Cellular Automata Markov Chain Research Articles

Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-Markov chain modelling: A case of Babol city, Iran

An urban heat island is one of the most vital environmental risks in urban areas. The aim of this study was to assess the spatial-temporal patterns of land use changes and heat island intensity for the city of Babol, Iran, between 1985 and 2015 and to predict likely future heat island intensity variations. Multi-temporal Landsat images were acquired and analysed in this study. First, single channel algorithms were used to calculate the land surface temperature (LST), and a Maximum Likelihood Algorithm was utilized to classify images. Second, land use changes (LUCs) and LST were examined, and the relationship between the fractional vegetation cover (FVC) and land-use changes was analysed using the normalized land surface temperature. By using the mean and the standard deviation of the normalized thermal images, the area was divided into five thermal categories: very low, low, medium, high and very high. Then, by applying the heat island intensity index, the heat island changes in the studied period were investigated. Possible future land use changes were investigated using a cellular automata-Markov model, and the heat island intensity changes were anticipated. The results indicate that the area of built-up land increased by 92% between 1985 and 2015, and that the area of agricultural land noticeably decreased. The built-up land changes trend has an inverse relationship with the trend of FVC changes and follows the same trend as the normalized surface temperature changes. Most changes in the surface temperature of the area are located within 0–800 m of a built-up area. The main reason for these changes could be the conversion of agricultural and green space land areas into built-up land. The largest area of the temperature categories in all years is the medium temperature category, which covers the suburban land areas. The low- and very low-temperature categories, which follow a decreasing trend, are related to land areas far from the city. In addition, the high- and very high-temperature categories, whose areas increased annually, are adjacent to the city core and the exits from the city. The average surface temperature in all land uses increased during the studied period. Nevertheless, the rate of temperature rise is higher in the built-up uses. The index ratio of the heat island during this period shows an increasing trend, and its value changed from 0/5 in 1985 to 0/67 in 2015. An increase in the heat island intensity has a direct relationship with the area population growth and, thus, the increase in the built-up land area. Anticipation of land use changes and the process of heat island intensity variations for the studied area show alarming results that call for decision-makers to address this important challenge.

Scenario-based analysis of land-use competition in central Iran: Finding the trade-off between urban growth patterns and agricultural productivity

This study attempts to evaluate different land-use scenarios in a highly growing region and also to find the trade-off between urbanization and agricultural productivity in the Isfahan City, central Iran. In doing so, several modeling tools including satellite image processing, Multi Criteria Evaluation (MCE), Cellular Automata-Markov Chain (CA-MC) model and scenario analysis are employed to delineate highly competitive zones and also to detect sustainable trajectories of future urban growth in the region. In this regard, suitability surfaces depicting the potential of the area for urbanization and agricultural activities are created using the MCE method and these layers were then used to produce continuous and categorized layers of land-use competition in the research area. These layers are also implemented to develop multiple growth scenarios through the CA-MC model and possible land-use competition zones are identified under historical and trend-breaking options. Finally, by integrating the total set of urban growth layers derived from predictive scenarios in this study, an innovative categorization scheme is developed to study the effect of each scenario on the intensity of land-use competition and agricultural productivity loss in the area. The results of this study provide a holistic insight regarding current conditions of the Isfahan City and also a simulation environment for planners to evaluate the possible outcomes of their decisions on fertile soils and future urban growth patterns.

Quantifying the Spatiotemporal Pattern of Urban Expansion and Hazard and Risk Area Identification in the Kaski District of Nepal

The present study utilized time-series Landsat images to explore the spatiotemporal dynamics of urbanization and land use/land-cover (LULC) change in the Kaski District of Nepal from 1988 to 2016. For the specific overtime analysis of change, the LULC transition was clustered into six time periods: 1988–1996, 1996–2000, 2000–2004, 2004–2008, 2008–2013, and 2013–2016. The classification was carried out using a support vector machine (SVM) algorithm and 11 LULC categories were identified. The classified images were further used to predict LULC change scenarios for 2025 and 2035 using the hybrid cellular automata Markov chain (CA-Markov) model. Major hazard risk areas were identified using available databases, satellite images, literature surveys, and field observations. Extensive field visits were carried out for ground truth data acquisition to verify the LULC maps and identify multihazard risk areas. The overall classification accuracy of the LULC map for each year was observed to be from 85% to 93%. We explored the remarkable increase in urban/built-up areas from 24.06 km2 in 1988 to 60.74 km2 by 2016. A majority of urban/built-up areas were sourced from cultivated land. For the six time periods, totals of 91.04%, 78.68%, 75.90%, 90.44%, 92.35%, and 99.46% of the newly expanded urban land were sourced from cultivated land. Various settlements within and away from the city of Pokhara and cultivated land at the river banks were found at risk. A fragile geological setting, unstable slopes, high precipitation, dense settlement, rampant urbanization, and discrete LULC change are primarily accountable for the increased susceptibility to hazards. The predicted results showed that the urban area is likely to continue to grow by 2025 and 2035. Despite the significant transformation of LULC and the prevalence of multiple hazards, no previous studies have undertaken a long-term time-series and simulation of the LULC scenario. Updated district-level databases of urbanization and hazards related to the Kaski District were lacking. Hence, the research results will assist future researchers and planners in developing sustainable expansion policies that may ensure disaster-resilient sustainable urban development of the study area.

