Agricultural Land Abandonment and Retirement Mapping in the Northern China Crop-Pasture Band Using Temporal Consistency Check and Trajectory-Based Change Detection Approach

Du, Zhenrong; Yang, Jianyu; Ou, Cong; Zhang, Tingting

doi:10.1109/tgrs.2021.3121816

Cited by 15 publications

(9 citation statements)

References 59 publications

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“…In addition, because of the presence of mixed pixels, errors in the analysis of cropland dynamics may come from the spectral similarities between cropland and grassland. While we did not explicitly test the accuracy of the CLCD in the farming-pastoral zone, previous studies have shown this possibility [5]. Despite these limitations, our results support the CLCD's potential for analyzing cropland dynamics.…”

Section: Uncertainty Of Data and Methodssupporting

confidence: 41%

“…The rational use of limited cropland resources, especially high-quality cropland for potential food supply, is essential to guarantee sustainable food security and harmonize with environmental change [4], particularly in China with a vast and growing population. As one of China's most fragile eco-climatic zones, the northern farming-pastoral zone has undergone cropland abandonment and land reclamation over several decades [5,6]. There is evidence that irrational human activities induced by population growth and urbanization, such as overgrazing, have greatly altered the ecosystem of northern China, leading to severe desertification [7].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, many Chinese researchers who rely on remote sensing technologies often evaluate cropland changes at discrete time points, instead of monitoring changes over a continuous period due to dataset update limitations that do not align with typical crop rotations [12,13]. Some studies use the annual change detection method to explore cropland abandonment or land reclamation [5,14]. Although the annual change detection method provides insight into recent land cover status, this information may differ from actual land use resulting from the cropping system.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring of Cropland Abandonment and Land Reclamation in the Farming–Pastoral Zone of Northern China

Ye,

Hu,

Feng

et al. 2024

Remote Sensing

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The farming–pastoral zone in northern China is one of the most ecologically sensitive areas globally, having experienced extensive cropland abandonment and land reclamation over decades, primarily influenced by policy adjustment and global warming. However, the spatiotemporal patterns and suitability of long-term cropland change remain poorly understood. Using the annual China land cover dataset (CLCD), we provide a cropland abandonment and land reclamation mapping approach based on actual land use processes (rather than land cover conditions) to investigate spatiotemporal features of abandonment and reclamation and evaluate the rationality. Our findings show that: (1) Returning farmland to forest and grassland has been a clear trend in the study area over the past 30 years. Specifically, cropland use has undergone three phases of change, i.e., cropland contraction and expansion alternately (before 2000), followed by substantial abandonment (after 2000), and low-intensity reclamation (after 2010). (2) In the last decade, the intensity of the abandonment of cropland with high and moderate suitability is low. The rate of abandonment decreased, while the intensity of land reclamation was relatively high. The rate of the reclamation increased, and the spatial distribution of cropland tended to be reasonable. Our study emphasizes the importance of monitoring actual cropland changes based on land use processes, and this method can be effectively extended to regional or global long-term cropland monitoring.

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Section: Uncertainty Of Data and Methodssupporting

confidence: 41%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring of Cropland Abandonment and Land Reclamation in the Farming–Pastoral Zone of Northern China

Ye,

Hu,

Feng

et al. 2024

Remote Sensing

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“…In this way, it is essentially the classification of the LULC types in multiple years. Based on the classification results, certain rules are designed to discriminate the cropland that has changed to other types as abandoned cropland [6]. In addition to hard classification, some studies have improved the time trajectory-based methods into soft classification, that is to perform change detection algorithms such as LandTrendr [7] and CCDC [8], using the time curves of classification probability of cropland component [9].…”

Section: Under the Rapid Expansion Of Urbanization Growing Problemsmentioning

confidence: 99%

Statistical Texture Learning Method for Monitoring Abandoned Suburban Cropland Based on High-Resolution Remote Sensing and Deep Learning

Shen

Deng

Wen

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Cropland abandonment is crucial in agricultural management and has a profound impact on crop yield and food security. In recent years, many cropland abandonment identification methods based on remote sensing observation data have been proposed, but most of these methods are based on coarse-resolution images and use traditional machine learning methods for simple identification. To this end, we perform abandonment recognition on high-resolution remote sensing images. According to the texture features of the abandoned land, we combine the method of statistical texture learning and propose a new deep learning framework called PSPNet-STL. The model integrates high-level semantic feature extraction and deep mining of low-level texture features to identify cropland abandonment. First, we labeled the abandoned cropland area and built the HRAC dataset, a high-resolution cropland abandonment dataset. Secondly, we improved PSPNet by fusing statistical texture learning modules to learn multiple texture information on lowlevel feature maps, and combined high-level semantic features for cropland abandonment recognition. Experiments are performed on the HRAC dataset. Compared with other methods, the proposed model has the best performance on this dataset, both in terms of accuracy and visualization, proving that deep mining of low-level statistical texture features is beneficial for crop abandonment recognition.

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“…It has been recently reported that abandonment can be ephemeral [23], particularly in cropland-scarce regions. Hence, obtaining an improved understanding of the dynamics of cropland abandonment and recultivation is crucial in order to assess its impact on the delivery of multiple ecosystem services and support policy-makers in developing sustainable solutions for the future [24,25].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Spatiotemporal Dynamics of Cropland Abandonment and Recultivation across the Yangtze River Basin

Long,

Sun,

Wellens

et al. 2024

Remote Sensing

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Whether China can achieve the United Nations’ Sustainable Development Goals (SDGs) largely depends on the ability of main food-producing areas to cope with multiple land use change challenges. Despite the fact that the Yangtze River basin is one of the key regions for China’s food security, the spatiotemporal dynamics of cropland abandonment and recultivation remain largely unexplored in this region. The present study assesses the evolution of the agricultural system within the Yangtze River basin between 2000 and 2020 by mapping cropland abandonment and recultivation using MODIS time series and multiple land cover products. The results highlight a widespread cropland abandonment process (i.e., 10.5% of the total study area between 2000 and 2020), predominantly in Western Sichuan, Eastern Yunnan, and Central Jiangxi. Although 70% of abandoned cropland is situated in areas with slopes less than 5°, the highest rates of abandonment are in mountainous regions. However, by 2020, 74% of this abandoned cropland had been recultivated at least once, whereas half of the abandoned croplands got recultivated within three years of their initial abandonment. Hence, as this is one of the first studies that unravels the complex interaction between cropland abandonment and recultivation in a spatiotemporal explicit context, it offers (i) scientists a novel methodological framework to assess agricultural land use issues across large geographical entities, and (ii) policy-makers new insights to support the sustainable transition of the agricultural sector.

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Agricultural Land Abandonment and Retirement Mapping in the Northern China Crop-Pasture Band Using Temporal Consistency Check and Trajectory-Based Change Detection Approach

Cited by 15 publications

References 59 publications

Monitoring of Cropland Abandonment and Land Reclamation in the Farming–Pastoral Zone of Northern China

Monitoring of Cropland Abandonment and Land Reclamation in the Farming–Pastoral Zone of Northern China

Statistical Texture Learning Method for Monitoring Abandoned Suburban Cropland Based on High-Resolution Remote Sensing and Deep Learning

Mapping the Spatiotemporal Dynamics of Cropland Abandonment and Recultivation across the Yangtze River Basin

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