Low Resolution Satellite Images Research Articles

Modelling and analyzing the water and carbon dynamics of Mediterranean macchia by the use of ground and remote sensing data

Mediterranean ecosystems are particularly vulnerable to the environmental changes which have occurred in the last decades. Evaluating the ecosystem response to these changes is therefore a top priority, particularly concerning water and carbon dynamics. The Pianosa Island is a well known test site where numerous environmental surveys have been performed to fully characterize the most typical vegetation type, Mediterranean macchia. A first measurement campaign concerning both vegetation and soil properties was performed in 2001, and was repeated in 2010 only for soil properties. Vegetation cover was characterized again by means of an aircraft high resolution LiDAR dataset taken in 2009. Additional medium and low spatial resolution satellite images (Landsat OLI and MODIS) and aircraft photos were recovered from various sources. The availability of these datasets offers a unique opportunity to develop and test a methodology capable of modelling and analyzing the water and carbon dynamics of Mediterranean macchia during the 2001–2010 decade. To this aim, simulation procedures integrating remotely sensed and ancillary data were first tuned towards the available observations of vegetation biomass and soil carbon content. These procedures were then applied to produce daily estimates of macchia actual evapotranspiration (AET), gross primary production (GPP) and net ecosystem exchange (NEE), whose accuracy was assessed against corresponding eddy covariance flux tower observations taken in two years (2007–2008). The results obtained are satisfactory and support the capability of the modelling approach to reproduce both the water and carbon dynamics of macchia during the study decade. From an ecological viewpoint, both fluxes are increasing in this time period, mainly depending on similarly increasing spring rainfall. The macchia ecosystem behaves as a net sink of carbon, which is stored primarily in soil (≅90%) and secondarily in vegetation.

Estimation of weighting distribution using fuzzy memberships and wavelet transformation with PSO optimization in satellite image enhancement

Satellite image enhancement suffers from edge localization and distortion of detail. A novel satellite image enhancement is presented which enhance the edge detail as well as a quality of low-resolution satellite images. In the proposed approach, we improve and preserve the edge detail of a significant region using particle swarm optimization and fuzzy logic in the high spectral region. Further, the fusion of detail images is performed to conquer the limitation of intensity distortion and over saturation in the region using an estimation of weights. The magnitude of weights is estimated by fuzzy membership functions to extract optimal spectral information. The measure of enhancement and contrast assessment function criterion are used to examine the performance of proposed method in comparison with other two recent techniques.

ESTIMATION OF SUBPIXEL SNOW-COVERED AREA BY NONPARAMETRIC REGRESSION SPLINES

Abstract. Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the tradeoff between temporal and spatial resolution of satellite imageries. Soft or subpixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed snow cover fraction methods applied on Moderate Resolution Imaging Spectroradiometer (MODIS) data have evolved from spectral unmixing and empirical Normalized Difference Snow Index (NDSI) methods to latest machine learning-based artificial neural networks (ANNs). This study demonstrates the implementation of subpixel snow-covered area estimation based on the state-of-the-art nonparametric spline regression method, namely, Multivariate Adaptive Regression Splines (MARS). MARS models were trained by using MODIS top of atmospheric reflectance values of bands 1-7 as predictor variables. Reference percentage snow cover maps were generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also employed to estimate the percentage snow-covered area on the same data set. The results indicated that the developed MARS model performed better than th

Algorithm of Bowtie effect rapid removing for low and medium resolution satellite images

Algorithm of Bowtie effect rapid removing for low and medium resolution satellite images

Crosswalk Localization from Low Resolution Satellite Images to Assist Visually Impaired People.

In this article, we propose a model for crosswalk detection and localization by using satellite images captured from Google Maps, for the purpose of assisting visually impaired people. The detection is performed by an SVM classifier, which is combined with Google Road Map to speed up computation time and to eliminate some possible false alarms. We assume that a visually impaired person holds a smartphone with an embedded GPS, which is used to initialize the extraction of images from Google Maps, as well as to assist its user by providing audio feedback of the nearest detected crosswalk. This issue brings forward significant interest and it is also very challenging, mainly due to illumination changes, occlusion, image noise and resolution, besides the quality of crosswalks that sometimes are badly painted in many developing countries. Experimental results indicate that the proposed model works well in low resolution images, effectively detecting and localizing crosswalks in simulated scenarios.

Method for Burned Forest Biomass Estimation at Subcompartment Level Using GF-1 images and GIS Datasets

To estimate the burned biomass and get the burned fuel types information by forest fire for the need of Chinese forestry management, basing on the fuel load from digital reference data, the combustion factor gotten from fieldwork, and the results of burned scar mapping by using the No.1 High-Resolution satellite (GF-1) of Chinese, the burned biomass estimation method at the subcompartment level has been developed using satellite images and Geography datasets. The method has been validated by the selected forest fire, which had taken place in Huangcaobai of Anning City, Yunnan province in year 2012.The total burned biomass is about 1.18 × 108 kg by using the panchromatic and multispectral scanners (PMS) image of GF-1; however, it is about 1.11 × 108 kg by using the Wide Coverage Image (WFV) of GF-1. The difference between them is 7.10 × 106 kg. This study also supplies a method for the single forest fire case when the fire radiative power (FRP) or fire radiative energy (FRE) of detected active fire points by using sparse low spatial resolution satellite images doesn’t satisfied the condition of Power Law distribution or Gaissian function.

Insights into the evolution of the Yenkahe resurgent dome (Siwi caldera, Tanna Island, Vanuatu) inferred from aerial high-resolution photogrammetry

The Yenkahe dome (Tanna Island, Vanuatu) is one of the most spectacular examples of presently active post-caldera resurgence, exhibiting a very high uplift rate over the past 1000years (156mm/year on average). Although numerous inhabited areas are scattered around the dome, the dynamics of this structure and associated hazards remain poorly studied because of its remote location and dense vegetation cover. A high-resolution photogrammetric campaign was carried out in November 2011 over the dome. Georeferenced photographs were treated by “Structure from Motion” and “Multiple-view Stereophotogrammetry” methods to produce a 3D-digital surface model (DSM) of the area and its associated orthophotograph. This DSM is much more accurate than previously available SRTM and Aster digital elevation models (DEMs), particularly at minimal (coastline) and maximal altitudes (Yasur culmination point, ~390m). While previous mapping relied mostly on low resolution DEMs and satellite images, the high precision of the DSM allows for a detailed structural analysis of the Yenkahe dome, notably based on the quantification of fault displacements. The new structural map, inferred from the 3D reconstruction and morphological analysis of the dome, reveals a complex pattern of faults and destabilization scars reflecting a succession of constructive and destructive events. Numerous landslide scars directed toward the sea highlight the probable occurrence of a tsunami event affecting the south-eastern coast of Tanna. Simulations of landslide-triggered tsunamis show the short time propagation of such a wave (1–2min), which could affect coastal localities even following relatively small destabilized volumes (a few million cubic meters).

Satellite Image Resolution Enhancement Using Dyadic-integer Coefficients Based Bi-orthogonal Wavelet Filters

Abstract The acquisition of high satellite Imagery becomes difficult due to the instrument limitation and imperfect imaging optics. Low spatial resolution satellite images consist a lot of mixed pixels which degrade the detection and recognition performance in civil and military applications. In this paper, a satellite image resolution enhancement using Dyadic-Integer coefficients based bi-orthogonal wavelet filters is proposed. Dyadic-integer-coefficient based wavelet filters are derived from the construction of a half-band polynomial. The splitting approach is used to develop the integer-coefficient based half-band polynomial. The possibility of these dyadic-integer coefficients based wavelet filters is explored in the field of image enhancement using sub-pixel image registration. The two-resolution frames are registered at a specific shift from one another to restore the resolution lost by the sensors. The discrete wavelet transform (DWT) obtained from the designed coefficients is applied on these two low-resolution images to obtain the high resolution image. The proposed method has been tested on satellite images. The quantitative (peak signal-to-noise ratio and root mean square error) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.

