The project is produced for ES 771 - Remote Sensing course. Purpose for this project is principally to analyze the vegetation cover of the Emajõe delta area. River Emajõgi is one the largest rivers in the Estonia, it is 100 km long and flows from Lake Võrtsjärv to Lake Peipsi. Emajõe Suursoo (Great Fen of Emajõgi River) is situated in the delta region of the Emajõgi River in the western part of the vast depression of Lake Peipsi.
The mire has been created by process of paludification of mineral ground due to transgression of Lake Peipsi in connection with the land uplift in SE Estonia. The mire reserve covers 3/4 of the greatest delta region in Estonia and is divided by tributaries of the Emajõgi River into separated parts. Marginal parts, Jõmmsoo, Varnja and Pedaspää mire are represented as transitional fens, and Meerapalu is in bog stage already. In centre of Emajõe Suursoo mire spread bare and wet high - sedge fen with sparse birches, black alders and willows. Rivers are surrounded with large sedge-meadow marsh. (Aaviksoo, et al 1997).
Estonia is situated in the North - East Europe, on the east cost of the Baltic Sea (between 57º 30`34`` and 59º 49`12``N, and 21º 45`49`` and 28º 12`44``E). Total area of Estonia is 45,227.6 km2. As part of the East - European Plain, Estonia is a generally flat country, where uplands and plateau - like areas alternate with lowlands, depressions and large valley - like forms. Because of this reason wetlands (mires, floodplains, coastal grasslands, etc.) are common features in the Estonian landscape, covering approximately 30% of the country's total land area (J. Paal et al., 1998).
Map of the Emajõe Suursoo Mire Reserve area
In map is shown Emajõe - Suursoo mire of Emajõgi delta area. With red line is depicted conservation reside area. (The map is taken from book "Kaug ja lähivõtteid 30 Eesti soost" - Aerial Views and Close-up Pictures of 30 Estonian Mires; Aaviksoo et al,1997)
Landsat Remote Sensing
Remotely sensed satellite observations from space
have fundamentally changed the way in which scientists study the atmosphere,
oceans, land vegetation, glaciers, sea ice, and other environmental aspects
of the Earth's surface. In the past only way to investigate a given area
was through direct observation and sampling on the ground and from ships
or aircraft (Aber 2000). Using remote sensing has given possibility to
study inaccessible area of the globe, what were poorly known to last days.
Since 1960s remote sensing has been continuously used to investigate the Earth.
Landsat 7 is the up-to-date satellite, it is built by NASA and launched on April 15, 1999.
Landsat 7 carries an enhanced thematic mapper plus (ETM+), which should provide continuity of Landsat TM data with enhanced spectral and spatial resolution. Landsat 7 operates in seven bands of visible and infrared energy with resolution of 30 m. The ETM+ adds a panchromatic band (0.5-0.9 micrometers) at 15 m resolution (Aber 2000).
To interpret Landsat and other remote sensing images have been used three general methods: Photo interpretation - visual interpretation of images based on feature tone (color), pattern, shape, texture, etc.
Spectral analysis - identification of surface materials on the basis of spectral signatures. The combination of energy reflected and emitted from an object is its spectral signature. In principle, each object has a unique spectral signature, which could be used for identifiation much like a finger print (Aber 2000). Under clear, sunlit conditions, many objects have characteristic spectral signatures in the visible and short infrared portions of the spectrum (Aber 2000) and vegetation is one better interpretable object in this wave band.
Data integration - merging of remote sensing data with other types of data, such as digital elevation models.
For interpretation the work being is used visual interpretation and spectral analysis.
The image from Emajõgi delta was taken on July 10, 1999 by the Landsat ETM+ satellite. The image consist of six bands (1-5 and 7).
The vegetation cover was analyzed in the present studies. Vegetation is the dominant and important component in most ecosystems and useful indicator environmental conditions. Many remote sensing mechanisms operate in the green, red and near infrared regions of the electromagnetic spectrum. They can discriminate radiation absorption and reflectance of vegetation. Changes in vegetation are useful for recognizing changes in other environmental factors. Identifying vegetation in remote sensing images depends on plant characteristics: leaf shape and size, overall plant shape, water content, and associated background (e.g. soil types and spacing of the plants) (Short, 1998). For interpretation is used single band images (bands 3, and 4) and bands compositions. For natural color composition are used bands 1, 2, 3; for standard false color composition are used bands 2, 3, 4; for special composite image are used bands 3, 4, 5 and for NDVI image is calculated NDVI values. Ultimately for estimating different vegetation and land cover types and ranging vegetated areas and other areas is created cluster classification map.
Band 3 image
Band 4 image
Bands 3 and 4. Single band images. Black - and - white images represent light intensity variations for a single band. Band 3 ( red) is strongly absorbed by active vegetation, whereas band 4 (near infrared) is strongly reflected. Because of this reason, vegetated areas are bright in band 4 image, and dark in band 3 image (J. Aber 2000). On left side in band 3 image with light depicted area is settled region and some areas of mire are quite light. Waterbodies and some areas in mire are black. Absence very light tones in settled area refers, that in this region do not appear big buildings or road or other human products. Vegetation on this settlement is well kept. In band 4 image waterbodies have black and other areas have different gray undertone. Mires and settled regions have similar undertone scale, but mire areas is shown as quite smooth area comparing with settled regions on left side in image. It is interesting to note, that some mire areas, such as Varnja, Pedaspää, and Meerapalu mires what are bright in band 4 image is a little lighter in band 3 image too. It can draw a conclusion, that on these mire areas absent vegetation strongly absorbed red (band 3) light. Obviously it is due to absent forest and these parts of mire are dryer than surrounded mire areas.
Landsat ETM+, bans 3 and 4; acquired 10/07/1999; image processing by Reet Nemliher
Landsat ETM+ bands were used to study vegetation cover.
The Normalized Difference Vegetation Index (NDVI) is indicator for the conditions of vegetation using the absorption (visible red) and the reflectance (near IR) patterns of active vegetation. Positive values indicate appearance active vegetation and negative values indicate a lack of vegetation. The NDVI is computed using formula (Rouse et al., 1974):
(short-IR - red)/(short-IR + red)=NDVI
NDVI values extend from -1.0 to+1.0. Typical NDVI values for active terrestrial vegetation range from 0.1 to 0.6. Materials that reflect red more strongly than near IR (clouds, snow, water) result in negative values. Bare rocks and soils reflect red and near IR about equally, so their NDVIs are around zero (Aber 2000). Both terrestrial and marine vegetation biomass is mapped and analyzed on basis vegetation indices, and the status of vegetation and vegetation changes over multiyear periods is possible documented.
Brown areas in image are waterbodies. With green
color is presented forested areas, and yellow areas in mire are seemingly
without forest, settled areas are depicted with yellow colors too. On Emajõgi
delta area becoming marshy is to continued to novaday. As shown in image,
northernmost part of lake Koosa has been quite a lot swamped already.
For image displaying is used NDVI 256 palette.
Landsat ETM+, bands 3 and 4; acquired 10/07/1999; image processing by Reet Nemliher 12/00
Natural color image.
For preparing natural color image are used bands 1, 2, 3. Natural color image simulates real conditions of scene. Vegetated areas in image is represented in green undertones, black areas are waterbodies. The white and pink areas in left side in image are settled regions. Varnja, Jõmmsoo, Pedaspää fens and Meerapalu bog areas do not have usual green tone, what point to absent forest in this area. The pink pach on left side in image is an agricultural field.
Landsat ETM+, bands 1, 2, 3; acquired 10/07/1999; image processing by Reet Nemliher 12/00
landsat ETM+; bands 2, 3, 4; acquired 10/07/1999; image processing by Reet Nemliher 12/00
Special composite image. Used are bands 3, 4, and 5. In such combination most common forest types are possible to distinguish. Pine is depicted as olive-green and brown, and deciduous trees are painted with yellow-green color. Spruce in Emajõgi delta area absent, because in such combination it have to display as dark green patches. In mire areas lilac and brown color depicted vegetation is problematic. Brown might be dwarf pine.
Settled and agricultural land are depicted with lavender and pink colors.
Landsat ETM+, bands 3, 4, 5; acquired 10/07/1999; image processing by Reet Nemliher 12/00
For creating cluster classification map is used 8-bit 345 composite image. For planning land use and reaching conservation residue area is very useful to have general review kind of vegetation, and area dimension. Using cluster classification it make possible to estimate clustered areas reach. In Emajõgi Delta region fen areas enfold 124,5 km2, deciduous forest 86,6 km2, and pine forest enfold 21,1 km2 wide area. In this image dryer transitional fens and bogs areas very distinctly discernible from most wet fen areas. In earlier images are shown already, that settled areas and dryer mire areas taken similar reflection and because of this on this map are they classified as the same cluster. Therefore it is not possible to estimate transitional fens and bogs reaching.
Landsat ETM+, bands 3, 4, 5; acquired 10/07/1999; image processing by Reet Nemliher 12/00
I wish to thank Dr. James Aber for interesting
course and for helpful and forebearing instructions.
Also I thank my colleagues for all-round help.
References and related links
K. Aaviksoo, H. Kadarik, V. Masing.1997. Kaug ja lähivõtteid 30 Eesti soost. - Aerial views and close-up pictures of 30 Estonian mires. Tallinn, pp.96
Aber, J.S. 2000. ES 771 - Remote Sensing (internet course in fall semester
EGEA Northern Regional congress
Overwiev from Estonian Nature
Paal, J., Ilomets, M., Fremstad, E., Moen, A., Borset, E., Kuusemets, V., Truus, L., Leibak, E. 1998 Estonian Wetland Inventory 1997. Tartu, pp. 166
Short, N.M. 1998. The Remote Sensing Tutorial.