Classification of land use/land cover using artificial intelligence (ANN-RF)

Alshari, Eman A.; Abdulkareem, Mohammed Basil; Gawali, Bharti W.

doi:10.3389/frai.2022.964279

Cited by 16 publications

(7 citation statements)

References 88 publications

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“…LULC data are necessary for some planning and administration activities, and it is a critical component for illustrating and comprehending the earth as a system. It also plays an essential role in earth-atmosphere interactions [45].…”

Section: Applications For Land Observation Satellites 41 Lulc Change ...mentioning

confidence: 99%

Artificial Intelligence Techniques for Observation of Earth’s Changes

Alshari¹,

Gawali²

2023

Satellite Altimetry - Theory, Applications and Recent Advances

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This chapter discusses the primary components that contribute to the observation of Earth’s changes, including Land Observation Satellites, land classification techniques and their stages of development, and Machine Learning Techniques. It will give a comprehensive summary of the development stages of high-resolution satellites. It also details land classification with artificial intelligence algorithms. It will also give knowledge of classification methodologies from various Fundamentals of Machine Learning Classifiers: Pixel-based (PB), Sub-pixel-based (SPB), Object-based (OB), Knowledge-based (KB), Rule-based (RB), Distance-based (DB), Neural-based (NB), Parameter Based (PB), object-based image analysis (OBIA). It includes several different classifiers for LULC Classification. This chapter will include two applications for land observation satellites: The first is land use and land cover change observation with a practical example (study land use and land cover classification for Sana’a of Yemen as a case study from 1980 to 2020). The second application is satellite altimetry monitoring changes in mean sea level. The most significant contributions of it are the integration of these components. This chapter will be crucial in helping future researchers comprehend this topic. It will aid them in selecting the most appropriate and effective satellites for monitoring Earth’s changes and the most efficient classifier for their research.

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Section: Applications For Land Observation Satellites 41 Lulc Change ...mentioning

confidence: 99%

Artificial Intelligence Techniques for Observation of Earth’s Changes

Alshari¹,

Gawali²

2023

Satellite Altimetry - Theory, Applications and Recent Advances

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“…Recently, machine learning techniques have been effectively applied to improve the accuracy of land cover/land use classification (Talukdar et al, 2020). Machine learning algorithms are commonly used from multispectral satellite imagery from multispectral satellite imagery, including Random Forest -RF (Gislason et al, 2006;Kumar & Sinha, 2020), Support Vector Machine -SVM (Basheer et al, 2022;Huang & Song, 2016), Classification and Regression Tree -CART (Nguyen, 2020;Yang & Li, 2013), Artificial Neural Network -ANN (Alshari et al, 2023). The results obtained in these studies show that machine learning techniques help significantly improve the accuracy in forest cover classification compared to traditional classification methods.…”

Section: Introductionmentioning

confidence: 99%

Assessing forest cover changes in Dak Lak province (Central Highlands of Vietnam) from multi-temporal Landsat data and machine learning techniques

Tran,

Trinh,

Vambol

et al. 2024

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Dak Lak is a province in the Central Highlands region of Vietnam, with a large area of forests and forestry land. However, the forest cover has changed dramatically in recent times due to the influence of human activities and climate change. This article presents the results of assessing forest cover changes in Dak Lak province from Landsat satellite image data for the period 2000 – 2020. Three Landsat satellite image scenes, including Landsat 5 TM images taken in March 2000 and February 2010 and Landsat 8 OLI image taken in February 2020 are used to classify forest cover. Three common machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART) and the traditional maximum likelihood classification algorithm are used to classify forest cover in the study area, thereby choosing the method with the highest accuracy. The results show that the RF algorithm has the highest accuracy in classifying forest cover from multi-temporal Landsat images by comparing the overall accuracy value and the Kappa coefficient. The obtained results are used to build forest cover change maps in the period 2000 - 2010, 2010 - 2020 and 2000 - 2020. The results received in the study provide information to help managers in monitoring and protecting forest resources.

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“…Other studies have demonstrated the effectiveness of machine learning (ML) algorithms in accurately classifying remotely sensed data for quantifying woodland cover over spatially extensive regions (Pal, 2005;Rogan et al, 2008;Camargo et al, 2019;Talukdar et al, 2020;Alshari et al, 2023). There has been a recent increase in interest in the utilization of sophisticated machine learning (ML) algorithms, including support vector machines , with a spatial resolution of cm.…”

Section: Introductionmentioning

confidence: 99%

Examining changes in woody vegetation cover in a human-modified temperate savanna in Central Texas between 1996 and 2022 using remote sensing

Olariu,

Wilcox,

Popescu

2024

Front. For. Glob. Change

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Savanna ecosystems across the globe have experienced substantial changes in their vegetation composition. These changes can be attributed to three main processes: (1) encroachment, which refers to the expansion of woody plants into open areas, (2) thicketization, which is characterized by the growth of sub-canopy woody plants, and (3) disturbance, defined here as the removal of woodland cover due to both natural forces and human activities. In this study, we utilized Landsat surface reflectance data and Sentinel-1 SAR data to track the progression of these process from 1996 to 2022 in the significantly modified Post Oak Savannah ecoregion of Central Texas. Our methodology employs an ensemble classification algorithm, which combines the results of multiple models, to develop a more precise predictive model, along with the spectral–temporal segmentation algorithm LandTrendr in Google Engine (GEE). Our ensemble classification algorithms demonstrated high overall accuracies of 94.3 and 96.5% for 1996 and 2022, respectively, while our LandTrendr vegetation map exhibited an overall accuracy of 80.4%. The findings of our study reveal that 9.7% of the overall area experienced encroachment of woody plants into open area, while an additional 6.8% of the overall area has transitioned into a thicketized state due to the growth of sub-canopy woody plants. Furthermore, 5.7% of the overall area encountered woodland disturbance leading to open areas. Our findings suggest that these processes advanced unevenly throughout the region, resulting in the coexistence of three prominent plant communities that appear to have long-term stability: a dense deciduous shrubland in the southern region, as well as a thicketized oak woodland and open area mosaic in the central and northern regions. The successional divergence observed in these plant communities attests to the substantial influence of human modification on the landscape. This study demonstrates the potential of integrating passive optical multispectral data and active SAR data to accurately map large-scale ecological processes.

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Classification of land use/land cover using artificial intelligence (ANN-RF)

Cited by 16 publications

References 88 publications

Artificial Intelligence Techniques for Observation of Earth’s Changes

Artificial Intelligence Techniques for Observation of Earth’s Changes

Assessing forest cover changes in Dak Lak province (Central Highlands of Vietnam) from multi-temporal Landsat data and machine learning techniques

Examining changes in woody vegetation cover in a human-modified temperate savanna in Central Texas between 1996 and 2022 using remote sensing

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