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Soraya Violini
Seminary – Master in Emergency Early Warning and Response Space Applications.Mario Gulich
Institute, CONAE. Argentina
October, 2013
Deforestation: Change
Detection in Forest Cover
using Remote Sensing
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Contents
Abstract……………………………………………………………………………..1
Chapter 1
Introduction….………………………………………………………………..........2
Chapter 2: Forest and Deforestation2.1 Definitions…..…..……………………………………………………………...3
2.2 Forest and Problem……………...…………………………………………….4
2.3 Lucha contra la Deforestacion……………………………………………......5
Chapter 3: Remote Sensing y Deforestation
3.1 Vegetation Reflectance………….……………………………………..……..11
3.2 Technical of Change Detection….…………………………………………...14
Chapter 4
Conclusions……………………………………………………………………… ..24
Bibliography……………………………………………………………….... ........25
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Abstract
The conservation and development of forests are vital to the welfare ofhuman beings. Forests management is essential to maintain social, economic and ecological
services.
Forrest monitoring allows to track their state of health and productivity, in order to
conduct proper management, according to the state of resources, to enhance their
functionality and promote conservation.
Remote sensors, optical and radar, offer the possibility of locating changes in forest
areas using various analysis techniques, ranging from the purely visual interpretation to the
implementation of a fully automated algorithm.
This report is a review of the literature on the techniques used to observe changes in
forest cover and monitoring through remote sensing.
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CHAPTER 1
Introduction
Forests provide support for one billion people that live in extreme poverty around
the world, and provide remunerative employment to more than one hundred million. They
contain more than 80% of terrestrial biodiversity (FAO, 2012) and provide essential
environmental services such as soil conservation, watershed management, protection
against floods and landslides, and provide industrial wood (UN).
According to the International Tropical Timber Organization (ITTO) of the UN, it is
estimated that deforestation and forest degradation rise 12.9 million hectares per year and
the current area of degraded forest is 850 million hectares. Most of the changes in forest-
based ecosystems due to: a) conversion of land cover, b) land degradation c) intensification
of land use (Lambin, 1997). These changes have resulted in coverage to a wide variety of
ecological impacts, ranging from local to global scale, including changes in productivity
and forests composition, nutrient dynamics, species diversity, and increased atmospheric
carbon dioxide (Braswell et al 2003).
One way of assessing changes in land use is based on the measurement of changes
in vegetal and no vegetal cover (Bochco, 2001). Technological progress allows a
comprehensive understanding of any region of the earth's surface from satellite images
(Chuvieco et al, 2002). These images of the Earth have been widely used for change
detection, specifically to the mapping and monitoring deforestation. This has favored
international efforts to establish permanent programs (Vargas and Chuvieco, 1991).
This work is intended as a review to the approaches used to monitor deforestation
processes, listing and describing the techniques used to observe changes in forest cover and
monitoring through remote sensing.
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CHAPTER 2
Forests and Deforestation
2.1. Definitions
Forests
The concepts of Forests and Deforestation were and are defined in different ways
depending on the organization to which we refer. According to FAO (2000) forests and
natural plantation with canopy cover greater than 10% and a surface to 0.5 ha.; determined
by the presence of tree and the absence of other predominant land use, which trees should
be able to reach a minimum height of 5m.
In Argentina, the law 26.331/07 of Minimum Standards for Environmental
Protection of Native Forests, defines forests as natural forest ecosystems composed
predominantly of mature native tree spices, with various species of flora and fauna
associated, along with the surrounding medium - soil, subsoil, atmosphere, climate, water-,
forming an interdependent web with its own characteristics and multiple functions, which
in its natural state, give to the system a dynamic equilibrium condition that provides various
environmental services to society, as well as diverse natural resources with the possibilityof economic use.
Deforestation
The Program Forest Resources Assessment (FRA, 2006) defines deforestation as the
conversion of forests to other land use or cover reduction, less than 10% of their Total (0.5
ha.). Also the United Nations Framework Convention on Climate Change (UNFCCC)
defines deforestation as the direct conversion of human-induced forest land to non-forest
land.
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2.1. Forests and Problem
The forests of the world cover about 3.4 billion hectares (Figure 1). They are
sources of raw materials and food, and are critical to maintaining agricultural productivity
and ecological balance of the entire planet.
Figure 1: World map of forest distribution.
Forests and woodlands are varied, from the dense jungles of the tropics to the East
African savannah woodlands of mangroves to lush boreal and temperate forests.
In Latin America, tropical forests constitute the largest reserves of this type of
forests worldwide, but there are disappearing at a rate of about 1.3 percent annually.
In Argentina, the richest forested regions in species are the Paranaense Forest and
“Yungas” Forest. By mid 2004 Argentina's native forests spread over approximately
36 million hectares, this means only 15% of the country. Between 1880 and 2003
destroyed approximately 78% of native forest cover throughout Argentina.
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Figure 2: Phytogeographical regions of Argentina (Cabrera).
The lack of a system for the continuous monitoring of the forest cover, including
forest inventory or geographical information system to periodically update the changes in
land use, prevent obtaining actual figures regarding the disappearance of woodlands. To the
above, are added, controls inefficient and little or no supervision over the damage to the
forest cover by total and selective harvesting, authorized annually; therefore, there are no
updated figures on the extent of commercial forest loss and total deforestation (FAO,
1995).
2.2. Combating Deforestation
Concern about the destruction of the world's forests, has increased considerably in
the past two decades and has led to several initiatives to reverse this trend and develop
strategies and measures for sustainable forest management (ITTO, 2002).
World Forestry Program
In February 1997, the Intergovernmental Panel on Forests (IPF) of the Commission on
Sustainable Development (CSD) defined the global forest program. He reaffirmed that the
conservation and sustainable development of forests are matters of international concern.
La YungaBos ue de Calden
Selva Misionera
Bos ue Cha ueño
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The Group adopted proposals for action and reach consensus on key issues such as national
forest programs and forest assessments. Here are some of the major issues and
recommended actions were the result of the process of IPF:
National forest programs should be holistic, intersectoral and interactive, and
consistent with the policies and national and local strategies. Should involve all
stakeholders, promote secure land tenure and integrate the conservation and
sustainable use of biological resources.
Indigenous peoples and local communities have traditional rights to be respected.
Working with them is essential to identify, preserve and promote traditional
knowledge related to forests.
The national forestry research capabilities should be improved and create regional
and global networks of research to facilitate information exchange, foster
interdisciplinary research and disseminate the results. Detailed studies are needed of
the underlying causes of deforestation and environmental degradation.
You need to have better evaluation methodologies to obtain reliable estimates of all
forest goods and services, especially those that are not generally traded.
Measures are needed to improve access to markets for forest goods and services,
including the reduction of tariff and non-tariff barriers to trade, in accordance with
existing international obligations and commitments. It is necessary to adopt innovative methods to make more effective use of existing
financial mechanisms and generate new and additional resources, both nationally
and internationally.
The investment policies and regulations should aim to attract domestic investment
of foreign and local communities for sustainable forest-based industries,
reforestation, conservation and protection of forests. The use of appropriate
economic instruments and incentives and market-based increase income and to
mobilize domestic financial resources.
Cooperation should be encouraged in the transfer of technology related to forests,
through public sector investment and private joint ventures, exchange of
information and a closer relationship between forestry institutions.
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Improving information systems would improve coordination and data sharing on the
implementation of national forest programs, programming of official development
assistance, the provision of new and additional financial resources, private sector
investment and development and technology transfer.
Should be clarified the roles and mandates of the relevant international
organizations and mechanisms to increase cooperation, eliminate gaps and avoid
duplication (FAO).
International Tropical Timber Agreement
The International Tropical Timber Organization (ITTO) is an intergovernmental
organization that promotes conservation and sustainable management, sustainable use and
trade of tropical forest resources. Its 60 members represent about 80 percent of the world'stropical forests and accounted for 90 percent of world trade in tropical timber. Documents
develop ITTO internationally agreed policy to promote the conservation and sustainable
management of forests and assists tropical member countries to enable them to adapt such
policies to local circumstances and to implement them through projects.
In addition, ITTO collects, analyzes and disseminates data on the production and
trade of tropical timber and funds a variety of projects and activities for developing
industries at both community and industrial scales. All projects are funded by voluntarycontributions from members, mainly from consumer member. Since it became operational
in 1987, ITTO has funded more than 750 projects, pre-projects and activities with a total
value of over 300 million U.S. dollars. The main donors are the governments of Japan,
Switzerland and the United States of America.
Reducing Emissions from Deforestation and Degradation (REDD)
Deforestation and forest degradation due to agricultural expansion, conversion to
pastureland, infrastructure development, fires, destructive logging, including nearly 20 % of
global emissions of greenhouse gases.
Limiting the impact of climate change within limits that society can tolerate, the
global average temperature must be stabilized within the range of two degrees Celsius
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above the current temperature. This is almost impossible without reducing emissions from
the forest sector, including mitigation measures.
The initiative for Reducing Emissions from Deforestation and Degradation (REDD,
2008), is an effort to create a financial value for the carbon stored in forests. This provides
incentives to developing countries to reduce emissions from forested lands and invest in
routes of low-carbon sustainable development. “REDD +" goes beyond deforestation and
forest degradation, and includes the role of conservation, sustainable management of forests
and enhancement of forest carbon stocks.
Panama is one of the 9 countries in the world with resources approved by the UN
REDD Program to help design and organize the steps necessary to achieve " readiness" to
allow the country to be ready to implement activities and mechanisms to reduce emissions.
The Joint UN REDD in Panama will contribute to: design a legal framework
validated for the implementation of the national REDD + strategy , develop an operational
framework for the implementation of the REDD + strategy , strengthen national capacities
for the implementation of the REDD + strategy , design system and benefit payments ;
create a national forest inventory and monitoring carbon ; establish a baseline emissions
scenario and design a carbon accounting system and emissions information generation .
The Joint UN- REDD in Panama defines the minimum of preparation, and supports
the country to chart a path towards achieving the implementation of REDD +.
Native Forest Law
In late 2007, Argentinian Congress passed Law 26.331 of Minimum Standards for
Environmental Protection of Native Forests, regulated in February 2009.
The Forest Act provides that the provinces will make the land of its native forests
(OTBN) through a participatory process, categorizes possible uses for forest lands: from
conservation to the possibility of transformation for agriculture, through sustainable forest
use. So forest zoning as follows:
Category I (red): High conservation value areas that must not be removed or used
for timber extraction and forest should remain forever. Include nature reserves and
their surrounding areas, which have outstanding biological values, and / or sites that
protect important watershed (headwaters of rivers and streams).
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CHAPTER 3
Sensors Remotes y Deforestation
3.1. Reflectance de la vegetation
All geographical elements (forests, crops, rivers lakes, buildings, etc.) transformed
differentially receiving electromagnetic radiation from the sun. Each object type represents
a specific type of level in terms of:
Received Radiation = Reflected Radiation + Radiation Absorbed + Radiation Transmitted
The variation of reflectance (reflected radiation) as a function of wavelength
spectral signature called, that is, the function that describes the amount of reflected
radiation with respect to the wavelength of this radiation, the spectral signature is an
objector element (Figure 3).
Figure 3: Spectral signatures and resolution of Landsat multispectral bands
(www.tecnologia.net/teledeteccion/).
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Vegetation is a resource with many varieties, and different characteristics depending
on the species (leaf, stem, trunk, humidity, etc.), and besides that, for living being, are
subject to change depending on environmental conditions and internal thereof.
Healthy vegetation has a low reflectivity in the visible, but with a peak in the green
color due to chlorophyll and air bags which are generated in the intermediate tissue of
leaves. This reflectivity is very high in the near infrared due to low energy absorption by
the plants in this band. Mid-infrared is a particularly important because decrease in those
wavelengths in which water of the plant absorbs energy.
The ill vegetation has decrease of the reflectivity in the infrared, but an increase in
the red and blue (visible).
An additional factor that affects the vegetation, is the amount of wáter it containes.
When this increases, reflectivity decreases and viceversa (inversely proportional), due to
the behavior of water with respect to radiation. Mid-infrared shows a particularly important
decrease in those wavelengths in which wáter contained in plants absorbs energy (Figure
4).
Figure 4: Reflectivity of vegetation in the spectrum (//tecnologia.net/teledeteccion/).
Vegetation Indexes
Investigators have developed techniques for qualitatively and quantitatively
assessing the vegetation from spectral measurements. Vegetation indexes (VIs) take
advantage of vegetation's reflective contrast (as mentioned before) between the NIR and
visible red (VIS) wavelengths, sometimes with additional channels included; these are one
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of the most widely used remote sensing measurements. Generally, are analyzed the
reflectance at 660 nm in the red range of the spectrum and the reflectance at 870 nm in the
NIR range. At higher vegetation vigor, more high contrast between reflectance values in
these regions.
Most formulas of vegetation indexes are based on ratios or linear combination and
exploit differences in the reflectance patterns of green vegetation and other objects.
Following a list of the principal Vegetation indexes:
Vegetation Indexes Definition
RVI Ratio Vegetation Índex
nir RVI
r
NDVI Normalized Difference Vegetation Index nir r NDVI nir r
PVI Perpendicular Vegetation Index 2
*
1
nir M r Q PVI
M
DVI Difference Vegetation Index
DVI nir r
IPVI Infrared Percentage Vegetation Índex
1
2
NDVI IPVI
WDVIWeighted Difference Vegetation
*WDVI nir M r
SAVISoil Adjusted Vegetation Index
1nir r
SAVI Lnir r L
TSAVITransformed Soil Adjusted Vegetation Index
2* *
* * 1
B nir B r QTSAVI
r B nir Q M X M
NIR = value near infrared reflectivity
R = Value of reflectivity in the red
L = soil adjustment factor (L = 0.5; Huete 1998)
M = Slope of the land
Q = y-intercept of the line of the soil
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3.2. Change detection techniques
Satellite images are a valuable tool for quick access to those areas under ecological
disasters. Their wide coverage area, his ability to quickly view and evaluate the situation of
those places, where the same consequences of the disaster prevent or hinder other types of
approach, are key factors in the management of the recovery actions after the event.
The use of remote sensing in monitoring deforestation processes is performed both
visual and digital analysis. Among the most interesting approaches should consider
employing linear spectral mixing analysis , in order to extract sub - pixel information from
NOAA - AVHRR images ( Braswell et al. , 2003 ; Hlavka and Spanner , 1995 ; Kressler
and Steinnocher , 1999 ) and software CLASlite ( Asner et al. , 2009 ) , the use of radar
images , in sectors with very frequent cloud coverage ( Thapa et al. , 2013 ) , multitemporal
analysis ( Lambien and Ehrlich , 2010 ; Chuvieco , 2002 Chuvieco Vargas , 1991 ).
Visual Analysis
For the detection of changes, such as deforestation and fragmentation, visual
interpretation procedure is appropriate since replacing farmland forest represents changes in
the spectral values of the contrasting images as well as ways that favor their identification
features (Figure 5).
Figure 5: Combinations of color Landsat image Paysandú forestation (//teledet.com).
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Linear Spectral Mixing Analysis
In the mixing model are estimated the proportions of the components of mixed
pixels, from the spectral response of the components, this model is especially useful in low
spatial resolution images.
The Advanced Very High Resolution Radiometer (AVHRR) is a sensor on board
the satellite platform of the National Oceanic and Atmospheric Administration (NOAA)
and was originally designed for meteorological applications. However, in recent years, is
used to monitor the surface of the earth, particularly the vegetation dynamics regional and
global level (spatial resolution of 1.1 km), using reflective bands (580-680 nm in the visible
red, 725-1100 nm in the near infrared).
The use of this sensor may be combined with other high spatial resolution, such as
Landsat providing more detailed information to calibrate AVHRR data.In Hlavka and Spanner (1995) the model was applied to the radiance (L) of a given
pixel, considering that DN are a linear function of radiation, which groups coverage that
wanted to distinguish (clearcutting, forest and succession) has spectral signatures different
and that the patches contain all three types of covers.
True
Color
(3, 2, 1)
False Color NIR
(4, 3, 2) False Color
Corine
(4, 5, 3)
SWIR
(GeoCover)
(7, 4, 2)
Trees and
shrubs
Olive Green Red Brown-Orange Various Green
Crops Green tolight Green
Rose to Red Yellowish Various Green
Wetlands Dark Greento Black
Dark Red Black Various Green
Water Blue-Green Blue Black Black to DarkBlue
Urban
Areas White to
Light BlueBlue to Grey Gray-green to
Green-BlueViolet
Soil White toLight Blue
Blue to Grey Blue-Green toWhite
Violet to rose
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L (p, b) = Ml(b) * Fl(p) + M2(b) * F2(p) + M3(b) * F 3 b ) + e(p, b) (1)
Subject to the constraint
F l (p) + F2 (p) + F3 (p) = 1
Where Ml (b), M2 (b), and M3 (b) are the mean L values for classes 1, 2, and 3 in
band b; and Fl (p), F2 (p), and F3 (p) are the fractions of the three classes. The error term e
(p, b) is a term representing the combined effect of local deviations of L values of the
components from their average values. Braswell et al. (2003), applied this technique using
reflectance data from MISR and MODIS sensors and ISODATA unsupervised
classification of land use from Landsat ETM + (30 m resolution) of which were recognized
5 types of cover (Figure 6).
Figure 6: Observed and predicted values of forest, secondary, and cleared fractionalareas for the reference case in Ruropolis (Braswell et al., 2003).
For the evaluation of this methodology results, Steinnocher Kessler (1999) propose
three methods: visual analysis, average squared error calculation and the calculation of theoverflow fraction. The RMS error can be calculated for each pixel or the whole image,
smaller RMS, better the model. In the third test, the fraction of land cover components
should be between 0 and 1, if the model is not well-built fractions values are outside this
range.
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Other studies estimated the percentage of pixel occupied by forest and no forest in
MODIS images. Hansen et al. (2008) propose an approach that uses a regional coverage
MODIS product to calibrate high-resolution Landsat imagery. The method is an automated
decision tree algorithm that uses an algorism tool to characterize forest cover, evaluate
presence of clouds and shadows, and generate maps identifying as afforestation those pixels
with more than 60 % coverage and no afforestation, those with less this value. Then are
performed Landsat compositions and finally, the maps are elaborated (Figure 7).
Figure 7: Flow diagram of multi-resolution forest cover mapping and change detectionmethodology where the following text sub headings from the Methods section are highlighted:1) Generate regional MODIS 250 m forest non-forest cover map forest non-forest cover map,2) Georectification/resampling of satellite data, 3) Landsat normalization, 4) Landsat cloudand shadow flagging, 5) Landsat decision tree forest mapping procedure, 6) Landsatcompositing, 7) Landsat forest change mapping (Hansen et al., 2008).
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CLASlite Software
CLASlite is a highly automated system for converting satellite imagery from its
original format (unprocessed), through calibration, pre-processing, atmospheric correction
and cloud masking steps, Monte Carlo Spectral Mixture Analysis and expert classificationto obtaining high-resolution images output (Figure 8).
Figure 8: Automated System CLASlite (http://claslite.stanford.edu).
This tool was designed specifically to support forest monitoring for REDD program.
Its outputs include maps of percent cover of living and dead vegetation, bare soil and other
substrates in addition to quantitative measures of the uncertainty in each pixel of the image.
These maps are interpreted in terms of forest cover, deforestation and forest disturbance
using automated decision trees.
The output images CLASlite can enter directly to other screening programs,
geographic information systems (GIS), Google Earth or other display systems.
A detail of system processes were described by Asner et al. (2009) (Figure 9):
http://claslite.stanford.edu/http://claslite.stanford.edu/http://claslite.stanford.edu/http://claslite.stanford.edu/
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Initial Image Masking
Water bodies are masked automatically to be removed in subsequent analyzes. This
is accomplished by detecting the reflectance properties only of water (reduced from blue to
near infrared) and by clouds leftovers mask by identifying pixels that appear negative
reflectance values .
On Landsat images, it only has the option to hide a portion of the pixels with clouds
using the thermal band, to reduce the processing time for the next step.
Sub - pixel Analysis
The core process is a sub CLASlite - model called AutoMCU (Automated Monte
Carlo unmixing), which provides a quantitative analysis of the fraction or percent cover (0-
100 %) of live and dead vegetation and bare substrate within each pixel. Live vegetation or photosynthetic vegetation (PV) has unique spectral properties associated with leaf
photosynthetic pigments and water content of the canopy. Senescent vegetation fraction is
called non-photosynthetic vegetation (NPV), is seen in the spectrum as bright surface
material spectral features associated with carbon compounds from the plant.
The AutoMCU is based on a probabilistic algorithm that use three spectral libraries
derived from field measurements and hyperspectral satellite images to decompose each
pixel image using a linear equation. Iteratively selects a PV, NPV and bare substrate
spectrum of each library, and the pixel reflectance unmixing in housing constituent
fractions using the equation. The random selection process is repeated until the solution
converges to a mean value for each fraction of covered surface.
To assess performance, pixel by pixel, standard deviation and RMSE images are
generated, which allows the user to identify areas of concern (Figure 10).
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Figure 10: Processing stream for the Automated Monte Carlo
Unmixing (AutoMCU) sub-model within CLASlite (Asner et al .,
2009)
Secondary Image Masking
After determining PV fractional coverage, VPN and bare substrate within each pixel
of the image, there is a secondary masking and a scale change. The masking is applied with
a threshold value selected by the user based on RMSE derived from the AutoMCU model.
