Space Applications Institute Characterization of the Impact of Forest Architecture on AVHRR Bands 1 and 2 Observations

Transcription

1 Space Applications Institute Characterization of the Impact of Forest Architecture on AVHRR Bands 1 and 2 Observations by Yves M. Govaerts EC Joint Research Centre January 1997

2 i Table of Contents Overview Acknowledgments List of Acronyms i ii ii iii 1 Introduction 1 2 Modeling of the radiation transfer Canopy structural properties Optical properties Characterization of the atmospheric properties Radiation transfer modeling Sensitivity to media properties Effects of the branches Effect of canopy gaps and tree crowns Effect of the dominant trees Effect of the sky radiation Atmospheric effects on the space-borne observations Detectability from space-borne observations 27 5 Conclusions 32 References 32

3 ii Overview Evergreen tropical forests may be subject to seasonal physiological processes such as temporal variation in the amount of falling plant leaves. This report investigates the impact of such processes on satellite remote sensing data. For this purpose, two artificial tropical forests with different architectural properties have been generated on the basis of field observations. The bidirectional reflectance of these two scenes are compared both at the top of the canopy and at the satellite level to assess the potential detectability of such processes. The intensity of these spectral signatures are compared with perturbing effects such that the observation geometry and the atmospheric transmittance variations. We finally compared the simulated reflectances at the satellite level with AVHRR observations. We found that the temporal variation range of spaceborne observed reflectances cannot be explained in terms of the seasonal variations of forest properties but are essentially due to the permanent presence of broken cloud fields during the rainy season. Acknowledgments This research activity has been supported by the TREES II project. The author thanks S. Jacquemoud whose provided the leaf spectral properties inverting the PROSPECT model against the LOPEX data base. S. Brownlee provided the AVHRR reflectance values and observation geometries. The artificial tropical forests have been designed with the help of C. de Wasseige and V. Van Mol. F. Achard and P. Defourny were instrumental in setting and fostering this project as part of the collaboration between the Belgian Office for Scientific, Technical and Cultural Affairs and the EC Joint Research Centre. Jim Downing and Simon Pinnock reviewed the final draft of this report.

4 iii List of Acronyms AVHRR BRF CSG DBH DS LAD LAI LND NIR RS SAA SZA TOC VAA VZA Advanced Very High Resolution Radiometer Bidirectional Reflectance Factor Constructive Solid Geometry Diameter at Breath Height Dry Season Leaf Area Density Leaf Area Index Leaf Normal Distribution Near-Infrared Rainy Season Sun Azimuth Angle Sun Zenith Angle Top of Canopy View Azimuth Angle View Zenith Angle

5 1 INTRODUCTION 1 1 Introduction This work was undertaken to study radiative properties of different tropical forest architectures in the context of the TREES II project. This project aims at producing an operational method for a global tropical forest inventory from the NOAA-AVHRR data according to a predefined legend. The developed method consists of automated classification of each image individually and the subsequent assignment of class labels, with associated probabilities, to all of the resulting classes. Despite very encouraging results obtained during the first phase of this project (Achard and Estreguil 1995, D Souza et al. 1995, Mayaux et al. 1997), further research and development efforts are necessary to improve the definition of class labels and the classification of the entire tropical belt on the basis of AVHRR observations. Indeed, space-borne observations can potentially be used to monitor the spatial and temporal variations of earth surface biological or ecological properties provided that the observed radiances can be converted into the parameters of interest with appropriate algorithms or expertise. In this context and according to the TREES project objectives, the following issues need to be addressed: 1. The characterization of the impact of various forest canopy architectures on the satellite remote sensing data, as observed with the AVHRR instrument; 2. The documentation of the evolution of a specific forest canopy in time, and in particular the documentation of the feasibility to observe these temporal changes on the basis of remote sensing data; 3. The issue of detectability of various forest types. In this context, it would be very desirable to be able to detect not only the presence but also the type of forest. The immediate objective of this study is to document the impact of the forest architectural properties and their seasonal variations on the radiation regime in the canopy and the reflectances at the satellite level in the optical spectral region. To achieve this objective, five logical phases have been identified: 1. Data collection: Detailed data are required on the structure and physical properties of the various strata of each of the studied tropical forest canopies. Typically, such data include all aspects of the geometrical description of the canopy (tree density, dimensions, branching patterns, leaf size, number and orientation distributions, etc.) as well as the optical properties of the radiatively active constituent of the forest (e.g., the reflectance and transmittance of leaves, trunks, and underlying soil). Most of the information has been collected during a field campaign in the Ngotto forest located in Central African Republic (CAR). This campaign has been carried out by the Department of Environmental Sciences and Land Use Planning of the Catholic University of Louvain and the Forest Management and Spatial Information Techniques Unit of the University of Gent. Additional data not available from the field campaign or from the TREES project itself have been gathered from the literature or from alternative sources.

6 2 MODELING OF THE RADIATION TRANSFER 2 2. Scene creation: These data are used to generate different forest canopy types in a format compatible with the use of radiative transfer models. A specific computer program has been developed for that purpose. 3. Modeling of the observed radiances at the satellite level: The modeling of the observed radiances is performed with state of the art radiative transfer models. The reflectances at the canopy level are computed with Raytran, a three-dimensional Monte Carlo ray tracing radiative transfer model that permits a detailed description of the scene structural properties. The reflectances at the satellite level are computed with the 6S code. 4. Sensitivity studies: Different sensitivity studies have been carried out to investigate the spectral signatures of the physiological processes. The intensity of these signatures are compared with angular and atmospheric perturbing effects. 5. Comparisons with AVHRR observations. The reflectance of the generated scenes are computed at the satellite level and compared with AVHRR observations. This report is divided into three main sections. Section (2) contains the characterization of the artificial tropical forest structural and optical properties, the atmospheric properties and the description of the radiative transfer models. Section (3) shows the results of various sensitivity studies on the radiation transfer which illustrate the effects of the observation geometry, canopy architecture and atmospheric scattering on the observed reflectances. Finally, the detectability issue of tropical forests seasonal processes from AVHRR observations is addressed in Section (4). 2 Modeling of the radiation transfer For the purpose of the radiative transfer computation in the canopy, a virtual tropical rain forest has been generated with geometrical modeling techniques. The architectural property of this virtual scene is described with geometrical primitives which are combined with the Constructive Solid Geometry (CSG) technique to elaborate more complex objects. Optical properties are next defined for each of these objects. This artificial scene has been constructed on the basis of field campaign observations conducted jointly by the Department of Environmental Sciences and Land Use Planning of the Catholic University of Louvain and the Forest Management and Spatial Information Techniques Unit of the University of Gent from November 1995 to May 1996 in the Ngotto forest in CAR. This forest is located in an area between 3 52 and 3 57 North and and East. The architectural measurement protocol is described elsewhere as well as the observation results (Van Mol et al. 1997). These observations reveal spatial disparities in the forest architectural properties. We decided, however, to generate a generic artificial forest on the basis of the mean observed values such as the tree height, the trunk density, the crown radius, etc. in the Ngotto forest. The atmospheric transmittance properties, not measured during the field campaign, have been chosen to represent a standard tropical atmospheric profile.

7 2 MODELING OF THE RADIATION TRANSFER 3 Figure 1: Perspective view of an individual tree branching structure and crown shape of the upper portion of the canopy. 2.1 Canopy structural properties The structural analysis of tropical rain forest reveals a wide variety of trees species with different architectural properties and spatial arrangements. A preliminary analysis of the vertical dimensions of the forest recognizes however a limited number of taxum organizations: (1) canopy trees, (2) understorey trees, (3) shrubs, (4) liana or woody climbers, (5) understorey herbs (Tomlinson 1983). While these elements are of primary importance for the forest ecological dynamics, such a detailed description is not necessary for the purpose of radiative transfer computations. Conversely, the parameters which control the transfer of radiation in the canopy and contribute to the observed reflectance at the NOAA-AVHRR sensor spatial resolution, i.e., 1 km, need to be correctly represented. We therefore focused on the accurate description of the tree crown structure, the gap pattern and spatial distribution and finally the LAI. The artificial forest is consequently assumed to be composed of two main horizontal parts: a uniform understorey and an upper layer composed of individual trees with a DBH greater than a fixed value. The bottom part is composed of the understorey and the dominated trees with a DBH lower than 20 cm. This stratum is represented with uniform pseudo-turbid media characterized by an LAI, a vertical thickness, a leaf diameter and a leaf normal distribution (LND). A flat soil bounds these layers. The upper part is composed of individual trees categorized in classes characterized by a

8 2 MODELING OF THE RADIATION TRANSFER 4 Name Height LAI LND sub erectophile 0.18 sub uniform 0.18 crown uniform 0.18 total Table 1: Parameters of the vertical structure of the artificial tropical forest during the rainy season. Distances are given in meters. cylindrical trunk of diameter and height. Only trees with a trunk diameter bigger than 20 cm are represented. The tree crown shape is assumed to be ellipsoidal with a horizontal axis and a vertical axis. For each tree class, and are actually uniformly distributed between a lower and a upper limit. The tree architecture is composed of seven branches of diameter. This value is adjusted such that 5% of the horizontally projected crown surface is covered by the branches. The branch zenith angle is equal to cotg. The azimuth angle between the branches is constant and equal to. A perspective view of an individual tree is shown on Figure (1). The individual trees are divided into classes each of which is characterized by different dimensions and trunk densities. For the scene generation, circular gaps of radius! are first uniformly distributed. The tree trunks of each class are next pseudo-randomly located in a square area of size ", except in the gaps. A minimum distance # between the trees is assumed. This distance can be adjusted for each tree class. If this value differs from class to class, the maximum value of # between two classes is actually used. When two crowns intersect, one of them is carved out so that the intersected volume is counted only once. The same process is applied when several crowns intersect. $ min max min max density # Name Z A B C D X E F G I J K Table 2: Values of the parameters for the tree crown layer. Distances are given in meters. Density is given number of trees per ha. Each crown is assumed to be a pseudo-turbid medium composed of finite size oriented scatterers. This medium is characterized by a leaf area density (LAD), a leaf normal distribution and the leaf diameter. All tree crowns have the same internal structural properties. The LAD

