Browsing by Author "George P. Petropoulos"

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A novel deep learning change detection approach for estimating spatiotemporal crop field variations from Sentinel-2 imagery
(Elsevier B.V., 2024) Neelam Dahiya; Gurwinder Singh; Dileep Kumar Gupta; Kleomenis Kalogeropoulos; Spyridon E. Detsikas; George P. Petropoulos; Sartajvir Singh; Vishakha Sood
The analysis of crop variation and the ability to quantify it is a critical and challenging task. Remote sensing (RS) has proven to be an effective tool for monitoring crops and detecting seasonal variations worldwide. This opens new opportunities for developing effective crop monitoring models, with deep learning models showing great promise. This study presents a deep learning-based U-Net v5 Change Detection (UCD) model capable of identifying and monitoring the spatio-temporal variations in crop fields. The application of the model is demonstrated using Sentinel-2 imagery over Patiala district in India to monitor the seasonal crop variation (rabi crop) during 2017–2018. The results have shown that the UCD model has achieved better results (95.6–98.4%) in accuracy for classified maps and more than (91.6%–96.6%) in accuracy for change maps. This study will be useful for crop monitoring, precision agriculture and crop yield prediction and can assist in decision and policy making towards a more sustainable environment. © 2024
An integrated spatiotemporal pattern analysis model to assess and predict the degradation of protected forest areas
(MDPI, 2020) Ramandeep Kaur M. Malhi; Akash Anand; Prashant K. Srivastava; G. Sandhya Kiran; George P. Petropoulos; Christos Chalkias
Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model's predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis-with the use of FRAGSTATS 4.2 software-was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction. © 2020 by the authors.
Appraisal of Climate Response to Vegetation Indices over Tropical Climate Region in India
(MDPI, 2023) Nitesh Awasthi; Jayant Nath Tripathi; George P. Petropoulos; Dileep Kumar Gupta; Abhay Kumar Singh; Amar Kumar Kathwas; Prashant K. Srivastava
Extreme climate events are becoming increasingly frequent and intense due to the global climate change. The present investigation aims to ascertain the nature of the climatic variables association with the vegetation variables such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). In this study, the impact of climate change with respect to vegetation dynamics has been investigated over the Indian state of Haryana based on the monthly and yearly time-scale during the time period of 2010 to 2020. A time-series analysis of the climatic variables was carried out using the MODIS-derived NDVI and LAI datasets. The spatial mean for all the climatic variables except rainfall (taken sum for rainfall data to compute the accumulated rainfall) and vegetation parameters has been analyzed over the study area on monthly and yearly basis. The liaison of NDVI and LAI with the climatic variables were assessed at multi-temporal scale on the basis of Pearson correlation coefficients. The results obtained from the present investigation reveals that NDVI and LAI has strong significant relationship with climatic variables during the cropping months over study area. In contrast, during the non-cropping months, the relationship weakens but remains significant at the 0.05 significance level. Furthermore, the rainfall and relative humidity depict strong positive relationship with NDVI and LAI. On the other, negative trends were observed in case of other climatic variables due to the limitations of NDVI viz. saturation of values and lower sensitivity at higher LAI. The influence of aerosol optical depth was observed to be much higher on LAI as compared to NDVI. The present findings confirmed that the satellite-derived vegetation indices are significantly useful towards the advancement of knowledge about the association between climate variables and vegetation dynamics. © 2023 by the authors.
Appraisal of SMAP operational soil moisture product from a global perspective
(MDPI AG, 2020) Swati Suman; Prashant K. Srivastava; George P. Petropoulos; Dharmendra K. Pandey; Peggy E. O'Neill
Space-borne soil moisture (SM) satellite products such as those available from Soil Moisture Active Passive (SMAP) offer unique opportunities for global and frequent monitoring of SM and also to understand its spatiotemporal variability. The present study investigates the performance of the SMAP L4 SM product at selected experimental sites across four continents, namely North America, Europe, Asia and Australia. This product provides global scale SM estimates at 9 km x 9 km spatial resolution at daily intervals. For the product evaluation, co-orbital in situ SM measurements were used, acquired at 14 test sites in North America, Europe, and Australia belonging to the International Soil Moisture Network (ISMN) and local networks in India. The satellite SM estimates of up to 0-5 cm soil layer were compared against collocated ground measurements using a series of statistical scores. Overall, the best performance of the SMAP product was found in North America (RMSE = 0.05 m3/m3) followed by Australia (RMSE = 0.08 m3/m3), Asia (RMSE = 0.09 m3/m3) and Europe (RMSE = 0.14 m3/m3). Our findings provide important insights into the spatiotemporal variability of the specific operational SM product in different ecosystems and environments. This study also furnishes an independent verification of this global product, which is of international interest given its suitability for a wide range of practical and research applications. © 2020 by the authors.