Vehicle Detection and Classification from High Resolution Satellite Images

Abstract. In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus the High Resolution Satellite Imagery (HRSI) proved to be a suitable alternative to aerial photogrammetric data to provide a new data source for object detection. Since the traffic rates in developing countries are enormously increasing, vehicle detection from satellite data will be a better choice for automating such systems. In this work, a novel technique for vehicle detection from the images obtained from high resolution sensors is proposed. Though we are using high resolution images, vehicles are seen only as tiny spots, difficult to distinguish from the background. But we are able to obtain a detection rate not less than 0.9. Thereafter we classify the detected vehicles into cars and trucks and find the count of them.

Validación local de la temperatura superficial del mar del sensor MODIS en aguas someras del Mediterráneo occidental

The sea surface temperature (SST) estimated from MODIS Aqua products (daytime and nighttime 11 μm and night 4 μm) has been correlated with field data taken at three depths (15, 50, 100 cm) in a Western Mediterranean coastal area. The comparison has allowed us to analyze the uncertainty in the estimation of this parameter in coastal waters using low spatial resolution satellite images. The results show that the daytime SST_11 μm product obtains fittest statistical values: RMSE (root mean square error) and r2 (Pearson’s correlation coefficient) of 1°C and 0.96, respectively, for 50 cm depth.

Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.

Assessing the quality of temporal high-resolution classifications with low-resolution satellite image time series

Upcoming temporally and spatially high-resolution satellites such as Venus, SENTINEL-2, and Landsat Data Continuity Mission (LDCM) will provide very valuable data for land-cover and vegetation monitoring. However, owing to cloud cover and even to some rapid changes, a higher temporal resolution may be needed for some applications. In this work, we propose using the higher temporal resolution of satellites with mid to low spatial resolutions such as the upcoming PROBA-V. The aim of this work is to study how images provided by satellites with a lower spatial resolution but with a higher temporal one can be used to obtain information about the temporal classification derived from satellite image time series (SITS) provided by high-resolution satellites such as Venus, SENTINEL-2, or LDCM.We show that the low-spatial-resolution SITS can be used to inform about the stability and relevance of high-spatial-resolution classification. Experiments include a wide variety of resolution ratios and study the use of each ratio from global to class-specific invalidation of the high-resolution classification map (computed from the high-spatial-resolution SITS).This work contributes to the assessment of the usefulness of the joint use of PROBA-V data and Venus/SENTINEL-2/LDCM images for land-cover monitoring.

Adaptive Contrast Stretching for Land Observation in Cloudy Low Resolution Satellite Imagery

Although low spatial resolution satellite images like MODIS and GOCI can be important to observe land surface, it is often difficult to visually interpret the imagery because of the low contrast by prevailing cloud covers. We proposed a simple and adaptive stretching algorithm to enhance image contrast over land areas in cloudy images. The proposed method is basically a linear algorithm that stretches only non-cloud pixels. The adaptive linear stretch method uses two values: the low limit (L) from image statistics and upper limit (U) from low boundary value of cloud pixels. The cloud pixel value was automatically determined by pre-developed empirical function for each spectral band. We used MODIS and GOCI images having various types of cloud distributions and coverage. The adaptive contrast stretching method was evaluated by both visual interpretation and statistical distribution of displayed brightness values.

