Variations in soil organic carbon storage and stability with vegetation restoration stages on the Loess Plateau of China

Wang, Anning; Zha, Tonggang; Zhang, Zhiqiang

doi:10.1016/j.catena.2023.107142

Cited by 6 publications

(1 citation statement)

References 75 publications

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“…Thus, accurate and detailed LULC data are helpful not only in understanding the LULC changes induced by human activities in the basin and their accompanying ecological and environmental effects but also in understanding the results of major ecological implementation. Additionally, such data are primary data for studies on biodiversity, sustainable resource use and soil carbon stocks (Watson et al, 2001;Yang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Which land cover product provides the most accurate land use land cover map of the Yellow River Basin?

Zhang,

Tian,

Zhang

et al. 2023

Front. Ecol. Evol.

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Precise land use land cover (LULC) data are essential for understanding the landscape structure and spatial pattern of land use/cover in the Yellow River Basin (YRB) to regulate scientific and rational territorial spatial planning and support sustainable development. However, differences in the multiple sets of LULC products in portraying the land composition of the YRB limit our understanding of the land cover composition in this region. To address this issue, this study chose five sets of open and high spatiotemporal LULC data in 2020, namely, CLCD, LSV10, ESRI10, CLC_FCS30, and Globeland30, to evaluate the accuracy and consistency of classification in the YRB. Our results show that: (1) The LULC composition of the YRB in 2020 was mapped consistently by the five datasets. Grasslands, croplands, and woodlands constitute the major LULC types, accounting for 96% of the total area of the study area. (2) The correlation coefficients of the LULC types of any two of the five datasets ranged from 0.926 to 0.998, showing high land compositional consistency. However, among the five datasets, there were considerable differences in the areas of a single LULC type. (3) The classification consistencies of croplands, woodlands, grasslands, and water bodies were higher than 60% in any two datasets. The spatial consistencies of grasslands, croplands, and woodlands were higher than those of other LULC types. An area with better consistency can reach more than 50% of the average area of the corresponding land types, but grasslands were mixed with other LULC types in ESRI10 and GLC_FCS30. (4) According to the accuracy assessments, LSV10 data have the highest overall classification accuracy, 79.32%, and the classification accuracy of major land types is also higher than 70%; GLC_FCS30 data have the lowest overall accuracy, 70.14%. Based on these results, LSV10 can more accurately demonstrate LULC than the other four datasets. This study can be used as a reference for selecting land cover data, and it also highlights that the necessary assessments of consistency and accuracy are essential when conducting land use/cover change studies in a specific region.

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Section: Introductionmentioning

confidence: 99%