↓79 |
Submitted manuscript Tobias Kuemmerle, Patrick Hostert, Véronique St-Louis, and Volker C. Radeloff
Land cover modifications include changes in land use patterns. Eastern Europe provides unique opportunities to study such changes, because much farmland became parcelized in the post-socialist period (i.e. large fields were broken up into smaller ones). Classification-based remote sensing approaches, however, do not capture these changes and new approaches based on continuous indicators are needed. Our goal was to use image texture to map farmland field size in the border region of Poland, Slovakia, and Ukraine. We fitted linear regression models to relate field size to image texture from Landsat TM/ETM+ images. Texture measures explained up to 93% of the variability in field size. Our field size map showed marked differences among countries. These differences appear to be related to socialist land ownership patterns and post-socialist land reform strategies. Image texture has great potential for mapping land use patterns and may contribute to a better understanding of land cover modifications in Eastern Europe and elsewhere.
Land use change is one of the primary drivers of environmental change in the earth system (Steffen et al., 2004;Foley et al., 2005). An improved understanding of how land use decisions are made is urgently needed to better assess the consequences of land use change for ecosystem services and human-wellbeing (Rindfuss et al., 2004;GLP, 2005). Institutions, laws, and political and socio-economic conditions form the background for land use decisions and may increasingly outrank other factors as determinants of land use (Kaimowitz et al., 1999;Geist and Lambin, 2002;Lambin and Geist, 2006). Linking land use change with its political and socio-economic boundary conditions however, remains a challenge (Rindfuss et al., 2004;GLP, 2005), partly because it may manifest itself in both conversions (changes from one thematic class to another) and modifications (subtle changes within a thematic class) of land cover (Lambin and Geist, 2006). However, to date, most studies assessing broad-scale factors of land use change focus on land cover conversions such as deforestation (e.g.Mertens et al., 2000) or urbanization (e.g.Seto and Kaufmann, 2003). This is problematic because land cover modifications are widespread and possibly more important than land cover conversions (Lambin et al., 2001). For example, the area affected by forest degradation in the Amazon (e.g. through selective logging) equals at least the area affected by forest conversions (Asner et al., 2005). Agricultural intensification increased the world’s food production substantially (Matson et al., 1997), but decreased farmland biodiversity (Donald et al., 2002). Despite their importance, land cover modifications have so far been relatively neglected (Lambin and Geist, 2006) and there is a need to quantify their extent, and to assess their relationship to broad-scale political, institutional, and socio-economic conditions.
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One prominent case of land cover modifications occurs when the size or configuration of land management units within a land cover class is altered. Such dynamics in land use patterns often take place when changes in politics or socio-economics trigger changes in land use practices, land-management policies, or land-ownership structures (GLP, 2005;McConnell and Keys, 2005). Central and Eastern Europe’s farmland provides a good example of such a process (Swinnen and Mathijs, 1997;Lerman et al., 2004). After World War II, socialist governments across Eastern Europe intensified agriculture and shifted ownership from private citizens to the state (i.e. collectivization,Lerman, 2001;van Dijk, 2003). This transformation was accompanied by widespread spatial reorganization of land management units. Small pre-socialist farms were dissolved and large, state-controlled agricultural enterprises managed almost all farmland (Lerman, 2001).
Patterns of farmland changed again drastically after the breakdown of the Soviet Union in 1990, when most Eastern European countries privatized and individualized land management (Lerman, 2001), leading to widespread land ownership transfers and the downsizing of farms (Lerman et al., 2004). Land use patterns changed in many areas, as socialist farmland fields were subdivided (Sabates-Wheeler, 2002;van Dijk, 2003). This physical fragmentation of farmland (hereafter called parcelization) has many economic and ecological consequences. For example, parcelization decreases agricultural efficiency (Sabates-Wheeler, 2002) and may lead to abandonment of commonly used infrastructure (Penov, 2004). However, parcelization increases farmland biodiversity (Benton et al., 2003), and soil stability (Van Rompaey et al., 2003). Despite the significance of parcelization for rural Eastern Europe, surprisingly little is known about Eastern Europe’s land use patterns and how they changed since 1990.
This lack of information is unfortunate because studying land use patterns offers unique opportunities to better understand the effects of changing institutions, politics, and socio-economics on land use decisions. Moreover, field size can be interpreted as an indicator of land ownership and land management, particularly when comparing land use patterns among different countries with similar environmental conditions. However, mapping field size in Eastern Europe is challenging, because cadastral data are largely unavailable or of unknown accuracy. Remote sensing is an alternative that can overcome some of these problems.
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Very few studies used remote sensing for automated assessments of field size. This is not surprising, because most conventional image classification and change detection methods stratify images into discrete classes (Southworth et al., 2004;Lambin and Geist, 2006). Using classification based methods to map field size requires classifying all occurring crop types. Such detailed classifications are only possible for detailed time series of satellite images or where crops have unique spectral properties, for example in the case of rapeseed (Elliott et al., 2004). In most cases, however, acquiring training data for detailed crop type classifications is not feasible, and the spectral similarity of crops inhibits detailed classifications. Field boundaries can also be delineated using image segmentation (Evans et al., 2002;Lloyd et al., 2004). Yet, this is only possible where fields are much larger than the dimensions of a pixel, because mixed pixels result in poor boundary discrimination (Turner and Congalton, 1998;Silleos et al., 2002;Ozdogan and Woodcock, 2006). Small fields are common in Eastern Europe due to farmland parcelization (Sabates-Wheeler, 2002;van Dijk, 2003) and this inhibits the use of image segmentation to delineate field boundaries.
An alternative is to characterize land cover using continuous variables, which can detect subtle changes (Southworth et al., 2004;Turner, 2005). A few such methods exist (e.g. change vector analysis, spectral mixture analysis,Coppin et al., 2004), but are based on spectrally homogeneous land cover types (i.e. changes in the signal are related to changes in land cover condition). This is problematic in the case of farmland, where different crops and phenology result in high spectral variability. Moreover, many methods focus on the spectral domain only (Coppin et al., 2004;Southworth et al., 2004), but the spatial domain also contains important information (Chica-Olmo and Abarca-Hernandez, 2000;Cihlar, 2000). Methods based on continuous data that integrate the spatial domain (Southworth et al., 2004;Turner, 2005) and allow for mapping structural modifications of land cover, such as farmland parcelization, are therefore needed.
Image texture measures tonal variations in the spatial domain by quantifying the variability and spatial distribution of grey level values (Baraldi and Parmiggiani, 1995;Chica-Olmo and Abarca-Hernandez, 2000). Because structural information can be important to discriminate between land cover categories, texture measures have widely been used in land cover classifications (Berberoglu et al., 2000;Presutti et al., 2001). For example, texture measures improve classification of forests (Franklin et al., 2000;Coburn and Roberts, 2004), urban areas (Dekker, 2003), and agricultural crops (Anys and He, 1995;Lloyd et al., 2004). Texture measures have much less frequently been used to derive continuous, quantitative variables, and existing studies have mostly assessed vegetation structure in natural ecosystems (Wulder et al., 1998;Asner et al., 2002;Asner et al., 2003). However, to our knowledge no study used texture to map field sizes. This is unfortunate because small fields likely result in high local heterogeneity due to the variability in crop types and phenology, whereas large fields are locally homogeneous. Measures of spatial autocorrelation are sensitive to these field-size-dependent textural characteristics (Lloyd et al., 2004;Ozdogan and Woodcock, 2006). Image texture therefore, should be able to quantify differences in land use patterns and provide an indicator of field size.
