Geospatial modelling of urban growth for sustainable development in the Niger Delta Region, Nigeria

Musa, Sulaiman Ibrahim; Hashim, Mazlan; Reba, Mohd Nadzri Md

doi:10.1080/01431161.2018.1539271

Cited by 20 publications

(15 citation statements)

References 68 publications

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“…To improve the accuracy of these models, the transition probabilities and suitability (i.e., probability whether and where LULC change will occur) are frequently expressed as functions of multiple explanatory variables such as topography, socio-economic metrics and distance functions. The relative influence of these variables can then be weighted in a Multi Criteria Evaluation (MCE) [45,46,59,79,80]. In a slightly different approach, ANNs like the Multilayer Perceptron (MLP) are often combined with MC to calculate the transition potentials as functions of multiple change drivers [47][48][49][50][51]60,[72][73][74]81,82,131,135,137].…”

Section: Categorization Of Forecasting Methodsmentioning

confidence: 99%

“…The most important applications in EO-based forecasting of the anthroposphere are LULC (54%) and crop yield (40%). In most LULC simulations, forecasts focus on urban sprawl or LULC change in an urban environment, simulating more general LULC maps of urban centers [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55] or binary urban/non-urban masks [56][57][58][59][60][61][62][63][64]. Musa et al [36] reviewed urban modeling studies and showed that modeling approaches based on CA are most popular in the scientific literature due to their flexibility and ability for spatially explicit simulation.…”

Section: Research Topicsmentioning

confidence: 99%

“…Some studies, in contrast, focus on the loss of valuable or protected ecosystems such as forests or wetlands to agricultural or built-up LULC classes [78][79][80][81][82][83]. By altering or updating some of the input variables, some studies simulate future LULC under different scenarios to reflect different land planning policies or change trajectories [42,46,61,64,68,72,75,77,81]. Almost all of these LULC forecasts are acquired by the application of CA-like methods, except for Hailu et al [78], who forecast the future probability of coffee abundance in Ethiopia based on climate projections.…”

Section: Research Topicsmentioning

confidence: 99%

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Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Koehler

Kuenzer

2020

Remote Sensing

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Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.

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Section: Categorization Of Forecasting Methodsmentioning

confidence: 99%

Section: Research Topicsmentioning

confidence: 99%

Section: Research Topicsmentioning

confidence: 99%

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Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Koehler

Kuenzer

2020

Remote Sensing

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“…A factor is a criterion that influences the suitability of the particular land cover in a particular area. Factors vary in values; however, landuse cover change projections are usually stretched from 0 to 255 (Musa, Hashim, and Reba 2018). These factors are grouped into five groups, namely; Environmental factors, i.e., constrained areas, local-scale neighbourhood, i.e., one land use affected by the other, spatial characteristics of the cities, i.e., distance and access to the city centre.…”

Section: Geospatial Modelling Frameworkmentioning

confidence: 99%

“…Also, the shape of the chosen contiguity filter influences the suitability maps. This could result from inefficient digitization of some of the dataset, and factors and constraints could have influenced the results (Musa, Hashim, and Reba 2018). Another common source of cell-by-cell error is inefficient georeferencing and geometric correction of the comparison map (Eastman 2012).…”

Section: Validation Of Land Use/cover Simulationmentioning

confidence: 99%

Predicting land use/cover changes and its association to agricultural production on the slopes of Mount Kilimanjaro, Tanzania

et al. 2021

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Increasing demand for food production results in Land use and land cover (LULC) changes, which afflicts the provision of ecosystem services in high mountain areas. This work used time-series LULC and selected spatial metrics to predict the LULC changes for Kikafu-Weruweru-Karanga (KWK) watershed (on the southern slopes of Mt. Kilimanjaro) for the next decade. LULC maps were generated by classifying time-series satellite images. We further predicted the implications for selected staple crop production over the next decade. The simulated LULC shows expansion in built-up (by 32.55%/27.04 km 2 ) and agriculture (by 39.52%/52.0 km 2 ) areas from 2018 to 2030. These results suggest that urbanization is likely the next biggest threat to water availability and food production. Grasslands and wetlands are expected to decrease by 57.24% and 39.29%, respectively. The forest area is likely to shrink by 6.37%, about 9.82 km 2 , and 1.26 km 2 being converted to agriculture and built-up areas, respectively. However, expansion in agricultural land shows very little increase in staple food crop production records, suggesting that farm size plays a minor role in increasing crop production. Predicting the near future LULC around KWK is useful for evaluating the likelihood of achieving development and conservation targets that are set locally, nationally and internationally.

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Detection and prediction of land use changes and population dynamics in the Gorganrud River basin, Iran

et al. 2023

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Analyzing land use/land cover change is a fundamental tool for evaluating the environmental consequences of human activities. This research was conducted to detect and predict likely land use changes in the Gorganrud River basin, Iran, and to estimate past and future population growth as a driving force in land use change and degradation. First, land use maps for 1999, 2009, and 2017 were prepared. Then, the likely land use changes for 2030 and 2040 were predicted using the Land Change Modeler (LCM) in TERRSET software. Results indicate that the percentage of changes in agricultural and residential areas, bare lands, and semi‐dense forests from 1999 to 2017 were +4.2, +0.62, +1.76, and +3.15, respectively, while the percentage of changes in rangelands, dense forests, and water bodies were −8.7, −0.37, and −0.63. Analysis of changes from 2017 to 2040 indicates that the percentage of changes in croplands, dense forests, and bare lands may reach −4.42, −2.35, and −2.74, respectively. Conversely, the area of rangelands, semi‐dense forests, water bodies, and residential areas would likely increase by +7.78%, +1.02%, +0.04%, and + 0.7%, respectively. The population density in 2011 and 2016 was 94 and 97 persons/km2, respectively, whereas the 5‐year population growth rate was 3.5%. Better conservation practices to prevent deforestation and inappropriate growth of residential areas, in line with forest replantation to prevent the conversion of semi‐dense forests into rangelands, are some of the management strategies required in the study area. Population control and redistribution are other prescribed actions based on the research findings.

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Geospatial modelling of urban growth for sustainable development in the Niger Delta Region, Nigeria

Cited by 20 publications

References 68 publications

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Predicting land use/cover changes and its association to agricultural production on the slopes of Mount Kilimanjaro, Tanzania

Detection and prediction of land use changes and population dynamics in the Gorganrud River basin, Iran

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