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Large Scale Tropical Forest Change
Monitoring Using Multiple Resolution
Satellite Data :from Hot Spot Detection
to Global Deforestation Assessment ?
Hervk ~eanjean',Frkdkric
--
chard^ and Jean-Paul h4alingreau3
-
Abstract - The rate of tropical forest degradation and deforestation is a serious
threat on environmental issues, socio-economic balances and land use planning
and is considered as a major concern for decision-makers at global, regional
and local scales. Appropriate tools are needed to address this multiscale and
multipurpose issue. Sampling techniques can be applied in large forest surveys
using high resolution satellite data. "Wall to wall" coverage is a feasible
alternative, but due to the high cost of data, low resolution satellite data are
more adapted to this method, provided that estimations are corrected using
multistage procedures. Recent advances have proved that this approach is
feasible for mapping and assessing tropical forest on large scale with reasonable
accuracy. However, for forest change detection and monitoring, few methods
have been developed so far enabling the estimate of change at global scale. In
this paper, change detection based on a combination of LRSD such as AVHRR
data and HRSD such as TM and SPOT data is addressed. A preliminary
investigation is carried out in a pilot area in Southeast Asia where changes
detected with HRSD (hot spot areas) are compared with AVHRR forest class
proportions and fragmentation patterns. A discussion is then proposed on the
necessary steps towards an operational system for global tropical forest
monitoring.
INTRODUCTION
Tropical forests constitute fragile ecosystems which shelter most of the world
animal and vegetal species. Playing an essential role in global environmental and
climatic balance, tropical forests are threatenend by a fast disappearance whith dramatic
consequences on biodiversity loss, shortage of wood and non-wood products, soil
degradation and erosion, deterioration of hydrological systems, disruption of local socioeconomic development.. .
According to the last estimates of F A 0 FRA 1990, 15.4 millions ha of tropical
forests have disappeared each year between 1980 and 1990, which represent an average
deforestation rate of 0.8 % of total tropical forest resources. In 1980, tropical forests
were estimated at 1,910 millions ha, and 1,756 millions ha in 1990. The deforestation
mechanism has become a critical issue not only for the countries where this problem is a
real concern, but also for the international scientific community who has launched
Remote sensing and CIS expert in forestry, SCOT Conseil, 1 rue HermEs, F-31526 Ramonville France
MTV Group, TREES Project Manager, Institute of Remote Sensing Applications, 1-21020 Ispra Italy
Head of Monitoring Tropical Vegetation Unit, TREES Project Coordinator, Institute of Remote Sensing Applications,
1-21020 Ispra Italy
important research programmes to better evaluate its exact magnitude and to improve the
knowledge of the different factors interacting in this complex processus. Intensive
logging practices, shifting cultivations and conversion of forest lands to agricultural
areas are some of the main causes, direct or undirect, of deforestation. Above all, the
modes of appropriation of forest lands by local farmers represent in many cases a critical
issue which interacts with legal aspects of land management. To tackle the
environmental effects of deforestation, one of the necessary actions consists in building
up a continuous monitoring systems along three major steps : (1) development of sound
methods for estimating forest areas (static approach), (2) development of sound methods
for estimating forest cover changes on a quantitative and structural basis (dynamic
approach) and (3) modeling of deforestation processes (anticipating approach).
Considerable advances have been made on the development of methods for retreiving
forest proportions based on CRSD. Few methods have been developed for change
detection and quantification, and they are generally using sampling techniques with
HRSD. The modeling of deforestation requires to consider many parameters which
encompass not only the vegetation characteristics of the area, but also physical and
anthropic characteristics.
This paper is mainly addressing the second issue, after reviewing some techniques
for global estimates of forest areas.
GLOBAL ESTIMATE OF FOREST COVER PROPORTION
The main actors
As far as global assessment is concerned, CRSD such as AVHRR data can provide
valuable information for forest resources assessment. Future sensors (vegetation
instrument on board SPOT 4 and MODIS) are expected to give further possibilities in
this field.
Several institutions or programmes have already started significant activities in
global survey of tropical forests : (i) the European Union with the TREES Project
(TRopical Ecosytem Environment observation by Satellites) where wall to wall coverage
is made using AVHRR data with a sample of HRSD for calibration purposes, (ii) F A 0
with the Forest Resources Assessment 1990 project where TM data are sampled over the
entire tropical belt, (iii) IUFRO (International Union of Forest Research Organizations)
which has published some guidelines for forest monitoring (Paivinen, 1994), (iv) NASA
which is carrying out several projects in South America and Africa (Landsat Pathfinder
project in the framework of the Global Change Research Programme : Chomentowski et
al., 1994), (v) UNEPIGRID which has undertaken some global analysis with AVHRR
data in the amazon basin, and has been involved with TREES in Southeast Asia, (vi) the
Woods Hole Research Centre which has realized a map of South America using
different sensors, (vii) IUCN (International Union for Conservation of Nature and
Natural Resources) and WCMW (World Conservation and Monitoring Centre) which
published in the 1990's the Conservation Atlas of Tropical Forests, (viii) IGBP
(International Global Biosphere Programme) which is working on global change issues.
