Lijun Zuo

Accurate delineation of urban-rural boundaries is fundamental for understanding urbanization dynamics and informing sustainable land-use and ecological governance. Yet most existing approaches suffer from limited accuracy and oversimplification of fragmented urban edges. To address these limitations during China’s rapid urbanization, we propose a high-precision framework for nationwide boundary extraction by integrating multi-source remote sensing data. Specifically, we construct a Nighttime Light Adjusted Urban Impervious Surface Index (NAIUI) that fuses impervious surface density with nighttime light intensity to enhance boundary delineation. Applied to China from 1985 to 2020, our approach reveals that urban built-up areas expanded from 22,165 km ² to 143,283 km ² (annual growth of 3,461 km ² ), with marked east-west heterogeneity. Validation shows an overall accuracy of 94.9% and a Kappa coefficient of 81.5%, outperforming existing national-scale products. By providing more reliable, fine-scale boundaries over long time series, the framework can support evidence-based spatial planning, infrastructure allocation, and ecological management, and it offers a practical platform for future integration with deep-learning methods to achieve near real-time monitoring.

In a context of economic growth and increasing urban populations, assessing the impact of urban-cropland interactions on crop production in developed countries offers insights about future global scenarios. We quantitatively assessed the impact of urbanization on crop production in the United States since 2000 by combining earth observation products and crop modelling. We found that urbanization has appropriated 2.1 M ha of highly productive cropland, leading to an overall decline in U.S. cropland area over time and a production loss of 15 million tons of maize, soybean, and wheat. Avoiding the negative impacts of urbanization on crop production and the environment will require proper land-use planning and urban design and yield intensification on existing cropland.

The inherent spatial heterogeneity of land types often leads to a class imbalance in remote sensing-based classification, reducing the accuracy of minority class detection. Consequently, current land use datasets are often inadequate for the specific needs of soil erosion studies. In response to the need for soil conservation in dry–hot valley regions, this study integrated multi-source remote sensing imagery and constructed three high-precision imbalanced sample datasets on the Google Earth Engine (GEE) platform to perform land use classification. The degree of class imbalance was quantified using the imbalance ratio (IR), and the impact of sample imbalance on the classification accuracy of different land use types in a typical dry–hot valley was analyzed. The results show that (1) Feature selection significantly improved both classification accuracy and computational efficiency. The period from February to April each year, between 2018 and 2023, was identified as the optimal time window for land use classification in dry–hot valleys. (2) Constructing composite images over longer time scales enhanced classification performance: using a 2020 annual composite image combined with a Gradient Tree Boosting classifier yielded the highest accuracy, indicating that longer temporal synthesis improves classification results. (3) The effect of class imbalance on classification accuracy varied by land type: woodland (the majority class) was least affected by imbalance, whereas minority classes such as cultivated land, garden plantations, and grassland were highly sensitive to imbalance. In imbalanced scenarios, minority classes are prone to omission errors, leading to notable accuracy declines; producer’s accuracy (PA) decreased by 46%, 42%, and 25% for cultivated land, garden plantations, and grassland, respectively, as IR increased (with PA dropping faster than user’s accuracy, UA). Cultivated land was especially sensitive and frequently overlooked under high imbalance conditions compared to gardens and grasslands. Despite overall accuracy improving with higher IR, the accuracy of these minority classes dropped significantly, underscoring the importance of addressing the class imbalance in land use classification for erosion-prone areas.

