(2) CESBIO, 18 Av. Ed.Belin, F-31055 Toulouse Cedex. 1. Introduction. During the phase I of the VEGETATION Preparatory Programme, studies on coniferous ...
Monitoring of forest ecosystems at regional scale using VEGETATION daily-data : First results on the Landes maritime pine forest (SW France)
Dominique Guyon 1, Benoît Duchemin 2, Jean-Pierre Lagouarde 1, Paul Berbigier 1 (1) INRA, Unité de Recherche en Bioclimatologie, BP81, F-33883 Villenave d’Ornon Cedex (2) CESBIO, 18 Av. Ed.Belin, F-31055 Toulouse Cedex
1. Introduction During the phase I of the VEGETATION Preparatory Programme, studies on coniferous and deciduous forests were carried out using VEGETATION data simulated from AVHRR/NOAA and Landsat TM time-series (Lagouarde et al., 1998). They demonstrated the potential of VEGETATION daily-data for monitoring both large-scale spatial heterogeneity and temporal of biophysical variables which determine surface-vegetation-atmosphere heat and mass transfers, forest growth, forest yield and other environmental processes changes (Duchemin et al., 1999a). Several examples dealing with the determination of phenological cycle duration (Duchemin et al., 1999b) and with the estimation of albedo and photosynthetic radiation absorbed (fAPAR) by forest canopies (Duchemin, 1999) have already been given. We present here some applications performed with actual VEGETATION data for estimating these variables on the Landes maritime pine forest. The study is based on a VEGETATION daily-data set (VGT-P product) acquired during one cycle of vegetation (31 March to 7 November 1998) just after the launch of the satellite.
2. Experimental sites and ground measurements The study is based on the 3 test sites already chosen in the framework of the VEGETATION preparatory programme. They are located in the Landes forest which covers about 1 million hectares in the South West of France and where maritime pine is dominant. The sites mainly differ in pedoclimatic conditions of growth, understorey vegetation composition and forest structure. A detailed description can be found in Duchemin (1999). The site HL (for ‘Humid Lande’) have been selected because it includes a INRA experimental stand. The surface fluxes and a large set of micrometeorological variables have been continuously monitored since 1997 in the framework of the EUROFLUX project (Aubinet et al., 2000). In particular, albedo and PAR spectral albedo have been measured above the canopy using sensors mounted at the top of a 40meters tower. HL and ML (for ‘Mésophile Lande’) sites consist of patchworks of homogenous even-aged stands. This results in a very irregular spatial structure of the forest at 1km² scale. For ML which covers about 40 km², the proportion -within 1km² pixels- of clear-cuts or young stands where the trees cover fraction is negligible varies between 5% and 95%. The site OCL (for ‘Old Coastal Lande’) which covers about 35 km² on old coastal dunes is a particular case. Because of the persistence of traditional sylvicultural practices, all stands are uneven-aged and they are never clear-cutted. In this case the pine cover fraction within 1km² pixels is always very high.
3. VEGETATION data and pre-processing 3.1 Data used We used the time-series of VGT-P data obtained over the Landes forest from 31 March to 7 November1998 : all measurements of top of atmosphere (TOA) reflectance and ancillary data (location, acquisition time and geometry, atmosphere gaseous contents, calibration parameters) were provided. 3.2 Cloud screening Cloud detection is based on the comparison of blue TOA reflectance and TOA NDVI against threshold values. The pixels are kept only if all their neighbours inside a 5x5 window are detected as cloud-free. Despite this spatial criterion being severe, observations contaminated by clouds shadows are not always clearly identified. The frequency of cloud-free data availability is significantly increased by the capability of the VEGETATION system to repeat observations at morning and at noon. It globally results in one clear measure obtained every 3.7 days on average on the 3 test sites. 3.3 Angular sampling Figure 1 displays the distribution of angular samples observed on the 3 sites. Repeated morning and noon observations increase azimuth sampling. Autumn configurations are closer to perpendicular plane whereas spring and summer configurations are closer to principal plane. In autumn sun zenith angle is always greater than view zenith angle.
90
90
100
120
60
50
150
30
180
330
240
300 270
VIEW ZENITH ANGLE
60
50
150
0
210
100
120
30
180
0
210
330
240
300 270
SOLAR ZENITH ANGLE
Figure 1 : Distribution of angular samples found in 1998 data set for cloud-free observations (sites HL, ML, and OCL). The radius length represents the zenith angle. The radius direction represents the relative azimuth angle. (o spring and summer ; x autumn)
3.4 Atmospheric corrections Surface reflectance is retrieved using the atmospheric correction algorithm SMAC (Rahman et Dedieu, 1994). Values of H2O and O3 concentrations are provided by the VGT-data base. Ozone contents are obtained from monthly climatology. Water vapour contents are estimated at 6, 12 and 18 UT from meteorological mesoscale modelling. We have used only the 12.00 UT estimates because the time of satellite observations varies between 10 and 12 UT.
