Mapping shoreline changes due land reclamation using Landsat TM data

Remote sensing sources very useful to capture continuous, repeatedly and recently data. Change detection technique using various type of satellite images in Remote Sensing have been using frequently and continuously previously. Edge change detection used is very sensitive to detect linear feature such as shoreline. Mapping shoreline changes due to only coastal reclamation for urban development purposes are using edge change detection technique in Envi 5.0 software and ArcGIS 10.2 for develop the databases. In order to mapping this changes, images pre-processing, filtering option until feature extraction stage will been used. Geographical Information System (GIS) as a tool for data input either spatial or attribute, data management, data display and manipulation. Therefore, both Remote Sensing and GIS known as a powerful approach to gather new information from primer to secondary data. New information will be tested by statistical of filtering and feature extraction technique and accuracy of Ground Control (GC) distortions. This testing will be produced very accurate of coastal changes area and shoreline changes due to coastal reclamation for urban development purposes

GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images

Automatic extraction of buildings in remote sensing images is an important but challenging task and finds many applications in different fields such as urban planning, navigation and so on. This paper addresses the problem of buildings extraction in very high-spatial-resolution (VHSR) remote sensing (RS) images, whose spatial resolution is often up to half meters and provides rich information about buildings. Based on the observation that buildings in VHSR-RS images are always more distinguishable in geometry than in texture or spectral domain, this paper proposes a geometric building index (GBI) for accurate building extraction, by computing the geometric saliency from VHSR-RS images. More precisely, given an image, the geometric saliency is derived from a mid-level geometric representations based on meaningful junctions that can locally describe geometrical structures of images. The resulting GBI is finally measured by integrating the derived geometric saliency of buildings. Experiments on three public and commonly used datasets demonstrate that the proposed GBI achieves the state-of-the-art performance and shows impressive generalization capability. Additionally, GBI preserves both the exact position and accurate shape of single buildings compared to existing methods

The use of high resolution digital surface models for change detection and viewshed analysis in the area around the pyramids of Giza, Egypt

One of the biggest threats to cultural heritage is related to their rapidly changing and developing surroundings. The Giza pyramid plateau is a prime example of this phenomenon, as it is threatened by the enormous urban expansion of Cairo over the last decades. Documenting, monitoring and modelling such a pressure requires accurate and detailed geographic data, which can be derived from recent up-to-date, high resolution satellite images. Remote sensing techniques have proven to be very useful to visualize and analyze urban sprawl and land use changes in two dimensions. The impact assessment of urban sprawl near specific heritage sites, however; needs to be complemented with accurate 2.5D-information. In an attempt to do so, digital surface models (DSMs) from Ikonos-2 (2005) and GeoEye-1 stereoscopic images (2009 and 2011) have been computed in order to analyze recent urban changes. Change detection methods are mainly developed for large scale high resolution aerial images; however this paper focuses on the one hand DSM creation and its challenges resulting in an improvement of 2.5D change detection method for small scale satellite imagery in mainly informal areas. On the other hand a view shed evolution is presented. The combination of the enhanced digital terrain extraction (eATE) module of Erdas Imagine® and ground control points collected in the field provides accurate and high resolution DSMs. The impact of shadow and different urban morphologies however influence the pixel-wise comparison of the two DSMs, which results in different approaches for different city districts. The resulting 2.5D change model clarifies not only the urban sprawl, but also the increase in building levels, directly related to pressure on the famous pyramids. This pressure is furthermore analyzed by creating different view sheds through time from the plateau towards the city and vice versa. An integration of population statistics complements the model, hence allowing it to become a useful policy instrument

Urban detection, delimitation and morphology: comparative analysis of selective "megacities"

Postprint (published version

Non-Local Compressive Sensing Based SAR Tomography

Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse reconstruction problem and, hence, can be solved using compressive sensing (CS) algorithms. This paper proposes solutions for two notorious problems in this field: 1) TomoSAR requires a high number of data sets, which makes the technique expensive. However, it can be shown that the number of acquisitions and the signal-to-noise ratio (SNR) can be traded off against each other, because it is asymptotically only the product of the number of acquisitions and SNR that determines the reconstruction quality. We propose to increase SNR by integrating non-local estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible. 2) CS-based inversion is computationally expensive and therefore barely suitable for large-scale applications. We introduce a new fast and accurate algorithm for solving the non-local L1-L2-minimization problem, central to CS-based reconstruction algorithms. The applicability of the algorithm is demonstrated using simulated data and TerraSAR-X high-resolution spotlight images over an area in Munich, Germany.Comment: 10 page

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales