Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements

Kukkonen, Mikko; Kotivuori, Eetu; Maltamo, Matti; Korhonen, Lauri; Packalén, Petteri

doi:10.14214/sf.10360

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…In study III, DIPC data were adjusted horizontally and vertically with ALS data, and DIPC heights were normalized based on ALS DTM. If ALS data are not available, one option is to use DIPC-based DTM in height normalization (Kukkonen et al 2021a). As DIPC-based normalization may perform poorly in areas with a dense canopy, the metrics that are not dependent on the DTM should be studied more in the future (Giannetti et al 2018).…”

Section: Are Als-based Models Transferable To Dipc Data?mentioning

confidence: 99%

“…One possible option is to use satellite image data in species-specific prediction (Kukkonen et al 2018). Kukkonen et al (2021a) have already demonstrated the use of spectral information from Sentinel-2 images in the prediction of species proportions in DIPC-based forest inventories without new in-situ field measurements. Species proportions were then used to derive the species-specific V predictions.…”

Section: Further Studiesmentioning

confidence: 99%

“…In the study of Kukkonen et al (2021a), total V was predicted similarly to our approach in study III using the ALS-based model from a nearby area and DIPC metrics in the prediction. The results reported by Kukkonen et al (2021a) are promising; the species-specific error rates for V were in line with the traditional ALS-based inventories (e.g., Packalén and Maltamo 2007, Kukkonen et al 2018, Kukkonen et al 2019a). In the context of nationwide ALS inventories, satellite images of species proportions should be similarly tested.…”

Section: Further Studiesmentioning

confidence: 99%

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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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Section: Are Als-based Models Transferable To Dipc Data?mentioning

confidence: 99%

Section: Further Studiesmentioning

confidence: 99%

Section: Further Studiesmentioning

confidence: 99%

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

Kotivuori¹

2022

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“…Remote sensing has some limitations in assessing forest growth properly. Current remote sensing technologies are rather inefficient for assessing past forest growth, and they have difficulties assessing shares of different tree species [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Decision Support Tool for Tree Species Selection in Forest Regeneration Based on Harvester Data

Saksa

Uusitalo

Lindeman

et al. 2021

Forests

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Precision forestry—i.e., the division of a stand to smaller units and managing of the stand at a micro-stand level—provides new possibilities to increase forest growth, arrange forest stand structure and enhance forest health. In the regeneration phase by adjusting the tree species selection, soil preparation, intensity of regeneration measures (method, planting density, and material), and young stand management procedures according to precise information on soil properties (e.g., site fertility, wetness, and soil type) and microtopography will inevitably lead to an increase in growth of the whole stand. A new approach to utilizing harvester data to delineate micro-stands inside a large forest stand and to deciding the tree species to plant for each micro-stand was piloted in central Finland. The case stands were situated on Finsilva Oyj forest property. The calculation of the local growth (m3/ha/year) for each 16 × 16-m grid cell was based on the height of the dominant trees and the stand age of the previous tree generation. Tree heights and geoinformation were collected during cutting operation as the harvester data, and the dominant height was calculated as the mean of the three largest stems in each grid cell. The stand age was obtained from the forest management plan. The estimated local growth (average of nine neighboring grid cells) varied from 3 to 14 m3/ha/year in the case stands. When creating micro-stands, neighboring grid cells with approximately the same local growth were merged. The minimum size for an acceptable micro-stand was set to 0.23 ha. In this case study, tree species selection (Scots pine or Norway spruce) was based on the mean growth of each micro-stand. Different threshold values, varying from 6 to 8 m3/ha/year, were tested for tree species change, and they led to different solutions in the delineation of micro-stands. Further stand development was simulated with the Motti software and the net present values (NPVs (3%)) for the next rotation were estimated for different micro-stand solutions. The mixed Norway spruce–Scots pine stand structure never produced a clearly economically inferior solution compared to the single species stand, and in one case out of six, it provided a distinctly better solution in terms of NPV (3%) than the single species option did. Our case study showed that this kind of method could be used as a decision support tool at the regeneration phase.

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Distribution, ecology, and threats assessment of 11 endemic frankincense tree taxa (Boswellia) in the Socotra Archipelago (Yemen)

Maděra,

Vahalík,

Hamdiah

et al. 2024

Plants People Planet

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Societal Impact StatementConserving frankincense trees (Boswellia) is crucial for both ecological and socio‐economic reasons. Surveying these trees in the field and using remote sensing unmanned aerial vehicles in the Socotra Archipelago, we found that Socotran frankincense trees are threatened by forest fragmentation, overgrazing, and increasingly frequent extreme climate events. A better understanding of the distribution and the threats of these important insular species will improve the conservation policy of the local authorities and benefit local communities in the Socotra Archipelago. At the same time, this work serves as a good practice example to guide conservation efforts for other culturally important threatened tree species around the world, therefore helping to sustain local livelihoods, fostering ecological resilience, and supporting socio‐economic stability.Summary Globally, frankincense trees (Burseraceae: Boswellia) are increasingly under threat because of habitat deterioration, climate impacts, and the olibanum trade. Despite harboring nearly half of the species in the genus, up‐to‐date insights are lacking for the insular endemic frankincense trees of the Socotra Archipelago UNESCO (United Nations Educational, Scientific and Cultural Organization) World Heritage Site (Yemen). We combined georeferencing of individual trees in the field with remote sensing applying unmanned aerial vehicles (UAVs) to evaluate Boswellia distribution and (sub)population sizes in the entire Socotra Archipelago. We counted 17,253 trees across all 11 taxa and we surveyed almost 55% directly in the field, collecting individual information on threats and health indicators. We estimate that the current total population sizes of the relatively common Socotran Boswellia taxa (Boswellia elongata, Boswellia popoviana, and Boswellia ameero) consist of a few thousand mature individuals with fragmented distribution of which a large proportion occurs in highly disjunct relictual stands, while the more range‐restricted species survive only through a few hundred (Boswellia nana and Boswellia samhaensis) to fewer than a hundred trees (Boswellia scopulorum). Our field data show that the Socotran frankincense trees are threatened by fragmentation and overgrazing resulting in a lack of natural regeneration, in combination with effects of extreme climate events (e.g., higher frequency and intensity of cyclones and prolonged drought) and potential future infrastructure developments; the species are less impacted by resin collection. We provide recommendations to strategize urgent protection of the declining Socotran frankincense trees, and we update their conservation status, resulting in an endangered status for seven and a critically endangered status for four taxa.

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Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements

Cited by 6 publications

References 32 publications

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

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

Decision Support Tool for Tree Species Selection in Forest Regeneration Based on Harvester Data

Distribution, ecology, and threats assessment of 11 endemic frankincense tree taxa (Boswellia) in the Socotra Archipelago (Yemen)

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