AIMC Topic: Satellite Imagery

Clear Filters Showing 31 to 40 of 112 articles

Evaluating the performance of random forest, support vector machine, gradient tree boost, and CART for improved crop-type monitoring using greenest pixel composite in Google Earth Engine.

Environmental monitoring and assessment
The development of machine learning algorithms, along with high-resolution satellite datasets, aids in improved agriculture monitoring and mapping. Nevertheless, the use of high-resolution optical satellite datasets is usually constrained by clouds a...

Remote sensing estimation of aboveground biomass of different forest types in Xinjiang based on machine learning.

Scientific reports
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...

Pollution risk assessment in sub-basins of an open dump using drones and geographic information systems.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
The sustainable management of municipal solid waste (MSW) presents a pressing global challenge. This study introduces an innovative methodology for analysing open dumps in Tucumán, Argentina, using unmanned aerial vehicles (UAVs) and DroneDeploy soft...

Integration of remote sensing and machine learning algorithm for agricultural drought early warning over Genale Dawa river basin, Ethiopia.

Environmental monitoring and assessment
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
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
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
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
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)
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
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...