ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 555–563

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ISPRS Journal of Photogrammetry and Remote Sensing

journal homepage: www.elsevier .com/ locate/ isprs jprs

Pre-processing of a sample of multi-scene and multi-date Landsat imageryused to monitor forest cover changes over the tropics

Catherine Bodart a, Hugh Eva a, René Beuchle a,⇑, Rastislav Raši a, Dario Simonetti b, Hans-Jürgen Stibig a,Andreas Brink a, Erik Lindquist c, Frédéric Achard a

a Joint Research Centre of the European Commission, Institute for Environment and Sustainability, Global Environment Monitoring Unit, TP 440, I-21027 Ispra (VA), Italyb Reggiani SpA, Joint Research Centre of the European Commission, Institute for Environment and Sustainability, GEM Unit, TP 440, I-21027 Ispra (VA), Italyc United Nations Food and Agriculture Organization (FAO), Forestry Department, Viale delle Terme di Caracalla, I-00153 Rome, Italy

a r t i c l e i n f o a b s t r a c t

Article history:Received 28 April 2010Received in revised form 5 January 2011Accepted 17 March 2011Available online 29 April 2011

Keywords:ForestryCalibrationMatchingLandsatGlobal

0924-2716/$ - see front matter � 2011 Internationaldoi:10.1016/j.isprsjprs.2011.03.003

⇑ Corresponding author. Tel.: +39 0332785201.E-mail address: rene.beuchle@jrc.ec.europa.eu (R.

In support to the Remote Sensing Survey of the global Forest Resource Assessment 2010, the TREES-3 pro-ject has processed more than 12,000 Landsat TM and ETM+ data subsets systematically distributed overthe tropics. The project aims at deriving area estimates of tropical forest cover change for the periods1990–2000–2005. The paper presents the pre-processing steps applied in an operational and robust man-ner to this large amount of multi-date and multi-scene imagery: conversion to top-of-atmosphere reflec-tance, cloud and cloud shadow detection, haze correction and image radiometric normalization. Theresults show that the haze correction algorithm has improved the visual appearance of the image and sig-nificantly corrected the digital numbers for Landsat visible bands, especially the red band. The impact ofthe normalization procedures (forest normalization and relative normalization) was assessed on 210image pairs: in all cases the correlation between the spectral values of the same land cover in both imageswas improved. The developed automatic pre-processing chain provided a consistent multi-temporal dataset across the tropics that will constitute the basis for an automatic object-based supervisedclassification.� 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier

B.V. All rights reserved.

1. Introduction

Tropical regions have been undergoing rapid changes in forestcover since the 1980s (Achard et al., 2002; Hansen et al., 2008a;DeFries et al., 2002). These changes have impacts on our globalenvironment, in particular through carbon emissions to the atmo-sphere and loss of biodiversity. Tropical deforestation accounts forapproximately 15% of global CO2 emissions (van der Werf et al.,2009) and forest clearing in the humid tropics reduces habitatthroughout one of the most bio-diverse ecosystem on Earth (GlobalBiodiversity Outlook 3, 2010). In addition, the modification of for-est resources may have a long-term impact on ecological and so-cio-economic stability and sustainable food production throughforestry exploitation and associated agricultural activities. A globaland systematic monitoring of forest cover is important to assessaccurately these potential impacts (Lepers et al., 2005; Ramankuttyet al., 2007) and to better inform decision-makers at national andinternational level, in particular in the context of a potential REDDmechanism (Reduction of Emissions from Deforestation and

Society for Photogrammetry and R

Beuchle).

Degradation) of the UNFCCC (United Nations Framework Conven-tion on Climate Change) (Mollicone et al., 2007). Remote sensingimagery offers repetitive data acquisition, a synoptic view ofinaccessible areas and consistent image quality. For land covermapping at global or continental scale, coarse spatial resolutionimagery, such as AVHRR (Advanced Very High Resolution Radiom-eter), SPOT VEGETATION, MERIS (The Medium Resolution ImagingSpectrometer) or MODIS (Moderate Imaging Spectrometer) withpixel size between 250 m and 1 km, has been often considered asthe only solution to cover repetitively such large areas (Achardet al., 2007). Although the daily availability and the low cost ofthese data permit repetitive acquisitions in very cloudy regions likethe tropics, their coarse resolution only allows the detection oflarge scale deforestation patterns (Hansen et al., 2008a) and failsto characterize most of the forest changes and degradation pro-cesses as they often occur at the landscape level. To overcome thisproblem, large data sets of medium spatial resolution (�30 m)satellite imagery have been used for mapping and monitoring offorest cover at regional levels (INPE, 2009; Pekkarinen et al.,2009; Skole and Tucker, 1993). Hansen et al. (2008b) used a treecover product derived from 500 m resolution imagery (MODIS) tocalibrate a ‘wall-to-wall’ Landsat Thematic Mapper coverage of