Over Africa, other regional initiatives with environmental components are in progress or
in preparation : the CARPE (Central African Regional Program for Environment), and
RIMP (Regional Information Management Programme).
Existing methods for global forest survey
Four different methods can be applied for estimating tropical forest areas on a
global basis (Czaplewski, 1992). A first approach is consisting in compiling forest
statistics provided by national or regional forest inventories. F A 0 used this approach in
1980 and to a limited extent in 1990. The main problems of this method are the
discrepancies between the nomenclatures, the variables, the accuracy in the sampling
designs, the dates of the inventories. A second method is based on the analysis of a wall
to wall coverage of CRSD data (AVHRR data). In this approach, forest resources can be
mapped, e.g. located, and estimated using some AVHRR classification schemes.
However, the estimation of forest proportion is presenting a systematic bias due to
aggregation effects. The magnitude of this error depends on three factors : the spatial
resolution of the map, the initial proportions of forest in the landscape, and the spatial
arrangement of forest at fine resolution (Turner et al., 1989; Moody and Woodcock,
1994). It is therefore recommended to apply a correction function which may be derived
from a double sampling design with regression estimator. The regression is performed
between the auxiliary variable, measured from the AVHRR data set, and the target
variable, measured on a sample of HRSD sites. This method has been successfully
implemented by the TREES project (Mayaux et Lambin, 1995). The third method is
aimed at estimating forest proportion from a sample of high resolution images : this
method has been tested and implemented by F A 0 in the Forest Resources Assessment
1990. Moreover, if several images are acquired on each site, it is possible to estimate the
deforestation rate. F A 0 has estimated the deforestation rate between 1980 and 1990. In
the fourth approach, national mapping results are compiled at regional or global level :
as for the first method, this approach is limited by the discrepancies in nomenclature,
scales and dates.
The multi scale approach
The second method, based on CRSD such as AVHRR data, is the only one
providing some estimates of global forest resources and a location of those resources at
the same time. To increase the accuracy of the estimate, many investigators suggested to
use HRSD as reference data (Tucker and al., 1984; Nelson and Holben, 1988; Woodwell
and al., 1987; Malingreau and Tucker, 1988; Paivinen and Witt, 1988; Malingreau and
al., 1989; Stone and al., 1990; Nelson and Homing, 1993). The main advantage of this
approach is the daily availability of data, and the spatial resolution (1.1 Km) which is
more adapted to the scale of study. However, using coarse resolution satellite data leads
to a loss of details which depends on the spatial structure of the landscape (Woodcock
and Strahler, 1987; Townshend and Justice, 1988). The integration of high resolution
satellite data represents a double advantage with the possibility to first validate the broad
scale maps by comparing fine scale observations with AVHRR classification and second
to develop a correction function with respect to proportional errors in order to estimate
tropical forest areas from coarse resolution classifications.
Building a correction function :the lessons of the TREES project
The Joint Research Centre TREES project is aimed at mapping and estimating
tropical forest resources, detecting and monitoring changes and modeling deforestation
(Malingreau and al., 1993). A correction model for proportional errors has been tested
and developed. To calibrate broad scale tropical forest area estimates, an inverse
calibration model was applied (Brown, 1982; Czaplewski, 1992) by integrating in the
double sampling approach a measure of the spatial fragmentation of the forest-non forest
interface (Mayaux and Lambin, 1995). The inversion model can be expressed as (Eq. 1):
where PF, is the forest proportion at fine scale, PC, the forest proportion. at broad scale,
and Sc, a descriptor of the spatial pattern of the class i at coarse resolution. Several
tests have shown that the Matheron index (Kleinn et al., 1993) is a good descriptor of
the spatial structure in AVHRR classifications. It is defines as (Eq. 2) :
number of runs between forest and other cover type pixels
M=
(2)
,/(number of forest pixels),/(total number of pixels)
The two-step correction model for aggregation errors, in which forest cover
proportion at fine and coarse resolution as well as the fragmentation index are calculated
in l3x 13 AVHRR pixels blocks, was developed with two major regression phases : first,
a linear regression was performed within each equal-size subset of similar spatial pattern
between forest cover proportions at fine and coarse resolution, and second, a regression
was performed between the fragmentation measure and the intercept and the slope of the
previous regressions. This calibration procedure improved by 25.5 % the reliability of
the retrieval of forest cover proportions from coarse resolution data by comparison to a
simple correction function relating directly proportions at coarse and fine resolutions
(Mayaux and Lambin, 1995). Further improvements are in progress, with the
introduction of spectral mixture models at coarse resolution scale. The integration of
spatial information in the mixed pixel estimator improved significantly the reliability of
the model (Mayaux and Lambin, 1996).
