Oil spill detection using optical sensors: a multi-temporal approach

Oil pollution is one of the most destructive consequences due to human activities in the marine environment. Oil wastes come from many sources and take decades to be disposed of. Satellite based remote sensing systems can be implemented into a surveillance and monitoring network. In this study, a multi-temporal approach to the oil spill detection problem is investigated. Change Detection (CD) analysis was applied to MODIS/Terra and Aqua and OLI/Landsat 8 images of several reported oil spill events, characterized by different geographic location, sea conditions, source and extension of the spill. Toward the development of an automatic detection algorithm, a Change Vector Analysis (CVA) technique was implemented to carry out the comparison between the current image of the area of interest and a dataset of reference image, statistically analyzed to reduce the sea spectral variability between different dates. The proposed approach highlights the optical sensors’ capabilities in detecting oil spills at sea. The effectiveness of different sensors’ resolution towards the detection of spills of different size, and the relevance of the sensors’ revisiting time to track and monitor the evolution of the event is also investigated

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

Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependency in bi-temporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) It is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; 3) it is capable of adaptively learning the temporal dependency between multitemporal images, unlike most of algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode

Monitoring urban growth and land use land cover change in Al Ain, UAE using remote sensing and GIS techniques

Urbanization and industrialization cause a serious land degradation problem, including an increased pressure on natural resources such as deforestation, rise in temperature and management of water resources. The Urban Heat Island (UHI) effects of urbanization are widely acknowledged. Increase of impervious surface is a surrogate measure of urbanization and their effects on local hydrology is well reported in literature. This study investigates the spatial-temporal dynamics of land use and land cover changes in Al Ain, UAE, from 2006 to 2016. The Landsat images of two different periods, i.e., Landsat ETM of 2006 and Landsat 8 for 2016 were acquired from earth explorer site. Semi-supervised known as the hybrid classification method was used for image classification. The change detection was carried out through post-classification techniques. The study area was categorized into five major classes. These are agriculture, gardens, urban, sandy areas and mixed urban/sandy areas. It was observed that agricultural and urban land increases from 42,560 ha to 45,950 ha (8%) and 8150 ha to 9105 ha (12%), respectively. Consequently, the natural sandy area was reduced. It was also found that the urban area was expanded dramatically in the west and southwest directions. The outcomes of this study would help concerning authorities for a sustainable land and water resources management in the Al Ain region

Optimal land cover mapping and change analysis in northeastern oregon using landsat imagery.

Abstract The necessity for the development of repeatable, efficient, and accurate monitoring of land cover change is paramount to successful management of our planet’s natural resources. This study evaluated a number of remote sensing methods for classifying land cover and land cover change throughout a two-county area in northeastern Oregon (1986 to 2011). In the past three decades, this region has seen significant changes in forest management that have affected land use and land cover. This study employed an accuracy assessment-based empirical approach to test the optimality of a number of advanced digital image processing techniques that have recently emerged in the field of remote sensing. The accuracies are assessed using traditional error matrices, calculated using reference data obtained in the field. We found that, for single-time land cover classification, Bayes pixel-based classification using samples created with scale and shape segmentation parameters of 8 and 0.3, respectively, resulted in the highest overall accuracy. For land cover change detection, using Landsat-5 TM band 7 with a change threshold of 1.75 standard deviations resulted in the highest accuracy for forest harvesting and regeneration mapping