GIS Cellular Automata Using Artificial Neural Network for Land Use Change Simulation of Lagos, Nigeria

Okwuashi, Onuwa; Isong, Mfon; Eyo, Eyo; Eyoh, Aniekan E.; Nwanekezie, Okey; Olayinka, Dupe Nihinlola; Udoudo, Daniel Okon; Ofem, Beulah

doi:10.5539/jgg.v4n2p94

Cited by 15 publications

(3 citation statements)

References 17 publications

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“…For all image processing, ENVI 5.3, QGIS, and ArcGIS were used. In recent decades, a number of studies have revealed the capability of the Artificial Neural Network algorithm in calibrating the LUC change modeling to predict its future trends [46][47][48][49]. The Artificial Neural Network algorithm was created by imitating the nervous system of the human being [50].…”

Section: Image Classification and Accuracy Assessmentmentioning

confidence: 99%

Examining Land Use/Land Cover Change and Its Prediction Based on a Multilayer Perceptron Markov Approach in the Luki Biosphere Reserve, Democratic Republic of Congo

Michel

Wang

et al. 2021

Sustainability

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Villages within the Luki Biosphere Reserve and the surrounding cities have undergone rapid demographic growth and urbanization that have impacted the reserve’s natural landscape. However, no study has focused on the spatiotemporal analysis of its land use/land cover. The present research aims at providing a comprehensive analysis of land use/land cover change in the Luki Biosphere Reserve from the year 1987 to 2020, and to predict its future change for the year 2038. Landsat images were classified in order to provide land use/land cover maps for the years 1987, 2002, 2017 and 2020. Based on these maps, change detection, gradient direction, and landscape metric analyses were performed. In addition, land use/land cover change prediction was carried out using the Multilayer Perceptron Markov model. The results revealed significant land use/land cover changes in the Luki Biosphere Reserve during the study period. Indeed, tremendous changes in the primary forest, which lost around 17.8% of its total area, were noted. Other classes, notably savannah, secondary forest, built-up area, fallow land and fields had gained 79.35, 1150.36, 67.63, 3852.12 hectares, respectively. Based on the landscape metric analysis, it was revealed that built-up areas and fallow land and fields experienced an aggregation trend, while other classes showed disaggregation and fragmentation trends. Analysis further revealed that village expansion has significantly affected the process of land use/land cover change in the Luki Biosphere Reserve. However, the prediction results revealed that the primary forest will continue to increase while built-up area, fallow land and fields will follow a trend similar to a previous one. As for secondary forest and savannah, the forecast revealed a decrease of the extent during the period extending from 2020 to 2038. The present findings will benefit the decision makers, particularly in the sustainable natural resources management of the Luki Biosphere Reserve.

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Section: Image Classification and Accuracy Assessmentmentioning

confidence: 99%

Examining Land Use/Land Cover Change and Its Prediction Based on a Multilayer Perceptron Markov Approach in the Luki Biosphere Reserve, Democratic Republic of Congo

Michel

Wang

et al. 2021

Sustainability

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“…The observed well data were highlighted with pink color as found in Figure 16. The study was certain with correlation coefficient (R 2 ) [120][121][122] and kappa statistics [123][124][125][126] value. The correlation coefficient was found to be 0.73 (Figure 17), and the average kappa statistics value was 77% (Table 7).…”

Section: Resultsmentioning

confidence: 99%

Assessment of Groundwater Potential Zones of Upper Blue Nile River Basin Using Multi-Influencing Factors under GIS and RS Environment: A Case Study on Guder Watersheds, Abay Basin, Oromia Region, Ethiopia

