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1.

001-es BibID:BIBFORM109207
035-os BibID:(cikkazonosító)e14045 (Scopus)85149799289
Első szerző:Abriha Dávid (geográfus)
Cím:Smaller is better? Unduly nice accuracy assessments in roof detection using remote sensing data with machine learning and k-fold cross-validation / Dávid Abriha, Prashant K. Srivastava, Szilárd Szabó
Dátum:2023
ISSN:2405-8440
Megjegyzések:Deriving the thematic accuracy of models is a fundamental part of image classification analyses. K-fold cross-validation (KCV), as an accuracy assessment technique, can be biased because existing built-in algorithms of software solutions do not handle the high autocorrelation of remotely sensed images, leading to overestimation of accuracies. We aimed to quantify the magnitude of the overestimation of KCV-based accuracies and propose a method to overcome this problem with the example of rooftops using a WorldView-2 (WV2) satellite image, and two orthophotos. Random split to training/testing subsets, independent testing and different types of repeated KCV sampling strategies were used to generate input datasets for classification. Results revealed that applying the random splitting of reference data to training/testing subsets and KCV methods had significantly biased the accuracies by up to 17%; overall accuracies (OAs) can incorrectly reach >99%. We found that repeated KCV can provide similar results to independent testing when spatial sampling is applied with a sufficiently large distance threshold (in our case 10 m). Coarser resolution of WV2 ensured more reliable results (up to a 5?9% increase in OA) than orthophotos. Object-based pixel purity of buildings showed that when using a majority filter for at least of 50% of objects the final accuracy approached 100% with each sampling method. The final conclusion is that KCV-based modelling ensures better accuracy than single models (with better pixel purity on the object level), but the accuracy metrics without spatially filtered sampling are not reliable.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Urban environment
Roof classification
Accuracy assessment
Salt-and-pepper effect
Post-classification
Object-based pixel purity
Megjelenés:Heliyon. - 9 : 3 (2023), e14045, p.1-17. -
További szerzők:Srivastava, Prashant K. Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:Kooperatív Doktori Program
Egyéb
NKFI K 138079
Egyéb
NKFI K 142121
Egyéb
TKP2020-NKA-04
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2.

001-es BibID:BIBFORM078725
Első szerző:Lamine, Salim
Cím:Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS / Salim Lamine, George P. Petropoulos, Sudhir Kumar Singh, Szilárd Szabó, Nour El Islam Bachari, Prashant K. Srivastava, Swati Suman
Dátum:2018
ISSN:1010-6049 1752-0762
Megjegyzések:This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001-2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazards occurred in the area. To our knowledge, this is the first study of its kind, demonstrating the Hyperion capability in quantifying LULC changes with landscape metrics using FRAGSTATS® programme and PCA for understanding the land surface fragmentation characteristics and their changes. The suggested approach is robust and flexible enough to be expanded further to other regions. Findings of this research can be of special importance in the context of the launch of spaceborne hyperspectral sensors that are already planned to be placed in orbit as the NASA's HyspIRI sensor and EnMAP.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
Hyperspectral remote sensing
Support vector machines
FraGStatS
landscape fragmentation
Principal component analysis
Megjelenés:Geocarto International. - 33 : 8 (2018), p. 862-878. -
További szerzők:Petropoulos, George P. Singh, Sudhir Kumar (1970-) (geográfus) Szabó Szilárd (1974-) (geográfus) Bachari, Nour El Islam Srivastava, Prashant K. Suman, Swati
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3.

001-es BibID:BIBFORM097533
Első szerző:Lulla, Kamlesh
Cím:Mission to Earth : LANDSAT 9 will continue to view the world / Kamlesh Lulla, M. Duane Nellis, Bradley Rundquist, Prashant Srivastava, Szilard Szabo
Dátum:2021
ISSN:1010-6049 1752-0762
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Geocarto International. - 36 : 20 (2021), p. 2261-2263. -
További szerzők:Nellis, M. Duane Rundquist, Bradley Srivastava, Prashant K. Szabó Szilárd (1974-) (geográfus)
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4.

001-es BibID:BIBFORM116528
035-os BibID:(cikkazonosító)103507 (WoS)001135286500001 (Scopus)85179139332
Első szerző:Mahanta, Aiswarya Rani
Cím:Assessment of multi-source satellite products using hydrological modelling approach / Aiswarya Rani Mahanta, Kishan Singh Rawat, Nirmal Kumar, Szilard Szabo, Prashant K. Srivastava, Sudhir Kumar Singh
Dátum:2023
ISSN:1474-7065
Megjegyzések:Multi-source satellite products performance evaluation for varied geographical locations aids in quantification of hydrological variables and is useful in the strategy making and conservation of the hydrological resources available in a basin. The work was focused on assessing utility of multi-source satellite datasets to obtain the estimation of hydrologic variables and provide solution for areas that are poorly gauged. Assessment of the multisource-satellite products was performed for the poorly gauged river basin with the help of SWAT concerning the Palar River basin, India. We analysed time series at the monthly, seasonal, and annual scales to quantify surface runoff, water yield, ET, & PET at the calibration station and for the entire basin for the period 2003 to 2021. SWAT model estimated highest monthly water yield during November-December, with annual water yield being maximum (220 mm in 2010) and average (99.4 mm), which can be used to understand water resources for irrigation, drinking aspects, and net storage. Average monthly surface runoff patterns were similar for SWAT, TerraClimate, and FLDAS. The FLDAS and SWAT simulated surface runoff show a resemblance in pattern and magnitude for the monthly and annual time series of the average basin scenario. The monthly PET obtained from SWAT and ERA-5 show a similar pattern for the entire basin and at the calibration site. The ET derived from satellite observation has over-predicted the model output at both the calibration site and entire basin.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
FLDAS
SWAT
ERA-5
MODIS
MERRA-2
Water yield
Megjelenés:Physics And Chemistry Of The Earth. - 133 (2023), p. 1-18. -
További szerzők:Rawat, Kishan Singh Kumar, Nirmal Szabó Szilárd (1974-) (geográfus) Srivastava, Prashant K. Singh, Sudhir Kumar (1970-) (geográfus)
Pályázati támogatás:TKP2021-NKTA-32
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5.

