Polarimetric, two-color, photon-counting laser altimeter measurements of forest canopy structure

Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical reflectance data. However, these reflectance-based estimates have well-known limitations, such as the mere use of a linear relationship between the normalized difference hotspot and darkspot (NDHD) and CI, uncertainties in bidirectional reflectance distribution function (BRDF) models used to calculate the NDHD, and coarse spatial resolutions (e.g., hundreds of meters to several kilometers). To remedy these limitations and develop alternative methods for large-scale CI mapping, here we explored the use of spaceborne lidar—the Geoscience Laser Altimeter System (GLAS)—and proposed a semi-physical algorithm to estimate CI at the footprint level. Our algorithm was formulated to leverage the full vertical canopy profile information of the GLAS full-waveform data; it converted raw waveforms to forest canopy gap distributions and gap fractions of random canopies, which was used to estimate CI based on the radiative transfer theory and a revised Beer–Lambert model. We tested our algorithm over two areas in China—the Saihanba National Forest Park and Heilongjiang Province—and assessed its relative accuracies against field-measured CI and MODIS CI products. We found that reliable estimation of CI was possible only for GLAS waveforms with high signal-to-noise ratios (e.g., >65) and at gentle slopes (e.g., <12°). Our GLAS-based CI estimates for high-quality waveforms compared well to field-based CI (i.e., R2 = 0.72, RMSE = 0.07, and bias = 0.02), but they showed less correlation to MODIS CI (e.g., R2 = 0.26, RMSE = 0.12, and bias = 0.04). The difference highlights the impact of the scale effect in conducting comparisons of products with huge differences resolution. Overall, our analyses represent the first attempt to use spaceborne lidar to retrieve high-resolution forest CI and our algorithm holds promise for mapping CI globally.

Download Full-text

Drone-acquired data reveal the importance of forest canopy structure in predicting tree diversity

Forest Ecology and Management ◽

10.1016/j.foreco.2021.119945 ◽

2022 ◽

Vol 505 ◽

pp. 119945

Author(s):

Jian Zhang ◽

Zhaochen Zhang ◽

James A. Lutz ◽

Chengjin Chu ◽

Jianbo Hu ◽

...

Keyword(s):

Forest Canopy ◽

Canopy Structure ◽

Tree Diversity

Download Full-text

A Digital Terrain Modeling Method in Urban Areas by the ICESat-2 (Generating precise terrain surface profiles from photon-counting technology)

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.87.4.237 ◽

2021 ◽

Vol 87 (4) ◽

pp. 237-248

Author(s):

Nahed Osama ◽

Bisheng Yang ◽

Yue Ma ◽

Mohamed Freeshah

Keyword(s):

Urban Areas ◽

Spatial Clustering ◽

Photon Counting ◽

Ground Surface ◽

Ground Truth ◽

Surface Model ◽

Laser Altimeter ◽

Ground Truth Data ◽

Urban Scenes ◽

Terrain Surface

The ICE, Cloud and land Elevation Satellite-2 (ICES at-2) can provide new measurements of the Earth's elevations through photon-counting technology. Most research has focused on extracting the ground and the canopy photons in vegetated areas. Yet the extraction of the ground photons from urban areas, where the vegetation is mixed with artificial constructions, has not been fully investigated. This article proposes a new method to estimate the ground surface elevations in urban areas. The ICES at-2 signal photons were detected by the improved Density-Based Spatial Clustering of Applications with Noise algorithm and the Advanced Topographic Laser Altimeter System algorithm. The Advanced Land Observing Satellite-1 PALSAR –derived digital surface model has been utilized to separate the terrain surface from the ICES at-2 data. A set of ground-truth data was used to evaluate the accuracy of these two methods, and the achieved accuracy was up to 2.7 cm, which makes our method effective and accurate in determining the ground elevation in urban scenes.

Download Full-text

ESTIMATION OF REGIONAL FOREST ABOVEGROUND BIOMASS COMBINING ICESAT-GLAS WAVEFORMS AND HJ-1A/HSI HYPERSPECTRAL IMAGERIES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b7-731-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 731-737

Author(s):

Yanqiu Xing ◽

Sai Qiu ◽

Jianhua Ding ◽

Jing Tian

Keyword(s):

Carbon Cycle ◽

Aboveground Biomass ◽

Forest Canopy ◽

Field Investigation ◽

Support Vector ◽

Laser Altimeter ◽

Spectral Bands ◽

Spectral Imager ◽

Regional Forest ◽

Forest Aboveground Biomass

Estimation of forest aboveground biomass (AGB) is a critical challenge for understanding the global carbon cycle because it dominates the dynamics of the terrestrial carbon cycle. Light Detection and Ranging (LiDAR) system has a unique capability for estimating accurately forest canopy height, which has a direct relationship and can provide better understanding to the forest AGB. The Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) is the first polarorbiting LiDAR instrument for global observations of Earth, and it has been widely used for extracting forest AGB with footprints of nominally 70 m in diameter on the earth's surface. However, the GLAS footprints are discrete geographically, and thus it has been restricted to produce the regional full coverage of forest AGB. To overcome the limit of discontinuity, the Hyper Spectral Imager (HSI) of HJ-1A with 115 bands was combined with GLAS waveforms to predict the regional forest AGB in the study. Corresponding with the field investigation in Wangqing of Changbai Mountain, China, the GLAS waveform metrics were derived and employed to establish the AGB model, which was used further for estimating the AGB within GLAS footprints. For HSI imagery, the Minimum Noise Fraction (MNF) method was used to decrease noise and reduce the dimensionality of spectral bands, and consequently the first three of MNF were able to offer almost 98% spectral information and qualified to regress with the GLAS estimated AGB. Afterwards, the support vector regression (SVR) method was employed in the study to establish the relationship between GLAS estimated AGB and three of HSI MNF (i.e. MNF1, MNF2 and MNF3), and accordingly the full covered regional forest AGB map was produced. The results showed that the adj.R2 and RMSE of SVR-AGB models were 0.75 and 4.68 t hm−2 for broadleaf forests, 0.73 and 5.39 t hm−2 for coniferous forests and 0.71 and 6.15 t hm−2 for mixed forests respectively. The full covered regional forest AGB map of the study area had 0.62 of accuracy and 11.11 t hm−2 of RMSE. The study demonstrated that it holds great potential to achieve the full covered regional forest AGB distribution with higher accuracy by combing LiDAR data and hyperspectral imageries.

Download Full-text

Spatial Variation in Canopy Structure across Forest Landscapes

10.20944/preprints201806.0351.v1 ◽

2018 ◽

Author(s):

Brady S. Hardiman ◽

Elizabeth A. LaRue ◽

Jeff W. Atkins ◽

Robert T. Fahey ◽

Franklin W. Wagner ◽

...

Keyword(s):

Temperate Forest ◽

Forest Canopy ◽

Spatial Scales ◽

Canopy Structure ◽

Ecosystem Functions ◽

Lidar System ◽

Stable Point ◽

Eastern Usa ◽

Forested Landscapes ◽

Patch Scale

Forest canopy structure (CS) controls many ecosystem functions and is highly variable across landscapes, but the magnitude and scale of this variation is not well understood. We used a portable canopy lidar system to characterize variation in five categories of CS along N = 3 transects (140–800 m long) at each of six forested landscapes within the eastern USA. The cumulative coefficient of variation was calculated for subsegments of each transect to determine the point of stability for individual CS metrics. We then quantified the scale at which CS is autocorrelated using Moran’s I in an Incremental Autocorrelation analysis. All CS metrics reached stable values within 300 m but varied substantially within and among forested landscapes. A stable point of 300 m for CS metrics corresponds with the spatial extent that many ecosystem functions are measured and modeled. Additionally, CS metrics were spatially autocorrelated at 40 to 88 m, suggesting that patch scale disturbance or environmental factors drive these patterns. Our study shows CS is heterogeneous across temperate forest landscapes at the scale of 10’s of meters, requiring a resolution of this size for upscaling CS with remote sensing to large spatial scales.

Download Full-text