AIMC Topic: Satellite Imagery
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Remote sensing estimation of aboveground biomass of different forest types in Xinjiang based on machine learning.
Scientific reports
Feb 20, 2025
Forest aboveground biomass (AGB) is a key indicator reflecting the function and quality of forest ecosystems, and accurate large-scale estimations of forest AGB are essential for effective forest ecosystem management. However, owing to limitations in...
Integration of remote sensing and machine learning algorithm for agricultural drought early warning over Genale Dawa river basin, Ethiopia.
Environmental monitoring and assessment
Feb 4, 2025
Drought remains a menace in the Horn of Africa; as a result, the Ethiopia's Genale Dawa River Basin is one of the most vulnerable to agricultural drought. Hence, this study integrates remote sensing and machine learning algorithm for early warning id...
Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images.
Scientific reports
Jan 21, 2025
Climate change and human activities affect the biomass of different algal and the succession of dominant species. In the past, phytoplankton phyla inversion has been focused on oceanic and continental shelf waters, while phytoplankton phyla inversion...
Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning.
PloS one
Jan 15, 2025
In the production sector, the usefulness of predictive systems as a tool for management and decision-making is well known. In the agricultural sector, a correct economic balance of the farm depends on making the right decisions. For this purpose, hav...
Assessing the efficiency of pixel-based and object-based image classification using deep learning in an agricultural Mediterranean plain.
Environmental monitoring and assessment
Jan 10, 2025
Recent advancements in satellite technology have greatly expanded data acquisition capabilities, making satellite imagery more accessible. Despite these strides, unlocking the full potential of satellite images necessitates efficient interpretation. ...
Aboveground biomass estimation in a grassland ecosystem using Sentinel-2 satellite imagery and machine learning algorithms.
Environmental monitoring and assessment
Jan 6, 2025
The grassland ecosystem forms a critical part of the natural ecosystem, covering up to 15-26% of the Earth's land surface. Grassland significantly impacts the carbon cycle and climate regulation by storing carbon dioxide. The organic matter found in ...
Extraction of agricultural plastic greenhouses based on a U-Net convolutional neural network coupled with edge expansion and loss function improvement.
Journal of the Air & Waste Management Association (1995)
Dec 12, 2024
Agricultural plastic greenhouses (APGs) are crucial for sustainable agricultural planting, and accurate spatial distribution information acquisition is crucial. Deep learning network models can extract target features from remote sensing images more ...
Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning.
PloS one
Dec 12, 2024
Timely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and climate change adaptation in rice-producing countries such as Bangladesh. To address this need, this study introduce...
Particulate matter estimation using satellite datasets: a machine learning approach.
Environmental science and pollution research international
Dec 3, 2024
In the present work, it is the first time an interpretable machine learning model has been developed for the estimation of Particulate Matter 10 µm (PM) concentrations over India using Aerosol Optical Depth (AOD) from two different satellites, i.e. I...
Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.
International journal of environmental research and public health
Nov 19, 2024
This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resolution satellite imagery (Sentinel-2). By applying a deep convolutional neural network (DCNN), the study...
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