9 & 2 MODELING OF THE RADIATION TRANSFER 5 of the crowns is determined with LAD% LAI & (1) where LAI is the LAI of the tree crown layer, is the total thickness of this layer, & is the total volume of the tree crowns and & is the volume of the layer. Figure 2: Schematic representation of the virtual tropical forest transect. The lower part is composed of two uniform layers. The upper part is made up of individual trees. Practically, the lower part of the canopy is composed of two horizontal sub-layers whose characteristics are given in Table (1). The first sub-layer runs from 0 m up to 3 m with an LAI of 0.5. The second sub-layer ranges from 3 m up to 10 m with an LAI of 1.5. The upper part is composed of 12 individual tree classes whose values are given in Table (2). The total LAI of this layer is 3.3 and the tree density is 245/ha. The total LAI of the forest is 5.3. Fifteen circular gaps are generated per ha with a 30 m diameter. Figure (2) represents a schematic transect of this forest. These parameters have been adjusted such that rendered synthetic images of the artificial scene representing fish-eye pictures and aerial photographies match actual pictures taken with the same type of lens and viewing angles. Figures (3) and (4) show these artificial forest images. A perspective view of this forest is given in Figure (5). It is based on the structural properties observed during the rainy season, i.e., from March to October. Henceforth, it will be further referred to the RS target. For the purpose of the sensitivity study, a second artificial forest was generated to represent standard canopy structural properties during the dry season. It has indeed been observed that, while plant leaves of evergreen forest such as the Ngotto one fall continuously during the year, more tree leaves are lost during the dry season (from November to February). The dominant trees seem particularly subject to this defoliation. Consequently, this second forest has been designed by removing the leaves from the dominant tree crowns (classes A and Z). The LAD

10 2 MODELING OF THE RADIATION TRANSFER 6 Figure 3: Fish-eye view of the artificial forest tree crown structure during the rainy season.

11 2 MODELING OF THE RADIATION TRANSFER 7 Figure 4: Top view of a tree crown structure realization of the virtual tropical forest during the rainy season. The size of the target is 100 ' 100 m. The gaps in the canopy appear clearly.

12 2 MODELING OF THE RADIATION TRANSFER 8 Figure 5: Perspective view of a realization of the virtual tropical forest during the rainy season with the parameters of Tables (1) and (2). The size of the target is 100 ' 100 m.

13 2 MODELING OF THE RADIATION TRANSFER 9 Figure 6: Perspective view of a realization of the virtual tropical forest during the dry season with the parameters of Tables (1) and (2) but the crowns of classes A and Z are removed. The size of the target is 100 ' 100 m.

14 2 MODELING OF THE RADIATION TRANSFER 10 of the remaining tree crowns is unchanged. However, as the total volume & of the tree crowns decreases by 16%, so does the LAI in order to keep the LAD constant. The resulting LAI of this forest layer is Figure (6) shows a perspective view of this second canopy. It will be referred to as the DS target. 2.2 Optical properties Unfortunately, the field campaign measurements do not include any observation of the forest phyto-element optical properties. These properties have therefore been estimated on the basis of values found in the literature. Plants from different environments may vary in their capacity to absorb radiation. Leaves strongly absorb radiation in the visible spectral region (between 400 and 700 nm) and reflect or transmit most of the radiation in the near-infrared region, i.e., above 700 nm. It has been observed that plants can adapt their foliar structure according to their radiative environment. For instance, extreme shade-adapted plants often capture light for photosynthesis more efficiently (Lee 1986). Tropical plant leaves can therefore be categorized into Sun and Shade leaves which correspond to different adapted internal structures (Lee et al. 1990). Basically, the anatomical differences between these types originate from palisade cell shape adaptation and chloroplast distribution. These two species exhibit similar behavior in radiation absorption (Lee et al. 1990). We therefore assume that leaves of the different layers have the same optical properties. Their bihemispherical reflectance and transmittance spectra have been obtained by inverting the PROSPECT model (Jacquemoud et al. 1996) against the Figure 7: Observed leaf bihemispherical reflectance spectra (dashed lines). Mean leaf bihemispherical reflectance and transmittance spectra used for the radiative transfer model (solid line). The shaded areas correspond to the spectral response of AVHRR bands 1 (red) and 2 (nearinfrared). LOPEX data base (Hosgood et al. 1995). Figure (7) shows the leaf reflectance and transmit-

15 " " 2 MODELING OF THE RADIATION TRANSFER 11 tance spectrum (solid lines) in comparison with typical observed leaf reflectance spectra (dashed lines). The spectral response of AVHRR bands 1 and 2 are also shown on this figure. As can be seen, the spectral response of the bands is quite large and irregular in shape. Leaf spectral properties vary within these intervals. So does the solar spectrum. Radiative transfer simulations in the canopy are however monochromatic. Consequently, the leaf scattering properties are computed with a convolution between the leaf spectra, the sensor response and the solar spectrum (*),+-). + / :25476<;=2>4*6 4 (?),+-). + / 8:25476<;=2>4*6 4 where 02>4*6 is the leaf spectrum, 8:25476 is the Sun s spectrum and ;=25476 is the sensor response. For instance, the leaf reflectance in band 1 is computed as (*),+-). + /A@B2>4*6<8:25476<; ) 2>4*6 4 (. ),+-) + /A832>4*6; ) 2>4*6 4 is the leaf bihemispherical reflectance spectrum and ; ) 2>4*6 the AVHRR band 1 spectral response. The leaf transmittance is computed similarly, but uses instead the bihemispherical transmittance spectrum. For the trunk and the branches, we used a bihemispherical (2) (3) Figure 8: Mean soil bihemispherical reflectance spectrum used for the radiative transfer model (solid line). Trunk and branches bihemispherical spectrum (dashed line). The shaded areas correspond to the spectral response of AVHRR bands 1 (red) and 2 (near-infrared). spectrum published by Price (1995) whose values are shown on Figure (8), dashed line. The corresponding reflectance for bands 1 and 2 have been obtained using equation (2). The soil is characterized by the spectrum of an old litter (Figure 8, solid line). The monochromatic optical properties of the scene elements computed with equation (2) are summarized on Table (3).

16 2 MODELING OF THE RADIATION TRANSFER 12 RED NEAR-INFRARED Element Reflectance Transmittance Reflectance Transmittance leaf trunk branch soil Table 3: Spectral values for the various elements of the scene. 2.3 Characterization of the atmospheric properties On its way from the Sun to the surface and back to the sensor, radiation is scattered by atmospheric gaseous molecules and aerosol particles. Figure (9) shows the main gaseous and scattering transmittances as computed by the 6S code (Vermote et al. 1995) assuming a US62 vertical atmospheric profile and a continental aerosol type. The aerosol scattering transmittance can vary up to more than 40 % according to the aerosol optical thickness and affects principally AVHRR band 1 and to some extent band 2. Conversely, the water vapor absorption bands principally affect the AVHRR observations in band 2. It is therefore necessary to account for these atmospheric effects to simulate the BRF at the satellite level. Figure 9: Atmospheric gaseous and scattering transmittances at sea level for a standard US62 atmospheric profile as computed with the 6S code: water vapor transmittance (solid line), ozone transmittance (dotted line), Rayleigh scattering transmittance (dashed line), aerosol scattering transmittance for a visibility of 4 km (dashed dotted line), aerosol scattering transmittance for a visibility of 50 km (dotted dashed dotted line). The shaded areas correspond to AVHRR bands 1 and 2. The radiative transfer in the atmosphere is simulated with the 6S code, assuming clear sky conditions characterized by the US62 standard atmospheric vertical profile and continental

17 2 MODELING OF THE RADIATION TRANSFER 13 aerosol types. Only the atmospheric aerosol optical thickness C and the water vapor content can be adjusted to account for their actual ranges over tropical regions. 2.4 Radiation transfer modeling The radiation transfer in the canopy is computed with the Raytran model (Govaerts 1996). This model is based on Monte Carlo ray tracing techniques and enables computation of the bidirectional reflectance factor (BRF) as well as the vertical fluxes for complex three-dimensional artificial targets. Figure 10: Bidirectional reflectance factors in the red (left) and near-infrared (right) spectral regions for a Sun zenith angle of 30 corresponding to the rainy season virtual tropical forest. Values are represented in cylindrical coordinates. The distances from the origin of the horizontal axes represent the cosine of the viewing zenith angle. A 100 ' 100 m size artificial scene is generated with the defined architectural parameters. This scene is lit with 100 million rays in the red band and 50 million in the near-infrared band. A new scene is generated with the same parameters every 10 million rays to ensure a good statistical representation of the scene structural properties. An infinite horizontal repetition of the target is assumed for the lateral boundary conditions. The scene is lit with direct radiation, i.e., all the rays are parallel. Three Sun zenith angles are simulated: 0, 30 and 60. Figure (10) shows the bidirectional reflectance factors in the red and near-infrared spectral regions for a Sun zenith angle of 30 corresponding to the RS scene. The anisotropy of the reflected radiation can be clearly observed for both wavelengths. The peak in the illumination direction is due to the hot spot effect. The satellite level BRF is computed with the 6S code. For that purpose, we used the MVBP model (Pinty et al. 1990) to characterize the shape of the BRF at the top of the canopy for all the Sun zenith angles as required by the 6S code. The parameters of this model are obtained by its inversion against the reflectances computed with Raytran.

18 3 SENSITIVITY TO MEDIA PROPERTIES 14 3 Sensitivity to media properties The radiances that are observed at the satellite level over tropical forests have interacted at least with the atmosphere, the vegetation itself and the soil. The issue of detectability of specific forest ecological or physiological processes from remote sensing observations cannot be addressed without taking account of the interactions between these different media. Sensitivity studies have been carried out in two steps. First, we studied the effects of the canopy architecture on the anisotropy and intensity of the reflected signal. Three topics were covered: the effects of the branches, the effects of the gaps and tree crown structure and finally the effects of the dominant trees. Second, the atmospheric scattering and absorption effects on the ground level radiative measurements and satellite level observed BRF were investigated. We studied the effects of the sky radiation on the penetration of light in the canopy as well as the effects of the water vapor and aerosol contents on the satellite level observed reflectances. 3.1 Effects of the branches The presence of branches is most often neglected in the radiation transfer computation for forest canopies. However, trees do exhibit important branching structures which intercept radiation. We compared therefore the BRF at the top of the RS scene with the BRF of a target for which the branches have been removed. The results are shown on Figure (11) for a Sun zenith angle of 30. In the red spectral region, where the branch absorptance (0.835) is lower than the leaf absorptance (0.865), the reflectance of the RS canopy with branches is on the average 4% higher than the canopy without branches. The difference between the two cases is small because of the very high absorptance of the leaves at this wavelength. Most of the radiation is first absorbed by the leaves. The branches affect weakly the radiation transfer in the canopy. In the near-infrared region, on the contrary, the leaf absorptance is very low (0.106) so that most of the radiation can penetrate deeply into the canopy. The absorptance of the branches (0.661) is much higher than that of the leaves in this spectral region. Consequently, the branches reduce by 12% the reflectance of the canopy with a strong dependency according to the viewing angle. It is therefore expected that the more branches in the canopy, the darker the reflectance in the near-infrared band. This effect is almost insensitive to the Sun zenith angle. This sensitivity study clearly shows the importance of correctly accounting for the description of phytoelements with different optical properties, especially when multiple scattering is important. 3.2 Effect of canopy gaps and tree crowns The RS target has been carefully designed to represent as accurately as possible the gap spatial distribution and tree crown structure as it was observed during the field campaign. We now explore the importance of these structural properties both on the reflected radiation at the top of the forest and on the radiation regime in the canopy. For that purpose, we have generated a new scene, with exactly the same trunk density, branching structure and LAI as the RS target but where all the leaves of the different layers are uniformly distributed within the canopy, i.e.,

