Exploring the synergy between
environmental
geo-referenced field data and remote sensing spatial information in
Central Africa.
How to combine the CyberTracker with
the TREES information system.
Pascale Janvier, Philippe Mayaux
(JRC-TREES project GVM, Unit TP 440 I-21020, Ispra, Italy))
(pascale.janvier@libero.it,
philippe.mayaux@jrc.it )
- The
TREES project
An European initiative for
pan tropical deforestation monitoring of the Joint
Research Centre
A large-scale project
initiated in the early 1990s, the TREES (Tropical
Ecosystem Environment observations by Satellite) project
is dedicated to the development of techniques for global
tropical forest inventory using satellite imagery. The
TREES II project finalized the development of a prototype
information system for monitoring tropical forest cover
at a pan-tropical scale. The work was being progressively
adapted to meet the forest related information needs of
the services of the European Commission. In particular
efforts were made with respect to the European
Development Fund (EDF) regional ECOFAC project in Central
Africa.
The project released new
tropical forest maps derived from coarse spatial
resolution satellite images (Fig. 1). After the
publication of the Central Africa map in 1998, the
Vegetation map of South America at 1:5.000.000 scale was
published in September 1999. These maps are derived from NOAA-AVHRR 1km satellite data acquired in 1992/1993.
Those continental maps are currently updated using SPOT-4
VEGETATION data in the framework of the Global Land Cover
2000 project, a component of the Millenium Ecosystem
Assessment.
The objective of the maps
is to document the dense humid forest extent (both
lowland and upland) at a continental scale. The satellite
data were used to map three broad classes of vegetation -
dense humid forest, fragmented forest and non-forest. In
areas of rapid change the TREES map gives a relatively
up-to-date view of the forest cover, exhibiting a higher
spatial accuracy and precision than conventional maps. A
stratification of "deforestation hot spot
areas" was performed based on individual expertise.
Forest cover change during the period 1990-1997 was then
estimated from 100 sampled sites using high-resolution
optical remote sensing satellite imagery (Fig. 2).
Fig. 1- Mosaic of satellite images
over Central Africa
Fig. 2- High resolution image
The TREES project uses
also coarse resolution satellite imagery for the
day-to-day monitoring of forest condition. The project
used thousands of scenes acquired from the ATSR sensor
(on board ESA’s ERS-2 satellite) and from the
VEGETATION sensor (on board SPOT-4 satellite). The
evaluation of these data shows the potential for the
updating of existing vegetation maps at regional scale
and for identifying active deforestation areas. These
methods could now be integrated into an operational
analysis and monitoring system.
As well as paper maps, the
TREES project provides access to the information in
digital form via a bespoke GIS, the Tropical Forest
Information System (TFIS).
The TFIS is designed to
be:
- An archive of the
TREES project products and results; including
satellite classifications, GIS layers and
non-spatial information
- A tool for query,
browsing and visualization of data in the archive
- An analysis tool
bringing together diverse datasets
- A basis for modelling
deforestation processes.
It is comprised of four
components - a metadata database (Fig. 3), a spatial
database, an a-spatial database, and GIS software. The
system is implemented using standard software products in
MS Windows NT environment with GIS ArcInfo to process
vector data sets, Erdas Imagine for the satellite image,
MS Access to design the different databases (TFIS,
ECOFAC, IMAGES) and metadata catalogue and ArcView to
query, browse and visualize data sets.
Fig. 3 - TFIS Metadata
Browser
TFIS allows the users to
analyze the tropical forest maps in conjunction with
other spatial data sets such as elevation, other forest
maps (e.g. IUCN, WCMC) and non-spatial data. A dedicated
TFIS for the Central African ECOFAC project was installed
in Libreville. The project supported an on-site TFIS
within the Directorate General for Environment in
Brussels. A strategy was developed to provide an
interface available to a wider numbers of partners: An
Internet-TFIS prototype was developed.
- The
collaboration TREES-ECOFAC
- Since 1997, the TREES
project is collaborates with the ECOFAC
programme, also funded by the European Union,
working on similar areas, but at different scales
and with different techniques. In other words,
ECOFAC had accumulated an enormous amount of
field knowledge which was missing to TREES, and
TREES had a expertise in the set-up spatial
databases which was important for ECOFAC.
Moreover, the TREES project produced information
about the forest cover in a smaller scale but in
a quite exhaustive manner on the 6 continental
countries of Central Africa.
The respective scales of
the TREES and ECOFAC projects are complementary.
