Haitao Jing

Urban expansion and its driving factors are frequently analyzed within administrative regions to inform regional urban planning, yet such analyses often fall short at the natural basin scale (referring to the spatial extent defined by hydrological drainage boundaries) due to the scarcity of statistical data. Geographic and socio-economic spatial data can offer more detailed information across various research scales compared to traditional data (such as administrative statistical data, survey-based data, etc.), providing a potential solution to this limitation. Thus, this study took the Indus Basin as an example to reveal its urban expansion patterns and driving mechanism based on natural–economic–social time-series (2000–2020) spatial data, landscape expansion index, and geographical detector model (GDM). Future urban expansion distribution under different scenarios was also projected using Cellular Automata and Markov model (CA-Markov). The results indicated the following: (1) The Indus River Basin experienced rapid urban expansion during 2000–2020 dominated by edge-expansion, with urban expansion intensity showing a continuous increase. (2) Between 2000 and 2010 as well as 2010 and 2020, the dominant factor influencing urban expansion shifted from altitude to population (Pop), while the strongest interacting factors shifted from fine particulate matter (PM_2.5) and altitude to Gross Domestic Product (GDP) and Pop. (3) Future urban expansion probably occupies substantial mountainous area under the normal scenario, while the expansion region shifts towards the central plains to protect more ecological zones under a sustainable development scenario. Findings in this study would deepen the understanding of urban expansion characteristics of the Indus Basin and benefit its future urban planning.

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

Cotton (Gossypium hirsutum L.), as a significant economic crop, has undergone significant modernization in planting methods, and its smart irrigation management relies heavily on accurate cotton water content (CWC) estimation. Existing ground-based methods for measuring CWC are constrained by their limited scope and high monitoring costs. Although the development of unmanned aerial vehicle (UAV) technology has provided a new opportunity for large-scale CWC measurements, there remains a gap in the study of CWC estimation in cotton using multi-source and multi-stage data. In this study, we used UAV-based data, including texture features, vegetation indices, and a heat index, and applied four machine learning algorithms, i.e., partial least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XGB), to estimate CWC. The findings demonstrate that in a single growth stage, the boll setting stage performs the best, and multi-source and multi-stage inputs can improve the accuracy of CWC estimation, with the best performance of XGB (R² = 0.860). Overall, this study highlights that the synergistic use of multi-source and multi-stage data can effectively improve CWC estimation in cotton, suggesting UAV-based data will lead to a brighter future for precision agriculture.

In recent years, rapid urbanization in the Southern Jiangsu urban area has significantly altered land use patterns, impacting local ecosystem service value (ESV). This study examines land use changes (LUCs) from 2000 to 2020 and assesses ESV changes with the equivalent factor method. The PLUS model is also used to project future LUCs in line with the “Land and Space Planning of Jiangsu Province (2021–2035)”. The simulation considers scenarios for natural development (NDS), ecological protection (EPS), farmland protection (FPS), and comprehensive development (CDS) for the year 2035. The research findings show that over the past two decades, urbanized areas have persistently grown, whereas there has been a steady decline in the extents of farmland, forested areas, and grasslands. The total ESV has shown a decreasing trend, with the spatial distribution of ESV exhibiting higher values in the eastern regions and lower values in the west. The relationship between land use and ESV is negative, as evidenced by both the comprehensive land use (LA) and the Moran’s I index. Scenario predictions reveal that farmland, forest land, and grassland show a decreasing trend under the NDS, while it is the opposite under the EPS. The FPS is predominant in farmland area, and all land types in the CDS comply with national land planning requirements. Overall, the research findings reveal and predict the influence of LUCs on ESV in the study area under four scenarios, which is beneficial optimizing regional resource planning schemes and formulating ecological compensation policies.