19 3 SENSITIVITY TO MEDIA PROPERTIES 15 Figure 11: Effect of the branches on the bidirectional reflectance in the read and near-infrared spectral regions. Reflectance factors (left column) are shown in the principal plane for the RS target (solid lines) and the same scene but without branches (dashed line). The Sun zenith angle is 30. Positive viewing angles indicate backscattering directions. The relative differences in percent between the BRF of the two canopies are shown in the right column.

20 3 SENSITIVITY TO MEDIA PROPERTIES 16 Figure 12: Effect of the canopy gaps and tree crown structure on the top of canopy bidirectional reflectance in the red and near-infrared spectral regions. Reflectance factors (left column) are shown in the principal plane for the RS target (solid lines) and for the uniform one (dashed line). The Sun zenith angle is 0. Positive viewing angles indicate backscattering directions. The relative differences in percent between the BRF of the two canopies are shown in the right column.

21 3 SENSITIVITY TO MEDIA PROPERTIES 17 Figure 13: As in Figure (12) but for a Sun zenith angle of 30.

22 3 SENSITIVITY TO MEDIA PROPERTIES 18 Figure 14: As in Figure (12) but for a Sun zenith angle of 60.

23 3 SENSITIVITY TO MEDIA PROPERTIES 19 Figure 15: Net vertical flux (%) in the RS canopy (solid line) and in the uniform one (dashed line) in the red and near-infrared spectral regions. The relative difference between the two curves is indicated in percent. The first row corresponds to a Sun zenith angle of 0, the second one to 30 and the last one to 60.

24 3 SENSITIVITY TO MEDIA PROPERTIES 20 the gaps have been removed and the individual tree crown structure and understorey replaced by a unique uniform layer with an LAI of 5.3. Because of the absence of gaps in the uniform canopy, the corresponding LAD is lower than that of the RS target. We first examined the radiation transfer in the red spectral region. When the Sun zenith angle is equal to 0 (Figure 12), the light can penetrate deeper in the RS canopy upper part than in the uniform one because of the presence of large gaps in the former canopy. The scattered radiation is more absorbed (5%) in the RS scene than in the uniform case. This effect is due to the fact that the probability that scattered rays escape the canopy decreases as the scattering depth increases except in the backscattering direction where the reflectance of the RS target is higher than that of the uniform case. The corresponding vertical fluxes are shown on Figure (15). More or less 5% of the radiation reaches the soil, whatever the canopy structure. As the Sun zenith angle increases, the difference between the reflected radiation increases especially in the forward scattering direction. This is caused by the shadow cast by the tree crowns which darkens the scene BRF in that direction. Consequently, the RS canopy reflectance can be up to 30% lower than the reflectance of the uniform target in the forward direction for a Sun zenith angle of 60 (Figure 14). In the backscattering direction, the reflectance of the RS canopy is higher because of the stronger hot spot effect due to a higher LAD value. The presence of small gaps between the tree crowns of the RS canopy permits 1.1% of the downward flux to reach the soil, as opposed to only 0.35% in the case of the uniform canopy. The differences between the top of canopy BRF are less important in the near-infrared region than in the red one because of the contribution of the multiple scattering. For a Sun zenith angle of 0 (Figure 12) or 30, (Figure 13), the difference between the BRF of the two scenes does not exceeds D 3%. For large Sun zenith angles (Figure 14), the reflectance of RS target can be up to 15% lower than that of the uniform target in the forward scattering direction. This effect is also due to the shadow cast by the tree crowns. In brief, the effects of the forest architecture (tree crowns and gaps) on the radiation transfer are, with respect to the uniform case: 1. To decrease the reflectance in the forward scattering direction and to increase it in the backward one. 2. To increase the amount of radiation which reaches the soil. These effects are more important in the red band than in the near-infrared and are more pronounced for low Sun zenith angles. In the latter band, these effects are smoothed by the multiple scattering. The directional effects are much more important than the structural effect on the radiation transfer. For a Sun zenith angle of 60, the BRF may vary up to 400% in the principal plane in the red band and up to 200% in the near-infrared band at the top of the canopy. 3.3 Effect of the dominant trees The DS target has been designed to represent the canopy structure during the dry season, i.e., from November to February. The bulk of leaf fall from dominant trees occurs during this season.

