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

Ruotsalainen, Roope; Pukkala, Timo; Kangas, Annika; Tuominen, Sakari; Packalén, Petteri

doi:10.1139/cjfr-2018-0345

Cited by 9 publications

(8 citation statements)

References 30 publications

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“…Though additional improvements can be made to our estimates of forest characteristics, tradeoffs between processing, model development, and sampling costs related to improvements in accuracy should be weighed to evaluate the level of precision needed to inform decision making [34]. Given the amount of model error potentially introduced by co-registration errors [20], our EGAMs explained the majority of the variation that can be accounted for within the field data and provide a substantial improvement over previous efforts [5,6].…”

Section: Discussionmentioning

confidence: 99%

Estimating Forest Characteristics for Longleaf Pine Restoration Using Normalized Remotely Sensed Imagery in Florida USA

Hogland

Affleck

Anderson

et al. 2020

Forests

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Effective forest management is predicated on accurate information pertaining to the characteristics and condition of forests. Unfortunately, ground-based information that accurately describes the complex spatial and contextual nature of forests across broad landscapes is cost prohibitive to collect. In this case study we address technical challenges associated with estimating forest characteristics from remotely sensed data by incorporating field plot layouts specifically designed for calibrating models from such data, applying new image normalization procedures to bring images of varying spatial resolutions to a common radiometric scale, and implementing an ensemble generalized additive modeling technique. Image normalization and ensemble models provided accurate estimates of forest types, presence/absence of longleaf pine (Pinus palustris), and tree basal areas and tree densities over a large segment of the panhandle of Florida, USA. This study overcomes several of the major barriers associated with linking remotely sensed imagery with plot data to estimate key forest characteristics over large areas.

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Section: Discussionmentioning

confidence: 99%

Estimating Forest Characteristics for Longleaf Pine Restoration Using Normalized Remotely Sensed Imagery in Florida USA

Hogland

Affleck

Anderson

et al. 2020

Forests

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“…Also, other a-priori information, such as the geographical location and site type can be used for sample plot selection (Maltamo et al 2011). However, it has also been shown that if the number of sample plots is too small, the prediction accuracy is reduced, which will lead to suboptimal decisions in forest planning (Ruotsalainen et al 2019). The most cost-efficient method in an ALS-based inventory is to use ALS and field training data from former inventory projects for model fitting.…”

Section: Prediction Of Forest Attributes Without New In-situ Field Me...mentioning

confidence: 99%

Prediction of forest attributes using airborne laser scanning-based models without new in-situ field measurements

Kotivuori¹

2022

Diss. For.

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The era of airborne laser scanning (ALS) and the development of new forest inventory methods has reduced the need for field visits and overall inventory costs over the last two decades. Although the development of inventory methods has been considerable, some systematic field visits are usually always required. For example, the most common ALS inventory method, the area-based approach (ABA), leans on field sample plot measurements. Likewise in the ALS inventory, the ABA method can also be used in drone-based inventories with image point cloud (IPC) data. Due to the small areal coverage of the drones, local sample plot measurements in drone image point cloud (DIPC) inventories are not usually profitable. The objective of this thesis was to examine the performance of ALS-based forest attribute models in ALS- and DIPC-based ABA inventories without new in-situ field measurements. In this study, nationwide ALS models for three forest attributes (stem volume, above ground biomass and dominant height) were fitted for the whole of Finland, and regional-level error rates of the nationwide model predictions were assessed. As the nationwide models tended to exhibit systematic region-wise under- and over-predictions, different calibration methods were examined. First, calibration of nationwide models with a small number of new field measurements from the target area was simulated. Second, the nationwide stem volume model or its regional predictions was calibrated without new in-situ field measurements by three test scenarios: a) using additional calibration variables in the models to account for geographical and environmental conditions throughout the country, b) refitting of the models by using existing sample plots from nearby regions, and c) matching the regional-level predictions with national forest inventory data. The DICP-based forest inventory without new in-situ field measurements was evaluated by replacing the ALS metrics from the ALS-based models with DIPC metrics when the models were applied. In the DIPC inventory, the metrics used in the ALS models were selected carefully so that they would be similar to the corresponding DIPC metrics. The results showed that forest attributes can be predicted without new in-situ field measurements using nationwide ALS-based models with moderate error rates. The systematic errors associated with the nationwide models decreased when the models were fitted with additional calibration variables, such as degree days, precipitation, and tree species proportions. However, the measurement of a carefully selected set of sample plots (e.g., 20 plots) from the target area for the calibration of the nationwide model is recommended, in instances where it is economically feasible. Prediction of forest attributes using ALS-based models with DIPC metrics is possible provided the predictor variables describe the upper canopy layer. The lowest error rates in DIPC-based inventories were obtained when the ALS-based model was fitted in a nearby region and the inventory units were disaggregated to coniferous and deciduous dominated areas before the prediction.

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“…The accuracy of an ABA-based forest inventory depends on the precision and the coverage of the field-measured sample plot data (Maltamo et al 2011;White et al 2013;Ruotsalainen et al 2019). Moreover, the selection strategy of reference plots directly affects the value of the forest inventory data in decision-making (Ruotsalainen et al 2019).…”

Section: Measuring Forest Attributesmentioning

confidence: 99%

Producing information from airborne LiDAR data for peatland forest management

Niemi¹

2022

Diss. For.

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In Finland, peatland forests are significant for wood supply, although simultaneously, they are also important for biodiversity, carbon sequestration, water conservation, and recreation. In the 1960s and 1970s, peatland forests in Finland were extensively drained to increase tree growth and fulfil the needs of the forest sector. However, this extensive drainage has negatively impacted on the biodiversity of peatland ecosystems, and substantially increased nutrient and sediment emissions to lakes and rivers resulting in eutrophication, turbidity, and brownification of these water bodies. This dissertation presents a number of approaches to move peatland forest management in a more environmentally sound direction, which may increase the general acceptability of peatland forestry. Airborne LiDAR (Light detection and ranging, i.e., laser scanning) derived 3D point cloud provides useful data, for example, to estimate forest biomass, to identify low-productive peatland forests, to model overland water flows, and to identify wet areas. The strength of airborne LiDAR is the ability of laser pulses to pass through tree canopies and obtain accurate observations from the ground level. The information derived from airborne LiDAR can enhance the planning of peatland forest management, as much of the planning can be done remotely, and supplementary field work can be implemented in areas of strategic need. This study presented the novel idea of applying local binary patterns for the prediction of terrain trafficability, which should be considered in further studies and practice. The moisture index derived from the local neighborhood can reveal the small-scale variations in terrain moisture. This study also presented the novel idea to create spatial models to identify suitable locations for water protection structures, which may help forest managers to plan water protection of ditch network maintenance or peatland restoration operations. Overall, the utilization of airborne LiDAR-derived information for the development of peatland forestry practices shows great potential.

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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

Cited by 9 publications

References 30 publications

Estimating Forest Characteristics for Longleaf Pine Restoration Using Normalized Remotely Sensed Imagery in Florida USA

Estimating Forest Characteristics for Longleaf Pine Restoration Using Normalized Remotely Sensed Imagery in Florida USA

Prediction of forest attributes using airborne laser scanning-based models without new in-situ field measurements

Producing information from airborne LiDAR data for peatland forest management

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