MODEL PREDIKSI PERUBAHAN PENGGUNAAN LAHAN DENGAN PENDEKATAN SISTEM INFORMASI GEOGRAFIS DAN CELLULAR AUTOMATA MARKOV CHAIN: STUDI KASUS KABUPATEN ROKAN HULU, PROVINSI RIAU

Rokan Hulu Regency, Riau Province, is one of the largest oil palm plantation centers in Indonesia. The large growth of palm oil plantations in recent years has threatened the existence of protected areas and increased deforestation rates. This study aims to (1) find out the extent of land use change in Rokan Hulu Regency in 2000-2012, (2) predict land use change in Rokan Hulu regency by 2030. This research uses Geographic Information System approach with prediction model of Cellular Automata (CA) Markov Chain. The driving factors of land use change as input of CA Markov Chain model consist of distance to road, distance to river, distance to disturbance area, distance to Plantation Forest, distance to Forest Clearance Permits and slope data. The results showed that the area of deforestation reached 60.38% from 2000 to 2012, mostly in the northeastern region of Rokan Hulu Regency. The prediction model of land use change has an accuracy of 68.15% based on the kappa index agreement (KIA). By 2030, the area of forest land in Rokan Hulu is predicted to be 3.13%

A cost-effective and efficient framework to determine water quality monitoring network locations

A crucial part in designing a robust water quality monitoring network is the selection of appropriate water quality sampling locations. Due to cost and time constraints, it is essential to identify and select these locations in an accurate and efficient manner. The main contribution of the present article is the development of a practical methodology for allocating critical sampling points in present and future conditions of the non-point sources under a case study of the Khoy watershed in northwest Iran, where financial resources and water quality data are limited. To achieve this purpose, the river mixing length method (RML) was applied to propose potential sampling points. A new non-point source potential pollution score (NPPS) was then proposed by the analytic network process (ANP) to classify the importance of each sampling point prior to selecting the most appropriate locations for a river system. In addition, an integrated cellular automata–Markov chain model (CA–Markov) was applied to simulate future change in non-point sources during the period 2026–2036. Finally, by considering anthropogenic activities through land-use mapping, the hierarchy value, the non-point source potential pollution score values and budget deficiency in the study area, the seven sampling points were identified for the present and the future. It is not expected, however, that the present location of the proposed sampling points will change in the future due to the forthcoming changes in non-point sources. The current study provides important insights into the design of a reliable water quality monitoring network with a high level of assurance under certain changes in non-point sources. Furthermore, the results of this study should be valuable for water quality monitoring agencies looking for a cost-effective approach for selecting sampling locations.

Monitoring and Modeling of Spatiotemporal Urban Expansion and Land-Use/Land-Cover Change Using Integrated Markov Chain Cellular Automata Model

Spatial–temporal analysis of land-use/land-cover (LULC) change as well as the monitoring and modeling of urban expansion are essential for the planning and management of urban environments. Such environments reflect the economic conditions and quality of life of the individual country. Urbanization is generally influenced by national laws, plans and policies and by power, politics and poor governance in many less-developed countries. Remote sensing tools play a vital role in monitoring LULC change and measuring the rate of urbanization at both the local and global levels. The current study evaluated the LULC changes and urban expansion of Jhapa district of Nepal. The spatial–temporal dynamics of LULC were identified using six time-series atmospherically-corrected surface reflectance Landsat images from 1989 to 2016. A hybrid cellular automata Markov chain (CA–Markov) model was used to simulate future urbanization by 2026 and 2036. The analysis shows that the urban area has increased markedly and is expected to continue to grow rapidly in the future, whereas the area for agriculture has decreased. Meanwhile, forest and shrub areas have remained almost constant. Seasonal rainfall and flooding routinely cause predictable transformation of sand, water bodies and cultivated land from one type to another. The results suggest that the use of Landsat time-series archive images and the CA–Markov model are the best options for long-term spatiotemporal analysis and achieving an acceptable level of prediction accuracy. Furthermore, understanding the relationship between the spatiotemporal dynamics of urbanization and LULC change and simulating future landscape change is essential, as they are closely interlinked. These scientific findings of past, present and future land-cover scenarios of the study area will assist planners/decision-makers to formulate sustainable urban development and environmental protection plans and will remain a scientific asset for future generations.

