Summary of relevant works

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

Objective:

•Predict deforestation in tropical

forests with high cloud cover

and scan-off Landsat images

Study area:

Pre-processing chain:

Processing chain:

Soil – GV – NPV

R – G – B

Scan-off &

Data cloud gap

fill with ERDAS

Modeler (Leica

Post-processing:

Visual inspection

and edition of

Spectral mixture decomposition

with ImageTools (IMAZON, 2013):

*Green vegetation

*Non-photosynthetic vegetation

*Soils

*Shades

Radiance

Conversion

with ImageTools

(IMAZON, 2013)

Atmospheric

Correction with

FLAASH (ITT, 2013)

Normalized Difference

Fraction Index or NDFI

(Souza, 2006)

Modeler (Leica

Geosystems, 2007)

Haze filter

Enhancement

with ImageTools

(IMAZON, 2013)

NDFI classification

with ImageTools

(IMAZON, 2013)

and edition of

errors

High resolution

images & field data

on a stratified

sampling for

a confusion matrix

Modelling deforestation scenarios:

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Finding out the drivers of

deforestation & validation

of models (Dinamica Ego,

2009)

Predictors + deforestation

control maps

(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

bajo Presion” (RAISG, 2012)

Calculation of deforestation rates:

Appliance of “Simulate

Deforestation with Patch

formation and Expansion“ model

(Dinamica Ego, 2009)

Square matrix (25 km2)

for obtain deforestation

taxes (FAO & Puyravaud

formulas)

More information at: http://raisg.socioambiental.org/

bajo Presion” (RAISG, 2012)

Time period

Tax calculation

Site identification

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

Objective:

•Update the Potential Strata

Forest Map with a replicable

methodology

Study area:

Flowchart:

Decision tree formulas:

Predictor

variables

collection

Hexagon database

system for data

collection (25 ha

each analysis unit)

Possible unique

variable

combinations and

regresion formulas

Data extraction:

Ecuador regions Remaining strata forests

collection

(total 11)

Extraction of data

Outliers detection

and elimination

Samples

collection

Samples Output 1 + Output 2 +

1. Data extraction

2. Decision tree creation

3. Prediction & validation

4. Map plotting

5. Discussion & edition

Output N

Prediction & Validation:

Results:

Map plotting:

Final result

after editing the

errors

identified

Workshop for identify the

quality of the results

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

•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

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.r-

project.org/web/packages/rpart/index.html.