25 3 SENSITIVITY TO MEDIA PROPERTIES 21 Consequently, the LAI of the DS target is 16% lower than the RS target LAI. We now compare the BRF of the two scenes in the principal plane (Figures 16 to 18) in order to assess the impact of these physiological differences on the radiative transfer. In the red spectral region, the LAI decrease increases the reflectance due to the presence of less chlorophyll. This effect is clearly visible for a Sun zenith angle of 0 (Figure 16) where the BRF of the RS target is 10% lower than the BRF of the DS target. This behavior is further affected by the modification of the structural properties of the forest. The absence of the dominant tree crowns increases the number of gaps in the canopy. Consequently, the tree crown cast shadow effect that appears at high Sun zenith angles is enhanced as can be seen on Figure (18). For that illumination direction, the reflectance of the DS target is 10% higher than the reflectance of the RS target in the backscattering direction due to the LAI decrease and almost equivalent in the forward scattering direction. In that direction, the new canopy gaps effects compensate the LAI decrease. The difference between the two reflectance curves is minimal for a Sun zenith angle of 30. In the near-infrared region, where the plant leaf absorptance is very low, an increase of the LAI tends to increase the reflected radiation because of the increase of the multiple scattering. For a Sun zenith angle of 0, the BRF of the DS target is 12% higher than that of the RS target. For high Sun zenith angles, this difference increases slightly in the forward scattering direction for the reasons mentioned above. The difference between the BRF of the two targets depends both on the wavelength and on the observation geometry. This difference increases in the backscattering direction and for low Sun zenith angles in the red spectral region and in the forward scattering direction for high Sun zenith angles in the near-infrared band. The potential detectability of such ecological processes with remote sensing observations should therefore take advantage of these maximum differences. The spectral signature of these processes is much smaller than the angular variations in the reflectance. 3.4 Effect of the sky radiation LAI and FPAR field measurements are often based on the interpretation of radiation penetration in the canopy in the visible part of the solar spectrum. For instance, in the red spectral band, the downward flux at the soil level is 30% higher for the DS canopy (8.5%) than for the RS one (6.5%) for low Sun zenith angles and 20% higher for high Sun zenith angles. The hour of the day during which these measurements are performed may therefore influence the measured values. So far, the sensitivity studies have been carried out assuming direct illumination conditions, i.e., all the generated rays are parallel. The sky radiation, i.e., the diffuse radiation due to sunlight scattered by the atmosphere also influences the way the radiation penetrates into the canopy. Typically, in clear sky condition case, the diffuse radiation contributes from 10% to 30% of the total downward radiation according to the amount of aerosol in the atmosphere and the Sun zenith angle. For the present sensitivity study, we will assume an isotropic sky radiation accounting for 20% of the total incoming radiation. Results are shown on Figure (19).

26 3 SENSITIVITY TO MEDIA PROPERTIES 22 Figure 16: Effect of the dominant trees on the top of canopy bidirectional reflectance in the red and near-infrared spectral regions. Reflectance factors (left column) are shown in the principal plane for the RS target (solid lines) and for the DS one (dashed line). The Sun zenith angle is 0. Positive viewing angles indicate backscattering directions. The relative differences in percent between the two canopies are shown in the right column.

27 3 SENSITIVITY TO MEDIA PROPERTIES 23 Figure 17: As in Figure (16) but for a Sun zenith angle of 30.

28 3 SENSITIVITY TO MEDIA PROPERTIES 24 Figure 18: As in Figure (16) but for a Sun zenith angle of 60.

29 3 SENSITIVITY TO MEDIA PROPERTIES 25 Figure 19: Relative downward flux (%) in the RS canopy assuming direct illumination (solid line) and 20% of isotropic sky radiation (dashed line) in the red spectral region. The relative difference between the two curves is indicated in percent. The first row corresponds to a Sun zenith angle of 0, the second one to 30 and the last one to 60.

30 3 SENSITIVITY TO MEDIA PROPERTIES 26 Direct radiation penetrates more efficiently when large gaps are present in the canopy while diffuse radiation penetrates better in a homogeneous turbid medium where the distance between the scatterers is small. Therefore, the direct radiation penetrates better than the diffuse radiation in the canopy for low zenith angles because of the gaps. As the Sun zenith angle increases, the gap effect on the direct radiation decreases and the diffuse radiation penetrates better in the canopy than the direct radiation. For a Sun zenith angle of 30, both effects are compensated and there is almost no difference between the two cases. At large Sun zenith angles, the solar radiation cannot penetrate directly through the large canopy gaps. The downward radiation with a diffuse component penetrates better in the canopy than purely direct radiation in this case. The interpretation of ground radiative measurements should therefore carefully account for the sky radiation. The importance of this effect depends also on the canopy architecture and the Sun zenith angle. 3.5 Atmospheric effects on the space-borne observations So far, we have performed sensitivity studies on the BRF at the top of the canopy. The issue of different forest type detectability or ecological process monitoring from space-borne observations should also account for the atmospheric absorption and scattering effects on the propagation of Sun radiation. In Section (2.3), we saw that AVHRR band 1 is essentially affected by the atmospheric aerosol content whereas band 2 is sensitive to the amount of water vapor in the atmosphere. To illustrate the importance of these effects, the BRF of the RS canopy was computed at the satellite level for different aerosol and water vapor concentrations assuming clear sky conditions. In the red AVHRR band, we fixed the water vapor content at 4 gr/cme and computed the satellite level reflectance for two different aerosol optical thicknesses C : 0.1 and 0.8. Results are shown on Figure (20A) where the top of canopy reflectance is computed with the MVBP model inverted against the Raytran reflectances. The satellite level BRFs are simulated with the 6S code. Because of the high forest LAI value, the target is dark in this spectral band and its bihemispherical reflectance is much lower than the aerosol single scattering albedo. Consequently, the satellite level BRF is higher than the top of canopy value because of the aerosol scattering effect. As can be seen in the present case, variations of the aerosol optical thickness can be responsible for as much as 50% of the variation of the observed signal. In the near-infrared band, we fixed the aerosol optical thickness C at 0.2 and computed the satellite level reflectance for two different water vapor concentrations: 0.2 and 0.5 gr/cmfge. Results are shown on Figure (20B). The high value of water vapor gaseous transmittance in this spectral region tends to decrease the BRF at the satellite level with respect to the value at the canopy level. In the present clear sky case, different values of water vapor concentration can modify the satellite BRF by 10%.

