Ruren Li’s research while affiliated with Shenyang University and other places

In order to reveal the spatial variation characteristics and influencing factors of grassland net primary productivity (NPP) in China, this paper uses remote sensing data, land use data and meteorological data to simulate and estimate China’s grassland net primary productivity from 2001 to 2019 using the Carnegie-Ames-Stanford Approach (CASA). The trend analysis and complex correlation analysis were used to analyze the relationship with the temporal and spatial changes of grassland NPP from the perspectives of climate factors, topography, longitude and latitude. The results show that: 1) In the past 19 years, the China’s grassland NPP has generally shown a fluctuating upward trend, the spatial distribution of NPP variation shows a characteristic of low in the west and high in the east, with the increased area accounting for 70.39% of the total grassland area, and the low NPP values are mainly distributed in the northwestern part of Tibet and Qinghai and the central part of Inner Mongolia, the average annual NPP is 257.13 g C·m⁻²·a⁻¹. 2) The change of mean NPP value of grassland in China is more dependent on precipitation (p) than air temperature (T). 3) Grassland NPP showed a decreasing trend with the increase of altitude, and the NPP on the gradient with DEM between 200 m and 500 m was the highest (483.86 g·C·m⁻²·a⁻¹); The maximum annual mean value (448.42 g C·m⁻²·a⁻¹) is fallen over the sharp slope of 35°–45°; the NPP of grassland increases with the slope (from shade to sunny), and the NPP of grassland on the semi-sunny slope increases. The annual average NPP is the highest (270.87 g C·m⁻²·a⁻¹). 4) The mean value of grassland NPP was negatively correlated with the change of latitude, and showed a “wave-like” downward trend from south to north; the mean value of grassland NPP was positively related to the change of longitude. The correlation relationship shows a “stepped” upward trend from west to east.

Excessive emissions of greenhouse gases — of which carbon dioxide is the most significant component, are regarded as the primary reason for increased concentration of atmospheric carbon dioxide and global warming. Terrestrial vegetation sequesters 112–169 PgC (1PgC = 10¹⁵g carbon) each year, which plays a vital role in global carbon recycling. Vegetation carbon sequestration varies under different land management practices. Here we propose an integrated method to assess how much more carbon can be sequestered by vegetation if optimal land management practices get implemented. The proposed method combines remotely sensed time-series of net primary productivity datasets, segmented landscape-vegetation-soil zones, and distance-constrained zonal analysis. We find that the global land vegetation can sequester an extra of 13.74 PgC per year if location-specific optimal land management practices are taken and half of the extra clusters in ~15% of vegetated areas. The finding suggests optimizing land management is a promising way to mitigate climate changes.

Street greening, an indispensable element of urban green spaces, has played an important role in beautifying the environment, alleviating the urban heat island effect, and improving residents’ comfort. Vegetation coverage is a common index used for measuring street greening. However, there are some shortcomings in the traditional evaluation methods of vegetation coverage. Part of the vegetation coverage cannot be determined from a two-dimensional perspective, such as shrubs and green walls. In this paper, the Sentinel-2 image was used to extract the street fractional vegetation cover (SFVC) and the Baidu street view panoramas were used to extract the green view index (GVI). To overcome the lack of a single perspective from the street vegetation coverage evaluation, the above two indices were merged to construct a comprehensive street greening evaluation index (CSGEI). The research area is the Longhua District of Shenzhen city in Southern China. All three indices were divided into five classes using natural breakpoint methods based on previous research experience. The results showed that Baidu street view panoramas could effectively identify shrubs and green walls that were deficient in the Sentinel-2 image. The GVI is a supplement to the street vegetation coverage. The SFVC and GVI were divided into five classes, from L1 to L5 implying a gradual increase in the percentage of the vegetated area. The result has shown that the SFVC was in the L1, accounting for 53.68%. After index merging, the process of accounting for the L1 decreased to 31.29%. The multiperspective integrated CSGEI could comprehensively measure the distribution information of street greening and guide the planning and management of urban green landscapes.

Integration development of urban agglomeration is important for regional economic research and management. In this paper, a method was proposed to study the integration development of urban agglomeration by trajectory gravity model. It can analyze the gravitational strength of the core city to other cities and characterize the spatial trajectory of its gravitational direction, expansion, etc. quantitatively. The main idea is to do the fitting analysis between the urban axes and the gravitational lines. The correlation coefficients retrieved from the fitting analysis can reflect the correlation of two indices. For the different cities in the same year, a higher value means a stronger relationship. There is a clear gravitational force between the cities when the value above 0.75. For the most cities in different years, the gravitational force between the core city with itself is increasing by years. At the same time, the direction of growth of the urban axes tends to increase in the direction of the gravitational force between cities. There is a clear tendency for the trajectories of the cities to move closer together. The proposed model was applied to the integration development of China Liaoning central urban agglomeration from 2008 to 2016. The results show that cities are constantly attracted to each other through urban gravity.

Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000-2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

Featured Application The study assessed the reduced carbon uptake (RCU) due to human activities and highlighted the patterns of the impact on RCU from human-related driving forces so that optimized grassland management policies could be implemented to achieve more carbon sequestration from vegetation. Abstract The ever-rising concentration of atmospheric carbon is viewed as the primary cause for global warming. To discontinue this trend, it is of urgent importance to either cut down human carbon emissions or remove more carbon from the atmosphere. Grassland ecosystems occupy the largest part of the global land area but maintain a relatively low carbon sequestration flux. While numerous studies have confirmed the impacts on grassland vegetation growth from climate changes and human activities, little work has been done to understand the driving forces for a reduced carbon uptake (RCU)—a loss in vegetation carbon sequestration because of inappropriate grassland management. This work focused on assessing RCU in the grassland of Inner Mongolia and understanding the influential patterns of the selected variables (including grazing intensity, road network, population, and vegetation productivity) related to RCU. Neighborhood analysis was proposed to locate optimized grassland management practices from historical data and to map RCU. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were applied to explore the driving forces for RCU. The results indicated that the human-related factors, including stock grazing intensity, population density, and road network were likely to present a spatially varied impact on RCU, which accounted for more than 1/4 of the total carbon sequestration.

The global temperature could increase over 1.5 or even 2 °C by the middle of 21st century due to massive emissions of greenhouse gases (GHGs) — of which carbon dioxide (CO2) is the largest component1. Human activities emit more than 10 PgC (1PgC=1015gC) per year into the atmosphere1, which is regarded as the primary reason for increased atmospheric CO2 concentration and global warming2. Global vegetation sequesters 112–169 PgC each year3, about half of which is released back into the atmosphere through autotrophic respiration while the rest, termed as net primary production (NPP), is for balancing the CO2 emissions from human activities, microbial respiration, and decomposition4. Carbon sequestration from vegetation varies under different environmental conditions5 and could also be significantly altered by land management practices (LMPs)6. Adopting optimal land management practices (OLMPs) helps sequester more CO2 from the atmosphere and mitigate climate changes. Understanding the extra carbon sequestration with OLMPs, or termed as carbon gap, is an important scientific topic that is rarely studied. Here we propose an integrated method to identify the location-specific OLMPs and assess the carbon gap by using remotely sensed time-series of NPP dataset, segmented landscape-vegetation-soil (LVS) zones and distance-constrained zonal analysis. The findings show that the carbon gap from global land plants totaled 13.74 PgC per year with OLMPs referenced from within a 20km neighborhood, an equivalent of ~1/5 of the total sequestered net carbon at the current level; half of the carbon gap clusters in only ~15% of vegetated area. The carbon gap flux rises with population density and the priority for implementing OLMPs should be given to the densely populated areas to enhance the global carbon sequestration capacity.

Explicit information of tree species composition provides valuable materials for the management of forests and urban greenness. In recent years, scholars have employed multiple features in tree species classification, so as to identify them from different perspectives. Most studies use different features to classify the target tree species in a specific growth environment and evaluate the classification results. However, the data matching problems have not been discussed; besides, the contributions of different features and the performance of different classifiers have not been systematically compared. Remote sensing technology of the integrated sensors helps to realize the purpose with high time efficiency and low cost. Benefiting from an integrated system which simultaneously acquired the hyperspectral images, LiDAR waveform, and point clouds, this study made a systematic research on different features and classifiers in pixel-wised tree species classification. We extracted the crown height model (CHM) from the airborne LiDAR device and multiple features from the hyperspectral images, including Gabor textural features, gray-level co-occurrence matrix (GLCM) textural features, and vegetation indices. Different experimental schemes were tested at two study areas with different numbers and configurations of tree species. The experimental results demonstrated the effectiveness of Gabor textural features in specific tree species classification in both homogeneous and heterogeneous growing environments. The GLCM textural features did not improve the classification accuracy of tree species when being combined with spectral features. The CHM feature made more contributions to discriminating tree species than vegetation indices. Different classifiers exhibited similar performances, and support vector machine (SVM) produced the highest overall accuracy among all the classifiers.