As no information on aerosol was available, we tested various models of atmosphere, and finally used the same standard model of aerosol for the whole data set with an aerosol optical thickness of 0.3 at 550 nm. Of course, these approximations are more critical for visible channels, especially for blue band, than for infrared bands.
4 BRDF retrieval 4.1 BRDF observations The time evolution of the red, NIR and SWIR reflectance is presented in Figure 2. For ML and OCL sites, the study is limited to the pixels located in their central part, i.e. 7 and 6 pixels respectively. Because of its small size, only one pixel has been considered for HL. Every observed surface reflectance value results from an averaging over a 3x3 pixels neighbouring, which allows to reduce effects of registration errors. We can point out the high spatial variability of the reflectance, as indicated by the differences observed between and within sites for a given date in similar angular configurations (θs, ϕs, θv, ϕv). Moreover angular variations of the reflectance can be greater than seasonal variations. They are maximal in summer between observations facing the sun and observations with the sun backwards, especially in the near infrared. The observed directional effects are consistent with BRDF measurements from the spaceborne POLDER instrument (Hautecoeur et Leroy, 1999) and with observations from AVHRR (Duchemin, 1999).
0.21
SURFACE REFLECTANCE
SURFACE REFLECTANCE
0.12 0.10 0.08 0.06 0.04 0.02 0.00 0
60
120
180
240
300
360
0.19 0.17 0.15 0.13 0.11 0.09 0.07 0
DAY of YEAR
60
120
180
240
300
360
DAY of YEAR
RED
Short wave Infrared (SWIR)
SURFACE REFLECTANCE
0.34
0.30
0.26
Figure 2 : Surface reflectance observed over the 3 sites in the 3 bands (red, NIR, SWIR). • HL ο ML + OCL
0.22
0.18
0.14 0
60
120
180
240
300
360
DAY of YEAR
Near Infrared (NIR)
4.2 BRDF modelling BRDF is modelled with the kernel-driven semi-empirical model of Roujean et al (1992) : ρ(θs, θv ,ϕ)=k0 + k1f1 (θs, θv ,ϕ) + k2f2 (θs, θv ,ϕ) with θs : sun zenith angle
θv: view zenith angle
ϕ: relative azimuth angle
Two methodologies are performed to retrieve BRDF:
(1)
• For the first one, the model (1) is fitted and parameters k , k , k are estimated on each site • In the second method, the BRDF is expressed as a linear combination of the BRDF of 2 types of 0
1
2
surfaces : the component G includes clear-cut areas and stands where the pine cover fraction is negligible, the component H corresponds to closed canopies : ρ(θs, θv , ϕ, p) = (1 -p) ρG(θs, θv ,ϕ) + p ρH(θs, θv ,ϕ) with ρ G(θs, θv ,ϕ) = k0 G + k1 G f1 (θs, θv ,ϕ) + k2 G f2 (θs, θv ,ϕ) ρ H(θs, θv ,ϕ) = k0 H + k1 H f1 (θs, θv ,ϕ) + k2 H f2 (θs, θv ,ϕ) p is the fraction of the component H within pixels and is estimated from the proportion of stands older than 4 years. The BRDF model can be written : ρ(θs, θv ,ϕ)=k0 + k1f1 (θs, θv ,ϕ) + k2f2 (θs, θv ,ϕ)
with ki = (1 -p) kiG + p ki H
(2)
The ‘linear mixture’ model (2) is fitted only on the site ML where p is known and displays important variability. Six parameters are estimated : k0 G , k1 G , k2 G, , k0 H , k1 H , k2 H The models are fitted by a non-linear regression technique applied every day on sliding periods of 36 days. The fitting is processed only if the angular sampling per period is greater than 6 configurations (day of year, θs, θv, ϕ) per pixel.
0.020
0.020
0.015
0.015
RMSE
RMSE
4.3 Performances The best fits are obtained for the infrared channels. The correlation coefficient between observed and modelled reflectance is always significant, except for a few cases in the red channel. The relative error always reaches large values in the red band : RMSE (root mean square error) represents in average about 30-50% of the mean reflectance. These significant errors are probably caused by noise resulting from the inaccurate atmospheric correction. The use of the mixture model improves the infrared BRDF retrieval, with better results in Short Wave Infrared (SWIR) than in Near Infrared (NIR).