emote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

556 C. Bodart et al. / ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 555–563

the Congo Basin which was then the basis to produce a map ofdeforested areas. These studies have been conducted only atnational or regional levels. A spatially exhaustive (e.g. ‘wall-to-wall),global or pan-tropical forest monitoring exercise at medium or finespatial resolution would require a very large amount of satellite dataand resources for the processing and analysis thereof.

Statistical sampling techniques to derive area estimates ofland cover change from medium spatial resolution imagery pro-vide an alternative and cost-effective approach compared to‘wall-to-wall’ coverage (Gallego, 2004; Stehman et al., 2005).Stratified sampling of Landsat-type imagery has been imple-mented to monitor land cover change at pan-tropical (Hansenet al., 2008a; Richards et al., 2000) or continental level (Brinkand Eva, 2009). The Food and Agriculture Organization (FAO)of the United Nations applied a stratified random sampling ofLandsat imagery for the Remote Sensing Surveys (RSS) of theirprevious Forest Resources Assessments (FRA-1990 and FRA-2000) (FAO, 1996, 2001). Czaplewski (2003) demonstrated thatsample-based estimates of land cover change at global or conti-nental levels are statistically valid (with a relatively small stan-dard error) if the sampling intensity is sufficiently high. In thiscontext, the Joint Research Centre (JRC) of the European Com-mission (TREES-3 project) is now developing a methodology toestimate tropical forest cover changes for the periods 1990–2000–2005 based on multi-date Landsat sample sites distributedsystematically over the tropics (Sub-Saharan Africa, CentralAmerica, the Caribbean and the tropical part of South America,South and Southeast Asia including Papua New Guinea and theSolomon Islands). A subset of 20 � 20 km size was extracted ateach integer confluence of longitude and latitude resulting inabout 4000 sample sites over the tropics. This approach is ex-pected to deliver estimates of forest cover area and changes atcontinental and regional levels. The results will also be usedfor the tropical part of the forthcoming RSS of the FAO FRA-2010 which has been extended to all land areas globally, i.e.not only carried out for the pan-tropical zone as in previousFAO surveys. The same systematic sampling of 10 � 10 km sizesubsets is used corresponding to 9000 sample sites across theGlobe excluding Antarctica and some sample locations withindeserts and areas with permanent ice (Gerrand et al., 2009;Mayaux et al., 2005; Ridder, 2007).

This paper presents the different pre-processing steps aimed atproducing a multi-temporal data set with consistent radiometricand geometric properties so as to obtain unbiased forest changestatistics through robust classification results. These measuresshould significantly reduce the time needed by regional expertsto visually control and correct the land cover maps at each samplesite, which is an essential part of quality control in the project. Thechallenge was to develop a single approach to pre-process auto-matically a very large number of multi-date imagery form differentlocations in an operational and robust manner.

The methodology and results analyzed in this paper concern theimages of the reference years 1990 and 2000. Same processes wereapplied to images of the reference year 2005.

2. Data

2.1. Sampling strategy

The grid system selected for the global systematic sample is arectilinear grid based on integer degrees of geographical latitudeand longitude enabling a straightforward implementation and easylocation (Ridder, 2007). Although stratified sampling is often rec-ommended to efficiently estimate land cover changes (Achardet al., 2002; Brink and Eva, 2009; Hansen et al., 2008a; Richards

et al., 2000), a simple systematic sampling has been implementedfor the following reasons:

(i) this sampling scheme is intended to be used in future peri-ods (for year 2010 and later) and it is problematic to predictwith reliability where deforestation ‘hot spots’ will belocated in the future years (Geist and Lambin, 2002);

(ii) a systematic sample can be easily intensified for specificpurposes, in particular for assessment at national level orfor a particular ecosystem;

(iii) to monitor and assess their national forest, a number ofcountries supported by FAO are carrying out systematic fieldinventories based on an intensification of this global sam-pling scheme (www.fao.org/forestry/nfma).

