Geospatial modelling of changes in land use/land cover dynamics using Multi-layer Perceptron Markov chain model in Rajshahi City, Bangladesh

Dey, Nataraj Narayan; Rakib, Abdullah Al; Kafy, Abdulla Al; Raikwar, Vinay

doi:10.1016/j.envc.2021.100148

Cited by 63 publications

(26 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It comes from the quantitative description of system state and state transfer in system analysis. In Formula (4),

is the system state at time t ,

is the system state at time t + 1,

is the transition probability matrix in the state, and the calculation formula is as Formula (5) [ 64 , 65 ]:

where P is Markov probability matrix,

represents the probability of transferring from the current state i to another state j in the next time period. The low conversion probability is close to 0 and the high conversion probability is close to 1.…”

Section: Methodsmentioning

confidence: 99%

Spatial-Temporal Responses of Ecosystem Services to Land Use Transformation Driven by Rapid Urbanization: A Case Study of Hubei Province, China

Cui

Liu

Shan

et al. 2021

IJERPH

View full text Add to dashboard Cite

Exploring the changes of ecosystem services value caused by land use transformation driven by urbanization is crucial for ensuring the safety of the regional ecological environment and for enhancing the value of ecosystem services. Based on the land use remote sensing data during the rapid urbanization development period of Hubei Province from 1995 to 2015, this study analyzed the characteristics of land use/land cover change and land use transformation. The spatial–temporal response characteristics and evolution of ecosystem services value (ESV) to land use transformation driven by urbanization were measured by equivalent factor method, spatial autocorrelation analysis, hot spot analysis and gravity model. We found that: (1) Driven by urbanization, the most significant feature of land use transformation in Hubei Province was the expansion of the built-up land and the significant reduction of cropland and forest, among which 90% of the new built-up land was converted from cropland and forest. (2) This land use transformation became the main source of ESV losses. Especially, the sharp increase of the built-up land from 2010 to 2015, occupying cropland and forest, resulted in ESV losses of nearly USD 320 million. The service capacity of climate regulation, soil conservation, gas regulation and food production undertaken by cropland and forest decreased. (3) The ecosystem services value in the study area showed spatial distribution characteristics of high in the west and low in the middle and east regions. The center of gravity of ESV shifted from northwest to southeast. Due to the sharp increase of the built-up land from 2010 to 2015, the center of gravity shift rebounded. This study can help policymakers better understand the trade−offs between land use transformation and ecosystem services driven by urbanization.

show abstract

“…It comes from the quantitative description of system state and state transfer in system analysis. In Formula (4),

is the system state at time t ,

is the system state at time t + 1,

is the transition probability matrix in the state, and the calculation formula is as Formula (5) [ 64 , 65 ]:

where P is Markov probability matrix,

Section: Methodsmentioning

confidence: 99%

Spatial-Temporal Responses of Ecosystem Services to Land Use Transformation Driven by Rapid Urbanization: A Case Study of Hubei Province, China

Cui

Liu

Shan

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…It can be transformed into grid-based land use patterns at different locations. Competition between various land types, conversion rules of land use, and the spatial distribution of land use demand in each simulation year can be decided based on the probability of land use types (Dey et al, 2021).…”

Section: Clue-s Modelmentioning

confidence: 99%

Land use changes in Zhangjiakou from 2005 to 2025 and the importance of ecosystem services

Chi

et al. 2021

PeerJ

View full text Add to dashboard Cite

Changes in local land use affect regional ecological services, development planning, and optimal use of space. We analyzed the effects of changes in land use from 2000 to 2025 on the spatial distribution of ecosystem services using CLUS-S modeling to evaluate ecosystem functions in Zhangjiakou, China. We found that the urban ecosystem area in Zhangjiakou increased and farmland decreased between 2000–2025. Water conservation was relatively high and was concentrated in the nature reserves of southern Zhangjiakou. Soil conservation was mainly distributed in eastern and southern counties. The results of the CLUE-S model showed that the relative operating characteristics of the six land use types were > 0.70, and the logistic regression equation was able to successfully explain the distribution pattern of the different types of land use.

show abstract

“…The satellite constellations with multispectral sensors provide data that can deliver a consistent scheme for mining different information about the ecological environment, comprising the biophysical composition index, vegetation index, humidity, and impermeable surface indices (Gašparović et al, 2021;Piyoosh & Ghosh, 2018) The new technologies which retrieve the LST from thermal infrared data have been considerably developed since the beginning of twenty century, and now it is broadly applied in various industries (Abdullah-Al-Faisal et al, 2021). Currently, the relationships between LST and LULC using GIS and remotely sensed data are investigated by many studies at global and local scales (Dey et al, 2021;How Jin Aik et al, 2021;Naim & Kafy, 2021b;Ogunjobi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of Land Use/Land Cover Changes on the Surface Urban Heat Island intensity: The case study of Kabul city, Afghanistan

Sahak

Hotaki

Ahmadi

et al. 2022

Preprint

View full text Add to dashboard Cite

The purpose of this paper is to detect the areas which are affected by urban heat island (UHI) based on the relationship between land surface temperature and land use /land cover (LU/LC) changes during 1998, 2011, and 2020 for the major city of Kabul, the capital of Afghanistan, using multispectral and multi-temporal Landsat data (TM and OLI/TIRS). To achieve the objectives, the emissivity corrected land surface temperature method was examined to calculate Land Surface Temperature (LST), and the Land-use/Land-cover map was prepared using the support vector machine (SVM) supervised algorithm. The LU/LC was categorized into five major classes (vegetation, water-body, built-up, bare-land, and soil). According to the LST map estimated by processing the thermal band of the satellite image, areas influenced by Urban Heat Island (UHI) were detected to evaluate their anomalies to the existing LU/LC types. The findings of LST illustrate, that the mean recorded LST in 1998 was 39.42˚C, whereas the mean recorded LST in 2020 was 41.25˚C, which demonstrates a 1.83 ˚C increase for the whole study period. Specifically in 1998 only five districts (1,16,9,15, and 19) were affected by surface UHIs, while in 2011, the surface UHIs influenced areas increased to eight districts (15, 16, 1, 21, 17, 12, 13, and 8), and in 2020, the surface UHIs affected regions are improved to ten districts (1,19, 15,16, 9,17, 22, 11, 13, and 2) over the Kabul City. These variations and improvements are mostly due to the status of LU/LC in the study area, and it demonstrates the strong link between land cover and land surface temperature. Furthermore, Normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) were also extracted. The relationship of NDVI and NDBI with LST was evaluated. Based on the results, a strong negative relationship between LST and NDVI was observed, while a positive relationship between LST and NDBI was recorded. The findings of this work show that an increase in non-evaporation areas and a decline in the greenery surfaces increased the LST. Consequently, the outcomes of this study are significant for decision-makers and urban planners to manage the drawbacks of urbanization temperature in arid and semi-arid areas.

show abstract

Geospatial modelling of changes in land use/land cover dynamics using Multi-layer Perceptron Markov chain model in Rajshahi City, Bangladesh

Cited by 63 publications

References 62 publications

Spatial-Temporal Responses of Ecosystem Services to Land Use Transformation Driven by Rapid Urbanization: A Case Study of Hubei Province, China

Spatial-Temporal Responses of Ecosystem Services to Land Use Transformation Driven by Rapid Urbanization: A Case Study of Hubei Province, China

Land use changes in Zhangjiakou from 2005 to 2025 and the importance of ecosystem services

Evaluating the Impact of Land Use/Land Cover Changes on the Surface Urban Heat Island intensity: The case study of Kabul city, Afghanistan

Contact Info

Product

Resources

About