Prediction of future urban surface temperatures using medium resolution satellite data in Harare metropolitan city, Zimbabwe

The objective of the study was to determine the impact of urban growth on future micro-climate of Harare by predicting future distribution of land use and land cover, as well as land surface temperature using Cellular Automata Markov Chain analysis. Landsat series data was used to map Land Use and Land Cover (LULC) and land surface temperature distribution during the month of October for the year 1984, 1993, 2001 and 2015. The Cellular Automata Markov Chain analysis was used to determine long term landscape transformation at 10-year time steps from 2015 to 2045. We further tested the potential of a variety of vegetation and non-vegetation indices to predict land surface temperature. Results show that the Urban Index (UI), non-vegetation index was the best predictor of surface temperature, since it had the highest correlation with retrieved surface temperature (r = 0.9831). When tested against temperature derived from thermal band in October 2015, the mean absolute percentage error of the UI derived temperature was 5.27%. Based on changes which occurred between 1984 and 2015, the Cellular Automata Markov Chain analysis predicted that high density built-up areas will increase monotonically from 470.02 in 2015 to 490.36 km2 in 2045. Green spaces were predicted as decreasing from 57.42 to 27.85 km2, while croplands also decrease from 30.27 to 16.93 km2 between 2015 and 2040. Using UI as predictor of land surface temperature, we predicted that the 18–28 °C class will decrease in coverage between 2015 and 2040, while the 36–45 °C category will increase in proportion covered from 42.5 to 58% of city. We concluded that continued urban growth will increase warming and result in high future temperatures unless mitigation efforts are strengthened. The findings of this study are important in informing future development of cities to consider growth implications on future temperatures and thermal comfort of urban residents.

Modelling spatial changes in coastal areas of Samsun (Turkey) using a Cellular Automata-Markov Chain method

Modelling spatial changes in coastal areas of Samsun (Turkey) using a Cellular Automata-Markov Chain method

Tailoring a non-path-dependent model for environmental risk management and polycentric urban land-use planning.

This study attempts to develop a non-path-dependent model for environmental risk management and polycentric urban land-use planning in Gorgan Township area, Iran. Applying three suitability layers of environmental risk (soil erosion, flood risk, fire risk, and land susceptibility), urbanization potential, and integrated surface (environmental risk plus urbanization potential layers), a non-path-dependent Cellular Automata-Markov Chain (CA-MC) model was configured to execute three scenarios of polycentric urban growth allocation. Specifically, the modeling approach improved the traditional functionality of the CA-MC model from a prediction algorithm into an innovative land allocation tool. Besides, due to its flexibility, the non-path-dependent model was able to explicitly include different characteristics of the landscape structure ranging from physical land attributes to landscape functions and processes (natural hazards). Accordingly, three polycentric urban growth allocation efforts were undertaken and compared in terms of connectivity and compactness of the resultant patterns and consumption of other land resources. Based on results, the polycentric allocation procedure based on integrated suitability layer produced a more manageable pattern of urban landscape, while the growth option based on environmental risk layer was more successful for protecting farmlands against excessive urbanization. This study suggests that polycentric urban land-use planning under the strategy of rural land development programs is an available option for designing an urban landscape with lower exposure to natural hazards and more economic benefits to rural residents. Finally, the non-path-dependent modeling is a recommended approach, when highly flexible and interactive decision-support systems as well as trend-breaking scenarios are desired.

Rules versus layers: which side wins the battle of model calibration?

