A Short Presentation of the Land Change Modeler (LCM)

Eastman, J. Ronald; Toledano, James

doi:10.1007/978-3-319-60801-3_36

Cited by 47 publications

(40 citation statements)

References 11 publications

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“…The LCM is a tool to predict land changes for land planning (Eastman & Toledano, 2018). The LCM was used due to its simplicity, graphical illustration, and availability of different modeling approaches.…”

Section: Methodsmentioning

confidence: 99%

“…The LCM was used due to its simplicity, graphical illustration, and availability of different modeling approaches. The LCM utilizes historical maps of LULC to empirically model the association between LC transitions and descriptive variables to map future LULC scenarios (Eastman & Toledano, 2018). The LCM has been used in many studies worldwide as a new generation of models to detect and model LC change in a wide range of applications (Ansari & Golabi, 2019; Gibson et al, 2018; Li & Liu, 2017; Tarawally et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

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Analysis and prediction of land cover changes using the land change modeler (LCM) in a semiarid river basin, Iran

et al. 2021

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Predicting future land cover (LC) changes is an important step in the proper planning and management of watersheds. As a susceptible area to salinity and desertification, receiving only about 195 mm rainfall annually, the Hable‐Rud River basin is especially sensitive to land use/cover changes. Based on corrected LANDSAT satellite images for the years 1986, 2000, and 2017, the LC were extracted using the maximum likelihood (ML) method. LC changes were predicted by applying the land change modeler (LCM) for the basin. The kappa index for classification in 1986, 2000, and 2017 was 75, 78, and 81%, respectively. Using LCM, the prediction was accomplished for the year 2017 with a kappa index of above 74%. The LC map was predicted for year 2040. The analysis indicates that over the past 32 years, bare land, saline land, agricultural, industrial, and residential areas have increased by about 8, 6.2, 2.7, 0.63, and 0.48%, respectively; while rangeland area was decreased by 18%. Results also indicate that given the predicted LC for year 2040 in comparison with the reference year (2017), saline land, agricultural, industrial, and residential areas will be likely to continue to increase by about 3, 1.5, 0.7, and 0.8%, respectively, whereas bare land and rangeland will most likely decrease by about 4.55 and 1.45%, respectively. The findings of this study assist in analyzing the future trends of LC changes in the basin. This information can be used as a guide for land planners and managers in future land use planning of the area.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Analysis and prediction of land cover changes using the land change modeler (LCM) in a semiarid river basin, Iran

et al. 2021

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“…This modelling involved three steps: Step 1) exploring and summarising past change, which included correcting land-cover maps and selecting the datasets from the time series to be used in the land-use change model; Step 2) modelling transition potentials from native vegetation to anthropic land-use classes; and Step 3) calculating the probability of change for the modelled transitions within a predefined time period. These analyses were developed using the Geographic Information System (GIS) ArcMap 9.2 [ 56 ] and Land Change Modeller (LCM) [ 57 ], a tool commonly used for modelling of land-use change [ 58 , 59 ].…”

Section: Methodsmentioning

confidence: 99%

Advancing Land-Sea Conservation Planning: Integrating Modelling of Catchments, Land-Use Change, and River Plumes to Prioritise Catchment Management and Protection

et al. 2015

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Human-induced changes to river loads of nutrients and sediments pose a significant threat to marine ecosystems. Ongoing land-use change can further increase these loads, and amplify the impacts of land-based threats on vulnerable marine ecosystems. Consequently, there is a need to assess these threats and prioritise actions to mitigate their impacts. A key question regarding prioritisation is whether actions in catchments to maintain coastal-marine water quality can be spatially congruent with actions for other management objectives, such as conserving terrestrial biodiversity. In selected catchments draining into the Gulf of California, Mexico, we employed Land Change Modeller to assess the vulnerability of areas with native vegetation to conversion into crops, pasture, and urban areas. We then used SedNet, a catchment modelling tool, to map the sources and estimate pollutant loads delivered to the Gulf by these catchments. Following these analyses, we used modelled river plumes to identify marine areas likely influenced by land-based pollutants. Finally, we prioritised areas for catchment management based on objectives for conservation of terrestrial biodiversity and objectives for water quality that recognised links between pollutant sources and affected marine areas. Our objectives for coastal-marine water quality were to reduce sediment and nutrient discharges from anthropic areas, and minimise future increases in coastal sedimentation and eutrophication. Our objectives for protection of terrestrial biodiversity covered species of vertebrates. We used Marxan, a conservation planning tool, to prioritise interventions and explore spatial differences in priorities for both objectives. Notable differences in the distributions of land values for terrestrial biodiversity and coastal-marine water quality indicated the likely need for trade-offs between catchment management objectives. However, there were priority areas that contributed to both sets of objectives. Our study demonstrates a practical approach to integrating models of catchments, land-use change, and river plumes with conservation planning software to inform prioritisation of catchment management.

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“…Several other software tools allow modeling and forecasting of forest cover change (Mas et al, 2014). The most famous land cover change software tools include Dinamica-EGO (Soares-Filho et al, 2002), Land Change Modeller (Eastman & Toledano, 2017), and CLUE (Verburg & Overmars, 2009). Despite the many functionalities they provide, these software tools are not open source and might not all be cross-platform, scriptable, and completely user-friendly.…”

Section: Statement Of Needmentioning

confidence: 99%

forestatrisk: a Python package for modelling and forecasting deforestation in the tropics

Vieilledent¹

2021

JOSS

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A Short Presentation of the Land Change Modeler (LCM)

Cited by 47 publications

References 11 publications

Analysis and prediction of land cover changes using the land change modeler (LCM) in a semiarid river basin, Iran

Analysis and prediction of land cover changes using the land change modeler (LCM) in a semiarid river basin, Iran

Advancing Land-Sea Conservation Planning: Integrating Modelling of Catchments, Land-Use Change, and River Plumes to Prioritise Catchment Management and Protection

forestatrisk: a Python package for modelling and forecasting deforestation in the tropics

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