The Rapid expansion of the Lanzhou–Xining (Lanxi) urban cluster in China during recent decades poses a threat to the fragile arid environment. Quantitatively assessing the impact of urban expansion on vegetation in the Lanxi urban cluster has profound implications for future sustainable urban planning. This study investigated the urban expansion dynamics of the Lanxi urban cluster and its impacts on regional vegetation between 2001 and 2021 based on time series land cover data and auxiliary remote sensing data, such as digital elevation model (DEM) data, nighttime light data, and administrative boundary data. Thereinto, urban expansion dynamics were evaluated using the annual China Land Cover Dataset (CLCD, 2001–2021). Urban expansion impacts on regional vegetation were assessed via the Vegetation Disturbance Index (VDI), an index capable of quantitatively assessing the positive and negative impacts of urban expansion at the pixel level, which can be obtained by overlaying the Enhanced Vegetation Index (EVI) and rainfall data. The major findings indicate that: (1) Over the past two decades, the Lanxi region has experienced rapid urban expansion, with the built-up area expanding from 183.50 km² to 294.30 km², which is an average annual expansion rate of 2.39%. Notably, Lanzhou, Baiyin, and Xining dominated the expansion. (2) Urban expansion negatively affected approximately 53.50 km² of vegetation, while about 39.56 km² saw positive impacts. The negative effects were mainly due to the loss of cropland and grassland. Therefore, cities in drylands should balance urban development and vegetation conservation by strictly controlling cropland and grassland occupancy and promoting intelligent urban growth.

Accurate cultivated land parcel data are an essential analytical unit for further agricultural monitoring, yield estimation, and precision agriculture management. However, the high degree of landscape fragmentation and the irregular shapes of cultivated land parcels, influenced by topography and human activities, limit the effectiveness of parcel extraction. The visual semantic segmentation model based on the Segment Anything Model (SAM) provides opportunities for extracting multi-form cultivated land parcels from high-resolution images; however, the performance of the SAM in extracting cultivated land parcels requires further exploration. To address the difficulty in obtaining parcel extraction that closely matches the true boundaries of complex large-area cultivated land parcels, this study used segmentation patches with cultivated land boundary information obtained from SAM unsupervised segmentation as constraints, which were then incorporated into the subsequent multi-scale segmentation. A combined method of SAM unsupervised segmentation and multi-scale segmentation was proposed, and it was evaluated in different cultivated land scenarios. In plain areas, the precision, recall, and IoU for cultivated land parcel extraction improved by 6.57%, 10.28%, and 9.82%, respectively, compared to basic SAM extraction, confirming the effectiveness of the proposed method. In comparison to basic SAM unsupervised segmentation and point-prompt SAM conditional segmentation, the SAM unsupervised segmentation combined with multi-scale segmentation achieved considerable improvements in extracting complex cultivated land parcels. This study confirms that, under zero-shot and unsupervised conditions, the SAM unsupervised segmentation combined with the multi-scale segmentation method demonstrates strong cross-region and cross-data source transferability and effectiveness for extracting complex cultivated land parcels across large areas.

Research on the spatiotemporal changes in land use/cover (LUC) and carbon storage (CS) in the region of the Taihang Mountains in various developmental scenarios can provide significant guidance for optimizing the structure of LUC and formulating ecologically friendly economic development policies. We employed the PLUS and InVEST models to study change in LUC and CS in the Taihang Mountains from 1990 to 2020. Based on these results, we established three distinct development scenarios: a business-as-usual development scenario, a cropland protection scenario, and an ecological conservation scenario. Based on these three developmental scenarios, we simulated the spatiotemporal changes in LUC and CS in the Taihang Mountains in 2035. The results indicate that: (1) from 1990 to 2020, the CS in the Taihang Mountains increased from 1575.91 Tg to 1598.57 Tg, with a growth rate of approximately 1.44%. The primary source of this growth is attributed to the expansion of forests. (2) In the business-as-usual development scenario, the growth rate of CS in the Taihang Mountains was approximately 0.45%, indicating a slowdown in the trend. This suggests that economic development has the consequences of aggravating human–land conflicts, leading to a deceleration in the growth of CS. (3) In the cropland protection scenario, the increase in the CS in the Taihang Mountains was similar to the CS increase in the business-as-usual development scenario. However, the expansion of cropland dominated by impermeable surfaces, which indicates economic development, was considerably constrained in this scenario. (4) In the ecological conservation scenario, the increase in carbon storage in the Taihang Mountains was 1.16%, which is the fastest among all three scenarios. At the same time, there was a certain degree of development of impermeable surfaces, achieving a balance between economic development and ecological conservation.

The quality of the ecological environment determines human well-being, and the degree of ecological environment quality has a significant impact on regional sustainable development. Currently, the assessment content of ecological environment quality in Luoyang is relatively single-indicator-based and is insufficient to comprehensively reflect the changes in the ecological environment quality of Luoyang city. Therefore, the study aims to use the Remote Sensing Ecological Index (RSEI), a comprehensive evaluation model, with Landsat remote sensing images and statistical yearbooks as the data sources, to evaluate the spatiotemporal dynamic changes in the ecological environment quality of Luoyang city from 2002 to 2022 through trend analysis and mutation testing; the study employs geographical detectors to analyze the driving factors about the changes in ecological environment quality. The study found that: (1) the average RSEI value in Luoyang city has increased by 0.102 in the past 20 years, indicating an overall improvement in the ecological environment quality of Luoyang city. (2) The northern region of the study area has lower RSEI values, while the southern region has better ecological environment quality, which corresponds to the fact that the northern part of Luoyang city has intensive human activities, while the southern part is characterized by higher vegetation coverage in mountainous areas. (3) The proportion of areas with medium and above ecological environment quality grades have increased from 47.2% to 67.5%, indicating a positive trend in future ecological environment quality changes. (4) The population change was the strongest single factor influencing the ecological environment quality change in Luoyang city. The interaction between temperature and GDP was relatively the strongest. The current ecological environment status in the study area is the result of the combined effects of natural and anthropogenic factors. The research conclusions contribute to improving regional ecological environment quality and are of great significance for the regional ecological environment planning and the achievement of sustainable development goals.

Deep learning algorithms offer an effective solution to the inefficiencies and poor results of traditional methods for building a footprint extraction from high-resolution remote sensing imagery. However, the heterogeneous shapes and sizes of buildings render local extraction vulnerable to the influence of intricate backgrounds or scenes, culminating in intra-class inconsistency and inaccurate segmentation outcomes. Moreover, the methods for extracting buildings from very high-resolution (VHR) images at present often lose spatial texture information during down-sampling, leading to problems, such as blurry image boundaries or object sticking. To solve these problems, we propose the multi-scale boundary-refined HRNet (MBR-HRNet) model, which preserves detailed boundary features for accurate building segmentation. The boundary refinement module (BRM) enhances the accuracy of small buildings and boundary extraction in the building segmentation network by integrating edge information learning into a separate branch. Additionally, the multi-scale context fusion module integrates feature information of different scales, enhancing the accuracy of the final predicted image. Experiments on WHU and Massachusetts building datasets have shown that MBR-HRNet outperforms other advanced semantic segmentation models, achieving the highest intersection over union results of 91.31% and 70.97%, respectively.

Currently, mainstream 3D point cloud segmentation methods still have shortcomings, which have an impact on the accuracy of point cloud segmentation. In particular, by using the MLP layer with shared weights, PointNet, and its improved version, generate redundant channel features when mapping 3D point cloud data to high-dimensional space. This study intended to improve the PointNet network and introduce the key point attention and channel attention mechanisms into the PointNet model to build the KCA-PointNet network. Taking the point cloud data generated via oblique photography as the experimental data, the experimental results show that 1) after adding the KCA module, the segmentation accuracy of buildings, parks, and other places is greatly improved; 2) the segmentation accuracy of the combined use of the KA and the CA module is about 3% higher than that when they are used independently; and 3) the different connection modes of the KA and the CA modules also have an impact on the accuracy, and upon connecting the two modules in series, the data first pass through KA and the segmentation effect after CA is better than that of the other two connection methods. Through the improvement of the PointNet network, this study not only eliminated the redundant channel features, but also extracted the key points in the point cloud, reduced the possibility of losing important information in the process of point cloud data sampling, and effectively improved the accuracy and efficiency of point cloud segmentation. Furthermore, the results of this study will help to broaden the application scope of deep learning in the field of point cloud data, promote the development and innovation of point cloud segmentation algorithms, and lay a scientific foundation for deep learning point cloud segmentation.