The urban development driving the air pollution matter is one of the reason which is seriously affecting on public health. Besides the traditional on-land monitoring methods, the current space technology has been contributed to supervising and managing environment. Therefore, this research has studied the use of remote sensing data to detect PM10 from Landsat satellite image by Aerosol Optical Thickness (AOT) method for Ho Chi Minh City area. At the same time, the regression analysis is also used for establishing the relationship the PM10 data obtained at ground stations and AOT values from processed images. The analysis shows a good correlation coefficient 0.95 (and sig-F coefficient 0.0039 noticed that the regression equation is very meaningful). The distribution for PM10 aerosol pollution is focused on the urban area, traffic booth and industrial zones with the value approximately 200µg/m³, even some places have reached 300µg/m³. The results of this study have provided an image of general distribution for current pollution status and also supported to determine the specified polluted areas. It is very helpful and good supported to zoning and urban environmental management in accordance with urban development. As a result, this research has proved that remote sensing can be considered as a helpful and economic tool which supports to monitor the environment and climate change in big cities

The Carbon Use Efficiency (CUE) is the ratio of net primary production (NPP) to gross primary production (GPP) and shows the capacity of terrestrial ecosystems to transfer carbon from the atmosphere to biomass. After 2000 there were four anomalous years in the productivity of terrestrial ecosystems caused by extreme droughts and heat waves in Southeast Europe in 2000, 2003, 2007 and 2012. The aim of this study is to examine the CUE under the stress conditions using NPP/GPP data products from the MODIS (NASA) spectroradiometer. Under extreme weather conditions CUE varied between 0.44 and 0.49, i.e. the drought and heat waves reduced the CUE with 10 до 20% and as a result the region has shifted from a carbon sink to a carbon source. Lowest CUE was observed in 2007, when it was 20% lower than efficiency in a normal year. The stress affects most on forest biomes, which were the lowest effective. It was found that up to 1100 m.a.s.l. the CUE decreased linearly with elevation, because of the predominant deciduous broadleaf forests.

Accurate and rapid mapping of fire damaged areas is fundamental to support fire management, account for environmental loss, define planning strategies and monitor the restoration of vegetation. Under climate change conditions, drought severity may trigger tough fire regimes, in terms of number and dimension of fires. Year 2017 was characterized by a harsh fire season in the Mediterranean area, especially for Portugal, Italy, Spain, Croatia, Bosnia and Herzegovina. Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map areas damaged by fire in a accurately and prompt way.
Burnt Area Index (BAI), Normalized Burn Ratio (NBR) and its relative versions have been largely employed in the past to map burnt areas from high resolution optical satellite data, employing their spectral domains. New Sentinel-2 satellites carry more spectral information recorded in the red-edge spectral region, opening the way to the development of new indexes for burnt area mapping.
This study present a processing chain developed to perform post-fire mapping using Sentinel-2 data. It makes use of a newly developed Burnt Area Index for Sentinel-2 (BAIS2), based on Sentinel-2 spectral bands to detect burnt areas at 20 m spatial resolution. The new index has been tested on various study cases in Italy for summer 2017 fires, and compared to already existing indexes for detecting burnt areas. Results show a good performance of the index and highlighted critical issues related to the Sentinel-2 data preprocessing, that have been taken into account in the development of the processing chain. Such improvements significantly reduce the number of false positives detected in the post-fire mapping, using both a single image or a multitemporal approach based on change detection.

