An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Vu-Dong Pham, Farina de Waard, Fabian Thiel, Bernd Bobertz, Christina Hellmann, Duc-Viet Nguyen, Felix Beer, M Arasumani, Marcel Schwieder, Jörg Hartleib, David Frantz, Sebastian van der Linden
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引用次数: 0

Abstract

We present detailed annual land cover maps for the Baltic Sea region, spanning more than two decades (2000-2022). The maps provide information on eighteen land cover (LC) classes, including eight general LC types, eight major crop types and grassland, and two peat bog-related classes. Our maps represent the first homogenized annual dataset for the region and address gaps in current land use and land cover products, such as a lack of detail on crop sequences and peat bog exploitation. To create the maps, we used annual multi-temporal remote sensing data combined with a data encoding structure and deep learning classification. We obtained the training data from publicly available open datasets. The maps were validated using independent field survey data from the Land Use/Cover Area Frame Survey (LUCAS) and expert annotations from high-resolution imagery. The quantitative and qualitative results of the maps provide a reliable data source for monitoring agricultural transformations, peat bog exploitation, and restoration activities in the Baltic Sea region and its surrounding countries.

波罗的海地区年度土地覆被数据集,包含 2000 年至 2022 年 30 米处的作物类型和泥炭沼泽。
我们提供了波罗的海地区详细的年度土地覆被图,时间跨度超过 20 年(2000-2022 年)。这些地图提供了 18 个土地覆被等级的信息,包括 8 个一般土地覆被类型、8 个主要作物类型和草地,以及 2 个泥炭沼泽相关等级。我们的地图代表了该地区首个同质化的年度数据集,弥补了当前土地利用和土地覆被产品的不足,例如缺乏有关作物序列和泥炭沼开采的详细信息。为了绘制地图,我们使用了年度多时相遥感数据,并结合数据编码结构和深度学习分类。我们从公开的开放数据集中获取训练数据。我们利用土地利用/覆盖区框架调查(LUCAS)中的独立实地调查数据和高分辨率图像中的专家注释对地图进行了验证。地图的定量和定性结果为监测波罗的海地区及其周边国家的农业转型、泥炭沼泽开发和恢复活动提供了可靠的数据源。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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