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Only two prior studies used remote sensing to address field size in Eastern Europe. Visual interpretation of a 1998 Landsat Thematic Mapper (TM) image provided mean field size for six types of villages in southeast Poland and showed that traditional villages had much smaller fields compared to villages with more intensive land use (Angelstam et al., 2003). Similar visual interpretation in Albania revealed widespread parcelization between 1988 and 2003 (Müller and Munroe, 2007). Thus, existing studies were confined to small study areas within single countries. No study mapped field size from remote sensing images for larger areas in Eastern Europe or has compared land use patterns among countries.
Our goal was to map field sizes in 2000 in a study area in Eastern Europe (the border triangle of Poland, Slovakia, and Ukraine) using image texture. Our specific objectives were:
↓83 |
We studied field sizes in the border triangle of Poland, Slovakia, and Ukraine (Figure V-1). The boundaries of the study area were based on the extent of one Landsat TM scene (path/row 186/26), landscape features such as rivers and valleys, and administrative boundaries. The 17,800km2 study area is characterized by a moderately cool and humid climate (average annual temperature of 5.9°C, mean precipitation of 1,100-1,200mm,Augustyn, 2004). Bedrock is largely dominated by Carpathian flysh (sandstone and shale) (Denisiuk and Stoyko, 2000), but some andesite-basalts occur in the southwest of the study area (Herenchuk, 1968). Dominating soils are cambisols together with podzols in the mountains, whereas podzoluvisols, greysems, gleysols, and fluvisols dominate the plains. The region has mountainous topography (200 - 1,480m altitude). The three main altitudinal zones of potential natural vegetation are: the foothill zone (< 600m) dominated by broadleaved forest, mostly beech (Fagus sylvatica) and oak (Quercus robur, Quercus petraea); the montane zone (600-1,100m) characterized by beech, mixed with silver fir (Abies alba) and sycamore (Acer pseudoplatanus); and alpine meadows with dwarfed beech above the timberline (1,100-1,200m,Denisiuk and Stoyko, 2000).
Land use has substantially altered the study area, particularly in the 19th and 20th century. Population growth and agricultural intensification resulted in increasing agricultural area, mainly at the expanse of forests (Turnock, 2002;Augustyn, 2004). Today, the densely settled plains and the foothills of the study area are largely farmed (Kuemmerle et al., 2006). Forests dominate the montane zone (>60%,Kuemmerle et al., 2006), but farmland and pastures are widespread in mountain valleys, particularly in Slovakia and Ukraine, where population density is much higher than in Poland (Augustyn, 2004). Growing season length varies with altitude (from more than 270 days below 500m to less than 220 days above 800m) (Zarzycki and Glowacinski, 1970). Dairy farming and cattle breeding are important agricultural activities. Cereal (e.g. winter wheat, buckwheat), oil crops (e.g. rape, sunflowers), flax, corn, and potatoes are cultivated in the plains. Agriculture is an important source of income, but most of the agricultural goods are produced for local markets. Moreover, many land owners depend on subsistence farming, particularly in Ukraine.
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Almost all farmland in Slovakia and Ukraine was collectivized during socialism and managed in large, state-controlled agricultural enterprises. In Slovakia, cooperatives prevailed, and land owners retained property rights to their fields. In contrast, in Ukraine all land was owned by the state (Lerman, 1999;Csaki et al., 2003), but in Poland, most farmland was never collectivized (Lerman et al., 2004). A special case were some parts of the Polish region of the study area that were forcefully depopulated following border changes between the Soviet Union and Poland in 1947, and these lands were transferred into state ownership (Turnock, 2002;Augustyn, 2004). After 1990, Slovakia, Poland, and Ukraine launched land reforms to privatize farmland and to individualize land use; yet, the countries chose diverse land reform strategies (Lerman et al., 2004). As a result, the region has heterogeneous land use patterns and is particularly well suited to study how changes in land ownership and land management manifest themselves in land use pattern, and to explore the relationship of field size and image texture.
We acquired one Landsat TM (8/21/2000) and two Landsat Enhanced Thematic Mapper Plus (ETM+) images (6/6/2000 and 9/30/2000) from path/row 186/26 to map post-socialist field size patterns in our study area. Precise co-registering of is necessary for accurate analysis of multitemporal images (Coppin et al., 2004). We orthorectified the images and registered them to the Universal Transverse Mercator coordinate system (World Geodetic System 1984 datum and ellipsoid) (Hill and Mehl, 2003;Kuemmerle et al., 2006). Radiometric correction based on radiometric transfer models was carried out to minimize the effect of differing atmospheric and illumination conditions among images (Hill and Mehl, 2003;Kuemmerle et al., 2006). Thermal bands were not retained due to their lower spatial resolution. Three Ikonos images and twelve Quickbird images available via Google Earth (http://earth.google.com) were used to digitize farm fields as training samples (see section 3.2). All images had been acquired between 2002 and 2006, and together covered an area of 2,890km² (16% of the study area). The Ikonos images were georectified by us while the Quickbird images were already orthorectified. All images were pan-sharpened, with a spatial resolution of 1m for the Ikonos data and 0.67m for the Quickbird images.
To derive ground truth data, we digitized farm fields from IKONOS and Quickbird images for 35 independent sample plots, where each sample plot consisted of many fields. Sample locations were determined at random, maintaining a minimum distance of 1km between sample plots to reduce potential effects of spatial autocorrelation. This distance was chosen based on the range of positive spatial autocorrelation in semi-variograms of selected texture measures (see section 3.3). Variograms were based on 1,000 random locations, directional variograms were used to account for potential directionality, and Gaussian variogram models were fitted to estimate the range.
↓85 |
Field size differed greatly in the study area (<0.1ha to >100ha) and deciding upon the size of the sample plots carefully was therefore important. We defined a sample plot as a fixed number of fields, rather than a fixed area because no single sample plot size would have been well suited to all conditions. To determine the necessary number of fields, we evaluated how mean field size changed as a function of the number of fields. We did so for two circular test areas, one with very small fields (Poland, 151ha, n=331 fields), and one with large fields (Slovakia 1,787ha, n=173 fields). The locations of these test areas were determined based on field visits and expert knowledge. Fields were consecutively added to the calculation of the mean, based on their centroid’s distance to the center point of the test area. The curves of both test areas suggested that mean field size became stationary after approximately 30 fields were included (data not shown). We therefore digitized the 30 fields from the IKONOS and Quickbird images that were closest to the center of each of our 35 sample plots. We used less than 30 fields if sample plot size exceeded 200ha (including the field necessary to reach this limit). The area covered by a single sample plot ranged from 9 – 262ha. Non-farmland was not digitized and we digitized a total of 770 fields.
