Integration of High and Low Resolution NDVI Data for Monitoring Vegetation in Mediterranean Environments

Integration of High and Low Resolution NDVI Data for Monitoring Vegetation in Mediterranean Environments Fabio Maselli,* M. Amparo Gilabert,† and Claudio Conese‡

The integration of the useful features of high and low

spatial and temporal resolution satellite data is a major issue in remote sensing studies. The current work presents the development and testing of a procedure based on classification and regression analysis techniques for generating an NDVI data set with the spatial resolution of Landsat TM images and the temporal resolution of NOAA AVHRR maximum-value composites. The procedure begins with a classification of the high resolution TM data which yields land use references. These are degraded to low spatial resolution in order to produce abundance images comparable with the AVHRR data. Linear regressions are then applied between the AVHRR NDVI data and the abundance images to estimate the profiles of the pure classes, which are then merged to the high spatial resolution classification outputs to generate an integrated data set. Experiments carried out in an area of Tuscany (Central Italy) intercomparing different strategies for each methodological step (hard and fuzzy classification, mean and Gaussian degradation, uni- and multivariate regression) identified an optimum methodology composed of fuzzy classification, mean degradation, and multivariate regression procedures. Elsevier Science Inc., 1998

INTRODUCTION The development of models of climate, carbon cycles, hydrology, etc. depends on an unambiguous, replicable * IATA-CNR, Firenze, Italy † Departament de Termodina`mica, Facultat de Fı´sica, Universitat de Vale`ncia, Burjassot, Vale`ncia, Spain ‡ CeSIA—Accademia dei Georgofili, Firenze, Italy Address correspondence to F. Maselli, IATA-CNR, Ple. delle Cascine, 18. 50144-Firenze, Italy. Received 18 December 1996; revised 25 July 1997. REMOTE SENS. ENVIRON. 63:208–218 (1998) Elsevier Science Inc., 1998 655 Avenue of the Americas, New York, NY 10010

definition of the existing terrestrial vegetation (Sellers et al., 1995). Vegetation characteristics, including land cover and phenology, affect processes such as water cycling, absorption and reemission of solar radiation, momentum transfer, carbon cycling, and latent and sensible heat fluxes. Consequently, variations in the composition and distribution of vegetation represent one of the main sources of systematic change on local, regional, or global scale, and the ability to detect these variations using multitemporal remotely sensed image data is of utmost importance for both environmental research projects and management activities (Hall et al., 1995; Running et al., 1995). This problem is even more dramatic when dealing with areas where desertification processes may occur (Bolle, 1996). Traditionally, vegetation monitoring by remotely sensed data has been carried out by means of vegetation indices, which are mathematical transformations designed to assess the spectral contribution of green plants to multispectral observations. The potentials and limits of different vegetation indices are extensively discussed in the literature (see, e.g., Bannari et al., 1995; Eldvidge and Chen, 1995; Baret and Guyot, 1991). Vegetation indices are mainly derived from reflectance data from discrete red (R) and near-infrared (NIR) bands. They operate by contrasting intense chlorophyll pigment absorption in the R against the high reflectance of plant materials in the NIR. Such is the case of the well-known normalized difference vegetation index NDVI5(NIR2R)/(NIR1R) (Rouse et al., 1973), which is the most widely used index especially when analyzing data taken from satellite platforms. Even though several studies have shown the enormous potential of these indices for monitoring vegetation conditions, their operational utility when acquired from current systems is actually limited by problems of under0034–4257/98/$19.00 PII S0034–4257(97)00131–4