31 4 DETECTABILITY FROM SPACE-BORNE OBSERVATIONS 27 Figure 20: Atmospheric scattering effects for a Sun zenith angle of 30. (A) Red band. Solid line: top of canopy reflectance of the RS scene estimated with the MVBP model inverted against the Raytran reflectances. Dashed line: satellite level reflectance for an aerosol optical thickness C of 0.1 and water vapor content of 4 gr/cme. Dashed-dotted line is for CHJILKNM. (B) Nearinfrared. Solid line: top of canopy reflectance of the RS scene estimated with the MVBP model inverted against the Raytran reflectances values. Dashed line: satellite level reflectance for a water vapor concentration of 2 gr/cme and an aerosol optical thickness of 0.2. Dashed-dotted line is for a water vapor content of 5 gr/cme. 4 Detectability from space-borne observations In the previous section, we illustrated the relative importance of the observation geometry, canopy architecture and atmospheric scattering on the observed radiances. We showed that the directional effects are responsible for most of the observed variations, especially for large viewing and Sun zenith angles. The contribution of the atmospheric aerosol scattering and water vapor absorption must also be accounted for. For an Sun zenith angle of 30, they may be responsible for several tens of percent of the variations that can be observed in the clear sky case. The tropical forest seasonal ecological and physiological processes of interest are responsible for BRF variations of one order of magnitude lower than the perturbing effects such as the observation geometry or the atmospheric scattering and absorption. Consequently, the detection of such phenomena from space-borne observations may be possible provided that (1) the measurements are sufficiently accurate to detect these small variations and (2) the perturbing effects can be properly processed. The developed method relies on comparisons between remote sensing data and simulated values for clear sky conditions to assess to what extent the observed AVHRR data seasonal variations match the modeled spectral signatures of the tropical forest seasonal processes.

32 4 DETECTABILITY FROM SPACE-BORNE OBSERVATIONS 28 For this purpose, we selected a window, whose coordinates are North and East, in the Ngotto forest. Within this area, the forest is dense and its architectural properties are rather uniform (de Wasseige, personal communication). Specific days have been selected during the observation period (November 1995 to May 1996) based on the cloud conditions and the availability of AVHRR observations. These days are listed in Table (4) together with the observation geometries. As can be seen, most of the selected days correspond to the dry season because of the nearly constant presence of clouds during the rainy season. During the observation period, the Sun zenith angle (SZA) varies between 25 and 44 degrees while the viewing azimuth angle (VZA) ranges from 0 to 63 degrees. A cloud mask based on the GEMI (Pinty and Verstraete 1992) has been applied to reject cloudy pixels. The threshold value has been fixed at 0.3 such that pixels with a smaller GEMI value are rejected. The mean reflectances in bands 1 and 2 have been computed for the selected pixels. Typical reflectance values under clear sky conditions range between for band 1 and for band 2 during the dry season. During the rainy season, these values increase in both channels (Figure 21, black lines). As can be seen, the window internal reflectance variations are very small under clear sky conditions, providing a good estimate of the forest homogeneity in that area. Conversely, the range of observed reflectances within the window significantly increases in the case of partial cloud cover, showing the difficulty of masking clouds in such condition. The satellite level BRF for the DS and RS scenes were modeled next. In Section (2.1), we saw that the structural properties of these two canopies have been accurately represented on the basis of field observations. The optical properties, while not directly observed during the campaign, have been estimated with values found in the literature. The observation geometry is also precisely known for each day. Therefore, the only unknowns concerning the satellite level simulations are the atmospheric conditions. These conditions can be reasonably well represented assuming a US62 atmospheric vertical profile except for the water vapor and aerosol concentrations which considerably affect the AVHRR observations. We therefore considered water vapor content between 2 and 6 gr/cmfge and continental aerosol with an optical thickness at 550 nm varying between 0.05 and 1.3. In the red spectral band, there are no significant differences between the satellite level BRF of the two scenes for the simulated observation geometries (Figure 21, red and green lines). Indeed, we saw that the difference between the two target reflectances is a minimum for a Sun zenith angle of 30. In the near-infrared band, the RS scene, which corresponds to the forest properties during the rainy season, has an average BRF value slightly bigger than the DS canopy. The latter target does indeed have a smaller LAI value. Still, this difference is smaller than the variations due to the atmospheric conditions uncertainties. Analysis of the comparisons between the modeled BRF and the observed data leads to the following conclusions: 1. During the dry season, the modeled reflectance variations are within the range of the AVHRR data, except in the case of partial cloud cover. The properties of the described media composed of the soil, vegetation and atmosphere, therefore explain the observed data in cases of clear sky conditions.

