Mapping abandoned agricultural land in Kyzyl-Orda, Kazakhstan using satellite remote sensing

Löw, Fabian; Fliemann, Elisabeth; Abdullaev, Iskandar; Conrad, Christopher; Lamers, John P. A.

doi:10.1016/j.apgeog.2015.05.009

Cited by 81 publications

(64 citation statements)

References 63 publications

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“…This region experienced precipitous population growth in the mentioned countries, increasing from 61 million people in 1990 to 104 million by 2016; the rural population is strongly dependent on the domestic agricultural production [6]. Despite increasing food demand, official statistics and exemplary case studies with satellite imagery suggest that agricultural land abandonment is common in this region and most likely occurs on degraded, irrigated agricultural lands [7][8][9][10]. Due to economic restructuring, agricultural land abandonment primarily occurs in the downstream Amudarya and Syrdarya River basins resembling Soviet land-use legacies.…”

Section: Introductionmentioning

confidence: 99%

“…Machine-learning classifiers were found to be particularly useful to overcome the complexity related to the accurate separation of spectrally similar classes, such as abandoned cropland and cultivated cropland in drylands [36], particularly at the early stages of abandonment [7,29,33,37]. Vegetation recovery on abandoned irrigated fields in the drylands of the ASB generally follows a certain pathway.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, since fallow periods can be part of the typical crop rotation cycle, it is important to assess several consecutive years to determine whether a field has effectively been abandoned or if it is awaiting future use [7]. Using regular, consecutive image time series such as those provided by MODIS helps avoid misclassification of temporarily fallow-crop rotation [7,29].…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…Vegetation recovery on abandoned irrigated fields in the drylands of the ASB generally follows a certain pathway. It starts with the recovery of annual and multiyear herbaceous species [8,38,39] and gradually, perennial woody species such as shrubs (e.g., black saxaul) and some trees [7] can establish. Depending on time and hydrological and soil properties, bare areas without any vegetation change could also be observed [8,24].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Löw¹,

Prishchepov

Waldner

et al. 2018

Remote Sensing

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The fate of rangelands: Revealing past and predicting future land‐cover transitions from 1985 to 2036 in the drylands of Central Iran

et al. 2021

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Land serves as a vital production resource, and therefore land planning plays an important role in sustainable land‐use design. Iran includes large areas of the world's drylands, many of which are used as rangelands, and where the population has increased by 60% from 1985 to 2016. Further population increase in Iran would probably require more land resources to be allocated for human needs. However, the pace and patterns of these changes remain unclear. The aim of this study was to map land‐cover change from 1985 to 2016 and predict future land‐cover change in the Zayandehrood ecologic sub‐basins of Central Iran. By using multiseasonal LANDSAT imagery, we mapped land‐cover change with a Random Forest classifier for 1985, 1998, and 2016 with an overall accuracy of 80% for each period. Classification results revealed that from 1985 to 2016 residential areas doubled and industrial areas increased at the expense of rangelands. Our study also revealed cropland expansion at the expense of rangelands, cropland abandonment, and contraction of croplands due to residential and industrial development. Prediction of changes by 2036 with a multilayer perceptron neural network and Markov chain analysis showed further expansion of industries and residencies, particularly near the Ghamashlu Wildlife Refuge. Despite the partial restoration of rangelands in some parts of the world, our study provides evidence of ongoing rangeland degradation in Iran due to conversion to other land uses. Therefore, the study underscores the importance of sustainable land management of already cultivated areas and the development of strategies for the protection of global rangelands.

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Multiple trajectories of grassland fragmentation, degradation, and recovery in Russia's steppes

et al. 2021

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Over the 20th century, the Eurasian steppes underwent drastic land-cover changes.Much progress was made studying cropland expansion and the post-1990 (i.e., post-Soviet) agricultural land abandonment in Eurasia. However, the alteration of steppe landscapes may include other disturbances, such as oil and gas development, formal and informal roads and garbage dumps, which were not systematically documented.Considering the example of the steppe Orenburg Province in Russia, we reconstructed agricultural land-cover change dynamics using Landsat and Sentinel-2 imagery from 1990 to 2018. Furthermore, we used very high-resolution imagery and assessed the patterns and determinants of other steppe landscape anthropogenic disturbances. Our study showed that, despite steppe recovery due to widespread cropland abandonment from 1990 to 2018, the steppes, including the recovered steppe patches, underwent fragmentation due to informal roads, oil and gas development, shrub encroachment, garbage dumps and quarries, as well as abandonment of settlements and buildings. Only 6.4% of the sampled 7859 1Â1 km blocks in 2018 showing grassland extent had no documented disturbances. The mapped disturbances occurred primarily near settlements and roads, while some disturbances occurred in remote areas. Given the accessibility of steppes, our study calls for a urgent need to systematically document alternatives to agricultural land uses in the steppes of Eurasia and other parts of the global grassland biome.

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Mapping abandoned agricultural land in Kyzyl-Orda, Kazakhstan using satellite remote sensing

Cited by 81 publications

References 63 publications

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

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

The fate of rangelands: Revealing past and predicting future land‐cover transitions from 1985 to 2036 in the drylands of Central Iran

Multiple trajectories of grassland fragmentation, degradation, and recovery in Russia's steppes

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