Hailang Qiao

Chinese Academy of Sciences

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Zheng Niu

State Key Laboratory of Remote Sensing Science

Li Wang

Chinese Academy of Sciences

Kang Jun

State Key Laboratory of Remote Sensing Science

Shuai Gao

Aerospace Information Research Institute

Yuchu Qin

Beijing Institute of Big Data Research

Gang Sun

Gansu Agricultural University

Muhammad Shakir

Institute of Space Technology

Wen‐Yong Guo

East China Normal University

Chai Ziwei

Xin Zhao

Hohai University

Cooperative Institutions

Chinese Academy of Sciences

State Key Laboratory of Remote Sensing Science

Aerospace Information Research Institute

Institute of Remote Sensing and Digital Earth

Beijing Normal University

University of Chinese Academy of Sciences

Aarhus University

University of Science and Technology of China

Central South University

Xi'an University of Technology

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Extraction of the vertical distribution of biochemical parameters using hyperspectral LiDAR

Shuai Gao Zheng Niu Gang Sun Li Wang Hailang Qiao

The vertical distribution of plant physiological composition plays an important role in vegetation growth and carbon stock. The hyperspectral LiDAR was thought to be the most promising solution to assess the vertical distribution of vegetation physiological parameters, such as LAI (Leaf Area Index) and chlorophyll content. However, the instrument was not fully feasible, so the simulation and experiment of its response to these parameters was necessary. In this paper, the hyperspectral LiDAR waveform was simulated with the consideration of single scatter of plant radiative transfer model. The variation of LAI and chlorophyll along plant height was investigated and tree scenarios were assumed in the simulation. The hyperspectral lidar data experiment was also carried out to validate the simulation and method. The result showed that the hyperspectral LiDAR waveform could reflect the variation of plant physiological composition and facilitate the inversion algorithm design and it could play a significant role in parameter estimation and precision agriculture application in future.

Precision Agriculture

10.1109/igarss.2016.7729451

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Unmanaged naturally regenerating forests approach intact forest canopy structure but are susceptible to climate and human stress

One Earth (2024)

Wang Li Wen‐Yong Guo Maya Pasgaard Zheng Niu Li Wang

Forest structure

10.1016/j.oneear.2024.05.002

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Citations (1)

Bi-HRNet: A Road Extraction Framework from Satellite Imagery Based on Node Heatmap and Bidirectional Connectivity

Remote Sensing (2022)

Ziyun Wu Jinming Zhang Lili Zhang Xiongfei Liu Hailang Qiao

Today, with the rapid development of the geographic information industry, automatic road extraction from satellite imagery is a basic requirement. Most existing methods have been designed based on binary segmentation. However, these methods do not consider the topological features of road networks, which include point, edge, and direction. In this study, a topology-based multi-task convolution network is designed, namely Bi-HRNet, which can effectively learn the key features of nodes and their directions. First, the proposed network learns the node heatmap of roads, and then the pixel coordinates are extracted from the node heatmap via non-maximum suppression (NMS). At the same time, the connectivity between nodes is predicted. To improve the integrity and accuracy of connectivity, we propose a bidirectional connectivity prediction strategy, which can learn the bidirectional categories instead of direction angles. The bidirectional categories are designed based on “top-to-bottom” and “bottom-to-top” strategies, which can improve the accuracy of the connectivity between nodes. To illustrate the effectiveness of the proposed Bi-HRNet, we compare our method with several methods on different datasets. The experiments show that our method achieves a state-of-the-art performance and significantly outperforms various previous methods.

Convolution (computer science)

10.3390/rs14071732

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Citations (13)

The Construction of DEM in Mountain Plantation Landscape based on UAV Images

Yaogan jishu yu yingyong (2015)

Chai Ziwei Kang Jun Wang Li Xin Zhao Hailang Qiao

10.11873/j.issn.1004-0323.2015.3.0504

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Citations (2)

Identifying crown areas in an undulating area planted with eucalyptus using unmanned aerial vehicle near-infrared imagery

Remote Sensing Letters (2016)

Kang Jun Li Wang Kun Jia Zheng Niu Muhammad Shakir

In this letter, we propose an identification method of tree crown areas for imagery captured by a near-infrared camera on board an unmanned aerial vehicle platform over an undulating Eucalyptus planting area in Guangdong Province, China. The method extracts crown areas by applying mathematical morphology, unsupervised segmentation based on J-value segmentation, local spatial statistics, and Iterative Self-Organizing Data Analysis Technique Algorithm. Two morphology filters and four segmentation scales were compared between densely and sparsely planted plots as well as sunlit and shaded plots. The opening operation by the window size of 9×9 pixel and segmentation by the seed area sized 65×65 pixel achieved the best performance with overall accuracy of 91%, 93%, 89% and 91% in densely sunlit, sparsely sunlit, densely shaded and sparsely shaded plots.