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sampling in space and time (Richards, 1993; Bolle, 1996). While in fact high spatial resolution sensors such as Landsat Thematic Mapper (TM) or SPOT High Resolution Visible (HRV) have an adequate capacity for collecting information with high spatial detail, their temporal frequency is too low to follow rapidly changing phenomena connected to vegetation phenology or responses to environmental changes (Allan, 1990). The theoretical frequency of acquisition of these data (one to two times per month) is in fact always reduced by the presence of clouds and other atmospheric perturbations, so that no more than four to five good passages per year are usually available, often concentrated in one to two seasons. On the other hand, sensors onboard meteorological satellites such as the NOAA Advanced Very High Resolution Radiometer (AVHRR) are able to collect information very frequently but with poor spatial resolution. The efficient integration of the useful spatial and temporal features of the two data types has therefore become a major challenge for remote sensing experts interested in environmental applications (Puyou-Lascassies et al., 1994; Oleson et al., 1995). The paramount importance of the subject is testified by the numerous investigations performed in recent years, which have achieved varying degrees of success. A common and straightforward strategy is to assume a linear combination of the spectral signal to compare high and low resolution data by regression analysis techniques (Puyou-Lascassies et al., 1994; Kerdiles and Grondona, 1995; Oleson et al., 1995). This strategy will be followed in this article, which presents a procedure for integrating NDVI data from TM and AVHRR sensors by classification and regression analysis techniques. By this integration, NDVI estimates are produced with the spatial resolution of TM images and the temporal frequency of AVHRR data. The approach was developed and tested in the area around Radicondoli in Tuscany (Central Italy). This is a major test site for the EC project RESMEDES,1 which has as its major goal the monitoring of vegetation and land use changes to be incorporated in flux modeling of environmentally sensitive Mediterranean areas. STUDY AREA: RADICONDOLI Radicondoli is a test site of about 15315 km2 located near the center of Tuscany (43.18–43.298N, 10.95–10.578E). The terrain is mainly hilly, with elevations ranging from 400 m in the valleys to about 900 m in the upper zones. The area was selected to contain a sufficient number of AVHRR pixels (about 143145196) without substantially disturbing the assumption of uniformity of the main cover types. The limited size and range of elevation was 1 Remote Sensing of Mediterranean Desertification and Environmental Stability.

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in fact intended to guarantee the approximate intraclass homogeneity of vegetation features and phenological development which was necessary for subsequent analysis. According to the climate classification made by Rapetti and Vittorini (1995), the climate of the area can be defined as Mediterranean subhumid, characterized by a long arid season in summer. This Mediterranean climate is obviously more pronounced in the valleys, since the mean annual temperature and rainfall range from about 158C and 700–800 mm to 13–148C and 1000 mm, respectively, following the altitudinal gradient. The land is used prevalently for agriculture in the valleys (mainly spring crops and pastures) and for forests in the upper zones. The most common spring crops are wheat and alfalfa, while forests, which cover about 70% of the area, consist predominantly of mixed oak woods (Quercus pubescens L. and Quercus cerris L.) and some stands reforested with pines (Pinus nigra, L.). MATERIALS AND METHODS Hardware and Software Used In the current investigation most of the data processing was carried out by means of Fortran 77 and C programs written in house, running on a Digital Vax Station 3100. For preprocessing and simpler operations, the IDRISI for Windows 2.0 package (Clark Labs, 1997) was utilized on a Pentium 166 MHz personal computer. Satellite Data The high spatial resolution satellite data consisted of three Landsat-5 TM frames (192/30) taken on 1 June, 4 August, and 8 November 1990. These images were representative for three seasons (late spring, summer, and autumn), while no image was available for the winter/early spring period. From a visual inspection the three frames were shown to be cloud-free and practically unaffected by other atmospheric perturbations all over the Radicondoli area. A scene of 5123512 pixels was extracted from each of the three frames and georeferenced by a nearest-neighbor resampling algorithm trained on about 20 ground control points, with a final RMSE lower than 1 pixel on both axes. TM Bands 4 (NIR) and 3 (R) were then converted into apparent reflectance values (Gilabert et al., 1994). Neither atmospheric nor topographic corrections were performed at this stage, since the final purpose was the comparison with AVHRR data that were the same — uncorrected for these effects. From the two apparent reflectance images the NDVI was computed by the standard formula mentioned in the introduction section. The AVHRR data, already in the form of NDVI, were taken from the archives of Nuova Telespazio (Rome, Italy) within the framework of the RESMEDES Project. The standard procedure for the production of these data

