Monitoring and mapping vegetation cover changes in arid and semi-arid areas using remote sensing technology: a review
Vegetation cover change is one of the key indicators used for monitoring environmental
quality. It can accurately reflect changes in hydrology, climate, and human activities …
quality. It can accurately reflect changes in hydrology, climate, and human activities …
Next generation of global land cover characterization, mapping, and monitoring
C Giri, B Pengra, J Long, TR Loveland - International Journal of Applied …, 2013 - Elsevier
Land cover change is increasingly affecting the biophysics, biogeochemistry, and
biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to …
biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to …
[HTML][HTML] Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity
Vegetation productivity is a critical indicator of global ecosystem health and is impacted by
human activities and climate change. A wide range of optical sensing platforms, from ground …
human activities and climate change. A wide range of optical sensing platforms, from ground …
In-memory parallel processing of massive remotely sensed data using an apache spark on hadoop yarn model
W Huang, L Meng, D Zhang… - IEEE Journal of Selected …, 2016 - ieeexplore.ieee.org
MapReduce has been widely used in Hadoop for parallel processing larger-scale data for
the last decade. However, remote-sensing (RS) algorithms based on the programming …
the last decade. However, remote-sensing (RS) algorithms based on the programming …
An automated cropland classification algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data
PS Thenkabail, Z Wu - Remote Sensing, 2012 - mdpi.com
The overarching goal of this research was to develop and demonstrate an automated
Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify …
Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify …
Towards a polyalgorithm for land use change detection
One way of analyzing satellite images for land use and land cover change (LULCC) is time
series analysis (TSA). Most of the many TSA based LULCC algorithms proposed in the …
series analysis (TSA). Most of the many TSA based LULCC algorithms proposed in the …
Riesz-Quincunx-UNet Variational Auto-Encoder for Unsupervised Satellite Image Denoising
Multiresolution deep learning approaches, such as the UNet architecture, have achieved
high performance in classifying and segmenting images. Most traditional convolutional …
high performance in classifying and segmenting images. Most traditional convolutional …
[HTML][HTML] Combining machine learning, space-time cloud restoration and phenology for farm-level wheat yield prediction
AA Tesfaye, D Osgood, BG Aweke - Artificial Intelligence in Agriculture, 2021 - Elsevier
Though studies showed the potential of high-resolution optical sensors for crop yield
prediction, several factors have limited their wider application. The main factors are …
prediction, several factors have limited their wider application. The main factors are …
Geo-ICDTs: Principles and applications in agriculture
Geographical information, communication and dissemination technologies (Geo-ICDTs) is
an innovative initiative that integrates state-of-the-art technologies for geospatial information …
an innovative initiative that integrates state-of-the-art technologies for geospatial information …
Parallel relative radiometric normalisation for remote sensing image mosaics
C Chen, Z Chen, M Li, Y Liu, L Cheng, Y Ren - Computers & Geosciences, 2014 - Elsevier
Relative radiometric normalisation (RRN) is a vital step to achieve radiometric consistency
among remote sensing images. Geo-analysis over large areas often involves mosaicking …
among remote sensing images. Geo-analysis over large areas often involves mosaicking …