0.010
0.005
0.010
0.005
0.000
0.000 90
120
150
180
210
240
270
300
DAY of YEAR
90
120
150
180
210
240
270
300
DAY of YEAR
RED
Short Wave Infrared
0.020
RMSE
0.015
Figure 3 : Performance of models : time course of RMSE HL Model 1 ML Model 1 ML Model 2 OCL Model 1
0.010
0.005
0.000 90
120
150
180
210
DAY of YEAR
Near Infrared
240
270
300
5. Seasonal changes of reflectance and interest of SWIR The normalisation of surface reflectance is useful to remove directional effects and to enhance seasonal changes of forest characteristics. The parameter k0 provided the normalised reflectance : it represents the reflectance estimate at nadir view (θv = 0) with the sun at nadir (θs = 0). Figure 4 shows seasonal variations of normalised reflectance and derived vegetation indices. Remaining atmospheric noise related to the red channel does not facilitate the interpretation of results obtained with NDVI and VI(SWIR, Red), which reduces the practical interest of these vegetation indices. Moreover a detailed analysis of effects of phenological cycle of pine canopy and understorey vegetation requires ground observations which are not been made and VEGETATION multi-annual time-series not yet available. It is also necessary to validate the deconvolution of surface properties of forest and nonforest canopies by using for instance high spatial resolution reflectance measurements with SPOT4/HRVIR.
0.09
1.0
0.08 0.9 0.07 0.8
0.06
NDV I
K0
0.05 0.04
0.7 0.6
0.03 0.02
0.5 0.01 0.4
0.00 90
120
150
180
210
240
270
90
300
120
150
210
240
270
300
240
270
300
NDVI
RED 0.32
0.9
0.30
0.8
V I (SWI R, Red )
0.28 0.26 0.24
K0
180
DAY of YEAR
DAY of YEAR
0.22 0.20 0.18 0.16
0.7 0.6 0.5 0.4 0.3 0.2
0.14 90
120
150
180
210
240
270
90
300
120
150
180
210
DAY of YEAR
DAY of YEAR
VI(SWIR, Red)=(SWIR-Red)/(SWIR+Red)
Near Infrared
0.4
0.27 0.25
V I (N I R, SWI R)
0.23 0.21
K0
0.19 0.17 0.15 0.13
0.3
0.2
0.1
0.0
0.11 -0.1
0.09 90
120
150
180
210
DAY of YEAR
Short wave infrared
240
270
300
90
120
150
180
210
240
270
DAY of YEAR
VI(NIR, SWIR)=(NIR-SWIR)/(NIR+SWIR)
Figure 4 : Seasonal course of normalised reflectance and derived vegetation indices on the 3 sites ____ HL Model 1: 0 ML (ML, model 1) > non-forested surface of ML (ML, model 2 with p=0). We can expect the SWIR reflectance to be a valuable tool to estimate tree cover fraction. These preliminary results confirm observations from Landsat TM data and previous modelling results (Jolly et al., 1996; Lagouarde et al., 1998). They are consistent with those obtained by Brown et al. (2000) on coniferous boreal forests.
6. Estimation of hemispherical reflectance and albedo The hemispherical reflectance for each spectral band is estimated by angular integration of the BDRF model with the approximation used by Duchemin (1999). Albedo is derived from the spectral integration of hemispherical reflectance estimates , as given by Weiss et al. (1999) :
albedo(θ s ) ≈ 0.25ρ h (θ s , BLUE) + 0.13ρ h (θ s , RED) + 0.32ρ h (θ s , NIR ) + 0.24ρ h (θ s , SWIR ) Because of inaccurate atmospheric correction, the blue contribution is omitted and we estimated the spectral albedo from only Red to SWIR bands : albedo(θ s, Red to SWIR) ≈ 0.13ρ h (θ s , RED) + 0.32ρ h (θ s , NIR ) + 0.24ρ h (θ s , SWIR ) A comparison of ground measurements and estimates from VEGETATION is given in figure 4, despite they are not directly comparable because obtained at different space and time scales : ground measurements are performed at local scale and are averaged over ½-hour, whereas satellite-derived estimates are derived from a processing of sets of instantaneous observations (36-day periods) over 3*3 km². The uncertainties on albedo estimates are likely to be related to visible bands because atmospheric correction and insufficient angular sampling in autumn lead to large errors on the BRDF retrieval : as a matter of fact we observe a discrepancy in visible wavelengths during autumn. But seasonal variations of NIR and SWIR hemispherical reflectance estimates agree with those of albedo measurements.