Furthermore, a sampling strategy using smaller-size units buta larger number of samples provides more precise estimatesthan a strategy using larger-size units but a smaller number ofsamples with an equivalent total area (Stehman et al., 2005;Steininger et al., 2009). The systematic sampling approach hasalready been tested and compared with wall-to-wall referencedata over large areas such as the Brazilian Amazonia basin(Broich et al., 2009; Eva et al., 2010). It has also been intensifiedand tested for the Congo River basin region for the 1990–2000period (Duveiller et al., 2008) and at national/territorial levelfor the French Guiana territory (Eva et al., 2010) demonstratingits efficiency to estimate forest cover changes from continentalto sub-regional levels.

The areas processed in this study by the JRC TREES project areshown in Fig. 1. The sampling frame at each integer confluenceof the geographic grid (e.g. 10�0000000N 35�0000000E) results in2045 sample sites in Sub-Saharan Africa, 1230 in Central and SouthAmerica and the Caribbean and 741 in South and Southeast Asia.This sampling scheme corresponds to a sampling rate of circa 3%(Eva et al., 2010).

2.2. Satellite image extraction and selection

Subsets of 20 � 20 km size of Landsat Thematic Mapper (TM)and Enhanced Thematic Mapper (ETM+) images were used to coverthe sample sites for the reference years 1990 and 2000 (Fig. 2).Most of them were downloaded from the United States GeologicalSurvey’s (USGS) National Center for Earth Resources Observationand Science (http://glovis.usgs.gov) at full spatial resolution(30 m). Additional images were obtained from the CEOS (Commit-tee on Earth Observation Satellites) Land Surface Imaging Constel-lation program (LSI) to replace bad or missing data after screeningof USGS imagery (see next paragraph). The six reflective spectralbands (1–5 and 7) of Landsat TM and ETM+ images were usedfor the pre-processing and the thermal infrared band (6) servedfor the cloud masking. Table 1 lists the spectral range and resolu-tion for each band.

To find satisfactory imagery in terms of cloud cover and sea-sonal/radiometric characteristics Landsat scenes were screenedfrom 1984 until 1994 for the reference year 1990 and from 1997to 2003 for the reference year 2000, selecting visually the bestimages available for each sample site. This was carried out consis-tently across each continent by regional experts. The overall imagequality has been determined from the presence/absence of clouds(with a maximum threshold of 15%), haze, dropped lines and othersensor noise and from the relative accuracy of geo-localization.Additionally, the selection of the acquisition dates was based onthe following two criteria:

� Acquisition date nearest the target reference years, 1990 and2000.

Fig. 1. Study area and sampling scheme.

Fig. 2. Extraction of Landsat imagery. Sample frame: geographical grid (a sample at each longitude/latitude intersect 1� � 1�). Sample size: 20 � 20 km. Reference years: 1990and 2000.

Table 1Landsat Thematic Mapper and Enhanced Thematic Mapper data characteristics.

Description Wavelength (lm) Resolution (m)

Band 1 Blue 0.45–0.51 30Band 2 Green 0.52–0.60 30Band 3 Red 0.63–0.69 30Band 4 Near IR 0.77–0.90 30Band 5 Shortwave IR 1.55–1.75 30Band 6 Thermal IR 10.40–12.5 60Band 7 Shortwave IR 2.09–2.35 30

C. Bodart et al. / ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 555–563 557

� Acquisition dates from the same season, preferably the end ofthe local wet season (maximum vegetation discrimination).

Several sample sites with no data or poor quality images couldnot be processed and are considered ‘missing’. Alternative datasources are being sought for these sites.

Image quality has been documented in a database. In particular,the selected images still containing partial coverage of clouds,cloud shadows, haze and images containing large patches of denseevergreen forests have been flagged through visual qualitativeinterpretation for the application of further pre-processing stepsas described in the next chapter.

3. Methodology: pre-processing steps

A radiometric correction to a common radiometric scale is re-quired in order to apply standard supervised classification algo-rithms to the full imagery data set, making use of spectraltraining data of representative vegetation types (Song et al.,2001). Acquisition errors and irrelevant data (e.g. clouds andcloud shadows) must also be removed in the pre-processing(Coppin et al., 2004). Considering these requirements, we devel-oped a pre-processing chain capable of assessing and correctingboth geometrically and spectrally each pair of Landsat imagerysubset including the masking of clouds and cloud shadows andhaze removal. Fig. 3 represents an overview of these pre-pro-cessing steps applied selectively before the subsequent segmen-tation and the final land cover classification.