Tree (set theory)

10.1080/2150704x.2016.1168947

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Citations (7)

Classification of Small-Scale Eucalyptus Plantations Based on NDVI Time Series Obtained from Multiple High-Resolution Datasets

Remote Sensing (2016)

Hailang Qiao Mingquan Wu Muhammad Shakir Li Wang Kang Jun

Eucalyptus, a short-rotation plantation, has been expanding rapidly in southeast China in recent years owing to its short growth cycle and high yield of wood. Effective identification of eucalyptus, therefore, is important for monitoring land use changes and investigating environmental quality. For this article, we used remote sensing images over 15 years (one per year) with a 30-m spatial resolution, including Landsat 5 thematic mapper images, Landsat 7-enhanced thematic mapper images, and HJ 1A/1B images. These data were used to construct a 15-year Normalized Difference Vegetation Index (NDVI) time series for several cities in Guangdong Province, China. Eucalyptus reference NDVI time series sub-sequences were acquired, including one-year-long and two-year-long growing periods, using invested eucalyptus samples in the study region. In order to compensate for the discontinuity of the NDVI time series that is a consequence of the relatively coarse temporal resolution, we developed an inverted triangle area methodology. Using this methodology, the images were classified on the basis of the matching degree of the NDVI time series and two reference NDVI time series sub-sequences during the growing period of the eucalyptus rotations. Three additional methodologies (Bounding Envelope, City Block, and Standardized Euclidian Distance) were also tested and used as a comparison group. Threshold coefficients for the algorithms were adjusted using commission–omission error criteria. The results show that the triangle area methodology out-performed the other methodologies in classifying eucalyptus plantations. Threshold coefficients and an optimal discriminant function were determined using a mosaic photograph that had been taken by an unmanned aerial vehicle platform. Good stability was found as we performed further validation using multiple-year data from the high-resolution Gaofen Satellite 1 (GF-1) observations of larger regions. Eucalyptus planting dates were also estimated using invested eucalyptus samples and the Root Mean Square Error (RMSE) of the estimation was 84 days. This novel and reliable method for classifying short-rotation plantations at small scales is the focus of this study.

Thematic Mapper

10.3390/rs8020117

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Citations (23)

Estimation of leaf biochemical content using a novel hyperspectral full-waveform LiDAR system

Remote Sensing Letters (2014)

Li Wang Gang Sun Zheng Niu Shuai Gao Hailang Qiao

A novel hyperspectral full-waveform light detection and ranging (LiDAR) system was proposed in this study. First, the optical set-up of this system was introduced. Second, the spectral reflectance of two kinds of leaves was measured using this system as well as a common spectrometer. A workflow was proposed to show how to calculate spectral reflectance from the LiDAR data. Three vegetation indices (VIs) – simple ratio index, normalized difference vegetation index and photochemical reflectance index – were calculated using the reflectance derived from the data collected by these two instruments. Finally, the LiDAR-derived VIs were used to test for the estimation of leaf biochemical contents. Results show that: (1) LiDAR reflectance is different from that measured by spectrometer, but the LiDAR and spectrometer spectral shapes are clearly similar; (2) leaf biochemical contents can be successfully estimated by LiDAR-derived VIs with a root mean square error of 0.46–0.55% for nitrogen content, 1.42–2.98 mg g−1 for chlorophyll content and 0.35–0.56 mg g−1 for carotenoid content. These results demonstrate the possibilities for estimating leaf biochemical contents using the new hyperspectral full-waveform LiDAR data.

Photochemical Reflectance Index

Ranging

Content (measure theory)

10.1080/2150704x.2014.960608

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Citations (49)

Monitoring the geodynamic behaviour of earthquake using Landsat 8-OLI time series data: case of Gorkha and Imphal

Biswajit Nath Zheng Niu Shukla Acharjee Hailang Qiao

Abstract. Prediction of earthquake in advance is really a challenging task for the scientific community till now. But research results from various scientists regarding lineament extraction using satellite imageries help us to way forward for earthquake monitoring study. For the present study, Landsat 8 OLI Time series data analyzed by integrating four different remote sensing and GIS software’s for automatic lineament extraction, its change, including lineament lengths and directions study by creating rose diagrams and finally vertical surface transect profile curve drawing. Two recent major earthquakes (in different geological settings Gorkha of Nepal 7.8 Mw and Imphal, Manipur of Eastern India 6.8 Mw) epicenter based single tile and corresponding same temporal scenes (three for before and one for after quake respectively) were considered for each case to perform lineament extraction, length variation and vertical surface transect profile change analysis. The research results witnessed major variations in lineament number, lineament length and its trends. The major trends found in an ESE-WNW, N-E, N-S, E-W, NNE-SSW directions and ESE-WSW, ESE-WNW, NE-SW on pre-earthquake scenes compared to post earthquake ESE-WNW, NE-SW, NNE-SSW were found for Gorkha, and ESE-WSW for Imphal regions respectively and in both cases, it was observed that the lineation trends return to its earlier status after an earthquake strike. The results obtained using the automated and geo-integrated methods compared cross validation with each other showed our method worked practically for earthquake monitoring and one can apply this new novel combined approach to predict the probable earthquake occurrence in advance just a few days before it strikes.

Lineament

Epicenter

Quake (natural phenomenon)

10.5194/nhess-2017-10

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Citations (7)