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Changes in agricultural land use in Estonia in the 1990s detected with multitemporal Landsat MSS imagery
Landscape and Urban Planning 41 (1998) 193±201
Changes in agricultural land use in Estonia in the 1990s detected with multitemporal Landsat MSS imagery Urmas Peterson1,a,*, Raivo Aunapb b
a Tartu Observatory, ToÄravere, EE2444 Tartumaa, Estonia Institute of Geography, Tartu University, Vanemuise 46, EE2400 Tartu, Estonia
Abstract During the 1990s, the largest changes in land use in the Baltic states ocurred in the agricultural land sector. The changes can be summarised as the abandonment of arable land and the overgrowing of grasslands with shrubs. Both types of formerly agricultural land become successional old®elds. A national land-use map of Estonia is currently not available. Multitemporal Landsat Multispectral Scanner scenes combining winter, spring and summer data were used to classify land use in Estonia in 1990 and 1993. Satellite data discriminated `arable land' and `other,' but failed to differentiate `grasslands currently in use' from `successional old®elds'. One-third of the arable land in use in Estonia in 1990 had been abandoned by 1993. # 1998 Elsevier Science B.V. All rights reserved. Keywords: Land-use change; Landsat multispectral scanner; Abandoned lands; Re¯ectance pattern; Phenological aspects
1. Introduction The most extensive changes in land use/land cover during the last decade in Estonia have occurred in agricultural land use. Structural changes in Estonian economy, including agriculture started after independence from the Soviet Union was gained in 1991. In the course of privatisation collective and state farms were restructured and the property thereof was transferred into private ownership. Privatisation of agriculture was ®nished by 1995 when 50% of arable land was at the disposal of private farms, another 50% *Corresponding author. Tel.: +372 7 465827; fax: +372 7 465825; e-mail: [email protected] 1 Present address: Institute of Botany and Ecology, Tartu University, Lai 36, EE2400 Tartu, Estonia. 0169-2046/98/$19.00 # 1998 Elsevier Science B.V. All rights reserved. PII S0169-2046(98)00058-9
belonged to agricultural enterprises established on the basis of former state and collective farms (Laansalu, 1997). Since 1992 cultivated area of ®eld crops decreased from 1107 000 ha to 851 000 ha in 1995 or by 23%. Area of unused arable land that was 12 000 ha in 1992, increased to 254 000 ha by 1995, that is, 20 times (Anonymous, 1996). According to the data of the Estonian Statistical Of®ce, 22.5% of arable land was out of use in 1995. Data from enterprises were acquired from the farm returns, data from private farms were gathered in a survey (Anonymous, 1996). However, the reliability of such data is rather questionable, as with the expert estimates calculated area that is presently out of use is from 330 000 to 350 000 ha, that is, 29±31% of the total arable land (Vipper et al., 1996). The area is calculated according to the number of livestock and
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the corresponding area necessary for forage production per capita. The results of estimates predict that approximately 300 000±400 000 ha of arable land will cease to be used for agricultural production in the very near future. These areas will be invaded by forests by natural succession or will need to be reforested. Under such conditions, the market price for land will be very low. This will probably lead to the buying up of available land. Such a process, if not nationally regulated, will give rise to undesirable social, cultural and economic consequences. Satellite sensor data for determining crop acreages over extensive areas were ®rst used within the Large Area Crop Inventory Experiment programme (MacDonald and Hall, 1980). A general review of remote sensing for crop identi®cation and condition assessment is given by Bauer (1985), and more recently by Moran et al. (1997). Satellite data have been used to
create land-use and land-cover maps and to detect changes in vegetation over large areas. Papers by Fuller et al. (1994); Stone et al. (1994); Zhu and Evans (1994); Schriever and Congalton (1995); Wolter et al. (1995); Dymond et al. (1996); Steininger (1996) are mentioned here as some of the latest published in this ®eld. We have made an attempt to estimate the area of arable land in Estonia using low cost satellite data ± Landsat Multispectral Scanner (MSS) digital images. The area estimates for spring 1993 are compared with the area estimates for spring 1990, when the land reform had not yet started. 2. Methods We have estimated the extent of agricultural decrease with Landsat MSS scenes (Fig. 1). Landsat
Fig. 1. Location of study area, Estonia, satellite image frames and test sites (1). A boundary line (2) separates lower Estonia (flooded after the Ice Age by the Baltic sea and local glacial lakes) from upper Estonia.
U. Peterson, R. Aunap / Landscape and Urban Planning 41 (1998) 193±201
Fig. 2. Seasonal reflectance profiles of a barley field and a birch (Betula pendula) forest in the red spectral region (0.675 mm). Measured data are denoted by dots, best fit lines are cubic spline approximations. Note that the communities are hardly separable in midsummer, separability is better in late spring.
