Table Of ContentSPRINGER BRIEFS IN COMPUTER SCIENCE
Thiago Nunes Kehl
Viviane Todt
Maurício Roberto Veronez
Silvio Cesar Cazella
Real time
deforestation
detection using ANN
and Satellite images
The Amazon
Rainforest study case
123
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Thiago Nunes Kehl (cid:129) Viviane Todt
Maurício Roberto Veronez (cid:129) Silvio Cesar Cazella
Real time deforestation
detection using ANN
and Satellite images
The Amazon Rainforest study case
Thiago Nunes Kehl Viviane Todt
Vale do Rio dos Sinos University - UNISINOS Vale do Rio dos Sinos University - UNISINOS
São Leopoldo , Rio Grande do Sul , Brazil São Leopoldo , Rio Grande do Sul, Brazil
Maurício Roberto Veronez Silvio Cesar Cazella
Vale do Rio dos Sinos University - Federal University of Health Sciences
Advanced Visualization Laboratory – of Porto Alegre (UFCSPA)
VizLab/UNISINOS Porto Alegre , Rio Grande do Sul , Brazil
São Leopoldo , Rio Grande do Sul , Brazil
ISSN 2191-5768 ISSN 2191-5776 (electronic)
SpringerBriefs in Computer Science
ISBN 978-3-319-15740-5 ISBN 978-3-319-15741-2 (eBook)
DOI 10.1007/978-3-319-15741-2
Library of Congress Control Number: 2015936085
Springer Cham Heidelberg New York Dordrecht London
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Abst ract
T his study is an extended version of Kehl et al. ( Sustainability 4(10): 2566–2573,
2012), where the foremost aim was the development of a tool to detect daily defor-
estation in the Amazon rainforest, using satellite images from the MODIS/TERRA
(NASA—National Aeronautics and Space Administration , MODIS Website.
Available at: http://modis.gsfc.nasa.gov/about, 2014) sensor and Artifi cial Neural
Networks. The developed tool provides parameterization of the confi guration for
the neural network training to enable us to select the best neural architecture to
address the problem. The tool makes use of confusion matrices to determine the
degree of success of the network. A part of the municipality of Porto Velho, in
Rondônia state, is located inside the tile H11V09 of the MODIS/TERRA sensor and
was used as the study site. A spectrum-temporal analysis of the area was done on 57
images from May 20 to July 15, 2003 using the trained neural network. The analysis
enabled verifi cation of quality of the implemented neural network classifi cation and
also aided in understanding the dynamics of deforestation in the Amazon rainforest,
thereby highlighting the vast potential of neural networks for image classifi cation.
However, the complex task of detection of predatory actions at the beginning, i.e.,
generation of consistent alarms instead of false alarms, has not been solved yet.
Thus, the present article provides a theoretical basis and elaboration of practical use
of neural networks and satellite images to combat illegal deforestation.
Keyw ords Artifi cial neural networks, Satellite images classifi cation, Deforestation
detection, MODIS, Amazon rainforest
v
Contents
1 Introduction ................................................................................................. 1
1.1 Objectives ............................................................................................ 3
1.1.1 General ..................................................................................... 3
1.1.2 Specifi c ..................................................................................... 4
1.2 Contributions ........................................................................................ 4
1.3 Text Organization ................................................................................. 4
2 Literature Review ....................................................................................... 5
2.1 Remote Sensing ................................................................................... 5
2.1.1 Sensor MODIS/TERRA ........................................................... 8
2.2 Artifi cial Neural Network .................................................................... 9
2.2.1 Multilayer Perceptron .............................................................. 12
2.2.2 Back-Propagation ..................................................................... 13
2.3 Related Work........................................................................................ 15
2.3.1 Monitoring Systems ................................................................. 15
2.3.2 Orbital Images and Neural Networks ...................................... 16
3 Method ......................................................................................................... 19
3.1 Material Used ....................................................................................... 19
3.2 Development Tool ................................................................................ 19
3.2.1 Neural Module ......................................................................... 22
3.2.2 Data Storage ............................................................................. 26
3.2.3 Alarm Generation ..................................................................... 28
4 Modeling and Tool Use ............................................................................... 33
4.1 Modeling and Tool Use ........................................................................ 33
5 Results and Discussion ................................................................................ 39
5.1 Qualitative and Quantitative Analysis.................................................. 39
5.2 Temporal Analysis ............................................................................... 44
5.3 Conclusions and Future Work .............................................................. 48
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viii Contents
Appendix A: Training Dataset ......................................................................... 51
Appendix B: Test Dataset ................................................................................. 5 7
References .......................................................................................................... 6 1
Index ................................................................................................................... 65
List of Figures
Fig. 1.1 Differentiation between various levels of forest degradation.
Adapted from [9]. Licensed under CC BY-SA 3.0 ............................ 2
Fig. 1.2 Annual rate of deforestation in the Legal Amazon 1988–2012.
Adapted from earlier study [6]. Licensed under CC BY-SA 3.0 ....... 2
Fig. 2.1 Spectral signature for (a) Green leaf, (b) Dry leaf
in the visible band (B, G and R) and near infrared (IR).
Adapted from reference no. [21] ........................................................ 7
Fig. 2.2 Overview of the formal model of a neuron ........................................ 10
Fig. 2.3 A Multilayer Perceptron network with a hidden layer ....................... 12
Fig. 3.1 The Legal Amazon, the highlighted tile H11V09 is the area
under study. Adapted from reference no [10]. Licensed
under CC BY-SA 3.0 .......................................................................... 20
Fig. 3.2 Thematic map of tile H11V09 of the end of 2003 classifi ed
by INPE [6]. Licensed under CC BY-SA 3.0 ..................................... 21
Fig. 3.3 Flowchart of the reading process of a scene
with the trained network .................................................................... 22
Fig. 3.4 Collection of pixels of an image ........................................................ 25
Fig. 3.5 Image 166 with a high amount of clouds ........................................... 25
Fig. 3.6 Entity relationship diagram ................................................................ 27
Fig. 3.7 Difference in quality between two images.
Image (140) from May 20, 2003 (left). Image (142)
from May 22, 2003 (right) ................................................................. 28
Fig. 3.8 Three alarm levels of deforestation. Adapted from Todt [12] ........... 30
Fig. 4.1 Use case diagram tool ........................................................................ 34
Fig. 4.2 Main screen of the tool with the alarm list
and highlighted pixels ........................................................................ 34
Fig. 4.3 Screen displaying the current parameters
of the neural network ......................................................................... 35
Fig. 4.4 Interface for data manipulation .......................................................... 36
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