A WCS-BASED APPROACH TO INTEGRATE SATELLITE

IMAGERY DATA IN WILDFIRE SIMULATION

António Esteves and António Pina

Departamento de Informática, Universidade do Minho, Braga, Portugal

Keywords:

OGC Standards, WCS, Satellite Datasets, MODIS Sensor, Wildﬁre Simulation.

Abstract:

This paper describes the integration of multi-dimensional data from satellite sensors in a Civil Protection

application that simulates ﬁre spread. The approach uses standard Web Coverage Services from OGC to fetch

and process land cover and recently burned areas, available in the form of satellite imagery data previously

captured by the MODIS sensor, to automatically generate renovated fuel maps. The proposed architecture

is based on rasdaman, a domain-independent database management system (DBMS) that offers a suite of

WCS services on top of the DBMS. In the current work we extended rasdaman with facilities to: (i) insert

and retrieve multi-layer coverages from WCS, (ii) support new formats, such as HDF, adequate for satellite

imagery and multi-layer ﬁles, and (iii) support Coordinate Reference Systems. We also demonstrate that it

is feasible to use MODIS datasets to automatically compute valuable and regularly updated fuel maps, used

as input of ﬁre spread simulations. The results also show that in spite of using inexpensive general and low

resolution (500m) MODIS maps, we obtained quite acceptable results when compared with the static ones,

which are tailored and higher resolution (80m).

1 INTRODUCTION

This work is part of the Portuguese CROSS-Fire

project aimed to develop a grid-based risk manage-

ment decision support system, for the Civil Protec-

tion (CP) authorities, using wildﬁres as the main case

study, and FireStation (Lopes et al., 2002) a simulator

to compute the ﬁre spread over a complex topography.

The paper reports the work that has been carried

on to improve the quality of ﬁre spread simulations

by automatically renewing the old static fuel maps

used as input, with fresh data obtained from satellite

sensors, as an alternative to a time and money con-

suming human update. The approach uses standard

Web Coverage Services (WCS) from Open Geospa-

tial Consortium (OGC) to get and process land cov-

ers and recently burned areas, available in the form of

satellite imagery data, to automatically generate ren-

ovated fuel maps.

To integrate information models, encodings, and

metadata we use: i) a central core of Web services,

implemented as a set of Web Processing Service

(WPS) algorithms, ii) the EGEE/EGI to provide raw

technological capability provision, iii) a Spatial Data

Infrastructure (SDI), to provide the access and man-

agement of remote geospatial data from remote sen-

sors/satellites or in-situ sensors, and iv) a GUI that

provides users with visualization facilities, and meta-

data and spatial data queries (Pina et al., 2011).

The overall software development take advantage

of available implementations of the standards pro-

posed by the OGC: (i) the OGC Web Services (OGC-

WS), (ii) the OGC Sensor Web Enablement (OGC-

SWE) services, and (iii) gvSIG, a full-featured Open

Source GIS desktop GUI.

The simulator requires both static and dynamic in-

put data. The static data comprises: i) the terrain di-

vided into cells, each one characterized by its altitude

and fuel type, ii) the wind conditions, and iii) the con-

trol parameters. The dynamic data, is provided by a

Remote Weather Station Sensor Observation Service

(SOS) client. Given the potential of the imagery data

that may be obtained from satellite sensors we eval-

uated the possibility of computing improved and up-

dated fuel maps based on: i) updated vegetation, ii)

newly burned areas, and iii) land cover type, by get-

ting and processing data from MODIS (USGS, 2008)

sensors aboard of Terra and Aqua satellites. To main-

tain the commitment to OGC standards we use the

WCS (Whiteside and Evans, 2008) and its Web Cov-

erage Processing Service (WCPS) (Baumann, 2009)

extension.

238

Esteves A. and Pina A..

A WCS-BASED APPROACH TO INTEGRATE SATELLITE IMAGERY DATA IN WILDFIRE SIMULATION.

DOI: 10.5220/0003898002380241

In Proceedings of the 8th International Conference on Web Information Systems and Technologies (WEBIST-2012), pages 238-241

ISBN: 978-989-8565-08-2

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

Among the related work we should mention the

SAFORAH project, the work by Daniel Mandl at

NASA, Sentinel/Digital Asia, GEO Grid, and RGI-

project projects, and the tools developed by NASA to

process and deliver data from MODIS/Aster sensors:

the MODIS Rapid Response system and Fire Informa-

tion for Resource Management System.

2 OGC STANDARDS

The WCS standard speciﬁes 3 operations: GetCapa-

bilities, DescribeCoverage, and GetCoverage. These

operations allow spatial, temporal and band subset-

ting, scaling, reprojection, and format encoding.