MSS data were used due to their low price. Landsat MSS has four spectral bands, two in the visible and two in the near infrared (NIR) spectral region. The spatial resolution, (pixel size) is approximately 80 m on the ground. This spatial resolution is satisfactory compared to the ®eld size, that in Estonia is at least some hectares as a rule. All green plant communities are quite equally `green' during the growing period. The separability of communities with a similar phenological calendar is low in June and July. The similarity of the seasonal re¯ectance values of two communities in midsummer is demonstrated in Figs. 2 and 3. The seasonal re¯ectance pro®les from ground-based measurements (Nilson, 1988) are presented for the red and NIR spectral bands roughly corresponding to Landsat MSS bands 2 and 4. The communities are composed of species of very different life forms. Aboveground phytomass in a barley ®eld consists mainly of photosynthesising leaves; in a birch forest most of the phytomass is allocated to non-photosynthesising trunks and branches. Nevertheless they are dif®cult to differentiate from each other in midsummer. Clearly a better discrimination of the two is possible in spring. The advantages of multi-date data compared to single-date data become evident when subtle differences in phenology are to be determined. The speed and timing of green-up in spring in grassland communities are in¯uenced by the amount of plant litter present from the previous growing period. In litter-
195
Fig. 3. Seasonal reflectance profiles of a barley field and a birch (Betula pendula) forest, the same communities as in Fig. 2, but in the near infrared spectral region (0.790 mm). Measured data denoted by dots are plotted only for barley. Best fit lines are cubic spline approximations. The separability of the communities is poor during the most part of the growing period in this spectral region.
poor cases (grassland has been cut or grazed in the previous summer), the green-up in spring occurs earlier and more rapidly. Green-up is slightly delayed and proceeds more slowly when shoots have to sprout through a thick litter layer (grassland has been abandoned) (Fig. 4). Mowing of grassland for hay in June is expressed in an abrupt decrease in NIR re¯ectance and a sudden increase in red re¯ectance (Fig. 5). Therefore, pre-cut and after-cut satellite scenes are necessary in order to
Fig. 4. Daily reflectance change in near infrared (NIR) spectral band in spring is more rapid in communities with little litter from the previous growing season (1). Green-up in spring is delayed in communities with thick layer of plant litter (2).
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U. Peterson, R. Aunap / Landscape and Urban Planning 41 (1998) 193±201 Table 1 Summary of Landsat MSS imagery used
Fig. 5. Seasonal reflectance profiles of a grassland community in the red and near infrared (NIR) spectral bands. Note an abrupt decrease in NIR reflectance and increase in red reflectance after a severe disturbance. A similar effect can be observed in grasslands after mowing.
detect hay-mowing from satellite scenes. The phenological window for detecting abrupt midsummer changes is 2±3 weeks. The window must be covered by at least two satellite scenes. In late summer, a normally declining bell-shaped seasonal curve intersects the abrupt change curve. Therefore, it is not possible to discriminate reliably between the two types of curves with a longer time interval. Peak summer re¯ectance of successional communities decreases during the years of early succession due to increasing structural complexity corresponding to an increase in the amount of shadow in the canopy. Unfortunately, according to our observations (Peterson, 1992), the decrease in re¯ectance in successional communities in 2 or 3 years is too small to be reliably detected with the radiometric resolution of Landsat MSS. 2.1. Data set and test sites The study area, Estonia (45 000 km2), is covered by ®ve satellite Landsat orbit paths. Three scene frames from path 186 row 20, path 187 row 19 and path 188 row 19 were used in this study (see Fig. 1). Altogether 12 Landsat MSS scenes were used (see Table 1). Landsat MSS data selection was based on data availability of cloud-free acquisitions from spring, midsummer (June) and winter conditions. The spring season was taken to be from mid-April till end of May from the period of snowmelt till the germination
Scene path/row
Date
Season
186/20
6 28 4 20
May 1990 Aprill 1993 June 1992 February 1986
Spring Spring Summer Winter
187/19
13 21 3 27
May 1990 May 1993 June 1992 February 1983
Spring Spring Summer Winter
188/19
4 12 26 21
May 1990 May 1993 June 1986 February 1984
Spring Spring Summer Winter
time of spring crops. The summer period covered the peak in plant growing season, from June to early July. The winter period was characterized by snow covered ground with high solar elevation ± late February or March. The number of suitable scenes was low due to the 16-day cycle of Landsat satellite orbits and frequent cloud cover. However, at least one scene for every necessary season was available. Soviet topographic maps of scale 1:100 000 were used for ground truth data for testing accuracy of water, forest and wetland areas. Training areas for agricultural land were located on state farm maps, scale 1:10 000. Fields of different crops on the corresponding 1990 and 1993 maps were delineated by farm agronomists. Speci®c annual crops (e.g. wheat and barley) were naturally not separable in spring and were treated jointly as arable land. Each training area (altogether ®ve state farms, see Fig. 1) comprised 40±80 ®elds with a total of 800±1200 pixels containing arable land per state farm. 2.2. Geometric correction and image registration Landsat MSS scene path 187 row 19 has a central location relative to the study area. Scene 187/19 from May 13 1990 was selected as the master image and all other images were geometrically registered to it. In image-to-image registration 20 to 30 image control points were suf®cient to give the geometric registra-