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Table 1. Statistics of the TM and AVHRR NDVI Images Used in the Research (Minimum, Maximum, Mean, and Standard Deviation) Standard Minimum Maximum Mean Deviation TM June TM August TM November

0.008 0.004 0.008

0.820 0.792 0.843

0.628 0.436 0.480

0.137 0.188 0.169

AVHRR AVHRR AVHRR AVHRR AVHRR AVHRR AVHRR AVHRR AVHRR AVHRR AVHRR AVHRR

0.196 0.235 0.263 0.384 0.494 0.431 0.345 0.251 0.275 0.286 0.380 0.188

0.400 0.471 0.478 0.608 0.729 0.808 0.702 0.722 0.780 0.588 0.616 0.396

0.252 0.322 0.340 0.475 0.609 0.597 0.551 0.485 0.498 0.466 0.464 0.278

0.030 0.042 0.038 0.036 0.046 0.061 0.086 0.104 0.084 0.054 0.032 0.047

January February March April May June July August September October November December

each scene were retained for the classifications, as suggested by Horler and Ahern (1986). After finding the training statistics of each class [means, variance–covariance matrix, and frequency histograms, (Maselli et al., 1992)], the modified MLC was applied both in “hard” and “fuzzy” ways, in order to evaluate the possible differences deriving from the use of the relevant outputs (Wang, 1990a,b). As is well known, “hard” classifications assign every pixel uniquely to a class, while “fuzzy” procedures associate with each pixel continuous membership grades of all classes comprised between 0 and 1. The implications deriving from the use of the two systems have been recently discussed by Canters (1997). For the current case study, the classifier applied in a hard mode assigned a full membership grade to the class with the highest maximum likelihood a posteriori probability Pri, computed as

Y

Pri5Fmi·Ppi comprised the georeferencing of the original images by a cubic convolution algorithm, the radiometric calibration of the first two bands to derive apparent reflectances following Rao and Chen (1994), and the computation of NDVIs to finally obtain maximum value compositings (MVCs) (Holben, 1986) on a monthly basis. The final products were therefore 12 monthly NDVI MVCs covering all 1990, which were used for the current analysis. The TM and AVHRR NDVI data sets were characterized by computing their main statistics (minimum, maximum, mean, and standard deviation of each NDVI image, Table 1). Figure 1 shows a color composite of the study area produced using NOAA AVHRR and Landsat TM NDVIs in the three common dates (June, August, and November, 1990). High Spatial Resolution Land Cover Classifications The generation of a reference land use map was carried out by a supervised procedure based on a modified maximum likelihood classifier, MLC (Maselli et al., 1992). The land use references were directly identified on the TM images with the aid of air photos taken in 1991 and personal knowledge from direct ground surveys. Four land use classes were deemed sufficient to cover almost all the variability in vegetation features of the study area (coniferous forest, deciduous forest, spring crops, and pasture). Six plots of approximately 1 ha (333 TM pixels) were found for each class and were used as training sets. The number of training pixels for each class (54) was deemed sufficient thanks to the good spectral separability of the cover types considered in the multitemporal data set. The same number of pixels per class was independently found by the same procedure for classification testing. To avoid excessive redundancy of the spectral data, only Bands TM3, TM4, and TM5 from

4

o Fmi·Ppi, i 1 5

(1)

where Fmi and Ppi are the maximum likelihood density function and the prior probability of class i, respectively. The two terms were found as (Strahler, 1980; Maselli et al., 1992) Fmi5(2p)21/2n|Ci|21/2 e2(1/2)[(X2M)9Ci21(X2M)]

(2)

with n5number of measurement variables, Ci5variance–covariance matrix of class i, Mi5mean vector of class i, Xi5pixel vector, Ppi5Hi

Y

4

o Hi i 1 5

(3)

with Hi5frequency of class i at pixel vector derived from the histograms of the training sets. When utilized in a fuzzy mode, the classifier yielded the a posteriori probabilities of the study classes as output membership grades, thus retaining all information produced during the classification process (Wang, 1990a, b). By applying the hard classification, a conventional thematic map was thus produced (Fig. 2), while the output of the fuzzy procedure were four membership grade images. Few pixels remained unclassified, mainly relating to small water bodies and urban centers, bare soils and rocks. In addition to the classifications with the multitemporal data set, similar classifications were carried out with each TM scene in order to evaluate the influence of the acquisition period on the NDVI estimation process. The accuracy assessment was carried out by comparing the outputs of the hard classifications with the multitemporal and monotemporal sets to the test pixels. The

Figure 1. Color composites of NOAA AVHRR (full image) and Landsat TM (zoomed area) NDVIs from June (red), August (green), and November (blue) 1990, showing the position and features of the study area.

Figure 2. Classification of the study area obtained by the maximum likelihood hard procedure with the multitemporal data set (blue5coniferous forest, green5deciduous forest, red5spring crops, yellow5pasture).

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accuracies were measured by error matrices and summarized as Kappa coefficients of agreement as suggested by Congalton et al. (1983) and Rosenfield and FitzpatrickLins (1986). Regression Analysis For the integrated analysis with the low spatial resolution data, the outputs of the hard and fuzzy classifications with the complete TM data set had to be compared to the AVHRR NDVI images. To this purpose, each hard map was first transformed into four dichotomous membership grade images (i.e., images with pixels having 0 values for no attribution, 1 for full attribution). All membership grade images from both the hard and fuzzy classifications (dichotomous or continuous, respectively) were then degraded to produce abundance images with the AVHRR pixel size. The degradation was done in two ways: i) by simple average (mean filtering), as suggested by Kerdiles and Grondona (1995), and ii) by applying a twodimensional Gaussian filter to simulate the AVHRR point spread function, following Moreno and Melia´ (1994). The results of these operations were abundance images which reported the proportion of each land use class in every AVHRR pixel as derived from the high spatial resolution classifications. As already mentioned, the 12-month AVHRR NDVI profiles of the pure classes were derived assuming a linear mixture model for the composition of NDVI, with the proportions of the cover classes in each AVHRR pixel derived from the abundance images. Other assumptions were that the cover types tended to have invariant distributions during the study year and unique NDVI profiles. The implications of all these assumptions are discussed in a subsequent section. On these bases, uni- and multivariate linear regression analyses were performed between the abundance images and the monthly NDVIs to retrieve the pure class profiles (Anderson, 1984). In the univariate case, the NDVI values of each monthly composite were regressed against the proportion of each class and the “pure NDVI” was estimated by extrapolating the proportion to 1 (Kerdiles and Grondona, 1995). In the multivariate case, the same NDVI values were regressed contemporaneously on all four classes; the pure NDVI of each class was then found by extrapolating the proportion of that class to 1 and those of the other classes to 0 (Oleson et al., 1995). Globally, eight complete NDVI profiles of all classes were obtained from the classifications with the multitemporal data set (every combination from hard and fuzzy classifications, mean and Gaussian filterings, uni- and multivariate regressions). The accuracy of these profiles was evaluated by comparison with the mean NDVI values of the four classes derived from the apparent reflectance TM data. These mean values were computed from the hard multitempo-

ral classification, which was taken as a reference. The NDVI values estimated for the four classes from the AVHRR data at the dates of the three TM acquisitions were found by linear interpolation of the monthly profiles. In practice, 12 points (four classes by three TM acquisitions) were compared in each case. The determination coefficient of the regression, r 2, and the root mean square error (RMSE) (in NDVI units) were considered as criteria for evaluating the agreement between TM and AVHRR-derived NDVIs. NDVI profiles were also derived using the best classification and filtering methodologies with the single-date classifications, so as to evaluate the possible use of restricted TM data sets acquired in different periods. The accuracy evaluation was carried out by the same statistics as above (r 2 and RMSE). Production of Integrated Images The most accurate NDVI profiles found in the previous phase were finally used to produce NDVI images of all 12 months with high spatial resolution. For this purpose, a procedure proposed by our research group was employed (Maselli et al., 1995) based on the use of the high resolution classification outputs. For each month, the NDVI estimated for every TM pixel (NDVIest) was computed as 4

NDVIest5 o Pri·NDVIi i 51

(4)

where Pri is the membership grade of class i (comprised between 0 and 1) and NDVIi refers to the NDVI value of class i found by the regression analysis from the AVHRR data. The method of course yielded discontinuous or continuous outputs depending on the use of hard or fuzzy classification representations. During the computation of the high resolution images, all pixels not assigned to any of the four classes were considered as having NDVI close to 0 for the whole study year. The same assumptions previously mentioned were employed regarding the spatial invariance of the class distribution and the uniqueness of their NDVI profiles. The 12 high resolution NDVI images were finally subjected to a procedure for recalibrating the estimated NDVI values on the AVHRR index. In practice, the NDVI value of each high spatial resolution pixel for each month was adjusted so that the average of the relevant low resolution window would match that of the monthly AVHRR NDVI data (Chavez et al., 1991). RESULTS Comparison of the Two Data Sets A first evaluation of the correspondence between TM and AVHRR NDVI data was performed by comparing the relevant mean values found over the entire study

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Table 2. Kappa Accuracies Found for the Multitemporal and Single Date Classifications as Compared to the Test Pixels

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Table 4. Correlations between the Classification Outputs Degraded by Mean and Gaussian Filters for the Hard and Fuzzy Procedures

Classification

Kappa

Class

Hard Procedure

Fuzzy Procedure

June–August–September June August November

0.920 0.803 0.686 0.463

Coniferous forest Deciduous forest Spring crops Pastures

0.995 0.997 0.996 0.994

0.995 0.997 0.996 0.994

scene (Table 1). The mean TM NDVI values were actually very similar to those derived from AVHRR data interpolated at the three study dates, with a mean difference equal to 20.011. This confirmed the possibility of using TM NDVI data to evaluate the retrieval accuracy of class estimates from AVHRR images and justified the choice of not intercalibrating the two data sets prior to the regression analysis. Again from Table 1 it can be observed that the dynamic ranges and standard deviations of the AVHRR NDVI images were lower that those of the corresponding TM images, due to the coarser spatial resolution of the former and consequent averaging effect. It is also worth noting that while May–June was the period of maximum average vegetation activity, the largest ranges and standard deviations were reached in August–September for both data sets. As was visible from the images, the latter was the period of maximum contrast between green forests on the upper mountain zones and bare soils in the agricultural areas. High Spatial Resolution Classifications From the Kappa coefficients of Table 2, it can be seen that the four study classes were well discriminated by the multitemporal TM data set (Kappa50.920). The distribution of the four classes is shown in the classified image of Figure 2. As already mentioned, this classification was taken as a reference for the computation of the TM NDVI parameters (mean and standard deviation for each class in the three dates). Among the three single-date classifications, the best results were obtained from the June scene, while lower accuracies where achieved by the August and, in particular, the November scenes. Inaccuracies mainly derived from the confusion between the two forest types for the

June scene, while for the other two scenes errors also came from the bad discrimination of spring crops and pasture. This last error was particularly evident in the November scene, which could be explained considering that both these cover types are mainly sparse grasses in this period. AVHRR NDVI Profiles The comparison of the TM and AVHRR NDVI values gave interesting results for all the treatments examined. As regards the effect of the representation of the classification outputs, Table 3 shows that the differences in the outputs produced by the hard and fuzzy procedures were strongly reduced by both filtering processes. In practice, the degradation of the images led to a substantial loss of the higher amount of information retained by the fuzzy method. As a consequence, the two classification procedures produced rather similar estimates of the NDVI profiles (Table 5), but slightly better results were anyway obtained by the fuzzy representation. Secondly, the effects of the degradation methods were compared. As can be imagined, the simple average was easier to perform than the Gaussian filtering; nevertheless, they produced substantially similar results. Table 4 shows that the two filtering methods gave very similar degraded abundance images, which produced practically equivalent NDVI profiles (Table 5). Again from Table 5 it can be seen that the major effect on the NDVI estimation process was attributable Table 5. Determination Coefficients (r 2) and Root Mean Square Error (RMSE) for the Correlations between TM and Interpolated AVHRR NDVI Data Obtained by Different Classification, Degradation, and Regression Proceduresa Classification and Degradation Procedures

Table 3. Correlations between Hard and Fuzzy Classification Outputs for the Original Data (High Resolution) and the Data Degraded Using Mean and Gaussian Filters Class

Original

Mean Filter

Coniferous forest Deciduous forest Spring crops Pastures

0.965 0.956 0.948 0.882

0.999 0.998 0.998 0.989

Gaussian Filter 0.999 0.999 0.998 0.990

Hard and mean filter Hard and Gaussian filter Fuzzy and mean filter Fuzzy and Gaussian filter

Regression Procedure Univariate

Multivariate

r 50.768 RMSE50.108 r 250.764 RMSE50.114 r 250.781 RMSE50.119 r 250.769 RMSE50.122

r 250.847 RMSE50.066 r 250.853 RMSE50.064 r 250.864 RMSE50.062 r 250.857 RMSE50.063

2

a All correlations are highly significant, P,0.01; the results of the best estimation method are underlined.

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Figure 3. NDVI values throughout the year 1990 for each study class [1) coniferous forest; 2) deciduous forest; 3) spring crops; 4) pastures] as obtained from TM data [(d) with standard deviation bars] and by applying the methodology described to AVHRR data [(- -) by simple average; (—) by fuzzy classification, mean filtering and univariate regression; (—) by fuzzy classification, mean filtering, and multivariate regression; all curves are smoothed for better interpretation; DOY 5 day of year].

to the regression method. The univariate procedure in fact gave relatively poor estimates in all cases, while the multivariate method yielded results which were in absolute the best obtained in the study. Figure 3 shows the different NDVI profiles obtained from AVHRR data for each spectral class by using the best classification (fuzzy) and degradation (mean) and the two regression methods compared to the NDVI values computed from the TM data (mean6standard deviation). The averages of the AVHRR NDVI data corresponding to the TM pixels of each class are also shown. As could be expected, all three curves are good estimates of the reference TM NDVI values for class 2 (deciduous forest), which was very widespread in the study area. Even the simple average from the TM classification gave good estimates for this class which contained many almost pure AVHRR pixels. This was not the case for the other classes, the profiles of which were poorly estimated by both the simple average and the univariate regression methods. In particular, the former method tended, of course, to underestimate NDVI deviations from the mean monthly values, while the univariate regression procedure had the opposite effect. Conversely, the multivariate regression procedures provided good estimates in all cases, always comprised in the mean6standard deviation range of the TM NDVI values.

The profiles found by this last procedure were also quite realistic for all classes. Coniferous forests, which mainly cover the upper mountain zones, showed an NDVI maximum in July–August, with moderate variations during the year. Deciduous forests had a higher contrast from winter to spring–summer NDVI values, with a maximum in June. The profile of class 3 was typical of spring crops in Mediterranean areas, with a clear maximum in April and two minima in winter and summer due to the cold and arid seasons, respectively. The profile of pastures finally showed moderate variations throughout the year, with a smoother maximum from May to July and the same minima as in the previous case. The trials carried out with the single date fuzzy classifications also produced useful indications (Fig. 4). In contrast with what could be expected, the classification of the June scene produced the poorest NDVI estimates. This may indicate that this period of uniformly high vegetation activity is not the most appropriate for separating the NDVI profiles of the study classes. In effect, all classes showed medium-high NDVI values in June, while they were more differentiated in August and November due to the contrast between crops and deciduous forest and between pastures and coniferous forest, respectively. The fact that these estimation trends were different from those of the single-date classification accuracies can be

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Figure 4. Correlations between the mean TM NDVI values and the AVHRR NDVI values as obtained by applying the mean filtering and the multivariate regression procedure to the outputs from the fuzzy multitemporal and single date classifications.

partly explained considering that the latter utilized the information of all three original TM bands with relevant variabilities, while the present analysis was based only on the mean NDVI values of the four classes. Integrated Images From a visual examination of the 12 final NDVI images (Fig. 5) obtained by the optimal procedure developed (fuzzy classification, mean filtering, multivariate regression), it can be seen that they are informative on the spatial distribution of the index in the study area all over the year. Thanks to the recalibration procedure applied, the mean NDVI value in each low spatial resolution window is the same as that of the AVHRR NDVI, so that the images produced can be considered unbiased estimates with respect to the latter data. This is confirmed by Table 6, which shows that the mean values of the new NDVI images are actually almost identical to the relevant AVHRR values (Table 1). On the contrary, the standard deviations and dynamic ranges are larger than these and more similar to the TM statistics, which testifies to the increased information content brought by the enhanced spatial resolution. DISCUSSION The current work presents a methodology suited for merging the useful features of remote sensing data with different spatial and temporal resolutions. Some assump-

tions necessary for the correct working of the methodology have already been mentioned but are worth further discussion. A first assumption regards the spatial invariance of the vegetation classes considered during the growing season, which is realistic for the study area owing to its environmental features and agricultural practices. The spatial invariance of wheat and alfalfa during a season can in fact be safely assumed, since only one crop per year is usually cultivated in the whole study area. Where this is not the case, single date classifications performed independently during the year could be used together with collateral information to recover NDVI profiles for briefer periods. As regards the high spatial resolution data, the homogeneity of the spectral classes identified in the study area must be assumed, even if the subsequent regression analysis can cope with limited violations of this assumption. The selection of a relatively small study area and the use of a classification with a multitemporal data set as high spatial resolution reference tend to guarantee the approximate homogeneity of the spectral classes during the year. Moreover, the fuzzy approach can deal appropriately with the spectral variability deriving from mixtures among classes (Wang, 1990a,b). The classes must also be well spectrally separated and with different profiles expected in the low spatial resolution data. This was obtained in the current case by keeping their number low and identifying them directly

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Table 6. Statistics of the New NDVI Images Produced by the Complete Methodology (Minimum, Maximum, Mean, and Standard Deviation)

January February March April May June July August September October November December

Figure 5. Monthly high resolution NDVI images obtained for the year 1990 by applying the complete integration methodology to the TM and AVHRR data (1–125sequential month from January to December).

in the images on the basis of existing ground references and personal knowledge of the study area. Another requisite is that the classes have different spatial distributions, so as to obtain degraded abundance images with wide dynamic ranges. This also was satisfied in the present investigation since the main forest types, spring crops and pasture, were concentrated in different places. Concerning the low spatial resolution data, an adequate preprocessing phase has to be assumed, especially regarding the radiometric calibration, the georeferencing, and the reduction of disturbing effects. Actually, the AVHRR data used in the present research had already been preprocessed, which could be the cause of some problems. Even though the radiometric calibration method proposed by Rao and Chen (1994) can be considered reliable, the cubic convolution resampling algorithm, which was applied partly to reduce the effects of misregistrations, is known to slightly alter the radiometric properties of the data. In practice, the global result of this resampling and of the maximum value compositing process was a widespread overestimation of the true vegetation index,

Minimum

Maximum

Mean

Standard Deviation

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.529 0.635 0.608 0.745 0.847 0.875 0.882 0.847 0.992 0.690 0.671 0.541

