- Email: [email protected]
Use of NOAA satellite data for grassland studies in Poland
Acta Astronautica Vol. 25, No. 8/9, pp. 439--442, 1991
0094-5765/91 $3.00+ 0.00 Copyright O 1991 Pergamon Prum pk
Printed in Great Britain. All fights reserved
USE OF N O A A SATELLITE D A T A FOR G R A S S L A N D STUDIES IN POLANDI" ZmGNIEW BOCHENEK Institute of Geodesy and Cartography, Remote Sensing Centre, Jasna 2/4 00-950 Warsaw, Poland (Received 15 February 1991)
Abetract--NOAA data are appfied for developing a method of soil moisture and biomass assessment which would be operational for Polish grasslands, characterized by relatively high soil moisture content and great diversity of grass species.
!. INTRODUCTION
In 1987 a large project, sponsored by UNDP and FAO agencies, started at the Polish Remote Sensing Centre in Warsaw. The main objectives of this project are: - - to prepare a remote sensing based method for grassland soil moisture and biomass assessment based on data collected by meteorological satel-
lUtes
- - to improve a grass yield model through incorporation of remotely sensed data, characterizing growth stage and soil moisture conditions - - to create an information system of grassland monitoring, leading to forage yield estimates at regional level. The extensive studies on applying low-resolution satellite images for vegetation assessment have been performed by some scientists from European and American remote sensing centres. The results of these works, conducted mainly for semi-arid or tropical regions, prove the usefulness of the processed NOAA AVHRR data for large-area vegetation monitoring. Therefore it was decided to apply this type of data for developing a method of soil moisture and biomass assessment, which could be operational for Polish grasslands, characterized by relatively high soil moisture content and great diversity of grass species. 2. M E T H O D S
The approach assumed in this work is based on three-level data collection, in order to draw proper conclusions from analysis of satellite data. The following types of data were selected for the study area: k ground-truth data and ground spectral measurements 1"Paper IAF-90-119 presented at the 41st Congress of the International Astronautical Federation, Dresden, Fed. Rep. Germany, 8-12 October 1990.
- - aerial multispectrai photographs - - NOAA AVHRR and Landsat TM satellite images NOAA AVHRR images, collected several times during the growing season, were the basic source of information about location and spectral response of grasslands in visible, near-i.r, and thermal bands. These images are collected routinely at the receiving station located at the Institute of Meteorology and Water Management in Cracow (southern Poland). They are recorded on magnetic tapes and sent to the Polish Remote Sensing Centre in Warsaw for further processing. Analysis of satellite data has been done, utilizing two systems of digital image processing: E R D A S system installed on Compaq 386 computer and inhouse developed IPS system, prepared for IBM PC/AT. At the first stage of analysis raw NOAA AVHRR images covering the whole Poland were geometrically corrected, i.e. transformed to the topographic maps at a scale of 1:200,000, through the use of an adequate number of ground control points. Next, fragments of the georeferenced images covering western Poland were extracted and further processed. These fragments contain selected grassland test sites. The chosen test site has a large area of meadows, characterized by different soil moisture levels and plant compositions, as well as by various agricultural practices, due to mixed, private and state ownership. In order to have detailed information about spectral variability within the study area, Landsat TM image was also acquired for the selected region and optimum colour composite formed from three enhanced channels (TM 3, 4 and 5) was produced. NOAA AVHRR images, after their geometric correction were further analyzed using two approaches. One approach assumed application of AVHRR fari.r. channels (bands 4 and 5); it was aimed at evapotranspiration and soil moisture assessment, based on remotely sensed temperature information. The
439
440
ZBIGNIEWBOCHENEK
second approach, which will be presented in detail in this paper, assumes use of visible and near-i.r. AVHRR data (band I and 2) for grass biomass assessment. The following main steps of analysis of NOAA AVHRR images were realized: (1) Raw visible and near-i.r. A V H R R data were transformed into normalized difference vegetation indices (NDVI), applying the following formula: NDVI -- (AVHRR2 - AVHRRI)/ (AVHRR2 + AVHRR1) (2) NDVI images were created and visualized for the selected study region. (3) Values of the vegetation index were derived from NDVI images for individual NOAA pixels covering the selected grassland test site. These values were the main satellite data used for regression analyses and correlations with ground information. The following information was collected from ground level during the satellitepasses:
(I) Ground parameters characterizingstateof vegetation canopies, i.e. leaf area index (LAI) -- wet and dry biomass -- soil moisture at 4 depth layers
-
3. DATAANALYSISAND RESULTS A three-stage approach was assumed for analysis of multi-level data, i.e. correlations between ground, aerial and satellite data were studied in succession. At the first stage, ground spectral and agrometeorological measurements were thoroughly analyzed. Relationships between reflectance values recorded by spectral radiometer and ground parameters characterizing grass canopies: LAI, biomass and soil moisture were first studied. Two channels, corresponding to bands 1 and 2 of NOAA radiometer, were extracted from 7-band set of radiometric data. Next, NDVI index was calculated from reflectance values recorded in these channels for all points, where ground parameters were collected. In order to assess which spectral data arc most sensitive to change of ground parameters, regression analysis was done systematically for the following combinations: i.r. reflectance LAI red reflectance vs LAI - - NDVI vs LAI i.r. reflectance vs biomass - - NDVI vs biomass i.r. reflectance vs soil moisture - - NDVI vs soil moisture. -
-
-
-
-
-
-
-
-
(2) Ground spectral measurements of vegetation, performed with the use of a spectral radiometer. Data concerning spectral reflectance of grasses were collected, in order to support relationships between ground parameters and satellite data. 7-band spectral radiometer, operating in narrow visible and ncar-i.r, bands, constructed at the Polish Centre of Space Research, was used for these measurements. They were done at the selected points, representing different values of LAI, biomass and soil moisture, and spread quite uniformly throughout the study area. At the same time, while ground and satellitedata were collected, an aerial mission was carried out. Aerial photographs were taken, using a helicopter, from three altitudes:1600, 400 and I00 m. They were recorded with the use of Hasselblad cameras, in red infrared spectral bands. Photographs taken from 1600m altitude were utilized for preparing photomosaic at a scaleof I:I0,000covering the study area. The prepared photomosaic permitted precise selection of points of ground measurements and detailed study of variabilityof grass cover. Negatives of aerial photographs were used for measuring opticaldensity at the points selected for ground-truth data collection. As acquisition of ground, aerial and satellitedata was synchronized, it enabled their thorough analysis for three degrees of detailness.
Before the main series of regression studies had been started, two preliminary analyses were performed. The first analysis was aimed at evaluation of soil moisture- i.r. reflectance relations, depending on depth of soil moisture measurements. Soil moisture, . defined as the ratio of water to soil weight in unit volume, was determined at 4 layers: 0-10cm, 10-20cm, 20-30cm and 30-40crn, embracing the whole root zone of grasses. Correlation analysis was done for i.r. reflectance values, as well as for NDVI values, derived from radiometer red and i.r. reflectance. The results of this analysis are presented in Fig. 1. This study revealed that the best correlation between soil moisture and i.r. reflectance was for the surface soil layer (correlation coefficient r--0.84). Similar correlation has been obtained for surface soil moisture- NDVI relations. Slightly lower relationship has been found for mean soil moisture, averaged for the whole root profile (correlation coefficient r = 0.77). For all these relationships linear regression proved to be sufficient. The second preliminary analysis dealt with studying relations between i.r. reflectance values and angle of view of spectral radiometer, used for ground data collection. Orientation of radiometer was changed from nadir up to 45 °. The regression analysis revealed strong correlation between the studied parameters (mean correlation coefficient r •0.97). Slightly weaker relationship was observed between radiometer angle of view and red reflectance values (r ffi 0.88), while low correlation was obtained for
Grassland studies CHANG[ Of" ¢OMllt'~TION ¢OERRIClENT 0,III
''''I
....
I ....
I ....
I ' ' '."I
''
----
|R'" NOVI
....