Duguma

2022

Geofluids

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Groundwater is the most crucial resource for human beings and plays an important role in combating climate change and is substantial to human existence on the globe. Overall increased demand for water in different sectors, population growth, and unreliable rainfall necessitates the planning and management of groundwater. In this study, groundwater potential zones are delineated by combining remote sensing and geographical information system techniques in the Guder watersheds of the Upper Blue Nile Basin. Groundwater potential zones are prepared by using various multi-influencing factors like geomorphology, land use/cover, lithology, soil type, soil texture, drainage density, slope, lineament, rainfall, and elevation. These influencing factors’ features were given appropriate weightage according to Saaty’s AHP method, expert judgment, and their relative significance for groundwater occurrence. The groundwater potential zone was classified into different categories as very poor, poor, moderate, good, and very good according to quantile classification. This study reveals that about 33.6% of the Guder River Basin represents a good andvery good GWPZ category with an equal value of 16.8%,; while values 23.3%, 20.2%, and 22.9% were denoted by very poor, poor, and moderate groundwater potential zone, respectively. GWPZ was validated by field-collected data such as well discharge and soil depth. An accepted similarity was observed between delineated GWPZ and the basin’s soil depth graphically. The results of this study were also verified by correlation and kappa statistics values of 0.73and 77%, respectively. The study is certain with a sensible dimension of consistency in pairwise comparison between influencing and the overall weightage. The very high GWPZs are found in the northern part starting from the center longitude of the study area, more along with the northwestern, southern, and southwestern of the Guder subbasin. Low to very low groundwater potentiality has been seen at different distances from the center due to the presence of escapements, hills and steep side slopes, slopes, and rock surfaces. The study also revealed that the zone of high groundwater potential has high soil depth, and the zone of low groundwater showed low soil depth as the capacity of the aquifer to store water may depend on the depth of soil profile. This study attests to the GIS and remote sensing techniques as an effective model for delineation of GWPZs and can be applied at other basins of Ethiopia.

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“…The parameters to classify the values in these categories were based on the methods' accuracy classification as low agreement (below 40), moderate agreement (41 to 60), good agreement (61 to 75), excellent agreement (76 to 80), and almost perfect agreement (above 80) [17,[53][54][55].…”

Section: Overall Accuracy Assessmentmentioning

confidence: 99%

A Land Cover Change Detection Approach to Assess the Effectiveness of Conservation Projects: A Study Case on the EU-Funded LIFE Projects in São Miguel Island, Azores (2002–2021)

Tiengo,

Merino-De-Miguel,

Uchôa

et al. 2024

Land

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Small oceanic islands, such as São Miguel Island in the Azores (Portugal), face heightened susceptibility to the adverse impacts of climate change, biological invasions, and land cover changes, posing threats to biodiversity and ecosystem functions and services. Over the years, persistent conservation endeavors, notably those supported by the EU LIFE Programme since 2003, have played a pivotal role in alleviating biodiversity decline, particularly in the eastern region of São Miguel Island. This study advocates the application of remote sensing data and techniques to support the management and effective monitoring of LIFE Nature projects with land cover impacts. A land cover change detection approach utilizing Rao’s Q diversity index identified and assessed changes from 2002 to 2021 in intervention areas. The study analyzed the changes in LIFE project areas using ASTER, Landsat 8, and Sentinel 2 data through Google Earth Engine on Google Colab (with Python). This methodological approach identified and assessed land cover changes in project intervention areas within defined timelines. This technological integration enhances the potential of remote sensing for near-real-time monitoring of conservation projects, making it possible to assess their land cover impacts and intervention achievements.

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GIS Cellular Automata Using Artificial Neural Network for Land Use Change Simulation of Lagos, Nigeria

Cited by 15 publications

References 17 publications

Examining Land Use/Land Cover Change and Its Prediction Based on a Multilayer Perceptron Markov Approach in the Luki Biosphere Reserve, Democratic Republic of Congo

Examining Land Use/Land Cover Change and Its Prediction Based on a Multilayer Perceptron Markov Approach in the Luki Biosphere Reserve, Democratic Republic of Congo

Assessment of Groundwater Potential Zones of Upper Blue Nile River Basin Using Multi-Influencing Factors under GIS and RS Environment: A Case Study on Guder Watersheds, Abay Basin, Oromia Region, Ethiopia

A Land Cover Change Detection Approach to Assess the Effectiveness of Conservation Projects: A Study Case on the EU-Funded LIFE Projects in São Miguel Island, Azores (2002–2021)

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