Multidecadal evolution of the shoreline change at Agadir beach (Morocco): A GIS and high-resolution satellite imagery analysis

A semi-automatic road extraction method from high-resolution (1-m) satellite images is presented. As IKONOS, a high-resolution (1-m) satellite has been launched and several companies have plans to launch high-resolution satellites, extraction of man-made objects from high-resolution satellite images has been main concern of many scientists. The method consists of three phases; 1) NUBS (Non Uniform B-Spline) curve is formed by given seed points. 2) A road candidate area is made by straightening image along the NUBS curve. 3) Finally, road is extracted by a tracking algorithm which uses adaptive least squares correlation match method and linearity. Due to straightening image, the tracking algorithm extracts roads accurately even though there are road gaps, and the size of a matrix for least squares correlation match can be reduced. We test our method on high-resolution (1-m) satellite (IKONOS) image. The test result reveals our method is robust and can be one of the feasible solutions of mapping from high-resolution (1-m) satellite images.

Crown is a key part of tree. It's the main place that photosynthesis has been taken place. It's also the important energy sources that tree need to grow. So, some researchers often study how to monitor growth of tree, predict trees' life increment and judge the quality of wood, and so on by using remote sensing technique. The appearance of commercial high-resolution satellite data supplies new resources for people to study crown structure of a tree by using remote sensing technique. In this paper, method to detecting tree crown by using QuickBird image that covers the demonstration has been studied. Base on image process, Object-Oriented image analysis method has been taken. The tree crown has been effectively extracted from the QuickBird image by using Fuzzy Classification method that bases on samples. At the same time, it can give some experiment to process high spatial resolution satellite image and make a strong base for promoting the application of high-resolution satellite image in forestry and entironment construction of our country.

Digital Earth rendering applications, such as Google Earth and World Wind, allow us to explore real information of the Earth surface. To show the diverse details of the Earth surface, it requires high resolution satellite images. In some cases obtaining high resolution satellite images cost too much and sometimes there are even no such images for the interesting sites. In this paper, we present a method using example-based super-resolution techniques combined with image analogies framework to improve the visual quality of satellite images. Detailed high resolution and low resolution satellite images of the same site are regarded as example pairs to form a super-resolution filter. The filter effectively improves resolution of low-resolution satellite images. Moreover, it preserves the coherence of the images and improves the performance of the Digital Earth applications as well. The proposed method has been tested on the World Wind, experiment results show the effectiveness of our method.

The process of road extraction from high-resolution satellite images is complex, and most researchers have shown results on a few selected set of images. Based on the satellite data acquisition sensor and geolocation of the region, the type of processing varies and users tune several heuristic parameters to achieve a reasonable degree of accuracy. We exploit two salient features of roads, namely, distinct spectral contrast and locally linear trajectory, to design a multistage framework to extract roads from high-resolution multispectral satellite images. We trained four Probabilistic Support Vector Machines separately using four different categories of training samples extracted from urban/suburban areas. Dominant Singular Measure is used to detect locally linear edge segments as potential trajectories for roads. This complimentary information is integrated using an optimization framework to obtain potential targets for roads. This provides decent results in situations only when the roads have few obstacles (trees, large vehicles, and tall buildings). Linking of disjoint segments uses the local gradient functions at the adjacent pair of road endings. Region part segmentation uses curvature information to remove stray nonroad structures. Medial-Axis-Transform-based hypothesis verification eliminates connected nonroad structures to improve the accuracy in road detection. Results are evaluated with a large set of multispectral remotely sensed images and are compared against a few state-of-the-art methods to validate the superior performance of our proposed method.

Este trabajo presenta una alternativa metodológica para la generación de información usada en la planificación de las actividades forestales. Se tomó como sitio de experimentación un predio forestal ubicado al sur del estado Nuevo León. Las metodologías se basaron en el empleo de imágenes de satélite de alta resolución las cuales han sido validadas utilizando los materiales tradicionales (fotografías aéreas) que sirvieron para describir la distribución de los recursos forestales del área y para hacer una comparación en cuanto a la calidad de la información obtenida de estos dos materiales. Estos materiales fueron procesados con el apoyo de equipo y software especializado que se encuentra en el Laboratorio de Percepción Remota y Sistemas de Información Geográfica de la Facultad de Ciencias Forestales de la UANL. Se realizó una comparación de la rodalización efectuada sobre la ortofoto y en la imagen de satélite, con cubrimiento para el área de estudio, encontrando que la efectuada sobre la imagen de satélite presentó calidad suficiente para identificar mejor la composición de especies y la estructura de edades. El uso de imágenes de satélite de alta resolución para la elaboración de la cartografía forestal ofrece ventajas sobre las ortofotos, en especial por la resolución espectral, que facilita en algunos casos la rodalización, presentan la posibilidad de obtenerse repetidamente, se pueden procesar digitalmente y obtener otras variables como los índices de vegetación.

The detection of the road is one of an area of satellite image classification. The satellite image classification plays a vital role in various area of monitoring different resources available on the earth surface. Here, the high-resolution satellite data from Google earth is acquired from a different region of Mumbai, Maharashtra, India region for detection of road. This research paper used two different algorithms i.e. radial basis function neural network and Naive Bayes classifiers for the detection of reading features from the high-resolution satellite image. Both algorithms are implemented using the Matlab simulation toolbox. Radial Basis Function and Naive Bayes is a supervised classification technique applied on High-Resolution Satellite Image. Extraction of Road from the satellite image is a very difficult task because in the rural areas there are many unstructured roads which may consist of mud and concrete. After applying the algorithms on the image high-resolution satellite, the accuracy of classifiers is calculated using confusion matrix and Kappa coefficient. The accuracy of Naive Bayes found to be 91% with Kappa Value 0.698 and the accuracy of radial basis function found to be 99% with a Kappa value of 0.9831. The accuracy calculation using confusion matrix and Kappa value shows that the radial basis function neural network classifier is better than Naive Bayes classifiers for the detection of the road using high-resolution satellite image.

Shoreline change rates along Samborombón Bay, Río de la Plata estuary, Argentina

The information extracted from the high spatial resolution remote sensing images has become one of the important data sources of the GIS large scale spatial database updating. The realization of the building information monitoring using the high resolution remote sensing, building small scale information extracting and its quality analyzing has become an important precondition for the applying of the high-resolution satellite image information, because of the large amount of regional high spatial resolution satellite image data. In this paper, a clustering segmentation classification evaluation method for the high resolution satellite images of the typical rural buildings is proposed based on the traditional KMeans clustering algorithm. The factors of separability and building density were used for describing image classification characteristics of clustering window. The sensitivity of the factors influenced the clustering result was studied from the perspective of the separability between high image itself target and background spectrum. This study showed that the number of the sample contents is the important influencing factor to the clustering accuracy and performance, the pixel ratio of the objects in images and the separation factor can be used to determine the specific impact of cluster-window subsets on the clustering accuracy, and the count of window target pixels (Nw) does not alone affect clustering accuracy. The result can provide effective research reference for the quality assessment of the segmentation and classification of high spatial resolution remote sensing images.

This study is focused on the use of Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data along with high and very high resolution satellite images. The use of these data requires special attention and the elaboration of novel preprocessing methods, which is presented through two case studies. In the first one, the aim is to assess subpixel-scale changes associated to agricultural practice in an agricultural landscape. This requires the integration of temporal information from MODIS daily time series and spatial information from high resolution satellite images by means of subpixel unmixing. Our second case study is concentrated on the use of these images in direct radiometric rectification of high-resolution optical imagery. Our results show that the standard preprocessing is not sufficient for carrying out accurate subpixel analysis in a MODIS time series or for reliable radiometric rectification. The main reasons include raster-based processing not taking into account the changes in observation dimensions throughout the scene, and pixel mixing caused by the triangular point spread function (PSF). To resolve this, we propose a novel method based on the vector data model, in which each MODIS pixel is replaced by a polygon with its real size and orientation. Our results show this method yields a significant improvement in the radiometric fit of high-resolution and MODIS data.

On 07 February 2021, around 10:30 hrs local time catastrophic flash flood occurred in the Dhauliganga River (a tributary of the Ganga River) near Rini village at 2000 m above MSL (mean sea level) (Chamoli District), which killed 79 people and about 125 people were missing. Part of the area belongs to Nanda Devi Biosphere Reserve, which is completely protected from human interventions. Further, on the Dhauliganga River, two run-of-river hydroelectric power Rishiganga Small Hydro (13.2 MW) at 1975 m above MSL and Tapovan Vishnugad (520 MW) at 1795 m above MSL projects were also severely damaged due to the devastating flash flood. More than 150 workers were also trapped in the under-construction power tunnel of the Tapovan Vishnugad project. Initial assessment on the day of the event suggested that there was a glacial burst. Later, it was evaluated through time series of high spatial resolution remote sensing images of various satellites that a large part of a north-facing triangular-shaped slope at 5540 m above MSL had failed, which was also supporting a small hanging glacier. This landslide and, consequently, massive debris flow into the Raunthi Gadhera initially blocked the flow of Dhauliganga near Rini village [at 2000 m above MSL (mean sea level)], which later failed around 10:30 hrs on 07 February 2021 and brought a catastrophic flash flood in the Dhauliganga river. Further, remote sensing images acquired around 10:33 hrs of 07 February 2021 revealed a large dust cloud which clearly unravels the sequence of events from a high-altitude landslide, collapse of a small hanging glacier, and snow avalanche to catastrophic flooding. Even after the catastrophic flash flood of 07 February 2021, an elongated lake was created due to the blocking of the flow of the Rishi Ganga River. For detailed analysis, the calculation of dimension, area and volume of the failed slope was done using the high-resolution satellite images and digital elevation model using the RAMMS modelling technique. The north-facing triangular shape had a base of about 660 m and 1100 m height and the estimated total volume calculated was 20 million cubic meters, including rocks, snow, and ice. The debris flow runout simulation of the event was performed using the RAMMS debris flow model to calculate flow depth, flow velocity and maximum pressure. Also, from high-resolution satellite images, the dimensions of the artificial Rini Lake were estimated to have a length of about 800 m, a width at the front of about 100 m and a depth of about 46m, including freshly deposited debris and silt of about 10 m. To calculate the volume of the lake, simulation of lake was done in ArcView software using digital elevation model, and it came out to be ~5 million cubic meters. The paper also emphasizes monitoring of such vulnerable areas based on high-resolution time series satellite images, which are available on a regular basis to avoid the loss of human lives in the future.

In the early stages of post-disaster response, a quick and reliable damage assessment map is essential. As time is a critical factor, automated damage mapping from remotely sensed images is the expected solution to drastically reduce data acquisition and computation time. Recently, high-resolution satellite images, such as QuickBird data, have been in high demand by damage assessment analysts and disaster management practitioners. However, the existing automated mapping approaches hardly accommodate such high-resolution data. This research aims at developing a new context-based automated approach for earthquake damage mapping from high-resolution satellite images. Relevant contextual information (including structure, shape, size, edge texture, spatial relations) describing the damage situation is formulated and up-scaled on a morphological scale-space. Speed optimization is achieved by parallel processing implementation. The developed approach was tested with two QuickBird images acquired on 26 June 2005 and 3 June 2008 over YingXiu town, Sichuan, China, which suffered the devastating 12 May 2008 earthquake. In comparison to the reference, the developed mapping approach could achieve over 80% accuracy for computation of the damage ratio. Future research is planned to test the approach on various disaster cases for both optical and radar images using a grid-computing platform towards a cost-effective damage mapping solution.

Shoreline change studies in small-scale beaches require high-resolution satellite images. In this regard, high-resolution satellite images from Google Earth (GE) would be an alternative source however novel studies are needed to verify the effectiveness and the efficiency of applying those images for shoreline change detection in small-scale beaches. Addressing this gap, the current study attempts to develop a new method. Accuracies of delineated shorelines under different scenarios were used to develop relationships with digitizing methods and used eye-altitude to estimate the most effective, efficient and productive method. This was done using Digital Shoreline Analysis System (DSAS) in ArcGIS software. It was found that the eye-altitude influences on digitizing accuracy and it could be improved when increasing the zoom level of the image which is under investigation. Maximum zoom level (50 m) used in this study showed the highest accuracy in shoreline digitizing while the most productive eye-altitude for shoreline delineation was found as 300 m. The current study identified that GE coupled with DSAS tool in ArcGIS software can be used as an effective and efficient method for small-scale shoreline change analysis and it is suggested that this methodology could be adopted for other similar studies.

Combining Structure-from-Motion with high and intermediate resolution satellite images to document threats to archaeological heritage in arid environments

As the volume of marine transportation increases, it becomes increasingly important to monitor ships for efficient coastal monitoring and management. To this end, high-resolution satellite images can be utilized to surveil oceanic environments synoptically. In this study, high-resolution optical satellite image was used to detect ships and estimate the size of each ship in the Korean coastal region. All the pixels in an image were first classified into ship, ship shadow, wake, sea, and land by applying a maximum likelihood classifier. The positions corresponding to the boundary of the ship were obtained from the magnitude of the 2-dimensional gradient on the classified ship pixels, and then the length and width of the ship were estimated by applying an ellipse fitting method to the ship periphery. This method resulted, in slight overestimations of the sizes of the ships. In order to improve the accuracy of the estimated ship sizes, a correction formula was developed by investigating the errors of the estimated values and their potential relationships to the variables representing the spatial shape of the vessels, such as eccentricity, kurtosis. Applying the suggested formulation for ship size estimation improved accuracy by 54.41% compared to the estimated sizes obtained through ellipse fitting. We anticipate that our method of estimating the lengths of the vessels will contribute to identifying missing ships using high-resolution satellite images.

Phenotyping urban built and natural environments with high-resolution satellite images and unsupervised deep learning