Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation
Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness...
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my.uniten.dspace-362262025-03-03T15:41:37Z Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation Pande C.B. Srivastava A. Moharir K.N. Radwan N. Mohd Sidek L. Alshehri F. Pal S.C. Tolche A.D. Zhran M. 57193547008 57221943932 57193546415 56763877500 58617132200 57224683617 57208776491 57198446685 57553459500 India Maharashtra Classification (of information) Engines Forestry Image enhancement Land use Machine learning Satellite imagery Vegetation mapping Change detection Energy Google earth engine Google earths Land cover maps Land use and land cover Land-use and land-cover classifications Random forests Remote-sensing SDG Anthropocene cloud cover data set detection method image classification land cover land use change machine learning remote sensing satellite imagery Sustainable Development Goal Remote sensing Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30�m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of�Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes. ? The Author(s) 2024. Final 2025-03-03T07:41:37Z 2025-03-03T07:41:37Z 2024 Article 10.1186/s12302-024-00901-0 2-s2.0-85191348820 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191348820&doi=10.1186%2fs12302-024-00901-0&partnerID=40&md5=15694a845ecc055096119507fec3f391 https://irepository.uniten.edu.my/handle/123456789/36226 36 1 84 Springer Scopus |
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India Maharashtra Classification (of information) Engines Forestry Image enhancement Land use Machine learning Satellite imagery Vegetation mapping Change detection Energy Google earth engine Google earths Land cover maps Land use and land cover Land-use and land-cover classifications Random forests Remote-sensing SDG Anthropocene cloud cover data set detection method image classification land cover land use change machine learning remote sensing satellite imagery Sustainable Development Goal Remote sensing |
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India Maharashtra Classification (of information) Engines Forestry Image enhancement Land use Machine learning Satellite imagery Vegetation mapping Change detection Energy Google earth engine Google earths Land cover maps Land use and land cover Land-use and land-cover classifications Random forests Remote-sensing SDG Anthropocene cloud cover data set detection method image classification land cover land use change machine learning remote sensing satellite imagery Sustainable Development Goal Remote sensing Pande C.B. Srivastava A. Moharir K.N. Radwan N. Mohd Sidek L. Alshehri F. Pal S.C. Tolche A.D. Zhran M. Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation |
| description |
Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30�m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of�Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes. ? The Author(s) 2024. |
| author2 |
57193547008 |
| author_facet |
57193547008 Pande C.B. Srivastava A. Moharir K.N. Radwan N. Mohd Sidek L. Alshehri F. Pal S.C. Tolche A.D. Zhran M. |
| format |
Article |
| author |
Pande C.B. Srivastava A. Moharir K.N. Radwan N. Mohd Sidek L. Alshehri F. Pal S.C. Tolche A.D. Zhran M. |
| author_sort |
Pande C.B. |
| title |
Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation |
| title_short |
Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation |
| title_full |
Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation |
| title_fullStr |
Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation |
| title_full_unstemmed |
Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation |
| title_sort |
characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a google earth engine implementation |
| publisher |
Springer |
| publishDate |
2025 |
| _version_ |
1825816056031084544 |
| score |
13.244413 |