The collaboration is aiming at the improvement of
the management of the spatial data in order to
optimise the field patrols and to increase the
understanding of the animal migrations. The field
observations, collected by the CyberTracker system are transferred into a
relational database and integrated in
a Geographical Information System for the
spatial analysis. The TREES Project is in charge
of the set-up of the database and the
Geographical Information System, the vegetation
mapping and the archiving of spatial data
generated by ECOFAC. On the other hand, ECOFAC
contributes to the validation of the
interpretation of satellite datasets and a better
understanding of the biodiversity of the region.
Collecting field
data for validation of map derived from satellite
imagery:
Reliable analysis
of the satellite radar images comes up against of
the lack of appropriate field observations.
Effectively, the useful parameters for radar
signal modelling are quite specific and are not
available in the ecologic literature. It has been
decided to collect series of parameters about the
different types of forest such as open forest,
closed forest, or swamp forest and also the
description of the environment on the Odzala
site. (Odazla National Park – Congo) These
observations will allow to verify the assumption
announced during the analysis. An exhaustive list
of different types of vegetation has been
integrated within the CyberTracker.
- Using
the CyberTracker in the TREES project
-
- Integration
of the field data to the GIS
The main aims of this
study described below are to demonstrate the
benefits of integrating the results of the
CyberTracker into a Relational Data Base and into
a GIS, to explore the possibilities for spatial
analysis (Fig. 4). The potentials provided by the
GIS for selecting and displaying the results
according to the location, time or other data
attributes are known to be highly useful. The
methodology developed is summarised below:
-
- Step1:
Exporting from CyberTracker
Creation of
tables with attributes containing
acquisition time and lat/long position
and export in *.dbf or *.xls format
readable in MS Access.
-
- Step 2:
Storage in Relational Data Base
In MS Access:
Sort out, data harmonization and queries
for pre-analysis and to permit linkages
with GIS
-
- Step
3:Spatial Analysis using GIS
In ArcView:
SQL Connect, Queries, New tables,
Mapping, Overlaying and Spatial analysis.
Fig. 4 –Integration
of the field data to the GIS
-
-
- Data
cleaning and harmonization
- The
initial table called "cyber
Observation" is created from
the CyberTracker database and
contains the complete attributes
of the cyber such as lat/long,
observer, displacement, activity,
and index. It is imported in a
rough manner with all the errors
contained in the original
database.
In this table, the
same observation can be described
in different manner, with
capitals or not, a mix of
capitals and minuscule, with
accent or not. For the animal
species, the description can
correspond to one or many fields.
For example, an elephant
corresponds to only one field,
while a "céphalophe à dos
jaune" is first qualified as
"antilope", then
"Cephalophe" and
finally "Cephalophe a dos
jaune". This is a problem
for an accurate analysis (Fig. 5).
Fig. 5 - List fauna table in MS Access
To
facilitate the analysis, and to
produce a consistent Data Base, a
methodology (Fig. 6) has been
implemented to harmonize the data
by using cleaning functions and
queries within MS Access. This
step will improve by the quality
of the data.
Fig. 6 -
Design of the methodology
developed in MS Access
In
MS Access, a set of tables has
been designed, according to the
different types of observations
such as fauna, human and
vegetation. These tables were
populated with the cleaned
descriptions. These tables are
used for the data harmonization
processing and could be used, in
the future, for the translation
from English to French, and for
retrieving the scientific name of
species. A set of predefined
queries has been already
implemented regarding the list
tables. The cleaning process is
carried out using both the lists
and queries and is creating a new
table called "new cyber
observation" (Fig. 7).
Fig. 7-
Result of the cleaning function
- Development
of thematic queries
A set of thematic
queries has been developed using MS Access macro
commands. Running these macro commands
automatically generates new tables which are a
selection of records related to some specific
phenomena for the spatial analysis. A set of
tables has already been created such as the table
of the different species, the calculation of
distances using Euclidian distance formula, the
densities per quadrant, the distance covered per
quadrant and by month, etc.
For each species a set of
queries has been designed (e.g. elephant):
- Elephant_all_observations
- Elephant_fresh_dropping
- Elephant_fresh_tracks
- Elephant_indice
d’abondance (number of fresh
droppings per km of patrol)
- Elephant_indice
d’abondance (number of fresh tracks
per km of patrol).
-
-
- Loading
tabular data from the databases
- To
query, to visualize and for basic
spatial analysis, the database is
connected to a GIS ArcView (AV).
