Spectral cross-calibration of VIIRS enhanced vegetation index with MODIS: A case study using year-long global data

© 2015 by the authors; licensee MDPI, Basel, Switzerland. In this study, the Visible Infrared Imaging Radiometer Suite (VIIRS) Enhanced Vegetation Index (EVI) was spectrally cross-calibrated with the Moderate Resolution Imaging Spectroradiometer (MODIS) EVI using a year-long, global VIIRS-MODIS dataset at the climate modeling grid (CMG) resolution of 0.05°-by-0.05°. Our cross-calibration approach was to utilize a MODIS-compatible VIIRS EVI equation derived in a previous study [Obata et al., J. Appl. Remote Sens., vol.7, 2013] and optimize the coefficients contained in this EVI equation for global conditions. The calibrated/optimized MODIS-compatible VIIRS EVI was evaluated using another global VIIRS-MODIS CMG dataset of which acquisition dates did not overlap with those used in the calibration. The calibrated VIIRS EVI showed much higher compatibility with the MODIS EVI than the original VIIRS EVI, where the mean error (MODIS minus VIIRS) and the root mean square error decreased from -0.021 to -0.003 EVI units and from 0.029 to 0.020 EVI units, respectively. Error reductions on the calibrated VIIRS EVI were observed across nearly all view zenith and relative azimuth angle ranges, EVI dynamic range, and land cover types. The performance of the MODIS-compatible VIIRS EVI calibration appeared limited for high EVI values (i.e., EVI > 0.5) due likely to the maturity of the VIIRS dataset used in calibration/optimization. The cross-calibration methodology introduced in this study is expected to be useful for other spectral indices such as the normalized difference vegetation index and two-band EVI

Use of EO-1 Hyperion Data for Inter-Sensor Calibration of Vegetation Indices

Numerous satellite sensor systems useful in terrestrial Earth observation and monitoring have recently been launched and their derived products are increasingly being used in regional and global vegetation studies. The increasing availability of multiple sensors offer much opportunity for vegetation studies aimed at understanding the terrestrial carbon cycle, climate change, and land cover conversions. Potential applications include improved multiresolution characterization of the surface (scaling); improved optical-geometric characterization of vegetation canopies; improved assessments of surface phenology and ecosystem seasonal dynamics; and improved maintenance of long-term, inter-annual, time series data records. The Landsat series of sensors represent one group of sensors that have produced a long-term, archived data set of the Earth s surface, at fine resolution and since 1972, capable of being processed into useful information for global change studies (Hall et al., 1991)

Analisis orientado a objetos de imágenes de teledetección para cartografia forestal : bases conceptuales y un metodo de segmentacion para obtener una particion inicial para la clasificacion = Object-oriented analysis of remote sensing images for land cover mapping : Conceptual foundations and a segmentation method to derive a baseline partition for classification

El enfoque comúnmente usado para analizar las imágenes de satélite con fines cartográficos da lugar a resultados insatisfactorios debido principalmente a que únicamente utiliza los patrones espectrales de los píxeles, ignorando casi por completo la estructura espacial de la imagen. Además, la equiparación de las clases de cubierta a tipos de materiales homogéneos permite que cualquier parte arbitrariamente delimitada dentro de una tesela del mapa siga siendo un referente del concepto definido por su etiqueta. Esta posibilidad es incongruente con el modelo jerárquico del paisaje cada vez más aceptado en Ecología del Paisaje, que asume que la homogeneidad depende de la escala de observación y en cualquier caso es más semántica que biofísica, y que por tanto los paisajes son intrínsecamente heterogéneos y están compuestos de unidades (patches) que funcionan simultáneamente como un todo diferente de lo que les rodea y como partes de un todo mayor. Por tanto se hace necesario un nuevo enfoque (orientado a objetos) que sea compatible con este modelo y en el que las unidades básicas del análisis sean delimitadas de acuerdo a la variación espacial del fenómeno estudiado. Esta tesis pretende contribuir a este cambio de paradigma en teledetección, y sus objetivos concretos son: 1.- Poner de relieve las deficiencias del enfoque tradicionalmente empleado en la clasificación de imágenes de satélite. 2.- Sentar las bases conceptuales de un enfoque alternativo basado en zonas básicas clasificables como objetos. 3.- Desarrollar e implementar una versión demostrativa de un método automático que convierte una imagen multiespectral en una capa vectorial formada por esas zonas. La estrategia que se propone es producir, basándose en la estructura espacial de las imágenes, una partición de estas en la que cada región puede considerarse relativamente homogénea y diferente de sus vecinas y que además supera (aunque no por mucho) el tamaño de la unidad mínima cartografiable. Cada región se asume corresponde a un rodal que tras la clasificación será agregado junto a otros rodales vecinos en una región mayor que en conjunto pueda verse como una instancia de un cierto tipo de objetos que más tarde son representados en el mapa mediante teselas de una clase particular

An isoline-based translation technique of spectral vegetation index using EO-1 Hyperion data

The availability of similar satellite data products from multiple sensors has focused much attention on the issue of continuity across satellite data products from past, current, and future sensors. Hyperspectral datasets acquired over a variety of land cover types are extremely useful in attempting to resolve spectral differences in the global datasets from different sensors. The datasets from the Earth Observing 1 (EO-1) Hyperion sensor are very suitable for this purpose, as is airborne hyperspectral data. In this paper, we examine the possibility of translating vegetation index (VI) data between two sensors by using imagery from the Hyperion sensor and utilizing the vegetation isoline concept. The objectives of this paper are to introduce and test a VI translation technique, focused on the spectral differences associated with sensor spectral bandpass filters. The translation of global VI datasets from one sensor to another requires a methodology applicable over various land cover types and throughout the wide ranges in VI values. To meet these requirements, a technique is proposed that utilizes adjustable translation coefficients, based on an estimation of the leaf area index value relative to a numerical canopy model. The theoretical basis of the proposed translation algorithm is explained in terms of the vegetation isoline concept. Its performance was tested through a numerical experiment with a Hyperion image, focusing on the normalized difference vegetation index (NDVI) as a representative vegetation index. The results indicate the potential of the isoline-based translation technique for stable translation throughout wide ranges of NDVI values

Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies