Mapping individual trees with airborne laser scanning data in an European lowland forest using a self-calibration algorithm

Stereńczak, Krzysztof; Kraszewski, Bartłomiej; Mielcarek, Miłosz; Piasecka, Żaneta; Lisiewicz, Maciej; Heurich, Marco

doi:10.1016/j.jag.2020.102191

Cited by 23 publications

(15 citation statements)

References 43 publications

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“…In general, individual tree detection with the use of UAVs or airborne sensors shows promise [19][20][21][22][23][24][25][26][27]. However, tree identification depends on a variety of parameters defined while processing images or point clouds and, on the algorithms, employed [21].…”

Section: Discussionmentioning

confidence: 99%

“…An alternative for counting the number of trees would be the individual tree detection with the use of Unmanned Aerial Vehicle (UAV) systems or airborne sensors [19][20][21][22][23][24][25][26][27] where the detection rate can vary from 70% to 114%, depending on the type and characteristics of the sensor used, the algorithm method, the age, species, spacing and management conditions. This is a useful method for obtaining data, being a relatively inexpensive and automated process that can support several types of sensors [28,29] highlighted the potential of using data obtained by UAV systems in the forest inventory with many innovations based on the tree detection.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory

Stolle

Corte

Sanquetta

et al. 2021

Forests

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In this study, we estimate the forest stock volume by multiplying the number of trees detected remotely by the estimated mean individual volume of the population (individual approach). A comparison was made with the conventional inventory method (area approach), which included 100 simulations of a simple random sampling process and a Bootstrap resampling. The study area included three stands: stand 1, 16-year-old pine; stand 2, 7-year-old pine; and stand 3, 5-year-old eucalyptus. A census was carried out in each stand for the variables diameter and total height. Individual volume was estimated by a ratio estimator, and the sum of all volumes was considered as the total parametric volume. The area approach presented parametric values within the confidence interval for 91%, 94%, and 98% of the simulations for the three stands, respectively. The mean relative errors for the area approach were −3.5% for stand 1, 0.3% for stand 2, and −0.9% for stand 3. The errors in stands 1 and 3 were associated with the spatial distribution of the volume. The individual approach proved to be efficient for all stands, and their respective parametric values were within the confidence interval. The relative errors were 1% for stand 1, −0.7% for stand 2, and 1.8% for stand 3. For stand 1 and 3, this approach yielded better results than the mean values obtained by the area approach simulations (Bootstrap resampling). Future research should evaluate other remote sources of data and other forest conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory

Stolle

Corte

Sanquetta

et al. 2021

Forests

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“…Polygon layers representing the crowns of individual trees (with a given area and height) were used for the analyses and were created for the REMBIOFOR project "Remote sensing for determining wood biomass and carbon stocks in forests", which was conducted at the Forest Research Institute from 2014-2018. The segmentation method [58] used the CHM and adaptive kernel windows in relation to tree height. Taller trees were smoothed with a larger kernel window and shorter trees were smoothed with smaller kernel windows.…”

Section: Remotely Sensed Datamentioning

confidence: 99%

The Use of Remote Sensing Data to Estimate Land Area with Forest Vegetation Cover in the Context of Selected Forest Definitions

Hycza

Kamińska

Stereńczak

2021

Forests

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(1) Background: Like many other countries, Poland is obliged to report forest area to the Climate Convention (UNFCCC Kyoto Protocol) and the Food and Agriculture Organization of the United Nations (FAO/UN). Differences between national and international forest definitions lead to differences between actual and reported forest area. Remote sensing is a useful tool for estimating forest area for reporting purposes. One of the most important parts of the estimation is the choice of a basal area to calculate the percentage of vegetation cover. (2) Methods: Height, crown projection area, and minimum complex area were used to classify the area with forest vegetation. Percentage canopy cover was determined using three different methods based on segmentation polygons, triangular grid and canopy height model pixels. The accuracy of the above methods was verified by manual vectorization performed on a selected set of test plots in the Milicz study area according to the international definitions. The differences were examined using three statistical metrics. (3) Conclusions: This paper compares for the first time methods for determining the area for which canopy cover is calculated (using data from (ALS) and discusses the differences between them in the context of accuracy (the correspondence between the results and the reference data) and the complexity of the process (time and effort required to perform the analysis). This is important in the context of reporting, estimating carbon stocks and biodiversity to mitigate the effects of climate change. Method 2 proved to be the most accurate method, Method 1 was found to be the worst option. Accuracy was better in the case of the Kyoto Protocol definition.

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“…Individual trees are the main body of forests, and the investigation of individual tree species (ITS) is an essential part of forest resource surveys [5][6][7]. The ITS investigations that include the classification and distribution of the individual trees are helpful for the management and protection of forests [8][9][10]. Remote sensing technology has been widely employed in ITS classification because of its macroscopic, dynamic, and abundant acquisitions [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Individual Tree Species Classification Based on Convolutional Neural Networks and Multitemporal High-Resolution Remote Sensing Images

Guo

Jing

et al. 2022

Sensors

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The classification of individual tree species (ITS) is beneficial to forest management and protection. Previous studies in ITS classification that are primarily based on airborne LiDAR and aerial photographs have achieved the highest classification accuracies. However, because of the complex and high cost of data acquisition, it is difficult to apply ITS classification in the classification of large-area forests. High-resolution, satellite remote sensing data have abundant sources and significant application potential in ITS classification. Based on Worldview-3 and Google Earth images, convolutional neural network (CNN) models were employed to improve the classification accuracy of ITS by fully utilizing the feature information contained in different seasonal images. Among the three CNN models, DenseNet yielded better performances than ResNet and GoogLeNet. It offered an OA of 75.1% for seven tree species using only the WorldView-3 image and an OA of 78.1% using the combinations of WorldView-3 and autumn Google Earth images. The results indicated that Google Earth images with suitable temporal detail could be employed as auxiliary data to improve the classification accuracy.

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Mapping individual trees with airborne laser scanning data in an European lowland forest using a self-calibration algorithm

Cited by 23 publications

References 43 publications

Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory

Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory

The Use of Remote Sensing Data to Estimate Land Area with Forest Vegetation Cover in the Context of Selected Forest Definitions

Individual Tree Species Classification Based on Convolutional Neural Networks and Multitemporal High-Resolution Remote Sensing Images

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