Mapping the spatial and temporal patterns of fallow land in mountainous regions of China

Research output: Contribution to journal › Journal article › Research › peer-review

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Mapping the spatial and temporal patterns of fallow land in mountainous regions of China. / Song, Wen; Prishchepov, Alexander V.; Song, Wei.

In: International Journal of Digital Earth, Vol. 15, No. 1, 2022, p. 2148–2167.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Song, W, Prishchepov, AV & Song, W 2022, 'Mapping the spatial and temporal patterns of fallow land in mountainous regions of China', International Journal of Digital Earth, vol. 15, no. 1, pp. 2148–2167. https://doi.org/10.1080/17538947.2022.2148765

APA

Song, W., Prishchepov, A. V., & Song, W. (2022). Mapping the spatial and temporal patterns of fallow land in mountainous regions of China. International Journal of Digital Earth, 15(1), 2148–2167. https://doi.org/10.1080/17538947.2022.2148765

Vancouver

Song W, Prishchepov AV, Song W. Mapping the spatial and temporal patterns of fallow land in mountainous regions of China. International Journal of Digital Earth. 2022;15(1):2148–2167. https://doi.org/10.1080/17538947.2022.2148765

Author

Song, Wen ; Prishchepov, Alexander V. ; Song, Wei. / Mapping the spatial and temporal patterns of fallow land in mountainous regions of China. In: International Journal of Digital Earth. 2022 ; Vol. 15, No. 1. pp. 2148–2167.

Bibtex

@article{8496f1c8bcaa426c980318b3f757fcac,
title = "Mapping the spatial and temporal patterns of fallow land in mountainous regions of China",
abstract = "The rapid growth of the global population has resulted in a continuous increase in cropland intensity and a shortening of the fallow period as part of the cropland rotation cycle. Yet, there is a lack of systematic knowledge on the extent of fallow lands, particularly in complex landscapes, such as the mountainous regions of China. To fill this knowledge gap, taking Yuanyang County (YYC), Yunnan Province, China, as a case study, we tested a method to identify the spatial-temporal distribution of fallow land by mapping cropland with Landsat data. The overall accuracy of land cover classification, including cropland, ranged between 90.1% and 95.8% from 1998 to 2019. The average accuracy of fallow plots was 75.7% from 2001 to 2019. The annual fallow rate varied between 8.3% and 54.3%, with an average of 20.7%. Kernel density estimated with the probability density function showed that fallow varied between 5 and 13 blocks per km2, gradually decreasing from the central area to the periphery. Increasing elevation, the low value of regional domestic products, and the increased distance to rural settlements were closely related to the higher proportions of fallow land. The approach presented here can be applied to map fallow land in other regions.",
author = "Wen Song and Prishchepov, {Alexander V.} and Wei Song",
year = "2022",
doi = "10.1080/17538947.2022.2148765",
language = "English",
volume = "15",
pages = "2148–2167",
journal = "International Journal of Digital Earth",
issn = "1753-8947",
publisher = "Taylor & Francis",
number = "1",

}

RIS

TY - JOUR

T1 - Mapping the spatial and temporal patterns of fallow land in mountainous regions of China

AU - Song, Wen

AU - Prishchepov, Alexander V.

AU - Song, Wei

PY - 2022

Y1 - 2022

N2 - The rapid growth of the global population has resulted in a continuous increase in cropland intensity and a shortening of the fallow period as part of the cropland rotation cycle. Yet, there is a lack of systematic knowledge on the extent of fallow lands, particularly in complex landscapes, such as the mountainous regions of China. To fill this knowledge gap, taking Yuanyang County (YYC), Yunnan Province, China, as a case study, we tested a method to identify the spatial-temporal distribution of fallow land by mapping cropland with Landsat data. The overall accuracy of land cover classification, including cropland, ranged between 90.1% and 95.8% from 1998 to 2019. The average accuracy of fallow plots was 75.7% from 2001 to 2019. The annual fallow rate varied between 8.3% and 54.3%, with an average of 20.7%. Kernel density estimated with the probability density function showed that fallow varied between 5 and 13 blocks per km2, gradually decreasing from the central area to the periphery. Increasing elevation, the low value of regional domestic products, and the increased distance to rural settlements were closely related to the higher proportions of fallow land. The approach presented here can be applied to map fallow land in other regions.

AB - The rapid growth of the global population has resulted in a continuous increase in cropland intensity and a shortening of the fallow period as part of the cropland rotation cycle. Yet, there is a lack of systematic knowledge on the extent of fallow lands, particularly in complex landscapes, such as the mountainous regions of China. To fill this knowledge gap, taking Yuanyang County (YYC), Yunnan Province, China, as a case study, we tested a method to identify the spatial-temporal distribution of fallow land by mapping cropland with Landsat data. The overall accuracy of land cover classification, including cropland, ranged between 90.1% and 95.8% from 1998 to 2019. The average accuracy of fallow plots was 75.7% from 2001 to 2019. The annual fallow rate varied between 8.3% and 54.3%, with an average of 20.7%. Kernel density estimated with the probability density function showed that fallow varied between 5 and 13 blocks per km2, gradually decreasing from the central area to the periphery. Increasing elevation, the low value of regional domestic products, and the increased distance to rural settlements were closely related to the higher proportions of fallow land. The approach presented here can be applied to map fallow land in other regions.

U2 - 10.1080/17538947.2022.2148765

DO - 10.1080/17538947.2022.2148765

M3 - Journal article

VL - 15

SP - 2148

EP - 2167

JO - International Journal of Digital Earth

JF - International Journal of Digital Earth

SN - 1753-8947

IS - 1

ER -

ID: 326488649