Prospects of the ICESat-2 laser altimetry mission for savanna ecosystem structural studies based on airborne simulation data

Gwenzi, David; Lefsky, M. A.; Suchdeo, V. P.; Harding, David J.

doi:10.1016/j.isprsjprs.2016.04.009

Cited by 64 publications

(32 citation statements)

References 15 publications

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“…It is also confirmed by simulation studies which prove the optimal footprint diameters of 25 and 30 m [43]. Also, study focus on MABEL data showed similar findings that 50 m is not always the optimal size to estimate the maxH [28].…”

Section: Discussionsupporting

confidence: 64%

“…It can be seen that the geolocation between the SIMPL and G-LiHT data is not satisfying; therefore, an approach based on moving profiling [28] is implemented to match these two datasets. First, we extract the ground photons from our signal detection results and create a DTM profile; next we compare this profile with the DTM profile generated from G-LiHT.…”

Section: Co-registration Between Simpl and G-liht Datamentioning

confidence: 99%

“…These studies show that one of the challenges for application to vegetation study is the significant quantity of noise photons, with photon return time giving the appearance of originating in the atmosphere and even below the ground [23,24]. Several attempts have been made to separate the signal photons from the noise photons by methods such as spatial statistical based detection algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), ellipsoidal histogramming method, and a framework to retrieve ground and canopy height [25][26][27][28][29]. In addition, some studies focused on the application rather than the data pre-processing showed some promising results, especially for vegetation studies [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…However, studies focusing on the potential estimation of forest parameters by using the ATLAS-like data are still very limited. There are some findings including investigating the dryland ecosystem vegetation cover and biomass by using a combination of Landsat 8 and ATLAS-like data [34], the similarity comparison with airborne LiDAR system of the ICESat-2 laser altimetry for savanna ecosystem [28,35], and an automated approach for better estimating vegetation canopy height [36]. The study to explored potential for biomass retrieval over boreal ecosystems, using the radiative transfer model FLIGHT to simulate ICESat-2 [37].…”

Section: Introductionmentioning

confidence: 99%

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Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest

Chen

Pang

et al. 2019

Remote Sensing

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ICESat-2 is the new generation of NASA’s ICESat (Ice, Cloud and land Elevation Satellite) mission launched in September 2018. We investigate the potential of forest parameter estimation using metrics from photon counting LiDAR data, using an integrated dataset including photon counting LiDAR data from SIMPL (the Slope Imaging Multi-polarization Photon-counting LiDAR), airborne small footprint LiDAR data from G-LiHT and a stem map in Howland Research Forest, USA. First, we propose a noise filtering method based on a local outlier factor (LOF) with elliptical search area to separate the ground and canopy surfaces from noise photons. Next, a co-registration technique based on moving profiling is applied between SIMPL and G-LiHT data to correct geolocation error. Then, we calculate height metrics from both SIMPL and G-LiHT. Finally, we investigate the relationship between the two sets of metrics, using a stem map from field measurement to validate the results. Results of the ground and canopy surface extraction show that our methods can detect the potential signal photons effectively from a quite high noise rate environment in relatively rough terrain. In addition, results from co-registration between SIMPL and G-LiHT data indicate that the moving profiling technique to correct the geolocation error between these two datasets achieves favorable results from both visual and statistical indicators validated by the stem map. Tree height retrieval using SIMPL showed error of less than 3 m. We find good consistency between the metrics derived from the photon counting LiDAR from SIMPL and airborne small footprint LiDAR from G-LiHT, especially for those metrics related to the mean tree height and forest fraction cover, with mean R 2 value of 0.54 and 0.6 respectively. The quantitative analyses and validation with field measurements prove that these metrics can describe the relevant forest parameters and contribute to possible operational products from ICESat-2.

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Section: Discussionsupporting

confidence: 64%

Section: Co-registration Between Simpl and G-liht Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest

Chen

Pang

et al. 2019

Remote Sensing

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“…From the currently released data products [9], the photon counting approach introduced abundant noise appearing in the atmosphere and even below the ground, making it difficult to extract the correct canopy and ground surface in vegetation area [10]. A few studies have been done to detect the noise and separate the signal, such as a spatial statistical detection algorithm based on discrete mathematical concepts [11], an ellipse search area based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) [12] and an automated algorithm using the cumulative density of photons to identify cut off points of canopy top and ground [13]. In addition, a recent novel methodological framework to retrieve ground and canopy height for both MABEL and simulated ICESat-2 data achieved good results for various nighttime and daytime scenarios [14].…”

Section: Introductionmentioning

confidence: 99%

Ground and Top of Canopy Extraction From Photon-Counting LiDAR Data Using Local Outlier Factor With Ellipse Searching Area

Chen

Pang

et al. 2019

IEEE Geosci. Remote Sensing Lett.

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The future ICESat-2 is the next generation of NASA's ICESat (Ice, Cloud and land Elevation Satellite) mission scheduled to be launched in 2018. The new photon counting LiDAR onboard ICESat-2 introduced new challenges to the estimation of forest parameters and their dynamics, the greatest being the abundant photon noise appearing in returns from the atmosphere and below the ground. To identify the potential forest signal photons, we proposed an approach by using a local outlier factor (LOF) modified with ellipse searching area. Six test data from two kinds of the photon counting LiDAR data in the USA are used to test and evaluate the performance of our algorithm. The classification results for noise and signal photons showed our approach has a good performance not only in lower noise rate with relatively flat terrain surface but also works even for a quite high noise rate environment in relatively rough terrain. The quantitative assessment indicates that the horizontal ellipse searching area gives the best result compared with the circle or vertical ellipse searching area. These results demonstrate our methods would be useful for future ICESat-2 vegetation study.

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A main direction-based noise removal algorithm for ICESat-2 photon-counting LiDAR data

Pan,

Gao,

Wang

et al. 2024

J Geod

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Prospects of the ICESat-2 laser altimetry mission for savanna ecosystem structural studies based on airborne simulation data

Cited by 64 publications

References 15 publications

Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest

Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest

Ground and Top of Canopy Extraction From Photon-Counting LiDAR Data Using Local Outlier Factor With Ellipse Searching Area

A main direction-based noise removal algorithm for ICESat-2 photon-counting LiDAR data

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