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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Towards high throughput assessment of canopy dynamics: The estimation of leaf area structure in Amazonian forests with multitemporal multi-sensor airborne lidar

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Author(s):
Shao, Gang [1] ; Stark, Scott C. [1] ; de Almeida, Danilo R. A. [2] ; Smith, Marielle N. [1]
Total Authors: 4
Affiliation:
[1] Michigan State Univ, Dept Forestry, 480 Wilson Rd, E Lansing, MI 48824 - USA
[2] Univ Sao Paulo, Luiz de Queiroz Coll Agr, ESALQ, Dept Forest Sci, Av Padua Dias 11, BR-13418900 Piracicaba, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: REMOTE SENSING OF ENVIRONMENT; v. 221, p. 1-13, FEB 2019.
Web of Science Citations: 2
Abstract

Leaf area dynamics offer information about changes in forest biomass and canopy function critical to understanding the role of forests in the climate system and carbon cycle. Airborne small footprint lidar is a potential major source for the detection of variation in leaf area density (LAD), LAD vertical profiles, and total leaf area (leaf area index, LAI), from sites to regional scales. However, the sensitivities of lidar-based LAD and LAI estimation are not yet well known, particularly in dense forests, over landscape heterogeneity, sensor system, and survey differences, and through time. To address these questions, we compared 16 pairs of multitemporal airborne lidar surveys with four different laser sensors across six Amazon forest sites with resurvey intervals ranging from one to nine years. We tested whether the different laser sensors, and the pulse return density of laser sampling (variable between and within each survey) introduce systematic biases. Laser sensors created consistent biases that accounted for up to 18.20% of LAD differences between surveys, but biases could be corrected with a simple regression approach. Lidar pulse return density had little appreciable bias impact when above 20 returns per m(2). After correction, repeated mean and site maximum LAI estimates became significantly correlated (R-2 similar to 0.8), while LAD profiles revealed site differences. Heterogeneity and change in LAD structure were detectable at the ecologically relevant 1/4 ha forest neighborhood grid scale, as evidenced by the high correlation of profile variation between surveys, with the strength of correlation (R-2 value) significantly decreasing with increasing survey interval (0.74 to 0.16 from one to nine years), consistent with accumulating effects of forest dynamics. Sensor-induced biases trended towards correlation with lidar footprint (beam width). The LAD estimation and bias correction approach developed in this study provides the standardization critical for heterogeneous lidar networks that offer high throughput functional ecological monitoring of climatically important forests like the Amazon. (AU)

FAPESP's process: 17/03867-6 - Towards universal biological structural assessment of forests with LiDAR remote sensing for vegetation monitoring in a changing world
Grantee:Danilo Roberti Alves de Almeida
Support type: Scholarships abroad - Research Internship - Doctorate
FAPESP's process: 16/05219-9 - Monitoring forest landscape restoration through Light Detection and Ranging (LiDAR).
Grantee:Danilo Roberti Alves de Almeida
Support type: Scholarships in Brazil - Doctorate