001-es BibID:BIBFORM078726
Első szerző:Singh, Sudhir Kumar (geográfus)
Cím:Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India / Sudhir Kumar Singh, Prosper Basommi Laari, Sk. Mustak, Prashant K. Srivastava, Szilárd Szabó
Dátum:2018
ISSN:1010-6049 1752-0762
Megjegyzések:An integrated Markov Chain and Cellular Automata modelling (CA MARKOV), multicriteria evaluation techniques have been applied to produce transition probability. The unsupervised method was employed to classify the satellite images of year 1985, 1995, 2005 and 2015 to meet the magnitude of LULC change. Results showing the spatial pattern of the sub-basin is largely influenced by the biophysical and socio-economic drivers leading to growth of agricultural lands and built-up area in the basin. Simulated plausible future LULC changes for 2025 which is based on a CA MARKOV that integrates Markovian transition probabilities computed from satellite-derived LULC maps and a CA contiguity spatial filter (5 x 5). Further, the fragmentation analysis was performed to check the fragmentation scenario in the year 2025. The result for year 2025 with reasonably good accuracy will be useful to the planners, policy- and decision-makers.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
earth observation
LULC change
cellular automata
Markov chain analysis
India
Megjelenés:Geocarto International. - 33 : 11 (2018), p. 1202-1222. -
További szerzők:Laari, Prosper Basommi Mustak, Sk. Srivastava, Prashant K. Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:RH/751/2015
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6.

001-es BibID:BIBFORM068022
Első szerző:Singh, Sudhir Kumar (geográfus)
Cím:Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using Earth Observation data-sets / Sudhir Kumar Singh, Prashant K. Srivastava, Szilárd Szabó, George P. Petropoulos, Manika Gupta, Tanvir Islam
Dátum:2017
ISSN:1010-6049 1752-0762
Megjegyzések:Analysis of Earth observation (EO) data, often combined with geographical information systems (GIS), allows monitoring of land cover dynamics over different ecosystems, including protected or conservation sites. The aim of this study is to use contemporary technologies such as EO and GIS in synergy with fragmentation analysis, to quantify the changes in the landscape of the Rajaji National Park (RNP) during the period of 19 years (1990?2009). Several statistics such as principal component analysis (PCA) and spatial metrics are used to understand the results. PCA analysis has produced two principal components (PC) and explained 84.1% of the total variance, first component (PC1) accounted for the 57.8% of the total variance while the second component (PC2) has accounted for the 26.3% of the total variance calculated from the core area metrics, distance metrics and shape metrics. Our results suggested that notable changes happened in the RNP landscape, evidencing the requirement of taking appropriate measures to conserve this natural ecosystem.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
geographic information system
protected ecosystem
remote sensing
landscape pattern
fragmentation
ecological metrics
Megjelenés:Geocarto International 32 : 2 (2017), p. 113-127. -
További szerzők:Srivastava, Prashant K. Szabó Szilárd (1974-) (geográfus) Petropoulos, George P. Gupta, Manika Islam, Tanvir
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7.

001-es BibID:BIBFORM063093
Cím:Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information / Sudhir Kumar Singh, Sk. Mustak, Prashant K. Srivastava, Szilárd Szabó, Tanvir Islam
Dátum:2015
ISSN:2198-7491 2198-7505
Megjegyzések:Remote sensing and GIS are important tools for studying land use/land cover (LULC) change and integrating the associated driving factors for deriving useful outputs. This study is based on utilization of Earth observation datasets over the highly urbanized Allahabad district in India. Allahabad district has experienced intense change in LULC in the last few decades. To monitor the changes, advanced techniques in remote sensing and GIS, such as Cellular Automata (CA)-Markov Chain Model (CAMCM) were used to identify the spatial and temporal changes that have occurred in LULC in this area. Two images, 1990 and 2000, were used for calibration and optimization of the Markovian algorithm, while 2010 was used for validating the predictions of CA-Markov using the ground based land cover image. After validating the model, plausible future LULC changes for 2020 were predicted using the CAMCM. Analysis of the LULC pattern maps, achieved through classification of multi-temporal satellite datasets, indicated that the socio-economic and biophysical factors have greatly influenced the growth of agricultural lands and settlements in the area. The two urbanization indicators calculated in this study viz. Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used, which indicated a drastic change in the area in terms of urbanization. The predicted LULC scenario for year 2020 provides useful inputs to the LULC planners for effective and pragmatic management of the district and a direction for an effective land use policy making. Further suggestions for an effective policy making are also provided which can be used by government officials to protect this important land resource.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
LULC
Cellular automata
Markov chain analysis
Remote sensing and GIS
Predictive modeling
India
Megjelenés:Environmental Processes. - 2 : 1 (2015), p. 61-78. -
További szerzők:Singh, Sudhir Kumar (1970-) (geográfus) Mustak, Sk. Srivastava, Prashant K. Szabó Szilárd (1974-) (geográfus) Islam, Tanvir
Pályázati támogatás:Bolyai Scholarship of the Hungarian Academy of Sciences
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