Remote sensing and machine learning tools to support wetland monitoring: a meta-analysis of three decades of research

H Jafarzadeh, M Mahdianpari, EW Gill, B Brisco… - Remote Sensing, 2022 - mdpi.com
Despite their importance to ecosystem services, wetlands are threatened by pollution and
development. Over the last few decades, a growing number of wetland studies employed …

Machine learning-based spatial-temporal assessment and change transition analysis of wetlands: An application of Google Earth Engine in Sylhet, Bangladesh (1985 …

M Waleed, M Sajjad, MS Shazil, M Tariq, MT Alam - Ecological Informatics, 2023 - Elsevier
Wetlands are crucial ecosystems as they enhance the quality of groundwater, protect from
natural hazards, control erosion, and provide habitat to rare species of flora and fauna …

Novel hybrid models to enhance the efficiency of groundwater potentiality model

S Talukdar, J Mallick, SK Sarkar, SK Roy… - Applied Water …, 2022 - Springer
The present study aimed to create novel hybrid models to produce groundwater potentiality
models (GWP) in the Teesta River basin of Bangladesh. Six ensemble machine learning …

Evaluation and mapping of predicted future land use changes using hybrid models in a coastal area

H Ahmad, M Abdallah, F Jose, HE Elzain… - Ecological …, 2023 - Elsevier
Future prediction modeling of land use/land cover (LULC) is crucial for coastal regions due
to unique challenges and vulnerabilities associated with these areas. This research aims to …

Probabilistic coastal wetland mapping with integration of optical, SAR and hydro-geomorphic data through stacking ensemble machine learning model

P Prasad, VJ Loveson, M Kotha - Ecological Informatics, 2023 - Elsevier
The present study focuses on preparing the wetland map using earth observation data and
applying a novel ensemble model. Eight advanced machine learning algorithms were …

[HTML][HTML] Citrus orchard mapping in Juybar, Iran: Analysis of NDVI time series and feature fusion of multi-source satellite imageries

A Toosi, FD Javan, F Samadzadegan, S Mehravar… - Ecological …, 2022 - Elsevier
Nowadays crop mapping as an interdisciplinary hot topic attracted both agriculture and
remote sensing researchers' interests. This study proposed an automatic method to map …

Effects of multi-growth periods UAV images on classifying karst wetland vegetation communities using object-based optimization stacking algorithm

Y Zhang, B Fu, X Sun, H Yao, S Zhang, Y Wu… - Remote Sensing, 2023 - mdpi.com
Combining machine learning algorithms with multi-temporal remote sensing data for fine
classification of wetland vegetation has received wide attention from researchers. However …

Mapping small inland wetlands in the South-Kivu province by integrating optical and SAR data with statistical models for accurate distribution assessment

CB Géant, MN Gustave, S Schmitz - Scientific Reports, 2023 - nature.com
There are several techniques for mapping wetlands. In this study, we examined four
statistical models to assess the potential distribution of wetlands in the South-Kivu province …

[HTML][HTML] Mapping and classification of Liao River Delta coastal wetland based on time series and multi-source GaoFen images using stacking ensemble model

H Qian, N Bao, D Meng, B Zhou, H Lei, H Li - Ecological Informatics, 2024 - Elsevier
The precise mapping of coastal wetlands holds great significance for monitoring carbon
sequestration and storage within coastal ecosystems, particularly in light of climate change …

Comparative evaluation of operational land imager sensor on board landsat 8 and landsat 9 for land use land cover mapping over a heterogeneous landscape

Shahfahad, S Talukdar, MW Naikoo… - Geocarto …, 2023 - Taylor & Francis
Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in
sensor characteristics, which have significantly improved accuracy. In this study, a …