Quantifying forest land-use changes using remote-sensing and CA-ANN model of Madhupur Sal Forests, Bangladesh

Islam, Md Yachin; Karim, K.H. Razimul; Rashid, Kazi Jihadur

doi:10.1016/j.heliyon.2023.e15617

Cited by 9 publications

(5 citation statements)

References 51 publications

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“…In this work, to predict changes in LULC by 2030 and 2050, the combined CA-ANN model was used, and studies were carried out using the MOLUSCE plugin of QGIS software, the effectiveness and reliability of which have been confirmed in many works [32][33][34][35][36][37]. In our case, the results of the validation of the forecast model also turned out to be relatively high (≥0.85), which gives grounds to consider the effects of modeling future changes in LULC quite reliable since 0.75 is regarded as an acceptable threshold [36].…”

Section: Discussionmentioning

confidence: 99%

“…But by 2050, the rate of increase in sown areas and decrease in grasslands will decrease somewhat, reaching 48.76% and 16.82% of the district's area, respectively. According to some researchers, using the CA-ANN model for predicting LULC has advantages and disadvantages [37]. The advantage of CA-ANN is its higher forecasting accuracy compared to other models, which allows you to model various scenarios using input variables of multiple natures.…”

Section: Discussionmentioning

confidence: 99%

“…It is believed that CA-ANN accurately represents the complex spatial inhomogeneities of LULC [33]; therefore, it is successfully used to evaluate future LULC transformations. At the same time, the Modules for Land-Use Change Simulation (MOLUSCE) plugin, as part of QGIS, is often used to model future LU generations based on CA-ANN [34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

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Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan

Alipbeki,

Alipbekova,

Mussaif

et al. 2024

Agronomy

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Changes occurring because of human activity in protected natural places require constant monitoring of land use (LU) structures. Therefore, Korgalzhyn District, which occupies part of the Korgalzhyn State Natural Reserve territory, is of considerable interest. The aim of these studies was to analyze changes in the composition of the land use/land cover (LULC) of Korgalzhyn District from 2010 to 2021 and predict LU transformation by 2030 and 2050. Landsat image classification was performed using Random Forest on the Google Earth Engine. The combined CA-ANN model was used to predict LULC changes by 2030 and 2050, and studies were carried out using the MOLUSCE plugin. The results of these studies showed that from 2010 to 2021, there was a steady increase in the share of ploughable land and an adequate reduction in grassland. It is established that, in 2030 and 2050, this trend will continue. At the same time, there will be no drastic changes in the composition of other land classes. The obtained results can be helpful for the development of land management plans and development policies for the Korgalzhyn District.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan

Alipbeki,

Alipbekova,

Mussaif

et al. 2024

Agronomy

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“…The ANN model was adopted to improve the accuracy level of the model for the spatial relations among neighboring cell values of 2 × 2. The multi-layer perceptron (MLP) model repeats 1000 times at a rate of 0.01, with each iteration modifying the weights and prejudices of the neural network to shrink the error between the projected and detected land use (Islam et al, 2023).…”

Section: Projection Of Future Lulcmentioning

confidence: 99%

Un-planned Urban Growth Monitoring from 1991 to 2021 of Aizawl City, North-east India by Multi- Temporal Changes and CA-ANN Model

Lawmchullova,

Lalrinawma,

Rinkimi

et al. 2024

Preprint

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Monitoring urban landuse and landcover (LULC) change is a crucial element in developing cities like Aizawl to improve land use planning for future smart cities. The objective of the current study is to analyze the lulc changes of Aizawl city between 1991 and 2021 using multi-date Landsat images and a cellular automata-artificial neural network (CA-ANN) model to predict future scenarios. The present study is highly essential for examining the urban expansion in a vertical hill city and the historical influence of settlement patterns along the edges of hill ranges for proper land use planning. The automatic classification of support vector machines (SVM) in-built at Orfeo tool box (OTB) modules was employed for LULC pattern classification. The land cover change method of the semi-automatic classification plugin (SCP) was used to identify the past LULC using Landsat 4, 5, 7, and 8. The future LULC was stimulated using the machine-learning approaches modules for land use change evaluation (Molusce) plugin in QGIS 2.18. Also, we highlight the factors that influence future LULC changes and the impacts of unplanned hill cities from the results of multi-criteria evaluation (MCE) and analytical hierarchical process (AHP). The study reveals that built-up areas are continuously increasing while open forest, agricultural land, and fallow land are diminishing, even in the projected land use land cover thematic layer in 2031. The built-up area has seen the highest change, from 5.98–25.8% in 1991 to 2021; the rate of increase has been 0.636 km2/yr-1 during the last 30 years. Similarly, dense forest cover also increased from 12.14–18.72% from 1991 to 2021, while other landuse landcover patterns like open forest, fallow land, and agricultural land are declining due to urban expansion. The accuracy level of Kappa coefficients was 97.30% in 1991 and 100% in the years 2001, 2011, and 2021, respectively. This study can help town planners and decision-makers with the protection of greenery and proper town planning.

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“…Forest, one of the most abundant and ecologically appealing forest types (Islam et al 2023) but it has been severely damaged by agriculture and other human activities (Rahman et al 2009). Around 7,079.4 hectares of Sal Forest were destroyed between 1972 and 2013 as a result of the quick destruction and severe disruptions caused by both local and indigenous inhabitants (Al Faruq et al 2016).…”

Section: Madhupur Sal Forest Is a Plainland Salmentioning

confidence: 99%

Estimation of Aboveground Forest Biomass and Carbon Storage of Bangladesh

Mojumder,

Hossain,

Volk

2023

BIOTROPIA

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Forest biomass helps mitigate climate change impacts through sequestration of atmospheric carbon dioxide and potentially storing it for long periods of time. Deforestation and timber harvesting cause the reduction of forest biomass resulting in the reduced carbon sequestration capacity and alterednatural balance of forest ecosystems. We used remote sensing and GIS tools in the four important forest cover zones within five districts of Bangladesh to compare the aboveground forest biomass (AGB) changes between 2014 and 2020. We found an increased AGB in Sundarban mangrove forest from 89.73 Mg.h-1 in 2014 to 90.76 Mg.h-1 in 2020. Similarly, the AGB was found to be increased for Ukhiya hill forest from 7.89 Mg.h-1 in 2014 to 8.89 Mg.h-1 in 2020. Contrary, the average AGB content in Nijhum Dwip mangrove forest decreased from 44.36 Mg.h-1 in 2014 to 37.46 Mg.h-1 in 2020. The average AGB of Modhupur decidious forest also found to be decreased from 110.01 Mg.h-1 in 2014 to 107.22 Mg.h-1 in 2020. The decreased biomass contents could be attributed to anthropgenic factors as indicated by the presence of human activities and this informatin will be helpful for forest restoration and management in Bangladesh.

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Quantifying forest land-use changes using remote-sensing and CA-ANN model of Madhupur Sal Forests, Bangladesh

Cited by 9 publications

References 51 publications

Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan

Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan

Un-planned Urban Growth Monitoring from 1991 to 2021 of Aizawl City, North-east India by Multi- Temporal Changes and CA-ANN Model

Estimation of Aboveground Forest Biomass and Carbon Storage of Bangladesh

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