Qingwei Zhuang

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Zhenfeng Shao

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Xiao Huang

Emory University

Chaoya Dang

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Shixin Wu

Xinjiang Institute of Ecology and Geography

Yuzhen Li

Xihua University

Orhan Altan

Istanbul Technical University

Jiaxin Qian

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Deren Li

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Bowen Cai

Wuhan University

Xiaodi Xu

Nanjing Agricultural University

Cooperative Institutions

Chinese Academy of Sciences

Wuhan University

University of Chinese Academy of Sciences

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Xinjiang Institute of Ecology and Geography

Institute of Ecology and Geography

Shanghai Jiao Tong University

Russian Academy of Sciences

Marine Biological Laboratory

Research Center for Ecology and Environment of Central Asia

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Monitoring the impacts of cultivated land quality on crop production capacity in arid regions

CATENA (2022)

Qingwei Zhuang Shixin Wu Xiao Huang Lu Kong Yuyan Yan

Arable land

10.1016/j.catena.2022.106263

Cite

Citations (30)

Monitoring land surface thermal environments under the background of landscape patterns in arid regions: A case study in Aksu river basin

The Science of The Total Environment (2019)

Qingwei Zhuang Shixin Wu Yuyan Yan Yaxuan Niu Fan Yang

10.1016/j.scitotenv.2019.136336

Cite

Citations (24)

Evaluation of the resistance and resilience of terrestrial ecosystems to drought in southwest China

Journal of Hydrology (2024)

Yuzhen Li Qingwei Zhuang Hongfei Zhao Wenqiang Zhang Peng Cai

Resilience

Terrestrial ecosystem

Drought Resistance

10.1016/j.jhydrol.2024.132318

Cite

Citations (0)

Impact of global urban expansion on the terrestrial vegetation carbon sequestration capacity

The Science of The Total Environment (2023)

Qingwei Zhuang Zhenfeng Shao Deren Li Xiao Huang Yuzhen Li

Urban expansion

Terrestrial ecosystem

Global Change

10.1016/j.scitotenv.2023.163074

Cite

Citations (34)

Joint estimation of aboveground biomass using “Space-Air-Ground” data in the Qilian Mountains, China

Ecological Indicators (2022)

Zi‐Hui Zhang Shixin Wu Qingwei Zhuang Xiangyi Li Fanjiang Zeng

Accurate monitoring of aboveground biomass (AGB) and its spatial distribution is essential for sustainable management and development. The most frequently used method for estimating AGB with remotely sensed data is based on the relationship between AGB measured data and vegetation indices. In the past, there were some shortcomings in analyzing the spatial heterogeneity and solving scale difference. Therefore, this paper realized the space-air-ground integrated based on Sentinel-2 Multispectral Instrument (MSI), Unmanned Aerial Vehicle (UAV) and measured data. The results indicated that the RFR models have excellent adaptability to invert AGB of three different coverage regions in study area, with R2 of 0.69 and root mean square error (RMSE) of 116.22 kg·hm−2 for low-coverage region, R2 of 0.65 and RMSE of 164.16 kg·hm−2 for medium-coverage region, R2 of 0.73 and RMSE of 149.99 kg·hm−2 for high-coverage region. We found that the main driving vegetation indices was different in each region during the process of model building and it was related to the characteristics of vegetation indices. Moreover, regions with different coverage have different responses to topography. The findings showed that the spatial distribution and driving mechanism of AGB in the study area could be more clearly analyzed based on the coverage division. The introduction of coverage division reasonably enhanced the accuracy of RFR modeling AGB. This study confirmed that using the Sentinel-2 MSI, UAV images, measured data and the RFR model to realize the space-air-ground integrated is an applicable method for monitoring and assessment of AGB in Qilian Mountains, which can offer scientific basis and theoretical support to rational development of land resources and maintaining the balance of ecosystem.