3.1. Visual evaluation of the multi-temporal image registration

Most of the Landsat TM and ETM+ data selected are part of theGlobal Land Survey (GLS) data set (Gutman et al., 2008). These datahave been geometrically corrected and geo-referenced by the USGSto the UTM (Universal Transverse Mercator) projection with a

Conversion to TOA reflectance

Landsat ~ 1990

raw DN values

Landsat ~ 2000

raw DN values

Visual assessment of image co-registration

De-hazing

Cloudmask

Normalization

De-hazing

NormalizationNormalization

Landsat ~ 1990 and ~ 2000

Final pre-processed imagery

1

2

3

2

3 3

Fig. 3. Flow diagram of Landsat multi-date pre-processing chain. Clouds and cloudshadows masking, haze correction and radiometric normalization have been,respectively, applied on flagged images with (1) clouds, (2) haze or (3) evergreendense forest. When it is possible, a relative normalization of the 1990 image to the2000 image has been applied if not enough evergreen dense forest was present inthe sample site.

558 C. Bodart et al. / ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 555–563

spatial resolution of 30 m and a remaining root mean square errorof less than 50 m.

In order to check the effective precision of the spatial registra-tion between multi-date imagery, we visually assessed the relativeimage to image geo-location. Taking the 2000 image as the ‘master’we evaluated the linear X, Y pixel shift required to match the slaveimage (1990) to the master image using image overlay. In mostcases geo-location errors corresponded to a linear shift of 1 or 2pixels. Then raw Landsat scenes were translated and a new subsetof 20 � 20 km was extracted. In a few cases geo-location error wasnon-linear and the images were excluded from the study and re-placed by alternative data when possible.

3.2. Radiometric calibration

Radiometric calibration is the first step to reduce differencesarising from changing illumination conditions and instrument er-rors and differences (Chander et al., 2009). This calibration wasachieved by first converting raw digital numbers (DN) into at-sen-sor spectral radiance (Lsatk) for each band (k) following:

Lsatk ¼ gk � DNk þ ok ð1Þ

where gk and ok are the band specific gain and offset factors. ForLandsat-4/5 TM sensor, these coefficients were obtained fromChander et al. (2007), according to the acquisition and processingdate of the corresponding image. For Landsat-7 ETM+ images theavailable metadata file provided the gain and offset factors. Subse-quently, the at-sensor radiance was converted into top-of-atmo-sphere reflectance (uTOAk) according to:

uTOAk ¼p � Lsatk � d2

ESUNk � cos rsð2Þ

where ESUNk is the mean solar exoatmospheric irradiance obtainedfrom Chander and Markham (2003) for Landsat TM images andfrom USGS (2009) for Landsat ETM+ images. The solar zenith angle(rs) was derived from the sun elevation angle provided in the satel-lite scene’s metadata and the earth–sun distance (d) was calculatedusing the Julian Day (JD) as follows:

d ¼ 1� 0:01672: cosð0:01745ð0:9856ðJD� 4ÞÞÞ ð3Þ

Finally, reflectance values were scaled into the byte range of 0–255.Thermal bands (Band 6 on TM and 6.2 on ETM+), required for cloudmasking, have been converted from at-sensor spectral radiance toeffective at-sensor brightness temperature (T) in kelvin using thefollowing formula:

T ¼ K2= lnK1

Lsatkþ 1

� �ð4Þ

where K1 and K2 are the calibration constants obtained from Chand-er et al. (2009).

Since a sample site represents a limited portion of a Landsat fullscene (11% along the swath), the variation across the swath of theLandsat scene due to surface anisotropic effects has been consid-ered as insignificant within the sample site and has not been cor-rected specifically. Effects of different acquisition angles areeliminated during the normalization process.

3.3. Cloud and cloud shadow masking

The remaining clouds and cloud shadows in the selected imageswere masked in two steps. The first step was to detect all potentialcloud and cloud shadow pixels over the radiometric calibrated im-age using the automatic spectral rule-based preliminary mappingapproach proposed by Baraldi et al. (2006). This classification ap-proach is a system of decision rules designed for mapping cali-brated Landsat TM and ETM+ into a number of kernel spectralcategories. This detection system is based on prior spectral knowl-edge of target land covers taken from the remote sensing literatureand provides preliminary maps that can be used as input for fur-ther specific classification. In our procedure, potential clouds in-cluded all pixels classified as ‘‘Cloud Spectral Category’’ and‘‘Snow Spectral Category’’ while shadows corresponded to ‘‘WaterSpectral Category’’ and all ‘‘Shadow Leaf Spectral Category’’. Ini-tially, a high amount of commission and omission errors often ren-dered the direct use of resulting masks problematic. This led to thesecond step that consisted of a sequential application of a post-processing algorithm based on morphological and topologicalmethods designed to clean the results using the size, width andshape of clouds and shadows and the sun’s azimuth. A bufferwas also applied by iteration around clouds and shadows.

This refined mask was computed only on images where cloudswere visually identified.

3.4. Haze correction

Image contamination by haze is relatively frequent in tropicalregions. It can be characterized and identified as semi-transparentclouds and aerosol layers that alter the spectral signatures of ob-jects, especially in the visible bands (Zhang et al., 2002) and hin-ders visual interpretation. Therefore, when no alternative dataare available, the correction of haze is required before classificationand change detection. Partially contaminated images were cor-rected on the basis of the method proposed by Lavreau (1991).The fourth component of the tasseled cap transformation (TC4)was used to discriminate the haze effect. This parameter was com-puted using the six reflective bands of Landsat TM imagery and thecoefficients suggested by Crist et al. (1986):

TC4 ¼ 0:8461:TM1� 0:07031:TM2� 0:4640:TM3

� 0:0032:TM4� 0:0492:TM5� 0:0119:TM7þ 0:7879 ð5Þ

Lavreau (1991) corrected Landsat TM pixels values affected by hazeby subtracting a portion of the fourth tasseled cap feature for eachband according to the following formula:

C. Bodart et al. / ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 555–563 559

DNcork;i ¼ DNk;i � ðTC4i � TC40ÞAk ð6Þ

where DNcork,i = digital number for pixel i in band k after the hazecorrection, DNk,i = original digital number for pixel i in band k,TC4i = TC4 parameter for the pixel i (Eq. (5)), TC40 = correctionthreshold, Ak = band specific coefficients empirically derived fromeach band/TC4 relationship (1.88; 0.89; 1.02; 0.85; 1.40; 0.71 forbands 1–5 and 7).

The TC40 corresponds to the TC4 average value of haze-free pix-els in the image. This scene dependent threshold was estimatedusing the mode value of TC4.

In Eq. (5), the coefficients used to compute the TC4 parameterwere derived from the DN values of the six reflectance bands. Inthe same manner, DN values were used to approximate the rela-tionship between TC4 parameters and haze covering ground pixels(Ak) in Eq. (6). To apply these coefficients, TOA reflectance wastransformed to DN-like values using linear transformation. Thenhaze correction was performed and after the correction, the valueswere converted back into original TOA reflectance applying the in-verse transformation of the TOA reflectance to DN-like values. Anumber of sample Landsat scenes distributed across the three trop-ical continents were used to determine the coefficients of this lin-ear transformation.

3.5. Radiometric normalization

Different models and techniques have been developed to reducethe noise introduced in remotely sensed images by the variation inatmospheric conditions (Song et al., 2001). Complex radiativetransfer models (e.g. 6S) can produce accurate reflectance values,but require measurements of atmospheric optical properties atthe time of image acquisitions. Conversely, the Dark Object Sub-traction (DOS) method proposed by Chavez (1996) is based oninformation contained in the image. This approach assumes theexistence of dark objects with surface reflectance close to zero inall bands (e.g. deep water bodies). Their actual average value inthe Landsat scenes is attributed to the effect of the atmosphereand is subtracted from all pixels of the image.

However, in order to detect land cover conversion, the precisesurface reflectance is not necessary (Song et al., 2001). The relativenormalization of multi-temporal data can set the radiometric mea-surements to a common relative scale and, consequently, ensurethe spectral homogeneity of such data (Du et al., 2002; Vicente-Serrano and Lopez-Moreno, 2008). Relative normalization adjuststhe spectral values of all images to the values of one reference im-age. Commonly the reference image is selected as the most recentimage or the least affected by atmospheric effects. This approachrelies on the ability to identify stable targets between dates, namedPseudo-Invariant Features (PIF), and assumes that reflectance dif-ferences in these stable targets are due to atmospheric perturba-tions. A simple linear relationship among images across time isgenerally used to normalize images to the same reference level.

Initially developed to normalize images acquired at differentdates but at the same location, we have substantially simplifiedthis method to adapt it to multi-date images covering differentareas, e.g. to Landsat scenes with different paths and rows. Usingthis specific approach, dense evergreen forest pixels have beenconsidered as PIFs. A similar approach has been used by Hansenet al. (2008b) to normalize Landsat data on the basis of a 250 mresolution forest map in the Congo Basin produced from MODISimagery. Each reflective band (k) is normalized, i.e. rescaled tothe same reference forest value as follows:

uadjustedðkÞ ¼ uoriginalðkÞ � ~xforestðkÞ þ xref ðkÞ ð7Þ

where ~xforestk = median value of dense evergreen forest of the sam-ple site for the band k, xref(k) = reference dense evergreen forest

value for the band k computed from representative areas selectedvisually in 100 images across all continents (22, 16, 11, 63, 28, 10for bands 1–5 and 7).

This normalization algorithm, hereafter referred to as ‘forestnormalization’, has been applied to each sample image with signif-icant presence of dense evergreen forests (i.e. more than 2000 pix-els in the image). The median forest value parameter was extractedfrom a forest mask created as follows (Fig. 4):

(i) First, one forest mask was created for each image (1990 and2000) based on empirically determined thresholds of NDVIand bands 4 and 5 excluding cloud pixels.

(ii) To reduce the influence of spatial variability of forests, forestmasks of the years 1990 and 2000 were intersected.

(iii) In addition, this common forest mask was intersected with a250 m forest map derived from the Vegetation ContinuousField (VCF) product (Hansen et al., 2003).

The best intersection mask was ultimately selected as the finalforest mask for both images. In case none of these two masks wereconsidered suitable to identify dense evergreen forest, a forestmask was delineated manually on-screen.

For those sites with a less proportion of dense evergreen forests(i.e. less than 2000 pixels in the image), a relative normalizationhas been performed whenever possible by visually selecting anarea that did not change between the two dates. This approachhas been used to normalize the two images of the same samplelocation and reduce the misdetection of changes at this site, withthe most recent image considered as the well-calibrated reference(Teillet et al., 2001), i.e. the 1990 image is normalized to the 2000image.

4. Results and discussions

4.1. Results of the data screening

Landsat images pairs (for the 1990 and 2000 periods) have beenacquired from the USGS archive. Whenever possible, poor qualitydata were replaced by Landsat data from International GroundStations (INPE, Brazil; Geoscience Australia; GISTDA, Thailand) orby imagery from other sensor (SPOT). For some missing samplesites, mosaics were also created from adjacent Landsat scenes.Finally, good image pairs have been retrieved for 98%, 99% and98% of the total number of sites for Africa, South America andSoutheast Asia, respectively. More than 4000 Landsat scenes wereused to extract these data at each integer confluence site. Theremaining gaps, caused largely by persistent cloud cover, weremainly in the Guianas and the Choco region (Colombia) of SouthAmerica, in some of the African rainforest areas near the coast ofGabon, Cameroun and the Republic of Congo, and in Asia mainlyon the islands of Borneo and New Guinea. More details on theresults of data selection including the variation in date of imageacquisition and geometry precision as well as on the impact ofusing alternative data to GLS data set is described in a differentpaper (Beuchle et al., 2011).

4.2. Haze correction

The haze correction was applied to reduce the variability ofspectral values caused by the presence of haze. Only sample siteswith spatially non-homogeneous haze presence could be correctedeffectively by removing a portion of the fourth tasseled cap featurefrom each band.

After correction, reflectance values of areas affected by hazeshould be comparable with those without haze. Fig. 5 shows an

Forest mask ~ 1990NDVI<0.627, 15<B5<40, 25<B4<85B1>35, B6>30, StanDevB1>2.5

Landsat ~ 1990

TOA reflectance

Forest mask ~ 2000NDVI<0.627, 15<B5<40, 25<B4<85B1>35, B6>30, StanDevB1>2.5

Landsat ~ 2000

TOA reflectance

TREES Intersection 1990-2000

VCF MODIS Forest mask

40<percent cover<81

Intersection TREES-MODIS

Fig. 4. Dense evergreen forest masks selection for each sample site.

560 C. Bodart et al. / ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 555–563

example of a haze affected sample site in Malaysia (1�N–102�E) be-fore and after the correction as well as the haze mask resultingfrom the tasseled cap transformation. As a first result, the haze cor-rection improved considerably the visual interpretability of imagescontaminated by haze and therefore will facilitate the visual cor-rection of classification results by regional experts. Furthermore,to assess the impact of haze correction on spectral signatures,two test areas, one with haze and one without haze (red circlesin image), were selected in similar forest cover in this image andin 29 additional sample sites. The mean reflectance values in eachspectral band were computed for each test area after and beforehaze correction. Fig. 6 shows the comparison of the mean reflec-tance values of haze affected versus haze non-affected areas inband 3 before and after haze correction for the 30 sample sites.Table 2 lists the linear regression parameters obtained before andafter haze correction for all the spectral bands. Significantenhancement was achieved in the visible spectral bands, wherethe slope of the regression line is closer to unity and the correlationbetween the two data sets is improved. However, infrared bands 4,5 and 7 are overcorrected (the slope of the regression line de-creases in all cases when haze correction is applied).