The Earth’s land-covers are exposed to several types of environmental changes, issued by either human activities or natural disasters. On 15 April 2017, severe precipitations in the west and southwest regions of Iran caused flooding in the rivers of Ilam, Lorestan and Khuzestan provinces. The peak of this rainfall was in the Karoon Basin and the Dez Dam, causing an unprecedented flood in recent years with an intensity of eight thousand cubic meters per second. The occurrence of this flood has led to damages to these villages and agricultural plains. As well as, on 11 March 2011, an earthquake occurred at about 130 km of the east coast of Sendai City in Japan. This earthquake has been followed by a huge tsunami, which caused devastating damages over the wide areas in the eastern coastlines of Japan. Due to the occurrence of natural disasters across the world, there is a strong need to develop an automated algorithm for fast and accurate extraction of changed landscapes within the affected areas. Such techniques can accelerate the process of strategic planning and primary services for people to move into shelters, damage assessment, as well as risk management during a crisis. Therefore, a variety of change detection (CD) techniques has been previously developed, based on various requirements and conditions. However, the selection of the most suitable method for change detection is not easy in practice. To our best of knowledge, there is no existing CD approach that is both optimal and applicable in the cases of using a variety of optics and radar remote sensing images. In order to resolve these problems, an automated CD method based on Support Vector Data Description (SVDD) classifier is proposed. This method used the information contents of radar and optical data simultaneously by decision level fusing of obtained change maps from these data. In order to evaluate the efficiency of the proposed method and extract the damaged areas, two case studies consist of Sendai 2011’s tsunami and Shoosh 2017’s flood were considered. Various optical and radar remote sensing images from before and after of Sendai 2011’s tsunami and Shoosh 2017’s flood, acquired by IKONOS, Radarsat-2 and Sentinel-1, 2 were used, respectively. The proposed CD approach leads to an acceptable level of accuracy for both optical and radar imagery. The results confirmed the fundamental role and potential of using both optical and radar data for natural hazard damage detection applications.

Maize is the most commonly cultivated cereal in Africa in terms of land area and production. Low yields in this region are very often associated with issues related to low Nitrogen (N), such as low soil fertility or low fertilizer availability. Developing new maize varieties with high and reliable yields in actual field conditions using traditional crop breeding techniques can be slow and costly. Remote sensing has become an important tool in the modernization of field High Throughput Plant Phenotyping (HTPP), providing faster gains towards improved yield potential, adaptation to abiotic (water stress, extreme temperatures, salinity) and biotic (susceptibility to pests and diseases) limiting conditions, and even quality traits. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and the performance of the field-based Normalized Difference Vegetation Index (NDVI) and SPAD as phenotypic traits and crop monitoring tools for assessing maize performance under managed low nitrogen conditions. Phenotyping measurements were conducted on maize plants at two different levels: on the ground and from an airborne UAV (Unmanned Aerial Vehicle) platform. For the RGB indices assessed at the ground level, the strongest correlations to yield were observed with Hue, GGA (Greener Green Area) and GA (Green Area) at the ground level while GGA and CSI (Crop Senescence Index) were better correlated with grain yield at the aerial level. Regarding the field sensors, SPAD exhibited the closest correlation with grain yield, with a higher correlation when measured closer to anthesis. Additionally, we evaluated how these different HTPP data contributed to the improvement of multivariate estimations of crop yield in combination with traditional agronomic field data, such as ASI (Anthesis Silking Data), AD (Anthesis Data), and Plant Height (PH). PH was also estimated using 3D models produced using the Structure from Motion (SfM) algorithms from the RGB UAV aerial data. All multivariate regression models with an R² higher than 0.50 included one or more of these three agronomic parameters as predictive parameters, but with RGB indices at both levels increased to R2 over 0.60. As such, this research suggests that traditional agronomic data provide information related to grain yield in abiotic stress conditions, but that they may be potentially supplemented by RGB indices from either ground or UAV phenotyping platforms. Finally, in comparison to the same panel of maize varieties grown under optimal conditions, 11% of the varieties that were in highest yield producing quartile under optimal N conditions remained in the highest quartile when grown under managed low N conditions, suggesting that specific breeding for low N tolerance can still produce gains, but that low N productivity is not necessarily exclusive of high productivity in optimal conditions.

