Fabián Santos
05.06.2014

Deforestation on tropical forests from image processing of Landsat 7 with scan-off and clouds cover in the sector Auca Sur, Yasuni National Park – Ecuador (master degree dissertation)

Background:
• Ecuador had for some years, the highest deforestation rate in
Southamerica (FAO, 2010)
• Lack of deforestation methodologies which treat bad quality Landsat images
Pre-processing chain:
Objective:
• Predict deforestation in tropical forests with high cloud cover and scan-off Landsat images
Processing chain:
Soil – GV – NPV
R – G – B
Spectral mixture decomposition with ImageTools (IMAZON, 2013):
*Green vegetation
*Non-photosynthetic vegetation
*Soils
*Shades
Study area:
Post-processing:
Scan-off &
Data cloud gap fill with ERDAS
Geosystems, 2007)
Normalized Difference
Fraction Index or NDFI
(Souza, 2006)
Radiance
Conversion with ImageTools
(IMAZON, 2013)
Visual inspection errors
Atmospheric
Correction with
FLAASH (ITT, 2013)
NDFI classification with ImageTools
(IMAZON, 2013)
High resolution images & field data on a stratified sampling for a confusion matrix
Haze filter
Enhancement with ImageTools
(IMAZON, 2013)

Modelling deforestation scenarios:
14
13
12
11
10
9
8
1
7
2
3
4
6
5
Predictors + deforestation control maps
(Dinamica Ego, 2009)
Finding out the drivers of deforestation & validation of models (Dinamica Ego,
2009)
Appliance of “Simulate
Deforestation with Patch formation and Expansion“ model
(Dinamica Ego, 2009)
Results:
• Three model scripts for recover scan-off and cloud data gaps
• Kappa values for the deforestation maps (2000 –
2008) were over 0.76 and the calibration of the deforestation scenario achieve 0.67
• Square matrix style calculations, helps to reveals hotspots of deforestation
Other relevant issues:
• Imgtools (IMAZON, 2013) methodology was applied for the whole Amazon Region of
Ecuador and publicy on the Atlas of “Amazonia
Calculation of deforestation rates:
Time period
Tax calculation
Site identification
Square matrix (25 km 2 ) for obtain deforestation taxes (FAO & Puyravaud formulas)
More information at: http://raisg.socioambiental.org/

A framework for Mapping Potential Strata Forests on Ecuador (technical report)

Background:
• For REDD+ purposes, a
Potential Strata Forests Map was request for quantify greenhouse gases emissions
• Previous version of a potential map (MAE, 2013) had a not clear and replicable methodology
Flowchart:
Objective:
• Update the Potential Strata
Forest Map with a replicable methodology
Data extraction:
Hexagon database system for data collection (25 ha each analysis unit)
Predictor variables
(total 11)
Study area:
Ecuador regions Remaining strata forests
Decision tree formulas:
Possible unique variable combinations and regresion formulas
1.
Data extraction
2.
Decision tree creation
3.
Prediction & validation
4.
Map plotting
5.
Discussion & edition
Extraction of data Prediction & Validation:
Samples Output 1 + Output 2 + Output N
Outliers detection and elimination
Samples collection

Map plotting:
Workshop for identify the quality of the results
Final result after editing the errors identified
Results:
• A methodology based on a set of 7 scripts programmed on R (Ihaka, R. and R.
Gentleman ,2013) were developed
• Decision tree algorithm (Therneau, T., B.
Atkinson, et al. ,2013) achieved regression outputs of 0.86, 0.79, and 0.90 kappa values for the best models
Other relevant issues:
• The methodology could be applied in other distribution exercises, as for example species or crops potential distributions, also climate change scenarios and its implications over biodiversity, food security, etc.
• The output map was used on the REDD+ reference scenario of greenhouse emission calculation, achieving the transparence of the methodology applied and its replication as better data is available

Acknowledgments
References
•Dinamica Ego (2009). Dinamica EGO. 1.6 ed. Minas Gerais - Brazil, Centro de Sensoriamento Remoto, Universidade Federal de Minas Gerais.
•FAO (2010)."Global Forest Resources Assessment 2010.Progress towards sustainable forest management. Global tables.".Retrieved 10/07/2013, 2013, from http://www.fao.org/forestry/fra/fra2010/en/ .
•Ihaka, R. and R. Gentleman (2013). "The R Project for Statistical Computing." 3.0.1. from http://www.r-project.org/ .
•IMAZON (2010). Imgtools - Monitoramento da Amazônia
•ITT Visual Information Solutions (2009). ENVI 4.6 ed.
•Leica Goesystems (2007). ERDAS IMAGINE 9.2 ed.
•MAE (2013). Representación Cartográfica de los Estratos de Bosque del Ecuador Continental. Subsecretaría de Patrimonio
Natural. Quito - Ecuador, Ministerio del Ambiente del Ecuador (MAE).
•RAISG (2012). Amazonía Bajo Presión. A. Rolla, B. Ricardo, D. Larrea, J. Ulloa and N. Hernández. Sao Paulo - Brasil.
•Souza, C., D. Roberts, et al. (2005). Combining spectral and spatial information to map canopy damage from selective logging and forest fires. Remote Sensing of Enviroment 98 (2005) - ELSEVIER, 15.
•Therneau, T., B. Atkinson, et al. (2013). "rpart: Recursive Partitioning." from http://cran.rproject.org/web/packages/rpart/index.html
.