4.3. Radiometric normalization

Based on the selection of the best forest mask, 60% (715 sites) ofthe good-quality sample sites in South America could be normal-ized to the same forest reference value while 35% (250 sites) and

Fig. 5. Example of haze correction procedure. Site location: 1�N–102�E (Malaysia). Landshaze mask resulting from the tasseled cap transformation (c) after de-hazing. Red circle

20% (430 sites) of the good-quality data set of Southeast Asia andAfrica had enough dense evergreen forest cover to compute med-ian forest values.

Even very small changes in the vegetation cover due to differentseasonality make the relative normalization of the 1990 image tothe 2000 image very difficult. For that reason, only a subset of1990 images could be relatively normalized to the 2000 imagesbased on unchanged reference areas: 378 images (31%) for SouthAmerica, 154 images (21%) for Southeast Asia, 209 images (10%)in Africa.

To assess the impact of the normalization procedures we com-pared the raw input digital numbers and the normalized digitalnumbers for selected points in our data set where neither land cov-er change nor seasonal changes were evident between the twodates. In theory, a successful normalization procedure results inequivalent digital numbers. We extracted the digital numbers from210 sites out of the data set for 1990 and 2000 dates.

The extraction was performed for 130 points where forest nor-malization was applied and 80 points where relative normalizationof the 1990 image to the 2000 image could be done. Table 3 pre-sents the results for the three spectral bands used in the latter seg-mentation and classification procedures. The points were initiallyplaced at the center of the sample site and then retained if suitable(i.e. if they fell on a point where neither land cover change nor sea-sonal changes were evident). Where a change was seen the pointwas moved to the nearest ‘no change’ area. This procedure wasadopted so as to try and obtain a representative set. We note from

at ETM+ 14/07/2002 (combination of spectral bands 3–2–1) (a) before de-hazing (b)s correspond to test areas in forest cover with and without haze influence.

y = 0.3034x + 5.8241R2 = 0.1571

0

5

10

15

20

25

30

0 5 10 15 20 25 30

Mean reflectance (haze)BEFORE Haze Correction

Mea

n re

flect

ance

(No

haze

)

y = 0.8767x + 0.8066R2 = 0.8303

0

5

10

15

20

25

30

0 5 10 15 20 25 30

Mean reflectance (haze)AFTER Haze Correction

Mea

n re

flect

ance

(No

haze

)

Fig. 6. Comparison of mean reflectance values of forest cover under haze conditions and mean reflectance value of forest cover without haze in the same sample site beforeand after haze correction for the band 3.

Table 2Effect of haze correction in each spectral band. List of linear regression parameters.

Before haze correction After haze correction

Slope Y intercept R2 Slope Y intercept R2

Band 1 0.45 9.71 0.27 1.04 �0.92 0.98Band 2 0.47 6.45 0.25 0.99 0.41 0.91Band 3 0.30 5.82 0.16 0.88 0.81 0.83Band 4 1.00 �5.54 0.65 0.88 8.63 0.66Band 5 1.03 �5.33 0.66 0.74 7.86 0.57Band 7 0.51 2.26 0.26 0.47 4.44 0.28

Table 4The Jefferies–Matusita (JM) distance between broad land cover classes derived fromforest normalized and non-normalized data. Pe Max is the maximum probability ofclass ‘i’ being incorrectly classified as class ‘j’.

JM distance Pe Max (%)

RawDN

NormalizedDN

RawDN

NormalizedDN

Tree/shrub cover 1010 1061 24 22Shrub/other land

cover682 783 38 35

Tree/other land cover 1140 1207 18 14

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Table 3 that for all three spectral bands both the forest normaliza-tion and the relative normalization of the 1990 image to the 2000image have the effect of improving the correlation of the digitalnumbers. In all cases the correlation between the two data setsas measured by the square of the Pearson coefficient is improvedby the normalization processes. In all but one case of the forestnormalization the slope of the regression line between the twodates comes closer to unity.

To assess the impact of forest normalization on spectral separa-bility we aggregated the spectral signatures for the normalized andnon-normalized data of these points using the broad classes of treecover, shrub and other land cover. The Jefferies–Matusita (JM) dis-tance measure (Swain and Davis, 1978) was calculated for the classpairs. This index gives a measure of the probability of misclassifi-cation between two classes ‘i’ and ‘j’. A JM distance of 1414 (onethousand times the square root of 2) means that the probabilityof pixels from class ‘i’ being classified as class ‘j’ is zero. Where

Table 3Effect of forest normalization and relative normalization in bands 3–5. List of linearregression parameters between the digital numbers of selected points in 1990 versusthe digital numbers of the same points in 2000. The points were selected whereneither land cover change nor seasonal changes were evident between the two dates.