Assessing the influence of atmospheric and topographic correction and inclusion of SWIR bands in burned scars detection from high-resolution EO imagery: a case study using ASTER
(Kluwer Academic Publishers, 2015) Yahia Abbi Said; George P. Petropoulos; Prashant K. Srivastava
In the present study, the effect of atmospheric and topographic correction to burned area delineation from Earth Observation (EO) imagery is explored. Furthermore, the potential added value of the inclusion of the shortwave infrared (SWIR) bands for improving retrievals of burned area cartography is investigated. In particular, the capability of ASTER imagery when combined with the maximum likelihood (ML) and the support vector machines (SVMs) classification techniques is examined herein. As a case study, a Mediterranean site on which a fire event occurred in Greece during 2007 and for which post-fire ASTER imagery was available is used. The combination of topographic correction (orthorectification) with the inclusion of the SWIR bands returned the most accurate results in burned area detection. SVMs showed the highest accuracy, showing the most promising potential in delineating the burned areas. The most accurate results for burned scar mapping were obtained from the combined use of SVMs with an orthorectified image and SWIR spectral bands, at least this was the case in our study site. Our results offer a very important contribution to the understanding of the capability of high-resolution imagery such as that from ASTER in burned area estimation. This study also corroborates the usefulness of topographic correction as an image processing step to be incorporated in modelling schemes for delineating burned areas from such data. Findings potentially provide very useful information towards the development of EO-based techniques that aim to operationally provide services related to the estimation of burned area. This is of considerable scientific and practical value to the wider scientific and users’ community given the continuation of free access today to observations from space from high-resolution sensors globally. © 2015, Springer Science+Business Media Dordrecht.
Assessing the Use of Sentinel-2 in Burnt Area Cartography: Findings from a Case Study in Spain
(wiley, 2020) Craig Amos; Konstantinos P. Ferentinos; George P. Petropoulos; Prashant K. Srivastava
Remote sensing is increasingly being used as a cost effective and practical solution for the rapid evaluation of impacts from wildfires. The recent launch of the Sentinels offers a unique opportunity to assess the impacts of wildfires at both greater spatial and spectral resolutions provided by those Earth observing systems. In this study, an assessment of the Sentinels to map burnt areas is conducted by initially exploring the use of Sentinel-2 to detect burnt areas. The investigation attempted in particular to evaluate the use of different bands and derived indices that are commonly used to detect burnt areas. A range of spectral indices was used, including Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) for both the SWIR1 and SWIR2 por- tions of the EM spectrum. The three new Red Edge bands that come with the Sentinel-2A MSI sensor were also used. The Slope, Aspect, and Fractional Vegetation Cover and Terrain Roughness were all derived to produce environmental variables. The Copernicus Emergency Management Service has produced a grading map for the fire using 0.5 m resolution Pleiades imagery, which was used as reference. The visible part of the EM spectrum was not well suited to discern burned from unburned land cover. The NBRb12 (SWIR2) produced the best results for detecting burnt areas. The SAM classification resulted in a 73% overall accuracy. All in all, our study contrib- utes to the understanding of Mediterranean landscape dynamics. It also provides further evidence that use of Sentinel-2 technology, combined with GIS analysis techniques, can offer an effective tool in mapping wildfires. Â© 2020 John Wiley & Sons, Inc.
Assessment of a Dynamic Physically Based Slope Stability Model to Evaluate Timing and Distribution of Rainfall-Induced Shallow Landslides
(MDPI, 2023) Juby Thomas; Manika Gupta; Prashant K. Srivastava; George P. Petropoulos
Shallow landslides due to hydro-meteorological factors are one of the most common destructive geological processes, which have become more frequent in recent years due to changes in rainfall frequency and intensity. The present study assessed a dynamic, physically based slope stability model, Transient Rainfall Infiltration and Grid-Based Slope Stability Model (TRIGRS), in Idukki district, Kerala, Western Ghats. The study compared the impact of hydrogeomechanical parameters derived from two different data sets, FAO soil texture and regionally available soil texture, on the simulation of the distribution and timing of shallow landslides. For assessing the landslide distribution, 1913 landslides were compared and true positive rates (TPRs) of 68% and 60% were obtained with a nine-day rainfall period for the FAO- and regional-based data sets, respectively. However, a false positive rate (FPR) of 36% and 31% was also seen, respectively. The timing of occurrence of nine landslide events was assessed, which were triggered in the second week of June 2018. Even though the distribution of eight landslides was accurately simulated, the timing of only three events was found to be accurate. The study concludes that the model simulations using parameters derived from either of the soil texture data sets are able to identify the location of the event. However, there is a need for including a high-spatial-resolution hydrogeomechanical parameter data set to improve the timing of landslide event modeling. © 2023 by the authors.
