Spatiotemporal Patterns and Determinants of Cropland Abandonment in Mountainous Regions of China: A Case Study of Sichuan Province

Land, Год журнала: 2025, Номер 14(3), С. 647 - 647

Опубликована: Март 18, 2025

Cropland abandonment (CA) is an increasingly severe global issue, with significant implications for achieving the Sustainable Development Goal of Zero Hunger. In China, widespread CA particularly evident in remote mountainous regions. However, rugged terrain and highly fragmented cropland pose challenges mapping abandoned high precision using sensing technology. Moreover, result multi-level factors, yet previous studies have primarily analyzed its driving factors from a single level, leading to lack comprehensive understanding underlying mechanisms. We took Sichuan Province, located regions Western as case study, utilizing satellite-derived high-precision maps reveal spatiotemporal patterns CA. Additionally, we employed hierarchical linear model explore determinants their interactions at both county municipal levels. The results indicate that rate decreased continuously 6.75% 2019 4.47% 2023, exhibiting spatial clustering. High-value clusters were predominantly concentrated western areas, hotspots exhibited general migration trend northeast southwest. Furthermore, found influenced by 61% 39% variance being explained levels, respectively. agglomeration index (AI) key determinant Digital Elevation Model (DEM) distance roads also playing roles. At urbanization proportion non-agricultural employment (PNAE) are dominant increase PNAE weakens negative impact AI on rates. To curb recommend implementing land consolidation projects, improving rural transfer markets, strengthening legal mechanisms combat Our study has broad application prospects, providing critical support assessing ecological environmental consequences exploring potential reutilizing food production, bioenergy, carbon sequestration.

Язык: Английский

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Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model

Remote Sensing, Год журнала: 2025, Номер 17(5), С. 787 - 787

Опубликована: Фев. 24, 2025

Change detection of cultivated land parcels is critical for achieving refined management farmland. However, existing change methods based on high-resolution remote sensing imagery focus primarily cultivation type changes, neglecting the importance detecting parcel pattern changes. To address issue diverse types changes in parcels, this study constructs an automated workflow framework detection, unsupervised segmentation method SAM (Segment Anything Model). By performing spatial connection analysis units extracted by two phases and combining multiple features such as texture (GLCM), multi-scale structural similarity (MS-SSIM), normalized difference vegetation index (NDVI), precise identification areas was achieved. The results show that proposed achieved highest accuracy plain (precision: 78.79%, recall: 79.45%, IOU: 78.44%), confirming effectiveness method. This provides efficient low-cost distinction analyzing patterns using images, which can be directly applied real-world scenarios. significantly enhances automation timeliness unit offering important applications advancing precision agriculture sustainable resource management.

Язык: Английский

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The impact of high-standard farmland construction on farmland abandonment by farm households: evidence from rural China

Applied Economics, Год журнала: 2025, Номер unknown, С. 1 - 17

Опубликована: Март 7, 2025

Язык: Английский

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Evaluation of farmland production potential in key agricultural production areas on the Qinghai-Tibet Plateau under multi-scenario simulation

The Science of The Total Environment, Год журнала: 2024, Номер 951, С. 175741 - 175741

Опубликована: Авг. 23, 2024

Язык: Английский

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A deep learning classification framework for research methods of marine protected area management

Journal of Environmental Management, Год журнала: 2024, Номер 368, С. 122228 - 122228

Опубликована: Авг. 24, 2024

The latest emerging transdisciplinary marine protected area (MPA) research scheme requires efficient approaches for theoretically based and data-driven method integration. However, due to the rapid development diversification of methods, it is growingly difficult locate new methods in methodological dimensions integrate them utmost utility. This study proposes a deep learning-based classification framework MPA management focused particularly on data theory capabilities using natural language processing (NLP). It extracted keywords from academic sources performed clustering semantic similarity, generating benchmark texts abstract labeling. By training learning NLP model analyzing abstracts 9049 empirical articles 1986 2024, scores were attributed each article, total 19 major categories 110 segment branches identified qualitative, quantitative, mixed genres. Combination types summarized, yielding data-theory neutralization principle where average tend approximate 0.50. Applying broadens traditional boundaries integration extends synthesis higher numbers, practical 2paradigm future research. Implications include bridging social ecological data, theorizing emergent challenges complex systems integrating construction science. applicable quantification other environmental disciplines can serve as guidance multidisciplinary © 2017 Elsevier Inc. All rights reserved.

Язык: Английский

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Morphology's importance for farmland landscape pattern assessment and optimization: A case study of Jiangsu, China

Applied Geography, Год журнала: 2024, Номер 171, С. 103364 - 103364

Опубликована: Авг. 13, 2024

Язык: Английский

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