Raw DN Forest normalized DN

Slope Y intercept R2 Slope Y intercept R2

Band 3 0.94 20.21 0.52 0.79 3.95 0.70Band 4 0.37 58.04 0.10 0.62 21.71 0.61Band 5 0.78 24.82 0.60 0.91 4.46 0.78

Raw DN Relatively normalized DN

Band 3 1.18 14.24 0.65 0.80 4.42 0.87Band 4 0.86 28.82 0.53 0.81 11.40 0.76Band 5 0.77 24.92 0.57 0.77 10.74 0.85

classes are exactly the same (JM = 0) then there is a 50% probabilityof misclassification. From Table 4 we see that the effect of thenormalization on this data set is to increase the JM distance andtherefore to reduce the probability of misclassification in the threeclass pairs tree-cover/shrub-cover, tree-cover/other land cover andshrub-cover/other land cover.

5. Conclusions

The paper presents a robust and operational method designedto prepare a pan tropical data set of satellite image samples forforest change assessment for the periods 1990–2000 and for2000–2005. The methodology also accounts for future periodicassessments (e.g. 2010). The multi-temporal and multi-sceneimages were processed so as to obtain stable spectral characte-ristics prior to the latter supervised classification. Given the glo-bal nature of the exercise involving a very large number ofscenes (about 4000 sample sites) this required an extensive ef-fort to ensure the preprocessing was consistent across timeand space and the method was automatic and operational. Afterimage selection and standard calibration techniques, a simplebut robust haze correction method has been applied selectivelyon sample sites with spatially non-homogeneous haze presence.Lavreau’s (1991) initial study was carried out only on two Land-sat TM scenes, respectively, in Morocco and Tanzania. Our proce-dure was adjusted to TOA reflectance data and tested on a largedata set. The haze correction has not only considerably improvedthe appearance of the image, providing data much easier to visu-ally interpret, but also the internal consistency between haze af-fected and haze non-affected forest cover areas, especially for

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the band three which will be used in the segmentation and theclassification process. The procedure was not applied on infraredbands as the results showed that the slope of the regression linediverged from the unity when haze correction was applied onthese bands. Thin clouds have been subsequently removed byautomatic cloud and cloud shadow masking.

Finally, to reduce the effect of remaining external factors (dif-ferences in atmospheric conditions, temporal variations in theillumination and viewing angles and sensor calibration inconsis-tency) and to establish a better relationship between the satellitedata and the land cover changes, images have been calibrated tothe same radiometric scale by normalizing forest areas or otherunchanged land cover areas stable between 1990 and 2000. Therelative normalization improved the equivalence between digitalnumbers of the same land cover. This means that unchangedclasses will be given at least the same class label by a classifier,reducing therefore the misdetection of changes. The forest nor-malization had the additional advantage of improving the spec-tral separability between the forest class and other land coverclasses. These results agree with Hansen et al. (2008b) who ob-served a reduction of the misclassification rate by one-quarterafter the normalization process.

The results demonstrated the efficiency of our pre-processingchain in terms of spectral improvements and spatial coherencebut also the robustness of the method. Further studies would bevaluable to assess the impact of the radiometric calibration onclassification accuracy when the full data set of final maps willbe quality controlled by the experts.

Acknowledgments

A large proportion of the Landsat imagery (from Landsat-5TM or Landsat-7 ETM+ sensors) was obtained free of charge fromthe Global Land Survey (GLS) partnership between the U.S. Geo-logical Survey (USGS) and the National Aeronautics and SpaceAdministration (NASA) (see http://gls.umd.edu/). Alternative data(outside the available GLS archives) was searched on the USGSLandsat archive via GloVis interface (http://glovis.usgs.gov/) thenobtained in GLS format from USGS. Complementary Landsat-5TM and SPOT HRV data were obtained free of charge througha CEOS Land Surface Imaging Constellation Initiative from Land-sat receiving stations operated by GISTDA (Thailand), INPE(Brazil), and Geoscience (Australia) and from CNES (France).We thank the following scientists for technical discussions whichhelped to the development of the paper, in particular RenatoCunami (FAO/SDRN), Anssi Pekkarinen and Peter Strobl (JRC/IES).

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