Band selection algorithms for foliar trait retrieval using AVIRIS-NG: a comparison of feature based attribute evaluators
(Taylor and Francis Ltd., 2022) Ramandeep Kaur M. Malhi; Manish Kumar Pandey; Akash Anand; Prashant K. Srivastava; George P. Petropoulos; Prachi Singh; G. Sandhya Kiran; B.K. Bhattarcharya
Interband information overlapping enhances redundancy in hyperspectral data. This makes identification of application-specific optimal bands essential for obtaining accurate information about foliar traits. The current study investigated the performance of three novel Band Selection (BS) algorithms (i.e. the Chi-squared-statistics based attribute evaluator (CSS), the Recursive elimination of features-based attribute evaluator (REF) and the Correlation-based attribute subset evaluator (CBS)) in identifying the spectral bands of Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) from visible and Near Infrared (NIR) regions that are sensitive to variation in Chlorophyll Content (CC). Identified bands were employed to formulate Hyperspectral Indices (HIs) by incorporating combinations of Blue, Green, Red, and NIR regions. CC models were built by establishing a linear fit between ground CC and HIs. For all the three BS algorithms, optimum bands varied for visible and NIR regions. REF-HI (NIR,R), REF-HI(NIR,R + G), CSS-HI(NIR,R) and CSS-HI(NIR,R + G) had the best correlation with CC. HI(NIR,R) is identified as the best HI and REF the best BS algorithm for retrieving CC. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining
(Springer Science and Business Media Deutschland GmbH, 2019) Prashant K. Srivastava; George P. Petropoulos; Manika Gupta; Sudhir K. Singh; Tanvir Islam; Dimitra Loka
Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices. © 2018, Springer Nature Switzerland AG.
Drought identification and trend analysis using long-term chirps satellite precipitation product in bundelkhand, india
(MDPI AG, 2021) Varsha Pandey; Prashant K. Srivastava; Sudhir K. Singh; George P. Petropoulos; Rajesh Kumar Mall
Drought hazard mapping and its trend analysis has become indispensable due to the aggravated impact of drought in the era of climate change. Sparse observational networks with minimal maintenance limit the spatio-temporal coverage of precipitation data, which has been a major constraint in the effective drought monitoring. In this study, high-resolution satellite-derived Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data has been used for computation of Standardized Precipitation Index (SPI). The study was carried out in Bundelkhand region of Uttar Pradesh, India, known for its substantial drought occurrences with poor drought management plans and lack of effective preparedness. Very limited studies have been carried out in assessing the spatio-temporal drought in this region. This study aims to identify district-wide drought and its trend characterization from 1981 to 2018. The run theory was applied for quantitative drought assessment; whereas, the Mann-Kendall (MK) test was performed for trend analysis at seasonal and annual time steps. Results indicated an average of nine severe drought events in all the districts in the last 38 years, and the most intense drought was recorded for the Jalaun district (1983–1985). A significant decreasing trend is observed for the SPI1 (at 95% confidence level) during the post-monsoon season, with the magnitude varying from −0.16 to −0.33 mm/month. This indicates the increasing severity of meteorological drought in the area. Moreover, a non-significant falling trend for short-term drought (SPI1 and SPI3) annually and short-and medium-term drought (SPI1, SPI3, and SPI6) in winter months have been also observed for all the districts. The output of the current study would be utilized in better understanding of the drought condition through elaborate trend analysis of the SPI pattern and thus helps the policy makers to devise a drought management plan to handle the water crisis, food security, and in turn the betterment of the inhabitants. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Earth Observation for MONITORING AND MODELING LAND USE
(Elsevier, 2024) George P. Petropoulos; Daniela Fernanda Da Silva Fuzzo; Dimitris Triantakonstantis; João Alberto Fischer Filho; Prashant K. Srivastava; Salim Lamine
Earth Observation for Monitoring and Modeling Land Use presents a practical guide and theoretical overview of the latest techniques and Earth observation technologies applied to land use and land cover change through qualitative assessment of Earth observation technologies. The book's chapters include detailed case studies, Earth observation datasets, and detailed applications of the technologies covered that are presented in a way that each chapter is a self-contained guide on a specific application of Earth observation technologies to land use problems, ensuring all technical and background information is provided on each subject without the need for cross-referencing or searching for other sources. The book spatializes the understanding of monitoring land cover and use, and quantifies the challenges faced, allowing analysis of the dynamics of the territory in terms of occupation processes, land use, and its transformations. It focuses on practical applications of using remote sensing and modeling that support new research in relation to monitoring of land use and spectral modelling, elucidating the importance of advanced methodologies in the coverage and use mappings of the Earth. © 2025 Elsevier Ltd. All rights are reserved.