0,3 hemispherical reflectance (NIR)
0,25
0,2 hemispherical reflectance (SWIR) 0,15 albedo (Red to SWIR) albedo measured at 40m
0,1
albedo (PAR) measured at 40m
0,05
0 150
hemispherical reflectance (Red) 180
210
240
DOY
270
Figure 4 : Comparison of hemispherical reflectance and albedo estimates from VEGETATION with top of canopy albedo measured at the same time on the site HL, between 17 June and 21 September.
7. Conclusion The first results obtained on the Landes forest with spaceborne VEGETATION data are quite consistent with those obtained with AVHRR and Landsat TM data during the pre-launch phase studies. The practical interest of SWIR spectral band is confirmed. Good radiometric and geometric quality of VEGETATION data attenuates the effects of restricted angular sampling on BRDF estimates. The main critical point remains the estimation of surface reflectance from top of atmosphere measurements in visible spectral bands. However in near future a better use of blue and red channels is expected thanks to improvements of atmospheric corrections, by using aerosol optical thickness measurements with sunphotometer and Blue/SWIR ratio (cf. Maisongrande et al., 2000). 8. References1 AUBINET M., …, BERBIGIER P., (18 co-auteurs), 2000. Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology. Adv. Ecol. Research, 30: 113-175. BROWN L., CHEN J.M., LEBLANC S., CIHLAR J., 2000. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: an image and model analysis. Remote Sensing of Environment, 71:16-25. DUCHEMIN B., 1999. NOAA-AVHRR bidirectional reflectance: modeling and application for the monitoring of a temperate forest. Remote Sensing of Environment, 67:51-67. DUCHEMIN B., GOUBIER J., COURRIER G., 1999b. Monitoring phenological key-stages and cycle duration of temperate deciduous forest ecosystems with NOAA-AVHRR data. Remote Sensing of Environment, 67:68-82. DUCHEMIN B., GUYON D., LAGOUARDE J.P., 1997. Modélisation du cycle de végétation des écosystèmes forestiers tempérés à partir des données NOAA-AVHRR. Atelier de Modélisation de l’Atmosphère, Météo-France, Toulouse, 2-3 décembre 1997, 277-280. DUCHEMIN B., GUYON D., LAGOUARDE J.P., 1999a. Potential and limits of NOAA-AVHRR temporal composite data for phenology and water stress monitoring of temperate forest ecosysyems. Int. J. Remote Sens., 20:895-917. HAUTECOEUR O., LEROY M., 1999 : Validation of the spaceborne POLDER BRDF with an airborne experiment over the Landes forest (to be submitted to IJRS letters – May 4, 1999) JOLLY A., GUYON D., RIOM J. 1996. Utilisation des données du moyen infrarouge de Landsat Thematic Mapper pour la mise en évidence des coupes rases sur le Massif forestier landais. Int. J. Remote Sens., 17:3615-3645. LAGOUARDE J.P., GUYON D., DUCHEMIN B., BRUNET Y., BERBIGIER P., KERR Y., 1998: Potential of VEGETATION data used in combination with others sensors for monitoring of forests at regional scale. Vegetation Preparatory Programme, final report, Février 1998, 79p. MAISONGRANDE P, DUCHEMIN B,., DEDIEU G., LEROY M., DUBEGNY C., BERTHELOT B., 2000. New algorithmic concept on atmospheric and directional correction for surface reflectance retrieval. VEGETATION 2000. Belgirate, April 3-6 2000. MAISONGRANDE P., DUCHEMIN B., DEDIEU G., LEROY M., DUBEGNY C., BERTHELOT B., 2000. New algorithmic concept on atmospheric and directional correction for surface reflectance retrieval. Symposium VEGETATION 2000, Belgirate Italy, 3-6 April 2000. RAHMAN, G. DEDIEU, 1994. “SMAC : a simplified method for the atmospheric correction of satellite measurements in the solar spectrum” H. Int. J. Remote Sensing, 1994, vol.15, no.1, 123-143 ROUJEAN J.L., LEROY M., DESCHAMPS P.Y., 1992. A bidirectional reflectance model of the Earth’s surface for the correction of remote sensing data. Journal of Geophysical Research, Vol 97, 20455-20468. WANNER W., LI X., STRAHLER A., 1995. On the derivation of kernels for kernel-driven models of bidirectional reflectance. Journal of Geophysical Research, Vol 100, 21077-21089. WEISS M., BARET F., LEROY M., BÉGUÉ A., HAUTECOEUR O., SANTER R, 1999. Hemispherical reflectance and albedo estimates from the accumulation of across-track sun-synchronous satellite data. Journal of Geophysical Research, Vol 104, 22221-22232.
1
underlined: publications produced in the framework of the phase I of the VEGETATION preparatory programme
