The Laser Vegetation Detecting Sensor: A Full Waveform, Large-Footprint, Airborne Laser Altimeter for Monitoring Forest Resources

Yang, Huifen; Wu, Fayun; Sun, Zhongqiu; Lister, Andrew J.; Gao, Xianlian; Li, Weitao; Peng, Dong

doi:10.3390/s19071699

Cited by 12 publications

(4 citation statements)

References 56 publications

(58 reference statements)

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“…The tree heights (measured by a VERTEX LASER VL5 manufactured in Haglof, Sweden) and diameters at breast height (DBHs) were measured for all individual trees (measured by a caliper with a DBH greater than 5 cm) in the circle sample plot. TBSJDPT ( Figure 2) was put forward and used to locate the sample plot center and the individual trees [39]. The tree heights (measured by a VERTEX LASER VL5 manufactured in Haglof, Sweden) and diameters at breast height (DBHs) were measured for all individual trees (measured by a caliper with a DBH greater than 5 cm) in the circle sample plot.…”

Section: In Situ Sample Plot Data Collectionmentioning

confidence: 99%

Section: In Situ Sample Plot Data Collectionmentioning

confidence: 99%

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Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Yang

et al. 2020

Remote Sensing

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The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R 2 = 0.91, RMSE = 35.13 m 3 ha −1 , n = 321) and in the test phase (i.e., R 2 = 0.58, RMSE = 65.03 m 3 ha −1 , and n = 138). This model was followed by the SVR model (R 2 = 0.54, RMSE = 65.60 m 3 ha −1 , n = 321 in training; R 2 = 0.54, RMSE = 66.00 m 3 ha −1 , n = 138 in testing), which was slightly better than the MLR model (R 2 = 0.38, RMSE = 75.74 m 3 ha −1 , and n = 321 in training; R 2 = 0.49, RMSE = 70.22 m 3 ha −1 , and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 10 8 m 3 ) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 10 8 m 3 ). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.The forest stock volume (FSV, m 3 ha −1 ) is the sum of the stem volumes of all the living trees per unit area, and is one of key forest variables for forest resources management and assessments on local, region and country scales [1]. FSVs have a strong relationship with the aboveground biomass (AGB) and carbon stocks [2]. To understand the spatial distribution of carbon in forests and to derive predictions for monitoring carbon stock trends, the FSV must be quantified [3]. Traditionally, the FSV is estimated by sampling several plots, which involves substantial manpower, materials, and financial resources [4]. With the development of remote sensing technology, particularly the Landsat series since 1972, satellite imagery has played an important role in forest inventory. Various studies have been performed to estimate the forest variables ...

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Section: In Situ Sample Plot Data Collectionmentioning

confidence: 99%

Section: In Situ Sample Plot Data Collectionmentioning

confidence: 99%

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Yang

et al. 2020

Remote Sensing

Self Cite

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“…Today, single-wavelength and multi-wavelength (at two or three wavelengths) lidars in the visible and/or near-infrared range are used for forest monitoring [1][2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Multispectral lidar method for monitoring the forest ecosystem under the forest canopy

Белов

Belov

Городничев

et al. 2022

J. Phys.: Conf. Ser.

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This study demonstrates the potential of the multispectral lidar method to monitor the forest ecosystem under the forest canopy. The mathematical modeling results of forest territories elements classification on the created neural network using experimentally measured reflection coefficients are presented. It is shown that the neural network provides a high probability of correct classification for the forest ecosystem elements classification task (when using lidar measurement data about the height of the forest ecosystem elements). Laser pulse sounding at two wavelengths in near infrared spectral range 1064 and 2030 nm and the created neural network provide the probabilities of correctly classify the undergrowth of green broadleaved and coniferous trees, swamps and soils more than 0.84 and the probability of incorrect classification less than 0.08.

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“…Therefore, in this article, we propose to combine the respective advantages of the two types of methods. The specific objectives of this article are as follows: 1) Introduce a novel method based on the combination of deconvolution and Gaussian decomposition for more accurate peak position extraction, which will result in a more precise description of close object heights and the complex structure within large laser footprints; 2) present a thorough comparison between the deconvolution methods of the Wiener filter, the Richardson-Lucy algorithm, the regularization filter, and blind deconvolution, for applications in large laser footprints, to find the best deconvolution technique; and 3) give an insight into the advantages and limitations of the combination method compared with the benchmark Gaussian decomposition technique, in the expectation of providing a reference for the processing of future waveforms obtained by China's Gaofen-7 (GF-7) satellite and the terrestrial ecosystem carbon monitoring (TECM) mission, which are mainly for high-accuracy 3-D mapping [48] and forest monitoring [49]. GF-7 has a pulse length of 7 ns and a footprint of about 30 m, and was launched on 3 November 2019; TECM will be launched in 2020.…”

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confidence: 99%

A Combined Deconvolution and Gaussian Decomposition Approach for Overlapped Peak Position Extraction From Large-Footprint Satellite Laser Altimeter Waveforms

Zhang

Xie

Tong

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In satellite laser altimetry, it is a challenging task to accurately extract peak positions from full waveforms due to the overlapped or weak peaks within the large laser footprints, which substantially affects the subsequent applications. In this article, to improve the laser ranging resolution and accuracy, we propose a novel approach by combining deconvolution with Gaussian decomposition. The approach is applied in two main phases: 1) The deconvolution is first used to remove the system contribution (the transmit pulse spreading over several nanoseconds, system noise); and 2) Gaussian decomposition is then adopted to extract the peak parameters of each object. Experiments using simulated and ICESat waveforms were conducted to validate and evaluate the Manuscript

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The Laser Vegetation Detecting Sensor: A Full Waveform, Large-Footprint, Airborne Laser Altimeter for Monitoring Forest Resources

Cited by 12 publications

References 56 publications

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Multispectral lidar method for monitoring the forest ecosystem under the forest canopy

A Combined Deconvolution and Gaussian Decomposition Approach for Overlapped Peak Position Extraction From Large-Footprint Satellite Laser Altimeter Waveforms

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