The WCS-Transactional (WCS-T) extension to

WCS 2.0 allows the insertion and updating of cov-

erages stored on a WCS server. WCS-T speciﬁes an

additional Transaction operation.

The Web Coverage Processing Service (WCPS)

extension to WCS 2.0 deﬁnes a ﬂexible interface for

the navigation, extraction, and ad-hoc analysis of

large, multi-dimensional raster coverages. WCPS is

abstract in that it does not anticipate any particular

protocol, and speciﬁes an additional ProcessCover-

ages operation.

Figure 1: MODIS tiling scheme.

3 MODIS SENSOR

MODIS sensor is placed aboard of Terra and Aqua

satellites, both covering the entire surface of the

planet Earth. The planet is divided into 36X8

tiles, and the Portuguese continental land and islands

spread over 3 tiles: h16v05, h17v04 and h17v05.

The spatial resolution of MODIS data can be 250m,

500m or 1000m and classiﬁed in 3 categories: atmo-

spheric, land cover, and oceanographic data. NASA

provides this information at several levels, L0 up to

L4, where L0 is raw data and L4 are the most pro-

cessed products. MODIS L2 to L4 products are de-

ﬁned on a global sinusoidal grid. The grid is divided

into ﬁxed-area tiles of approximately 10x10 degrees

in size. Each tile is assigned a horizontal (h) and ver-

tical (v) coordinate ranging from 0 to 35 and 0 to 17,

respectively (ﬁgure 1) (Boschetti et al., 2009).

Produced from daily surface reﬂectance,

MCD45A1 is a monthly L3 500m product that

approximates the date of burning, and maps the

spatial extent of recent ﬁres. MCD12Q1 L3 product

describes land cover properties derived from one

year of observations. It is delivered yearly at 500m

resolution and incorporates 5 different land cover

classiﬁcation schemes: IGBP, UMD, LAI/FPAR,

NPP, and PFT. Using MODIS reﬂectances, vegetation

indices are computed daily. Difference Vegetation

Index (NDVI) and Enhanced Vegetation Index (EVI)

are among these indices. MOD13Q1 is a L3 grid

product delivered every 16 days at 500m spatial

resolution. In the present work, we are interested in

the level-3 (L3) land products described in table 1

and selected the IGBP scheme since it is the most

detailed and comprehends 17 classes of land cover

type.

Table 1: MODIS land products.

Product Product Resol. Temporal

Info. Code (m) Granularity

Burned Area MCD45A1 500 Monthly

Veg. Indices MOD13Q1 250 16 Day

Land Cover MCD12Q1 500 Yearly

4 ARCHITECTURE

The architecture that integrates the data provided

by MODIS on the ﬁre spread simulator is sup-

ported by the following technologies: the Hierar-

chical Data Format (HDF) used by NASA to pub-

lish MODIS data, the Java HDF Object Package

that provides an object-oriented interface to HDF

data objects (The_HDF_Group, 2010), and ras-

daman ((rasdaman GmbH), 2011), which is a domain-

independent database management system (DBMS)

with a WCS suite of services built on top of it. From

bottom to top, the architecture (ﬁgure 2) includes:

• The postgreSQL relational DBMS that manages

and stores the coverages and their metadata in two

separated databases;

• The core modules of rasdaman that mediate the

interaction between the WCS services (petascope)

and the DBMS, and the format converters;

• petascope implements a OGC-compliant WCS

2.0 suite of services: WCS, WCPS, and WCS-T;

AWCS-BASEDAPPROACHTOINTEGRATESATELLITEIMAGERYDATAINWILDFIRESIMULATION

239

Firestation

getCoverage ()

Format Converters

WCS-T client WCS client

WCS-T WCPS

WCS

Petascope

WCS Suite

(rasdaman)

TIFF

Converter

JPG

Converter

ArcGrid

Converter

rasdaman

...

Other Modules

(rascontrol, rasmgr, rasql processor, …)

rasql

Relational DataBase

Managment System

(postgreSQL)

sql

Coverage

WCS Suite

of Clients

Download HDF files

HDF processing algorithms

Generate XML files to insert data in WCS

GDAL

WCPS client

Local HDFs

Coverages

Metadata

Satellite Data Manager

MODIS

FTP

Figure 2: Proposed architecture, based on rasdaman, to ex-

tend the ﬁre spread simulator with satellite data.

