High Spatial Resolution Image Data Research Articles

A patch spectral purification method to extract field patch average parameters from moderate resolution data

Satellite remote sensing data with high spatial and temporal resolutions are required in the dynamic monitoring of agricultural information. However, currently it is not yet possible to embrace the advantages of both high spatial resolution and high temporal resolution in a single sensor. Combining the merits from different remote sensing data will greatly benefit agricultural applications. In this paper, a patch spectral purification (PSP) method is developed to estimate the pure reflectance of field patches by combining high and moderate spatial resolution image data. The PSP method adopts the linear mixing model to build equations from moderate spatial resolution images, estimates initial values of field patch reflectance by statistical means to provide constraints to the solution and solves the equation sets based on Bayes' theorem. We chose one farm in Xinjiang, China as the study area and took 250 m MODIS image data as an example to test the performance of the method. The sensitivity analysis and the validation indicate that the PSP method is capable of giving a good estimation of pure field patch reflectance from mixed pixels of 250 m MODIS images in the study area.

Remote sensing of the water quality of shallow lakes: A mixture modelling approach to quantifying phytoplankton in water characterized by high‐suspended sediment

Remote sensing has the potential to provide truly synoptic views of water quality, in particular, the spatial distributions of phytoplankton. Whilst the spectral capabilities of satellites used in ocean colour work have improved significantly over recent years, the application of satellite remote sensing to lake water is constrained by the need for high spatial resolution image data and thus remains limited by spectral resolution capabilities. This becomes a significant problem when attempting to quantify chlorophyll a (Chl a) in waters characterized by high and heterogeneous suspended sediment concentrations (SSC). The SSC dominates the spectral reflectance, masking the spectral influence from other components in broad spectral band systems, making Chl a determination from remote sensing imagery difficult. This paper presents a linear mixture modelling approach to derive accurate estimates of Chl a from Landsat Thematic Mapper (TM) imagery. This approach was tested in Lake Balaton, Europe's largest shallow lake characterized by high suspended sediment and, until recently, frequent eutrophic and hypereutrophic episodes. The last significant bloom occurred in September of 2000 and a Landsat TM image was acquired for 11th September, during which ground reference data of water quality was collected. The modelled image‐derived results of Chl a demonstrate an excellent correspondence (r2 = 0.95) between the ground‐based measurements of Chl a, and yield considerable detail of lake phytoplankton distributions. The September 2000 calibration was then successfully applied to a July 1994 Landsat TM image and validated with Chl a data collected coincidently within two days of the image. The comparability between water sample data and image results demonstrates that there is temporal stability and robustness in the approach and calibration described.

Integrating High and Moderate Spatial Resolution Image Data to Estimate Forest Age Structure

RÉSUMÉLa collecte de l'information spatiale pour mesurer les changements à l'état et à la condition de l'environnement est une composante fondamentale pour l'économie ou l'utilisation soutenable des forêts tropicales et subtropicales. L'âge est un attribut structural important des forêts de vieille-croissance qui influence la diversité biologique dans les forêts d'eucalyptus de l'Australie. La traduction des photographies aériennes a été traditionnellement utilisée pour tracer l'âge et la structure des peuplements de forêt, cependant, cette méthode est subjective et ne capture pas assez bien les variations d'échelle fine et horizontale qui sont nécessaires aux études écologiques. L'identification et le tracé des attributs structuraux végétatifs aux échelles fines et horizontale permettront la compilation d'information associée aux indicateurs de processus Montréal 1b et 1d, qui cherchent à déterminer les liens entre la répartition par âge et la diversité et l'abondance de populations de faune de forêt. Ce projet intégre des mesures des attributs structuraux dérivés d'un modèle d'altitude de la voûte avec les résultats d'un modèle d'analyse de mélanges géometrique-optique/spectral pour tracer la répartition par âge de forêt à l'échelle horizontale. La disponibilité de données à multiple-échelle permet le transfert des attributs à haute résolution pour la surveillance à l'échelle horizontale. Des données d'image multispectrales ont été obtenues à partir d'un capteur DMSV (vidéo digital multispectral) au-dessus de la forêt St. Mary's State Forest, au sud-est du Queensland, en Australie. Des niveaux locaux de variance de scène pour différents types de forêt calculés à partir des données DMSV ont été employés pour optimiser les données de densité d'arbre et de taille de la voûte dans un modèle géométrique-optique appliqué aux données du cartographe thématique de Landsat (TM).Des données aéroportées de module de balayage à laser obtenues au-dessus de la zone de projet ont été employées pour calibrer un filtre digital pour extraire des tailles d'arbre à partir d'un modèle digital d'élévation dérivé des stereopairs balayés de couleur. Les évaluations modelées de la taille d'arbre, de la taille de couronne, et de densité ont été utilisées pour produire une classification à étape de succession de forêt à l'échelle horizontale. Les résultats obtenus (exacts à 72%), limités dans la validation, démontrent quand même le potentiel de la méthodologie à multiple-échelle pour fournir l'information spatiale qui assisterait les objectifs de la politique de sylviculture (c.- à-d., la surveillance de la répartition par âge de forêt).

Investigating the near-IR properties of PN: Deep imaging of molecular hydrogen emission

We present the first results of a deep near-infrared narrowband imaging study of several planetary nebulae. The data were acquired using the University of Hawaii 2.2m telescope on Mauna Kea and the “QUIRC” 1024 × 1024 array camera. With these sensitive high spatial resolution imaging data, we are able to explore in detail where H2 is found relative to the ionized region in planetary nebulae at various stages of evolution (See Figure 1, for an example). The objects were selected from previous studies to have evidence for extended emission from H2, and they are at several differing phases of central star evolution. The presence of molecules in the UV–flooded environment of a planetary nebula cannot be explained without highly non-spherical geometries and high densities, or clumping. Although there can be an ambiguity in the excitation mechanism (UV photons or shocks), the location of H2 emission identifies regions of the nebula with sufficient dust shielding and density for the survival of molecular species. The emission morphology also provides information that helps identify which excitation mechanism is most likely. When combined with data provided by other molecular studies, we can examine how the photodissociation region evolves through the circumstellar envelope as the planetary nebula tracks across the top of the HR diagram. We can also consider how morphological evolution of the nebula, through interacting winds and shocks, might provide the conditions necessary for molecular survival.

An edge-restricted spatial interpolation algorithm

This study addresses how to generate high spatial resolution image data from a low spatial resolution imaging source. An edge-restricted spatial interpolation algorithm is developed to increase the image resolution and at the same time to enhance the sharp edges and details from the original imaging source. The algorithm is based on a cubic spline-under-tension interpolation kernel. The weights of the interpolation kernel can be adjusted adaptively according to the edge information in the neighborhood of the interpolated pixels. The algorithm can be applied to a relatively low spatial resolution image source, such as video, to generate highresolution image data for high-quality printing devices.