Antarctic land-cover classification using IKONOS and Hyperion data at Terra Nova Bay

Low or medium spatial resolution satellite images are used for environmental monitoring in remote, extremely cold areas such as Antarctica. However, they cannot provide detailed spatial and spectral information over Antarctic areas. For obtaining this information, we propose an Antarctic land-cover classification method using IKONOS and Hyperion data over Terra Nova Bay, Antarctica. High spatial resolution IKONOS imagery enabled the detection of detailed, accurate boundaries between areas of snow, ice, vegetation, water and rock. Rock types were classified in detail using high spectral resolution Hyperion imagery. Because Antarctic land-cover types exhibit unusual spectral features, a step-by-step classification using various data sets and methods was applied. Five soil types and two rock types were identified by applying spectral linear unmixing to Hyperion data. Our land-cover classification map shows small mossy areas near emperor penguin or skua habitat. Lakes, which are an important factor in environmental monitoring, are also clearly shown. The detailed rock and soil classification map revealed that biotite gneiss and clay soils are dominant. This Antarctic land-cover map is useful for determining a suitable site for an environment-friendly Antarctic station. Among the various land-cover types, the lakes and the vegetation, rock and soil areas are considered to be important environmental factors related to Antarctic living organisms.

De l'utilisation des séries temporelles SPOT-VEGETATION pour surveiller un front pionnier

Depuis les années 1960, la forêt amazonienne se réduit face à l'extension des surfaces commerciales agricoles. Ce phénomène a été renforcé par l'augmentation de la population due à l'immigration vers ces nouveaux secteurs défrichés. Le phénomène a alors tendance à s'amplifier aux dépens de la forêt. Dans ce contexte, il devient essentiel de surveiller ces fronts pionniers afin de caractériser les changements rapides qui s'y opèrent. Les satellites à haute résolution ne sont pas adaptés pour surveiller ces milieux très dynamiques car leur résolution temporelle est faible et leur traitement reste compliqué. Inversement, les données satellitaires à basse résolution semblent être plus adaptées pour ce genre d'analyses. En se basant sur des données à basse résolution obtenues à partir du satellite SPOT-VEGETATION cette étude propose une méthode de surveillance adaptée à des fronts de déforestations de petites étendues. La méthode peut être extrapolée à d'autres fronts pionniers afin de les surveiller et d'établir des diagnostics sur les dynamiques temporelle des transformations paysagères.

Feature Extraction from Rural Satellite Imagery Using Color Based CBIR Techniques

The ability to organize and retrieve visual information such as images is becoming a crucial problem for specialists, because processing visual information requires perceptual abilities not yet known to exist in computational form. Therefore the ability to retrieve visual information is a complex problem. The main retrieval pattern include the features of an image such as colour, and shape. The algorithms used to calculate the similarity between extracted feature with an image database, that utilizes colour features of the images as the basis of comparison and retrieval. An object-oriented definition also involves prescribing a set of pertinent features or pixels as well as a method such as a classification algorithm with the appropriate training data. A low resolution satellite image of a rural scenario has been selected for the analysis. The entire image scene is divided into four equal quadrants. The Content-based retrieval of image features have been carried out for the selected two quadrants I and II. The color classification of the image in two quadrants clearly indicates that there are four important features that are common to the images 1 The features identified include, Natural Vegetation, Water Bodies, Land, and Housing associated with the image. Further using the L*a*b color spectral distribution and histogram techniques an estimation of the different features identified in the image has been made for all the four quadrants. The results are presented in the paper.

Object-based image analysis of remote sensing data

Remote sensing has developed various methods and technologies for contactless and cost-effective mapping of large area land cover/land use maps and other thematic maps. The key factor for the availability and reliability of these maps for use in Earth sciences is the development of effective procedures for satellite data analysis and classification. The most appropriate approach for classifying low and medium resolution satellite images (pixel size is coarser than, or at best similar to, the size of geographical objects) is pixelbased classification in which an individual pixel is classified into the closest class based on its spectral similarity. With increasing spatial resolution, pixel-based classification methods became less effective, since the relationship between the pixel size and the dimension of the observed objects on the Earth's surface has changed significantly. Therefore object-oriented classification has become increasingly popular over the past decade. This combines segmentation (which is a fundamental phase of the approach) and contextual classification. Segmentation divides the image into homogeneous pixel groups (segments), which are – during the semantic classification process arranged into classes based on their spectral, geometric, textural and other features during. The intent of this paper is to present the theoretical argumentation and methodology of object-based image analysis of remote sensing data, provide an overview of the field and point out certain restrictions as regards the current operational solutions.

