoología Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Prieto, Yanina. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentina Fil: Manzano, Adriana Silvina. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentina

Land cover maps at different resolutions and mapping extents contribute to modeling and support decision making processes. Because land cover affects and is affected by climate change, it is listed among the 13 terrestrial essential climate variables. This paper describes the generation of a land cover map for Latin America and the Caribbean (LAC) for the year 2008. It was developed in the framework of the project Latin American Network for Monitoring and Studying of Natural Resources (SERENA), which has been developed within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLaTIF). The SERENA land cover map for LAC integrates: 1) the local expertise of SERENA network members to generate the training and validation data, 2) a methodology for land cover mapping based on decision trees using MODIS time series, and 3) class membership estimates to account for pixel heterogeneity issues. The discrete SERENA land cover product, derived from class memberships, yields an overall accuracy of 84% and includes an additional layer representing the estimated per-pixel confidence. The study demonstrates in detail the use of class memberships to better estimate the area of scarce classes with a scattered spatial distribution. The land cover map is already available as a printed wall map and will be released in digital format in the near future. The SERENA land cover map was produced with a legend and classification strategy similar to that used by the North American Land Change Monitoring System (NALCMS) to generate a land cover map of the North American continent, that will allow to combine both maps to generate consistent data across America facilitating continental monitoring and modeling. Fil: Blanco, Paula Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina Fil: Colditz, Rene R.. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad; México Fil: López Saldaña, Gerardo. Universidade Técnica de Lisboa; Portugal Fil: Hardtke, Leonardo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina Fil: Llamas, Ricardo M.. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad; México Fil: Mari, Nicolás A.. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias. Centro de Investigaciones de Recursos Naturales. Instituto de Clima y Agua; Argentina Fil: Fischer, Angeles. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias. Centro de Investigaciones de Recursos Naturales. Instituto de Clima y Agua; Argentina Fil: Caride, Constanza. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias. Centro de Investigaciones de Recursos Naturales. Instituto de Clima y Agua; Argentina Fil: Aceñolaza, Pablo Gilberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; Argentina. Universidad Autónoma de Entre Ríos; Argentina Fil: del Valle, Hector Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina Fil: Lillo Saavedra, Mario. Universidad de Concepción; Chile Fil: Coronato, Fernando Raul. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina Fil: Opazo, Sergio A.. Universidad de Magallanes; Chile Fil: Morelli, Fabiano. Centro de Previsao de Tempo e Estudos Climaticos. Instituto Nacional de Pesquisas Espaciais; Brasil Fil: Anaya, Jesús A.. Universidad de Medellin; Colombia Fil: Sione, Walter Fabian. Universidad Autónoma de Entre Ríos; Argentina. Universidad Nacional de Lujan; Argentina Fil: Zamboni, Pamela. Universidad Autónoma de Entre Ríos; Argentina Fil: Barrena Arroyo, Victor. Universidad Nacional Agraria La Molina; Perú

El objetivo de este trabajo fue evaluar la aplicacion de una metodologia de deteccion y monitoreo de los desmontes, resistente a la irregularidad de la adquisicion de imagenes en la region del Espinal entrerriano. El monitoreo de los cambios de uso y cobertura de las tierras es actualmente necesario para la politica gubernamental de manejo y conservacion de los recursos naturales. El area de estudio fue el Departamento Feliciano al norte de la Provincia de Entre Rios, region noreste de Argentina. La vegetacion es la tipica formacion arborea del Espinal entrerriano sometido a la actividad ganadera extensiva. La metodologia uso imagenes Landsat TM para formar un paquete multitemporal de bandas espectrales de la imagen de la segunda fecha y una banda intensidad del cambio obtenida por la tecnica RCEN. Sobre este paquete se aplico una tecnica de “segmentacion de imagenes por crecimiento de regiones” con semillado manual. Finalmente, se realizo el agrupamiento tematico basado en la interpretacion visual. En total, fueron detectados 1680ha desmontadas entre agosto de 2009 y diciembre de 2010, y 1140ha desmontadas entre diciembre de 2010 y abril de 2011. La segmentacion de imagenes con bandas “intensidad del cambio” con semillado manual obtuvo buenos resultados para la deteccion de desmontes. Este resultado fue corroborado por la fiscalizacion in situ de los organismos gubernamentales. Abstract The objective of this study was to evaluate the application of a methodology for detecting and monitoring forest clearance. The methodology should be unaffected to irregular image acquisition in the region of the “Espinal” (thorn forest) in northeastern Argentina. Monitoring changes in land use and cover is needed for government policies of conservation and management of natural resources. The study area was the Department Feliciano northern of Entre Rios province. The typical vegetation is Espinal thorn forest, subjected to selective logging for extensive livestock ranching. The methodology used Landsat TM images to form a multitemporal package with spectral bands of the second date and change intensity band obtained by the RCAN technique. On this package we applied a technique “Image segmentation by region growing” with manual seeding. Finally, the thematic aggregation based on visual interpretation was made. A total of 1680 ha cleared were detected between August 2009 and December 2010, and 1140ha cleared between December 2010 and April 2011. The segmentation of images with intensity bands change with manual seeding obtained god results for detecting forest clearing. This result was verified in situ by government agencies.