FOREST CHANGE DETECTION
Digital change detection methods
Forest ecosystems are in continuous evolution. Changes can be defined as "an
alteration in the surface components of the vegetation cover" (Milne, 1988) or as "a
spectrallspatial movement of a vegetation entity over time" (Lund, 1983). The intensity
of change can be abrupt (clear cutting, deforestation, and forest fires) or diffuse and
gradual (stands growth). Various classifications of forest change have been proposed by
Aldrich (1975) who suggested nine general forest disturbance classes, by Colwell (1980)
who proposed a more hierarchical framework, and by Hame (1986) who took a more
mechanistic view of the problem. Ecological aspects have been then developed by
Hobbs (1990), and Khorram (1994) who concentrated more on the spatial environment
in which the change occurs.
Critical issues in forest change detection are the appropriate selection of images, the
choice of sensors, the change categories and the change detection algorithms (Coppin,
1994). Moreover, the change interval length, e.g. the temporal resolution, is a key aspect
in change detection. The accuracy of forest cover change information is closely related
to the maximization of the signal-to noise ratio. When this ratio is not optimum, unreal
changes may be detected, and are caused by differences in scattering conditions,
differences in water content in the atmosphere, variations in the solar azimuth angles,
and sensors calibration inconsistencies (Hade, 1988). Most digital change detection
methods are based on per-pixel classifiers (Teuber, 1990). But the decision rules, which
comprise the complete range of pattern recognition techniques, constitute the most
sensitive aspect in any change detection model. Numerous detection algorithms have
been developed during the past years. They can be grouped into two main categories :
change measurement methods, and classification schemes (Malila, 1980; Pilon and al.,
1987). It is also possible to separate the simultaneous analysis of multitemporal data
versus comparative analysis of independent single date classifications (Singh, 1989).
Change detection methods may be characterized by two main phases : first the data
transformation procedure, and second the analysis techniques applied to delineate the
changed areas (Nelson, 1983). The transformation procedure can be no transformation
(raw data), images differencing, bands ratioing, vegetation index differencing, regression
between bands, multitemporal linear data transformation (principal component analysis
and tasseled cap), change vector analysis, and comparison of single date classifications.
The analysis techniques used to detect change are usually deriving from thresholding,
supervised or unsupervised classification, spatial analysis, or layered spectral/temporal
analysis. For a review of change detection techniques, see Singh (1989) and Coppin
(1994).
The multi scale approach in change detection
How to conciliate broad scale and fine scale observations
Most of the detection change studies have used high resolution satellite data since
the changes are easier to detect at fine scale. At broad scale, change vector analysis
techniques have been applied to characterize vegetation changes in Africa (Lambin and
Strahler, 1994). Very few studies have been carried out using several resolution satellite
data. As far as vegetation monitoring at global scale is concerned, there is a need to get
observations at different scales both in temporal and spatial terms (Malingreau and
Belward, 1992). A relationship between forest clearing rate based on MSS-TM analysis
and fire activity based on AVHRR interpretation was studied in Mato Grosso, Brazil
(Nelson and al., 1987). The results suggested to use AVHRR data as a stratification tool
rather than as a auxiliary variable in the sampling design. Townshend and Justice (1995)
have looked into the spatial frequencies of two MSS single date images and of the
change image obtained by substracting the NDVI. Considerable variability in the spatial
frequency was found on the two images, with a spatial resolution ranging from 125 m to
64 Km. But no relationship was detected between the spatial frequencies of the two
single date images and the change image.
To estimate changes, a simple approach can be applied consisting in substracting
one map (or classification) from another. But the bias and the resulting variance are
unknown and might be substantial. Statistical procedures were tested for estimating
change from thematic maps available for the same region at two times (Van Deusen,
1994). Subsampling was used to obtain estimates of the true class of some pixels at each
of the two times. Simulation results verified that the procedures produce unbiased
estimates and that associated variance estimates are reliable. Van Deusen pointed out
that this statistical approach could be applied on low resolution thematic maps by using
subsamples of high resolution data to refine the true class proportions. In a global forest
monitoring exercise, Beltz and al. (1992) recommended to use multi-stage sampling with
sampling units proportional to forest cover or deforestation, and sampling with partial
replacement.
