AIMC Topic: Remote Sensing Technology

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Enhanced hermit crabs detection using super-resolution reconstruction and improved YOLOv8 on UAV-captured imagery.

Marine environmental research
Hermit crabs are vital to coastal ecosystems, serving as environmental health indicators and contributing to seed dispersal, debris cleanup, and soil disturbance. Traditional hermit crabs survey methods, like quadrat sampling, are labor-intensive and...

Tracking the spatial and temporal evolution of salt marsh vegetation based on UAV sampling and seasonal phenology from Landsat data.

Journal of environmental management
Salt marshes, valued for their ecological importance, have been increasingly degraded in recent decades. Preserving salt marshes necessitates a critical approach that involves monitoring vegetation distribution and species composition. This study pre...

Soil moisture mapping in Indian tropical islands with C-band SAR and artificial neural network models.

Environmental monitoring and assessment
This study aims at analyzing the patterns of soil moisture in the South Andaman district using an integrated approach that incorporates Sentinel-1A C-band synthetic aperture radar (SAR) data and other auxiliary data from Sentinel-2A and Landsat 8. A ...

Enhanced wheat yield prediction through integrated climate and satellite data using advanced AI techniques.

Scientific reports
Wheat plays a vital role in Pakistan's economy and food security, making accurate yield forecasting essential for planning and resource management. Traditional approaches-such as manual field surveys and remote sensing-have been widely used, but thei...

Remote sensing-based detection of brown spot needle blight: a comprehensive review, and future directions.

PeerJ
Pine forests are increasingly threatened by needle diseases, including Brown Spot Needle Blight (BSNB), caused by . BSNB leads to needle loss, reduced growth, significant tree mortality, and disruptions in global timber production. Due to its severit...

Modeling climate change impacts and predicting future vulnerability in the Mount Kenya forest ecosystem using remote sensing and machine learning.

Environmental monitoring and assessment
The Mount Kenya forest ecosystem (MKFE), a crucial biodiversity hotspot and one of Kenya's key water towers, is increasingly threatened by climate change, putting its ecological integrity and vital ecosystem services at risk. Understanding the intera...

Development of deep learning quantization framework for remote sensing edge device to estimate inland water quality in South Korea.

Water research
Recent achievements in the fields of deep learning and remote sensing have led to their application in monitoring river water quality. One of the most researched methods is the estimation of total suspended solid (TSS) concentrations using multispect...

Improved integrated framework for flooded crop damage and recovery assessment: A multi-source earth observation and participatory mapping in Hadejia, Nigeria.

Journal of environmental management
Flooding has increasingly significant adverse effects on global food security, and there is a lack of a framework to effectively integrate remote sensing with survey data for accurate damage and recovery assessment. Also, optical satellite images for...

Inversion of lake transparency using remote sensing and deep hybrid recurrent models.

Ecotoxicology and environmental safety
Utilizing computer technology and remote sensing data, the extraction of water-related features of lakes has become a hot topic in lake ecological research. Addressing challenges like the high optical complexity of lake water bodies, the inadequacy o...

Estimating soil cadmium concentration using multi-source UAV imagery and machine learning techniques.

Environmental monitoring and assessment
Urbanization and industrialization have led to widespread soil heavy metals contamination, posing significant risks to ecosystems and human health. Conventional methods for mapping heavy metal distribution, which rely on soil sampling followed by che...