23 research outputs found

    Comparación de índices de vegetación a partir de imágenes modis en la región del libertador Bernardo O´Higgins, Chile, en el período 2001-2005

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    Se comparan cinco índices de vegetación, NDVI, SAVI, ARVI, GreenNDVI y EVI, calculados a partir de imágenes MODIS en la Región del Libertador General Bernardo O´Higgins, Chile. Se determina las características de sus comportamientos según cubiertas vegetales distintas, zonas agrícolas, praderas y forestal, y dos periodos contrastados a lo largo del año, verano e invierno de los años 2001, 2003 y 2005. Los resultados indican que si bien las tendencias generales de las mediciones de vigor vegetal que hacen estos índices son semejantes, existen diferencias localizadas que hacen evidente la necesidad de elegir correctamente el tipo de índice de acuerdo a las necesidades que cada investigación requiera. Se hace particular énfasis en la necesidad de contar con una mayor discriminación de cubiertas vegetales para hacer una evaluación más refinada de las semejanzas y diferencias entre los índices estudiados.Five vegetation indices, NDVI, SAVI, ARVI, GreenNDVI and EVI, obtained from MODIS images in the Región del Libertador General Bernardo O´Higgins, Chile, are compared. The characteristics of their behavior according different vegetation cover layers, agricultural, grasslands and forestal zones, in both dry and wet seasons, along the years in 2001, 2003 and 2005 are determined. Results shows that global tendencies in the behavior of this indices are similar, there are some localized differences that make evident the necessity of the correct election of the right index for each situation. The importance of a more detailed discrimination in land cover types in order to achieve a better assesstment of similarities and differences between the indices is emphatized

    Verification and Validation of NASA-Supported Enhancements to PECAD's Decision Support Tools

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    The NASA Applied Sciences Directorate (ASD), part of the Earth-Sun System Division of NASA's Science Mission Directorate, has partnered with the U.S. Department of Agriculture (USDA) to enhance decision support in the area of agricultural efficiency-an application of national importance. The ASD integrated the results of NASA Earth science research into USDA decision support tools employed by the USDA Foreign Agricultural Service (FAS) Production Estimates and Crop Assessment Division (PECAD), which supports national decision making by gathering, analyzing, and disseminating global crop intelligence. Verification and validation of the following enhancements are summarized: 1) Near-real-time Moderate Resolution Imaging Spectroradiometer (MODIS) products through PECAD's MODIS Image Gallery; 2) MODIS Normalized Difference Vegetation Index (NDVI) time series data through the USDA-FAS MODIS NDVI Database; and 3) Jason-1 and TOPEX/Poseidon lake level estimates through PECAD's Global Reservoir and Lake Monitor. Where possible, each enhanced product was characterized for accuracy, timeliness, and coverage, and the characterized performance was compared to PECAD operational requirements. The MODIS Image Gallery and the GRLM are more mature and have achieved a semi-operational status, whereas the USDA-FAS MODIS NDVI Database is still evolving and should be considere

    Dinámica del NDVI en distintas fases del fenómeno ENSO en la Reserva de Biósfera Laguna Blanca (Catamarca, Argentina)

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    Maggi, Alejandro Esteban. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Ingeniería Agrícola y Uso de la Tierra. Cátedra de Manejo y Conservación de Suelos. Buenos Aires, Argentina.Ponieman, Karen. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Ingeniería Agrícola y Uso de la Tierra. Cátedra de Manejo y Conservación de Suelos. Buenos Aires, Argentina.Castro, Nicolás Guillermo. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Ingeniería Agrícola y Uso de la Tierra. Cátedra de Manejo y Conservación de Suelos. Buenos Aires, Argentina.Di Ferdinando, Miguel Angel. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Ingeniería Agrícola y Uso de la Tierra. Cátedra de Manejo y Conservación de Suelos. Buenos Aires, Argentina.151-164El Niño-Oscilación del Sur (ENSO, por sus siglas en inglés) es un fenómeno oceánico-climático que muestra dos fases contrastantes y una fase intermedia o neutra que afectan a diferentes regiones del mundo. En algunas ecorregiones del Noroeste Argentino (NOA), dichas fases provocarían años con precipitaciones menores o mayores que el promedio histórico, correspondientes a El Niño y La Niña, respectivamente. Estas diferencias en la disponibilidad de agua causan cambios en la cobertura vegetal y en la degradación de la tierra. Los cambios producidos en el régimen hídrico por causas naturales o antrópicas afectan la productividad de los diferentes ecosistemas y se pueden inferir a través de las diferencias en los índices espectrales, como el Índice de Vegetación de Diferencia Normalizada (NDVI). Los objetivos de esta investigación fueron caracterizar la dinámica de la cobertura vegetal estimada mediante el NDVI y estudiar las relaciones temporales entre el ENSO, la precipitación y el NDVI en las comunidades más conspicuas de la Puna en la Reserva de Biósfera Laguna Blanca. Se obtuvieron datos de NDVI del sensor MODIS (Moderate Resolution Imaging Spectroradiometer) a partir de un mapa publicado de comunidades vegetales de la reserva; la precipitación, a partir del GPCC (Global Precipitation Climatology Centre) y el Índice Oceánico El Niño (ONI, por sus siglas en inglés), de la National Oceanic and Atmospheric Administration (NOAA). La máxima diferencia de este último índice entre las distintas fases del ENSO se alcanzó entre septiembre y febrero. Como consecuencia del régimen monzónico, las precipitaciones alcanzaron el máximo un trimestre después; las más altas fueron en eventos La Niña. El máximo NDVI también mostró un retraso de uno a dos trimestres respecto al ONI dependiendo la comunidad analizada. Se encontraron indicios que prueban la conexión entre el NDVI, las precipitaciones y las fases del ENSO en la Puna catamarqueña. En años extremos La Niña y El Niño, tanto en estepas de la Puna árida y semiárida se manifiestan diferencias significativas en el NDVI. Los resultados sugieren que el monitoreo del ONI permitiría anticipar la aplicación de estrategias adecuadas para el control de la desertificación