In the coming decades, Sub-Saharan Africa (SSA) faces the challenge of increasing the rate of food production, in a sustainable manner, to keep pace with the continued population growth. To do so, conservation agriculture (CA) is being proposed for developing countries in order to enhance soil health and productivity. On the other hand, maize is the main staple in SSA.  Thus, attempts to increase maize yields have to be focused in the selection of suitable genotypes and management practices for CA conditions, in which remote sensing tools may play a fundamental role towards overcoming the traditional limitations of data collection and processing in large scale phenotyping studies. We present the result of a study where Red-Green-Blue (RGB) and multispectral indices were evaluated for assessing maize performance under conventional ploughing (CP) and CA practices, and where the CA strategies resulted in higher yields. Eight hybrids under different planting densities and tillage practices were tested. The measurements were conducted on seedlings at ground level (0.8 m) and from an unmanned aerial vehicle (UAV) platform (30 m), causing a platform proximity effect on the images resolution that did not have a negative impact on the performance of the indices. Most of the indices calculated were significantly affected by the tillage conditions increasing their values from CP to CA, as the Green Area (GA) or the Normalized Difference Vegetation Index (NDVI). Indices derived from the RGB-images related to canopy greenness performed better at assessing yield differences, potentially due to the greater resolution of the RGB compared with the multispectral data, although this performance was more precise for CP than CA. The correlations of the multispectral indices with yield were improved by applying a soil-mask derived from a NDVI threshold and then aim the measurements on pixels corresponding to vegetation. This study highlights the applicability of remote sensing approaches based in the use of RGB images to assess crops performance and hybrid choice.

The Ma-le'l Dunes are located at the upper end of the North Spit of Humboldt Bay, California and are home to a range of plant and animal species. The goal of this study was to determine which classification method is most accurate in identifying dune features when performed on a larger, more diverse area. The data sources used for this study were orthomosaic UAV image [2017] with 14 cm spatial resolution and NAIP image [2016] with 1 m spatial resolution. The dune feature classes were compared with two images using supervised, unsupervised and feature extraction classification methods and accuracy assessment was performed using 50 ground control points. The classified feature classes were beach grass, shore pine, other vegetation, sand, and water. Overall, the NAIP classified map showed a higher accuracy for all classification methods than the UAV classified map with 86% overall accuracy for supervised classification. A feature extraction method showed a low accuracy for both NAIP (46%) and UAV ortho classified images [30%]. Of the classified methods for the UAV orthomosiac image, unsupervised classification showed a high accuracy [44%]. The Ma-le'l dune habitats are more heterogeneous and some classes were overlapped (i.e. beach grass and sand) due to high microtopographic variation of the dune, causing less accuracy for the feature extraction method. Monitoring dune habitat and geomorphic change over time with UAV images is important in order to implement best management practices for species conservation and to mitigate coastal vulnerabilities.    

With more than 450 terabytes of data acquired monthly, the Copernicus Satellites provide essential and free information to monitor our environment. However, with a download speed of 10Mps (average connection speed of the EU), more than 11 years would be needed to download such month of observations (not to mention the computing power needed for processing). In addition there are “knowledge barriers” which prevent adoption of such data by the users.

With the purpose to contribute overcoming these problems, the RUS (Research and user Support for Sentinel Core Products) project (funded by EC and managed by ESA) was opened to operations in October 2017. The service, offered at no cost, is run within a scalable cloud environment which allows to remotely store and process the data by bringing closer data and associated processing. Integral part of the solution is the exploitation and adaptation to the platform of Free and Open-Source Software (FOSS). In addition, technical and scientific support (eg training sessions) is provided to simplify the exploitation of Copernicus data. Trainings are carried out exploiting the FOSS installed on the VMs offered by the service. Face to face events are organized to meet the requirements of small groups of users which receive specific training on EO theory and step by step application of the theory in practical case-studies. Large Webinars organized every month provide detailed tutorials to exploit Sentinel data for application-oriented purposes. An e-learning platform will complement the practical part shown in the webinars, with technical background presented in an interactive way.

The service supports with ICT and expertise three different categories of users ranging from basic users in need for downloading support, R&D users in need for prototyping support and proficient users, in need for processing support. The uniqueness and innovative content of this service, relays in making the data more accessible to the users (alike other similar services) at no cost, together with providing technical and scientific support to the users, helping hence their activities. This contribution aims to raise the interest of potential users of the service, which facilitates the development of new science and prototyping of new applications based on Copernicus data. Our contribution to the 2nd International Electronic Conference on Remote Sensing (ECRS-2) is planned to be a Webinar during which we will demonstrate use of the RUS environment (and associated Open Source Toolboxes) for processing Copernicus data.