Continuous surface of urbanization suitability, as an input to many urban growth models (UGM), has a significant role on a proper calibration process. The present study evaluates and compares the simulation success of the Cellular Automata-Markov Chain (CA-MC) model through multiple methods. For this, a series of mapping algorithms are applied ranging from empirical methods such as multi-criteria evaluation (MCE) to statistical algorithms without spatially explicit suitability mapping rules such as logistic regression (LR) and multi-layer perceptron (MLP) neural network and finally statistical and spatially explicit rule-based methods such as SLEUTH-Genetic Algorithm (SLEUTH-GA) model. The CA-MC model was calibrated in three study locations including Azadshahr, Gonbad, and Gorgan cities in northeastern Iran. Applying Kappa-based indices (Kappa, K location, K Simulation, and K Transloc) and computing relative error (RE) values of landscape metrics, performance of the model was quantified and compared across the three study sites. The MCE and SLEUTH-GA methods, as the most data-demanding and the most computationally complex methods, respectively, yielded approximately similar results (especially in case of Kappa-based indices) and these methods were less successful compared to LR and MLP models. LR and MLP models were less data-demanding, while they produced approximately equal results. This study concludes that, when historical growth patterns feed an urbanization suitability mapping process, neither rules (SLEUTH-GA) nor layers (MCE) are effectively efficient when applied in a separated manner. Instead, methods with statistical rules and least-correlated input layers (LR and MLP) provide better simulation outputs. In contrast, methods such as MCE are more applicable when a non-path-dependent mapping procedure is desired since this method does not require training data (dependent variable) and the provided flexibilities in urbanization suitability mapping under various scenarios can improve the functionality of land-use change prediction algorithms into innovative land allocation tools.

Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia

This study analyses the expansion of urban growth and land cover changes in five Saudi Arabian cities (Riyadh, Jeddah, Makkah, Al-Taif and the Eastern Area) using Landsat images for the 1985, 1990, 2000, 2007 and 2014 time periods. The classification was carried out using object-based image analysis (OBIA) to create land cover maps. The classified images were used to predict the land cover changes and urban growth for 2024 and 2034. The simulation model integrated the Markov chain (MC) and Cellular Automata (CA) modelling methods and the simulated maps were compared and validated to the reference maps. The simulation results indicated high accuracy of the MC–CA integrated models. The total agreement between the simulated and the reference maps was >92% for all the simulation years. The results indicated that all five cities showed a massive urban growth between 1985 and 2014 and the predicted results showed that urban expansion is likely to continue going for 2024 and 2034 periods. The transition probabilities of land cover, such as vegetation and water, are most likely to be urban areas, first through conversion to bare soil and then to urban land use. Integrating of time-series satellite images and the MC–CA models provides a better understanding of the past, current and future patterns of land cover changes and urban growth in this region. Simulation of urban growth will help planners to develop sustainable expansion policies that may reduce the future environmental impacts.

Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Urban expansion is considered as one of the most important problems in several developing countries. Bangkok Metropolitan Region (BMR) is the urbanized and agglomerated area of Bangkok Metropolis (BM) and its vicinity, which confronts the expansion problem from the center of the city. Landsat images of 1988, 1993, 1998, 2003, 2008, and 2011 were used to detect the land use and land cover (LULC) changes. The demographic and economic data together with corresponding maps were used to determine the driving factors for land conversions. This study applied Cellular Automata-Markov Chain (CA-MC) and Multi-Layer Perceptron-Markov Chain (MLP-MC) to model LULC and urban expansions. The performance of the CA-MC and MLP-MC yielded more than 90% overall accuracy to predict the LULC, especially the MLP-MC method. Further, the annual population and economic growth rates were considered to produce the land demand for the LULC in 2014 and 2035 using the statistical extrapolation and system dynamics (SD). It was evident that the simulated map in 2014 resulting from the SD yielded the highest accuracy. Therefore, this study applied the SD method to generate the land demand for simulating LULC in 2035. The outcome showed that urban occupied the land around a half of the BMR.

Performance evaluation of land change simulation models using landscape metrics

This study proposes a landscape metrics-based method for model performance evaluation of land change simulation models. To quantify model performance at both landscape and class levels, a set of composition- and configuration-based metrics including number of patches, class area, landscape shape index, mean patch area and mean Euclidean nearest neighbour distance were employed. These landscape metrics provided detailed information on simulation success of a cellular automata-Markov chain (CA-Markov) model standpoint of spatial arrangement of the simulated map versus the corresponding reference layer. As a measure of model simulation success, mean relative error (MRE) of the metrics was calculated. At both landscape and class levels, the MRE values were accounted for 22.73 and 10.2%, respectively, which are further categorised into qualitative measurements of model simulation performance for simple and quick comparison of the results. Findings of the present study depict a hierarchical and multi spatial level assessment of model performance.

Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration

Uncontrolled, yet fragmented peripheral urban expansion has emerged as a menace to urban development. To cope with this rapid urban expansion process, identification of the forces responsible for this rapid urban expansion is a pre-requisite, especially when its threats to habitability are taken into consideration. This study tries to evaluate fragmented uncontrolled urban expansion faced by Kolkata using cellular automata-Markov chain. Urban growth patterns, land use/land cover transformations and spatial allocation correspondence with planning strategy is the main theme of this study. Depending upon the driving forces, the study result indicates a bi-directional urban development potential surface, which might be a result of the biased planning initiative along with middle-class residential demand. This simulation result provides evidence for the planning authority to evaluate the effectiveness of spatial allocation and urban expansion trends and provide flexibility to modify the current allocation scenario.

Prediction of Urban Growth through Cellular Automata-Markov Chain

Urban growth modelling can provide a useful tool to help decision-makers and urban planners evaluate different planning scenarios. Predicting the land uses and covers for sustainable utilization of lands cover is crucial due to rapid changes in the operations mainly arising from population growth and urbanization or perhaps because of changes in the stature of a city. The application of urban growth models such as CA-Markov could raise awareness about the future growth of the city aimed at consciously controlling and changing the land use planning in the future. The results of reviewing the urban growth process and land use changes around the city in the past mainly indicated the lands converted from agricultural use to urban areas across Bojnoord. Moreover, the hybrid model of CA-Markov was employed to predict the land use changes for the next 50 years with 10-year intervals between 2020 and 2070. The results showed that if the process of urban growth and land use changes in areas around the city persist, the urban areas will double by 2070 compared to 2009, while the agricultural lands will shrink to half. This could provide the context for environmental issues in the future. Hence, it is recommended that the inner capacity of the city be used instead of urban growth in the surrounding areas.

A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models

Spatially land-cover models are necessary for sustainable land-cover planning. The expansion of human-built land involves the destruction of forests, meadows and farmlands as well as conversion of these areas to urban and industrial areas which will result in significant effects on ecosystems. Monitoring the process of these changes and planning for sustainable use of land can be successfully achieved by using the remote sensing multi-temporal data, spatial criteria and predictor models. In this study, land-cover change analysis and modeling was performed for our study area in central Germany. An integrated Cellular Automata–Markov Chain land change model was carried out to simulate the future landscape change during the period of 2020–2050. The predictive power of the model was successfully evaluated using Kappa indices. As a consequence, land change model predicts very well a continuing downward trend in grassland, farmland and forest areas, as well as a growing tendency in built-up areas. Hence, if the current trends of change continue regardless of the actions of sustainable development, drastic natural area decline will ensue. The results of this study can help local authorities to better understanding the current situation and possible future conditions as well as adopt appropriate strategies for management of land-cover. In this case, they can create a balance between urban development and environmental protection.

Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

In this study, urban growth of the Atakum District in Samsun, Turkey, was simulated by Cellular Automata-Markov Chain (CA-MC) and Multi-layer Perceptron-Markov Chain (MLP-MC) hybrid models in a geographical information system (GIS) environment. Historical land use/land cover (LU/LC) data were extracted from 1989, 2000 and 2013 Landsat TM/ETM+/OLI images. Using the LU/LC data for the years 1989 and 2000, the urban growth for 2013 was simulated using the CA-MC and MLP-MC models. The simulation results were compared with the 2013 LU/LC data to assess the validity of the simulation. The MLP-MC method provided the best results according to the validation based on the kappa index of agreement. Based on this result, the urban growth for the year 2025 was simulated using MLP-MC. The simulation estimated an urban growth rate of 35.2% between 2013 and 2025, an increase in the area of artificial surfaces from 1681.9 ha to 2274.3 ha and the destruction of 511.7 ha of agricultural land and 4.4 ha of forest. The results of this study demonstrate that the urban growth models provide a better understanding of the current patterns and temporal dynamics and can predict future changes according to past and current dynamics. The results also show that simulations are most accurate when using a model that best conforms to the changes in the given study area.