    Integration of spatio-temporal vegetation dynamics into a distributed ecohydrological model: application to optimality theory and real-time watershed simulations

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    Spatio-temporal vegetation dynamics are important drivers to characterize seasonal to annual water and carbon budgets. Spatial adjustment and evolution of the ecosystem is closely related to the geomorphic, climatic, and hydrologic settings. In particular, lateral hydrologic redistribution along flowpaths control the long-term joint adjustments of vegetation and soil over successional and quasi-geological time scales. For this reason, it is complex and challenging to incorporate the many relevant processes and feedbacks between ecological and hydrological systems for the full simulation of water, carbon, and nutrient cycling. Recent developments in remote sensing technology provide the potential to link dynamic canopy measurements with integrated process descriptions within distributed ecohydrological modeling frameworks. In this dissertation, three research studies are presented concerning estimation of spatio-temporal vegetation dynamics in application into a distributed ecohydrological model at the Coweeta Long Term Ecological Research site. In Chapter 2, we test whether the simulated spatial pattern of vegetation corresponds to measured canopy patterns and an optimal state relative to a set of ecosystem processes, defined as maximizing ecosystem productivity and water use efficiency at the catchment scale. A distributed ecohydrological model is simulated at a small catchment scale with various field measurements to see if the evolved pattern of vegetation density along the flowpaths leads to system-wide emergent optimality for carbon uptake over and above the individual patch. Lateral hydrological connectivity determines the degree of dependency on productivity and resource use with other patches along flowpaths, resulting in different system-wide carbon and water uptake by vegetation. In Chapter 3, phenological signals are extracted from global satellite products to find the topography-mediated controls on vegetation phenology in the study site. It provides a basis to understand spatial variations of local vegetation phenology as a function of microclimate, vegetation community types, and hillslope positions. In Chapter 4, near real-time vegetation dynamics are estimated by fusing multi-temporal satellite images, and integrated into the catchment scale distributed ecohydrological simulation. Integration of spatio-temporal vegetation dynamics into a distributed ecohydrological model helps to simulate ecohydrological feedbacks between vegetation patterns and lateral hydrological redistribution by reducing uncertainty related to state and flux variables

    Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany.

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    This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat)

    Dinámica espacial de los pastizales en las microcuencas ganaderas de Pomacochas y Ventilla (Amazonas) utilizando datos Landsat en la plataforma de GEE, 1990 - 2020

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    En Perú, el monitoreo de los pastizales es fundamental para apoyar las políticas públicas relacionadas con la identificación, recuperación y manejo de los sistemas ganaderos. En este contexto, en este estudio se evaluó la dinámica espacial de los pastizales en las microcuencas de Pomacochas y Ventilla, Amazonas (Perú). Para ello, se utilizaron imágenes Landsat 5, 7 y 8 e índices de vegetación (índice de vegetación de diferencia normalizada (NDVI), Índice de Vegetación mejorado (EVI) e Índice de Vegetación Ajustado al Suelo (SAVI). Los datos se procesaron en Google Earth Engine para los años 1990, 2000, 2010 y 2020 mediante el uso del algoritmo de clasificación Random Forest (RF). Esto permitió el mapeo superficial de pastizales con presiones superiores al 85% en ambas microcuencas. Por su parte, la dinámica espacial de pastizales para el periodo 1990–2020, estuvo caracterizado por un incremento de 18% para Pomacochas (2457.03 a 3659. 37 ha) y de 9.5% en Ventilla (1932.38 a 4056.26 ha). En efecto, este estudio pretende brindar información útil para la planificación territorial y con potencial replicabilidad para otras regiones ganaderas del país. Además, podría utilizarse para mejorar la gestión de los pastizales y promover la ganadería semiextensiva

    Estimating leaf area index in savanna vegetation using remote sensing and inverse modelling

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    Leaf area index (LAI), defined as the one sided green leaf area per unit ground area, is a key parameter in ecosystem process models. Owing to the large area of the earth's surface that they occupy, savanna ecosystems represent the third largest terrestrial carbon sink. There is considerable uncertainty however, as to the functioning of these ecosystems, particularly as they respond to land cover changes. Consequently, ecosystem process models constitute one of the best methods available for investigating the effect this may have on terrestrial carbon cycling. If these models are to be used over large areas however, they need to be parameterised.This thesis develops a methodology to estimate LAI in savanna ecosystems, using remotely sensed earth observation (EO) data, laboratory bidirectional reflectance measurements (BRDF), physically based canopy reflectance models (CRMs), and artificial neural networks (ANN). First, the scattering behaviour of Kalahari soils was characterised, by making laboratory BRDF measurements. Soils were shown to be highly non-Lambertian. These measurements were then used to parameterise three different CRMs. Modelled reflectances were assessed with respect to Landsat ETM+ and Terra-MODIS reflectances. Results showed that a 1-D turbid medium provided the closest fit to the measurements. A series of model sensitivity analyses (SA) were performed, and it was shown that reflectance in the red and shortwave infrared displayed greatest sensitivity to LAI, sensitivity in the near-infrared was negligible. Model inversions were performed with ANN and different waveband combinations, and LAI was estimated. The results showed that LAI could be estimated with high accuracy, an RMSE of 0.3 1, and 0.18, from ETM+ and MODIS measurements, respectively. These results were promising, and with further improvements to models, coupled with more accurate input data, will see the use of EO data play an increasingly important role in understanding the functioning of these savanna ecosystems
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