Examining LiDAR – WorldView-3 data synergy to generate a detailed stand map in a mixed forest in the north-west of Turkey

Özkan, Ulaş Yunus; Demirel, Tufan; Özdemir, İbrahim; Sağlam, Serhun; Mert, Ahmet

doi:10.1016/j.asr.2020.02.020

Cited by 9 publications

(10 citation statements)

References 48 publications

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“…Also stand management regime of the forest is even-aged. Detailed information for the research area was presented by Ozkan et al (2020) Field Reference Data A eld survey was performed between June and August 2018 on the sample plots. A total of 45 samples were randomly selected from pure black pine and oak stands within a study area of 2172 hectares using stand maps produced based on the combination of aerial photograph and forest inventory data in the forest management plan.…”

Section: Study Areamentioning

confidence: 99%

The Influence of Widow Size on Remote Sensing-Based Prediction of Forest Structural Variables

Özkan

Demirel

2021

Preprint

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Background: Determining the appropriate window size is a critical step for estimating stand structural variables based on remote sensing data. Because the value of the reference laser and image metrics that affect the quality of the prediction model depends on window size. However, suitable window sizes are usually determined by trial and error. There are a limited number of published studies evaluating appropriate window sizes for different remote sensing data. This research investigated the effect of window size on predicting forest structural variables using airborne LiDAR data, digital aerial image and WorldView-3 satellite image.Results: In the WorldView-3 and digital aerial image, significant differences were observed in the prediction accuracies of the structural variables according to different window sizes. For the estimation based on WorldView-3 in black pine stands, the optimal window sizes for stem number (N), volume (V), basal area (BA) and mean height (H) were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. In oak stands, the R2 values of each moving window size were almost identical for N and BA. The optimal window size was 400 m2 for V and 600 m2 for H. For the estimation based on aerial image in black pine stands, the 800 m2 window size is optimal for N and H, the 600 m2 window size is optimal for V and the 1000 m2 window size is optimal for BA. In the oak stands, the optimal window sizes for N, V, BA and H were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. The optimal window sizes may need to be scaled up or down to match the stand canopy components. In the LiDAR data, the R2 values of each window size were almost identical for all variables of the black pine and the oak stands.Conclusion: This study illustrated that the window size has an effect on the prediction accuracy in estimating forest structural variables based on remote sensing data. Moreover, the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.

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Section: Study Areamentioning

confidence: 99%

The Influence of Widow Size on Remote Sensing-Based Prediction of Forest Structural Variables

Özkan

Demirel

2021

Preprint

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“…In addition, stand management regime of the forest is even-aged. Detailed information for the research area was presented by Ozkan et al (2020).…”

Section: Study Areamentioning

confidence: 99%

“…The texture properties were determined according to the Gray Level Co-occurrence Matrix (GLCM) (Haralick et al 1973). GLCM is widely utilized to describe the textural properties in remote sensing studies (Kayitakire et al 2006;Maltamo et al, 2006;Shamsoddini et al, 2013;Ozdemir and Donoghue 2013;Ozkan et al 2016Ozkan et al ,2020Meng 2016;Zhao et al 2018). There are many texture properties defined by GLCM in three main groups as Contrast, Orderliness and Descriptive (Ozdemir and Karnieli 2011).…”

Section: Predictor Variablesmentioning

confidence: 99%

The influence of window size on remote sensing-based prediction of forest structural variables

Özkan

Demirel

2021

Ecol Process

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Background Determining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data. Because the value of the reference laser and image metrics that affect the quality of the prediction model depends on window size. However, suitable window sizes are usually determined by trial and error. There are a limited number of published studies evaluating appropriate window sizes for different remote sensing data. This research investigated the effect of window size on predicting forest structural variables using airborne LiDAR data, digital aerial image and WorldView-3 satellite image. Results In the WorldView-3 and digital aerial image, significant differences were observed in the prediction accuracies of the structural variables according to different window sizes. For the estimation based on WorldView-3 in black pine stands, the optimal window sizes for stem number (N), volume (V), basal area (BA) and mean height (H) were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. In oak stands, the R2 values of each moving window size were almost identical for N and BA. The optimal window size was 400 m2 for V and 600 m2 for H. For the estimation based on aerial image in black pine stands, the 800 m2 window size was optimal for N and H, the 600 m2 window size was optimal for V and the 1000 m2 window size was optimal for BA. In the oak stands, the optimal window sizes for N, V, BA and H were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. The optimal window sizes may need to be scaled up or down to match the stand canopy components. In the LiDAR data, the R2 values of each window size were almost identical for all variables of the black pine and the oak stands. Conclusion This study illustrated that the window size has an effect on the prediction accuracy in estimating forest structural variables based on remote sensing data. Moreover, the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.

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“…Another successful example is the work performed by Peerbhay et al (2014) who distinguished Eucalyptus nitens from among 5 other species using Worldview 2 data obtaining accuracy indexes of above 80%. However, lower accuracies were obtained by Ozkan et al (2020) when testing the possibility of discriminating between Pinus pinaster and 9 other tree species including 3 other pine species. In fact, Fang et al (2020) have reported a large decline in accuracy when attempting to classify species from within the same genus.…”

Section: Introductionmentioning

confidence: 99%

FOREST COVER MAPPING AND PINUS SPECIES CLASSIFICATION USING VERY HIGH-RESOLUTION SATELLITE IMAGES AND RANDOM FOREST

Alonso

Picos

Armesto

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Advances in remote sensing technologies are generating new perspectives concerning the role of and methods used for National Forestry Inventories (NFIs). The increase in computation capabilities over the last several decades and the development of new statistical techniques have allowed for the automation of forest resource map generation through image analysis techniques and machine learning algorithms. The availability of large-scale data and the high temporal resolution that satellite platforms provide mean that it is possible to obtain updated information about forest resources at the stand level, thus increasing the quality of the spatial information. However, photointerpretation of satellite and aerial images is still the most common way that remote sensing information is used for NFIs or forest management purposes. This study describes a methodology that automatically maps the main forest covers in Galicia (Eucalyptus spp., conifers and broadleaves) using Worldview-2 and the random forest classifier. Furthermore, the method also evaluates the separate mapping of the three most abundant Pinus tree species in Galicia (Pinus pinaster, Pinus radiata and Pinus sylvestris). According to the results, Worldview-2 multispectral images allow for the efficient differentiation between the main forest classes that are present in Galicia with a very high degree of accuracy (91%) and ample spatial detail. Pinus species can also be efficiently differentiated (83%).

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Examining LiDAR – WorldView-3 data synergy to generate a detailed stand map in a mixed forest in the north-west of Turkey

Cited by 9 publications

References 48 publications

The Influence of Widow Size on Remote Sensing-Based Prediction of Forest Structural Variables

The Influence of Widow Size on Remote Sensing-Based Prediction of Forest Structural Variables

The influence of window size on remote sensing-based prediction of forest structural variables

FOREST COVER MAPPING AND PINUS SPECIES CLASSIFICATION USING VERY HIGH-RESOLUTION SATELLITE IMAGES AND RANDOM FOREST

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