Earth observation-based operational estimation of soil moisture and evapotranspiration for agricultural crops in support of sustainable water management
(MDPI, 2018) George P. Petropoulos; Prashant K. Srivastava; Maria Piles; Simon Pearson
Global information on the spatio-temporal variation of parameters driving the Earth's terrestrial water and energy cycles, such as evapotranspiration (ET) rates and surface soil moisture (SSM), is of key significance. The water and energy cycles underpin global food and water security and need to be fully understood as the climate changes. In the last few decades, Earth Observation (EO) technology has played an increasingly important role in determining both ET and SSM. This paper reviews the state of the art in the use specifically of operational EO of both ET and SSM estimates. We discuss the key technical and operational considerations to derive accurate estimates of those parameters from space. The review suggests significant progress has been made in the recent years in retrieving ET and SSM operationally; yet, further work is required to optimize parameter accuracy and to improve the operational capability of services developed using EO data. Emerging applications on which ET/SSM operational products may be included in the context specifically in relation to agriculture are also highlighted; the operational use of those operational products in such applications remains to be seen. © 2018 by the authors.
Evaluation of the Soil Moisture Operational Estimates From SMOS in Europe: Results Over Diverse Ecosystems
(Institute of Electrical and Electronics Engineers Inc., 2015) George P. Petropoulos; Gareth Ireland; Prashant K. Srivastava
This study presents the results of an extensive validation of the Soil Moisture and Ocean Salinity mission (SMOS) soil moisture operational product from selected European sites representative of a variety of climatic, environmental, biome, and seasonal conditions. SMOS soil moisture estimates were compared against corresponding in-situ measurements from the CarboEurope observational network. The agreement between the two datasets was evaluated on the basis of a series of statistical metrics. In addition, the effect of variability of site characteristics such as land cover, seasonality, and also that of the Radio Frequency Interference (RFI) effect on SMOS was explored. In overall, the SMOS soil moisture product estimates agreed reasonably well with near concurrent CarboEurope in-situ measurements acquired from the 0-5 cm soil moisture layer. Significant changes in the SMOS performance were observed with local adjustments, such as land cover and seasonal changes. Agreement was found to be higher over low vegetation cover and during the autumn season. The RFI contaminated pixels were filtered out from the pooled datasets, as well as from the seasonally discriminated datasets, which resulted in noticeably improved performances. This paper provides strong supportive evidence of the potential value of the SMOS soil moisture product for hydrometeorological and related studies. © 2015 IEEE.
Examining the variation of soil moisture from cosmic-ray neutron probes footprint: experimental results from a COSMOS-UK site
(Springer Science and Business Media Deutschland GmbH, 2023) Owen D. Howells; George P. Petropoulos; Dimitris Triantakonstantis; Zacharias Ioannou; Prashant K. Srivastava; Spyridon E. Detsikas; George Stavroulakis
Utilising cosmic-ray neutron probes is a relatively new approach in obtaining larger area soil moisture and various operational monitoring networks have been established worldwide utilising this technology to measure operationally this parameter. One such network located in the United Kingdom (UK) is the Cosmic-ray Soil Moisture Observing System, so-called COSMOS-UK, established in 2013. The present study aims at investigating the true footprint and the variations within the footprint detectable area at the COSMOS-UK sites using as a case study one such site located in Riseholme, UK. At the selected experimental site extensive fieldwork was conducted in July 2017 that allowed examining the agreement among the soil moisture data retrieved by the Time Domain Transmissometer (TDT) sensors and the corresponding estimates from the COSMOS-UK network station probe. The COSMOS-UK site footprint was compared using GPS-aided information from ground instrumentation, assisted by drone imagery acquisition and the implementation of geospatial interpolation methods in a Geographical Information System (GIS) environment. Altogether, this information was used for assessing the soil moisture footprint extent from the COSMOS-UK site. The COSMOS-UK station footprint was representative for an area shorter in size than the alleged footprint of 600 m diameter, as generally proposed in various relevant investigations. The COSMOS network slightly overestimated soil moisture content measured by the TDT sensor probes installed in the area. Our study findings although concern specifically the studied experimental site contribute towards efforts aiming at assessing the COSMOS-UK soil moisture measurement footprint showcasing the added value of geospatial analysis in this direction. © 2022, The Author(s).