Deforestation and disturbance mapping (multi-image mode)
CLASlite includes a fully automated ability to detect the change from a time series
of images taken from the same geographical area. Analysis of multiple images is the most
accurate method for detecting the loss of forests (deforestation), the gain (secondary
regrowth) or degradation (forest disturbance zones persistent).
Forest Cover Analysis (single-image mode)
Although it is preferable to use at least two consecutive images to detect
deforestation and forest disturbance, it is possible to map the forest canopy coverage using
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only one image. These images of forest cover often indicate areas of past logging and
disturbance.
Technical limitations
CLASlite outputs, especially maps disturbance usually require further analysis in
order to interpret the specific types of disturbances.
Radar
In areas characterized by constant cloudiness and fog, data from Synthetic
Aperture Radar are the only alternative for the study of vegetation cover.
According Thapa et al. (2013), the radar images ALOS - PALSAR (L-band) HH
-HV polarization and angle of 34.3 °, are highly applicable to the characterization of
several tropical forests.Image segmentation is used and applied thresholds for discriminating between
types of soil coverage on the basis of backscattered HH and HV bands. Another
segmentation technique, multiresolution is applied in order to minimize heterogeneity and
maximize homogeneity.
The segmentation procedure begins with a pixel of a single image object and
repeatedly merges in pairs in several loops to larger units, provided that an upper threshold
of homogeneity that cannot be exceeded locally. Then the objects in the image are labeled
by establishing standards for different types of land cover (Figure 11).
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Figure 11: Four land cover maps (A. 2007, B. 2008, C. 2009, and D. 2010) and locations of
concession and conservation areas including deforested landscape observed in 2007e2010 (E).
Multitemporal Analysis
Multitemporal analysis techniques from satellite imagery to track deforestation
provide a useful source for the management of the territories affected by this phenomenon.
The analysis is based on the interpretation and comparison of images from different dates
and different sensors (Landsat and Spot).
In the case of multi-sensor studies, you should keep in mind that the images should
be compatible. For this, it’s necessary to proceed with the normal pre-processing
(radiometric calibration, geometric and atmospheric correction setting) before starting the
multitemporal analysis.
For the study of deforestation is useful to use transformations of images that
reinforce plant component as indexes normalized difference vegetation index (NDVI ) (
critical value for ground cover around 0.1 and 0.5 dense vegetation ) indicating the state of
vitality of vegetation, reducing its value with the loss of vitality and facilitating the
identification of areas of forest cover changes . With a histogram segmentation, forest cover
of each image obtained from NDVI, defined categories of interest (afforestation andreforestation). Are obtained thus, binary maps of each date and multitemporal crossover is
performed to identify themselves with the changes and / or changes in the vegetation in the
area (Chuvieco, 2002 Chuvieco Vargas, 1991).
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Figure 13: Sample "funnel" to detect hot spots of deforestation (Lambin y Ehrlich, 1997).
Two variables highly related to deforestation processes can be derived directly from
large-scale maps of land cover such as that are produced by the TREES project (maps with
NOAA-AVHRR images 1.1 km resolution): the proportion of land cover and the spatial
fragmentation of these ones.
Maps are developed of areas that have high prior probability of deforestation,
according to a single variable and overlaps through a GIS. With the aim of increasing the
precision of hot spots, variables are combined (not more than 4 variables) according tological relationships (high density presence of fires with high rates of growth of the
population) (Lambin, 1997).
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CHAPTER 4
Conclusions
The conservation and management of forests are very important for the
development of human life. However, this is only possible if conservation measures
contemplate economic and social situations unfair in different countries. Developing
countries are those with the largest areas of tropical forests but also have countless
economic and social needs, so that, forest conservation is not a priority and in many cases,
are an obstacle development of economic activities such as agriculture, oil extraction,
extraction of minerals and timber.
The forest conservation policies have to offer financial compensation, to involve
communities (especially indigenous peoples), should be established based on multi-
disciplinary and jurisdictional studies and must be applied consistently at all scales, but
mostly it is necessary to adopt innovative techniques in order to make more efficient the
use of resources, since the monitoring of forest tracts to measure deforestation, degradation
and / or evolution of the forests are not easy.
In this sense, the remote sensors offer a quick and low cost way to map thousands of
acres per day to monitoring deforestation, logging and other disturbances, as well as,
following the recovery of forests. The access to images is becoming easier and cheaper and
analysis techniques range from visual to those fully automated and can when applied to
optical (spectral values) and radar sensors (backscatter values ).
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