+ u
o:
O, GI
0 , 58
O,,,l
,,
I,,,,I,,,,I S
.... 10
IS
I,~,I'~,~ 20
;5
30 cn
SOIL MOISTURE OEPI~H
Fig. 1. Comparison of correlation coefficients for different soil moisture depths.
NDVI values. The results of this analysis prove that normalized difference vegetation index is quite insensitive to light intensity variations caused by different sun-sensor orientation. Analysis relations between spectral reflectance and two ground parameters, characterizing closely grassland productivity, i.e. LAI and biomass, were studied. Regression analysis, performed for i.r. spectral reflectance values and for NDVI values, revealed low correlation between the studied spectral data and LAI/biomass values. These results implied that weak relation between the studied parameters was mainly caused by point character of ground spectral measurements (field of view of spectral radiometer was of 40 cm dia). So it was decided to aggregate measurements o f spectral reflectance, through the u s e of aerial photographs, instead of ground measurements. Utilizing photographs at a scale of 1:20,000 and applying proper densitometer parameters for measuring optical density we could determine reflectance values from a much larger area (of about 30 m dia). In this way it was possible to take into consideration local variability of environmental conditions. Results of regression analysis confirmed the above assumptions. A strong relationship has been found between LAI and optical density measured in i.r. band (correlation coefficient r = 0.83). This relationship is presented in Fig. 2. Similar results were obtained for correlation between i.r. optical density and dry biomass amount (r : 0.85). Although approach to the use of aerial photography for determining LAI/biomass values proved to be successful, the alternative method was also tested. This method assumes application of high-resolution satellite data, instead of aerial photographs. Landsat Thematic Mapper data were used at this part of analysis. First the selected points of ground measureAA 2 S / t ~ - C
441
merits were located on the satellite image. Next, reflectance values were extracted for all selected points from particular TM channels; they were correlated with LAI/biomass parameters. Four TM channels, i.e. TM3, TM4, TM5 and TM7 were taken into account, as well as normalized difference vegetation indices, formed from all combinations of these bands. The results of the regression analysis proved to be promising. Strong correlations were obtained for middle i.r. bands, i.e. TM5 and TM7 (r--0.86 and r--0.87, respectively for TM/biomass relations). Correlation coefficients were higher for these bands, than those obtained for ~ 1 and uear-i.r, channels. Also, NDVIs based on middle i.r. channels proved to be best correlated with LAI/biomass values. The strong relationships obtained in the course of analysis of aerial and Landsat TM data entitled us to the use of regression equations for determining LAI and biomass values for a greater number of points spread throughout the study area. An increase in the number of points, representing ground measurements, was important for the next stage of the works--analysis of NOAA AVHRR satellite data. At this stage of analysis NOAA grassland pixels were first located on the map at a scale of 1:50,000. Next, averaged LAI and biomass values were determined for each AVHRR pixel; they were derived from ground measurements supported with aerial/ TM relationships. In this way, a file containing NDVI values and corresponding LAI/biomass values for particular NOAA AVHRR pixels, was created. This file was used for final regression analysis between: --
normalized difference vegetation index and LAI difference vegetation index and biomass.
--normalized
Results of this analysis proved good correlation of the studied indices. The relationships were characterized by the following correlation coefficients: r = 0.82
for LAI-NDVI regression
r = 0.85
for biomass-NDVI regression. LAI - IR OPTICAL OEI~Si~Y
10,s+
i,s
./
m
/ •
.//__ t/
Sfl
-/
t,t
/
/ +,,
/
.,/r,,/,
ltl
/
1,2
/
/
// ./
/I
f/'~.
./i
°
/,"
/ /
,/,
.
-
.// //
n . , , ,J/,-,,, t . . . . a . . . . 1,3 1+6 I,| m. O~T~CAL O E . U ~
Ill
Fig. 2. LAI-i.r. optical densityml~lsion.
I,?