Texture measures quantify heterogeneity in the spatial distribution of grey values within a local neighborhood, either based on the 1st-order (occurrence) or 2nd-order (co-occurrence) grey level histogram (Haralick et al., 1973;Anys and He, 1995). Different texture measures capture different aspects of spatial variability. Their values also partly depend on the size of the moving window used to calculate them (Anys and He, 1995;Berberoglu and Curran, 2004). We selected 13 texture measures that are relevant to describe land cover features (Anys and He, 1995;Berberoglu and Curran, 2004), including many with low collinearity (Baraldi and Parmiggiani, 1995;St-Louis et al., 2006). The set of texture measures that we chose included five occurrence measures (range, mean, variance, entropy, and skewness), and eight co-occurrence measures (2nd mean, sum of squares variance, homogeneity, contrast, dissimilarity, 2nd entropy, angular second moment, and correlation) (Haralick et al., 1973;Anys and He, 1995). The grey-level co-occurrence matrix was calculated unidirectionally (135 degrees), because field visits and directional variograms did not suggest any particular textural orientation of farmland crops in our study area.
Texture measures were calculated for each of the six multispectral TM/ETM+ bands. We calculated texture measures for the June, August, and September images to test for phenology effects. We calculated the thirteen texture measures for seven window sizes (3, 5, 7, 9, 15, 21, and 51 pixels). In total, we calculated 1,638 texture measures (18 bands [3 images] * 13 texture measures * 7 window sizes). To relate field size to texture measures at the sample plot level we summarized texture by calculating mean and standard deviation of all pixels within a sample plot for each texture measure. The mean denotes the average texture for each sample plot, whereas the standard deviation texture is a measure of variability of texture per sample plot. All texture measures were calculated using ENVI/IDL image processing software (RSI, 2003).
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We fitted regression models to explore the relationship between field size and image texture. As the dependent variable, we used mean field size per sample plot. Histograms and normal quantile-quantile (QQ)-plots suggested a lognormal distribution, and we transformed field size to a normal distribution using the common logarithm. We used sample plot level mean and standard deviation texture as independent variables. Univariate and multiple regression models were fitted to determine texture measures that were good predictors of field size. To fit models and to select the best models, we used a best-subsets method based on the leaps procedure and an exhaustive search method (Miller, 1990;R Development Core Team, 2006). The best-subsets regression searches all possible combinations of n independent variables, where n is the number of covariates in a model, and ranks models according to their goodness-of-fit. Correlation matrices indicated strong collinearity between some input variables. To avoid over-fitting, we limited the maximum number of covariates in a model (n) to three (i.e. allowing for one-, two-, and three-dimensional models). Regression models were fitted for two groups of variables: models that used mean texture only (group I models), and models that incorporated mean and standard deviation texture (group II). Best models were derived for different selections of input variables. First, we selected best subsets for each window size and image, using 78 variables (6 bands * 13 texture measures) for group I models, and 156 variables (78 * 2) for group II models. Second, we selected the best subsets per window size when using input variables from all three images (78 * 3 = 234 for group I, 156 * 3 = 468 for group II) to assess whether combinations of texture derived from phenologically different dates improved predictions. Finally, we fitted models for each image based on the texture measures for all window sizes (78 * 7 = 546 for group I models, 156 * 7 = 1092 for group II models) to investigate whether combinations of texture measures from different window sizes improved predictions.
We derived the best one-, two-, and three-dimensional model for each of the groups of variables described above. Some of our input variables were collinear and we therefore expected several combinations of texture measures to predict field size equally well. Collinearity among input variables is not disadvantageous when using the best subsets routine, because all possible models with n covariates are compared. Models based on covariates with low collinearity likely explain more of the total variance than models with collinear covariates. To compare among models, we calculated two measures of goodness-of-fit: the adjusted coefficient of determination (R²), and the Bayesian Information Criterion (BIC,Schwarz, 1978). Both measures account for the number of covariates in a model, thus allowing comparison of models of different dimensionality. Given any two estimated models, the model with the lower BIC and the higher adjusted R² was preferred. We assumed models performed equally well if their adjusted R² values differed by less than 0.02 (equaling a BIC difference of ~3). We also calculated the p-value and the Bonferonni corrected p-value for all coefficients to evaluate their significance. The Bonferonni correction considers the number of input variables. Coefficients remain significant if their p-value is smaller than 0.05/n, where n denotes the number of input variables. To test the robustness of our models, we calculated cross-validation prediction errors using a leave-one-out procedure and a five-fold cross-validation for the best univariate and multiple regression models (Burman, 1989). We also controlled for the presence of spatial autocorrelation in the residuals in our best multiple regression models based on variograms and found no spatial autocorrelation. All statistical analyses were carried out using R 2.4.1 (R Development Core Team, 2006).
Once the best mean texture models (group I) were selected, we applied them to the entire study area to derive a map of field size. Models that used standard deviation texture (group II) performed better. However, we could not apply these models to full images, because the different sizes of the sample plots inhibited the spatially explicit estimation of standard deviation texture. All forests, water bodies, and settlements were masked out using previous land cover classifications (Kuemmerle et al., 2006;Kuemmerle et al., 2007). We excluded all areas above 1,000m elevation because farmland does not occur above this altitude in the Carpathians. Clouds in the September 2000 image (0.02% of the study area) were masked out. The best mean texture models (group I) were applied to all unmasked pixels to derive a map of field size for the year 2000. Our pixel-based models do therefore not allow for mapping the size or boundaries of single fields but rather estimate mean field size for the surroundings of a given pixel. Because field size was log-transformed, outliers resulted in unrealistically small or large field sizes. To account for this, we used a 5%-cutoff at the extreme ends of the field size distribution.
↓87 |
Texture measures explained the majority (i.e. up to 93%) of the variability in field size. Generally, models based on mean and standard deviation texture (group II models) explained more variability in field size than models based on mean texture only (group I models) (Table V-1 and Table V-2). Multiple regression models using two or three independent variables predicted field size substantially better than univariate models. The increase in adjusted R² was strongest from one to two dimensional models with an average of 0.17 (range: 0.09-0.29) for models that used mean texture (group I models, Table V-1), and 0.12 (range: 0.04-0.24) for models using mean and standard deviation texture (group II models, Table V-2). Adjusted R² values improved less when adding a third covariate (on average 0.07 for both, group I and group II models). All coefficients in the univariate models were highly significant (p<0.0001) and remained significant after Bonferonni correction. The significance of some coefficients decreased in the two- and three-dimensional models, but all coefficients were significant at p<0.05 and most coefficients were significant using the Bonferonni-corrected p-value (Table V-1 and Table V-2).
