Multispectral satellite imagery and machine learning for the extraction of shoreline indicators
Abstract Analysis of shoreline change is fundamental to a broad range of investigations
undertaken by coastal scientists, coastal engineers, and coastal managers. Multispectral …
undertaken by coastal scientists, coastal engineers, and coastal managers. Multispectral …
[PDF][PDF] A literature survey on smart cities
CT Yin, Z Xiong, H Chen, JY Wang… - Science China …, 2015 - researchgate.net
Rapid urbanization creates new challenges and issues, and the smart city concept offers
opportunities to rise to these challenges, solve urban problems and provide citizens with a …
opportunities to rise to these challenges, solve urban problems and provide citizens with a …
A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future …
Classification of remotely sensed imagery for land-cover mapping purposes has attracted
significant attention from researchers and practitioners. Numerous studies conducted over …
significant attention from researchers and practitioners. Numerous studies conducted over …
Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification
C A. Ramezan, T A. Warner, A E. Maxwell - Remote Sensing, 2019 - mdpi.com
High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to
map land covers over large geographic areas using supervised machine learning …
map land covers over large geographic areas using supervised machine learning …
Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery
Crop aboveground biomass (AGB) is one of the most important indicators in crop breeding
and crop management, and can be used for crop yield prediction. A number of vegetation …
and crop management, and can be used for crop yield prediction. A number of vegetation …
A review of landcover classification with very-high resolution remotely sensed optical images—Analysis unit, model scalability and transferability
As an important application in remote sensing, landcover classification remains one of the
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …
A survey of image classification methods and techniques for improving classification performance
Image classification is a complex process that may be affected by many factors. This paper
examines current practices, problems, and prospects of image classification. The emphasis …
examines current practices, problems, and prospects of image classification. The emphasis …
Object-based crop identification using multiple vegetation indices, textural features and crop phenology
JM Peña-Barragán, MK Ngugi, RE Plant… - Remote Sensing of …, 2011 - Elsevier
Crop identification on specific parcels and the assessment of soil management practices are
important for agro-ecological studies, greenhouse gas modeling, and agrarian policy …
important for agro-ecological studies, greenhouse gas modeling, and agrarian policy …
Estimating aboveground biomass using Sentinel-2 MSI data and ensemble algorithms for grassland in the Shengjin Lake Wetland, China
C Li, L Zhou, W Xu - Remote Sensing, 2021 - mdpi.com
Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem
health and is critical for water purification, carbon cycle, and biodiversity conservation …
health and is critical for water purification, carbon cycle, and biodiversity conservation …
The potential and challenge of remote sensing‐based biomass estimation
D Lu - International journal of remote sensing, 2006 - Taylor & Francis
Remotely sensed data have become the primary source for biomass estimation. A summary
of previous research on remote sensing‐based biomass estimation approaches and a …
of previous research on remote sensing‐based biomass estimation approaches and a …