Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia

Aryal, Jagannath; Sitaula, Chiranjibi; Frery, Alejandro C.

doi:10.1038/s41598-023-40564-0

Cited by 37 publications

(9 citation statements)

References 41 publications

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“…Classification of space images is a rather complex process, the methodology of which is constantly being improved [93][94][95], and the division into the number of classes depends on the predictors that prevail in the space under study [96,97]. Considering the results of these studies, we used one of the most reliable and, therefore, widely used algorithms-Random Forest-to classify Landsat satellite images.…”

Section: Discussionmentioning

confidence: 99%

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%

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 results underline the complex interplay between urban development and environmental changes in the region. In this regard, Aryal et al [48] indicated that the results could vary depending on the geographical characteristics of the study area. Although several factors influence urbanization, one of those detected in our research is migration, which played a crucial role in determining the Gelephu's LULC dynamics.…”

Section: Ndvi Change Detection and Statisticsmentioning

confidence: 99%

Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

Tempa,

Ilunga,

Agarwal

et al. 2024

Applied Sciences

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Gelephu, located in the Himalayan region, has undergone significant development activities due to its suitable topography and geographic location. This has led to rapid urbanization in recent years. Assessing land use land cover (LULC) dynamics and Normalized Difference Vegetation Index (NDVI) can provide important information about urbanization trends and changes in vegetation health, respectively. The use of Geographic Information Systems (GIS) and Remote Sensing (RS) techniques based on various satellite products offers a unique opportunity to analyze these changes at a local scale. Exploring Bhutan’s mandate to maintain 60% forest cover and analyzing LULC transitions and vegetation changes using Sentinel-2 satellite imagery at 10 m resolution can provide important insights into potential future impacts. To examine these, we first performed LULC mapping for Gelephu for 2016 and 2023 using a Random Forest (RF) classifier and identified LULC changes. Second, the study assessed the dynamics of vegetation change within the study area by analysing the NDVI for the same period. Furthermore, the study also characterized the resulting LULC change for Gelephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentinel-2 satellite data, which was then used for pre-and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Gelephu and Gelephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Gelephu between 2016 and 2023. The results serve as a basis for strategy adaption required to examine the environmental protection and sustainable development management, and the policy interventions to minimize and balance the ecosystem, taking into account urban landscape.

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“…6). Comparing these data points with the study area's current LULC facilitates the validation of the classi ed results (Aryal et al 2023). The confusion matrix is critical in analyzing key metrics based on reference or ground truth data, such as overall accuracy, producer accuracy, user accuracy, and the Kappa Coe cient of the classi ed maps.…”

Section: Post-classi Cationmentioning

confidence: 99%

Evaluation of SVM and RF Machine Learning Algorithms in Land Use/Land Cover Change Assessment: Tessa Watershed Case Study (Northwest of Tunisia)

BACCARI,

HAMZA,

SLAMA

et al. 2024

Preprint

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This study aims to highlight the changes in LULC (land use and Land cover) in the Tessa watershed (Northwest of Tunisia) between 1993 and 2023. Remote sensing coupled with geographic information systems (GIS) and statistical analysis, are used. Accuracy metrics make it possible to evaluate the performance of the image classification method, using the calculation of the producer’s accuracy, the user’s accuracy, overall accuracy, and the Kappa coefficient. Two Machine Learning (ML) algorithms related to the supervised classification are used for two Landsat images related to 1993 and 2023: the Support Vector Machine (SVM) and the Random Forest (RF). These algorithms are integrated into the SCP plugin of the QGIS software used in this study. The overall accuracy achieved by applying the SVM algorithm to the Landsat 5 TM image from 1993 is 88.24% with a Kappa value of 0.8, whereas the overall accuracy obtained for the Landsat 8 OLI image from 2023 is 99.4% with a Kappa value of 0.99. By applying the RF algorithm, the overall accuracy obtained for the 1993 Landsat 5 TM image is 86% with a Kappa value of 0.8, while for the 2023 Landsat 8 OLI image, the overall accuracy obtained is 81% with a Kappa value of 0.77. Using the transition matrix, it was possible to detect LULC changes spatiotemporally. A comparison of the classification results obtained from SVM and RF algorithms with ground truth showed that the SVM classifier was more accurate in the study area.

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Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia

Cited by 37 publications

References 41 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

Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

Evaluation of SVM and RF Machine Learning Algorithms in Land Use/Land Cover Change Assessment: Tessa Watershed Case Study (Northwest of Tunisia)

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