Human-caused land use and land cover change is one of the major drivers of global environmental change [1
]. Historically, agricultural expansion was the principal mode of land use change leading to, for example, an increasement of cropland areas by about 550% over the last three centuries [3
]. Nowadays, up to 38% of the land surface is used for agriculture and every year about 13 million hectares covered by natural vegetation are transformed into agricultural land [1
]. Next to agricultural expansion, agricultural intensification has recently become an important mode of reaching higher agricultural outputs [5
]. Especially since the advent of industrial fertilizer and the green revolution, intensification has been responsible for the majority of yield increases in recent decades [6
Both agricultural expansion and intensification entail substantial environmental trade-offs. Approximately 35% of the anthropogenic CO2
emissions since 1850 are directly traced back to land use changes [1
]. On the one hand, expansion of agriculture into native ecosystems is the major driver of biodiversity loss, releases huge amounts of carbon and plays a major role in changing the global carbon cycle [10
]. On the other hand, agricultural intensification can increase soil erosion, lower soil fertility, threaten biodiversity, pollute ground water and lead to the eutrophication of rivers and lakes, and contribute to climate change via the emission of CO2
and other green-house gases [8
]. Mapping the extent and the intensity of agriculture is therefore important for assessing environmental and socio-economic trade-offs of agriculture.
As demand for agricultural products (e.g., food, feed, bioenergy) continues to increase and land resources are increasingly becoming scarce [11
], identifying strategies for increasing agricultural production in sustainable ways has become a research priority [13
]. There are three strategies that could lead to increasing agricultural production: (1) cultivation of new farmland; (2) intensifying existing farmland; or (3) recultivation of unused or abandoned farmland. However, assessing where sustainable intensification or recultivation could be fostered, requires improved maps that go beyond broad land cover classes such as cropland and are sensitive to land use intensity and that include information on active and fallow/abandoned agriculture. Unfortunately, such information does not exist for most parts of the globe [15
Satellite remote sensing has doubtless become the most important technology to monitor agricultural land use and changes therein [17
]. Yet, existing approaches to do so have mainly focused on cropland extent and the proximate drivers, leading to changes in cropland area (e.g., deforestation due to agricultural expansion, agricultural abandonment) [22
]. What is generally lacking are methods able to capture the heterogeneity and the varying management intensity within the broad agricultural class.
One reason for the lack of approaches sensitive to land use intensity is that intensity in itself is a complex and multidimensional term. Agricultural land use intensity can be measured via inputs to agriculture (e.g., fertilizer, pesticide application rate), the outputs from agriculture (e.g., yields), or in terms of system properties that change due to management (e.g., human appropriation of net primary production) [15
]. Unfortunately, remote sensing can only rarely measure any of these dimensions directly and therefore a combination of remote sensing and ground-based data is often needed to determine land use intensity metrics. This is problematic because comprehensive and detailed in-situ data on land management is unavailable for most parts of the world, either because of the lack of monitoring schemes or the confidentiality issues [15
]. Approaches that allow to better characterize management intensity directly from satellite imagery are therefore potentially highly beneficial for a better understanding of the impacts of agriculture on the environment.
A promising avenue for a more nuanced representation of agriculture is to identify and map management regimes with different intensities [15
]. Such management regimes (or land systems) may be easier to map than all the individual dimensions of management intensity itself, yet provide a proxy variable for tracking changes in management intensity and their impacts (e.g., shifts from subsistence to capital intensive farming). A few studies have used such approaches recently at the global scale. For example, combining land cover data with human population density allowed deriving anthromes of varying land use intensity [28
]. Or, using clustering techniques on a comprehensive set of environmental and socio-economic variables allowed to map land system archetypes [29
Likewise, combining global data sets on cropland extent, yield gaps, livestock distribution, and market accessibility allowed to map different “land use systems”, which represented different levels of management intensity [29
]. Examples relying on remote sensing data to characterize land management regimes are even scarcer. At the regional scale, Landsat images were used to map swidden agricultural systems of varying management intensities for Laos [31
]. Furthermore, the spatial distribution of farming types in Ethiopia was analyzed based on Landsat, but additionally to remote sensing data, local spatial contextual information was needed [32
]. While these studies highlight the value of mapping management intensity regimes, there is a general lack of studies developing methods to map management regimes using remote sensing data.