33 4 DETECTABILITY FROM SPACE-BORNE OBSERVATIONS It is not possible to assess which targets fit better the observations during that season because of the absence of significant differences between the two target reflectances in the red band. There is a small difference in the near-infrared band which unfortunatly cannot be exploited because of the atmospheric condition uncertainties. 3. During the rainy season, the modeled data do not match the AVHRR observations, whatever the canopy type or the atmospheric profile. 4. The satellite level BRFs of the RS target is slightly bigger than those of the DS target in the near-infrared band. However, these differences do not help to explain the intensity of the observed reflectance increase.

34 4 DETECTABILITY FROM SPACE-BORNE OBSERVATIONS 30 Day Date SZA SAA VZA VAA Band 1 Band 2 Cond. 5 6/11/ P 10 11/11/ W 12 13/11/ S 14 15/11/ P 15 16/11/ bad image 22 23/11/ S 28 29/11/ W 41 12/12/ W 56 27/12/ Undefined 67 7/ 1/ W 72 12/ 1/ W 95 4/ 2/ W 97 6/ 2/ W / 2/ P / 2/ P / 2/ W / 2/ C 122 2/ 3/ W / 3/ P / 3/ P 159 8/ 4/ P / 4/ C 189 8/ 5/ C Table 4: Observation geometry and AVHRR reflectances for clear sky or partially clear sky conditions between November 1995 and May Day is the day number since November, The reflectances in bands 1 and 2 are the mean observed values in the window (3 50 1O 3 54 North and East). The last column gives the atmospheric conditions with the following meaning: C = cloudy, P = Partially cloudy; S = Clear sky; W = partially cloudy area but clear sky Window. W means that clear sky conditions are observed in the window while clouds are present in the surrounding area.

35 Q P Q P Q P Q P T S R R T S 4 DETECTABILITY FROM SPACE-BORNE OBSERVATIONS 31 Figure 21: Average AVHRR band 1 (top) and 2 (bottom) observed reflectances for clear sky or partially clear sky between November 1995 and May 1996 for the window ( North and East) located in the Ngotto forest (horizontal black dashes). The range between minimum and maximum observed reflectances in the window are indicated with black vertical lines. The modeled satellite level BRF of the RS scene is shown with red horizontal dashes for =0.4 and a water vapor concentration of 4 gr/cm. The vertical red lines show the simulated BRF variations when runs between 0.05 and 1.3 and the water vapor concentration between 2 and 6 gr/cm. The green color is for the DS scene. Days are numbered since November 1, The letters P, S, W and C indicate the cloud cover conditions (C = cloudy; P = Partially cloudy; S = Clear sky; W = partially cloudy area but clear sky Window). VU

36 5 CONCLUSIONS 32 5 Conclusions We investigated the spectral signature of tropical forest seasonal processes. For that purpose, two tropical forests with structural properties corresponding to two different seasons were described on the basis of field observations. These scenes have permitted assessment of the canopy architecture importance on radiation transfer. We saw that seasonal processes are responsible for a small variation of the BRF, mainly in the near-infrared spectral region due to an LAI change. These variations are however much smaller than the variations due to atmospheric properties or observation geometry changes. During the dry season, there is a good match between the simulation and the observation. It is however not possible to discriminate which target fits better the AVHRR observations. Such distinction would require very accurate atmospheric corrections and better angular observations. During the rainy season, the observed reflectances increase in both channels. As a result, the simulations do not fit the observations in this period. Consequently, the forest property seasonal variations cannot explain the space-borne observation changes. This discrepancy raises several issues such as the description accuracy of tropical seasonal changes. Basically, an increase of LAI decreases reflectance in the red band and increases the reflectance in the near-infrared one. An higher LAI value during the rainy season would be responsible for an increase of the band 2 reflectances, as observed, but a decrease of the reflectance in band 1. Oddly, an increase in channel 1 is actually observed during the rainy season. Erroneous LAI seasonal variation modeling does not explain the discrepancy. Conversely, the presence of undetected sub-pixel clouds would be responsible for an increase of the observed reflectances in both bands. It is however not possible to account for this feature in the radiative transfer models we used. Further efforts are necessary to document the importance of this effect. We therefore conclude that: 1. Tropical forest seasonal processes are potentially responsible for small reflectance variations of band 2 whose intensity is smaller than the perturbations due to the variations of the atmospheric properties. 2. AVHRR reflectance data increase in band 1 and 2 during the rainy season, but the intensity of this increase may not be explained in term of forest structural property seasonal variations. 3. The observed AVHRR variations are probably due to the presence of sub-pixel sized broken field clouds during the rainy seasons that cannot be properly detected. References Achard, F. and C. Estreguil (1995). Forest classification of Southeast Asia using NOAA AVHRR data. Remote Sensing of Environment 54,