Fusing Airborne Laser Scanning and Rapideye Sensor Parameters for Tropical Forest Biomass Estimation of Nepal

In forest inventories, regression models are often applied to predict quantities such as biomass at the level of sampling units. In this paper, we propose a model-based inference framework for combining sampling and model errors in the variance estimation. It was applied to airborne laser (LiDAR) data sets from Hedmark County, Norway, where the model error proportion of the total variance was found to be large for both scanning (airborne laser scanning) and profiling LiDAR when biomass was estimated. With profiling LiDAR, the model error variance component for the entire county was as large as 71% whereas for airborne laser scanning, it was 43% of the total variance. Partly, this reflects the better accuracy of the pixel-based regression models estimated from scanner data as compared with the models estimated from profiler data. The framework proposed in our study can be applied in all types of sample surveys where model-based predictions are made at the level of individual sampling units. Especially, it should be useful in cases where model-assisted inference cannot be applied due to the lack of a probability sample from the target population or due to problems of correctly matching observations of auxiliary and target variables.

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Estimation of forest biomass by means of genetic algorithm-based optimization of airborne laser scanning and digital aerial photograph features

Silva Fennica ◽

10.14214/sf.902 ◽

2013 ◽

Vol 47 (1) ◽

Cited By ~ 10

Author(s):

Sakari Tuominen ◽

Reija Haapanen

Keyword(s):

Genetic Algorithm ◽

Laser Scanning ◽

Aerial Photograph ◽

Forest Biomass ◽

Airborne Laser Scanning ◽

Airborne Laser

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Assessing the utility of airborne laser scanning derived indicators for tropical forest management

Southern Forests a Journal of Forest Science ◽

10.2989/20702620.2020.1733757 ◽

2020 ◽

Vol 82 (4) ◽

pp. 352-358

Author(s):

Vitor Antunes Martins da Costa ◽

Adeliton da Fonseca de Oliveira ◽

Jhonathan Gomes dos Santos ◽

Alex Augusto Abreu Bovo ◽

Danilo Roberti Alves de Almeida ◽

...

Keyword(s):

Forest Management ◽

Tropical Forest ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Airborne Laser ◽

Tropical Forest Management

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Qualifying the Information Detected from Airborne Laser Scanning to Support Tropical Forest Management Operational Planning

Forests ◽

10.3390/f12121724 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1724

Author(s):

Cristiano Rodrigues Reis ◽

Eric Bastos Gorgens ◽

Danilo Roberti Alves de Almeida ◽

Carlos Henrique Souza Celes ◽

Jacqueline Rosette ◽

...

Keyword(s):

Forest Management ◽

Tropical Forest ◽

Laser Scanning ◽

Sustainable Forest Management ◽

Point Clouds ◽

Field Work ◽

Airborne Laser Scanning ◽

Operational Planning ◽

Amazon Forest ◽

Airborne Laser

(1) Background: Forests throughout the world are managed to fulfil a range of commercial and ecosystem services. The same applies to managed areas of the Amazon forest. We explore a method of sustainable forest management (SFM) which anticipates the result of processes of natural mortality of large, mature trees that could fall and damage their neighbors. Collecting all the information required for planning logging in the Brazilian Amazon is, currently, a hard, time-consuming and expensive task. (2) Methods: This information can be obtained more quickly, accurately and objectively by including airborne laser scanning (ALS) products in the operational plan. We used ALS point clouds to isolate emergent crowns from the canopy height model. Then, we performed field work to validate the existence of these trees, and to understand how many commercial trees (tree diameter ≥ 50 cm) we identified by orienting the trees search through the emergent canopy model. (3) Results: We were able to detect 184 (54.4%) trees from 338 field-recorded individuals in 20 plots (totaling 8 ha). Of the detected trees, 66 individuals were classified as having potential for commerce. Furthermore, 58 individuals presented the best stem quality for logging, which represents more than seven high quality commercial trees per hectare. The logistic regression showed that the effects that positively influence the emergent crown formation are strongly presented in the commercial species. (4) Conclusions: Using airborne laser scanning can improve the SFM planning in a structurally complex, dense and mixed composition tropical forest by reducing field work in the initial stages of management. Therefore, we propose that ALS operational planning can be used to more efficiently direct field surveys without the need for a full census.

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