One important indicator to characterize agricultural land use intensity is field size, which is a proxy variable for the degree of mechanization. While small fields indicate low levels of mechanization, often accompanied by low levels of fertilizer and pesticide use, large fields tend to require a high degree of mechanization and are typical for industrialized agriculture [33
]. Mapping land management regimes that differ in field sizes (e.g., large-scale cropland and small-scale cropland) is challenging due to spectral similarities of fields and mixed signatures within groups of small fields. Studies mapping field sizes based on remote sensing data are scarce. While some remote sensing-based studies used predefined vector data or texture measures to analyze field sizes [35
] or an object-based approach using Landsat data to extract fields [40
], there is no classification-based approach to directly derive management regimes differing in field sizes up to now.
Object-based approaches use additional information compared to pixel-by-pixel approaches, for example, spatial context and object-based features such as spectral mean or variance. Object-based classification of land use and land cover often results in higher accuracies, when compared to pixel-based classifications [41
]. Some studies also used hierarchical classification approaches, mainly to integrate different data types into a comprehensive mapping framework [45
]. However, to our knowledge, no study has used segmentation algorithms to make better use of field size information inherent in satellite images in order to improve the mapping of agricultural management intensity.
Landsat has arguably become the most important sensor to characterize land cover and land use at regional to landscape scale [49
], especially since the recent opening of the United States Geological Survey (USGS) Landsat archives [53
]. However, Landsat data availability can be limited in regions of persistent cloud cover or due to the relatively low revisiting rates of the Landsat sensors (16 days). For example, the limits regarding image acquisition dates and temporal coverage are critical for the mapping accuracy of agricultural abandonment [54
Contrary to optical data, synthetic aperture radar (SAR) data are almost independent from weather conditions. Thus, multitemporal data sets covering any stage from one growing season in regions like Central Europe can reliably be produced by using SAR sensors. Several studies used SAR data for land use and land cover mapping [56
]. In addition, SAR data provides different, but complementary information on land cover, when compared to optical data. For example, discriminating vegetation species can be difficult due to their similar spectral signature. In this case, radar can contribute with signal differences in surface roughness, shape, and moisture content of the observed ground [59
Jointly using optical and SAR data to map land use and land cover change is therefore an attractive option, but has so far rarely been employed. This is unfortunate because such multisensor approaches can result in more reliable maps than using only one data source alone. For example, the fusion of multispectral and SAR data from an agricultural area outperforms the mono-sensoral approach in terms of the classification accuracy [60
]. Overall, several studies noted higher accuracies in the differentiation of classes by the combined use of optical and SAR data in context of land use and land cover mapping [61
], for example by minimizing spectral ambiguities and improving the characterization of phenological variability [65
]. However, none of these studies have assessed how the combined use of optical and SAR data can advance the mapping of management intensity of cropland.
Here, we explore the synergetic effect of multispectral Landsat and SAR data to map land management regimes, as proxies of land use intensity, in our study area in western Ukraine. Land use intensity here refers to the management intensity in terms of capital-related inputs such as industrial fertilizer, pesticides, or heavy machinery. Eastern Europe and especially western Ukraine are particular interesting for mapping land management regimes [22
]. Land management in the region has changed drastically in recent decades, triggered by the breakdown of the Soviet Union in 1991, when industrialized and the large agricultural fields, established during Soviet times, were abandoned [68
]. Furthermore, large fields were converted to small fields as subsistence agriculture became important after 1991 [66
]. Recently, global trends in food prices have led to a growing interest in the region, which triggered the recultivation of much farmland and a renaissance of industrial agriculture, including a consolidation of small fields into large ones [72
]. The rates and patterns of these trends remain unclear, however, especially with regard to changes in management intensity, which in this region is intimately linked to changes in agricultural field size. Mapping land management regimes can therefore offer important insights about land use change and ultimately the effect of economic and institutional drivers on land change in western Ukraine.
The overall goal of our study was to develop an approach for mapping agricultural land management regimes of different intensities for our study area in western Ukraine, and to use this methodology to assess the patterns and rates of agricultural land use in this region. To do so, we used field size (large-scale cropland and small-scale cropland) as a proxy for management intensity and evaluated the potential of object-based image analysis to merge multispectral and SAR images within a hierarchical classification framework in order to discriminate different land cover/use categories, including different land management regimes. Specifically, our objectives were to:
Analyze whether object-based mapping improves the separation of land management regimes
Assess whether the combination of multispectral and SAR data enhances the classification of land management regimes.
Map land management regimes and analyze them across gradients of soil marginality, elevation, and distance to markets.
We used three classification approaches—(1) pixel-based (Landsat); (2) object-based (Landsat); and (3) hierarchical object-based (Landsat + ERS)—to map land management intensity regimes. Generally, our classifications showed the substantial potential of improved characterizations of land management regimes when using multispectral and SAR data jointly in a hierarchical framework (Table 2
). The overall accuracy of the pixel-based classification (67.4%) was increased markedly in the object-based classification (78.3%). The hierarchical classification, integrating Landsat and ERS data as well as object-based features, outperformed both other approaches in terms of the classification accuracy (83.4%).
The confusion matrix of the pixel-based classification indicated the difficulty to classify large-scale cropland
) and small-scale cropland
) solely on pixel-level due to spectral ambiguities (i.e.
, field size cannot reliably be differentiated spectrally) (Table 3
). Furthermore, there was a noticeable confusion within active agriculture
) as well as between active agriculture
and fallow agriculture
. This caused relatively low producer’s and user’s accuracies for the agricultural classes in the pixel-based classification (Table 2
The object-based classification reduced the confusion between LSC
as well as the fallow
class substantially (Table 4
). Specifically, the misclassification of SSC
was reduced from 25.8% to 10.1% and the misclassification of fallow
from 25.8% to 5.6%. As a result, the producer’s accuracy of LSC
increased by about 34% from 51.6% to 85.4% and the user’s accuracy of LSC
rose by about 20% to 89.0%. Similar improvements between the pixel-based and object-based classification were observed for the producer’s accuracies of SSC
. However, there was still considerable remaining confusion between fallow
as well as pasture
. Thus, the producer’s accuracy of fallow
as well as the user’s accuracy of SSC
were still relatively low at about 56.0% (Table 2
With the use of additional SAR data within the hierarchical classification framework, the detection of fallow
areas was improved substantially, which consequently reduced the confusion between SSC
). This led to an increase in the producer’s accuracy of fallow
(74.0%) as well as the user’s accuracies of SSC
(71.4%) and pasture
(65.9%) (Table 2
Tests of statistical significance based on the McNemar statistics indicated that the object-based classification resulted in a significantly more accurate map (p <
0.001) compared to the pixel-based classification (Table 6
). The hierarchical classification performed well with regard to the classification accuracy, resulting in an overall accuracy that was significantly higher in comparison to the pixel-based (p <
0.001) and object-based (p <
0.05) approach. (Table 6
The map of land management regimes and land cover of our study area showed a heterogeneous landscape with mainly forest
in the northern part of Volodymyr-Volynskyi Raion and the southern part of Sokalskyi (Figure 5
). Ivanychivskyi and Horokhivskyi in the center of the study area were mainly covered by active farmland. Pasture
was mostly concentrated in the northern, southern, and western central part of the study area. The SSC
, mainly subsistence agriculture, can contain individual houses or small villages, since urban
represented cities with wide impervious surfaces and large urban structures. Consequently, the majority of the urban population lived in the big cities solely located in the western part of the study area while rural populations were concentrated in the central and eastern parts of the study area.
According to the error-adjusted area estimates (Figure 6
), our study area was mostly covered by large-scale cropland
(29%, ≈190,000 ha). Small-scale cropland
covered 15% (≈94,000 ha) and pasture
12% (≈77,000 ha). Therefore, the agricultural categories with active land use occupied approximately 53% of the study area, whereas about 22% (≈141,000 ha) of the study area was fallow
accounted for about 20% of the study area and the cities covered about 2% of the study region.
Comparing the distribution of land management regimes and land cover types along indicators of the marginality of agriculture revealed interesting patterns. Soil quality is a key element for agricultural productivity. As expected, our analysis revealed that the majority of large-scale cropland
and small-scale cropland
was cultivated in areas with comparatively good soils (Figure 7
). About 54% of the LSC
was cultivated on Phaeozems (28%) and Chernozems (25%). Even 60% of SSC
occurred on Phaeozems and Chernozems. Forest
areas were mainly located on Podzols (>
40%), the sum of these classes on Phaeozems or Chernozems was below 20%.
The distribution of each class also varied substantially with distance to cities (Figure 8
). The areas very close to the cities (0.5 km) in the study area contained over 25% of SSC
and only about 13% of LSC
, 4% pasture
, and 6% fallow
. With an increasing distance to cities (and thus local markets), SSC
decreased while the share of LSC
increased steadily. The dominance of land management regimes near cities changed from small-scale cropland
to large-scale cropland
between 2 and 4 km away from cities. Fallow
areas increased rapidly up to a distance of 4 km around cities.
Analyzing the distribution of our land management and land cover classes along elevation gradients showed interesting differences between the land management regimes (Figure 9
). The active farmland classes (LSC
) had their peaks at about 220 meter elevation, whereby LSC
was more equally distributed compared to SSC. Pasture
, and forest
occurred mainly at elevations between 180 and 200 m, whereby pasture
were similarly distributed.
A growing world population, diet changes, and an increasing role of bioenergy all contribute to a surging demand for agricultural products, and unless major shifts in consumptive behavior occur, this requires potentially a doubling of agricultural production by 2050 [12
]. Production increases can be achieved following three options: (1) expanding agriculture into natural ecosystems; (2) intensifying existing farmland; or (3) recultivating abandoned farmland. To decide which strategy is attractive to increase production while mitigating the environmental trade-offs of agriculture, first and foremost it is important to better map and understand spatial heterogeneity in agricultural management intensity, ranging from industrialized to abandoned lands. Mapping agricultural management regimes such as large-scale cropland
, and small-scale cropland
(indicating high and low management intensity, respectively), and fallow
land with remote sensing data provides interesting avenues to improve our understanding of the patterns of agricultural land use intensity, particularly where ground-data on the different aspects of management are scarce.
Our first objective was to analyze the value of an object-based approach in comparison with a pixel-based classification to map land management regimes. Our analyses clearly showed that the pixel-based approach was not capable of differentiating large-scale cropland
and small-scale cropland
with high accuracies (Table 2
), likely because of similar spectral characteristics of these classes. In our case, additional object-based features from a multilevel segmentation with different segment scales helped to overcome this problem. The spatial relationships and the different features within the segments provided information about (1) relatively homogenous large fields with one crop type and (2) small fields with inhomogeneous spectral characteristics due to multiple crops within a cluster of kitchen gardens, which is not included in pixel-by-pixel information. Therefore, multilevel object-based features were the key elements to distinguish large and small fields, and thus large-scale cropland
and small-scale cropland
in our study area.
Our second objective was to assess the value of SAR data (ERS-2 images in our case) within a hierarchical classification framework to enhance the mapping of land management regimes compared to using optical data (Landsat images in our case) alone. As we already noted, pasture and fallow fields can have similar spectral signatures as well as spatial similarities contrary to large-scale cropland and small-scale cropland. Therefore, the object-based approach, which was based on the optical data alone, did not noticeably improve the classification accuracy of pasture and fallow land in comparison to the pixel-based classification. However, the integration of SAR data within the hierarchical classification approach did appreciably improve the classification accuracies of both classes. This was likely due to two reasons. On the one hand, SAR data included complementary information to optical data. On the other hand, additional temporal information of SAR data (i.e., nine available scenes over the year), which enabled an enhanced extraction of information about different phenological stages of pasture and fallow, likely contributed to the higher classification accuracies, when using optical and SAR data jointly.
In general our results emphasize that the integration of both sources, i.e.
