Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

被引：252

作者：

Singh S.K. ^{[1
]}

Mustak S. ^{[2
]}

Srivastava P.K. ^{[3
,4
]}

Szabó S. ^{[5
]}

Islam T. ^{[6
,7
]}

机构：

[1] K. Banerjee Centre of Atmospheric and Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Allahabad

[2] School of Studies in Geography, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh

[3] Hydrological Sciences, NASA Goddard Space Flight Center, Greenbelt, MD

[4] Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD

[5] Department of Physical Geography and Geoinformatics, University of Debrecen, Debrecen

[6] NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD

[7] Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO

来源：

Environmental Processes | 2015年 / 2卷 / 01期

关键词：

Cellular automata; India; LULC; Markov chain analysis; Predictive modeling; Remote sensing and GIS;

D O I：

10.1007/s40710-015-0062-x

中图分类号：

学科分类号：

摘要：

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. © 2015 Springer International Publishing Switzerland.

引用

页码：61 / 78

页数：17

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