Exploring the effect of the first lockdown due to covid-19 to atmospheric NO2 using Sentinel 5P satellite data, Google Earth Engine and Geographic Information Systems
(Elsevier, 2024) Georgios Gkatzios; George P. Petropoulos; Spyridon E. Detsikas; Prashant K. Srivastava
Air pollution is a phenomenon that plagues modern societies, causing serious impacts on both the natural and man-made environment. Air pollution is linked to specific substances which, when their concentration exceeds certain limits, become harmful and are called pollutants. Such pollutants include carbon monoxide (CO) and carbon dioxide (CO2), particulate matter (PM10), nitrogen oxides (NOX), ozone (O3), and sulfur dioxide (SO2). Fluctuations in pollutant emissions are affected by various events. An example is the first lockdown implemented on March 23, 2020 as a result of the Covid-19 disease in an effort to protect citizens. The period of lockdown was characterized by the complete suspension of various types of activities, the reduction in transport means, as well as the decrease in industrial operations, activities that significantly contribute to increased emissions. The aim of the present study is to determine the distribution and changes in the concentration specifically of nitrogen dioxide (NO2) in the prefecture of Thessaloniki, Greece. The study period is defined as the corresponding time period 04 May–04 April 2019/2020, that is, one year before and during the first lockdown. More specifically, the correlation between the lockdown and atmospheric NO2 is investigated. As part of the analysis, area characteristics such as population density and land uses are also correlated with the distribution of NO2 concentrations. To satisfy the study objectives, the technologies of satellite remote sensing and Geographic Information Systems (GIS), which are a major pillar of geoinformatics, are used. More specifically, data recording NO2 concentrations are used, which have been collected via the Sentinel 5P satellite. Based on the results obtained, it is found that the month of 2020 during which the lockdown was applied showed a very small decrease in NO2 values (4.03%) compared to the corresponding month of 2019. © 2024 Elsevier Inc. All rights reserved.
Exploring the potential of SCAT-SAR SWI for soil moisture retrievals at selected COSMOS-UK sites
(Taylor and Francis Ltd., 2021) Owen D. Howells; George P. Petropoulos; Prashant K. Srivastava; Dimitrios Triantakonstantis; Ionut Sandric
The need for information on soil moisture at large scale to facilitate a sustainable intensification of agricultural land and to ensure food security due to increasing populations cannot be overstated. Remote sensing provides a platform for potential national coverage of soil moisture monitoring. This study explores the potential for using a Synthetic-Aperture-Radar Soil Water Index (SCAT-SAR SWI) product as a method of accurately monitoring soil moisture across the UK by comparing its output with the COSMOS-UK cosmic-ray soil moisture observation network. Using the daily data from these stations, SWI data from SCAT-SAR were compared during 2015 for the UK. Five COSMOS-UK network sites were selected across the UK for assessment, and the respected SCAT-SAR SWI pixels were extracted for their soil moisture values. Statistical test that were computed allowed quantifying the correlation between the truth data of the TDT soil moisture sensors and the COSMOS and SCAT-SAR soil moisture product. It was found that the SCAT-SAR product consistently underestimated the soil moisture with elevation affecting the level of agreement. The COSMOS network slightly overestimated soil moisture but was found, at least in this study, noticeably more accurate than the SCAT-SAR. The RMSD of the SCAT-SAR product was noticeably higher at sites with the highest elevation. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Exploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data
(Springer Verlag, 2019) Alexander Cass; George P. Petropoulos; Konstantinos P. Ferentinos; Andrew Pavlides; Prashant K. Srivastava
Earth Observation (EO) provides a unique means of obtaining information on land use/cover and of its changes, which is of key importance in many scientific and practical applications. EO data is already widely used, for example, in environmental practices or decision-making related to food availability and security. As such, it is imperative to examine the suitability of different EO datasets, including their synergies, in respect to their ability to create products and tools for such practices and to guide effectively such decisions. This work aims at exploring the added value of the synergistic use of optical and radar data (from the Landsat TM and Advanced Synthetic Aperture Radar (ASAR) sensors respectively). Such information can help towards improving the accuracy of land cover classifications from EO datasets. As a case study, the region of Wales in the UK has been used. Two classifications—one based on optical data alone and another one developed from the synergy of optical and RADAR datasets acquired nearly, concurrently were developed for the studied region. Evaluation of the derived land/use cover maps was performed on the basis of the confusion matrix using validation points derived from a Phase 1 habitat map of Wales. The results showed 15% increase in overall accuracy (84% from 69%) and kappa coefficient (0.81 from 0.65) using the synergistic approach over the scenario where only optical data were used in the classification. In addition, McNemar’s test was used to assess the statistical significance of the obtained results. Results of this test provided further confirmed that the use of optical data synergistically with the radar data provides more accurate land use/cover maps in comparison with the use of optical data alone. © 2019, Società Italiana di Fotogrammetria e Topografia (SIFET).
