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By F. Del Frate and M. Picchiani

Tor Vergata University, Italy
 

Landsat image of
The Castel Fusano pinewood (Landsat colour composite)

The ability to detect forest fires and assess their impacts is an important component of being able to monitor environmental changes. Optical satellite sensor data has been used successfully to map fire scars and post-fire recovery. However there are some limitations with this technology, the greatest being the inability to penetrate clouds. When a systematic survey of an area is required, the use of Synthetic Aperture Radar (SAR) imagery should also be considered. C-band space-borne radars such as those on ERS-1, ERS-2 and Envisat have considerable potential for monitoring and assessing the extent and severity of wild fires. However, the effects of soil moisture and low vegetation may affect the observed backscattering and have to be taken into account.

IMPORTANT NOTE: This lesson requires Bilko 3.4 from February 2010 or later, as earlier versions of the software can not open and display SMOS data as described in the lesson.

Introduction

The ability to detect fires and assess their impacts is an important component of being able to monitor environmental changes. In particular, the rate of biomass regrowth over burned areas can be a crucial factor in assessing the damage resulting from a forest fire. In some cases the recovery process is very fast and after only a few years the burned areas will be completely repopulated. In other cases such a process can take even decades hence the environmental and economic impact of the fire is much greater.

While there is a general understanding of how vegetation recovers after fire, the response over large areas can be complex because of variations in pre-fire vegetation cover, fire severity, and landscape-scale variations in topography and soil conditions.

Satellite data provide the possibility to monitor vegetation recovery over large areas. Indeed, previous studies have shown how measurements from spaceborne payloads may be used either for the fire impact assessment or for the characterization of the forest recovery from the fire (Background Refs. 3, 7, 8). In such a context, the use of synthetic aperture radar (SAR) data is attractive due to the near real-time information provided by the SAR images, irrespective of Sun illumination or the cloud cover, which makes this type of data very suitable for the management of emergencies over large areas.

Lesson Overview

This lesson shows how to use multi-temporal SAR data from ERS-2 to detect deforestation due to fires, and to monitor subsequent vegetation recovery. Typical pre-processing procedures for the analysis of SAR data will be introduced first. This will be followed by activities aiming at understanding the multi-temporal behaviour in forested and not forested (bare soil) areas. Using a dramatic fire event in the Mediterranean region as an example, it will be shown how the measured radar backscatter can be used to detect the fire scar and to monitor vegetation regrowth. We’ll also see how supporting information can be obtained from optical data (using Landsat-7 ETM+ as an example).

Aim and objectives

At the end of the lesson you should be able to

Lesson content

The lesson guides you through the steps needed to prepare a SAR time series of images so that they can be used to detect fire scars and then how to monitor forest regrowth also using Landsat optical data. The lesson is divided into 5 sections:

  1. Radiometric calibration and co-registration of a SAR time series
  2. Speckle reduction
  3. Analysis of forest and bare soil backscattering time series
  4. Fire scar detection with multi-temporal SAR data
  5. Use of optical data from Landsat for monitoring forest regrowth

Data and tools for this lesson

Files needed to complete the lesson acivities

All data and tools needed to complete the lesson activities are listed below. We also provide the original images of pre-processed data sets should you wish to download these.

Data in their original format

The following Level 1 precision image mode ERS-2 data from Castel Fusano (Rome) are used in the lesson in their original format. Each has a spatial resolution of about 20m.

Preprocessed data sets required for completing the lesson

These data are all made available as Bilko sets, and are included in the download file LearnEO_L7_tools.zip

coregistered_stack_cf.set: Bilko set consisting of the following SAR data, which must be kept the same folder as the set (dates of acquisition in brackets):

LE71910312000293EDC00.set: Bilko set consisting of the following data, which must be kept in the same folder as the set:

LE71910312001215EDC00.set: Bilko set consisting of the following data, which must be downloaded into the same folder as the set:

Bilko tools

Formula documents:

images_coregistration.tbl: Table of ground control points to co-registrate two images.

Original versions of data sets preprocessed for the lesson

The following images have been pre-processed and saved as Bilko sets in order to save down-load time and minimize the time needed to complete the lesson. They are not needed in order to complete the lesson, but are available for download.

Downloading the lesson

The lesson downloads contain everything you need to complete the lesson. This includes the data and tools listed above, and three PDF documents:

To download the lesson and data you must be a registered user. If you have not yet done so you can register here. Once your registration is confirmed you can download the lesson after submitting your registered e-mail address below. This will take you to the download page where you can select the documents, tools and data you wish to download.

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