ABSTRACT
Understanding the spatiotemporal dynamics of global 3D urban expansion over time is becoming increasingly crucial for achieving long-term development goals. In this study, we generated a global dataset of annual urban 3D expansion (1990-2010) using World Settlement Footprint 2015 data, GAIA data, and ALOS AW3D30 data with a three-step technical framework: (1) extracting the global constructed land to generate the research area, (2) neighborhood analysis to calculate the original normalized DSM and slope height of each pixel in the study area, and (3) slope correction for areas with a slope greater than 10° to improve the accuracy of estimated building heights. The cross-validation results indicate that our dataset is reliable in the United States(R2 = 0.821), Europe(R2 = 0.863), China(R2 = 0.796), and across the world(R2 = 0.811). As we know, this is the first 30-meter 3D urban expansion dataset across the globe, which can give unique information to understand and address the implications of urbanization on food security, biodiversity, climate change, and public well-being and health.
ABSTRACT
Farmland abandonment, a widespread phenomenon during land-use transition, leads to a cycling or vanishing evolution of farmland resources. As urbanization advances, an increasing number of agricultural laborers migrate from rural to urban areas, causing ongoing farmland abandonment. However, in contrast to the abandoned information extraction and driving mechanisms revelation, the potential risk of farmland abandonment has received insufficient attention. This study took Yangtze River Economic Belt of China as study area, selected multiple aspects to construct a risk assessment system for farmland abandonment, and applied time series change detection to verify the results. The results showed that (1) farmland abandonment risk, with a regional average value of 0.0978, has strong spatial heterogeneity, with high values clustering in Yunnan-Guizhou and Sichuan-Chongqing mountainous areas and low values distributed in the midstream and downstream plains and the Sichuan Basin. (2) The proportion of farmland area gradually decreased as the risk grade increased. Farmland, with low abandonment risk, occupied an area of 204,837 km2, constituting the highest percentage of 35.18% among the overall farmland, and was mainly distributed in the provinces of Jiangsu and Anhui. The area of farmland with high risk was 16,458 km2, only accounting for 2.83%, the majority of which was clustered in Sichuan and Yunnan provinces. (3) The Normalized Difference Vegetation Index (NDVI) time series change detection validated the reliability of the risk assessment system. Samples of farmland having low abandonment risk indeed had the lowest abandonment rate of 10%, and those which indicated high risk had the highest abandonment rate of 32%. We propose differentiated managements for farmland resources with high and low abandonment risk from the perspective of sustainable use. This study provides a more reasonable and scientific system for farmland abandonment risk assessment and helps to fill the research gap.