Forecasting Arabian Sea level rise using exponential smoothing state space models and ARIMA from TOPEX and Jason satellite radar altimeter data
(John Wiley and Sons Ltd, 2016) Prashant K. Srivastava; Tanvir Islam; Sudhir K. Singh; George P. Petropoulos; Manika Gupta; Qiang Dai
Sea level rise is a threat to coastal habitation and is corroborating evidence for global warming. The present study investigated the combined use of quantitative forecasting methods for sea level rise using exponential smoothing state space models (ESMs) and an autoregressive integrated moving average (ARIMA) model fed with sea level data over 17 years (1994–2010). Two levels of ESMs were employed: double (model levels with trend) and triple (model levels, trend and seasonal decomposition). The overall data analysis revealed the better performance of ARIMA in terms of index of agreement (d = 0.79), root-mean-square error (RMSE = 32.8 mm) and mean absolute error (MAE = 25.55 mm) than the triple ESM (d = 0.76; RMSE = 39.86 mm; MAE = 35.02 mm) and double ESM (d = 0.14; RMSE = 52.71 mm; MAE = 45.99 mm) models. The present study results suggest that the rate of Arabian Sea level rise is high, and if this is not taken into consideration many coastal areas may become subject to climate-change-induced habitat loss in future. © 2016 Royal Meteorological Society
Future pathway for research and emerging applications in GPS/GNSS
(Elsevier, 2021) Manish Kumar Pandey; Prashant K. Srivastava; George P. Petropoulos
Satellite navigation system has an unparalleled advantage in navigational technologies due to its high-precision delivery of location in terms of position, time, and velocity on any object or person. It had found its application in the areas ranging from transportation, geodesic, communication, disaster prevention to its handling to security, etc., to name a few. This chapter provides an overview of the satellite navigation system and the associated vulnerabilities to explore the future pathway for research and emerging applications. This chapter begins with a brief introduction of the satellite navigation systems and briefly describes the constellations of the Global Navigation Satellite System (GNSS). The evolution of the GNSS is discussed along with its market emergence and convergence followed by a detailed discussion on the challenges and vulnerabilities of the GNSS. These challenges and vulnerabilities provide a pathway for future research and help the researchers in developing emerging applications. © 2021 Elsevier Inc. All rights reserved.
Future perspectives and challenges in hyperspectral remote sensing
(Elsevier, 2020) Prem Chandra Pandey; Heiko Balzter; Prashant K. Srivastava; George P. Petropoulos; Bimal Bhattacharya
Remote sensing (RS) technology has rapidly advanced in terms of radiometric, spatial, and spectral resolution. This trend has led to increasing complexity of data types ranging from low to high spatial and spectral resolutions and data dimensionality. In the chapters of this book, the state of the art has been presented, outlining the advantages of hyperspectral imaging (HSI) systems over multispectral data, and key future challenges and research directions with HSI have been illustrated. This chapter provides a perspective on the evolution of hyperspectral RS methods and applications along with challenges and barriers faced during research and innovation activities. The promise of upcoming missions with higher spatial and spectral resolution sensors in orbit in the near future will increase the utility of hyperspectral data in several research domains and will likely increase the number of users of HSI for soils, forestry, agriculture, urban, and cryosphere research. This chapter is intended as a resource to be aware of challenges and the future potential of hyperspectral RS to current and prospective users of high spectral resolution data to extract meaningful information for their research and applications. © 2020 Elsevier Ltd All rights reserved.