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

Abstract

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 multitemporal 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.

Estimation of Surface Runoff from Semi-arid Ungauged Agricultural Watershed Using SCS-CN Method and Earth Observation Data Sets

“…Many studies have proved the use of EO data namely groundwater [27,46,48,51,52], river water quality [55], coastal water [22], lake and wetlands [5,47,53,59,60], land use/land cover mapping [45,46,48,50,51], land use change trajectories [56], land use/land cover modeling [28,49], urban land use dynamics [4], hydrological modeling [31], forest mapping [44], cyclone tracking [16], soil characterization [37], climate change [54], slope estimation [57], landscape ecology [47,53], ocean studies [35,36] and watershed management [67], watershed prioritization [68]. GIS processing has become a critical step in hydrologic modeling since it contributes to generate model parameters in a spatially distributed manner.…”

Identifying Categorical Land Use Transition and Land Degradation in Northwestern Drylands of Ethiopia

“…LULC studies focused on understanding the patterns, processes and dynamics of land transitions and its drivers over time [12][13][14][15][16]. The processes of these land use transitions can be categorized into either random or systematic changes based on identifying their pattern of categorical changes [12,17].…”

Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach

“…The CA-Markov is a combination model of Markov model which describes the probability of land cover changes between start and end period by developing a transition probability matrix between them and CA model which allows the transition probabilities of one pixel to be function of adjacent pixel [14]. The CAMarkov model has a capability to simulate and to predict LULC changing status in future with remotely sensed data [19]. It is helpful to the research area in establishing and optimizing urban development decisions at different spatial-temporal dimensions [14,[19][20][21], when simulating and prediction urban landscape changes comprehensively in quantity and spatial aspects.…”

“…It is helpful to the research area in establishing and optimizing urban development decisions at different spatial-temporal dimensions [14,[19][20][21], when simulating and prediction urban landscape changes comprehensively in quantity and spatial aspects. As given in [19] by Singh et al, the Markov chain process can be described as…”

“…While it is a challenge to choose appropriate landscape indexes that would reflect the landscape properties and is not necessary to calculate all landscape indexes for a special landscape, since some of these indexes are strongly correlated. The choice of landscape pattern indexes can be based on both quantitative analysis and knowledge of the study area [19,22,23]. One of previous study suggests the combining use of Spearman's correlation coefficient (SCC) and principal component analysis (PCA) method to reduce redundancies [22].…”