Ronggao Liu

Institute of Geographic Sciences and Natural Resources Research

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Yang Liu

Dantherm Power (Denmark)

Jiyuan Liu

Sichuan University

Jing M. Chen

Nanjing University

Rong Shang

Fujian Normal University

Quansheng Ge

Chinese Academy of Sciences

Shunlin Liang

University of Hong Kong

Mingzhu Xu

Fujian Normal University

Weimin Ju

Nanjing University

Chaoyang Wu

Institute of Geographic Sciences and Natural Resources Research

Xuexin Wei

Institute of Geographic Sciences and Natural Resources Research

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Chinese Academy of Sciences

127

University of Chinese Academy of Sciences

Institute of Geographic Sciences and Natural Resources Research

Advanced Lightweight Engineering (Netherlands)

Peking University

State Key Laboratory of Remote Sensing Science

Sun Yat-sen University

Beijing Normal University

Nanjing University

Tsinghua University

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しきい値分割に基づく黒竜江省森林タイプのリモートセンシング認識【JST・京大機械翻訳】

Dili Kexue Jinzhan (2016)

Yuan Yujuan Yin Yunhe Erfu Dai Ronggao Liu Shaohong Wu

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Estimation of crop leaf area index using MODIS directional reflectances data

Yang Liu Ronggao Liu Siliang Liu

Leaf area index (LAI) is an essential parameter for monitoring crop growth dynamic. An algorithm, which is based on physical model and neural networks to derive crop LAI from MODIS land surface reflectance, is presented. This algorithm utilizes the directional reflectances instead of the BRDF normalized data to avoid complex BRDF normalization and the error from it. The estimated LAI is compared with existing LAI products. Results show that it is consistent with MODIS (RMSE = 0.4994) and CYCLOPES (RMSE = 0.6658) LAI products in temporal and spatial patterns. The algorithm is validated against ground measurements of annual crop LAI in 2004 in Hengshui, China. The neural network derived LAI could represent the spatial pattern of the field LAI. However, all these LAI products are lower than field measurements. It would be suggested that the physical model should be modified to adapt to the dense crop in Northern China.

Normalization

10.1109/igarss.2010.5652674

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Decoding the inconsistency of six cropland maps in China

The Crop Journal (2024)

Yifeng Cui Ronggao Liu Zhichao Li Chao Zhang Xiao‐Peng Song

Accurate cropland information is critical for agricultural planning and production, especially in food-stressed countries like China. Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades, considerable discrepancies exist among these products both in total area and in spatial distribution of croplands, impeding further applications of these datasets. The factors influencing their inconsistency are also unknown. In this study, we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020, including three state-of-the-art 10-m products (i.e., Google Dynamic World, ESRI Land Cover, and ESA WorldCover) and three 30-m ones (i.e., GLC_FCS30, GlobeLand 30, and CLCD). We also investigated the effects of landscape fragmentation, climate, and agricultural management. Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy (92.3%). These maps collectively overestimated Chinese cropland area by up to 56%. Up to 37% of the land showed spatial inconsistency among the maps, concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps, cropland fragmentation and management practices such as irrigation. Our work shed light on the promotion of future cropland mapping efforts, especially in highly inconsistent regions.

Ground truth

Fragmentation

Land Cover

10.1016/j.cj.2023.11.011

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A thermal index from modis data for dust detection

Yang Liu Ronggao Liu

With increasing land degradation and deforestation, dust storms have been an essential factor of air pollution and global climate and biogeochemical cycle. A new index, Thermal Infrared Integrated Dust Index (TIIDI), has been developed to detect dust over land and ocean. This dust detection algorithm is based on brightness temperature difference of four thermal infrared channels, including BTD3.7-11, BTD8.6-11 and BTD12-11. BTD12-11 is mainly used to identify cloud, BTD8.6-11 is used as an indicator of airborne dust and surface sand, and BTD3.7-11 is used to separate dark surface and represent the intensity of dust storm. The algorithm is applied to monitor atmospheric dust storms over various landcover types, including ocean, dark object (vegetation), and bright surface such as desert by using MODIS data. The results show that the TIIDI could distinguish mineral dust from cloud and land surface over bright surface and ocean. The index provides a dust indicator both day and night for global apply.

Dust storm

Mineral dust

10.1109/igarss.2011.6050054

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A Tailings Pond Identification Method Based on Spatial Combination of Objects

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2019)

Kun Liu Ronggao Liu Yang Liu

Tailings ponds pose a significant risk to the safety of the surrounding residents and the local ecological environment. Therefore, it is necessary to efficiently and accurately monitor tailings ponds. However, the internal structures of tailings ponds are heterogeneous, and they are typically identified through manual interpretation. In this paper, an identification method for four main structures of tailings ponds, namely, starter dams, embankments, deposited beach, and water body, is proposed based on the spatial combinations among them. First, hierarchy objects were established based on GaoFen-2 imagery. Then, candidate objects (such as embankment-like) were identified using the spectral features and the number of parallel lines. Subsequently, rural settlement-like objects were eliminated as interfering objects based on their shapes and distributions. Finally, four structures of tailings ponds could be identified based on their spatial combination. Six cases of tailings ponds were selected for validation. Interference categories were eliminated step by step (79.64%, 38.21%, and 39.75% for case A), and all four structures were identified. The overall identification accuracies were 88.14%-96.21%. The average accuracy is 32.02% higher than that of comparison experiments using the random forest. The method was proved to be applicable to the automatic identification of mining areas, which is of great significance for efficient and accurate supervision of mining safety.