Some texture measures predicted field size better than others. First-order entropy was the best single predictor of field size (Figure V-2), and most of the best univariate models were based on either 1st or 2nd-order entropy, or angular second moment. The measures used in the best univariate models were highly collinear (for example, a correlation coefficient of 0.99 between 1st and 2nd-order entropy). In the multiple regression models, these measures were complemented by 1st and 2nd-order mean, variance, and correlation (Table V-3).
Table V1: Regression models for different combinations of texture measures and window sizes for models that included mean texture (group I models). Best models for each subgroup (one-, two-, or three-dimensional) are in bold. Acronyms: #V = number of input variables, WS = window size, adjR² = adjusted R², BIC = Bayesian Information Criterion, #BM = number of equally good best models (i.e. difference in adj. R² < 0.02 to the absolute best model); Significance: p<0.0001=***, <0.001=**, <0.01=*, <0.05=a; b indicates cases were all coefficients remained significant after Bonferonni correction.
#V |
WS |
one-dimensional models |
two-dimensional models |
three-dimensional models |
||||||||||||
adjR² |
BIC |
p-value |
#BM |
adjR² |
BIC |
p-values |
#BM |
adjR² |
BIC |
p-values |
#BM |
|||||
June 2000 |
78 |
3 |
0.59 |
-25.15 |
*** b |
1 |
0.74 |
-38.17 |
**/*** b |
2 |
0.76 |
-38.53 |
**/*/*** |
33 |
||
78 |
5 |
0.58 |
-24.58 |
*** b |
1 |
0.69 |
-32.13 |
*/*** |
11 |
0.75 |
-38.22 |
***/***/** b |
19 |
|||
78 |
7 |
0.56 |
-22.91 |
*** b |
2 |
0.68 |
-31.63 |
**/*** |
8 |
0.77 |
-40.24 |
***/***/** b |
9 |
|||
78 |
9 |
0.55 |
-22.26 |
*** b |
1 |
0.69 |
-32.43 |
***/*** b |
6 |
0.77 |
-40.28 |
***/***/** b |
14 |
|||
78 |
15 |
0.49 |
-17.73 |
*** b |
1 |
0.69 |
-32.89 |
***/*** b |
2 |
0.76 |
-39.11 |
***/*/*** b |
18 |
|||
78 |
21 |
0.46 |
-15.73 |
*** b |
2 |
0.68 |
-31.17 |
***/*** b |
2 |
0.73 |
-34.63 |
***/a/*** |
41 |
|||
78 |
51 |
0.45 |
-14.71 |
*** b |
1 |
0.57 |
-21.28 |
***/*** b |
1 |
0.67 |
-27.72 |
**/***/*** b |
8 |
|||
546 |
all |
0.59 |
-25.15 |
*** b |
2 |
0.74 |
-38.17 |
**/*** |
4 |
0.78 |
-41.96 |
**/***/*** |
>200 |
|||
August 2000 |
78 |
3 |
0.55 |
-21.56 |
*** b |
1 |
0.64 |
-27.04 |
***/*** b |
11 |
0.74 |
-35.55 |
**/***/*** b |
4 |
||
78 |
5 |
0.55 |
-21.57 |
*** b |
2 |
0.65 |
-27.86 |
***/*** b |
9 |
0.69 |
-29.68 |
*/***/* |
25 |
|||
78 |
7 |
0.53 |
-20.34 |
*** b |
3 |
0.65 |
-27.95 |
***/*** b |
13 |
0.69 |
-30.04 |
a/*/a |
85 |
|||
78 |
9 |
0.51 |
-18.97 |
*** b |
3 |
0.67 |
-30.37 |
***/*** b |
6 |
0.74 |
-35.82 |
*/***/*** |
6 |
|||
78 |
15 |
0.46 |
-15.37 |
*** b |
1 |
0.66 |
-29.04 |
***/*** b |
13 |
0.69 |
-30.14 |
*/*/*** |
69 |
|||
78 |
21 |
0.41 |
-12.49 |
*** b |
2 |
0.64 |
-27.54 |
***/*** b |
12 |
0.68 |
-28.50 |
a/***/*** |
76 |
|||
78 |
51 |
0.39 |
-11.39 |
*** b |
1 |
0.60 |
-23.23 |
***/*** b |
4 |
0.66 |
-26.38 |
*/a/*** |
35 |
|||
546 |
all |
0.55 |
-21.57 |
*** b |
5 |
0.73 |
-37.69 |
***/*** b |
24 |
0.79 |
-43.52 |
***/**/** |
93 |
|||
September 2000 |
78 |
3 |
0.52 |
-19.38 |
*** b |
1 |
0.67 |
-30.25 |
***/*** b |
9 |
0.71 |
-32.06 |
*/***/*** |
21 |
||
78 |
5 |
0.52 |
-19.62 |
*** b |
2 |
0.68 |
-31.49 |
***/*** b |
10 |
0.73 |
-35.06 |
*/a/* |
14 |
|||
78 |
7 |
0.52 |
-19.49 |
*** b |
2 |
0.70 |
-33.21 |
***/*** b |
8 |
0.84 |
-52.27 |
***/***/*** b |
6 |
|||
78 |
9 |
0.51 |
-18.62 |
*** b |
2 |
0.72 |
-36.13 |
***/*** b |
6 |
0.82 |
-49.00 |
***/***/*** b |
4 |
|||
78 |
15 |
0.48 |
-16.72 |
*** b |
4 |
0.74 |
-38.64 |
***/*** b |
4 |
0.80 |
-45.19 |
***/***/** b |
6 |
|||
78 |
21 |
0.46 |
-15.65 |
*** b |
3 |
0.67 |
-29.98 |
***/*** b |
8 |
0.73 |
-34.69 |
***/***/* |
12 |
|||
78 |
51 |
0.43 |
-13.88 |
*** b |
2 |
0.57 |
-20.82 |
***/*** b |
12 |
0.63 |
-24.22 |
***/**/*** b |
10 |
|||
546 |
all |
0.52 |
-19.62 |
*** b |
7 |
0.74 |
-38.64 |
***/*** b |
35 |
0.85 |
-54.92 |
***/***/*** |
43 |
|||
all three images |
234 |
3 |
0.59 |
-25.15 |
*** b |
1 |
0.74 |
-38.17 |
**/*** b |
2 |
0.78 |
-42.76 |
**/***/* |
18 |
||
234 |
5 |
0.58 |
-24.58 |
*** b |
1 |
0.69 |
-32.13 |
*/*** |
37 |
0.77 |
-40.11 |
***/***/*** b |
35 |
|||
234 |
7 |
0.56 |
-22.91 |
*** b |
2 |
0.70 |
-33.21 |
***/*** b |
22 |
0.84 |
-52.27 |
***/***/*** b |
6 |
|||
234 |
9 |
0.55 |
-22.26 |
*** b |
1 |
0.72 |
-36.13 |
***/*** b |
8 |
0.82 |
-49.00 |
***/***/*** b |
4 |
|||
234 |
15 |
0.49 |
-17.73 |
*** b |
3 |
0.75 |
-40.61 |
***/*** b |
9 |
0.81 |
-46.60 |
*/***/*** |
23 |
|||
234 |
21 |
0.48 |
-15.73 |
*** b |
5 |
0.77 |
-40.53 |
***/*** b |
14 |
0.83 |
-46.83 |
*/***/*** |
51 |
|||
234 |
51 |
0.45 |
-14.71 |
*** b |
2 |
0.71 |
-34.45 |
***/*** b |
6 |
0.79 |
-43.81 |
***/**/*** |
14 |
|||
↓88 |
Some texture measures were rarely included in the two- and three-dimensional models (e.g. homogeneity, contrast, and dissimilarity). Texture measures included in one of the best multiple regression models all displayed a low degree of collinearity (for example, correlation coefficients of <0.10 between mean and entropy).