10.1016/j.ecolind.2022.108866

Cite

Citations (1)

Quantifying the US Crop Yield in Response to Extreme Climatic Events from 1948 to 2013

2014 AGU Fall Meeting (2014)

Zhenong Jin Qingwei Zhuang

Source

Cite

Citations (0)

Integrating Zhuhai-1 Hyperspectral Imagery With Sentinel-2 Multispectral Imagery to Improve High-Resolution Impervious Surface Area Mapping

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2022)

Xiaoxiao Feng Zhenfeng Shao Xiao Huang Luxiao He Xianwei Lv

Mapping impervious surface area (ISA) in an accurate and timely manner is essential for a variety of fields and applications, such as urban heat islands, hydrology, waterlogging, and urban planning and management. However, the large and complex urban landscapes pose great challenges in retrieving ISA information. Spaceborne hyperspectral (HS) remote sensing imagery provides rich spectral information with short revisit cycles, making it an ideal data source for ISA extraction from complex urban scenes. Nevertheless, insufficient single-band energy, the involvement of modulation transfer function (MTF), and the low signal-to-noise ratio (SNR) of spaceborne HS imagery usually result in poor image clarity and noises, leading to inaccurate ISA extraction. To address this challenge, we propose a new deep feature fusion-based classification method to improve 10 m resolution ISA mapping by integrating Zhuhai-1 HS imagery with Sentinel-2 multispectral (MS) imagery. We extract deep features that include spectral and spatial features, respectively, from MS and HS imagery via a 2-D convolutional neural network (CNN), aiming to increase feature diversity and improve the model's recognition capability. The Sentinel-2 imagery is used to enhance the spatial information of the Zhuhai-1 HS image, improving the urban ISA retrieval by reducing the impact of noises. By combining the deep spatial features and deep spectral features, we obtain joint spatial-spectral features, leading to high classification accuracy and robustness. We test the proposed method in two highly urbanized study areas that cover Foshan city and Wuhan city, China. The results reveal that the proposed method obtains an overall accuracy of 96.72% and 96.75% in the two study areas, 18.78% and 8.66% higher than classification results with only HS imagery as input. The final ISA extraction overall accuracy is 95.42% and 95.50% in the two study areas, the highest among the comparison methods.

Impervious surface

10.1109/jstars.2022.3157755

Cite

Citations (9)

Assessing the impact of drought-land cover change on global vegetation greenness and productivity

The Science of The Total Environment (2022)

Jinlong Chen Zhenfeng Shao Xiao Huang Qingwei Zhuang Chaoya Dang

Land Cover

Global Change

Terrestrial ecosystem

10.1016/j.scitotenv.2022.158499

Cite

Citations (60)

Quantitative the urban vegetation carbon storage by constructing seamless and dense time series remote sensing data

Ya Zhang Zhenfeng Shao Xiaodi Xu Qingwei Zhuang

 Abstract Urban vegetation is an integral component of the terrestrial ecosystem, and accurate calculation of its carbon storage is essential. Net primary productivity (NPP), as a crucial element in the dynamic surface carbon cycle, plays a key role in characterizing the resilience of terrestrial ecosystems. It also serves as a fundamental indicator for measuring carbon storage. However, the insufficient coverage of effective image data within cities hampers the acquisition of high-quality Normalized Difference Vegetation Index (NDVI) data, posing a challenge for estimating carbon storage across different vegetation periods. Consequently, continuous monthly NPP estimation remains a challenge. In response to this issue, the primary objective of this study is to propose a novel approach for estimating intensive time series NPP by reconstructing NDVI data. We utilized the GF-SG algorithm to construct a densely sampled NDVI time series dataset by merging Landsat 8 and MODIS data, coupled with meteorological information. These inputs were then employed in an improved CASA model, enabling the generation of monthly NPP results at a 30m resolution. Using the Pearl River Delta (PRD) region as a case study, we investigated temporal and spatial variations in NPP resulting from changes in land cover and their impact on vegetation from 2017 to 2020. Our findings suggest that establishing a consistent time series of NDVI data contributes to reducing the uncertainty associated with NPP inversion outcomes. The annual cumulative NPP values varied significantly among different land cover types. Particularly, forest areas exhibited significantly higher cumulative NPP compared to other land types, with the order of NPP values being Crops>Grass>Wetlands. The transformation of land cover within the study area resulted in a substantial reduction in NPP, amounting to a decrease of 29.07 GgC. Importantly, even in the absence of image data, this method can estimate the net primary productivity of vegetation while maintaining high accuracy. Crucially, this approach can support long-term and large-scale monitoring of vegetation carbon storage within urban agglomerations. Keywords: Urban vegetation, carbon storage, Net primary productivity, Improved temporal NDVI, High observation frequency

10.5194/egusphere-egu24-650

Cite

Citations (0)

Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery

International Journal of Applied Earth Observation and Geoinformation (2025)

Xiaodi Xu Ya Zhang Peng Fu Chaoya Dang Bowen Cai

Tree canopy

10.1016/j.jag.2024.104348

Cite

Citations (0)