Please click here to download the map associated with this article. In many rangeland ecosystems, the role of wildfires has been the object of detailed studies. However, in Argentina and especially in the Monte biome this knowledge is limited. This region is prone to wildfires during late spring and summer, when high winds and temperatures are coupled with low relative humidity. In addition, this region also presents cases of extreme wildfire behaviour. The aim of this study is to develop a probability-based model for estimating wildfire risk at a regional scale based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Hot Spot data. The map presented here covers an area of approximately 246,000 km at a 1:100,000 scale. It is the result of fitting the 2001–2010 hot spot time series with a logistic regression model to 15 potentially explanatory environmental and anthropogenic variables. At a regional scale, mean net primary production, spring temperature, spring and summer precipitation, ...

[EN] There are large omission errors in the estimation of burned area in map products that are generated at a global scale. This error is then inherited by other models, for instance, those used to report Greenhouse Gas Emissions using a “bottom up” approach. This study evaluates temporal methods to improve burned area detection using Landsat 5-TM and 8-OLI. In this process, the normalized burn ratio (NBR) was used to highlight burned areas and thresholds to classify burned and non-burned areas. In order to maximize the burned area detection two alternatives to the temporal dNBR method were evaluated: the relative form of the temporal difference RdNBR and the use of time series metrics. The processing, algorithm development and access to Landsat data was made on the Google Earth Engine GEE platform. Three regions of Latin America with large fire occurrence were selected: The Amazon Forest in Colombia, the transition from Chiquitano to Amazon Forest in Bolivia, and El Chaco Region in Argentina. The accuracy assessment of these new products was based on burned area protocols. The best model classified 85% of burned areas in the Chiquitano Forests of Bolivia, 63% of the burned areas of the Amazon Forests of Colombia and 69% of burned areas in El Chaco of Argentina., [ES] Los productos globales de área quemada tienden a omitir una importante extensión de área afectada por el fuego, este error luego se traslada a otros modelos, por ejemplo, en las estimaciones nacionales de gases efecto invernadero utilizando el método conocido como “bottom up”. En este trabajo se evalúan métodos temporales para mejorar la identificación de áreas quemadas con datos de resolución espacial media (Landsat 5-TM y 8-OLI). En este proceso se utiliza el índice de proporción de quema normalizada (NBR) para resaltar las áreas quemadas y el método de selección de umbrales para separar las áreas quemadas de las no quemadas. Con el fin de maximizar la detección de área quemada se utilizaron dos métodos alternativos al método temporal dNBR: la forma relativa del método temporal, RdNBR, y el uso de métricas de series de tiempo. El procesamiento, el desarrollo del algoritmo y el acceso a los datos Landsat fue realizado en la plataforma de Google Earth Engine, GEE. Se evaluaron tres regiones con alta ocurrencia del fuego en Latino América: los bosques de la Amazonía colombiana, la transición de Bosque Chiquitano a Bosque Amazónico en Bolivia y El Chaco en Argentina. La evaluación de la calidad de los productos generados se basa en los protocolos de área quemada. Los resultados muestran que el mejor modelo identifica el 85% de las áreas quemadas en el Bosque Chiquitano de Bolivia, el 63% en los Bosques Amazónicos de Colombia y el 69% en El Chaco de Argentina., Los autores quieren agradecer el apoyo institucional de la Universidad de Medellín (UDEM) y de la Universidad Autónoma de Entre Ríos (UADER). El proyecto fue realizado en el contexto de REDLATIF y liderado por la línea de investigación de “Geomática aplicada a los recursos naturales” del grupo GEMA, con el apoyo de la Vicerrectoría de investigación de la UDEM. Convenio 294 código 789

Lymantria dispar is a quarantine pest absent in South America. A regional phytosanitary surveillance plan was developed to control this pest due to its high risk of introduction through global commerce. The objective of this study was to estimate the potential distribution ranges of L. dispar in a new geographical area, using a deductive approach to prioritize areas for preventive monitoring. In South America, forest susceptibility was evaluated under different climate scenarios. In Argentina a phenology model was simulated. According to historical climatic conditions in South America, the areas with higher risk of establishment are the Andean forests from northern Argentina to Venezuela, the Brazilian Atlantic forest, littoral coastal areas of Uruguay and Argentina and the Mediterranean area of central and southern Chile. Based on estimations of IPCC, the effects of climate change would cause a reduction of the susceptible area to L. dispar invasion in future decades. Although there were statistically significant differences among the areas with high risk of invasion, estimated by the two methodological approaches, susceptible areas for preventive monitoring were identified., Lymantria dispar es una plaga cuarentenaria ausente de Sudamérica, para la que se ha elaborado un plan de vigilancia fitosanitaria regional, debido su elevado riesgo de introducción involuntaria a través del comercio. El objetivo de este trabajo fue estimar la distribución geográfica potencial de L. dispar en una nueva área utilizando un enfoque deductivo, a fin de priorizar ambientes en riesgo para su monitoreo preventivo. En Sudamérica se evaluó la susceptibilidad de la cobertura boscosa ante diferentes escenarios climáticos. A escala local, en Argentina, se simuló la fenología potencial de la especie. A base de las condiciones climáticas históricas de Sudamérica, L. dispar podría establecerse en los bosques andinos desde el norte de Argentina, hasta Venezuela, la mata atlántica brasileña, áreas costeras litorales de Uruguay y Argentina y el área mediterránea del centro y sur de Chile. Los efectos del cambio climático estimados por el IPCC, provocarían una disminución de este área susceptible en las próximas décadas. Aunque existieron diferencias significativas en la superficie clasificada como de mayor riesgo fitosanitario en las dos escalas de estudio (subcontinental vs. regional), se lograron identificar las principales áreas susceptibles para el monitoreo preventivo de L. dispar.

This paper presents results of the AQL2004 project, which has been developed within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLatif). The project intended to obtain monthly burned-land maps of the entire region, from Mexico to Patagonia, using MODIS (moderate-resolution imaging spectroradiometer) reflectance data. The project has been organized in three different phases acquisition and preprocessing of satellite data; discrimination of burned pixels; and validation of results. In the first phase, input data consisting of 32-day composites of MODIS 500-m reflectance data generated by the Global Land Cover Facility (GLCF) of the University of Maryland (College Park, Maryland, USA) were collected and processed. The discrimination of burned areas was addressed in two steps searching for "burned core" pixels using postfire spectral indices and multitemporal change detection and mapping of burned scars using contextual techniques. The validation phase was based on visual analysis of Landsat and CBERS (China-Brazil Earth Resources Satellite) images. Validation of the burned-land category showed an agreement ranging from 30% to 60%, depending on the ecosystem and vegetation species present. The total burned area for the entire year was estimated to be 153 215 km2. The most affected countries in relation to their territory were Cuba, Colombia, Bolivia, and Venezuela. Burned areas were found in most land covers; herbaceous vegetation (savannas and grasslands) presented the highest proportions of burned area, while perennial forest had the lowest proportions. The importance of croplands in the total burned area should be taken with reserve, since this cover presented the highest commission errors. The importance of generating systematic products of burned land areas for different ecological processes is emphasized. © 2008 by the Ecological Society of America.