Mapping cropland abandonment and distinguishing from intentional afforestation with Landsat time series
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Mapping cropland abandonment and distinguishing from intentional afforestation with Landsat time series. / Hong, Changqiao; Prishchepov, Alexander V.; Jin, Xiaobin; Zhou, Yinkang.
In: International Journal of Applied Earth Observation and Geoinformation, Vol. 127, 103693, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Mapping cropland abandonment and distinguishing from intentional afforestation with Landsat time series
AU - Hong, Changqiao
AU - Prishchepov, Alexander V.
AU - Jin, Xiaobin
AU - Zhou, Yinkang
N1 - Publisher Copyright: © 2024 The Author(s)
PY - 2024
Y1 - 2024
N2 - Detecting cropland abandonment in a timely manner is essential to unlock the potential of such abandoned lands, for instance, to alleviate world hunger and steer environmental restoration. However, the challenge remains how to separate cropland abandonment from spectrally similar land-cover change trajectories, such as intentional afforestation (i.e., tree plantations) on former agricultural lands. Taking the South Sichuan province of China as a study area, this study developed a new approach by integrating land-cover change trajectories mapped using the random forest classifier and LandTrendr, as well as NDVI change based on Landsat time series to reveal abandoned cropland from 2003 to 2018. Results showed that the developed methodology could help to distinguish cropland abandonment with 76% producer's and 80% user's accuracies. The study showed that, by 2018, 0.37 million ha (approximately 15.54%) of previously cultivated land became truly abandoned, and 0.53 million ha (approximately 22.27%) of previously cultivated land became intentionally afforested. Annual abandonment rates were high at the beginning of the study period and low by 2018. Overall, our study highlights how the magnitude and pace of NDVI change helped to distinguish abandoned cropland from other land uses, such as intentional afforestation. The method can be adapted to map cropland abandonment accurately elsewhere; thus, our results can assist in evaluating land-use policies which aimed at guiding the cropland abandonment process.
AB - Detecting cropland abandonment in a timely manner is essential to unlock the potential of such abandoned lands, for instance, to alleviate world hunger and steer environmental restoration. However, the challenge remains how to separate cropland abandonment from spectrally similar land-cover change trajectories, such as intentional afforestation (i.e., tree plantations) on former agricultural lands. Taking the South Sichuan province of China as a study area, this study developed a new approach by integrating land-cover change trajectories mapped using the random forest classifier and LandTrendr, as well as NDVI change based on Landsat time series to reveal abandoned cropland from 2003 to 2018. Results showed that the developed methodology could help to distinguish cropland abandonment with 76% producer's and 80% user's accuracies. The study showed that, by 2018, 0.37 million ha (approximately 15.54%) of previously cultivated land became truly abandoned, and 0.53 million ha (approximately 22.27%) of previously cultivated land became intentionally afforested. Annual abandonment rates were high at the beginning of the study period and low by 2018. Overall, our study highlights how the magnitude and pace of NDVI change helped to distinguish abandoned cropland from other land uses, such as intentional afforestation. The method can be adapted to map cropland abandonment accurately elsewhere; thus, our results can assist in evaluating land-use policies which aimed at guiding the cropland abandonment process.
KW - Biomass change
KW - Cropland abandonment
KW - Land-cover probability
KW - Random forest
KW - Remote sensing
U2 - 10.1016/j.jag.2024.103693
DO - 10.1016/j.jag.2024.103693
M3 - Journal article
AN - SCOPUS:85183935173
VL - 127
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
SN - 1569-8432
M1 - 103693
ER -
ID: 389592732