Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques

S Basheer, X Wang, AA Farooque, RA Nawaz, K Liu… - Remote Sensing, 2022 - mdpi.com
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 …

Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove map**

AB Baloloy, AC Blanco, RRCS Ana… - ISPRS Journal of …, 2020 - Elsevier
Abstract Advancement in Remote Sensing allows rapid mangrove map** without the need
for data-intensive methodologies, complex classifiers, and skill-dependent classification …

[HTML][HTML] Analysis of land use and land cover using machine learning algorithms on google earth engine for Munneru River Basin, India

KN Loukika, VR Keesara, V Sridhar - Sustainability, 2021 - mdpi.com
The growing human population accelerates alterations in land use and land cover (LULC)
over time, putting tremendous strain on natural resources. Monitoring and assessing LULC …

Modelling of land use land cover changes using machine learning and GIS techniques: a case study in El-Fayoum Governorate, Egypt

I Atef, W Ahmed, RH Abdel-Maguid - Environmental Monitoring and …, 2023 - Springer
Land use/land cover (LULC) changes can occur naturally or due to human activities. In this
study, the maximum likelihood algorithm (MLH) and machine learning (random forest …

Cloud approach to automated crop classification using Sentinel-1 imagery

A Shelestov, M Lavreniuk, V Vasiliev… - … Transactions on Big …, 2019 - ieeexplore.ieee.org
For accurate crop classification, it is necessary to use time-series of high-resolution satellite
data to better discriminate among certain crop types. This task brings the following …

Analysis of desertification trends in Central Asia based on MODIS Data using Google Earth Engine

I Aslanov, N Teshaev, K Khayitov… - E3S Web of …, 2023 - e3s-conferences.org
Desertification is a significant environmental issue affecting arid and semi-arid regions
globally, including Central Asia. Monitoring and analyzing desertification trends is crucial for …

[HTML][HTML] The google earth engine mangrove map** methodology (Geemmm)

JMM Yancho, TG Jones, SR Gandhi, C Ferster, A Lin… - Remote Sensing, 2020 - mdpi.com
Mangroves are found globally throughout tropical and sub-tropical inter-tidal coastlines.
These highly biodiverse and carbon-dense ecosystems have multi-faceted value, providing …

[PDF][PDF] Comparison of machine learning algorithms for land use and land cover analysis using Google Earth engine (Case study: Wanggu watershed)

S Aldiansyah, RA Saputra - … Journal of Remote Sensing and Earth …, 2023 - researchgate.net
Human population growth and land use and land cover (LULC) change have always
developed side by side. Considering selection of a good Machine Learning (ML) classifier …

Evaluating mangrove conservation and sustainability through spatiotemporal (1990–2020) mangrove cover change analysis in Pakistan

H Gilani, HI Naz, M Arshad, K Nazim, U Akram… - Estuarine, Coastal and …, 2021 - Elsevier
This study provides the first comprehensive mangrove cover change assessment from 1990
to 2020, at five-year intervals, across all five mangrove areas in Pakistan, ie Indus Delta …

[HTML][HTML] Spatiotemporal assessment of deforestation and forest degradation indicates spillover effects from mining activities and related biodiversity offsets in …

S Eckert, L Schmid, P Messerli… - … Applications: Society and …, 2024 - Elsevier
Mining has severe environmental and social impacts. To compensate for the environmental
damage caused at mining sites, mining companies are required to engage in biodiversity …