Satellite Remote Sensing Detection of Forest Degradation by Integrating NDFI and the BFAST Algorithm

Authors

  • Erith Alexander Muñoz The Food and Agriculture Organization of United Nations, Quito-Ecuador http://orcid.org/0000-0002-2219-1632
  • Alfonso Zozaya Universidad Tecnolgica Metropolitana (UTEM), Santiago de Chile https://orcid.org/0000-0003-3357-3887
  • Erik Lindquist The Food and Agriculture Organization of United Nations, Rome-Italy

Keywords:

Forest Degradation, NDFI, BFAST, Data Fusion, Forest Monitoring, Remote Sensing.

Abstract

In this paper, results related with the assessment of the capability to detect forest degradation by analyzing NDFI
time series through the BFAST algorithm are presented. Recent studies have shown the potential of the BFAST algorithm applied to a time-series of satellite-derived spectral indices such as NDVI or NDMI to detect unambiguous and subtle perturbations of the forest cover canopy both positive (e.g. regeneration) and negative (e.g. deforestation). Similarly, these results suggest the feasibility to distinguish between several types of forest degradation and their causal agents such as selective logging and forest fire. In this context, the results derived from this research show that using NDFI as a data source in the BFAST algorithm improves the detection of forest degradation, and additionally provides information to understand both temporal and spatial approaches
related with the dynamics of perturbations of the forest canopy

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Author Biographies

Erith Alexander Muñoz, The Food and Agriculture Organization of United Nations, Quito-Ecuador

Erith Mu˜noz holds a B.Sc. degree (2007) in Phisics
and a M.Sc. degree (2012) in Electrical Engineering
from the University of Carabobo, Venezuela. In 2014
received a M.Sc. degree on Remote Sensing from
the National University of C´ordoba in Argentina.
Since 2015 he has been working for the Food and
Agriculture Organization as a Regional Consultant
for Latin Am´erica and the Caribbean in remote
sensing techniques applications on forest monitoring
in the context of the UN-REDD program. He is
currently working on his PhD research in the field
of Forest Monitoring using Remote Sensing Techniques by analysing time
series of satellites products derived from the fusion of optical and SAR data.

Alfonso Zozaya, Universidad Tecnolgica Metropolitana (UTEM), Santiago de Chile

A. J. Zozaya: received the B.Sc. degree in Electronic
Engineering, with a major in Telecommunication,
from the Polytechnic Institute of the National
Armed Forces of Venezuela (I.U.P.F.A.N.),
Maracay, Venezuela, in 1991, and his PhD degree
from the Polytechnic University of Catalonia (UPC),
Barcelona, Spain, in the area of Signal Theory and
Communications in 2002. He worked as a Professor
at the University of Carabobo, Valencia, Venezuela
from 1994 to 2014. He worked as a senior researcher
at the Ecuadorian Space Institute, Quito, Ecuador, in
the area of synthetic aperture radars in the periods from September 2014 to
September 2015 and from August 2016 to August 2017. Currently, he is with
the Universidad Politcnica Metropolitana, Santiago de Chile, where he works
as a Full Professor at the Department of Electricity. His research areas of
interest are applied electromagnetic, computational electromagnetic, digital
signal processing, RF circuits design, and synthetic aperture radars.

Erik Lindquist, The Food and Agriculture Organization of United Nations, Rome-Italy

Erik Lindquist: is currently a Forestry Officer with
the United Nations Food and Agriculture Organization
based in Quito, Ecuador. He has 23 years of
experience in remote sensing and GIS. He started his
career working for the US Forest Service as a plant
ecologist in the Wind River Mountains of Wyoming.
He then moved to central Africa and worked with
the Wildlife Conservation Society leading botanical
and wildlife surveys in the Democratic Republic
of Congo. His current focus with FAO is creating
platforms that facilitate the use of geospatial data
for autonomous land surface monitoring at national scales, especially in
developing countries. In collaboration with the international Global Forest
Observations Initiative, national research institutions, and Ministries of Environment
the FAO has launched a cloud-based computing platform called
SEPAL (System for Earth Observation Data Acquisition, Processing and
Analysis for Land Monitoring). SEPAL is a big-data processing platform
that combines super-computing power, open-source geospatial data processing
software and modern geospatial data infrastructures like Googles Earth Engine
to enable researchers and technicians anywhere in the world to create data
and produce locally relevant results that can affect decision making. Erik has
a Bachelors degree in Botany from Miami University (Ohio) and a PhD in
Geospatial Science and Engineering from South Dakota State University

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Published

2020-05-15

How to Cite

Muñoz, E. A., Zozaya, A., & Lindquist, E. (2020). Satellite Remote Sensing Detection of Forest Degradation by Integrating NDFI and the BFAST Algorithm. IEEE Latin America Transactions, 18(7), 1288–1295. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/2437

Issue

Vol. 18 No. 7 (2020): Ordinary Issue

Section

Articles