Songjia Chen

Dr. Songjia Chen


Research Focus & Metrics

Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
17 January 2025 in Remote Sensing
Verified
Journal Article
Open access
PDF file
JLJie Li
Jie Li
FQFen Qin
Fen Qin
YWYingping Wang
Yingping Wang
XZXiuyan Zhao
Xiuyan Zhao
MYMengxiao Yu
Mengxiao Yu
JYJunhua Yan
Junhua Yan
The ecosystem water use efficiency (WUE) plays a critical role in many aspects of the global carbon cycle, water management, and ecological services. However, the response mechanisms and driving processes of WUE need to be further studied. This research was conducted based on Gross Primary Productivity (GPP), Evapotranspiration (ET), meteorological station data, and land use/cover data, and the methods of Ensemble Empirical Mode Decomposition (EEMD), trend variation analysis, the Mann–Kendall Significant Test (M-K test), and Partial Correlation Analysis (PCA) methods. Our study revealed the spatio-temporal trend of WUE and its influencing mechanism in the Yellow River Basin (YRB) and compared the differences in WUE change before and after the implementation of the Returned Farmland to Forestry and Grassland Project in 2000. The results show that (1) the WUE of the YRB showed a significant increase trend at a rate of 0.56 × 10−2 gC·kg−1·H2O·a−1 (p < 0.05) from 1982 to 2018. The area showing a significant increase in WUE (47.07%, Slope > 0, p < 0.05) was higher than the area with a significant decrease (14.64%, Slope < 0, p < 0.05). The region of significant increase in WUE in 2000–2018 (45.35%, Slope > 0, p < 0.05) was higher than that of 1982–2000 (8.23%, Slope > 0, p < 0.05), which was 37.12% higher in comparison. (2) Forest WUE (1.267 gC·kg−1·H2O) > Cropland WUE (0.972 gC·kg−1·H2O) > Grassland WUE (0.805 gC·kg−1·H2O) under different land cover types. Forest ecosystem WUE has the highest rate of increase (0.79 × 10−2 gC·kg−1·H2O·a−1) from 2000 to 2018. Forest ecosystem WUE increased by 0.082 gC·kg−1·H2O after 2000. (3) precipitation (37.98%, R > 0, p < 0.05) and SM (10.30%, R > 0, p < 0.05) are the main climatic factors affecting WUE in the YRB. A total of 70.39% of the WUE exhibited an increasing trend, which is mainly attributed to the simultaneous increase in GPP and ET, and the rate of increasing GPP is higher than the rate of increasing ET. This study could provide a scientific reference for policy decision-making on the terrestrial carbon cycle and biodiversity conservation.

12 November 2024 in Remote Sensing
Verified
Journal Article
Open access
PDF file
SCSongjia Chen
Songjia Chen
JYJunhua Yan
Junhua Yan
YWYingping Wang
Yingping Wang
Zhongbing ChangZhongbing Chang
Zhongbing Chang
GYGuangcan Yu
Guangcan Yu
MYMengxiao Yu
Mengxiao Yu
Clarifying the relationship between human activities and the provision of ecosystem services has received significant interest in recent years because of a growing need for sustainable socio-ecological system development. Using multi-source remote sensing data, we assessed the spatial and temporal distribution of the human footprint index and five ecosystem services under four human activity gradients from 2010 to 2020 in the Xiangjiang River Basin. The five ecosystem services include water supply, soil conservation, food production, habitat quality, and carbon sequestration. The relationship between human footprint and ecosystem services was analyzed from quantitative and spatial perspectives. The results showed that over the past 10 years, water supply and habitat quality decreased by 4.59% and 16.49%, respectively. The other three services increased, and the upstream area of the basin had a higher level of ecosystem services provision. The human footprint index increased by 28.83% over the 10 years and was characterized by point and patchy clustering in the middle and lower reaches. In terms of quantitative characteristics, the relationship between human footprint and ecosystem services was primarily negative. The ecosystem services were sensitive to the human footprint index within the 0−0.4 range. In terms of spatial characteristics, the relationship was dominated by trade-offs. The risky “high–low” trade-offs were mainly distributed in the middle and lower reaches. As the gradients of human activity increased, the maximum fluctuation in ESs was 43%, and the maximum fluctuation in human footprint was 28%, making their relationship more complex. Our results identified response thresholds of ecosystem services to human activities, providing a guide for ecological management and sustainable development of basins.

20 January 2025 in Forests
Verified
Journal Article
Open access
PDF file
LQLin Qiu
Lin Qiu
Zhongbing ChangZhongbing Chang
Zhongbing Chang
XLXiaomei Luo
Xiaomei Luo
SCSongjia Chen
Songjia Chen
JJJun Jiang
Jun Jiang
LLLi Lei
Li Lei
Research on monitoring forest disturbances and analyzing its driving factors is crucial for the sustainable management of forest ecosystems. To quantitatively identify the spatial distribution and dynamic changes of forest disturbance and its driving factors in Guangdong Province from 1990 to 2019, the long-term Landsat time series imagery and the LandTrendr change detection algorithm were utilized. The impact of forest disturbances on four types of landscape fragmentation (attrition, perforation, shrinkage, and subdivision) was analyzed using the Forman index. The Geodetector model was used to analyze the driving factors of forest disturbance from human activity and the natural environment. The results showed that the LandTrendr algorithm achieved a Kappa coefficient of 0.79, with an overall accuracy of approximately 82.59%. The findings indicate a consistent increase in shrinkage patches, both in quantity and area. Spatially, the centroids of forest fragmentation processes exhibited a clear inland migration trend, reflecting the growing ecological pressures faced by inland forest ecosystems. Furthermore, interactions among driving factors, particularly between population density and economic factors, significantly amplified their combined impacts. The correlation between forest disturbances and socio-economic factors revealed distinct regional variations, highlighting significant differences in forest disturbance dynamics across cities with varying levels of economic development. This study provides critical insights into the spatiotemporal dynamics of forest disturbances under rapid urbanization and economic development. It lays the groundwork for sustainable forest management strategies in Guangdong Province and may contribute to global discussions on managing forest ecosystems during periods of rapid socio-economic transformation.