Data-driven Analysis for Future Land-use Change Prediction : Case Study on Seoul
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Urban industrial land size prediction is an important task to guide urban development.But traditional forecasting methods do not meet the requirements of transformation of urban development.This paper conducted a systematic study of the methods currently used to predict the scale of industrial land and analyzed of the applicability of traditional forecasting methods in the context of urban transformation.Then three systematic steps were proposed to reflect the new requirements for industrial land:choose the type of industrial land,select the scientific prediction methods and determining the predicted outcome.And finally,specific prediction methods of industrial land,residential real estate land and non-residential real estate land were illustrated,which are most important in cities.
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Based on remote sensing and geographical information system,land use database of 1998 and 2002 in Xinjian County were established.Two commonly used prediction models,which are Markov model based on transformation matrix and GM(1,1) based on Grey theory respectively were applied to predict land use change direction in the future.The results indicated that coincidence degree between two prediction models was very high.Arable land and unused land will decrease persistently.Forest land and construction land showed acceleration tendency.Grassland and water body will be leveling-off in the future.Furthermore,driving forces of land use change are analyzed in this paper.The research results could provide scientific basis for land use planning and land use policy constitution.
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Estimating greenhouse gases from the Agriculture, Forestry and Other land Use (AFOLU) sector is very challenging partly due to the unavailability of data (particularly for land use and land use change sectors) and inadequate experts to analyze this data in case it is available. We used Collect Earth together with Machine Learning techniques to be able to predict and classify all the land use types based on some few points collected using Collect Earth. We investigated the adoption of this tool and technology in Rwanda to help its national and sub-national inventories. The use of Collect Earth and the Machine Learning (ML) implementation will help Rwanda monitor and predict its Land Use, Land Use Change and Forestry in a cost effective manner whiles enhancing the quality of reports submitted to national and international bodies whiles introducing a new approach. Among the classification algorithms we tested, we had an overall classification accuracy of 97% using the Classification and Regression Trees (CART) algorithm to predict the six land Use classes across the country.
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With the rapid development of the society of China, a large number of land problems such as unused land or inefficient used land for construction exist in the process of land usages, which leads to a waste of massive land resources. In the management of land resources, not only solving the existing problems of the land, but also prediction of the problems of the land and prevention on land abuse are in demand urgently of the urbanization. In this paper, based on the land data of different time of Hannan district in Wuhan City, the algorithm of Random Forest is used to analyze and predict the problem of unused land for construction. The results show that with more related features of land problems, the algorithm of Random Forest has good performance on the unused land discovery and prediction.
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We develop a parcel-based spatial land use change prediction model by coupling machine learning and interpretation algorithms such as cellular automata and decision tree in a Geographic Information System environment. We collect and process historical land use data and various driving factors that affect land use changes in Hunterdon County of New Jersey using decision tree J48 Classifier to develop a set of transition rules that illustrate the land use change processes during the period 1986-1995. Then we apply the derived transition rules to the 1995 land use data in a cellular automata model Agent Analyst to predict spatial land use pattern in 2004. We validate these by the actual land use in 2002. The developed decision tree-based cellular automata model has a reasonable overall accuracy of 84.46 percent in predicting land use changes. It shows a much higher capability in predicting quantitative changes (92.5%) that location changes (74.8%) in land use. With such an encouraging measure of validity, we use the model to simulate the 2011 land use patterns in Hunterdon County based on the actual land uses in 2002. We build two scenarios: the “business as usual” scenario and the “policy” scenario (with imposed government policy). The simulation results show that successfully implementing current land use policies such as down-zoning, open space, and farmland preservation could prevent 973 agricultural and 870 forest parcels (a total of 2,856 hectares) from future urban encroachment in Hunterdon County during the period 2002-2011. It becomes a significant policy instrument for government to reckon with. Key words: land use change, cellular automata, decision tree, parcel, geographic information system, J48 Classifier, Agent Analyst, Hunterdon County
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Land use and land cover (LULC) models are essential for analyzing LULC change and predicting land use requirements and are valuable for guiding reasonable land use planning and management. However, each LULC model has its own advantages and constraints. In this paper, we explore the characteristics of LULC change and simulate future land use demand by combining a CLUE-S model with a Markov model to deal with some shortcomings of existing LULC models. Using Beijing as a case study, we describe the related driving factors from land-adaptive variables, regional spatial variables and socio-economic variables and then simulate future land use scenarios from 2010 to 2020, which include a development scenario (natural development and rapid development) and protection scenarios (ecological and cultivated land protection). The results indicate good consistency between predicted results and actual land use situations according to a Kappa statistic. The conversion of cultivated land to urban built-up land will form the primary features of LULC change in the future. The prediction for land use demand shows the differences under different scenarios. At higher elevations, the geographical environment limits the expansion of urban built-up land, but the conversion of cultivated land to built-up land in mountainous areas will be more prevalent by 2020; Beijing, however, still faces the most pressure in terms of ecological and cultivated land protection.
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Land cover changes prediction is important for a better understanding of dynamic
landscape change and consciousness on the sustainable development. Geographical
information system (GIS) and remote sensing have been used in this research as its consider
as the most effective method in order to monitor the changes of the land use and land cover.
The aim of this research is to forecast land use 2020 refer to land cover changes between
year 1997 and 2008 using Cellular Automata (CA) and evaluate it with existing
development planning land use of Perlis for 2020. The prediction of the CA model was
successfully applied into this research by using modelling tools namely Methods of Land
Use Change Evaluation (MOLUSCE plugin in Quantum GIS). The processing involving
two stages which are the data preparation and prediction of land use. For the data land use
of Perils for year 1997 and 2008 are have been classified into four (4) classes while the
existing planning map for year 2020 as the reference to the prediction also have been
classified equivalent with the land use classes. The validation result of the prediction shows
78% similarity with the existing Development Land Use Planning 2020, which indicates
the validity of the model for the future prediction. In conclusion, based on the behavior of
cells changes using CA method can be a useful tools for government planners to observe
development pattern in this country and enable them to use land source in better way.
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This paper analyzes the relevant contents of forest land quality classification and evaluation,inquires into technology of forest land assessment based on forest land classification,and then studies technical framework on forest land classification and evaluation.
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