Chunpeng Chen

Dr. Chunpeng Chen

East China Normal University, Shanghai, China
+1
orcid

Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

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

Representative Publications

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29 March 2023 in Remote Sensing
Verified
Journal Article
Open access
PDF file
CCChunpeng Chen
Chunpeng Chen
BTBo Tian
Bo Tian
WWWenting Wu
Wenting Wu
YDYuanqiang Duan
Yuanqiang Duan
YZYunxuan Zhou
Yunxuan Zhou
Ce ZhangCe Zhang
Ce Zhang
The rapid, up-to-date, cost-effective acquisition and tracking of intertidal topography are the fundamental basis for timely, high-priority protection and restoration of the intertidal zone. The low cost, ease of use, and flexible UAV-based photogrammetry have revolutionized the monitoring of intertidal zones. However, the capability of the RTK-assisted UAV photogrammetry without ground control points, the impact of flight configuration difference, the presence of surface water in low-lying intertidal areas on the photogrammetric accuracy, and the potential of UAV/satellite Synergy remain unknown. In this paper, we used an RTK-assisted UAV to assess the impact of the above-mentioned considerations quantitatively on photogrammetric results in the context of annual monitoring of the Chongming Dongtan Nature Reserve, China based on an optimal flight combination. The results suggested that (1) RTK-assisted UAVs can obtain high-accuracy topographic data with a vertical RMSE of 3.1 cm, without the need for ground control points. (2) The effect of flight altitude on topographic accuracy was most significant and also nonlinear. (3) The elevation obtained by UAV photogrammetry was overestimated by approximately 2.4 cm in the low-lying water-bearing regions. (4) The integration of UAV and satellite observations can increase the accuracy of satellite-based waterline methods by 51%. These quantitative results not only provide scientific insights and guidelines for the balance between accuracy and efficiency in utilizing UAV-based intertidal monitoring, but also demonstrate the great potential of combined UAV and satellite observations in identifying coastal erosion hotspots. This establishes high-priority protection mechanisms and promotes coastal restoration.

17 June 2022 in Remote Sensing
Verified
Journal Article
Open access
PDF file
WWWenting Wu
Wenting Wu
YGYiwei Gao
Yiwei Gao
CCChunpeng Chen
Chunpeng Chen
YSYu Sun
Yu Sun
Hua SuHua Su
Hua Su
The coastline plays an important role in indicating the conditions of social-economic development in the coastal zone. In this study, an integrated assessment framework was proposed to address the provincial and county-level spatiotemporal dynamics of continental coastlines from the perspectives of length, position, composition, and anthropogenic utilization quantitatively, and to explore the exact impacts of urbanization on coastline changes in the Fujian Province over the period from 1985 to 2020. Results showed that the total length of coastlines decreased first and then increased due to the different patterns of economic development. The proportion of artificial coastlines and the index of coastal utilization degree increased rapidly during the same period. Moreover, the seaward movement of coastlines due to the coastal reclamation projects resulted in a considerable increment in land areas. The pressure brought by the continuous concentration of population, built-up areas, and industrial districts under the rapid urbanization was the primary factor that increased the degree of anthropogenic disturbances in the coastal zone. Furthermore, the policies issued by the local or central government can be critical tipping points for coastline changes in different periods.

2 March 2024 in Remote Sensing
Verified
Journal Article
Open access
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GZGan Zhiquan
Gan Zhiquan
SGShurong Guo
Shurong Guo
CCChunpeng Chen
Chunpeng Chen
HZHanjie Zheng
Hanjie Zheng
YHYuekai Hu
Yuekai Hu
WWWenting Wu
Wenting Wu
Tidal flats in northern China are essential parts of the East Asian-Australasian Flyway, the densest pathway for migratory waterbirds, and are of great ecological and economic importance. They are threatened by human activities and climate change, raising the urgency surrounding tracking the spatiotemporal dynamics of tidal flats. However, there is no cost-effective way to map morphological changes on a large spatial scale due to the inaccessibility of the mudflats. In this study, we proposed a pixel-based multi-indices tidal flat mapping algorithm that precisely characterizes 2D/3D morphological changes in tidal flats in northern China using time-series remote sensing data. An overall accuracy of 0.95 in delineating tidal flats to a 2D extent was achieved, with 11,716 verification points. Our results demonstrate that the reduction in sediment discharge from rivers along the coastlines of the Yellow and Bohai Seas has resulted in an overall decline in the area of tidal flats, from 4856.40 km2 to 4778.32 km2. Specifically, 3D analysis showed that significant losses were observed in the mid-to-high-tidal flat zones, while low-elevation tidal flats experienced an increase in area due to the transformations in mid-to-high-tidal flats. Our results indicate that the sediment inputs from rivers and the succession of native vegetation are the primary drivers leading to 2D/3D morphological changes of tidal flats following the cessation of extensive land reclamation in northern China.

30 May 2025 in Forests
Verified
Journal Article
Open access
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YLYang Li
Yang Li
SZShaokun Zhou
Shaokun Zhou
YHYongping Hou
Yongping Hou
YHYuekai Hu
Yuekai Hu
CCChunpeng Chen
Chunpeng Chen
YSYuhong Song
Yuhong Song
Mountain forests in biodiversity hotspots show complex responses to climate and topographic gradients. However, the effect of synergistic controls of elevation and climate on Net Primary Productivity (NPP) dynamics remain insufficiently quantified in complex mountains. Southwest China’s mountains are Asia’s most biodiverse temperate region with pronounced vertical ecosystem stratification, representing a critical continental carbon sink. This study investigated the spatiotemporal dynamics and driving mechanisms of NPP in Southwest China’s typical mountain ecosystems over the past three decades using a high-resolution modeling framework integrated with relative importance analysis, a Geodetector, and an elevation-dependent model. The results showed that (1) NPP revealed a significant increasing trend, rising from 634 ± 325 to 748 ± 348 g C m−2 yr−1 (mean rate 4 g C m−2 yr−1) from 1990 to 2018. Spatially, the most rapid increases occurred in eastern regions. (2) Rising CO2 and climate warming (dominate 17% regions) drove interannual NPP growth, with elevation thresholds dictating driver dominance. The CO2 governed low elevation, while temperature controlled higher elevation (>4800 m). (3) The elevation-dependent model revealed a more complex and nonlinear relationship between NPP and elevation, identifying three distinct phases: the saturation phase (3500 m) with continuously attenuated NPP losses (NPP average loss of 10.5 g C m⁻2 yr⁻1 per 100 m) reflecting high-elevation vegetation adaptation to extreme conditions. (4) Land cover dominated NPP spatial heterogeneity and was amplified by interactions with elevation and temperature, highlighting a vegetation–climate–topography coupling mechanism that critically shapes productivity patterns. Biodiversity-rich widespread mixed forests underpinned the region’s high productivity. Mountain protection should focus on protecting existing evergreen forests from fragmentation, while forestation should prioritize the establishment of biodiversity-rich mixed forest. These findings established a comprehensive framework for spatiotemporal analysis of driving mechanisms and enhanced the understanding of NPP dynamics in complex mountain ecosystems, informing sustainable management priorities in mountain regions.

30 April 2025 in Geo-spatial Information Science
Verified
Journal Article
Open access
QDQiannan Ding
Qiannan Ding
CCChunpeng Chen
Chunpeng Chen
BTBo Tian
Bo Tian
WJWenxiang Ji
Wenxiang Ji
BZBin Zhang
Bin Zhang
QYQing Yuan
Qing Yuan