Using Ca-Markov Model to Model the Spatiotemporal Change of Land Use/Cover in Fuxian Lake for Decision Support

Li, S. H.; Jin, Baoxuan; Wei, Xingfeng; Jiang, Yongyao; Wang, J. L.

doi:10.5194/isprsannals-ii-4-w2-163-2015

Cited by 30 publications

(22 citation statements)

References 14 publications

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“…Prediction of LU/LC conditions for the 2030 and 2045 periods were undertaken with Cellular Automata-Markov (CA-Markov) model. CA-Markov predicts not only the trend but also the spatial structure of different LU/LC categories (Arsanjani et al 2011;Wang et al 2012;Li et al 2015). The model is widely applied in LU/ LC change modeling elsewhere (Kamusoko et al (2009) in Zimbabwe; Arsanjani et al 2011 0 was assigned for non constraint and constraint criterions, respectively.…”

Section: Land Use/land Cover Predictionmentioning

confidence: 99%

Evaluation and prediction of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia

et al. 2017

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Background: Land use/land cover (LU/LC) change is the challenging and continuous drivers of environment change. Understanding the rate and process of change is, therefore, basic for managing the environment. This study was intended to analyze the LU/LC changes from 1985 to 2015 periods, and predict the situation to 2030 and 2045 in the Andassa watershed of Blue Nile basin, Ethiopia. The hybrid classification technique for extracting thematic information from satellite images and CA-Markov model for prediction of LU/LC were employed. Results: Cultivated land was expanding from 62.7% in 1985 to 73.1% in 2000 and to 76.8% in 2015. The area of builtup also slightly increased (0.1-1.1%) between 1985 and 2015 periods. In contrast, forest, shrubland and grassland were reduced from 3.5 to 1.9%, 26.2 to 15.3% and 7.6 to 4.9% in 1985 and 2015 periods, respectively. The increase of cultivated land and built-up area, and the withdrawing of forest, shrubland and grassland were further continued in 2030 and 2045 periods. Conclusion: Significant amount of LU/LC conversions had occurred in the watershed from 1985 to 2015 periods, and expected to continue in 2030 and 2045 periods. Thus, appropriate interventions to revert the trends are very much critical.

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Section: Land Use/land Cover Predictionmentioning

confidence: 99%

Evaluation and prediction of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia

et al. 2017

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“…Land-Use/Land Cover Changes (LULCC) has major effects on a number of factors of ecological-environment like ecosystem functioning, climate change, deforestation etc. (Li et al, 2015) [12]. To understand the process of LULCC, such as deforestation or continuous urbanization, modeling can inform scientists and policymakers about the possible future conditions under different scenarios (Zadbagher et al, 2018) [25].…”

Section: Introductionmentioning

confidence: 99%

Integration of SimWeight and Markov Chain to Predict Land Use of Lavasanat Basin

Mirakhorlo

Rahimzadegan

2018

NMCE

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Production and prediction of land-use/land cover changes (LULCC) map are among the significant issues regarding input of many environmental and hydrological models. Among various introduced methods, similarity-weighted instance-based machine learning algorithm (SimWeight) and Markov-chain with lower complexity and proper performnce are frequently used. The main aim of this study is utilizing SimWeight along with Markov chain to predict land-use map of Lavasanat basin located in northeast of Tehran for the year 2018. In this regrad, eight driver variables and two land-use maps of the sudy area which were created from two Landsat-5 TM image sensor for the years 2000 and 2011 were considered as input. To evaluate the result of SimWeight, Receiver Operating Characteristic was used. The Landuse map of year 2018 was predicted using the proposed method. To evaluate this map, a landuse map of 2018 was produced using classification of a Landsat-8 OLI image. The results of model and value of area under curve (AUC) for transition potential map was about 0.78, which indicated good performance. Furthermore, the comparison of two produced and predicted land-use maps of 2018 shows great similarity. Generally, the results indicated the proper performance of the propsed method to predict LULCC.

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“…Major spatiotemporal statistical methods include empirical orthogonal functions [10], Markov chain [11], Markov-Bayes [12] and space-time Kriging methods [13]. These methods are widely used for monitoring national geographic conditions, simulating changes in land use over the past decades and forecasting future trends in land use [14]. With the increase in size and complexity of spatiotemporal data, traditional statistical analyses are not able to meet real-time requirements.…”

Section: Related Workmentioning

confidence: 99%

A High Performance, Spatiotemporal Statistical Analysis System Based on a Spatiotemporal Cloud Platform

Jin¹,

Song

Zhao³

et al. 2017

IJGI

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Abstract:With the increase in size and complexity of spatiotemporal data, traditional methods for performing statistical analysis are insufficient for meeting real-time requirements for mining information from Big Data, due to both data-and computing-intensive factors. To solve the Big Data challenges in geostatistics and to support decision-making, a high performance, spatiotemporal statistical analysis system (Geostatistics-Hadoop) is proposed in this paper. The proposed system has several features: (1) Hadoop is enhanced to handle spatial data in a native format and execute a number of parallelized spatial analysis algorithms to solve practical geospatial analysis problems; (2) the Oozie-based workflow system is utilized to ease the operation and sharing of spatial analysis services; and (3) a private cloud platform based on Eucalyptus is leveraged to provide on-the-fly and elastic computing resources. Experimental results show that Geostatistics-Hadoop efficiently conducts rapid information mining and analysis of big spatiotemporal data sets, with the support of elastic computing resources from a cloud platform. The adoption of cloud computing and the Hadoop cluster to parallelize statistical calculations significantly improves the performance of Big Data analyses.

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Using Ca-Markov Model to Model the Spatiotemporal Change of Land Use/Cover in Fuxian Lake for Decision Support

Cited by 30 publications

References 14 publications

Evaluation and prediction of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia

Evaluation and prediction of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia

Integration of SimWeight and Markov Chain to Predict Land Use of Lavasanat Basin

A High Performance, Spatiotemporal Statistical Analysis System Based on a Spatiotemporal Cloud Platform

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