Goodness-of-fit varied strongly among Landsat bands used to derive the texture measures, but model predictions were similar for collinear Landsat TM/ETM+ bands (e.g. bands in the visual domain). Most texture measures included in our best models were based on short wavelength infrared (SWIR) and visible bands (Table V-3).
Table V2: Regression models for different combinations of texture measures and window sizes for models that included mean and standard deviation texture (group II models). Best models for each subgroup (one-, two-, or three-dimensional) in bold. Acronyms: #V = number of input variables, WS = window size, adjR² = adjusted R², BIC = Bayesian Information Criterion, #BM = number of equally good best models (i.e. difference in adj. R² < 0.02 to the absolute best model); Significance: p<0.0001=***, <0.001=**, <0.01=*, <0.05=a; b indicates cases were all coefficients remained significant after Bonferonni correction.
#V |
WS |
one-dimensional models |
two-dimensional models |
three-dimensional models |
||||||||||||
adjR² |
BIC |
p-value |
#BM |
adjR² |
BIC |
p-values |
#BM |
adjR² |
BIC |
p-values |
#BM |
|||||
June 2000 |
156 |
3 |
0.59 |
-25.15 |
*** b |
2 |
0.81 |
-49.28 |
***/*** b |
6 |
0.83 |
-50.87 |
***/a/*** |
78 |
||
156 |
5 |
0.74 |
-41.11 |
*** b |
1 |
0.79 |
-45.39 |
***/** |
24 |
0.84 |
-53.40 |
**/***/** |
54 |
|||
156 |
7 |
0.78 |
-46.19 |
*** b |
3 |
0.83 |
-54.03 |
*/*** |
14 |
0.86 |
-58.99 |
**/***/** b |
63 |
|||
156 |
9 |
0.78 |
-46.97 |
*** b |
1 |
0.83 |
-53.77 |
*/*** |
15 |
0.85 |
-56.38 |
***/*/* |
115 |
|||
156 |
15 |
0.72 |
-38.80 |
*** b |
1 |
0.80 |
-47.02 |
*/*** |
4 |
0.83 |
-51.49 |
**/***/*** |
10 |
|||
156 |
21 |
0.68 |
-33.31 |
*** b |
1 |
0.76 |
-42.00 |
**/*** |
6 |
0.82 |
-49.48 |
***/***/** |
19 |
|||
156 |
51 |
0.68 |
-33.31 |
*** b |
1 |
0.74 |
-38.21 |
***/** |
18 |
0.80 |
-46.15 |
***/***/*** b |
19 |
|||
1092 |
all |
0.78 |
-46.97 |
*** |
3 |
0.84 |
-54.67 |
***/** |
95 |
0.89 |
-65.63 |
***/***/*** |
127 |
|||
August 2000 |
156 |
3 |
0.59 |
-24.94 |
*** b |
1 |
0.71 |
-34.65 |
***/*** b |
2 |
0.75 |
-37.76 |
***/***/** b |
19 |
||
156 |
5 |
0.67 |
-32.90 |
*** b |
1 |
0.71 |
-34.30 |
a/** |
21 |
0.78 |
-42.20 |
***/***/*** b |
12 |
|||
156 |
7 |
0.67 |
-32.54 |
*** b |
1 |
0.73 |
-36.66 |
***/*** b |
14 |
0.79 |
-43.16 |
***/***/*** b |
40 |
|||
156 |
9 |
0.63 |
-28.79 |
*** b |
1 |
0.74 |
-38.63 |
***/*** b |
10 |
0.80 |
-44.65 |
***/***/*** b |
55 |
|||
156 |
15 |
0.54 |
-20.78 |
*** b |
2 |
0.74 |
-38.74 |
***/*** b |
10 |
0.84 |
-52.68 |
***/***/*** b |
6 |
|||
156 |
21 |
0.50 |
-18.05 |
*** b |
3 |
0.74 |
-39.15 |
***/*** b |
8 |
0.83 |
-50.32 |
***/***/** |
20 |
|||
156 |
51 |
0.50 |
-18.05 |
*** b |
3 |
0.70 |
-33.34 |
***/*** b |
4 |
0.82 |
-50.00 |
***/***/*** b |
12 |
|||
1092 |
all |
0.67 |
-32.90 |
*** b |
2 |
0.76 |
-42.09 |
***/*** b |
105 |
0.86 |
-58.94 |
***/***/*** b |
36 |
|||
September 2000 |
156 |
3 |
0.55 |
-21.79 |
*** b |
1 |
0.67 |
-30.25 |
***/*** b |
13 |
0.75 |
-36.99 |
**/***/** |
34 |
||
156 |
5 |
0.57 |
-23.2 |
*** b |
1 |
0.68 |
-31.5 |
***/*** b |
15 |
0.77 |
-40.06 |
**/***/** b |
11 |
|||
156 |
7 |
0.59 |
-24.92 |
*** b |
1 |
0.70 |
-33.21 |
***/*** b |
15 |
0.84 |
-52.27 |
***/***/*** b |
6 |
|||
156 |
9 |
0.60 |
-25.95 |
*** b |
2 |
0.72 |
-36.13 |
***/*** b |
8 |
0.82 |
-49.00 |
***/***/*** b |
4 |
|||
156 |
15 |
0.58 |
-24.13 |
*** b |
5 |
0.76 |
-40.73 |
***/*** b |
10 |
0.82 |
-49.95 |
***/***/** |
39 |
|||
156 |
21 |
0.55 |
-22.13 |
*** b |
1 |
0.75 |
-40.32 |
***/*** b |
3 |
0.80 |
-45.99 |
***/***/* |
28 |
|||
156 |
51 |
0.55 |
-22.13 |
*** b |
1 |
0.72 |
-36.18 |
***/*** b |
4 |
0.82 |
-49.95 |
***/***/** b |
10 |
|||
1092 |
all |
0.6 |
-25.95 |
*** b |
3 |
0.77 |
-43.39 |
***/*** b |
66 |
0.85 |
-56.16 |
***/**/*** b |
155 |
|||
all three images |
468 |
3 |
0.59 |
-25.15 |
*** b |
3 |
0.81 |
-49.28 |
***/*** b |
6 |
0.85 |
-54.68 |
***/***/*** b |
31 |
||
468 |
5 |
0.74 |
-41.11 |
*** b |
1 |
0.80 |
-48.14 |
***/* |
22 |
0.87 |
-61.49 |
***/***/* |
97 |
|||
468 |
7 |
0.78 |
-46.19 |
*** b |
3 |
0.84 |
-54.71 |
***/** |
36 |
0.92 |
-77.31 |
***/***/** |
64 |
|||
468 |
9 |
0.78 |
-46.97 |
*** b |
1 |
0.85 |
-58.35 |
**/*** |
17 |
0.93 |
-84.13 |
***/***/*** b |
39 |
|||
468 |
15 |
0.72 |
-38.80 |
*** b |
1 |
0.82 |
-51.31 |
***/*** b |
19 |
0.91 |
-75.25 |
***/***/*** b |
74 |
|||
468 |
21 |
0.68 |
-33.31 |
*** b |
1 |
0.81 |
-49.85 |
***/*** b |
10 |
0.90 |
-70.15 |
***/***/*** b |
83 |
|||
468 |
51 |
0.68 |
-33.31 |
*** b |
1 |
0.79 |
-45.47 |
***/*** b |
7 |
0.90 |
-68.33 |
***/***/*** b |
19 |
|||
↓89 |
The SWIR bands were particularly important for the univariate mean texture models (group I); whereas texture measures based on the visible bands were mostly included in the univariate group II and in the two- and three-dimensional models. As expected due to the collinearity among different texture measures and Landsat bands, several models performed equally well (i.e. difference of adjusted R² <0.02). The number of similar models was generally lower for group I models compared to group II models and increased with the number of covariates allowed (Table V-1 and Table V-2).
The goodness-of-fit of the regression models varied among window sizes and was best at small window sizes (Table V-1 and Table V-2). Our univariate models revealed that most texture measures had a clear peak in goodness-of-fit at small or intermediate window sizes and R² values decreased rapidly for larger window sizes (Figure V-3). Combining texture measures from different window sizes did not substantially improve model predictions (i.e. increase in adjusted R² <0.02), both for group I models (Table V-1), and for group II models (Table V-2).
↓90 |
Table V3: Example of the number of times each texture measure was included in the series of regression models containing one (n=1), two (n=2 models), or three (n=33) covariates that performed equally well (i.e. diff. in adjusted R² <0.02) for mean texture of June 2000 (window size 3, total number of variables = 78). Acronyms: range (RA), 1st-order mean (M1), variance (VA), 1st-order entropy (E1), skewness (SK), 2nd-order mean (M2), sum of squares variance (SS), homogeneity (HO), contrast (CO), dissimilarity (DI), 2nd-order entropy (E2), angular second moment (SM), correlation (CR), near infrared band (NIR), short wavelength infrared bands (SWIR1, SWIR2).
|
RA |
M1 |
VA |
E1 |
SK |
M2 |
SS |
HO |
CO |
DI |
E2 |
SM |
CR |
Blue |
5 |
1 |
3 |
1 |
4 |
4 |
19 |
||||||
Green |
8 |
1 |
6 |
1 | |||||||||
Red |
4 |
4 | |||||||||||
NIR |
1 |
2 |
1 | ||||||||||
SWIR1 |
1 | ||||||||||||
SWIR2 |
30 |
1 |
1 |
6 |
|||||||||
Comparing regression models based on texture measures from different images revealed moderate differences in goodness-of-fit. For group I models, texture measures from the June and September images yielded higher model predictions than models based on the August image (Table V-1), but combining texture measures from all three images did not increase goodness-of-fit substantially. This was different for group II models. Goodness-of-fit was comparable among the three dates, however, predictions improved when combining texture from different images (Table V-2). Our best model explained 93% of the variance and used three covariates: mean angular second moment (September 2000 image, TM band 6), standard deviation of 1st-order entropy (June 2000, band 1), and standard deviation variance (August 2000, band 3); all calculated for a window size of 9 pixels.
Among all the models fitted, we selected the best one-, two-, and three-dimensional model for group I and group II based on the adjusted R² and BIC statistics, and chose only models where all coefficients remained significant after Bonnferoni correction. In cases where several models performed equally well (i.e. difference in adjusted R² <0.02), we selected the model that was derived using a smaller selection of input variables, resulting in six best models. Cross-validation for these six models (bold models in Table V-1 and Table V-2) showed that the robustness of the multiple regression models was relatively high (Table V-4)). Prediction errors of the multiple regression models were substantially lower than those of the univariate models (by a factor of 2-3). Errors ranged from 0.20 to 1.41 (log field size) and were lower for group II compared to group I models. The prediction errors were similar when using a leave-one-out strategy or a five-fold cross-validation approach (Table V-4).
↓91 |
Table V4: Mean prediction errors of mean field size (log) for the one- two-, and three-dimensional group I (mean texture) and group II (mean and standard deviation texture) models. Cross-validation was carried out for the best models per subgroup (bold models in Table 1 and Table 2) using a leave-one-out strategy and a five-fold cross-validation approach.
one-dimensional model |
two-dimensional model |
three-dimensional model |
||||
leave-one-out |
five-fold |
leave-one-out |
five-fold |
leave-one-out |
five-fold |
|
group I models |
1.41 |
1.40 |
0.94 |
0.90 |
0.59 |
0.63 |
group II models |
0.80 |
0.84 |
0.61 |
0.58 |
0.25 |
0.26 |
↓92 |
We used the absolute best two- and three-dimensional mean texture models (group I) based on the adjusted R2 to map field size in our study area. Because our ultimate goal was to predict field size, we did not have to consider other equally good models.
The optimal two-dimensional model used two September 2000 texture measures calculated at a window size of 15 pixels (2nd-order mean and correlation from band 3) and explained about 74% of the variance in field size at the sample plot level (Table V-1). The absolute best three-dimensional mean texture model relied on three September 2000 texture measures derived for a window size of 7 pixels (variance of TM band 1, correlation of band 2, and 2nd-order mean of band 3) and had an adjusted R² of 0.84 (Table V-1). Applying these best two- and three-dimensional models to all unmasked pixels of the September 2000 image yielded field sizes between 0.07-142ha and 0.04-1,565ha, respectively (10th and 90th percentile of all estimated pixels). The field size map revealed diverse spatial patterns of field size across our study area, and maps from the two-dimensional and three-dimensional models were highly similar (Figure V-4). Large fields dominated the plains in the north and south whereas mountain valleys were dominated by small fields. Field visits and visual comparison with the Quickbird and Ikonos images confirmed these patterns.
↓93 |
Figure V5: Distribution of field sizes for the Polish, Slovak, and Ukrainian region of the study area. Whiskers indicate the 90th and 10th percentiles. | ||
Field size patterns in Poland, Slovakia, and Ukraine differed markedly. Poland had small fields in most areas (Figure V-5), but some large fields (>1ha) occurred in the valleys along the Polish-Slovak border and in the northwest of the study area (figure V-4). In Slovakia, field sizes were substantially larger than in the other two countries (Figure V-5). In particular, the southern plains were characterized by very large fields, often exceeding 100ha. Mountain valleys had a mix of large and small fields, with valleys in the North exhibiting a higher percentage of large fields than valleys in the South. Ukraine showed the most heterogeneous patterns of field sizes. Although the overall distribution of field sizes was similar to Poland’s distribution (Figure V-5), small and large fields were much more clustered in Ukraine. Mountain valleys were characterized by very small fields (<0.1ha, figure V-4). Large fields were mainly found in the northern and southern plain, but the pattern was more heterogeneous than in Slovakia, and clusters of large and small fields occurred next to each other. Very small fields occurred often in the vicinity of larger settlements (Figure V-4).
Figure V6: Distribution of field sizes per elevation zone and country. Boxplot whiskers extend to 1.5 times the interquartile range. | ||
↓94 |
Field size co-varied with altitude in all three countries (Figure V-6). In Poland, fields were smaller at low altitudes and increased with elevation. In Slovakia and Ukraine, field sizes were much larger at lower altitudes compared to intermediate altitudes. At higher altitudes, areas of small and large fields occurring side by side whereas field size consistently decreased along the altitudinal gradient in Ukraine, and the highest mountain valleys there displayed smallest field sizes (Figure V-6). Three field visits (summer 2004, spring 2005, and spring 2006) included all three countries and confirmed the plausibility of the land use patterns in our field size maps.
We found a strong relationship between field size and Landsat TM/ETM+ texture measures and we used our models to map field size patterns for our full study area. We therefore suggest that texture measures bear considerable potential to map land use patterns and changes therein. This may be especially important in areas that are undergoing rapid change and where alternative data sources (e.g. cadastral maps) are not readily available or of unknown reliability, such as in Eastern Europe and the former Soviet Union.
Our predictions of field size varied based on the texture measures used, but some clear patterns emerged. As expected, the best predictors of field size were texture measures related to the local heterogeneity of grey level values. However, different texture measures quantify different aspects of this heterogeneity. We found measures that characterize the “orderliness” of an image (i.e. regular distribution of grey values,Hall-Beyer, 2007), such as entropy and the second angular moment, to be most sensitive to variations in field size. Entropy measures the degree of disorder or textural uniformity of grey level values (1st-order entropy) or grey level value pairs (2nd-order entropy) (Anys and He, 1995;Baraldi and Parmiggiani, 1995;Anys et al., 1998). Angular second moment (sometimes also referred to as energy) is strongly, but inversely related to entropy, and measures the uniformity of an image (Haralick et al., 1973;Gong et al., 1992;Baraldi and Parmiggiani, 1995). Farmland fields, patches of similar grey values, are often organized in distinct geometric patterns (e.g. along valleys, or perpendicular to roads to provide easier access to farmers). This likely explains why measures such as entropy and angular second moment predicted field size best.
↓95 |
Variance, correlation, and 1st and 2nd-order mean were, in addition to the above measures of orderliness, often included in the best multiple regression models (2 or 3 covariates). Variance describes the variability of grey level values within a given window (Haralick et al., 1973). In other words, variance directly relates to our underlying hypothesis that local heterogeneity is highest where small fields dominate. Correlation is a measure of grey level linear dependency in an image (Haralick et al., 1973) and uncorrelated to the measures of orderliness. Linear dependencies are characteristic for agricultural land use patterns (i.e. farmland fields are often rectangular), thereby explaining the sensitivity of the correlation feature towards field size. First and 2nd-order mean (the average or expected combination of two co-occurring grey level values within a window) both relate to purely spectral rather than textural characteristics. In univariate models, these measures predicted field size poorly. However, 1st and 2nd-order mean based on bands from the visible domain were frequently included in our best multiple regression models, likely because they provided additional information for separating soil and vegetation patches (i.e. fields in agricultural areas).
Some texture measures predicted field size poorly and were not included in any of the best models. Particularly, measures that quantify image contrast (e.g. dissimilarity, contrast, or homogeneity) yielded lower predictions than those measures that quantify the organization of contrasting features (i.e. image orderliness). The weak relationship of contrast measures and field size was not surprising, because the degree of image contrast is not related to particular land use patterns. Moreover, contrast measures are particularly sensitive to periodic features in an image (Baraldi and Parmiggiani, 1995). In agricultural landscapes with many different crop types and bare fields, such reoccurring patterns are scarce. Other measures that were poor predictors of field size included statistical parameters that are not related to the spatial organization of grey-level values (e.g. histogram skewness).
Selecting an appropriate window size is a crucial step when characterizing image features based on texture (Anys and He, 1995). Texture measures calculated using intermediate window sizes (e.g. 7, 9, or 15 pixels) yielded the best field size predictions (Table V-1 and Table V-2). At such window sizes, many small fields (e.g. in areas of subsistence farming) are found within a chosen window, and result in high local heterogeneity. Large fields on the other hand, were still relatively homogeneous at such window sizes. These differences translated into distinct textural characteristics that were useful to map field size (Ozdogan and Woodcock, 2006). Most texture measures displayed a clear peak in predictions at these intermediate window sizes, and decreased rapidly for larger windows. This also indicated that the range of window sizes tested was sufficient.
↓96 |
Landsat bands in different spectral domains predicted field size differently. The short-wavelength infrared (SWIR) bands and the bands in the visible domain captured much of the variation in farm fields, making texture measures calculated from these bands highly suited for field size mapping. The SWIR bands are particularly sensitive to variations in moisture content, and are important for mapping agricultural areas, for separating senescent and green vegetation, and to differentiate soils types (Cohen and Goward, 2004). The visible bands are especially helpful to separate vegetation and bare soil. On the other hand, senescent vegetation and soils are spectrally relatively similar in the near-infrared domain, thus explaining the lower predictions from texture measures based on the NIR band. Predictions from texture measures calculated from the SWIR and visible bands were fairly comparable, suggesting that senescent vegetation/soil discrimination was more important than separating senescent and green vegetation to map field size in our case.
Several combinations of texture measures, Landsat TM/ETM+ bands, and window sizes resulted in comparable predictions of field size in both univariate and multiple regression models. This was expected, because some Landsat TM/ETM+ bands are highly collinear (Small, 2004), several of our texture measures are strongly correlated (Baraldi and Parmiggiani, 1995;St-Louis et al., 2006), and texture measures calculated using similarly sized windows did not differ substantially (Figure V-3). Being conservative in the number of covariates included in our models was therefore important. We used a maximum number of three covariates and the leaps procedure was effective in selecting only variable combinations that displayed a low degree of collinearity (e.g. correlation coefficients among the variables in the best 3-dimensional mean texture (group I) model were 0.20, 0.31, and -0.69). We suggest that the strong collinearity among some of our input variables did not hinder our methodology, but simply led to a higher number of models that predicted field size equally well. Predicting field size likely does not depend on the exact combination of texture measures, window sizes, and Landsat bands. This is an advantage for transferring our methodology to other regions, because testing all possible combinations of input parameters is not necessary to find a model with similar goodness-of-fit than the absolute best model. We also suggest that reducing the dimensionality of the feature space (e.g. principal component transformation) may not be necessary, because the leaps procedure effectively selects variable combinations that explain the total variance best. We tested our models using the first three principal components per image instead of the original six Landsat TM/ETM+ bands, but this did not improve model predictions (results not shown).
