Improving forest management planning by means of airborne laser scanning and dynamic treatment units based on spatial optimization

Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m collected over 350 km of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce ( (L.) Karst.), Scots pine ( L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.22Picea abiesPinus sylvestris

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The effects of sample plot selection strategy and the number of sample plots on inoptimality losses in forest management planning based on airborne laser scanning data

Canadian Journal of Forest Research ◽

10.1139/cjfr-2018-0345 ◽

2019 ◽

Vol 49 (9) ◽

pp. 1135-1146 ◽

Cited By ~ 2

Author(s):

Roope Ruotsalainen ◽

Timo Pukkala ◽

Annika Kangas ◽

Jari Vauhkonen ◽

Sakari Tuominen ◽

...

Keyword(s):

Forest Management ◽

Laser Scanning ◽

Net Present Value ◽

Training Data ◽

Selection Strategy ◽

Sample Plot ◽

K Nearest Neighbors ◽

Management Planning ◽

Forest Management Planning ◽

Stand Attributes

In forest management planning, errors in predicted stand attributes might lead to suboptimal decisions that result in decreased net present value (NPV). Forest inventory data will have higher value if the amount of suboptimal decisions can be decreased. Therefore, the value of information can be measured through the decrease in inoptimality losses, which are the NPV differences between the optimal and suboptimal decisions. In this study, four alternative sample plot selection strategies with different numbers of sample plots were compared in terms of expected mean inoptimality losses. Stand-level mean inoptimality losses varied between €41.1·ha–1 and €80.7·ha−1, depending on the sample plot selection strategy and the number of sample plots used as training data in the k-nearest neighbors imputation method. Mean inoptimality losses decreased substantially when the number of sample plots increased from 25 to 100, and the decreasing trend continued until 500 sample plots. Total inoptimality losses can decrease by approximately €1 million in an inventory area of 100 000 ha when the number of sample plots is increased from 100 to 500. The measurement of more sample plots can be justified as long as the field measurement costs do not exceed the decrease in inoptimality losses.

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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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