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tion accuracy of 1.5 pixels. Images were ®nally registered to the Soviet Reference System Grid using control points on 1:100 000 scale maps. The images were then resampled to give 50 m output pixels. The nearest neighbour resampling method was used. No radiometric calibration was considered necessary. Landsat MSS raw digital numbers (DN) were used. 2.3. Image classification The ®rst step in classi®cation was the discrimination of water and terrestrial environments (see Fig. 7). Water bodies were identi®ed from midsummer scenes in low water table conditions. Water was thresholded according to Landsat MSS DN in the NIR re¯ectance band (MSS band 4). This discrimination rule is quite straightforward due to the great contrast in the NIR spectral region between water and terrestrial land surfaces. Land/water boundary pixels and coastal areas with shallow water and/or patchy marine/terrestrial environments are areas which can cause confusion. The second step was a re®nement of terrestrial areas, differentiating forests from non-forests. These classi®cation criteria de®ne whether the vegetation retains perennial aboveground structures (i.e. trees and woody stemmed shrubs) or survives non-growing seasons as seed or below-ground structures only (annual crops and grasses) (Running et al., 1995). The distinction merely requires that remote sensing can detect the presence/absence of aboveground biomass during the non-growing season. This was done by thresholding snow-covered winter scenes into `dark' permanent vegetation stands above the snow cover as forests and into `white' non-forests (open land) showing purely snow-covered areas. Landsat MSS band 2 was used. Soviet topographic maps, scale 1:100 000 were used for `ground truth' data to determine the thresholding DN level in MSS band 2. Wetlands were delineated from spring and early summer image pairs. Wetlands have low NIR to red re¯ectance contrast. Therefore, the re¯ectance change in wetlands at the beginning of the growing period is also insigni®cant compared to that of meadows, agricultural crops and deciduous forests. Wetlands were delineated as areas of low NIR to red re¯ectance ratio (MSS band 4 to band 2 ratio) in spring and in early
Fig. 6. Seasonal near infrared to red ratio index profiles of wetland communities compared to a birch (Betula pendula) forest. Wetland communities are dominated by bog whortleberry (Vaccinium uliginosum) wild rosemary (Ledum palustre), and Sphagnum sp. Note the delayed beginning of seasonal growth in wetland communities compared to the profile of the birch forest.
summer (beginning of June). An example of seasonal pro®les of NIR to red ratio index in wetland communities is presented in Figs. 6 and 7. On areas with `water,' `forests' and `wetlands' masked out, arable land was determined from principal component images. Principal component analysis
Fig. 7. Steps in the classification, imagery used and thematic layers generated.
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(PCA) was performed on the four MSS bands in spring-time scenes. The second principal component was used to delineate arable land. Pixel values in the second principal component image are calculated with strong negative loadings of visible bands and weak positive loadings of NIR bands. PCA enhanced the difference between ploughed land, that is, seasonally bare areas in spring with weak visible to NIR contrast, and grasslands and winter crops, that is, ground with vegetation cover with strong visible to NIR contrast. Winter crops as seasonally bare areas can be differentiated from grasslands in September images. This was not done in this study. Classi®cation was performed as thresholding into `arable land' and `other.' Prior to classi®cation, the study area was strati®ed into three strata. The stratum boundaries coincided roughly with satellite scene frames. Threshold level was determined as best ®t DN within training areas. The classi®cation process extrapolated the thresholding level to all pixels within a stratum, identifying all pixels as `arable land' or `other.' Beaches on the seashore were confused with arable land. A 3-pixel wide buffer area was de®ned along the coastline and `maritime ®elds' within this area were classi®ed as beaches. We did not ®nd any method to reliably de®ne urban areas from Landsat MSS scenes. Urban areas are clearly visible particularly on snow-covered winter scenes as `grayish dirty spots' but are not separable from other classes on the basis of pixel brightness. Major towns were digitized manually from winter scenes and `arable land in the urban environment' (in reality concrete covered parking lots and roofs of major buildings) was masked out. Gravel pits, peat milling areas and oil shale mines in northeastern Estonia were also manually digitized and masked out. These areas were classi®ed as `arable land' in early summer images. They are identi®able as `permanently bare areas' and separable from arable land, as `seasonally bare areas,' when late summer images are included in the classi®cation. Post-classi®cation ®ltering, that is, simplifying of data was not used. Removal of single isolated pixels as `erroneously classi®ed' was not justi®ed in the case of arable land.
3. Results and discussion A land-use map of Estonia was produced using Landsat MSS data. Winter, spring and summer satellite images were combined. The map, based on a 50 m grid, recorded land-use types consisting of water bodies, wetlands, forests, arable land (spring crops), grasslands together with winter cereals, permanently bare areas (beaches and mining areas) and urban areas. A sample of the land-use map representing the distribution of arable land is presented in Fig. 8. From the archive data set of Landsat MSS images at EURIMAGE a list of cloud-free images covering Estonia was compiled (cloud cover 20% or less). We could not ®nd any cloud-free image pairs for the Landsat image frames 187/19 and 188/19 either for spring (April, early May) nor for midsummer (June and July). One cloud-free image pair was available for frame 186/20 for spring 1990 and one for summer 1992. From the summer of 1993 downloading of Landsat MSS data in Europe was terminated. We also tried to differentiate `grasslands' and `old®elds' in a Landsat Thematic Mapper winter image, path 186, row 19 from 16 March 1996. The old®elds abandoned 2 or 3 years earlier had a dense stand of dead plant stems above the snow cover. Viewed obliquely on the ground they were clearly different from ®elds under white snow cover. Unfortunately, the presence of dead stems had not reduced re¯ectance suf®ciently to be detectable with Landsat TM radiometric resolution. From this we conclude that there is little possibility to discriminate between `grasslands currently in use' and `grasslands abandoned upto 2 years ago' using Landsat data. Results of the analysis of arable land distribution are presented below. Arable land is spread sporadically all over Estonia although clustered more densely in some areas than in the others. The general pattern of distribution has not changed signi®cantly since the beginning of the century as described in the land-use revision in 1925 by Laasi (1933). According to the density of arable land two distinct districts can be identi®ed (Fig. 8). The ®rst district with a high concentration of arable land is of a triangle-like shape pointing north. This area coincides with upper Estonia, which has not been ¯ooded by the
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199
Fig. 8. Distribution of arable land in Estonia in 1990. The results of classification of satellite scenes.
waters of the Baltic sea and local glacial lakes after the last Ice Age (compare with Fig. 1). The remainder, the major part of Estonia is sparsely covered with arable land. This is lower Estonia, a plane lowland covered with wetlands and forests. Within these two major
regions minor irregularities are observable. One is a gap in the densely populated triangle to the north of Lake VoÄrtsjaÈrv in central Estonia. This area is the bed of post-glacial Great-VoÄrtsjaÈrv. The former lake bottom is now covered with swampy forests and bogs.
Fig. 9. Decrease of arable land in Estonia from 1990 to 1993: (1) decrease less than 30%; (2) 30±35%; (3) 35±40%; (4) 40±50%; (5) decrease more than 60%.
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Other irregularities with sparse arable land denote belts of swampy forests and meadows separating upland areas. Some areas within the sparsely populated region with extremely little arable land are located in the transitional zone from upper Estonia to lower Estonia, Estonia intermedia. This zone is a wide belt running obliquely from the northern coast to the Gulf of PaÈrnu in the southwest. Arable land in this zone is located in areas with better drainage, close to rivers. The changes in arable land use from 1990 to 1993 are presented as averaged mean values of districts (Fig. 9). A change in a ®eld-by-®eld basis does not necessarily denote any trend but may merely be one step in crop rotation. Within the dominating trend arable land decrease has been the most severe in the Ida-Viru district, an area of oil shale mining. The agglomeration of shale re®nement industries has occupied a signi®cant part of the district's arable land for decades. The drainage regime of the ®elds in this area has been severely disturbed. The over-drained ®elds above mining areas suffer from temporary drought in summer. Probably the area covered by satellite image (path 187 row 19, see Fig. 1) is not representative of the whole district. A strip of arable land along the northern coast is not within the image area. Therefore, the trend (63% decrease) from 1990 to 1993 may be overestimated. The decrease in arable land use is signi®cant in the marginal districts of Estonia including the islands and also in the district surrounding the capital Tallinn. The decrease has been the smallest in the central districts of upper Estonia and in the westernmost district of continental Estonia ± LaÈaÈnemaa, where the proportion of arable land was the smallest before the land reform. 4. Conclusions A land-use map of Estonia has been compiled from multitemporal Landsat MSS data. The chosen classi®cation is a `simple but accurate' separation of a few land-use types. Landsat MSS scenes combining winter, spring and summer data were used to classify land use in Estonia in 1990 and 1993, respectively. Satellite data were used successfully to discriminate
`arable land' and `other,' but failed to differentiate `grasslands currently in use' from `successional old®elds.' Abandonment rate of arable land as a national total in Estonia was 32% compared to the baseline date, 1990.
Acknowledgements This study was supported by International Science Foundation Grant LCY 000 and by Estonian Science Foundation Grant 1744.
References Anonymous, 1996. Agriculture in 1995. Statistical Office of Estonia, Tallinn, Estonia. Bauer, M.E., 1985. Spectral inputs to crop identification and condition assessment. Proceedings of IEEE 73, 1071±1085. Dymond, J.R., Page, M.J., Brown, L.J., 1996. Large area vegetation mapping in the Gisborne district, New Zealand, from Landsat TM. Int. J. Remote Sensing 17, 263±275. Fuller, R.M., Groom, G.B., Jones, A.R., 1994. The land-cover map of Great Brittain: An automated classification of Landsat Thematic Mapper data. Photogrammetric Engineering and Remote Sensing 60, 553±562. Laansalu, A., 1997. Agricultural policy: Overview of agricultural policy and strategy. In: Koov, M. (Ed.), Agriculture in Estonia 1996. Ministry of Agriculture of Estonia, Tallinn, Estonia, pp. 12±15. Laasi, H., 1993. Distribution of arable land in Estonia (in Estonian with a summary in German). Publicationes Instituti Geographiae Universitatis Tartuensis 20, 92±97. MacDonald, R.B., Hall, F.G., 1980. Global crop forecasting. Science 208, 670±679. Moran, M.S., Inoue, Y., Barnes, E.M., 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment 61, 319± 346. Nilson, T., 1988. Spectral-Temporal Reflectance Profiles For Some Cereals, Academy of Science, Tartu. Peterson, U., 1992. Reflectance factor dynamics of boreal forest clear-cut communities during early secondary succession. Int. J. Remote Sensing 13, 2247±2262. Running, S.W., Loveland, T.R., Pierce, L.L., Nemani, R.R., Hunt, E.R., Jr., 1995. A remote sensing-based vegetation classification logic for global land cover analysis. Remote Sensing of Environment 51, 39±48. Schriever, J.R., Congalton, R.G., 1995. Evaluating seasonal variability as an aid to cover-type mapping from Landsat Thematic Mapper data in the northeast. Photogrammetric Engineering and Remote Sensing 61, 321±327.
U. Peterson, R. Aunap / Landscape and Urban Planning 41 (1998) 193±201 Steininger, M.K., 1996. Tropical secondary forest regrowth in the Amazon: age, area and change estimation with Thematic Mapper data. Int. J. Remote Sensing 17, 9±27. Stone, T.A., Schlesinger, P., Houghton, R.A., Woodwell, G.M., 1994. A map of vegetation of South America based on satellite imagery. Photogrammetric Engineering and Remote Sensing 60, 541±551. Vipper, H., Masso, V., Kuill, T., 1996. Abandonment of land makes harm to Estonia (in Estonian). Agriculture 9, 6±7.
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Wolter, P.T., Mladenoff, D.J., Host, G.E., Crow, T.R., 1995. Improved forest classification in the Northern Lake States using multi-temporal Landsat imagery. Photogrammetric Engineering and Remote Sensing 61, 1129±1143. Zhu, Z., Evans, D.L., 1994. U.S. forest types and predicted percent forest cover from AVHRR data. Photogrammetric Engineering and Remote Sensing 60, 525±531.
Changes in agricultural land use in Estonia in the 1990s detected with multitemporal Landsat MSS imagery Urmas Peterson1,a,*, Raivo Aunapb b
a Tartu Observatory, ToÄravere, EE2444 Tartumaa, Estonia Institute of Geography, Tartu University, Vanemuise 46, EE2400 Tartu, Estonia
Abstract During the 1990s, the largest changes in land use in the Baltic states ocurred in the agricultural land sector. The changes can be summarised as the abandonment of arable land and the overgrowing of grasslands with shrubs. Both types of formerly agricultural land become successional old®elds. A national land-use map of Estonia is currently not available. Multitemporal Landsat Multispectral Scanner scenes combining winter, spring and summer data were used to classify land use in Estonia in 1990 and 1993. Satellite data discriminated `arable land' and `other,' but failed to differentiate `grasslands currently in use' from `successional old®elds'. One-third of the arable land in use in Estonia in 1990 had been abandoned by 1993. # 1998 Elsevier Science B.V. All rights reserved. Keywords: Land-use change; Landsat multispectral scanner; Abandoned lands; Re¯ectance pattern; Phenological aspects
1. Introduction The most extensive changes in land use/land cover during the last decade in Estonia have occurred in agricultural land use. Structural changes in Estonian economy, including agriculture started after independence from the Soviet Union was gained in 1991. In the course of privatisation collective and state farms were restructured and the property thereof was transferred into private ownership. Privatisation of agriculture was ®nished by 1995 when 50% of arable land was at the disposal of private farms, another 50% *Corresponding author. Tel.: +372 7 465827; fax: +372 7 465825; e-mail: [email protected] 1 Present address: Institute of Botany and Ecology, Tartu University, Lai 36, EE2400 Tartu, Estonia. 0169-2046/98/$19.00 # 1998 Elsevier Science B.V. All rights reserved. PII S0169-2046(98)00058-9
belonged to agricultural enterprises established on the basis of former state and collective farms (Laansalu, 1997). Since 1992 cultivated area of ®eld crops decreased from 1107 000 ha to 851 000 ha in 1995 or by 23%. Area of unused arable land that was 12 000 ha in 1992, increased to 254 000 ha by 1995, that is, 20 times (Anonymous, 1996). According to the data of the Estonian Statistical Of®ce, 22.5% of arable land was out of use in 1995. Data from enterprises were acquired from the farm returns, data from private farms were gathered in a survey (Anonymous, 1996). However, the reliability of such data is rather questionable, as with the expert estimates calculated area that is presently out of use is from 330 000 to 350 000 ha, that is, 29±31% of the total arable land (Vipper et al., 1996). The area is calculated according to the number of livestock and
194
U. Peterson, R. Aunap / Landscape and Urban Planning 41 (1998) 193±201
the corresponding area necessary for forage production per capita. The results of estimates predict that approximately 300 000±400 000 ha of arable land will cease to be used for agricultural production in the very near future. These areas will be invaded by forests by natural succession or will need to be reforested. Under such conditions, the market price for land will be very low. This will probably lead to the buying up of available land. Such a process, if not nationally regulated, will give rise to undesirable social, cultural and economic consequences. Satellite sensor data for determining crop acreages over extensive areas were ®rst used within the Large Area Crop Inventory Experiment programme (MacDonald and Hall, 1980). A general review of remote sensing for crop identi®cation and condition assessment is given by Bauer (1985), and more recently by Moran et al. (1997). Satellite data have been used to
create land-use and land-cover maps and to detect changes in vegetation over large areas. Papers by Fuller et al. (1994); Stone et al. (1994); Zhu and Evans (1994); Schriever and Congalton (1995); Wolter et al. (1995); Dymond et al. (1996); Steininger (1996) are mentioned here as some of the latest published in this ®eld. We have made an attempt to estimate the area of arable land in Estonia using low cost satellite data ± Landsat Multispectral Scanner (MSS) digital images. The area estimates for spring 1993 are compared with the area estimates for spring 1990, when the land reform had not yet started. 2. Methods We have estimated the extent of agricultural decrease with Landsat MSS scenes (Fig. 1). Landsat
Fig. 1. Location of study area, Estonia, satellite image frames and test sites (1). A boundary line (2) separates lower Estonia (flooded after the Ice Age by the Baltic sea and local glacial lakes) from upper Estonia.
U. Peterson, R. Aunap / Landscape and Urban Planning 41 (1998) 193±201
Fig. 2. Seasonal reflectance profiles of a barley field and a birch (Betula pendula) forest in the red spectral region (0.675 mm). Measured data are denoted by dots, best fit lines are cubic spline approximations. Note that the communities are hardly separable in midsummer, separability is better in late spring.
MSS data were used due to their low price. Landsat MSS has four spectral bands, two in the visible and two in the near infrared (NIR) spectral region. The spatial resolution, (pixel size) is approximately 80 m on the ground. This spatial resolution is satisfactory compared to the ®eld size, that in Estonia is at least some hectares as a rule. All green plant communities are quite equally `green' during the growing period. The separability of communities with a similar phenological calendar is low in June and July. The similarity of the seasonal re¯ectance values of two communities in midsummer is demonstrated in Figs. 2 and 3. The seasonal re¯ectance pro®les from ground-based measurements (Nilson, 1988) are presented for the red and NIR spectral bands roughly corresponding to Landsat MSS bands 2 and 4. The communities are composed of species of very different life forms. Aboveground phytomass in a barley ®eld consists mainly of photosynthesising leaves; in a birch forest most of the phytomass is allocated to non-photosynthesising trunks and branches. Nevertheless they are dif®cult to differentiate from each other in midsummer. Clearly a better discrimination of the two is possible in spring. The advantages of multi-date data compared to single-date data become evident when subtle differences in phenology are to be determined. The speed and timing of green-up in spring in grassland communities are in¯uenced by the amount of plant litter present from the previous growing period. In litter-
195
Fig. 3. Seasonal reflectance profiles of a barley field and a birch (Betula pendula) forest, the same communities as in Fig. 2, but in the near infrared spectral region (0.790 mm). Measured data denoted by dots are plotted only for barley. Best fit lines are cubic spline approximations. The separability of the communities is poor during the most part of the growing period in this spectral region.
poor cases (grassland has been cut or grazed in the previous summer), the green-up in spring occurs earlier and more rapidly. Green-up is slightly delayed and proceeds more slowly when shoots have to sprout through a thick litter layer (grassland has been abandoned) (Fig. 4). Mowing of grassland for hay in June is expressed in an abrupt decrease in NIR re¯ectance and a sudden increase in red re¯ectance (Fig. 5). Therefore, pre-cut and after-cut satellite scenes are necessary in order to
Fig. 4. Daily reflectance change in near infrared (NIR) spectral band in spring is more rapid in communities with little litter from the previous growing season (1). Green-up in spring is delayed in communities with thick layer of plant litter (2).
196
U. Peterson, R. Aunap / Landscape and Urban Planning 41 (1998) 193±201 Table 1 Summary of Landsat MSS imagery used
Fig. 5. Seasonal reflectance profiles of a grassland community in the red and near infrared (NIR) spectral bands. Note an abrupt decrease in NIR reflectance and increase in red reflectance after a severe disturbance. A similar effect can be observed in grasslands after mowing.
detect hay-mowing from satellite scenes. The phenological window for detecting abrupt midsummer changes is 2±3 weeks. The window must be covered by at least two satellite scenes. In late summer, a normally declining bell-shaped seasonal curve intersects the abrupt change curve. Therefore, it is not possible to discriminate reliably between the two types of curves with a longer time interval. Peak summer re¯ectance of successional communities decreases during the years of early succession due to increasing structural complexity corresponding to an increase in the amount of shadow in the canopy. Unfortunately, according to our observations (Peterson, 1992), the decrease in re¯ectance in successional communities in 2 or 3 years is too small to be reliably detected with the radiometric resolution of Landsat MSS. 2.1. Data set and test sites The study area, Estonia (45 000 km2), is covered by ®ve satellite Landsat orbit paths. Three scene frames from path 186 row 20, path 187 row 19 and path 188 row 19 were used in this study (see Fig. 1). Altogether 12 Landsat MSS scenes were used (see Table 1). Landsat MSS data selection was based on data availability of cloud-free acquisitions from spring, midsummer (June) and winter conditions. The spring season was taken to be from mid-April till end of May from the period of snowmelt till the germination
Scene path/row
Date
Season
186/20
6 28 4 20
May 1990 Aprill 1993 June 1992 February 1986
Spring Spring Summer Winter
187/19
13 21 3 27
May 1990 May 1993 June 1992 February 1983
Spring Spring Summer Winter
188/19
4 12 26 21
May 1990 May 1993 June 1986 February 1984
Spring Spring Summer Winter
time of spring crops. The summer period covered the peak in plant growing season, from June to early July. The winter period was characterized by snow covered ground with high solar elevation ± late February or March. The number of suitable scenes was low due to the 16-day cycle of Landsat satellite orbits and frequent cloud cover. However, at least one scene for every necessary season was available. Soviet topographic maps of scale 1:100 000 were used for ground truth data for testing accuracy of water, forest and wetland areas. Training areas for agricultural land were located on state farm maps, scale 1:10 000. Fields of different crops on the corresponding 1990 and 1993 maps were delineated by farm agronomists. Speci®c annual crops (e.g. wheat and barley) were naturally not separable in spring and were treated jointly as arable land. Each training area (altogether ®ve state farms, see Fig. 1) comprised 40±80 ®elds with a total of 800±1200 pixels containing arable land per state farm. 2.2. Geometric correction and image registration Landsat MSS scene path 187 row 19 has a central location relative to the study area. Scene 187/19 from May 13 1990 was selected as the master image and all other images were geometrically registered to it. In image-to-image registration 20 to 30 image control points were suf®cient to give the geometric registra-
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tion accuracy of 1.5 pixels. Images were ®nally registered to the Soviet Reference System Grid using control points on 1:100 000 scale maps. The images were then resampled to give 50 m output pixels. The nearest neighbour resampling method was used. No radiometric calibration was considered necessary. Landsat MSS raw digital numbers (DN) were used. 2.3. Image classification The ®rst step in classi®cation was the discrimination of water and terrestrial environments (see Fig. 7). Water bodies were identi®ed from midsummer scenes in low water table conditions. Water was thresholded according to Landsat MSS DN in the NIR re¯ectance band (MSS band 4). This discrimination rule is quite straightforward due to the great contrast in the NIR spectral region between water and terrestrial land surfaces. Land/water boundary pixels and coastal areas with shallow water and/or patchy marine/terrestrial environments are areas which can cause confusion. The second step was a re®nement of terrestrial areas, differentiating forests from non-forests. These classi®cation criteria de®ne whether the vegetation retains perennial aboveground structures (i.e. trees and woody stemmed shrubs) or survives non-growing seasons as seed or below-ground structures only (annual crops and grasses) (Running et al., 1995). The distinction merely requires that remote sensing can detect the presence/absence of aboveground biomass during the non-growing season. This was done by thresholding snow-covered winter scenes into `dark' permanent vegetation stands above the snow cover as forests and into `white' non-forests (open land) showing purely snow-covered areas. Landsat MSS band 2 was used. Soviet topographic maps, scale 1:100 000 were used for `ground truth' data to determine the thresholding DN level in MSS band 2. Wetlands were delineated from spring and early summer image pairs. Wetlands have low NIR to red re¯ectance contrast. Therefore, the re¯ectance change in wetlands at the beginning of the growing period is also insigni®cant compared to that of meadows, agricultural crops and deciduous forests. Wetlands were delineated as areas of low NIR to red re¯ectance ratio (MSS band 4 to band 2 ratio) in spring and in early
Fig. 6. Seasonal near infrared to red ratio index profiles of wetland communities compared to a birch (Betula pendula) forest. Wetland communities are dominated by bog whortleberry (Vaccinium uliginosum) wild rosemary (Ledum palustre), and Sphagnum sp. Note the delayed beginning of seasonal growth in wetland communities compared to the profile of the birch forest.
summer (beginning of June). An example of seasonal pro®les of NIR to red ratio index in wetland communities is presented in Figs. 6 and 7. On areas with `water,' `forests' and `wetlands' masked out, arable land was determined from principal component images. Principal component analysis
Fig. 7. Steps in the classification, imagery used and thematic layers generated.
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(PCA) was performed on the four MSS bands in spring-time scenes. The second principal component was used to delineate arable land. Pixel values in the second principal component image are calculated with strong negative loadings of visible bands and weak positive loadings of NIR bands. PCA enhanced the difference between ploughed land, that is, seasonally bare areas in spring with weak visible to NIR contrast, and grasslands and winter crops, that is, ground with vegetation cover with strong visible to NIR contrast. Winter crops as seasonally bare areas can be differentiated from grasslands in September images. This was not done in this study. Classi®cation was performed as thresholding into `arable land' and `other.' Prior to classi®cation, the study area was strati®ed into three strata. The stratum boundaries coincided roughly with satellite scene frames. Threshold level was determined as best ®t DN within training areas. The classi®cation process extrapolated the thresholding level to all pixels within a stratum, identifying all pixels as `arable land' or `other.' Beaches on the seashore were confused with arable land. A 3-pixel wide buffer area was de®ned along the coastline and `maritime ®elds' within this area were classi®ed as beaches. We did not ®nd any method to reliably de®ne urban areas from Landsat MSS scenes. Urban areas are clearly visible particularly on snow-covered winter scenes as `grayish dirty spots' but are not separable from other classes on the basis of pixel brightness. Major towns were digitized manually from winter scenes and `arable land in the urban environment' (in reality concrete covered parking lots and roofs of major buildings) was masked out. Gravel pits, peat milling areas and oil shale mines in northeastern Estonia were also manually digitized and masked out. These areas were classi®ed as `arable land' in early summer images. They are identi®able as `permanently bare areas' and separable from arable land, as `seasonally bare areas,' when late summer images are included in the classi®cation. Post-classi®cation ®ltering, that is, simplifying of data was not used. Removal of single isolated pixels as `erroneously classi®ed' was not justi®ed in the case of arable land.
