WIND-SCATTEROMETER OBSERVATION OVER AMAZON RAINFOREST

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1 8 th International Conference on Environmental Science and Technology Lemnos island, Greece, 8 10 September 2003 WIND-SCATTEROMETER OBSERVATION OVER AMAZON RAINFOREST K. TOPOUZELIS 1 and I. H. WOODHOUSE 2 1 Remote Sensing Laboratory, National Technical University of Athens, Heroon Polytechniou 9, 15773, Zografos, Athens, Greece 2 School of GeoSciences (Geography), The University of Edinburgh EXTENDED ABSTRACT Windscatterometers (WSC) carried on the ERS 1 and 2 satellites were primarily designed to measure the backscatter coefficient as a function of incidence angle above the world s oceans, in order to determine wind speed and direction. A number of recent studies have demonstrated that scatterometers can be used to monitor surface parameters. This article presents an analysis of ERS Wind-Scatterometer (WSC) data over Amazon Rainforest. The consider period expands from August 1991 to July The main question that was addressed was whether the tropical rainforest got drier or wetter the last eight years. Analysis performed for: i) Monthly average (91-99), ii) Area-based, iii) Monthly-bashed (for specific year) and iv) Yearly-based (for specific month). Analysis saw a negative slope ( ) of the radar cross section over rainforest, at 40 0 angle of incidence. Key words: Remote Sensing, Radar, Windscatterometer 1. INTRODUCTION 1.1 Rainforests A rainforest is a moist, densely wooded area usually found in a warm, tropical wet climate. Annual rainfall is about 2000 mm and ranges as high as mm) in some tropical rain forests. The average temperature in most rain forests is 27 0 C [1]. In rainforests, canopy trees may rise 15 to 45 meters (50 to 150 feet) before forming a dense ceiling of branches and leaves. Broadleaf evergreen trees, vines, and nutrient-poor soils are common characteristics of this kind of forest. Tropical rainforests are the natural habitant of more than 80% of all plant and animal species of the world, while covering only approximately 7% of the land surface on Earth [2]. Rainforests maintain a rough balance between the biomass of the world s vegetation and carbon dioxide. Rainforest keep increases in carbon dioxide in check by converting carbon into cellulose and releasing oxygen through photosynthesis. However the concentration of carbon dioxide in the atmosphere has been increasing steadily since Mature tropical forests are in carbon balance, absorbing as much CO 2 as is released. These mature forests may become a major source of CO 2 on deforestation or a major sink of CO 2 on regeneration [3]. Large scale deforestation in the tropics normally results in decreasing humidity and rainfall, which in turn decreases agricultural and forestry yields and increases the probability of large scale forest fires [4]. 790

2 1.2 Amazon Rainforest The Amazon rainforest in South America is the world s largest. The Brazilian Amazon comprises the states of Acre, Amapa, Amazonas, Para, Rodonia, Roraima, Tocanins and portions of Maranhao and Goias. This area in total is approximately 5 million square kilometres, large enough to accommodate the entire Western Europe. According the Annual Report of Amazon Institutional Program (National Remote Sensing Agency of Brazil INPE) approximately 4 of the 5 millions of the area is covered by forest formations. Also according to the same report the gross deforestation for the period is km 2 and in total the gross deforestation from 1977 to 1988 is km Radar Backscatter Stability of Pure Rainforest Precise calibration of spaceborne radar instruments (SAR and scatterometers) is difficult to achieve using prelaunch calibration alone so extensive postlaunch calibration campaigns are often carried out shortly after launch of a new instrument [5]. The most common approach for postlaunch calibration is to use targets of known radar cross section such as extensive forests or agricultural field. These targets have to be uniform for large area and their average radar cross section needs to me stable for extended time periods. For the ERS-1 Wind Scatterometer the only place in the world that meets the requirements for such a target is the Brazilian rainforest [6]. The Brazilian rainforest is remarkably uniform over distances of hundreds of kilometres; it remains fully foliated throughout the year; it is dense enough so that the radar should not be affected by changes in ground conditions; its response is essentially independent of polarization and incidence angle [6]. However, these regions do exhibit some degree of spatial and temporal variability that, if unaccounted for, can lead to biases and increased uncertainly in the calibration of radar instrument [5]. In this study there is an attempt to examine the stability of radar backscatter over pure rainforest areas using windscatterometer (WSC) data. Assuming that a predetermined area is studied over an extend period of time (in this case eight years) the radar backscatter for such an area is expected to be very stable. The seasonal variability can be identified during the years [5] and the average measurement should follow a horizontal trend. Any deviations from the above have to be examined under two basic factors: a) Instrument calibration problem and b) Radar backscatter theory. The excellent calibration between the antennas of the WSC instrument has as a result a very good radiometric stability with a radiometric resolution better than 0.3 db [7]. The excellent calibration and maintenance of the instrument guarantee high quality data, which allow a precise evaluation of the spatial and temporal variability of the normalized radar cross section of the Earth s surface [8]. According to the radar backscatter theory the radar scatter is determined by: i) frequency, ii) polarization, iii) geometric characteristics, iv) incidence angle and v) dielectric properties. Frequency, polarisation and geometric characteristics remain constant over time. Moreover using the same incidence angle for the whole duration of the study the radar backscatter shall not be affected. Thus the dielectric properties are one parameter that can influence that amount of the radar backscatter. Over such a target as tropical rainforest the term dielectric properties is translated as the water content of rainforest. The other potential parameter is changes in atmospheric attenuation due to variations in intensity or frequency of rain cells. This effect is likely to be balanced by the associated 791

3 increase in backscatter from the underlying forest once the rain cell moves on, Since the increased forest moisture will be a longer lasting effect, it is statistically likely to dominant influence. Precipitation in these areas varies from 2000 mm to 1000 mm. Previous studies have shown that is a correlation between backscatter and WSC data [5]. A closer look is needed so as to better understand how the water content trend fluctuates over time. The question that this study will aim to provide an answer to, is whether the tropical rainforest got drier or wetter in the last 8 years. In case of wetter conditions occur radar backscatter increases and vice versa. The fact that the water content of rainforests is in any way connected with global warming, is not proven scientifically. Nonetheless, it is believed that signs of decreasing humidity and rainfall (water content) in rainforest came as a result of large scale deforestation and consequent derangement of carbon cycle [9] [3]. Reduced evaporation and the accelerated draining of precipitation frequently leads to soil erosion and water shortages not only in the regions immediately affected by deforestation [4]. The fluctuation of water content in rainforest can be used as one of the parameters in the study of global warming. 2. METHODOLOGY The methodology used in this study is illustrated in Figure 1. The Brazilian Amazon in South America expands in an area approximately 5 million square kilometres. From that area, using three basic criteria, 12 small representative patches selected both in North and South hemisphere. Windscatterometer data extracted from ESA CDs: Database of Global C-Band Radar Backscatter (Aug 91-Aug99) for the selected patches. Afterwards analysis of a) Monthly average (91-99), b) Area-based, c) Monthly- based (for specific year) and d) Yearly-based (for specific month) accomplished and graphs plotted. An attempt to correlate the radar backscatter with El Niño Southern Oscillation (ENSO) events preformed. Determine areas for study avoiding deforestation - rivers - big slopes Extract WSC data for selected areas from ESA s CD: Database of global C-Band Radar Backscatter i) Monthly Average analysis ii) Area-based analysis iii) Monthly-based analysis (for specific year) iv) Yearly-based analysis (for specific month) Plot Graphs Correlation with El Niño Southern Oscillation (ENSO) events Results Figure 1: Flow diagram of methodology used 792

4 3. DESCRIPTION OF THE DATASETS 3.1 Determination of the study areas Figure 2 shows the12 representative patches collected. These patches selected under the following three principles: a) Containment of dense forest For the study is complete dense forest was required. Deforestation must be avoided because it will influence the backscatter measurement. Three maps of deforestation were used in order to avoid any deforested area (From National Remote Sensing Agency of Brazil (INPE) [10], United Nations Environment Program (UNEP) and World Bank World Conservation Monitoring Centre). b) Avoidance of large water surfaces Water surface is another parameter that can influence the radar backscatter measurement over rainforest. The Amazon River is the world's second longest river (behind the Nile) approximately 6,437 kilometres long. Backscatter measurements will not respond to dense forest if the collected patches contain part of the river. Nevertheless, the coarse WSC footprint of 50 x 50 kilometres and the complicated river network force to avoid only the mainstem of the Amazon river. c) Avoidance of big slopes Slope refers to the geometrical characteristics of the patches. Maybe the geometrical characteristics of the area remains constant over time but the similarity of the areas is needed. Backscatter measurements will be affected if the selected patches are over areas with big slope. 3.2 WSC datasets The dataset used for this study contained in ESA s CD: Database of Global C-Band Radar Backscatter ERS Scatterometer Data Aug 91 Aug 99. The entire data stored on the CD was resampled onto en equal-area map projection (Goode Homolosive) with a grid spacing of 50 km and variations of incidence angles of 5 degrees. Using the CDs Graphical User Interface (GUI) time series of the Mean Normalized Radar Cross Section at 40 0 of incidence angle extracted. In total data of 185 grids extracted. Table 1 and figure 2 shows the division of the grids in each area. Each time series contains 96 mean measurements one for every month (Aug 91 Jul 99). In total 185 x 96 = 17,760 measurements analysed. Area Total No of Grids Table 1: Grids distribution to areas selected 793

