Modeling Spatiotemporal Patterns of Land Use/Land Cover Change in Central Malawi Using a Neural Network Model

Mungai, Leah M.; Messina, Joseph P.; Zulu, Leo C.; Qi, Jiaguo; Snapp, Sieglinde S.

doi:10.3390/rs14143477

Cited by 15 publications

(6 citation statements)

References 52 publications

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“…The LUCC is often regarded as one of the most important planning concerns [113,114]. These results show that the complexity of LUCC makes it hard to simulate using a single model and set of assumptions, particularly in metropolitan settings.…”

Section: Discussionmentioning

confidence: 99%

An Integrated Modelling Approach to Urban Growth and Land Use/Cover Change

Azizi

Soltani

Bagheri

et al. 2022

Land

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Long-term sustainable development in developing countries requires researching and projecting urban physical growth and land use/land cover change (LUCC). This research fills a gap in the literature by exploring the issues of modelling coupled LUCC and urban growth, their causes, and the role of policymakers. Tabriz metropolitan area (TMA), located at north-west Iran, was chosen as a case study to design an integrated framework using four well-established methods: cellular automata (CA), Markov chains (MC), logistic regression (LR), and stepwise weight assessment ratio analysis (SWARA). Northern, north-west, and central TMA were affected the worst by urbanisation and the loss of cultivated and grassland between 1990 and 2020. The accessibility of arterial roadways and proximity to major cities influenced these changes. Three scenarios characterise LUCC dynamics: the uncontrolled growth scenario (UGS) and the historical trend growth scenario (HTGS) foresee significant loss of cultivated land and continued urban expansion above the long-term average in 2050, while the environmental protection growth scenario (EPGS) promotes sustainable development and compact urbanisation. This study's methodology can follow and investigate the coupled dynamics of LUCC and urban growth spatially and temporally.

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Section: Discussionmentioning

confidence: 99%

An Integrated Modelling Approach to Urban Growth and Land Use/Cover Change

Azizi

Soltani

Bagheri

et al. 2022

Land

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“…Research to date have not yet determined how long-term changes in land use and long-term average climate relate to one another in driving and shaping the landscape in this geographical region. So far, the existing studies (e.g., Mungai et al, 2022 ; Mwale et al, 2014 ) have only modelled decadal land use-land cover change (LULCC) at the district level with no simulation on how the regional landscape will transition under climatic, topographic, and socioeconomic drivers. As a result, there is a paucity of direct knowledge on the combined effects of the land use and climate on land cover across the region.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and simulating landscape changes: how do long-term changes in land use and long-term average climate affect regional biophysical conditions in southern Malawi?

Nkolokosa,

Stothard,

Jones

et al. 2023

Environ Monit Assess

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We set out to reveal the effects of long-term changes in land use and long-term average climate on the regional biophysical environment in southern Malawi. Object-oriented supervised image classification was performed on Landsat 5 and 8 satellite images from 1990 to 2020 to identify and quantify past and present land use-land cover changes using a support vector machine classifier. Subsequently, using 2000 and 2010 land use-land cover in an artificial neural network, land use-land cover for 2020 driven by elevation, slope, precipitation and temperature, population density, poverty, distance to major roads, and distance to villages data was simulated. Between 1990 and 2020, area of land cover increased in built-up (209%), bare land (10%), and cropland (10%) and decreased in forest (30%), herbaceous (4%), shrubland (20%), and water area (20%). Overall, the findings reveal that southern Malawi is dominantly an agro-mosaic landscape shaped by the combined effects of urban and agricultural expansions and climate. The findings also suggest the need to enhance the machine learning algorithms to improve capacity for landscape modelling and, ultimately, prevention, preparedness, and response to environmental risks.

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“…The evolution process of LUCC is very complex, and it exhibits certain patterns in time and space [7,8]. The study of temporal and spatial evolution patterns of LUCC is of great significance to the detection of abnormal behaviors in the process of land use as well as in the identification of temporal and spatial changes in the process of land use.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatiotemporal Aggregation of Land Use Change Processes Considering the Shape of Land Units

Liang

Nie²,

Luo³

et al. 2023

Sustainability

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The processes of land use and cover change (LUCC) are highly diverse and complex, being heavily influenced by natural factors, economic factors, and other related factors. These changes have a significant impact on ecological environments and landscapes, and serve as a reflection of human activity, limited by natural factors. As a result, LUCC has been widely studied across multiple scientific disciplines. In particular, considerable progress has been made with regard to traditional methods of analyzing land use structures, which focus on the overall differences in the land use structure in each spatiotemporal snapshot. However, these methods have overlooked the continuity in the evolution of each land use unit between different snapshots, impeding the development of a comprehensive model for the spatiotemporal evolution of land use processes. In this work, land use change process (LUCP)—constructed using multiple land use data points from different points in time—was employed as the basis to develop a method to measure the spatiotemporal distance between irregular land patches in evolution sequences based on LUCP. Furthermore, the spatiotemporal distribution model was analyzed using Monte Carlo simulation and measurements of the shortest spatiotemporal distance of LUCP. This work employs land use data for Huainan in China, a typical coal resource city, from 2008 to 2017 for an empirical study. A typical kind of spatiotemporal evolution of LUCP (evolution from farmland to grassland within any two years) is evaluated. Taking into account the shape of land use units, the spatiotemporal distances between irregular evolutionary sequences are measured using buffer-based superposition. The results show that the expected mean nearest neighbor distance for the irregularly evolving sequence of land use units is 0.085 in the completely random CSR model, whereas the mean nearest neighbor distance is 0.037 in the real observation model. These results indicate that such LUCPs have generally shown a spatiotemporal aggregation pattern over the past 10 years. However, since the z-score is 1.03, which is in the range of −1.65 to 1.65, this aggregation pattern is not statistically significant. These experiments demonstrate the validity of using the method proposed herein to study similar problems. The results of this work provide valuable insight into the spatiotemporal evolution process of land use units, which could be instrumental in exploring the potential spatiotemporal model of LUCP evolution.

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Modeling Spatiotemporal Patterns of Land Use/Land Cover Change in Central Malawi Using a Neural Network Model

Cited by 15 publications

References 52 publications

An Integrated Modelling Approach to Urban Growth and Land Use/Cover Change

An Integrated Modelling Approach to Urban Growth and Land Use/Cover Change

Monitoring and simulating landscape changes: how do long-term changes in land use and long-term average climate affect regional biophysical conditions in southern Malawi?

Analysis of Spatiotemporal Aggregation of Land Use Change Processes Considering the Shape of Land Units

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