scholarly journals Characterizing a New England Saltmarsh with NASA G-LiHT Airborne Lidar

2019 ◽  
Vol 11 (5) ◽  
pp. 509 ◽  
Author(s):  
Ian Paynter ◽  
Crystal Schaaf ◽  
Jennifer Bowen ◽  
Linda Deegan ◽  
Francesco Peri ◽  
...  

Airborne lidar can observe saltmarshes on a regional scale, targeting phenological and tidal states to provide the information to more effectively utilize frequent multispectral satellite observations to monitor change. Airborne lidar observations from NASA Goddard Lidar Hyperspectral and Thermal (G-LiHT) of a well-studied region of saltmarsh (Plum Island, Massachusetts, United States) were acquired in multiple years (2014, 2015 and 2016). These airborne lidar data provide characterizations of important saltmarsh components, as well as specifications for effective surveys. The invasive Phragmites australis was observed to increase in extent from 8374 m2 in 2014, to 8882 m2 in 2015 (+6.1%), and again to 13,819 m2 in 2016 (+55.6%). Validation with terrestrial lidar supported this increase, but suggested the total extent was still underestimated. Estimates of Spartina alterniflora extent from airborne lidar were within 7% of those from terrestrial lidar, but overestimation of height of Spartina alterniflora was found to occur at the edges of creeks (+83.9%). Capturing algae was found to require observations within ±15° of nadir, and capturing creek structure required observations within ±10° of nadir. In addition, 90.33% of creeks and ditches were successfully captured in the airborne lidar data (8206.3 m out of 9084.3 m found in aerial imagery).

2019 ◽  
Vol 189 ◽  
pp. 103974 ◽  
Author(s):  
Alexandre Nicolae Lerma ◽  
Bruce Ayache ◽  
Beatrice Ulvoas ◽  
François Paris ◽  
Nicolas Bernon ◽  
...  

2015 ◽  
Vol 42 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Yanjun Su ◽  
Qinghua Guo ◽  
Danny L. Fry ◽  
Brandon M. Collins ◽  
Maggi Kelly ◽  
...  

2016 ◽  
Vol 8 (3) ◽  
pp. 240 ◽  
Author(s):  
Ibrahim Fayad ◽  
Nicolas Baghdadi ◽  
Jean-Stéphane Bailly ◽  
Nicolas Barbier ◽  
Valéry Gond ◽  
...  

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.


2021 ◽  
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
André Caceres Carrilho ◽  
Guilherme Gomes Pessoa

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