5 Figure 2: Areas collected for further study (background form the Oxford world atlas) 4. DATASETS ANALYSIS RESULTS Analysing the data the Average Monthly NRCS at 40 0 angle of incidence, from August 1991 to August 1999 plotted. Figure 3 illustrates the distribution of the radar backscatter within the years. Linear trend line is also plotted and its regression equation and R- squared value are available. The variation of sigma nought at 40 0 incidence angle over the eight years period is only 0.53dB with maximum value of 7.55dB (March 94) and lowest value of 8.08dB (October 92) This variation verifies the high level of stability in radar backscatter over tropical rainforests which present as a large, uniform target able to be used as calibration target for spaceborne microwave instruments Average Monthly NRCS σ 0 40 (91-99) (Areas 1-12) y = x R 2 = db Aug 91 Feb 92 Aug 92 Feb 93 Aug 93 Feb 94 Aug 94 Feb 95 Aug 95 Feb 96 Aug 96 Feb 97 Aug 97 Feb 98 Aug 98 Feb 99 Figure 3:Average Monthly NRCS at 40 0 angle of incidence, from August 1991 to August 1999 (all the areas) 794

6 The gradient of the slope of the regression line is very small throughout the eight-year period with a value of ( ). Nevertheless, a negative slope of the radar cross section over rainforest from August 91 to August 99 is clearly shown. The seasonality over the study regions is also depicted, with a dry season between June and December and wet season between January and May. As dryer month is presented October and as wetter March. The value of the correlation coefficient (R 2 ), as expected due to seasonal variation, is also very small (0.0381) but that does not mean that there is no good fit over the selected areas. Area-based Analysis Furthermore Monthly NRCS (Aug 91 to Aug 99) for each area individually plotted. The main scope of these plots was the examination of similarity between the areas. The seasonal pattern was recognizable, with low values of backscatter within the dryer months (Aug-Sept) and higher to wetter months (Feb-Apr). The biggest variation between the areas was presented to Area 12 with a value of 1.13dB while lowest value is to Area 2 with value of 0.46dB. The biggest value of radar backscatter (-7.01dB) observed for Area 11 on August 91 and the lowest (-8.33dB) for Area 12 on October 92. From the examination of correlation coefficient (r) between each area and the average (average calculated without area examined) it can be seen that there is strong correlation between the areas (r min =0.72 for Area 8, r max =0.92 for Area 9, r average =0.82). Further examination of the slope gradient shows stability to the value of ( ) as only four areas (Area 2, 4, 8, 10) have a considerable difference ( ). The areas selected confirm the selection criteria, as further exclusion analysis shows no variation to the mean plot. Monthly-based Analysis (for specific year) Monthly-based analysis examined in order to study the change of radar backscatter for each year ( ) individually. Seasonal variation, previously discussed, was also clearly presented for each year. The slope gradient was almost stable to the value of 0.02 and the correlation coefficient between the average contribution and each year fluctuate between 0.89 (for 1995) and 0.98 for These observations reinforce the belief that tropical rainforest is very stable target for the microwave instruments. There was some obvious non-normal values, but in general the data seem to have normal distribution during the years. This stability provides individual importance to the Average Monthly NRCS plot (Figure 3) and its slope. An attempt to analyse the behaviour of the radar cross section under a yearly average base was performed. No constructive results can be extracted from this analysis due to the fact that it is not physically understood what an average radar backscatter for a whole year means. Comparison with the Average Monthly NRCS at 40 0 incidence angle confirms this opinion. Yearly -based Analysis (for specific month) A yearly base analysis made in order to correlate the El Niño Southern Oscillation (ENSO) events with the backscatter measurements. During an ENSO event a noticeable drought observed over the Brazil, especially the time period near Christmas. Recent ENSO events occurred in , and Any correlation with the radar measurements for the period October to February for the above years was unsuccessful. Furthermore unexplainable peaks (January-March 1994 and August- November 1991) failure to clarify. 795

7 5. CONCLUDING REMARKS Rainforests are commonly used for postlaunch calibration of Windscattrometer (WSC) instruments due to stability of backscatter measurements over time [6]. However some degree of spatial and temporal variability do exhibit in these regions [5]. In this study there was an attempt to examine the stability of random backscatter coefficient over pure rainforest areas using WSC data. The main question that tried to answer to, was whether the tropical rainforest got drier or wetter the last eight years. An attempt to correlate the variety in water content to rainforest, with global warming established. The area of study located over Amazonia rainforest, Brazil. According to Moore (1989), the only place in the world that meets the requirement for calibration target (ERS-1 WSC) is the Brazilian rainforest. The Brazilian Amazon rainforest expands in an area approximately five million square kilometres. From that area 12 representative patches selected, using three basic criteria: a) Containment of dense rainforest. Deforestation has to be avoided because it will influence the backscatter measurement and will produce mistaken results. b) Avoidance of large water surfaces. According to backscatter theory water surfaces presents very unlike measurements from land. Avoiding the mainstem of Amazon river more accurate results will be occupied. c) Avoidance of big slopes. In order to achieve better similarity between the patches selected big mountains with considerable slopes must be avoided. For the selected patches (in total 185 grids of 50 x 50 km) time series of the mean Normalized Radar Cross Section (NRCS) at 40 0 of incidence angle extracted. The data was stored to ESA s CD: Database of Global C-Band Radar Backscatter ERS Scatterometer Data Aug 91 Aug 99. Datasets analysed under four perspectives: i) Area-based analysis The scope of this analysis was to examine the similarity between the patched selected. Results showed that there is strongly correlation between the areas with the average correlation coefficient (r) equal to From the plots showed that seasonal pattern is recognizable and the slope gradient is stable to ii) Monthly-based analysis (for specific year) Each year ( ) examined individually in order to analyse the behaviour of the backscatter in shorter time period. The analysis showed that rainforests act very stable all these years to WSC measurements. This confirmed by the normal distribution of the data, which observed during the years. Noticeable is that between the years r fluctuates from 0.89 to iii) Yearly-based analysis (for specific month) A yearly-based analysis completed in order to correlate the El Niño Southern Oscillation (ENSO) events with the backscatter measurements. Unfortunately any correlation was unsuccessful. Furthermore inexplainable peaks failure to clarify. iv) Average Monthly analysis The distribution of the radar backscatter within the years plotted for all the areas selected (Figure 3). From the graph showed that: a) the variation of σ 0 is very small (0.53dB) and b) the gradient of the slope of the regression line is The former observation strengths the belief that tropical rainforest are unique for calibration target. Due to strong stability of radar backscatter over rainforest particular important is the latter observation, 796

8 the fact that a negative slope of radar cross section over rainforest of Brazil for the period Aug 91 Aug 99 exist. As rainforests maintain a rough balance between the world s vegetation and carbon dioxide this negative slope can be connected with greenhouse effect. The difficulty of quantifying the interrelation of rainforest water content and CO 2 concentration cannot provide confident results. Decreasing humidity and rainfall over rainforest it is believed that is a sign of large scale deforestation but no scientific proof is given. Furthermore computer models failure to simulate the atmosphere s CO 2 concentration. Climate is such a complex system that different inputs to different models produce very different answer. Under this point of view, the negative slope of WSC measurements observed over Brazilian rainforest (the most stable ecosystem for spaceborne scatterometers) can be used as an individual parameter to the whole attempt of understanding the global warming and its effects. REFERENCES 1. Crump D., (1994), Exploring your World: The adventure of Geography, National Geography Society 2. Whitmore T., (1984), Tropical rain forests of the far east, Oxford Press 3. Curran P. and Foody G., (1994), The Use of Remote Sensing to Characterize the Regenerative States of Tropical Forests, Environmental Remote Sensing from Regional to Global Scales, Wiley 4. Kuntz S. and Siegert F., (1999), Monitoring of Deforestation and land use in Indonesia, International Journal of Remote Sensing, Vol 20, no 14, p Woodhouse I., Sanden J. and Hoekman D., (1999), Scatterometer Observations of Seasonal Backscatter Variation Over Tropical Rain Forest, IEEE Transactions on Geoscience and Remote Sensing, Vol 37, No 2, p Moore R.,(1982), Radar Calibration, Proceedings of EARSel Workshop Austria 1982, ESA Scientific and Technical Publication Branch, Frison L. and Mougin E., (1996), Use of ERS-1 wind scatterometer data over land surfaces, IEEE Transactions on Geoscience and Remote Sensing, 34, Wismann V., (1999), A Database of Global C-band NRCS Derived from ERS Scatterometer Data, IEEE Geoscience and Remote Sensing Society Newsletter, September Seyler F., Bernoux M., Cerri C., (1998), Landsat TM image texture and moisture variations of the soil surface under rainforest of the Rodonia State, Brazil, International Journal of Remote Sensing, Vol 19, no 7, p National Remote Sensing Agency of Brazil (INPE), Annual Report of Amazon Institutional Program, Funcate 797

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