3. Results and discussion A land-use map of Estonia was produced using Landsat MSS data. Winter, spring and summer satellite images were combined. The map, based on a 50 m grid, recorded land-use types consisting of water bodies, wetlands, forests, arable land (spring crops), grasslands together with winter cereals, permanently bare areas (beaches and mining areas) and urban areas. A sample of the land-use map representing the distribution of arable land is presented in Fig. 8. From the archive data set of Landsat MSS images at EURIMAGE a list of cloud-free images covering Estonia was compiled (cloud cover 20% or less). We could not ®nd any cloud-free image pairs for the Landsat image frames 187/19 and 188/19 either for spring (April, early May) nor for midsummer (June and July). One cloud-free image pair was available for frame 186/20 for spring 1990 and one for summer 1992. From the summer of 1993 downloading of Landsat MSS data in Europe was terminated. We also tried to differentiate `grasslands' and `old®elds' in a Landsat Thematic Mapper winter image, path 186, row 19 from 16 March 1996. The old®elds abandoned 2 or 3 years earlier had a dense stand of dead plant stems above the snow cover. Viewed obliquely on the ground they were clearly different from ®elds under white snow cover. Unfortunately, the presence of dead stems had not reduced re¯ectance suf®ciently to be detectable with Landsat TM radiometric resolution. From this we conclude that there is little possibility to discriminate between `grasslands currently in use' and `grasslands abandoned upto 2 years ago' using Landsat data. Results of the analysis of arable land distribution are presented below. Arable land is spread sporadically all over Estonia although clustered more densely in some areas than in the others. The general pattern of distribution has not changed signi®cantly since the beginning of the century as described in the land-use revision in 1925 by Laasi (1933). According to the density of arable land two distinct districts can be identi®ed (Fig. 8). The ®rst district with a high concentration of arable land is of a triangle-like shape pointing north. This area coincides with upper Estonia, which has not been ¯ooded by the
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Fig. 8. Distribution of arable land in Estonia in 1990. The results of classification of satellite scenes.
waters of the Baltic sea and local glacial lakes after the last Ice Age (compare with Fig. 1). The remainder, the major part of Estonia is sparsely covered with arable land. This is lower Estonia, a plane lowland covered with wetlands and forests. Within these two major
regions minor irregularities are observable. One is a gap in the densely populated triangle to the north of Lake VoÄrtsjaÈrv in central Estonia. This area is the bed of post-glacial Great-VoÄrtsjaÈrv. The former lake bottom is now covered with swampy forests and bogs.
Fig. 9. Decrease of arable land in Estonia from 1990 to 1993: (1) decrease less than 30%; (2) 30±35%; (3) 35±40%; (4) 40±50%; (5) decrease more than 60%.
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Other irregularities with sparse arable land denote belts of swampy forests and meadows separating upland areas. Some areas within the sparsely populated region with extremely little arable land are located in the transitional zone from upper Estonia to lower Estonia, Estonia intermedia. This zone is a wide belt running obliquely from the northern coast to the Gulf of PaÈrnu in the southwest. Arable land in this zone is located in areas with better drainage, close to rivers. The changes in arable land use from 1990 to 1993 are presented as averaged mean values of districts (Fig. 9). A change in a ®eld-by-®eld basis does not necessarily denote any trend but may merely be one step in crop rotation. Within the dominating trend arable land decrease has been the most severe in the Ida-Viru district, an area of oil shale mining. The agglomeration of shale re®nement industries has occupied a signi®cant part of the district's arable land for decades. The drainage regime of the ®elds in this area has been severely disturbed. The over-drained ®elds above mining areas suffer from temporary drought in summer. Probably the area covered by satellite image (path 187 row 19, see Fig. 1) is not representative of the whole district. A strip of arable land along the northern coast is not within the image area. Therefore, the trend (63% decrease) from 1990 to 1993 may be overestimated. The decrease in arable land use is signi®cant in the marginal districts of Estonia including the islands and also in the district surrounding the capital Tallinn. The decrease has been the smallest in the central districts of upper Estonia and in the westernmost district of continental Estonia ± LaÈaÈnemaa, where the proportion of arable land was the smallest before the land reform. 4. Conclusions A land-use map of Estonia has been compiled from multitemporal Landsat MSS data. The chosen classi®cation is a `simple but accurate' separation of a few land-use types. Landsat MSS scenes combining winter, spring and summer data were used to classify land use in Estonia in 1990 and 1993, respectively. Satellite data were used successfully to discriminate
`arable land' and `other,' but failed to differentiate `grasslands currently in use' from `successional old®elds.' Abandonment rate of arable land as a national total in Estonia was 32% compared to the baseline date, 1990.
Acknowledgements This study was supported by International Science Foundation Grant LCY 000 and by Estonian Science Foundation Grant 1744.
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