Based on AV’s SQL connection
feature, a connection has been
established to the database
server, (MS Access), a SQL query
allow retrieving requested
records from the database. The
retrieved records become a new
table in the project. It is
possible to develop some other
specific queries (Fig. 8) for
creating and editing spatial
data.
Fig. 8 – SQL
Query to create new tables
- Visualization
and spatial analysis
Our awareness of the
complexities of the world increases our desire to
understand the nature of the spatial data and
spatial pattern. The role of visualization in
geographical analysis is not limited to maps and
remotely sensed imagery, but extends to numeric
and statistical analysis as well. A series of
exercises has been done to illustrate these
talks. In the following example (Fig. 9), we show,
for each quadrant, the frequency of the patrols
during one year overlaid with the number of
elephant dropping observations over a year
period. From such a display, it is possible to
establish that the number of dropping
observations is spatially correlated with the
patrol effort.Fig. 9 –Spatial analysis
-
- Forest
mapping
- Accurate estimation
of tropical forest cover is arguably the most
important scientific technical aspect of TREES.
The TREES project aims to estimating forest cover
using satellite images, 1km resolution. Because
of the coarse spatial resolution of these images
the problem of spatial aggregation exists.
Improvement of the forest estimation is achieved
by using the high images (TM), 30m resolution,
(Fig.10) to calibrate the coarse resolution
classification. At the regional scale, TM was
used by the TREES project as the reference
training dataset. ECOFAC was also a beneficiary
of this expertise in the interpretation of high
spatial resolution satellite images (SPOT,
Landsat).
Fig. 10 – High
resolution image on Odzala National Park
Fig. 11- Overlaid with the
CyberTracker vegetation observation
Indeed, we produced a
vegetation map for the national park of Odzala (Congo)
derived from Landsat ETM data. The field observations
collected by CyberTracker represented a help for image
geo-referencing and interpretation and for map validation
at a level never achieved before. The two Landsat 7 ETM
images were classified into 9 classes (Fig. 12). A part of
the Cyber observations was used during the
interpretation, the rest for the validation (Fig. 13).
It must be underlined that
the satellite technologies participate from two sides to
a better management of the protected areas : with the
global positioning systems for geo-referencing the field
data (CyberTracker) and the Earth observation images for
mapping (Landsat).
Fig. 12 –
Vegetation map
Fig.
13 - Discrepancy between the TREES map and the CyberTracker
observations
- Future
In the future, we envisage
to improve the following points:
-
- The
classifications can be refined using new
sensors, as Ikonos, 1m spatial
resolution, (Fig. 14) and integrate
automatically the CyberTracker
observations in the process of
classification
- A
characterization of the park appropriate
to the management and to the ecological
analysis (percentage of different type of
vegetation per quadrant, distance to
villages…)
- A better
definition of the required accuracy
- A more
rigorous calculation of the distance
(Euclidian distance formula)
- An
optimisation of the frequency of the data
analysis. Monthly, yearly
- A stability
in the structure of the CyberTracker
(need for the relational database)
Fig. 14 - Ikonos Image.
- References
- Achard F., Eva H.,
Glinni A., Mayaux P., Richards T., Stibig H.J.,
1997, Identification of deforestation hot spot
areas in the humid Tropics, TREES publication
series B, Research report no 4., EUR 18079 EN,
European Commission, Luxembourg, 100 p.
- Eva, H.D., G.,
Glinni, A., Janvier, P., and Blair-Myers, C.,
1999, Vegetation Map of South America, Scale
1/5M, TREES Publications Series D, N°2, European
Commission, Luxembourg, EUR 18658.
- Mayaux, P., Janodet
E., Blair-Myers, C. and P. Janvier, 1997,
Vegetation Map of Central Africa at 1:5,000,000,
TREES Series D: Thematic output No 1, EUR 17322
EN.
- Mayaux, P., De
Grandi, G.F., and Malingreau, J.P., 2000, Central
Africa forest cover revisited: a new approach
based on a multi-satellite analysis, Remote
Sensing of Environment, 71:183-196.
- Richards T.S., J.
Gallego, and F. Achard, 2000, Sampling for forest
cover change assessment at the pan-tropical
scale, Int. J. Remote Sensing, 21
(6&7):1473-1490
- Stibig H.-J., F.
Achard , H. Eva , P. Mayaux, P. Janvier and T.
Richards, 2000, Monitoring of Tropical Forest
Cover at global and regional scale using Earth
Observation Satellite Data, XXI IUFRO World
Congress Kuala Lumpur, Malaysia 7-12 August 2000.
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