E xamples of processing results obtained by exploiting the service within training sessions account for: retrieval of the deformation fields associated to the Amatrice earthquake (August 2016), ship detection in the gulf of Trieste, monitoring of agricultural fields in California (November 2016-November 2017), mapping the Malawi flood (January 2015), the differential interferogram of the recent M7.3 Iran-Iraq earthquake (November 2017) and burned area mapping in Portugal.

Large tankers collisions with rocky shoals, platform accidents, pipelines ruptures and operative discharges are main contributors to oil pollution in the World’s oceans. Consequently, the release of the petroleum pollutants into coastal waters harms severely the environmental ecosystem. According to the European Space Agency (ESA), an estimate of 4.5 million tons are spilled on annual basis worldwide. Therefore, having oil pollution monitoring system is something crucial for the preservation of the coastal ecosystem. Recent remote sensing techniques combine between aircrafts and satellite surveillance in order to increase the probability of early spill detection, as well as to cover large spill areas.

We are working on a project that targets eventually to incorporate MIMO radar on drone for oil spill detection. The project will provide a quick assessment tool for oil spill accidents similar to what happened in the summer of 2006 in Lebanon, where 15000 tons of heavy fuel oil spilled in the Mediterranean Sea. In addition, the MIMO radar drones will be prominent by its providence to high spectral resolution, its allowance to parallel scanning, and its relative low cost. The project is composed of three phases. The first phase deals with the problem formulation and treatment using theoretical calculation and numerical simulations. The second phase deals with the prototype leading to the product in the third phase. As researchers, we are focusing on the first and second phases. Experimental measurements conducted in the laboratories in France are used to validate the models, to study the effectiveness of the algorithms and to provide the prototype. The product development in the final phase is left for application engineers.

Specifically for this abstract, we develop radar’s algorithms that take into account both the mathematical and the physical modeling of the sea surface covered by oil slicks. In these detection algorithms, we use the statistical characterization of the power reflectivity and its distribution under various scenarios (noise levels, oil thicknesses and electromagnetic wave’ frequencies). We first propose a single frequency oil spill detector that uses multiple observations of power reflection coefficients over several scanning iterations for the sea area. Increasing the number of observations leads to an increase in the certainty of the detector. Second, we address the correctness and the effectiveness of this detector for different scenarios using Monte- Carlo simulations. Results show the inability of this detector to effectively distinguish between oil slicks and oil-free slicks. An upgrade of this detector is the multi-frequency multi-snapshot detector where several electromagnetic frequencies are used when scanning the area. Performance analysis of the second detector proves its ability to overcome the drawbacks of the first detector by providing accurate detection.

In 2015, the Environment Agency of Abu Dhabi has developed an extensive Abu Dhabi Habitat, Land Use, Land Cover Map based on very high resolution satellite imagery acquired between 2011 and 2013. This was the first integrated effort at such a scale. This information has greatly helped in assisting in environmental conservation and preservation activities along with future infrastructure planning. This map has created an excellent baseline and provides a great opportunity for efficient monitoring. In this work, as an ongoing effort, we aim to establish a framework for continuous monitoring and short term updates to the maps to quickly capture the needs and enable efficient planning. We make use of the spectral-spatial approaches based on object-based image analysis to adapt the existing change detection methods such as iteratively-reweighted multivariate alteration detection (IR-MAD) to accurately identify the changes first even under varied image acquisition conditions. Then, the baseline maps are used to train classifiers such as random forest (RF) and support vector machines (SVM) in a spectral-spatial framework based on segmentation and/or morphological attribute profiles to build the updated land cover maps. Our aim is to develop an autonomous framework for a quick updation of land cover maps irrespective of the source of the satellite imagery. As a part of this work, we are also investigating the development of an operational change detection framework based on freely available data such as images from Sentinel and LandSat satellites.