The effect of Landsat ETM/ETM + image acquisition dates on the detection of agricultural land abandonment in Eastern Europe

Prishchepov, Alexander V.; Radeloff, Volker C.; Dubinin, Maxim; Alcántara, Camilo

doi:10.1016/j.rse.2012.08.017

Cited by 156 publications

(113 citation statements)

References 46 publications

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“…This is the first such a study for Ukraine for producing multi-year crop type inventories, as previous studies focused on other regions (AAFC, 2013;Boryan et al, 2011;Johnson & Mueller, 2010), single year and end of season crop type maps only (Kussul et al, 2016;Prishchepov et al, 2012;Skakun et al, 2016b;Waldner et al, 2016). The proposed approach is very useful for operational crop mapping, as it can be applied, while satellite imagery being acquired and ingested into the classifier to provide both in season and end of season crop maps.…”

Section: Discussionmentioning

confidence: 94%

“…In particular, optical instruments onboard remote-sensing satellites provide imagery in multiple spectral bands, usually in visible, near-infrared, short wave infrared, and thermal infrared. However, these data can be contaminated with cloud cover that, in many cases, makes it very difficult to acquire imagery in an optimal time range to discriminate crops (PaxLenney & Woodcock, 1997;Prishchepov, Radeloff, Dubinin, & Alcantara, 2012). On the other hand, synthetic aperture radar (SAR) instruments offer unique features to image crops due to their allweather capabilities and ability to capture crop characteristics different from those derived from optical instruments (Skakun, Kussul, Shelestov, Lavreniuk, & Kussul, 2016b;Stefanski, Chaskovskyy, & Waske, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery

Kussul

Lavreniuk

Shelestov

et al. 2018

European Journal of Remote Sensing

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Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery

Kussul

Lavreniuk

Shelestov

et al. 2018

European Journal of Remote Sensing

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“…Multiple images in each year allow capturing vegetation peaks in all fields. This technique was previously used by other researchers (Turner and Congalton 1998;Wolter et al 1995;Prishchepov et al 2012;Brandt et al 2013). Forest, residential and water body classes were derived using supervised (maximum likelihood) method.…”

Section: Resultsmentioning

confidence: 99%

A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran

Mohammady

Moradi

Zeinivand

et al. 2014

Int. J. Environ. Sci. Technol.

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Land use classification is often the first step in land use studies and thus forms the basis for many earth science studies. In this paper, we focus on low-cost techniques for combining Landsat images with geographic information system approaches to create a land use map. In the Golestan region of Iran, we show that traditional supervised and unsupervised methods do not result in sufficiently accurate land use maps. Therefore, we evaluated a synthetic approach combining supervised and unsupervised methods with decision rules based on easily accessible ancillary data. For accuracy assessment, confusion matrices and kappa coefficients were calculated for the maps created with the supervised, unsupervised and synthetic approaches. Overall accuracy of the synthetic approach was 98.2 %, which is over the 85 % level that is considered satisfactory for planning and management purposes. This shows that integration of remote sensing data, ancillary data and decision rules provides better classification accuracy than traditional methods, without significant additional use of resources.

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“…Remote sensing is a well-known alternative to assess large-scale land-use change. Much progress has been made in mapping land-use/land-cover changes (LULCCs) in drylands using geographic information systems (GIS) and remote sensing, such as with 30-m multiannual imagery from Landsat [25,26], high-resolution RapidEye or Sentinel [7,27,28] and 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data [29,30]. MODIS data fit well for assessing and mapping LULCC and crop types [31] or land abandonment over large regions in a regular manner [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series

Löw¹,

Prishchepov

Waldner

et al. 2018

Remote Sensing

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Abstract:Cropland abandonment is globally widespread and has strong repercussions for regional food security and the environment. Statistics suggest that one of the hotspots of abandoned cropland is located in the drylands of the Aral Sea Basin (ASB), which covers parts of post-Soviet Central Asia, Afghanistan and Iran. To date, the exact spatial and temporal extents of abandoned cropland remain unclear, which hampers land-use planning. Abandoned land is a potentially valuable resource for alternative land uses. Here, we mapped the abandoned cropland in the drylands of the ASB with a time series of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from [2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016]. To overcome the restricted ability of a single classifier to accurately map land-use classes across large areas and agro-environmental gradients, "stratum-specific" classifiers were calibrated and classification results were fused based on a locally weighted decision fusion approach. Next, the agro-ecological suitability of abandoned cropland areas was evaluated. The stratum-specific classification approach yielded an overall accuracy of 0.879, which was significantly more accurate (p < 0.05) than a "global" classification without stratification, which had an accuracy of 0.811. In 2016, the classification results showed that 13% (1.15 Mha) of the observed irrigated cropland in the ASB was idle (abandoned). Cropland abandonment occurred mostly in the Amudarya and Syrdarya downstream regions and was associated with degraded land and areas prone to water stress. Despite the almost twofold population growth and increasing food demand in the ASB area from 1990 to 2016, abandoned cropland was also located in areas with high suitability for farming. The map of abandoned cropland areas provides a novel basis for assessing the causes leading to abandoned cropland in the ASB. This contributes to assessing the suitability of abandoned cropland for food or bioenergy production, carbon storage, or assessing the environmental trade-offs and social constraints of recultivation.

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The effect of Landsat ETM/ETM + image acquisition dates on the detection of agricultural land abandonment in Eastern Europe

Cited by 156 publications

References 46 publications

Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery

Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery

A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran

Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series

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