442
Zmom~v
Similar correlations between NDVI values derived from N O A A A V H R R data and LAI/biomass vahtes were obtained for two missions carried out in May and July 1989. 4. CONCLUSIONS Results of the work carried out during the 1989 growing season demonstrated, that the method of determining LAI and biomass amount on the basis of visible and near-i.r. A V H R R data can be in Polish environmental conditions effective tool for assessing parameters, important for grassland productivity estimates. This method is based on ground measurements, performed at representative points which are condensed through the use of aerial photographs or high-resolution satellite images. Although fairly strong correlations were obtained, there is still need of their statistical confirmation, through collection and analysis of new ground and satellite data. Therefore, the extended field/aerial/satellite campaign has been carried out during 1990 growing season. This campaign incorporated wider range of environmental conditions, due to differentiated agricultural practices throughout study area. If the relations between agronomic and satellite data will be validated, a method
of yield forecast for large Polish grasslands, based on model incorporating parameters derived from satelrite data could be operationally applied. ~ C E S 1. K. P. Gallo and T. K. Flesh, Large area crop monitoring with the NOAA AVHRR; estimating the ~lldn~ stage of corn development. Remote Se~. F,~vfr, 27, I (1989). 2. B. N. Holben, Characteristics of maximum-value com. posite images from temporal AVHRR data. Int. J. Remote Sens. 7, 1417 (1986). 3. A. R. Huete, A soil adjusted vegetation index. Remote Sens. Envir. 25, 295 (1988). 4. A. R. Huete and R. D. Jackson, Suitability of spectral indices for evaluating vegetation characteristicsof add rangelands. Remote Sens. Envir. 23, 213 (1987). 5. P. J. Pinter, H. L. Kelly and S. Schnell, Spectral estimation of alfalfabiomass under conditions of variable cloud cover. Proceedingsof the 18th Conference on Agriculturaland Forest Meteorology (1987). 6. K. Rasmussen, S. Folving, J. Holm and H. Sogaard, Microcomputer technologies for deriving agrocllmato-
logical parameters and vegetations indicators from satelllte data. Final Report, Copenhagen (1987). 7. J. R. G. Towushend and C. O. Justice, Analysis of the dynamics of African vegetation using norrnA!iTeddifference vegetation index. Int..l. Remote Sens. 7, 1435 (1986).
0094-5765/91 $3.00+ 0.00 Copyright O 1991 Pergamon Prum pk
Printed in Great Britain. All fights reserved
USE OF N O A A SATELLITE D A T A FOR G R A S S L A N D STUDIES IN POLANDI" ZmGNIEW BOCHENEK Institute of Geodesy and Cartography, Remote Sensing Centre, Jasna 2/4 00-950 Warsaw, Poland (Received 15 February 1991)
Abetract--NOAA data are appfied for developing a method of soil moisture and biomass assessment which would be operational for Polish grasslands, characterized by relatively high soil moisture content and great diversity of grass species.
!. INTRODUCTION
In 1987 a large project, sponsored by UNDP and FAO agencies, started at the Polish Remote Sensing Centre in Warsaw. The main objectives of this project are: - - to prepare a remote sensing based method for grassland soil moisture and biomass assessment based on data collected by meteorological satel-
lUtes
- - to improve a grass yield model through incorporation of remotely sensed data, characterizing growth stage and soil moisture conditions - - to create an information system of grassland monitoring, leading to forage yield estimates at regional level. The extensive studies on applying low-resolution satellite images for vegetation assessment have been performed by some scientists from European and American remote sensing centres. The results of these works, conducted mainly for semi-arid or tropical regions, prove the usefulness of the processed NOAA AVHRR data for large-area vegetation monitoring. Therefore it was decided to apply this type of data for developing a method of soil moisture and biomass assessment, which could be operational for Polish grasslands, characterized by relatively high soil moisture content and great diversity of grass species. 2. M E T H O D S
The approach assumed in this work is based on three-level data collection, in order to draw proper conclusions from analysis of satellite data. The following types of data were selected for the study area: k ground-truth data and ground spectral measurements 1"Paper IAF-90-119 presented at the 41st Congress of the International Astronautical Federation, Dresden, Fed. Rep. Germany, 8-12 October 1990.
