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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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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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