Next Article in Journal
Monitoring Tamarix Changes Using WorldView-2 Satellite Imagery in Grand Canyon National Park, Arizona
Next Article in Special Issue
Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine
Previous Article in Journal
Vegetation Productivity Dynamics in Response to Climate Change and Human Activities under Different Topography and Land Cover in Northeast China
Previous Article in Special Issue
Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Remote Sensing
Volume 13
Issue 5
10.3390/rs13050974
remotesensing-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

by
Lorena Alves Santos
1,*,
Karine Ferreira
1,
Michelle Picoli
1,
Gilberto Camara
1,
Raul Zurita-Milla
2 and
Ellen-Wien Augustijn
2
1
Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), Avenida dos Astronautas, 1758, Jardim da Granja, Sao Jose dos Campos, SP 12227-010, Brazil
2
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Lluís Pesquer Mayos
Remote Sens. 2021, 13(5), 974; https://doi.org/10.3390/rs13050974
Received: 30 December 2020 / Revised: 22 February 2021 / Accepted: 25 February 2021 / Published: 4 March 2021
(This article belongs to the Special Issue Geospatial Statistics and Spatiotemporal Analysis Based on Remote Sensing Imagery)
View Full-Text Download PDF
Browse Figures

Abstract

The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types. View Full-Text
Keywords: data training; time series; clustering; spatiotemporal patterns data training; time series; clustering; spatiotemporal patterns
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
SciFeed

Share and Cite

MDPI and ACS Style

Santos, L.A.; Ferreira, K.; Picoli, M.; Camara, G.; Zurita-Milla, R.; Augustijn, E.-W. Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series. Remote Sens. 2021, 13, 974. https://doi.org/10.3390/rs13050974

AMA Style

Santos LA, Ferreira K, Picoli M, Camara G, Zurita-Milla R, Augustijn E-W. Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series. Remote Sensing. 2021; 13(5):974. https://doi.org/10.3390/rs13050974

Chicago/Turabian Style

Santos, Lorena A., Karine Ferreira, Michelle Picoli, Gilberto Camara, Raul Zurita-Milla, and Ellen-Wien Augustijn. 2021. "Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series" Remote Sensing 13, no. 5: 974. https://doi.org/10.3390/rs13050974

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop