The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments.

Net primary productivity (NPP) plays a vital role in the globe carbon cycle. Quantitative assessment of the effects of climate changes and human activities on net primary productivity dynamics is vital for understanding the driving mechanisms of vegetation change and sustainable development of ecosystems. This study investigates the contributions of climatic factors and human activities to vegetation productivity changes in China from 2000 to 2020 based on the residual trend analysis (RESTREND) method. The results showed that the annual average net primary productivity in China was 325.11 g C/m²/year from 2000 to 2020 and net primary productivity showed a significantly increasing trend (p<0.05) at a rate of 2.32 g C/m²/year. Net primary productivity increased significantly (p<0.05) across 40.90% of China over the study period, while only 1.79% showed a significantly declining trend (p<0.05). The contributions of climatic factors and human activities to net primary productivity increase were 1.169 g C/m²/year and 1.142 g C/m²/year, respectively. Climate factors contributed positively mainly in Sichuan Basin, the Loess Plateau, the Mongolian Plateau, and Northeast China Plain. Positive contributions of human activities to net primary productivity mainly occurred in the Loess Plateau, Central China, and the Greater Khingan Mountains. The effects of climatic factors and human activities on net primary productivity changes varied among sub-regions. In Tropical Monsoon Climate Region and Subtropical Monsoon Climate Region, human activities had greater impacts on net primary productivity increase than climate factors, while climate factors were the dominant factor for net primary productivity recovery in other sub-regions. In addition, during 2000–2020, net primary productivity was dominated by both climate factors and human activities in 49.84% of China, while areas dominated solely by climate factors and human activities accounted for 13.67% and 10.92%, respectively. Compared to changed land cover types, the total net primary productivity as well as the increase of total net primary productivity in China was mostly contributed by unchanged land cover types, which contributed more than 90%.

Urban expansion often occurs at the expense of cropland loss, posing challenges to sustainable urban growth and food security. However, detailed investigations into urban expansion and cropland loss remain limited, particularly in regions with varying levels of urbanization. Here, we take Guangdong Province, China, as a case study to exemplify how urban expansion affects cropland using remotely sensed land use products. We adopted geospatial analysis, correlation indicators, and landscape metrics to uncover their spatial relationships at 10-m spatial resolutions. Results showed that urban areas increased by 6335 km² while cropland decreased by 3780 km² from 2017 to 2022. Notably, 41 % of newly expanded urban areas were from croplands, and 45 % of lost croplands were converted to urban areas. Western Guangdong experienced the largest extent of urban expansion and cropland loss, emerging as a hotspot region in recent years. Additionally, our analysis observed the increasing compactness of urban areas and the growing fragmentation of cropland landscapes over time. These findings shed light on the intricate dynamics between urban expansion and cropland loss in rapidly urbanizing regions, which provide valuable insights for sustainable urban development, agricultural practice, and land management in the future.

Detecting land use or land cover changes is a challenging problem in analyzing images. Change-detection plays a fundamental role in most of land use or cover monitoring systems using remote-sensing techniques. The reliability of individual automatic change-detection algorithms is currently below operating requirements when considering the intrinsic uncertainty of a change-detection algorithm and the complexity of detecting changes in remote-sensing images. In particular, most of these algorithms are only suited for a specific image data source, study area and research purpose. Only a number of comprehensive change-detection methods that consider the reliability of the algorithm in different implementation situations have been reported. This study attempts to explore the advantages of combining several typical change-detection algorithms. This combination is specifically designed for a highly reliable change-detection task. Specifically, a fusion approach based on reliability is proposed for an exclusive land use or land cover change-detection. First, the reliability of each candidate algorithm is evaluated. Then, a fuzzy comprehensive evaluation is used to generate a reliable change-detection approach. This evaluation is a transformation between a one-way evaluation matrix and a weight vector computed using the reliability of each candidate algorithm. Experimental results reveal that the advantages of combining these distinct change-detection techniques are evident.

Vegetation plays an irreplaceable role for urban ecosystem services. Urban greenness represents all vegetation cover in and around cities. Understanding spatiotemporal patterns of the changes in urban greenness (CUG) provides fundamental clues for urban planning. The impact on CUG can be roughly categorized as being climate-induced and human-induced. Methods for mapping human-induced CUG (H-CUG) are rare. In this paper, a new framework, known as Localized Spatial Association Analysis under Temporal Context (LSAA-TC), was proposed to explore H-CUG. Localized spatial association analysis (LSAA) was performed first to extract local spatial outliers (LSOs), or locations that differ significantly in urban greenness from those located in the neighborhood. LSOs were then analyzed under the temporal context to map their intertemporal variations known as spatiotemporal outliers. We applied LSAA-TC to mapping H-CUG in the Wuhan Metropolitan Area, China during 2000–2015 using the vegetation index from Moderate-resolution Imaging Spectroradiometer (MODIS) 13Q1 as the proxy for urban greenness. The computed H-CUG demonstrated apparent spatiotemporal patterns. The result is consistent with the fact that the traditional downtown area presents the lowest H-CUG, while it is found that the peripheral area in the circular belt within 14–20 km from the urban center demonstrates the most significant H-CUG. We conclude that LSAA-TC can be a widely applicable framework to understand H-CUG patterns and is a promising tool for informative urban planning.

Rapid urbanization has threatened sustainable urban development in many cities across the globe, causing green space loss and vegetation cover degradation which reduce carbon sequestration. Optimal land management practices (LMPs) in an urban context are known as ways capable of promoting urban vegetation growth and contributing to carbon sequestration. Due to variations of physical, biological, and social structures in urban areas, policymakers often lack relevant information to decide and implement site-specific LMPs. Here we try to extract the areas in need of the optimal LMPs, identify location-dependent optimal LMPs, and assess how much more carbon can be captured by applying a combination of segmenting homogeneous urban environments and neighborhood-based analysis. As one of the most developed cities in China, the greater Guangzhou area (GGA) was selected as a case study. We found that the carbon uptake from the urban vegetation in GGA could be improved on average by 185 gC m⁻² yr⁻¹ in flux (or 1.3 TgC yr⁻¹ in total) with optimal LMPs, equivalent to a ~30% increase considering the current level of 662 gC m⁻² yr⁻¹ in flux (4.4 TgC yr⁻¹ in total). The carbon uptake potential was found to differ considerably across locations and among different ecosystem types, highlighting spatially varied priorities for implementing optimal LMPs over the space. This study reveals the usefulness of the model in assessing carbon uptake potential from optimal LMPs and emphasizes that future urban planning may consider the importance of optimal LMPs in enhancing vegetation carbon uptake in urban planning.