• The WCS suite of clients implements the clients

that dialogue with petascope WCS services, gen-

erating the adequate XML requests and parsing

the XML answers from the WCS servers;

• The Satellite data manager provides: i) a mod-

ule to download HDF ﬁles from the MODIS site

to a local directory; ii) a collection of algorithms

to process the HDF ﬁles; iii) a module to gener-

ate the XML ﬁles necessary to insert datasets and

their metadata in WCS.

The developed HDF processing algorithms permit

to: (1) generate datasets for tiles missing on MODIS

site (full ocean tiles), (2) extract a dataset from an

HDF, (3) merge datasets when the requested region

spans more than one tile, (4) reproject a dataset in

order to combine datasets that use different projec-

tions, (5) extract a subset from a dataset, (6) modify

the resolution of a dataset for combining/comparing

datasets with different resolutions, (7) modify the

range of a dataset, (8) compare datasets, and (9) com-

bine datasets in a fuel map. Comparing datasets is

needed, for example, to validate if a dynamic land

cover scheme may replace a static scheme (ﬁgure 3).

The algorithm that combines a land cover dataset with

one or more burned area datasets, in order to update

the yearly produced land cover with the monthly pro-

duced burned areas, is summarized in ﬁgure 4.

5 RESULTS

Initially, we compared the static and the MODIS-

based land cover classiﬁcation schemes, by previ-

ously replacing each class of the MODIS map by

Local

multi-dataset files (HDF)

Extract

Dataset

single-dataset

files

(HDF)

Merge

Datasets

merged dataset

(HDF & ArcGrid)

Reproject

Dataset

reprojected

dataset

(HDF & ArcGrid)

subset

(HDF & ArcGrid)

MODIS dynamic

landcover

(HDF & ArcGrid)

ADAI static

landcover

(ArcGrid)

landcover

difference

(HDF & ArcGrid)

1 2

Extract

Subset

Modify

Resolution

Modify

Landcover

Scheme

Compare

Landcovers

subset with

modified resolution

(HDF & ArcGrid)

Modify

Resolution

Figure 3: Processing ﬂow to compare static (ADAI) land

cover scheme with MODIS-based scheme.

landcover

Combine

landcover

and burned

areas

burned

areas

dynamic

fuel map

Local

Figure 4: Processing ﬂow to compute a MODIS-based dy-

namic fuel map.

its static (ADAI

) equivalent, to be able to calculate

the difference between the maps. Based on the re-

sults from this comparison, a mapping from static to

MODIS land cover classiﬁcation scheme was elabo-

rated (Esteves, 2011). To quantify the quality of the

mapping MODIS 7→ static land cover schemes, we

measured the difference among the static classes, con-

sidering 6 fuel characteristics of each class: fuel load-

ing dead after 1 hour/10 hours/100 hours, fuel load-

ing live, heat content, and moisture content of extinc-

tion (Cruz, 2005). Subsequently, we estimated the

maximum value of the difference for each character-

istic (1 to 6), among all land cover classes (0 to 16).

Finally, the global difference between two static land

cover classes was computed. Figure 5 displays the

results of comparing MODIS-based with static land

cover map. Black colour pixels represent a perfect

match (0% difference) between maps and a lighter

ADAI - Associação para o Desenvolvimento da Aerod-

inâmica Industrial, Coimbra, Portugal, www.adai.pt.

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240

Figure 5: Difference between the MODIS-based and static

land cover maps for Portuguese land.

colour means a greater difference. The mean differ-

ence is 14% and the average difference is 2%.

6 CONCLUSIONS

rasdaman is a running project still lacking some fa-

cilities. In the course of this work, we made sev-

eral extensions, including: i) insertion and retrieving

of multi-layer coverages from WCS, ii) support for

formats, such as HDF, for satellite imagery and for

multi-layer ﬁles, and iii) support to Coordinate Ref-

erence Systems. It is well known that MODIS reso-

lution is moderate. However, our results prove that

it may be used to improve the quality of static fuel

maps, by combining fresh burned areas information

with the land cover datasets, and to feed Firestation

with the generated valuable fuel. These maps may

be further improved with vegetation data produced

more frequently. In the future, we plan to evaluate the

architecture with higher resolution sensors/satellites,

such as ASTER/Terra, ETM+/Landsat-7, SAR/ESR-

2 and ASAR/Envisat, PROBA, SPOT-4/5, AVNIR-

2/ALOS, or DEIMOS-1. We also plan to contribute,

with others academics, industrial institutions and Por-

tuguese Civil Protection, to create a national platform

for fast and reliable wildﬁre management, based in

a distributed computing infrastructure built on top of

the EGI/Grid middleware.

REFERENCES

Baumann, P. (2009). The OGC Web Coverage Processing

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