Individual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads

Pires, Raul de Paula; Olofsson, Kenneth; Persson, Henrik J.; Lindberg, Eva; Holmgren, Johan

doi:10.1016/j.isprsjprs.2022.03.004

Cited by 28 publications

(13 citation statements)

References 41 publications

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“…Additionally, a generated prediction leftovers are entropy encoded using a learning-based method, which enhances the efficiency of compression and reduces the computational burden. In contrast to Raster image compression techniques proposed in, authors demonstrate so the approach provides considerable increases in the bit rate [27]. shows a few exemplary frames, the typical number of points throughout time, and the accompanying raw, uncompressed data bitrates (in MBytes/s).…”

Section: Approaches Based On Color Indicesmentioning

confidence: 96%

An Analysis of Image Processing In Forestry and Agriculture Review

Mosslah,

Abbas

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Numerous techniques have been demonstrated for computer technologies to increase agricultural output. One method that is becoming into a valuable tool for picture processing. In order to help academics and farmers improve agricultural practices, this study provides a brief assessment on the use of image processing in forestry and agriculture. Modern agricultural techniques, pesticides and herbicides, plant nutrition management and plant development monitoring benefit greatly from image processing techniques. A future potential for image processing in various agricultural business situations is highlighted in this research. Segmentation in this context refers to the division of pixels in a picture into plant and nonplant pixels. For subsequent plant analysis, like plant categorization (in another meaning: determining this plant was considered either a crop or maybe a weed. Based on this analysis, the successful work depends herbicide application in smart agricultural applications, In this process, excellence is crucial. Fragmentation is first focused on through survey and then image pre-processing is carefully examined. Here, at this stage, the backgrounds of the plants are segmented (i.e., the soil attachments are isolated on one side and the plant on the other). The main plant harvesting strategies are three of which are segmentation based on threshold, segmentation based on deep learning and segmentation based on color index. The focus of our review is on color index methods due to their abundance in the literature. Therefore, according to literature research completed in last decade, specifically starting in the year 2008 to 2021, a thorough evaluation of the Color index-based categorization approaches are shown. Finally, we list the difficulties and a few chances for new breakthroughs in this field.

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Section: Approaches Based On Color Indicesmentioning

confidence: 96%

An Analysis of Image Processing In Forestry and Agriculture Review

Mosslah,

Abbas

2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Nowadays, precision forestry, forest management planning, and forest growth modeling play an increasingly important role in forestry applications, and more attention is paid to the acquisition of individual tree parameters (tree height and DBH) of each tree species in the stand, and the research has begun to shift from the overall stand to the study of individual trees. 5,6 The acquisition of individual tree parameters (tree height and DBH) through automatic individual tree detection is also becoming more and more important in forestry applications. There are certain limitations in obtaining the parameter information of individual trees through multispectral data.…”

Section: Introductionmentioning

confidence: 99%

“…Because the image can only provide 2D data and cannot provide the vertical structure information of trees, more studies focus on the stand level to obtain the average or sum of the stands. Nowadays, precision forestry, forest management planning, and forest growth modeling play an increasingly important role in forestry applications, and more attention is paid to the acquisition of individual tree parameters (tree height and DBH) of each tree species in the stand, and the research has begun to shift from the overall stand to the study of individual trees 5 , 6 . The acquisition of individual tree parameters (tree height and DBH) through automatic individual tree detection is also becoming more and more important in forestry applications.…”

Section: Introductionmentioning

confidence: 99%

Combining multisource remote sensing data to calculate individual tree biomass in complex stands

Lian,

Zhang,

Wang

et al. 2024

J. Appl. Rem. Sens.

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Accurate estimation of forest individual tree characteristics and biomass is very important for monitoring global carbon storage and carbon cycle. In order to solve the problem of calculating individual biomass of various tree species in complex stands, we take terrestrial laser scanning data, unmanned aerial vehicle-laser scanning data, and multispectral data as data sources and extract spectral characteristics, vegetation index characteristics, texture characteristics, and tree height characteristics of diverse forest areas through multispectral classification of tree species. Based on the random forest (RF) algorithm, the extracted features were superimposed and optimized, and the tree species were classified according to the multispectral data combined with field investigation. Then multispectral classification data combined with light detection and ranging (LIDAR) point cloud data were used to classify point cloud species, and then individual tree parameters were extracted for the divided point cloud species, and stand biomass was obtained using the tree biomass calculation model. The results showed that all kinds of tree species could be identified based on RF algorithm by combining multispectral data and LIDAR data. The overall classification accuracy was 66% and the kappa coefficient was 0.59. The recall rate of poplar, cypress, and lacebark-pine was about 75%, except for willow and clove trees, which were blocked by large crown width and caused multiple detection and missed detection. The R 2 of diameter at breast height was 0.85, and the root-mean-square error (RMSE) was 5.90 cm. The R 2 of the tree height was 0.90, and the RMSE was 1.78 m. Finally, the biomass of each tree species was calculated, and the stand biomass was 66.76 t∕hm 2 , which realized the classification of the whole stand and the measurement of the biomass of each tree. Our study proves that the application of combined multisource remote sensing data to forest biomass estimation has good feasibility.

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“…In contrast, the most accurate manual method for obtaining the stem curve involves using a logging machine to cut down a tree [6]. In addition, the laser scanning-derived stem curves can be used alongside tree height to estimate stem volume [7][8][9][10][11], which may provide a more accurate estimate of the stem volume compared to national allometric models that have traditionally been used [7]. