Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites

Griffiths, Patrick; Kuemmerle, Tobias; Baumann, Matthias; Radeloff, Volker C.; Abrudan, I. V.; Lieskovský, Juraj; Munteanu, Catalina; Ostapowicz, Katarzyna; Hostert, Patrick

doi:10.1016/j.rse.2013.04.022

Cited by 250 publications

(155 citation statements)

References 58 publications

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“…After the succession of agricultural production, shrubs and grasses encroached on abandoned fields-or fields remained bare and devoid of vegetation due to salinization ( Figure 1). Due to a water shortage (e.g., droughts) or farmers' decisions, intermitted fallow periods may be longer than under the current management practices [26] but such fields do not necessarily meet the given definition of abandoned. Abandoned fields are characterized by a typical and recognizable vegetation succession, starting with the recovery of annual and multiyear herbaceous species [8,38,39], perennial woody species such as shrubs (e.g., black saxaul) and some trees [7].…”

Section: Definition Of Cropland Abandonmentmentioning

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%

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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: Definition Of Cropland Abandonmentmentioning

confidence: 99%

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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show abstract

“…This resulted in three raster data stacks that contained three to four monthly composites representing the study area in one of the two dry seasons and at least one of the two wet seasons. Such seasonal composites representing key phenological time windows can be helpful for separating certain land cover and land use classes in a reliable way [49,50].…”

Section: Study Areamentioning

confidence: 99%

Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective

et al. 2017

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This study spatially assesses, quantifies, and visualizes the agricultural expansion and land use intensification in the northwestern foothills of Mount Kenya over the last 30 years: processes triggered by population growth, and, more recently, by large-scale commercial investments. We made use of Google Earth Engine to access the USGS Landsat data archive and to generate cloud-free seasonal composites. These enabled us to accurately differentiate between rainfed and irrigated cropland, which was important for assessing agricultural intensification. We developed three land cover and land use classifications using the random forest classifier, and assessed land cover and land use change by creating cross-tabulation matrices for the intervals from 1987 to 2002, 2002 to 2016, and 1987 to 2016 and calculating the net change. We then applied a landscape mosaic approach to each classification to identify landscape types categorized by land use intensity. We discuss the impacts of landscape changes on natural habitats, biodiversity, and water. Kappa accuracies for the three classifications lay between 78.3% and 82.1%. Our study confirms that rainfed and irrigated cropland expanded at the expense of natural habitats, including protected areas. Agricultural expansion took place mainly in the 1980s and 1990s, whereas agricultural intensification largely happened after 2000. Since then, not only large-scale producers, but also many smallholders have begun to practice irrigated farming. The spatial pattern of agricultural expansion and intensification in the study area is defined by water availability. Agricultural intensification and the expansion of horticulture agribusinesses increase pressure on water. Furthermore, the observed changes have heightened pressure on pasture and idle land due to the decrease in natural and agropastoral landscapes. Conflicts between pastoralists, smallholder farmers, large-scale ranches, and wildlife might further increase, particularly during the dry seasons and in years of extreme drought.

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“…Many previous studies on forest disturbance assessments have focused on coniferous forests and mixed evergreen and deciduous forests in North America [9,[17][18][19]24,48,53,54]. In the subtropical forest regions of China, detection of forest disturbance and recovery has not received much attention.…”

Section: Introductionmentioning

confidence: 99%

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Liu

Wei

et al. 2017

Remote Sensing

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Detection of forest disturbance and recovery has received much attention during the last two decades due to its important influence on forest carbon budget estimation. This research used Landsat time-series data from 1984 to 2015 to examine forest disturbance and recovery in a subtropical region of eastern Zhejiang Province, China, through the LandTrendr algorithm. Field inventory data and high spatial resolution images were used to evaluate the disturbance and recovery results. This research indicates that high producer and user accuracies for both disturbance and recovery classes were obtained and three levels of disturbance and recovery each can be detected. Through incorporation of climate data and disturbance results, drought events also can be successfully detected. More research is needed to incorporate multisource data for detection of forest disturbance types in subtropical regions.

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Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites

Cited by 250 publications

References 58 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

Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

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