In East Africa, lack of agriculture inputs and unstable climates lead to 50% yield gaps, making intercropping—the planting of more than one crop in the same parcel of land—a common agricultural management practice among smallholder farmers to improve land-use efficiency and reduce risks. In Kenya, where maize is the staple food, maize is often intercropped with beans, legumes, and potatoes. Despite its widespread, agricultural statistics on intercropping are currently sparse, and remote sensing approaches for large-scale crop monocultures are often unsuitable for intercropping monitoring. Mapping intercropping at national scale is extremely challenging because of heterogeneous landscapes, lack of cloud-free satellite imagery, and the scarcity of high-quality ground-based situ data in these regions. This study addressed these challenges using a phenology-assisted automated mapping framework on Google Earth Engine (GEE) to create 10m-resolution maps of monoculture and intercropped maize across Kenya for the long and short rainy seasons of 2023.First, we computed 10-day median composites of Sentinel-2 optical reflectance data for each pixel in the region to build monoculture/intercropped/non-maize Random Forest (RF) classifiers. Several thousand crop ground labels were collected during field surveys in 2023, including monoculture maize (mono-maize), intercropped maize (in-maize), and other crops (e.g., wheat, rice, coffee, tea, sugarcane, potatoes, beans, etc.). To address the limited availability of intercropped maize samples, a novel phenology-based approach was implemented. Maize was first differentiated from other crops by analyzing TCARI and OSAVI during the vegetative phase and ARI during maturity. Additionally, lower greenness and moisture levels in intercropped systems, which have larger planting width and more short-term crops, were detected using the SWIR1/NDVI ratio, effectively distinguishing mono-maize from in-maize. Automatically derived monoculture/intercropped maize samples and 40% of ground samples were used for training, while the remaining ground data were used for accuracy assessment. For the long rainy season, the overall accuracy (OA) was 0.88, with an F1-score of 0.87 for mono-maize and 0.78 for in-maize. For the short rainy season, OA dropped to 0.85, with F1-scores of 0.82 for mono-maize and 0.72 for in-maize. Misclassification primarily arose from phenological similarities between mono-maize and in-maize and increased planting of other crops with similar patterns during the short rainy season. Results revealed that 854,432 hectares of mono-maize were concentrated in the Western region and Rift Valley plateau during the long rainy season, while 1,061,701 hectares of in-maize were widely distributed across the region, particularly near Mount Kenya and the Eastern region. In the short rainy season, reduced and erratic precipitation led to decreased maize planting, with more farmers opting for intercropped systems and short-term crops to reduce risks of crop failure. We are convinced that this study is a crucial first step to demonstrate the potential of Sentinel-2 data and phenology-based automated mapping for large-scale monitoring of intercropping, providing critical insights for agricultural monitoring in sub-Saharan Africa. It serves as a foundation for developing a regional archive of monoculture and intercropped crop systems and addressing key agricultural challenges across the region.

The mapping and dynamic monitoring of large-scale cropland erosion rates are critical for agricultural planning but extremely challenging. In this study, using field investigation data collected from 20,155 land parcels in 2817 sample units in the National Soil Erosion Survey, as well as land use change data for two decades from the National Land Use/Cover Database of China (NLUD-C), we proposed a new point-to-surface approach to quantitatively assess long-term cropland erosion based on the CSLE model and non-homologous data voting. The results show that cropland in Yunnan suffers from serious problems, with an unsustainable mean soil erosion rate of 40.47 t/(ha·a) and an erosion ratio of 70.11%, which are significantly higher than those of other land types. Engineering control measures (ECM_S) have a profound impact on reducing soil erosion; the soil erosion rates of cropland with and without ECMs differ more than five-fold. Over the past two decades, the cropland area in Yunnan has continued to decrease, with a net reduction of 7461.83 km² and a ratio of −10.55%, causing a corresponding 0.32 × 10⁸ t (12.12%) reduction in cropland soil loss. We also quantified the impact of different LUCC scenarios on cropland erosion, and extraordinarily high variability was found in soil loss in different basins and periods. Conversion from cropland to forest contributes the most to cropland erosion reduction, while conversion from grassland to cropland contributes 56.18% of the increase in soil erosion. Considering the current speed of cropland regulation, it is the sharp reduction in land area that leads to cropland erosion reduction rather than treatments. The choice between the Grain for Green Policy and Cropland Protecting Strategy in mountainous areas should be made carefully, with understanding and collaboration between different roles.

Large scale cropland erosion rates mapping and dynamic monitoring are critical for agricultural planning but extremely challenging. In this study, by using field investigation data collected from 20,155 land parcels in 2,781 sample units in the National Soil Erosion Survey, land use change data for two decades from the National Land Use/Cover Database of China (NLUD-C), we proposed a new point to surface approach to quantitatively assess long-term cropland erosion based on the CSLE model and non-homogeneous voting. The results show that cropland in Yunnan suffers from serious problem with unsustainable mean soil erosion rate of 40.47t/(ha·a) and erosion ratio of 70.11%, which are significantly higher than those of other land types. Engineering control measures (ECMS) have a profound impact on reducing soil erosion, soil erosion rate of cropland with and without ESMs differs by more than five times. Over the past two decades, the cropland area in Yunnan continues to decrease, with a net reduction of 7461.83 km2 and a ratio of −10.55%, which causes corresponding 0.32×108 t (12.12% ) decrease in cropland soil loss. We also quantified the impact of different LUCC scenarios on cropland erosion, and extraordinarily high variability was found in soil loss in different basins and periods. Conversion from cropland to forest contributes the most to cropland erosion reduction, while conversion from grassland to cropland contributes 56.18% of the increase in soil erosion. Considering the current speed of cropland regulation, it is the sharp reduction in land area that leads to cropland erosion reduction rather than treatments. The dilemma between the Grain for Green Policy and Cropland Protecting Strategy in mountainous areas should be treated carefully with shared understanding and collaborations from different roles.

Based on stratified random sampling, this paper obtains the first-hand data on the food consumption of 70 rural households in Kashgar, Xinjiang through field su Based on stratified random sampling, this paper obtains the first-hand data on the food consumption of 70 rural households in Kashgar, Xinjiang through field su

Water-conservation enhancement is a crucial objective of regional ecological restoration projects in arid and semiarid areas, and it is significantly influenced by land use/cover change (LUCC). The Grain for Green Project (GFGP) is a common strategy for ecological restoration. However, insufficient attention has been paid to the impact of reforestation patterns and the underlying surface characteristics on the effectiveness of GFGP in enhancing water conservation. In this study, a Soil and Water Assessment Tool (SWAT) scenario-based simulation was conducted to assess changes in water-conservation depth (WCD) in the Zhangjiakou section of the Guanting Reservoir basin. Redundancy analysis (RDA) and a mixed linear model were employed to determine the effects of different reforestation patterns and their underlying slope gradient and soil-type characteristics on WCD variation. The results showed that there were differences in the effect characteristics of reforestation patterns and different vegetation types on WCD changes; the effectiveness of increased water conservation is associated with the adaptation of reforestation plants to underlying characteristics. Returning farmland to evergreen forests was the most effective approach, leading to a relative increase in WCD that was 2.6 times greater than the relative increase in total WCD. WCD decreased with the slope gradient, with WCD decreasing by 0.2 mm for every 1° increase in slope. Converting grassland to evergreen forests on slopes greater than 16.19° and converting deciduous forests to grassland on slopes less than 16.19° would further increase WCD, promoting the synergistic development of ecosystem services. This study provides insights into the development of more efficient reforestation strategies to enhance water conservation in a complex terrain area.

Satellite remote sensing of grassland ecosystem restoration requires considering both the above-ground biomass and soil information, and the latter is even more crucial due to the value and restoration difficulty of soil productivity. In this study, we proposed an approach to support the restoration pattern for mountainous grasslands at regional scale. The approach integrates different aspects and key processes, including degradation status, restoration potential and recovery capability, compared to a reference state. Specifically, we illustrated the method with the case of grasslands in southwestern China from a conservation perspective. Soil erosion conditions, net primary productivity and regrowth rate of grasslands were selected as indicators to reveal restoration possibilities. The results showed that the method proposed for remote sensing identification of grassland distribution has an overall accuracy of 88.21% at the regional scale. 59.54% of grasslands in Zhaotong are being eroded with an unsustainable erosion rate greater than the tolerant soil loss, and the average annual soil erosion rate is 952.17 t/(km²·a). Meanwhile, there is obvious spatial heterogeneity in soil erosion factors, vegetation restoration potential and regrowth rate, and the dry–hot valley of Jinsha River in the southwest is much more sensitive to climate change and vulnerable than other regions. The grassland vegetation cover revealed a fluctuating trend and protection of grassland vegetation on soil from erosion has an obvious lag, restoration efforts should be focused on the months before the arrival of the rainy season. In light of various grassland types, the overlay zoning results suggest various restoration patterns of natural repair and manual intervention should be employed for different grasslands. Urgent action is needed to face the challenge and process of grassland degradation and restore its sustainability with shared understanding by taking the stakeholders, collaborations and mutual relationships among different roles into account (e.g., scientist, government and herdsman).