0.252 0.322 0.340 0.475 0.609 0.597 0.551 0.486 0.498 0.467 0.464 0.279

0.072 0.099 0.099 0.123 0.161 0.166 0.176 0.190 0.164 0.133 0.120 0.093

especially for low values, with a subsequent smoothing effect on the images which is difficult to quantify. As already said, this problem was unavoidable in the present case and is, however, very common in operational applications, since NOAA NDVI data are often provided in an even more uncorrected form, especially for historical time series. The study also assumes a linear combination of high spatial resolution NDVI values to generate the low resolution signal, which should be strictly true only for the original bands. Recent studies of Kerdiles and Grondona (1995) have however demonstrated that this assumption implies only very minor inaccuracies. Another question concerns the spectral equivalence of TM and AVHRR NDVI data for the comparison of the profiles. At this point, it is worth remembering that, even though the data acquired in the TM Channels 3 and 4 correspond fairly well with those acquired in the AVHRR Channels 1 and 2 (Bolle, 1996), there exist some discrepancies between the relevant NDVI data sets owing to slightly different band widths, sensor view angles, overpass time, and, consequently, atmospheric and directional perturbations (Bannari et al., 1995). Despite these error sources, the mean NDVI values from the two sensors found experimentally over the whole study area were actually very similar, which justifies the comparisons performed. CONCLUSIONS In summary, the following main methodological conclusions can be drawn from the results of the research: 1. The hard and fuzzy representations of the classification outputs, which are different at the original high spatial resolution, are almost equivalent when degraded at the low resolution. Consequently, which procedure is used has almost no effect on the regression estimates. The outcomes of

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the fuzzy approach are, however, more informative about class distribution and actually produce slightly better estimates. Moreover, they are more suitable for the final generation of images with integrated features. 2. The degradation method has little effect on the final results. This can be attributed to the fact that Gaussian degradation is anyway a rough simulation of the AVHRR point spread function when precise information on the atmospheric and illumination conditions at the acquisition times and on the image preprocessing phases is not available (Moreno and Melia´, 1994). Considering that Gaussian filtering is much more expensive than mean filtering in terms of computing time, this suggests that the simpler method can be used every time that a strict conformity to theoretical bases is not necessary. 3. The univariate linear regression method produces results which are relatively unstable and inaccurate with respect to the high spatial resolution data, while the multivariate procedure gives very good estimates. The additional complexity of using multivariate analysis is in effect limited, so that this approach can be highly recommended for similar applications. 4. Restricted high spatial resolution data sets (i.e., single date classifications) can be used for the integration procedure with only limited decreases in NDVI estimation accuracy if suitable dates are selected. This is particularly important for operational purposes when the availability of high spatial resolution data can be a major limiting factor. As regards the final part of the methodology, it must be emphasized that the recalibration of the estimated high resolution NDVI images on the AVHRR data is useful for introducing the intraclass information which is contained in the latter. This operation also makes the high resolution estimates unbiased with respect to the monthly AVHRR NDVI data. Globally, the approach proposed was demonstrated to be suitable for producing NDVI images with high spatial and temporal resolutions. As such, it could have great utility as a means of efficiently integrating remotely sensed data with different features for vegetation monitoring. It is worth noting again that high spatial and temporal resolution estimates of vegetation conditions are not directly obtainable from current systems due to the mentioned limitations. The methodology developed is not strictly linked to the data set currently utilized and could as well be applied to other imagery from present of future satellite systems. Further research is foreseen in this direction. Also, future investigations will address the application of the approach to the analysis of multiyear data sets, which

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is of paramount importance for studies on environmental modelling. This research was supported by the EC Environment and Climate Research Programme (RESMEDES Project, Contract No. ENV4-CT95–0094, Climatology and Natural Hazards). Within the framework of the Project, Nuova Telespazio provided the NOAA-AVHRR data and the Free University of Berlin the Landsat TM scene of August 1990; these contributions are gratefully acknowledged. The authors also want to thank the Project Coordinator, Professor H. J. Bolle, for his helpful comments on the first draft of the article. Other thanks are due to the three anonymous referees whose comments improved the quality of the article.

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