A GIS-based model to analyze the spatial and temporal development of oil palm land use in Kuala Langat district, Malaysia

In Malaysia, areas under oil palm plantations have increased dramatically since the early twentieth century and have resulted in multiple conversions of land change. This paper presents a spatial and temporal model for simulation of oil palm expansion in the Kuala Langat district, Malaysia. The model is an integration of cellular automata (CA), multi-criteria evaluation (MCE), and Markov chain (MC) analysis while MCE provides transition rules of CA iterations and MC analysis assigns a transition probability to each single pixel at the time steps. Evaluation criteria consist of constraints and nine suitability factors indicating environmental and socio-economic issues of oil palm development. In the first simulation, changes of six land-cover classes were projected to the year 2008 based on transitions between 1997 and 2002. Two measures of quantity disagreement and allocation disagreement were adopted to validate model outcome. The simulation of land-cover change of the year 2020 was done based on the transition observed between 1997 and 2002 regarding the satisfactory agreement of the projection and the reference data at the first simulation. The results, based on five landscape metrics, indicated continuous spatial patterns of oil palm plantations but more fragmented spatial patterns of other land classes by the year 2020.

Assessment of future water resources and water scarcity considering the factors of climate change and social–environmental change in Han River basin, Korea

Water resources are influenced by various factors such as weather, topography, geology, and environment. Therefore, there are many difficulties in evaluating and analyzing water resources for the future under climate change. In this paper, we consider climate, land cover and water demand as the most critical factors affecting change in future water resources. We subsequently introduce the procedures and methods employed to quantitatively evaluate the influence of each factor on the change in future water resources. In order to consider the change in land cover, we apply the Multi-Regression approach from the cellular automata-Markov Chain technique using two independent variables, temperature and rainfall. In order to estimate the variation of the future runoff due to climate change, the data of the SRES A2 climate change scenario were entered in the SLURP model to simulate a total of 70 years, 2021–2090, of future runoff in the Han River basin in Korea. However, since a significant amount of uncertainties are involved in predicting the future runoff due to climate change, 50 sets of daily precipitation data from the climate change scenario were generated and used for the SLURP model to forecast 50 sets of future daily runoff. This process was used to minimize the uncertainty that may occur when the prediction process is performed. For future water balance analysis, the future water demand was divided into low demand, medium demand and high demand categories. The three water demand scenarios and the 50 daily runoff scenarios were combined to form 150 sets of input data. The monthly water balance within the Han River basin was then calculated using this data and the Korean version of Water Evaluation and Planning System model. As a result, the future volume of water scarcity of the Han River basin was predicted to increase in the long term. It is mostly due to the monthly shift in the runoff characteristic, rather than the change in runoff volume resulting from climate change.

灾后生态恢复评价研究进展

Post-disaster ecological restoration assessment as one of the key research directions of restoration ecology assumes,with reference to specific goals and systems,the role of assessing and appraising the change of structure, function,quality,health and safety of damaged or degraded ecosystem during the restoration succession,after an interval of certain time following natural or induced disaster. It is inevitably the requirement for determining the degree of ecosystem restoration,testing the effect of restoration pattern and screening out the feasible ecological restoration project. Ever since the 90s of the last century,it has by no means halted at any occasions in enticing the attention more than ever from the research fellows,in parallel with the improvement of research methods and hiked public awareness of necessity of disaster prevention and mitigation. Post-disaster ecological restoration assessment is conducted mainly towards highly frequent disaster with severe ecological damage,covering six major types of disasters,namely forest fire,earthquake and geological disaster,drought,mining and ground subsidence,volcanic eruption and hurricane. Research content extends to three aspects,i.e. ecological succession under various time series,differentiation and correlation analysis on restoration effect of the affected ecosystems under various conditions and trend prediction of the ecological restoration of the affected ecosystems. Based on various indices of the reference systems,the assessment indices could be categorized as two parts: 1) the remote sensing indices based on pre-disaster reference systems,such as NDVI,vegetation coverage,leaf area index,land use type and landscape pattern indices,etc.; 2) ground indices based on unaffected reference systems or on restoration property, such as species composition,biomass,species richness,thickness of trees and shrubs,tree height and DBH,and soilindices in terms of soil moisture,pure soil composition,organic carbon and nutrient( N、P、Ca and K) content,etc. Presently there are four key assessment methods,which are single factor comparison method( comparison of time series between vegetation and soil factors),comprehensive index method( comparison of ecological safety,health and stability before and after disaster),simulation method( cellular automata model and Markov chain intended to simulate and predict dynamic changes of damaged ecosystem) and inverse method( assessment of the effect of disaster prevention and mitigation intended to reconstruct the ecological restoration condition). In wake of the problems like absence of systematic feature, overly unitary reference system and inefficacy in guiding the subsequent restoration of the disaster areas,prospective researches on technical framework process,classification and induction of index system,formulation of the ecological restoration standard threshold and application of ecological restoration assessment should be revved up for the good of showcasing the scientific and objective features of the assessment conclusions and of rendering the boost to the ecosystem management.