Mapping of summer crops in the State of Paraná, Brazil, through the 10-day spot vegetation NDVI composites

The search for low subjectivity area estimates has increased the use of remote sensing for agricultural monitoring and crop yield prediction, leading to more flexibility in data acquisition and lower costs comparing to traditional methods such as census and surveys. Low spatial resolution satellite images with higher frequency in image acquisition have shown to be adequate for cropland mapping and monitoring in large areas. The main goal of this study was to map the Summer crops in the State of Paraná, Brazil, using 10-day composition of NDVI SPOT Vegetation data for 2005/2006, 2006/2007 and 2007/2008 cropping seasons. For this, a supervised digital classification method with Parallelepiped algorithm in multitemporal RGB image composites was used, in order to generate masks of Summer cultures for each 10-day composition. Accuracy assessment was performed using Kappa index, overall accuracy and Willmott's concordance index, resulting in good levels of accuracy. This methodology allowed the accomplishment, with free and low resolution data, of the mapping of Summer cultures at State level.

Evaporative water use of different land uses in the upper-Thukela river basin assessed from satellite imagery

Abstract Evaporative water use of various land use classes in the upper-Thukela river basin was estimated using the public domain version of the Surface Energy Balance Algorithm for Land (SEBAL) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. Twenty eight images were analyzed covering the period between June 2005 and September 2006. The South Africa land use map developed in the year 2000 was used to compute the evaporative water use of the various land uses in 13 Quaternary Catchments (QCs) in the upper-Thukela river basin. There was a good correlation between the SEBAL estimates of total evaporation and ground measurements from a Large Aperture Scintillometer installed at a site in one of the QCs in the study area. It was observed that the land uses that generated relatively large volumes of evaporative water were forestry (i.e. Eucalyptus, Pine, mixed species & indigenous), “water bodies” (i.e. water supply reservoirs, farm reservoirs) and wetlands. Total evaporation rates for all land use classes were high during the summer season (wet), with Eucalyptus ranging between 3 mm d−1 during the winter season (dry) and about 5 mm d−1 during the summer season. Bare rocks and eroded soil surface land use class had the least evaporative water use i.e., less than 1 mm d−1. The relatively low evaporation rates over “water bodies” during the dry winter season could be attributed to the averaging of mixed pixels (wet and dry) due to change in areal coverage and which could not be captured in the 1 km × 1 km low resolution MODIS images. Thus, the potential for applying remote sensing techniques, using low resolution satellite images, to quantify water use by various land uses in the Thukela river basin was explored with promising results.

Fusion of low and high resolution satellite images to monitor changes on costal zones

A low resolution ETM+ and higher resolution IKONOS images obtained in 2000 - 2003, respectively, are compared using post-classification comparison algorithm to detect changes due to a new major highway construction initiated in 2000. The spatial resolution difference between the input images, which may lead to wrong registration, thereby, wrong post-classification comparison results, are minimized by fusing the 30 meter resolution ETM+ multispectral bands with its 15 meter resolution panchromatic band using a trous wavelet transform image fusion method. The IKONOS image has color change on the sea surface especially at the river mouth and its vicinity because of mud and sediments carried by Degirmendere Creek. Therefore, maximum Likelihood, Spectral Angular Mapping, Fisher Linear Likelihood, and ECHO classifiers are used for image classification. The best results are obtained from the ECHO classifier since it considers both spectral and spatial variations in the input images. The results show that the land fill on costal zone due to new high way construction is detected successfully with ECHO classifier. It is also seen that improving the spatial resolution of the ETM+ via image fusion minimizes the impact of misclassification on final change image generated by post-classification comparison. Key words: Change detection, image fusion, a trous wavelet transform, ETM+, IKONOS, ECHO classifier.