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Near real-time monitoring of cassava cultivation area

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Title Near real-time monitoring of cassava cultivation area
 
Creator Phan, Trong Van
Reymondin, Louis
Vantalon, Thibaud
Delaquis, Erik
Nguyen, Thuy Thanh
Mienmany, Bandit
 
Subject cassava
forest cover
remote sensing
machine learning
conservación de la naturaleza
satélites de observación terrestre
mandioca-yuca
 
Description Remote sensing technologies and deep learning/machine learning approaches play valuable roles in crop inventory, yield estimation, cultivated area estimation, and crop status monitoring. Satellite-based remote sensing has led to increased spatial and temporal resolution, leading to a better quality of land-cover mapping (greater precision, and detail in the number of land cover classes). In this work, we propose to use a long short-term memory neural network (LSTM), an advanced technical model adapted from artificial neural networks (ANN) to estimate cassava cultivation area in southern Laos. LSTM is a modified version of a Recurrent Neural Network (RNN) that uses internal memory to store the information received prior to a given time. This property of LSTMs makes them advantageous for time series regression. We employ Landsat-7/8 and Sentinel-2 time-series datasets and crop phenology information to identify and classify cassava fields using multi-sources remote sensing time-series in a highly fragmented landscape. The results indicate an overall accuracy of > 89% for cassava and > 84% for all-class (barren, bush/grassland, cassava, coffee, forest, seasonal, and water) validating the feasibility of the proposed method. This study demonstrates the
potential of LSTM approaches for crop classification using multi-temporal, multi-sources remote sensing time series.
 
Date 2022-11-26
2023-01-18T09:45:28Z
2023-01-18T09:45:28Z
 
Type Conference Paper
 
Identifier Phan, T.V.; Reymondin, L.; Vantalon, T.; Delaquis, E.; Nguyen, T.T.; Mienmany, B. (2022) Near real-time monitoring of cassava cultivation area. Asian Federation for Information Technology in Agriculture (AFITA) conference 2022, 6th edition: Promoting Smart Technologies for Sustainable Agriculture. 9 p.
https://hdl.handle.net/10568/127367
 
Language en
 
Rights CC-BY-NC-SA-3.0
Open Access
 
Format 9 p.
application/pdf