A correction function for change detection
As for the estimate of forest areas, an inverse correction model can be built to refine
the estimate of forest cover changes. A simple model can be built by relating the
estimate of change at broad scale with the estimate of change at fine scale (Eq. 3 ) :
where APF, and APci are the estimates of changes at fine scale and coarse scale of
class i between time tl and t2. The introduction of the spatial structure in the model
would lead to the following equation (Eq. 4) :
where Sc, (t
,) and Sc (t ,) are the measurements of the forest spatial patterns at time t
and t2. SC,(t characterizes the spatial structure at the inital stage, e.g. at the beginning
of the time inteval during which the change in forest proportion will be estimated : this a
priori knowledge of the spatial arrangement of forest cover can be considered as an
indicator of future deforestation. A fragmented pattern with many forest-non forest
contacts may be more sensitive to deforestation than a continuous block of forest cover.
However, this indicator of future deforestation and the magnitude of change are very
much depending on the type of deforestation, e.g. logging activities, clear cutting,
shifting cultivations ...etc. On the contratry, Sc, (t )characterizes the spatial structure at
the end of the interval, and thus can be considered as an indicator of past deforestation
activities. When CRSD data are not available at time tl and t2, the correction model can
be simplified as follows (Eq. 5), whith t, being between tl and t2 :
,
Preliminary results on a test site
A study was carried out on a test area located in Vietnam where deforestation is
very active, and field observations were available. An AVHRR classification from 199293 and a TM scene (124152 from December 30, 1990) were provided by the TREES
project. A SPOT image was purchased (KJ 277-326 from February 25, 1995). The
acquisition periods of the TM and SPOT scenes correspond to the dry season. Geometric
corrections were performed between all satellite data with a common projection system.
The total RMS between the SPOT scene and the TM scene was 0.13 pixel. The RMS
obtained between AVHRR data and the TM scene was 0.1 1 AVHRR pixel. A
multitemporal unsupervised classification was performed using the three bands of SPOT
and the first five bands of TM. The classes were then grouped into four classes :
unchanged forest, unchanged non forest, deforested areas (between 1990 and 1995), and
clouds or shadows. The AVHRR classification is giving three main classes : dense forest
(forest cover percentage over 70 %), fragmented forest (forest cover percentage between
40 and 70 %), and non forest (forest cover percentage below 40 %). Forest cover
proportions were calculated in 9x9 blocks of AVHRR pixels.
.
Figure 1 Relationship between the deforestation rate at fine scale and the
proportion of AVHRR fragmented forest.
Proportion of AVHRR fragmented forest (1993)
The relationship between the deforestation rate measured at fine scale and the proportion
of AVHRR fragmented forest (figure 1) shows a rather good correlation (coefficient of
determination is 0.61). It can be noticed that the deforestation rate increases with the
proportion of fragmented forest, but this trend stops at a proportion of 50 %. A similar
trend is observed with the Matheron index, with a less good correlation with the
deforestation rate (r2 = 0.33). On the contrary, when AVHRR dense forest increases, the
deforestation rate decreases. It can be concluded that the deforestation rate is in this case
governed by the fragmentation pattern observed at broad scale. The AVHRR
classification and the TMISPOT classification are presented in figure 2.
Figure 2 . Left : multitemporal classification TM(1990)-SPOT(1995)with unchanged forest
(black), unchanged non forest (white) and deforestated areas (grey). Right : AVHRR
classification (1993) with dense forest (black), fragmented forest (grey) and non forest
(white).
It can be concluded that there is a consistent relationship between the estimate of
deforestation at fine scale and forest cover parameters at broad scale. The AVHRR
fragmented forest class is already containing some information of the degree of
fragmentation within the pixel. The fragmentation pattern measured with the Matheron
index in a block of pixels is likely to improve the correction model. However, more
HRSD sites are needed to test the model and its real performance. Moreover, it is
suggested to develop a more complete model integrating the estimate of forest cover
change at broad scale (eq. 4). For this purpose, a more complete set of AVHRR data is
required. Phase 2 of the TREES project which has just been launched should focus on
global forest cover change with the acquisition and processing of new AVHRR data set.
It can be also pointed out that the correction model should pave the way for a global
deforestation model with the integration of auxiliary parameters such as socio-economic
data, population figures, infrastructure, and local environment conditions (topography,
soils...). The Tropical Forest Information System (TFIS) set up by the TREES project
will be the basis for reaching this final goal.
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