sits: Data Analysis and Machine Learning on Earth Observation Data Cubes with Satellite Image Time Series
Using time series derived from big Earth Observation data sets is one of the leading research trends in Land Use Science and Remote Sensing. One of the more promising uses of satellite time series is its application to classify land use and land cover since our growing demand for natural resources has caused significant environmental impacts.
This book presents sits, an open-source R package for land use and land cover change mapping using satellite image time series. The package uses machine learning techniques to classify image time series obtained from data cubes. Methods available include linear and quadratic discrimination analysis, support vector machines, random forests, boosting, deep learning, and convolutional neural networks. The package also provides functions for post-processing and sample quality assessment.
Who this book is for
The target audience for sits is the new generation of specialists who understand the principles of remote sensing and can write scripts in R. Ideally, users should have basic knowledge of data science methods using R. If you are new to R, we highly recommend the excellent self-learning book “Hands-On Programming with R” by Garrett Golemund from RStudio. If you want more information on the data science methods used in the book, please look at the following references:
Wickham, H.; Golemund, G., “R for Data Science”. O’Reilly, 2017.
James, G.; Witten, D.; Hastie, T.; Tibshirani, R. “An Introduction to Statistical Learning with Applications in R”. Springer, 2013.
Goodfellow, I; Bengio, Y.; Courville, A. “Deep Learning”. MIT Press, 2016.
How to use this book
This book describes sits version 0.13.1. Download and install the package as explained in the Setup. Start at Chapter 1 to get an overview of the package. Then feel free to browse the chapter for more information on topics you are interested in.
|Chr 1||Provides an overview of sits.|
|Chr 2||Describes how to work with Earth observation data cubes.|
|Chr 3||Describes how to access information from time series.|
|Chr 4||Improving the quality of the samples used in training models|
|Chr 5||Presents the machine learning techniques available in sits.|
|Chr 6||Describes how to classify satellite images associated with Earth observation data cubes.|
|Chr 7||Describes smoothing methods to reclassify the pixels based on the machine learning probabilities|
|Chr 8||Presents the validation and accuracy measures.|
|Chr 9||Presents case studies of LUCC classification.|
|Chr 10||How to develop extensions to sits.|
Main reference for sits
if you use sits, please cite the following paper:
Rolf Simoes, Gilberto Camara, Gilberto Queiroz, Felipe Souza, Pedro R. Andrade, Lorena Santos, Alexandre Carvalho, and Karine Ferreira. “Satellite Image Time Series Analysis for Big Earth Observation Data.” Remote Sensing, 13, p. 2428, 2021. https://doi.org/10.3390/rs13132428.
Publications using sits
This section gathers the publications that have used sits to generate the results.
Lorena Santos, Karine R. Ferreira, Gilberto Camara, Michelle Picoli, Rolf Simoes, “Quality control and class noise reduction of satellite image time series.” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 177, pp 75-88, 2021. https://doi.org/10.1016/j.isprsjprs.2021.04.014.
Lorena Santos, Karine Ferreira, Michelle Picoli, Gilberto Camara, Raul Zurita-Milla and Ellen-Wien Augustijn, “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.
Rolf Simoes, Michelle Picoli, Gilberto Camara, Adeline Maciel, Lorena Santos, Pedro Andrade, Alber Sánchez, Karine Ferreira & Alexandre Carvalho. “Land use and cover maps for Mato Grosso State in Brazil from 2001 to 2017.” Nature Scientific Data 7, article 34 (2020). DOI: 10.1038/s41597-020-0371-4.
Michelle Picoli, Ana Rorato, Pedro Leitão, Gilberto Camara, Adeline Maciel, Patrick Hostert, Ieda Sanches, “Impacts of Public and Private Sector Policies on Soybean and Pasture Expansion in Mato Grosso—Brazil from 2001 to 2017.” Land, 9(1), 2020. DOI: 10.3390/land9010020.
Karine Ferreira, Gilberto Queiroz et al., “Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products.” Remote Sensing, 12, 4033, 2020.
Adeline Maciel, Lubia Vinhas, Michelle Picoli and Gilberto Camara, “Identifying Land Use Change Trajectories in Brazil’s Agricultural Frontier.” Land, 9, 506, 2020. DOI: 10.3390/land9120506. DOI: 10.3390/rs12244033.
Alber Sanchez, Michelle Picoli, et al., “Land Cover Classifications of Clear-cut Deforestation Using Deep Learning.” In: SIMPÓSIO BRASILEIRO DE GEOINFORMÁTICA (GEOINFO), 2019, São José dos Campos. São José dos Campos: INPE, 2019. On-line.
Lorena Santos, Karine Ferreira, et al., “Self-Organizing Maps in Earth Observation Data Cubes Analysis.” 13th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM+ 2019), Barcelona, Spain, June 26-28, 2019.
- Michelle Picoli, Gilberto Camara, et al., “Big Earth Observation Time Series Analysis for Monitoring Brazilian Agriculture.” ISPRS Journal of Photogrammetry and Remote Sensing, 2018.
Reproducible papers used in building sits functions
We thank the authors of these papers for making their code available to be used in sits.
 Appel, Marius, and Edzer Pebesma, “On-Demand Processing of Data Cubes from Satellite Image Collections with the Gdalcubes Library.” Data 4 (3): 1–16, 2020.
 Hassan Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller, “Deep learning for time series classification: a review.” Data Mining and Knowledge Discovery, 33(4): 917–963, 2019.
 Pebesma, Edzer, “Simple Features for R: Standardized Support for Spatial Vector Data.” R Journal, 10(1):2018.
 Pelletier, Charlotte, Geoffrey I. Webb, and Francois Petitjean. “Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series.” Remote Sensing 11 (5), 2019.
 Wehrens, Ron and Kruisselbrink, Johannes. “Flexible Self-Organising Maps in kohonen 3.0.” Journal of Statistical Software, 87, 7 (2018).