- - aerial multispectrai photographs - - NOAA AVHRR and Landsat TM satellite images NOAA AVHRR images, collected several times during the growing season, were the basic source of information about location and spectral response of grasslands in visible, near-i.r, and thermal bands. These images are collected routinely at the receiving station located at the Institute of Meteorology and Water Management in Cracow (southern Poland). They are recorded on magnetic tapes and sent to the Polish Remote Sensing Centre in Warsaw for further processing. Analysis of satellite data has been done, utilizing two systems of digital image processing: E R D A S system installed on Compaq 386 computer and inhouse developed IPS system, prepared for IBM PC/AT. At the first stage of analysis raw NOAA AVHRR images covering the whole Poland were geometrically corrected, i.e. transformed to the topographic maps at a scale of 1:200,000, through the use of an adequate number of ground control points. Next, fragments of the georeferenced images covering western Poland were extracted and further processed. These fragments contain selected grassland test sites. The chosen test site has a large area of meadows, characterized by different soil moisture levels and plant compositions, as well as by various agricultural practices, due to mixed, private and state ownership. In order to have detailed information about spectral variability within the study area, Landsat TM image was also acquired for the selected region and optimum colour composite formed from three enhanced channels (TM 3, 4 and 5) was produced. NOAA AVHRR images, after their geometric correction were further analyzed using two approaches. One approach assumed application of AVHRR fari.r. channels (bands 4 and 5); it was aimed at evapotranspiration and soil moisture assessment, based on remotely sensed temperature information. The
439
440
ZBIGNIEWBOCHENEK
second approach, which will be presented in detail in this paper, assumes use of visible and near-i.r. AVHRR data (band I and 2) for grass biomass assessment. The following main steps of analysis of NOAA AVHRR images were realized: (1) Raw visible and near-i.r. A V H R R data were transformed into normalized difference vegetation indices (NDVI), applying the following formula: NDVI -- (AVHRR2 - AVHRRI)/ (AVHRR2 + AVHRR1) (2) NDVI images were created and visualized for the selected study region. (3) Values of the vegetation index were derived from NDVI images for individual NOAA pixels covering the selected grassland test site. These values were the main satellite data used for regression analyses and correlations with ground information. The following information was collected from ground level during the satellitepasses:
(I) Ground parameters characterizingstateof vegetation canopies, i.e. leaf area index (LAI) -- wet and dry biomass -- soil moisture at 4 depth layers
-
3. DATAANALYSISAND RESULTS A three-stage approach was assumed for analysis of multi-level data, i.e. correlations between ground, aerial and satellite data were studied in succession. At the first stage, ground spectral and agrometeorological measurements were thoroughly analyzed. Relationships between reflectance values recorded by spectral radiometer and ground parameters characterizing grass canopies: LAI, biomass and soil moisture were first studied. Two channels, corresponding to bands 1 and 2 of NOAA radiometer, were extracted from 7-band set of radiometric data. Next, NDVI index was calculated from reflectance values recorded in these channels for all points, where ground parameters were collected. In order to assess which spectral data arc most sensitive to change of ground parameters, regression analysis was done systematically for the following combinations: i.r. reflectance LAI red reflectance vs LAI - - NDVI vs LAI i.r. reflectance vs biomass - - NDVI vs biomass i.r. reflectance vs soil moisture - - NDVI vs soil moisture. -
-
-
-
-
-
-
-
-
(2) Ground spectral measurements of vegetation, performed with the use of a spectral radiometer. Data concerning spectral reflectance of grasses were collected, in order to support relationships between ground parameters and satellite data. 7-band spectral radiometer, operating in narrow visible and ncar-i.r, bands, constructed at the Polish Centre of Space Research, was used for these measurements. They were done at the selected points, representing different values of LAI, biomass and soil moisture, and spread quite uniformly throughout the study area. At the same time, while ground and satellitedata were collected, an aerial mission was carried out. Aerial photographs were taken, using a helicopter, from three altitudes:1600, 400 and I00 m. They were recorded with the use of Hasselblad cameras, in red infrared spectral bands. Photographs taken from 1600m altitude were utilized for preparing photomosaic at a scaleof I:I0,000covering the study area. The prepared photomosaic permitted precise selection of points of ground measurements and detailed study of variabilityof grass cover. Negatives of aerial photographs were used for measuring opticaldensity at the points selected for ground-truth data collection. As acquisition of ground, aerial and satellitedata was synchronized, it enabled their thorough analysis for three degrees of detailness.
Before the main series of regression studies had been started, two preliminary analyses were performed. The first analysis was aimed at evaluation of soil moisture- i.r. reflectance relations, depending on depth of soil moisture measurements. Soil moisture, . defined as the ratio of water to soil weight in unit volume, was determined at 4 layers: 0-10cm, 10-20cm, 20-30cm and 30-40crn, embracing the whole root zone of grasses. Correlation analysis was done for i.r. reflectance values, as well as for NDVI values, derived from radiometer red and i.r. reflectance. The results of this analysis are presented in Fig. 1. This study revealed that the best correlation between soil moisture and i.r. reflectance was for the surface soil layer (correlation coefficient r--0.84). Similar correlation has been obtained for surface soil moisture- NDVI relations. Slightly lower relationship has been found for mean soil moisture, averaged for the whole root profile (correlation coefficient r = 0.77). For all these relationships linear regression proved to be sufficient. The second preliminary analysis dealt with studying relations between i.r. reflectance values and angle of view of spectral radiometer, used for ground data collection. Orientation of radiometer was changed from nadir up to 45 °. The regression analysis revealed strong correlation between the studied parameters (mean correlation coefficient r •0.97). Slightly weaker relationship was observed between radiometer angle of view and red reflectance values (r ffi 0.88), while low correlation was obtained for
Grassland studies CHANG[ Of" ¢OMllt'~TION ¢OERRIClENT 0,III
''''I
....
I ....
I ....
I ' ' '."I
''
----
|R'" NOVI
....
+ u
o:
O, GI
0 , 58
O,,,l
,,
I,,,,I,,,,I S
.... 10
IS
I,~,I'~,~ 20
;5
30 cn
SOIL MOISTURE OEPI~H
Fig. 1. Comparison of correlation coefficients for different soil moisture depths.
NDVI values. The results of this analysis prove that normalized difference vegetation index is quite insensitive to light intensity variations caused by different sun-sensor orientation. Analysis relations between spectral reflectance and two ground parameters, characterizing closely grassland productivity, i.e. LAI and biomass, were studied. Regression analysis, performed for i.r. spectral reflectance values and for NDVI values, revealed low correlation between the studied spectral data and LAI/biomass values. These results implied that weak relation between the studied parameters was mainly caused by point character of ground spectral measurements (field of view of spectral radiometer was of 40 cm dia). So it was decided to aggregate measurements o f spectral reflectance, through the u s e of aerial photographs, instead of ground measurements. Utilizing photographs at a scale of 1:20,000 and applying proper densitometer parameters for measuring optical density we could determine reflectance values from a much larger area (of about 30 m dia). In this way it was possible to take into consideration local variability of environmental conditions. Results of regression analysis confirmed the above assumptions. A strong relationship has been found between LAI and optical density measured in i.r. band (correlation coefficient r = 0.83). This relationship is presented in Fig. 2. Similar results were obtained for correlation between i.r. optical density and dry biomass amount (r : 0.85). Although approach to the use of aerial photography for determining LAI/biomass values proved to be successful, the alternative method was also tested. This method assumes application of high-resolution satellite data, instead of aerial photographs. Landsat Thematic Mapper data were used at this part of analysis. First the selected points of ground measureAA 2 S / t ~ - C
441
merits were located on the satellite image. Next, reflectance values were extracted for all selected points from particular TM channels; they were correlated with LAI/biomass parameters. Four TM channels, i.e. TM3, TM4, TM5 and TM7 were taken into account, as well as normalized difference vegetation indices, formed from all combinations of these bands. The results of the regression analysis proved to be promising. Strong correlations were obtained for middle i.r. bands, i.e. TM5 and TM7 (r--0.86 and r--0.87, respectively for TM/biomass relations). Correlation coefficients were higher for these bands, than those obtained for ~ 1 and uear-i.r, channels. Also, NDVIs based on middle i.r. channels proved to be best correlated with LAI/biomass values. The strong relationships obtained in the course of analysis of aerial and Landsat TM data entitled us to the use of regression equations for determining LAI and biomass values for a greater number of points spread throughout the study area. An increase in the number of points, representing ground measurements, was important for the next stage of the works--analysis of NOAA AVHRR satellite data. At this stage of analysis NOAA grassland pixels were first located on the map at a scale of 1:50,000. Next, averaged LAI and biomass values were determined for each AVHRR pixel; they were derived from ground measurements supported with aerial/ TM relationships. In this way, a file containing NDVI values and corresponding LAI/biomass values for particular NOAA AVHRR pixels, was created. This file was used for final regression analysis between: --
normalized difference vegetation index and LAI difference vegetation index and biomass.
