International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
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Advances in Deforestation Detection: A Review of Forest Image Analysis Techniques Mr. Saurabh Miri, Mr. Ashish Tamrakar Student, Dept. of Computer Science & Engineering, Rungta College, Raipur, India Associate Professor, Dept of Computer Science & Engineering, Rungta College, Raipur, India -------------------------------------------------------------------------***-----------------------------------------------------------------------In order to detect changes in wooded areas, forest image Abstract - Global ecosystems are seriously threatened by
analysis include processing and analyzing satellite pictures, aerial photos, and drone-captured images. These photos offer important information on changes in land use, canopy density, and vegetation health. Researchers can identify illicit logging activities, categorize forest and nonforest regions, and track forest regeneration initiatives by using complex algorithms.
deforestation, which also contributes to environmental degradation, biodiversity loss, and climate change. Mitigating these effects requires effective forest cover monitoring, and deforestation detection has been transformed by recent developments in forest image processing. This study examines the most recent methods used in the analysis of forest images, with an emphasis on change detection algorithms, artificial intelligence (AI), and remote sensing technologies. It emphasizes how deep learning methods, such Convolutional Neural Networks (CNNs), can evaluate data from satellites and drones to accurately identify changes in forest cover.
2.REVIEW OF LITERATURE 2.1. Analyzing NASA Satellite image dataset to track forest cover change over one year. Kumar, R. R., Rani, N., Kajale, H. V., & Kaur, D. (2024). Indian Scientific Journal Of Research In Engineering And Management
Key Words: Deforestation detection , Forest image analysis, Remote sensing, Artificial intelligence (AI),Convolutional Neural Networks (CNNs) ,Satellite imagery.
The purpose of this research article is to monitor and comprehend changes in forest cover over the past year by analyzing the NASA satellite picture collection. A key environmental indicator, changes in forest cover have an impact on ecosystem services, biodiversity, and climate control. It draws attention to how well segmentation approaches work in a variety of forestry applications, including individual tree monitoring and accurate tree species identification. Improving ecological monitoring and forest inventory procedures depends on this contribution The review discusses the difficulties encountered when putting various segmentation strategies into practice, such as occlusions, overlapping branches, and inconsistent data quality. The report lays the groundwork for future research targeted at solving these challenges by recognizing them. Common machine learning techniques used in research concerning land cover classification and remote sensing because of its resilience and capacity to manage big datasets, Random Forest is frequently Utilized.
I.INTRODUCTION One of the most urgent environmental problems of the twenty-first century is deforestation, or the extensive removal of forest cover. Because they regulate the climate, protect biodiversity, and act as carbon sinks, forests are essential to sustaining ecological equilibrium. However, the world's forest loss has been greatly accelerated by logging, infrastructural development, urbanization, and agricultural expansion. The Food and Agriculture Organization (FAO) has recently reported that the world loses almost 10 million hectares of forest each year, which significantly contributes to climate change and global carbon emissions. Accurate, fast, and scalable techniques for identifying changes in forest cover are necessary for deforestation monitoring and mitigation. Even if they are dependable, traditional techniques like field surveys and manual mapping are frequently time-consuming, labor-intensive, and have limited spatial coverage. Because of this, there has been an increasing trend toward automated and semiautomatic methods that take advantage of developments in artificial intelligence (AI), geographic information systems (GIS), and remote sensing technologies. One of the most effective methods for identifying, measuring, and reporting deforestation is the study of forest images.
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Validation can also be accomplished by using statistical tests to examine the results' significance. This aids in determining if the changes that have been noticed are statistically significant or if they could have happened by accident. Using this method, the dataset is divided into subgroups, and the model is trained on some of these subsets while being validated on others. Evaluation of the results' generalizability to a different dataset is aided by cross-validation.
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