Detection of Urban Land Use Land Cover Dynamics Using GIS and Remote Sensing: A Case Study of Axum Town, Northern Ethiopia

Gebremedhin, Amare; Biruh, Welday; Govindu, Vanum; Demissie, Biadgilgn; Mehari, Abadi

doi:10.1007/s12524-019-00974-y

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…An example of a general exponential trend is the Hosftee and Brussel equation (Hofstee & Brussel, 1995) defined in Equation (16).

A_{f} = Ab {((), 1 goodbreak+ \frac{(RUE)}{100})}^{((), ((), t goodbreak+ normalΔ t) goodbreak- t)}

where

A

is the current area of interest,

b

is the initial year,

RUE

is the rate of the spatial expansion of the variable of interest,

t + Δ t

is the future time to predict, and

t

is the current time. GebreMedhin et al (2019) use Equation (16) to solve land cover prediction problems for urban land use.…”

Section: Forecasting Techniquesmentioning

confidence: 99%

“…On the other, it is essential to satisfy the increasing demand for natural resources and work from new people arriving in cities to keep the advantages. Approaches that study urban growth are (Aliani et al, 2019; GebreMedhin et al, 2019; Meng et al, 2011; Norman et al, 2009; Pal et al, 2016; Rafiee et al, 2009).…”

Section: Forecasting Applicationsmentioning

confidence: 99%

“…These applications generally use satellite images. Works associated with this topic are (Ben Abbes et al, 2018; Das & Ghosh, 2016; GebreMedhin et al, 2019). Other urbanization problems include the metro ridership prediction Ma et al (2019).…”

Section: Forecasting Applicationsmentioning

confidence: 99%

“…Detection of urban land use land cover dynamics using GIS and remote sensing: A case study of Axum Town, Northern Ethiopia(GebreMedhin et al, 2019). SummaryThis work predicts the possible level of urban expansion from 2016 to 2025 to plan infrastructure development.…”

mentioning

confidence: 97%

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A literature review on satellite image time series forecasting: Methods and applications for remote sensing

Lara‐Alvarez,

Flores,

Rodriguez‐Rangel

et al. 2024

WIREs Data Min & Knowl

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Satellite image time‐series are time series produced from remote sensing images; they generally correspond to features or indicators extracted from those images. With the increasing availability of remote sensing images and new methodologies to process such data, image time‐series methods have been used extensively for assessing temporal pattern detection, monitoring, classification, object detection, and feature estimation. Since the study of time series is broad, this article focuses on analyzing articles related to forecasting the value of one or more attributes of the image time‐series. The image time series forecasting (ITSF) problem appears in different disciplines; most focus on improving the quality of life by harnessing natural resources for sustainable development and minimizing the lethality of dangerous natural phenomena. Scientists tackle these problems using different tools or methods depending on the application. This review analyzes the field's leading, most recent contributions, grouping them by application area and solution methods. Our findings indicate that artificial neural networks, regression trees, support vector regression, and cellular automata are the most common methods for ITSF. Application areas address this problem as renewable energy, agriculture, and land‐use change. This study retrieved and analyzed relevant information about the recent activity of image time series forecasting, generating a reproducible list of the most pertinent articles in the field published from 2009 to 2021. To the author's best knowledge, this is the first review presenting and analyzing a reproducible list of the most relevant state‐of‐the‐art articles focusing on the applications, techniques, and research trends for ITSF.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Technologies > Machine Learning Technologies > Prediction

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“…An example of a general exponential trend is the Hosftee and Brussel equation (Hofstee & Brussel, 1995) defined in Equation (16).

A_{f} = Ab {((), 1 goodbreak+ \frac{(RUE)}{100})}^{((), ((), t goodbreak+ normalΔ t) goodbreak- t)}

where

A

is the current area of interest,

b

is the initial year,

RUE

is the rate of the spatial expansion of the variable of interest,

t + Δ t

is the future time to predict, and

t

is the current time. GebreMedhin et al (2019) use Equation (16) to solve land cover prediction problems for urban land use.…”

Section: Forecasting Techniquesmentioning

confidence: 99%

Section: Forecasting Applicationsmentioning

confidence: 99%

Section: Forecasting Applicationsmentioning

confidence: 99%

mentioning

confidence: 97%

See 2 more Smart Citations

A literature review on satellite image time series forecasting: Methods and applications for remote sensing

Lara‐Alvarez,

Flores,

Rodriguez‐Rangel

et al. 2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…Removal of vegetation and faulty agricultural practices lead to an increase in the concentration of CO 2 , and thus alter the climate pattern [17,18]. Therefore, the quantification and understanding of the land use dynamics at spatial and temporal scales are critical for the monitoring of urban dynamics and development of effective policies [19]. Climate variability is an intricate phenomenon considered as the changes in the mean state of the climate over a short time span (monthly and yearly), including extreme weather events (drought, flood, and cyclone).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Influence of Land Use/Land Cover Alteration on Climate Variability: An Analysis in the Aurangabad District of Maharashtra State, India

et al. 2022

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Examining the influence of land use/land cover transformation on meteorological variables has become imperative for maintaining long-term climate sustainability. Rapid growth and haphazard expansion have caused the conversion of prime agricultural land into a built-up area. This study used multitemporal Landsat data to analyze land use/land cover (LULC) changes, and Terra Climate monthly data to examine the impact of land transformation on precipitation, minimum and maximum temperature, wind speed, and soil moisture in the Aurangabad district of Maharashtra state in India during 1999–2019. Multiple linear regression and correlation analysis were performed to determine the association among LULC classes and climatic variables. This study revealed rapid urbanization in the study area over the years. The built-up area, water bodies, and barren lands have recorded a steep rise, while the agricultural area has decreased in the district. Drastic changes were observed in the climatic variables over the years. The precipitation and wind speed have shown decreasing trends during the study period. A positive relationship between soil moisture and agricultural land was found through a correlation analysis. Conspicuous findings about the positive relationship between the agricultural land and maximum temperature need further investigation. A multiple linear regression analysis demonstrated a negative relationship between the built-up area and precipitation. The intensity of the precipitation has reduced as a consequence of the developmental activities in the study area. Moreover, a positive relationship was observed between the built-up area and maximum temperature. Thus, this study calls for policy implications to formulate a futuristic land-use plan considering climate change projection in the district.

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Assessing the impact of urbanization on groundwater quality of lahore region, Pakistan

Ismail

Ahmed

Bakar

2023

Environ Sci Pollut Res

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Detection of Urban Land Use Land Cover Dynamics Using GIS and Remote Sensing: A Case Study of Axum Town, Northern Ethiopia

Cited by 5 publications

References 21 publications

A literature review on satellite image time series forecasting: Methods and applications for remote sensing

A literature review on satellite image time series forecasting: Methods and applications for remote sensing

Assessing the Influence of Land Use/Land Cover Alteration on Climate Variability: An Analysis in the Aurangabad District of Maharashtra State, India

Assessing the impact of urbanization on groundwater quality of lahore region, Pakistan

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