Mapping forest aboveground biomass with a simulated ICESat-2 vegetation canopy product and Landsat data

Narine, Lana L.; Popescu, Sorin C.; Zhou, Tan; Srinivasan, Shruthi; Harbeck, Kaitlin

doi:10.15287/afr.2018.1163

Cited by 19 publications

(23 citation statements)

References 34 publications

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“…Using an ICESat-2 track over vegetation in Finland, the authors reported a horizontal accuracy of 5 m and vertical RMSEs of 0.85 m and 3.2 m for terrain and canopy heights, when comparing with spatially coincident airborne lidar data. Using ICESat-2-like data, synergies between ICESat-2 and Landsat are also well-noted [13,16,17] and given the near-global coverage of ICESat-2 over a nominal mission period of 3 years and spatially continuous Landsat imagery, opportunities for achieving wall-to-wall global coverage of vegetation parameters are encouraging.…”

Section: Introductionmentioning

confidence: 90%

“…This study utilized available airborne lidar-derived AGB estimates for SHNF previously used to model AGB from simulated ICESat-2 data [16,17], and methods for upscaling were implemented to cover the extent of the ICESat-2 transect. AGB for SHNF was estimated from airborne lidar and ground inventory data [14].…”

Section: Reference Aboveground Biomass (Agb) Mapmentioning

confidence: 99%

“…AGB values (strong beam) were assigned to 30 m pixels based on the proportion of the 100-m segment across a pixel. Estimated AGB was then upscaled to represent the 30-m pixel, given the estimated area of the segment within a pixel [16,17]. The machine-learning technique, RF, was used for modeling AGB using the ModelMap R package [25] and the model was evaluated with a separate test set (20% of the data) through R 2 and RMSE.…”

Section: Mapping Agb With Icesat-2 and Landsat 8 Operational Land Imager (Oli)mentioning

confidence: 99%

“…Given the novelty of ICESat-2 data, specifically PCL data from a space-based platform, investigations to better understand its use and limitations for characterizing vegetation are warranted. Earlier work conducted with processed, simulated ICESat-2 data, yielded promising results for estimating and mapping AGB over forests in southeast Texas [14,16,17]. More recently, the accuracy of retrieving canopy heights with ICESat-2 data from the strong beam, was demonstrated [15].…”

Section: Introductionmentioning

confidence: 99%

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Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example

Narine

Popescu

Malambo³

2020

Remote Sensing

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National Aeronautics and Space Administration’s (NASA’s) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides rich insights over the Earth’s surface through elevation data collected by its Advanced Topographic Laser Altimeter System (ATLAS) since its launch in September 2018. While this mission is primarily aimed at capturing ice measurements, ICESat-2 also provides data over vegetated areas, offering the capability to gain insights into ecosystem structure and the potential to contribute to the sustainable management of forests. This study involved an examination of the utility of ICESat-2 for estimating forest aboveground biomass (AGB). The objectives of this study were to: (1) investigate the use of canopy metrics for estimating AGB, using data extracted from an ICESat-2 transect over forests in south-east Texas; (2) compare the accuracy for estimating AGB using data from the strong beam and weak beam; and (3) upscale predicted AGB estimates using variables from Landsat multispectral imagery and land cover and canopy cover maps, to generate a 30 m spatial resolution AGB map. Methods previously developed with simulated ICESat-2 data over Sam Houston National Forest (SHNF) in southeast Texas were adapted using actual data from an adjacent ICESat-2 transect over similar vegetation conditions. Custom noise filtering and photon classification algorithms were applied to ICESat-2’s geolocated photon data (ATL03) for one beam pair, consisting of a strong and weak beam, and canopy height estimates were retrieved. Canopy height parameters were extracted from 100 m segments in the along-track direction for estimating AGB, using regression analysis. ICESat-2-derived AGB estimates were then extrapolated to develop a 30 m AGB map for the study area, using vegetation indices from Landsat 8 Operational Land Imager (OLI), National Land Cover Database (NLCD) landcover and canopy cover, with random forests (RF). The AGB estimation models used few canopy parameters and suggest the possibility for applying well-developed methods for modeling AGB with airborne light detection and ranging (lidar) data, using processed ICESat-2 data. The final regression model achieved a R2 and root mean square error (RMSE) value of 0.62 and 24.63 Mg/ha for estimating AGB and RF model evaluation with a separate test set yielded a R2 of 0.58 and RMSE of 23.89 Mg/ha. Findings provide an initial look at the ability of ICESat-2 to estimate AGB and serve as a basis for further upscaling efforts.

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Section: Introductionmentioning

confidence: 90%

Section: Reference Aboveground Biomass (Agb) Mapmentioning

confidence: 99%

Section: Mapping Agb With Icesat-2 and Landsat 8 Operational Land Imager (Oli)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example

Narine

Popescu

Malambo³

2020

Remote Sensing

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“…The aims of the ICESat-2 mission include to continue estimation of ice sheet elevation and sea ice freeboard to determine their changes (Markus et al, 2017). Besides, the detection of ground under dense canopy (Herzfeld et al, 2014), generating wall-towall AGB maps (Narine et al, 2019), estimating vegetation cover and biomass in a dry land ecosystem (Glenn et al, 2016) have been done based on the simulated data. It should be mentioned that the real data of the satellite have not been yet released.…”

Section: Adm-aeolus Satellitementioning

confidence: 99%

History and Applications of Space-Borne Lidars

Fouladinejad¹,

Matkan²,

Hajeb³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. LIDAR (Light Detection and Ranging) is a laser altimeter system that determines the distance by measuring pulse travel time. The data from the LIDAR systems provide unique information on the vertical structure of land covers. Compared to ground-based and airborne LIDARs providing a high-resolution digital surface model, space-borne LIDARs can provide important information about the vertical profile of the atmosphere in a global scale. The overall objective of these satellites is to study the elevation changes and the vertical distribution of clouds and aerosols. In this paper an overview on the space-borne laser scanner satellites are accomplished and their applications are introduced. The first space-borne LIDAR is the ICESat (Ice, Cloud and land Elevation Satellite) satellite carrying the GLAS instrument which was launched in January 2003. The CALIPSO (the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, 2006), CATS-ISS (the Cloud-Aerosol Transport System, 2015), ADM-Aeolus (Atmospheric Dynamics Mission, 2018), and ICESat-2 (Ice, Cloud and land Elevation Satellite-2, 2018) satellites were respectively lunched and began to receive information about the vertical structure of the atmosphere and land cover. In addition, two ACE (The Aerosol-Cloud-Ecosystems, 2022) and EarthCARE (Earth Clouds, Aerosols and Radiation Explorer, 2021) space-borne satellites were planned for future. The data of the satellites are increasingly utilized to improve the numerical weather predictions (NWP) and climate modeling.

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