Where the Methane Is—Insights from Novel Airborne LiDAR Measurements Combined with Ground Survey Data

Airborne Light Detection and Ranging (LiDAR) has been very effectively used in collecting terrain information over different scales of area. Inevitably, filtering the non-ground returns is the major step of digital terrain model (DTM) generation and this step poses the greatest challenge especially for tropical forest environment which consists of steep undulating terrain and mostly covered by a relatively thick canopy density. The aim of this research is to assess the performance of the Progressive Morphological (PM) algorithm after the implementation of local slope value in the ground filtering process. The improvement on the PM filtering method was done by employing local slope values obtained either using initial filtering of airborne LiDAR data or ground survey data. The filtering process has been performed with recursive mode and it stops after the results of the filtering does not show any improvement and the DTM error larger than the previous iteration. The revised PM filtering method has decreasing pattern of DTM error with increasing filtering iterations with minimum ±0.520 m of RMSE value. The results also suggest that spatially distributed slope value applied in PM filtering algorithm either from LiDAR ground points or ground survey data is capable in preserving discontinuities of terrain and correctly remove non-terrain points especially in steep area.

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Application and Validation of a Model for Terrain Slope Estimation Using Space-Borne LiDAR Waveform Data

Remote Sensing ◽

10.3390/rs10111691 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1691 ◽

Cited By ~ 1

Author(s):

Xuebo Yang ◽

Cheng Wang ◽

Sheng Nie ◽

Xiaohuan Xi ◽

Zhenyue Hu ◽

...

Keyword(s):

Surface Characteristics ◽

Airborne Lidar ◽

Estimation Accuracy ◽

Estimation Model ◽

Large Area ◽

Waveform Data ◽

Terrain Slope ◽

Lidar Measurements ◽

Slope Estimation ◽

Study Sites

The terrain slope is one of the most important surface characteristics for quantifying the Earth surface processes. Space-borne LiDAR sensors have produced high-accuracy and large-area terrain measurement within the footprint. However, rigorous procedures are required to accurately estimate the terrain slope especially within the large footprint since the estimated slope is likely affected by footprint size, shape, orientation, and terrain aspect. Therefore, based on multiple available datasets, we explored the performance of a proposed terrain slope estimation model over several study sites and various footprint shapes. The terrain slopes were derived from the ICESAT/GLAS waveform data by the proposed method and five other methods in this study. Compared with five other methods, the proposed method considered the influence of footprint shape, orientation, and terrain aspect on the terrain slope estimation. Validation against the airborne LiDAR measurements showed that the proposed method performed better than five other methods (R2 = 0.829, increased by ~0.07, RMSE = 3.596°, reduced by ~0.6°, n = 858). In addition, more statistics indicated that the proposed method significantly improved the terrain slope estimation accuracy in high-relief region (RMSE = 5.180°, reduced by ~1.8°, n = 218) or in the footprint with a great eccentricity (RMSE = 3.421°, reduced by ~1.1°, n = 313). Therefore, from these experiments, we concluded that this terrain slope estimation approach was beneficial for different terrains and various footprint shapes in practice and the improvement of estimated accuracy was distinctly related with the terrain slope and footprint eccentricity.

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Monitoring tropical forest carbon stocks and emissions using Planet satellite data

Scientific Reports ◽

10.1038/s41598-019-54386-6 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 18

Author(s):

Ovidiu Csillik ◽

Pramukta Kumar ◽

Joseph Mascaro ◽

Tara O’Shea ◽

Gregory P. Asner

Keyword(s):

Climate Change ◽

Large Scale ◽

Cost Effective ◽

Carbon Stocks ◽

Airborne Lidar ◽

Aboveground Carbon ◽

Lidar Measurements ◽

High Resolution Map ◽

Aboveground Carbon Density ◽

Planet Satellite

AbstractTropical forests are crucial for mitigating climate change, but many forests continue to be driven from carbon sinks to sources through human activities. To support more sustainable forest uses, we need to measure and monitor carbon stocks and emissions at high spatial and temporal resolution. We developed the first large-scale very high-resolution map of aboveground carbon stocks and emissions for the country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into a random forest machine learning regression workflow, obtaining an R2 of 0.70 and RMSE of 25.38 Mg C ha−1 for the nationwide estimation of aboveground carbon density (ACD). The diverse ecosystems of Peru harbor 6.928 Pg C, of which only 2.9 Pg C are found in protected areas or their buffers. We found significant carbon emissions between 2012 and 2017 in areas aggressively affected by oil palm and cacao plantations, agricultural and urban expansions or illegal gold mining. Creating such a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries will serve as a transformative tool to quantify the climate change mitigation services that forests provide.

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