Comparison and assessment of coarse resolution land cover maps for Northern Eurasia

Pflugmacher, Dirk; Krankina, Olga N.; Cohen, Warren B.; Friedl, M. A.; Sulla‐Menashe, Damien; Kennedy, Robert E.; Nelson, Peter O.; Loboda, Tatiana V.; Kuemmerle, Tobias; Dyukarev, E. A.; Elsakov, Vladimir; Харук, В. И.

doi:10.1016/j.rse.2011.08.016

Cited by 80 publications

(60 citation statements)

References 52 publications

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“…The updated data are for years [2005][2006], and 2009, including two images. With a nominal pixel size of 300-m × 300-m, GlobCover represents the highest spatial resolution global land cover dataset currently available [29]. The overall accuracy of GlobCover data weighted by the class area reaches 67.5% [48] and 73% [49].…”

Section: Validation Approachmentioning

confidence: 99%

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Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Zhao

Zhou

et al. 2013

Forests

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Abstract:Forests are one of the most important components of the global biosphere and have critical influences on the Earth's ecological balance. Regularly updated forest cover information is necessary for various forest management applications as well as climate modeling studies. However, map products are not widely updated at continental or national scales because the current land cover products have overly coarse spatial resolution or insufficiently large training data sets. This study presents the results of forests distribution and variation information over China using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data with the first layer of MODIS Land Cover Type product (MODIS LC-1). The NDVI time series histogram characteristic curves for forestland were estimated from MODIS LC-1 and MODIS NDVI time series data.

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Section: Validation Approachmentioning

confidence: 99%

“…Kennedy [22] built a trajectory-based change detection method for automated characterization of forest disturbance dynamics. Then LandTrendr [23] and TimeSync [24] were further developed to detect trends in forest disturbance and recovery using yearly Landsat time series and applied widely [25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Zhao

Zhou

et al. 2013

Forests

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“…At the same time, a fusion of machine-learning classifiers based on strata (e.g., soil types, landforms, administrative boundaries and further stratum-specific classifiers) may boost classification accuracy, particularly where one classifier is not able to accurately separate classes [36]. However, maps created with different classification methods but with similar accuracies may yield spatial disagreement of classified patterns, i.e., errors are rarely equally distributed in a map [49][50][51]. Thus, a major challenge for mapping large-scale, abandoned cropland is to achieve spatial continuity and consistency in the final map.…”

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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“…Although they offer valuable information on LULC status, there are still important challenges for assessing and comparing these datasets, especially considering the discrepancies among these products and the different classification schemes applied (Jung et al, 2006). Moreover, even though there are several scientific papers assessing and comparing the classification accuracy of the available global land cover datasets (Brovelli et al, 2015;Büttner et al, 2012;EEA, 2006;Fritz and See, 2005;Giri et al, 2005;Jung et al, 2006;Kaptué Tchuenté et al, 2011;McCallum et al, 2006;Mora et al, 2014;Pflugmacher et al, 2011), there are few effective and user-friendly tools for visually comparing their similarities and discrepancies (EEA, 2016;ESRI Ireland, 2011;NGCC, 2016).…”

Section: Introductionmentioning

confidence: 99%

Land User and Land Cover Maps of Europe: A Webgis Platform

Brovelli

Fahl

Minghini

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents the methods and implementation processes of a WebGIS platform designed to publish the available land use and land cover maps of Europe at continental scale. The system is built completely on open source infrastructure and open standards. The proposed architecture is based on a server-client model having GeoServer as the map server, Leaflet as the client-side mapping library and the Bootstrap framework at the core of the front-end user interface. The web user interface is designed to have typical features of a desktop GIS (e.g. activate/deactivate layers and order layers by drag and drop actions) and to show specific information on the activated layers (e.g. legend and simplified metadata). Users have the possibility to change the base map from a given list of map providers (e.g. OpenStreetMap and Microsoft Bing) and to control the opacity of each layer to facilitate the comparison with both other land cover layers and the underlying base map. In addition, users can add to the platform any custom layer available through a Web Map Service (WMS) and activate the visualization of photos from popular photo sharing services. This last functionality is provided in order to have a visual assessment of the available land coverages based on other user-generated contents available on the Internet. It is supposed to be a first step towards a calibration/validation service that will be made available in the future.

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Comparison and assessment of coarse resolution land cover maps for Northern Eurasia

Cited by 80 publications

References 52 publications

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

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

Land User and Land Cover Maps of Europe: A Webgis Platform

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