Identification

Tailings dam

10.1109/jstars.2019.2904297

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Global leaf chlorophyll content (LCC) product from MODIS imagery (2000-2020)

Zenodo (CERN European Organization for Nuclear Research) (2021)

Mingzhu Xu Ronggao Liu Jing M. Chen Yang Liu Rong Shang

The spatial and temporal distribution of leaf chlorophyll content (LCC) is critical for understanding the capacity of vegetation photosynthesis. Here, a global 8-day leaf chlorophyll content (LCC) dataset at 500-m resolution was generated from MODIS data using a matrix system with two pairs of vegetation indices. The following paper should be cited when using the data: Xu, M., Liu, R., Chen, J.M., Liu, Y., Wolanin, A., Croft, H., He, L., Shang, R., Ju, W., Zhang, Y., He, Y., Wang, R., 2022. A 21-year time-series of global leaf chlorophyll content maps from MODIS imagery. IEEE Trans. Geosci. Remote Sens. https://doi.org/10.1109/TGRS.2022.3204185. Detailed description of data organization can be found in the uploaded document "1Readme.docx". The whole dataset of MODIS LCC product is from 2000 to 2020. Due to the data volume limitation of Zenodo, currently the data from 2000-2010 can be downloaded through Google Drive sharing link: https://drive.google.com/drive/folders/11eXetjsAB_ZjFqGs8SXEWzd6byr_8LoW?usp=sharing Related dataset: Mingzhu, X., Liu, R., Chen, J. M., Shang, R., & Liu, Y. (2022). Global leaf chlorophyll content product from MERIS imagery (GLOBMAP MERIS LCC) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10467919 For any other questions, please send email to Mingzhu Xu (xumzhu@gmail.com).

Content (measure theory)

10.5281/zenodo.5805575

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Automatic lunar crater detection based on DEM data using a max curvature detection method

IEEE Geoscience and Remote Sensing Letters (2024)

Quan Duan Ronggao Liu Yang Liu

10.1109/lgrs.2024.3368705

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Nitrogen Availability Dampens the Positive Impacts of CO₂ Fertilization on Terrestrial Ecosystem Carbon and Water Cycles

Geophysical Research Letters (2017)

Liming He Jing M. Chen Holly Croft Alemu Gonsamo Xiangzhong Luo

Abstract The magnitude and variability of the terrestrial CO 2 sink remain uncertain, partly due to limited global information on ecosystem nitrogen (N) and its cycle. Without N constraint in ecosystem models, the simulated benefits from CO 2 fertilization and CO 2 ‐induced increases in water use efficiency (WUE) may be overestimated. In this study, satellite observations of a relative measure of chlorophyll content are used as a proxy for leaf photosynthetic N content globally for 2003–2011. Global gross primary productivity (GPP) and evapotranspiration are estimated under elevated CO 2 and N‐constrained model scenarios. Results suggest that the rate of global GPP increase is overestimated by 85% during 2000–2015 without N limitation. This limitation is found to occur in many tropical and boreal forests, where a negative leaf N trend indicates a reduction in photosynthetic capacity, thereby suppressing the positive vegetation response to enhanced CO 2 fertilization. Based on our carbon‐water coupled simulations, enhanced CO 2 concentration decreased stomatal conductance and hence increased WUE by 10% globally over the 1982 to 2015 time frame. Due to increased anthropogenic N application, GPP in croplands continues to grow and offset the weak negative trend in forests due to N limitation. Our results also show that the improved WUE is unlikely to ease regional droughts in croplands because of increases in evapotranspiration, which are associated with the enhanced GPP. Although the N limitation on GPP increase is large, its associated confidence interval is still wide, suggesting an urgent need for better understanding and quantification of N limitation from satellite observations.

Water Use Efficiency

Terrestrial ecosystem

Carbon sink

Water cycle

Stomatal Conductance

FluxNet

10.1002/2017gl075981

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Panoramic reconstruction of central green belt of different levels of highway based on UAV platform

Tao Duan Lingzhi Sang Pengcheng Hu Ronggao Liu Lin Wang

In order to reconstruct the panoramic model and extract plant community structure characteristics rapidly and accurately for the highway central green belt, a method for rapidly acquiring images of the targets highway central green belt using quad rotor unmanned aerial vehicle (UAV) and digital image processing techniques is explored. It has the advantages of real-time, high flexibility and low cost to obtain the plant information of the green belt in the middle of the road based on the low-altitude unmanned aerial vehicle. The results show that the images acquired with a lower flight height (30 m and 50 m) can be used to produce rather high quality Ortho-mosaic, 3D point cloud reconstruction and ideal digital surface model. The density of 3D reconstruction point cloud for the target sections is even, and the single-pixel resolution of the ortho-mosaic image can reach 0.67 cm and 1.0 cm, respectively. Thus, it can be seen that the UAV platform can obtain high-precision panoramic reconstruction of monitoring areas with different road types.

Digital camera

Low altitude

10.1109/ictis.2019.8883772

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Evaluating the vegetation destruction and recovery of Wenchuan earthquake using MODIS data

Natural Hazards (2010)

Yang Liu Ronggao Liu Quansheng Ge

Natural hazard

10.1007/s11069-010-9511-z

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Citations (50)