Model predictions were fairly stable for comparable input variable selections from different images throughout the year, particularly for multiple regression models that used standard deviation texture. For mean texture models, the autumn image (September) yielded higher predictions, likely due to the presence of green vegetation, senescent crops, harvested fields, and bare soil. This spectral diversity of crop types resulted in higher local heterogeneity where land use patterns are dominated by small fields, and therefore a possible explanation for better predictions compared to the June and August image, where crop types are spectrally more homogeneous. However, the difference in goodness-of-fit among models from different images was relatively small (i.e. difference in adjusted R² <0.06). Using combinations of input variables from different images did not improve model predictions substantially. We therefore suggest that a single image suffices to predict field sizes from texture measures.
↓97 |
Applying the multiple regression models fitted at the sample plot level to our full study area was successful (Figure V-4). Both, the two-dimensional and three-dimensional models, yielded comparable patterns of field size for our study area. A disadvantage was the log-transformed nature of the dependent variable, which exponentially amplified outliers in the texture measures (for example due to errors of commission in the water and settlement masks, etc.), particularly in the map generated from the three-dimensional model. Cutting the extreme ends of the field size distribution partly addressed this problem, but this approach requires expert knowledge regarding the possible range of field sizes. Unrealistically high and low field sizes in the map generated from the three-dimensional model may also indicate over-fitting. However only a very small fraction of the study area was affected (predictions of <0.01ha for ~3% of the study area; >300ha for ~2% of the study area) and the cross-validation results did not suggest over-fitting. The robustness of our multiple regression models was also confirmed by the low cross-validation errors.
Our results show that image texture is a useful tool to map field size for areas with a high proportion of mixed pixels as well as for areas with very large fields. The field size maps proved useful to identify land use patterns and to compare these patterns among countries. We therefore suggest that texture has significant potential to monitor agricultural intensification and changes in land use patterns in Eastern Europe and in other regions of the world. Because texture is easily derivable from raw image data, it may represent an important variable (Southworth et al., 2004;Turner, 2005) to assess landscape structure and land dynamics based on the spatial domain, and to assess structural land cover modifications in human-dominated landscapes. Moreover, land use pattern information is important to understand the relationship of land tenure and land use change. Incorporating land use patterns in land use change models has so far largely been based on cadastral maps (Verburg, 2006). Such data are unavailable in many regions in the world, particularly those that experience rapid land use change, and field size variables based on image texture may be a useful alternative.
We found marked differences in field size among the Polish, Slovak, and Ukrainian region of our study area. The study area was part of the Austro-Hungarian Empire for approximately 150 years before 1914. During that time, land management was relatively homogeneous (Turnock, 2002). Therefore today’s differences in field size among countries likely originated in socialist and post-socialist times. In our case, these differences are related to land ownership patterns and land management in socialist times, combined with different strategies to re-privatize farmland and individualize land use in the post-socialist period.
↓98 |
Poland had the smallest field sizes, particularly in areas below 500m elevation. The reason is likely that Poland was the only Eastern European country where collectivization failed (van Dijk, 2003;Lerman et al., 2004), small family farms persisted, and fields were never aggregated (Lerman et al., 2004). However, the exceptions were Polish mountain valleys, where border changes between the Soviet Union and Poland after 1947 led to a depopulation and the establishment of large-scale, state owned farms (Turnock, 2002;Augustyn, 2004). After 1990, private farmland changed little, whereas state land was auctioned off, set aside, or converted to forest (Augustyn, 2004). Our results show the largest fields in the Polish part of our study area at altitudes above 500m, mostly clustered in the mountain valleys close to the border with Slovakia (Figure V-4).
In Slovakia, all farmland became collectivized in socialist times and small farms were dissolved into large-scale, state-controlled agricultural enterprises (Lerman, 2001;Csaki et al., 2003). Although land owners retained the title to their land, land was managed by the state (van Dijk, 2003;Lerman et al., 2004). After 1990, Slovakia privatized the agricultural sector and restituted farmland to former owners, but this has not led to widespread parcelization and farming often continues on large fields (Trzeciak-Duval, 1999;Csaki et al., 2003). This is reflected in our results by the high share of large fields, particularly in the southern plains. In such areas, socialistic land use patterns were effectively preserved (Mathijs and Swinnen, 1998). Likely explanations are the relatively slow pace of reform (Csaki et al., 2003) and Slovakia’s land owners, who often chose to lease their land to the successor organizations of the former cooperatives (Trzeciak-Duval, 1999;Lerman, 2001). In Slovak mountain valleys household fields occur next to fields managed by large-scale agricultural enterprises. Moreover, farmland abandonment was widespread in Slovak Mountain valleys resulting in relatively large, homogeneous areas, thus explaining the occurrence of very large fields in these areas.
In Ukraine, we found heterogeneous patterns of field size, and strong differences between mountain valleys and the plains in the North and South. In socialistic times, all farmland in Ukraine was state-owned and managed in large-scale farming enterprises (Ash and Wegren, 1998;Lerman, 2001). After the breakdown of the Soviet Union in 1991, Ukraine chose to distribute farmland among the workers of the state farms and collectives (Lerman et al., 2004). Land reform, however, was slow and much of the farmland is still managed by large-scale successor organizations. As a consequence, we found clusters of large fields, particularly in the plains. Parcelization occurred in some areas (Ash and Wegren, 1998;Lerman et al., 2004) and we found evidence of parcelization in the vicinity of larger settlements, where people use farmland for subsistence farming, and land is accessible and potentially more valuable. Mountain valleys were almost exclusively characterized by very small field sizes, because Ukrainian mountain valleys have a high population density, and many people depend on subsistence farming.
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Studying land use patterns in areas that are undergoing political and economic transitions allows assessing the effects of changing institutions, land management policies, and land ownership on land change. Our method permitted the cross-border comparison of field size and land use pattern, and revealed marked differences among the Polish, Slovak, and Ukrainian regions of our study area. These differences are likely related to land ownership and land management in socialist times as well as dissimilarities in land reform strategies after 1990. Mapping these differences would not have been possible using traditional classification-based methods, and image texture proved to be a reliable continuous indicator to map structural land cover modifications, such as the parcelization of farmland in Eastern Europe. Texture may thus contribute to an improved understanding of the spatial extent, causes, and consequences of land cover modifications in other regions of the world as well.
The authors are grateful to P. C. Alcántara Concepción, M. Dubinin, A. Janz, N. S. Keuler, D. Müller, A. Prishchepov, and S. Schmidt for valuable discussions and helpful comments on prior versions of this manuscript. We thank A. Damm and A. Janz for programming assistance and T. Schiller for helping with the data processing. This research would not have been possible without support by NASA’s Land Cover and Land Use Change (LCLUC) Program and by the International Office of Humboldt-Universität zu Berlin.
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