, multisensor data as well as object-based features from different scales, proved useful in terms of the mapping accuracy. This is in accordance with the results achieved by other studies [62
], for example, where crop type mapping was enhanced by classifying multiple segmentation levels from SAR and multispectral data [63
The algorithms that were used in our study proved to be well suited for mapping land management regimes. The Superpixel Contour algorithm was able to separate the Landsat images into meaningful regions, as the visual inspection and the significant improvement of the classification accuracies by the generated objects confirmed. This is also in accordance with the results achieved by Stefanski et al.
], where the Superpixel Contour algorithm was analyzed in more detail. The Random Forest classifier also performed very well, as it was already shown in previous studies (e.g., [63
]), and seems adequate for handling multisensor data as well as different multilevel features.
Our third objective was to explore the spatial distribution of land management regimes. Generally, the patterns observed in our study region were well in accordance with patterns that we would predict based on classical land rent theory [93
, less intensive or no land use on the most marginal plots (higher elevations, less suitable soils, far away from markets). Interestingly, small-scale cropland was most widespread in the vicinity of cities (Figure 8
), whereas large-scale agriculture (i.e.
, potentially more capital intensive) was found away from cities. Two factors explain this pattern. First, during the Soviet time, large industrialized farms were established and these were often far away from settlements and cities. Second, with the breakdown of the Soviet Union, subsistence agriculture became more important and thus farmland in the vicinity of cities was used for small-scale farming and gardening, explaining the concentration of small-scale cropland
close to the cities.
The pattern of the class distribution with regard to elevation can be explained by the local topography. Large-scale cropland
and small-scale cropland
is basically concentrated in the center of the study area, where the elevation is higher compared to the north and south, where pasture
, and forest
occurred mainly. The occurrence of farmland abandonment in Eastern Europe on lower elevations was surprising, and on first glance not in line with land rent theory. However, other studies [68
] have found similar patterns and the factors mentioned above (i.e.
, collapse of large corporate farms after the breakdown of the Soviet Union, concentration of farming around settlements) explain these patterns as well. Furthermore, some of the lower areas in our study region are frequently flooded (especially as drainage dikes were abandoned), making these areas not well-suited for agriculture.
Earlier studies have found substantial potential for recultivation in the region [68
]. Our results suggest—about 22% of the whole study area was fallow in 2010—some potential for recultivation of abandoned farmland. However, advanced processes of forest succession (i.e.
, high amount of woodland) causes increasing recultivation costs [95
]. In this context, the additional use of approaches that quantify succession seems sensible [96
]. About 15% of the study area was small-scale cropland
with low intensive farming, which suggests some potential for agricultural intensification. We caution though, that the socio-economic and environmental impacts of intensification, and recultivating currently idle cropland, have to be taken into account carefully.
Our study demonstrated that agricultural management regimes can be reliably determined from remote sensing imagery alone when field size can be used as a proxy, which is an important finding given that ground data on management practices is not available for large parts of the world [15
]. Nevertheless, several avenues for further improving our approach are possible. First, auxiliary data may be capable of improving the precision of area estimates. For example, spatial relationships can contribute to classify land management regimes more accurately or population density data may be useful to improve the precision of land cover change estimation [97
]. In this context, non-parametric methods that can deal with continues and categorical data like Random Forests appear to be appropriate to integrate and classify diverse datasets, including multisensor data, terrain models, or categorical variables, for example, derived from soil maps [78
Second, ground data on management or yields could further help to generate a better map of intensity, whereby geostatistical approaches are suitable to integrate such data. Analyzing management regimes over several years may also further improve the precision of mapping management regimes. For example, using previous analyses as prior knowledge or prior probabilities may improve mapping accuracies [98
Third, auxiliary data can be used to avoid misinterpretations of land management regimes maps. For example, Figure 6
implied that 22% of the study area was fallow
and therefore this region should offer great potential for recultivation. However, analyzing the proportion of fallow
land regarding its underlying soil type revealed that 45% of the fallow
areas were on Podzol (Figure 7
). As Podzols are generally less attractive soils for cropping due to the low nutrient status, low level of available moisture, and low pH values [88
], realistic potentials for recultivation in this region have to be further analyzed.