Land-use land-cover classification by machine learning classifiers for satellite observations—A review
Rapid and uncontrolled population growth along with economic and industrial development,
especially in develo** countries during the late twentieth and early twenty-first centuries …
especially in develo** countries during the late twentieth and early twenty-first centuries …
Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review
Spatio-temporal land-use change modeling, simulation, and prediction have become one of
the critical issues in the last three decades due to uncertainty, structure, flexibility, accuracy …
the critical issues in the last three decades due to uncertainty, structure, flexibility, accuracy …
Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques
Accurate land use land cover (LULC) classification is vital for the sustainable management
of natural resources and to learn how the landscape is changing due to climate. For …
of natural resources and to learn how the landscape is changing due to climate. For …
[HTML][HTML] Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main …
Modeling land use land cover (LULC) change is crucial to understand its spatiotemporal
trends to protect the land resources sustainably. The appraisal of this study was to model …
trends to protect the land resources sustainably. The appraisal of this study was to model …
Detecting and analyzing land use and land cover changes in the region of Al-Jabal Al-Akhdar, Libya using time-series landsat data from 1985 to 2017
The region of Al-Jabal Al-Akhdar in northeastern Libya has undergone rapid, wide-ranging
changes in the land use and land cover (LULC) intensified by the conversion of natural …
changes in the land use and land cover (LULC) intensified by the conversion of natural …
RETRACTED ARTICLE: Multi-temporal image analysis for LULC classification and change detection
GN Vivekananda, R Swathi, A Sujith - European journal of remote …, 2021 - Taylor & Francis
Statement of Retraction We, the Editor and Publisher of the journal European Journal of
Remote Sensing, have retracted the following articles that were published in the Special …
Remote Sensing, have retracted the following articles that were published in the Special …
Modeling land use change using cellular automata and artificial neural network: The case of Chunati Wildlife Sanctuary, Bangladesh
Land use changes generally affect the integrity of an ecosystem. The effect of this change
can be very severe if the conversion disrupts a crucial habitat of major plants and animals …
can be very severe if the conversion disrupts a crucial habitat of major plants and animals …
Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process
Land use and land cover change (LULCC) has directly played an important role in the
observed climate change. In this paper, we considered Dujiangyan City and its environs …
observed climate change. In this paper, we considered Dujiangyan City and its environs …
Spatio-temporal patterns of land use/land cover change in the Bhutan–Bengal foothill region between 1987 and 2019: study towards geospatial applications and …
M Chamling, B Bera - Earth Systems and Environment, 2020 - Springer
Monitoring of land use and land cover (LULC) change is fundamental aspect of the
landscape dynamics or environmental health evaluation at different spatio-temporal scales …
landscape dynamics or environmental health evaluation at different spatio-temporal scales …
Analysis of the current and future prediction of land use/land cover change using remote sensing and the CA‐Markov model in Majang forest biosphere reserves of …
S Tadese, T Soromessa, T Bekele - The scientific world journal, 2021 - Wiley Online Library
This study aimed to evaluate land use/land cover changes (1987–2017), prediction (2032–
2047), and identify the drivers of Majang Forest Biosphere Reserves. Landsat image (TM …
2047), and identify the drivers of Majang Forest Biosphere Reserves. Landsat image (TM …