Spartina alterniflora, after introduced to China, has rapidly expanded along the coastline and become one of the top invasive plants in coastal wetland. While it has been well accepted that phenological information derived from multi-temporal remotely sensed data improves vegetation mapping accuracy, previous research primarily relied on scene-based features for invasive plant mapping. In the coastal regions with frequent cloud cover such as South China coast, extracting phenological features at scene level was impossible due to lack of sufficient cloud-free imageries. In this study, we aimed to propose a simple phenological vegetation index (PVI) using pixel-level composition of Sentinel-2 observations with the assist of Google Earth Engine platform. By developing and comparing six PVIs, separability analysis showed that phenological normalized vegetation difference index (PNDVI) of S. alterniflora and other land cover types were more separable than other PVIs and single-season NDVI. Based on the PNDVI, we further proposed supervised and unsupervised Otsu thresholding methods for S. alterniflora extraction. The overall accuracies of supervised Otsu-PNDVI-thresholding method using 10-fold cross validation reached 97.84%, and that of the unsupervised Otsu-PNDVI-thresholding method reached 97.20%. Kappa Z-test statistics showed that both supervised and unsupervised Otsu-PNDVI-thresholding methods yielded statistically similar accuracies as random forest (RF) classifiers based on six PVIs, and much higher accuracies than scene-based classification. The success of the unsupervised Otsu-PNDVI-thresholding method suggested that the method was practical and operational for S. alterniflora mapping and expansion monitoring in wide area such as South China coast.

As a significant manifestation of human activities influencing natural environment, rapid urbanization has enhanced economic prosperity while simultaneously posing threats to ecological quality. Beijing–Tianjin–Hebei (BTH), the core region of the Yangtze River Delta (CYRD), and the Pearl River Delta (PRD) stand as three major economic centers characterized by the highest level of urbanization in China, encompassing areas of heightened ecological sensitivity. Nevertheless, the ecological quality at the scale of urban agglomerations remains ambiguous, with many studies failing to develop a comprehensive and effective method for comparing diverse urban agglomerations. Consequently, this study integrates multi-source remote sensing data, including information on land cover and other socio-economic parameters, to construct the Ecological Quality Index (EQI) based on the “Function–Interaction–Pressure–Stability” (FIPS) framework. Through a stratified determination of indicator weights grounded in both objective importance and empirical knowledge, we mapped the spatiotemporal changes of EQI and analyzed the impact of urbanization on ecological quality in three urban agglomerations from 2001 to 2020. We determined the following: (1) The calculated EQI can further capture the nuanced details with better performance at both underlining the discrepancy of highs and lows of EQI and describing the spatial detail of urban agglomerations’ characteristics. (2) Substantial disparities in EQI and its changes are evident across different urban agglomerations. Notably, only the average EQI improves in PRD, while ecological degradation is prominent in specific regions, such as the southeastern plains of BTH area, along the Yangtze River, and around Shanghai in CYRD and central PRD. The CYRD exhibits the largest affected area. (3) Urbanization predominantly influences ecological quality through land cover transitions. In expansion areas, ecological deterioration is significantly more pronounced, constituting approximately 90% of the total area. (4) Despite significant urbanization, city-level analysis in CYRD reveals a better coordination between urban expansion and ecological protection, with a lower intensity of ecological degradation compared to urban expansion rates. Conversely, some cities in the BTH, despite modest urban expansion, exhibit substantial declines in ecological quality, highlighting the need for targeted policy interventions. In conclusion, this study elucidates the intricate relationship between urbanization and ecological quality, offering valuable insights for the development of targeted protection strategies and sustainable urban planning.

After 2000, China’s vegetation underwent great changes associated with climate change and urbanization. Although many studies have been conducted to quantify the contributions of climate and human activities to vegetation, few studies have quantitatively examined the comprehensive contributions of climate, urbanization, and CO₂ to vegetation in China’s 32 major cities. In this study, using Global Land Surface Satellite (GLASS) fractional vegetation cover (FVC) between 2001 and 2018, we investigated the trend of FVC in China’s 32 major cities and quantified the effects of CO₂, urbanization, and climate by using generalized linear models (GLMs). We found the following: (1) From 2001 to 2018, the FVC in China generally illustrated an increasing trend, although it decreased in 23 and 21 cities in the core area and expansion area, respectively. (2) Night light data showed that the urban expansion increased to varying degrees, with an average increasing ratio of approximately 168%. The artificial surface area increased significantly, mainly from cropland, forest, grassland, and tundra. (3) Climate factors and CO₂ were the major factors that affected FVC change. The average contributions of climate factors, CO₂, and urbanization were 40.6%, 39.2%, and 10.6%, respectively. This study enriched the understanding of vegetation cover change and its influencing factors, helped to explain the complex biophysical mechanism between vegetation and environment, and guided sustainable urban development.

The normalized difference vegetation index (NDVI) is one of the most common metrics used to describe vegetation dynamics. Unfortunately, low-quality pixels resulting from contamination (by features including clouds, snow, aerosols, and mixed factors) have impeded NDVI products’ widespread application. Researchers have thought of several ways to improve NDVI quality when contamination occurs. However, most of these algorithms are based on the noise-negative deviation principle, which aligns low-value NDVI products to an upper line but ignores cases where absolute surface values are low. Consequently, to fill in these research gaps, in this article, we use the random forest model to produce a set of high-quality NDVI products to represent actual surface characteristics more accurately and naturally. Climate and geographical products are used as model inputs to describe environmental factors. They represent the random forest (RF) model that establishes relationships between MODIS NDVI products and meteorological products in high-quality areas. In addition, auxiliary data and empirical knowledge are employed to meet filling requirements. Notably, the random forest (RF) algorithm exhibits a mean absolute error (MAE) of 0.024 and a root mean squared error (RMSE) of 0.034, in addition to a coefficient of determination (R²) value of 0.974. Furthermore, the MAE and RMSE of the RF-based method decreased by 0.014 and 0.019, respectively, when compared to those of the STSG (spatial–temporal Savitzky–Golay) plan and by 0.013 and 0.015, respectively, when compared to the LSTM (long short-term memory) method. R² increased by 0.039 and 0.027, respectively, compared to the STSG and LSTM methods. We introduced a novel series of NDVI products that demonstrated consistent spatial and temporal connectivity. The novel product exhibits enhanced adaptability to intricate environmental conditions and promises the potential for utilization in investigating vegetation dynamics within the Chinese region.

Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. Fine-scale representation of vegetation indices is necessary for regions with complex topography and high diversity species. However, the advanced very-high-resolution radiometer (AVHRR), which covers an extensive time range with high temporal resolution, does not provide normalized difference vegetation index (NDVI) data with sufficient spatial resolutions for a detailed analysis of vegetation changes. The Moderate Resolution Imaging Spectroradiometer (MODIS), which has a higher temporal and spatial resolution, has only been limited to the last few decades. To deal with these issues, we propose a Multi-scale Residual Convolutional Neural Net-work (MRCNN) that utilizes a multi-scale structure with a residual convolutional neural network to combine MODIS NDVI and AVHRR NDVI data. The MRCNN algorithm improved Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) by 0.026 and 0.032, respectively, resulting in a 64.38% improvement for MAE and 62.79% improvement for RMSE compared to AVHRR NDVI. It also increased the Peak-Signal-to-Noise Ratio (PSNR) by 28.5% and the Structural Similarity index (SSIM) by 16.2%. The MRCNN method accurately captures the actual state of MODIS NDVI and consistently tracks changing trends in the vegetation index. It is exact in complex terrain and diverse vegetation areas. This method enhances the spatial resolution of AVHRR NDVI and significantly improves the accuracy of monitoring nationwide vegetation index changes over 30 years. The findings establish a solid scientific foundation for implementing ecological conservation measures and promoting sustainable vegetation growth.

Vegetation net primary productivity (NPP) is a key indicator for assessing vegetation dynamics and carbon cycle balance. Xinjiang is located in an arid and ecologically fragile region in northwest China, but the current understanding of vegetation dynamics in the region is still limited. This study aims to analyze Xinjiang’s NPP spatial and temporal trends, using random forest regression to quantify the extent to which climate change and human activities affect vegetation productivity. CMIP6 (Coupled Model Intercomparison Project Phase 6) climate scenario data help assess vegetation restoration potential and future risks. Our findings indicate that (1) Xinjiang’s NPP exhibits a significant increasing trend from 2001 to 2020, with three-quarters of the region experiencing an increase, 2.64% of the area showing significant decrease (p < 0.05), and the Ili River Basin showing a nonsignificant decreasing trend; (2) precipitation and radiation are major drivers of NPP variations, with contribution ratios of 35.13% and 30.17%, respectively; (3) noteworthy restoration potential exists on the Tian Shan northern slope and the Irtysh River Basin, where average restoration potentials surpass 80% relative to 2020, while the Ili River Basin has the highest future risk. This study explores the factors influencing the current vegetation dynamics in Xinjiang, aiming to provide references for vegetation restoration and future risk mitigation, thereby promoting sustainable ecological development in Xinjiang.