--normalized
Results of this analysis proved good correlation of the studied indices. The relationships were characterized by the following correlation coefficients: r = 0.82
for LAI-NDVI regression
r = 0.85
for biomass-NDVI regression. LAI - IR OPTICAL OEI~Si~Y
10,s+
i,s
./
m
/ •
.//__ t/
Sfl
-/
t,t
/
/ +,,
/
.,/r,,/,
ltl
/
1,2
/
/
// ./
/I
f/'~.
./i
°
/,"
/ /
,/,
.
-
.// //
n . , , ,J/,-,,, t . . . . a . . . . 1,3 1+6 I,| m. O~T~CAL O E . U ~
Ill
Fig. 2. LAI-i.r. optical densityml~lsion.
I,?
442
Zmom~v
Similar correlations between NDVI values derived from N O A A A V H R R data and LAI/biomass vahtes were obtained for two missions carried out in May and July 1989. 4. CONCLUSIONS Results of the work carried out during the 1989 growing season demonstrated, that the method of determining LAI and biomass amount on the basis of visible and near-i.r. A V H R R data can be in Polish environmental conditions effective tool for assessing parameters, important for grassland productivity estimates. This method is based on ground measurements, performed at representative points which are condensed through the use of aerial photographs or high-resolution satellite images. Although fairly strong correlations were obtained, there is still need of their statistical confirmation, through collection and analysis of new ground and satellite data. Therefore, the extended field/aerial/satellite campaign has been carried out during 1990 growing season. This campaign incorporated wider range of environmental conditions, due to differentiated agricultural practices throughout study area. If the relations between agronomic and satellite data will be validated, a method
of yield forecast for large Polish grasslands, based on model incorporating parameters derived from satelrite data could be operationally applied. ~ C E S 1. K. P. Gallo and T. K. Flesh, Large area crop monitoring with the NOAA AVHRR; estimating the ~lldn~ stage of corn development. Remote Se~. F,~vfr, 27, I (1989). 2. B. N. Holben, Characteristics of maximum-value com. posite images from temporal AVHRR data. Int. J. Remote Sens. 7, 1417 (1986). 3. A. R. Huete, A soil adjusted vegetation index. Remote Sens. Envir. 25, 295 (1988). 4. A. R. Huete and R. D. Jackson, Suitability of spectral indices for evaluating vegetation characteristicsof add rangelands. Remote Sens. Envir. 23, 213 (1987). 5. P. J. Pinter, H. L. Kelly and S. Schnell, Spectral estimation of alfalfabiomass under conditions of variable cloud cover. Proceedingsof the 18th Conference on Agriculturaland Forest Meteorology (1987). 6. K. Rasmussen, S. Folving, J. Holm and H. Sogaard, Microcomputer technologies for deriving agrocllmato-
logical parameters and vegetations indicators from satelllte data. Final Report, Copenhagen (1987). 7. J. R. G. Towushend and C. O. Justice, Analysis of the dynamics of African vegetation using norrnA!iTeddifference vegetation index. Int..l. Remote Sens. 7, 1435 (1986).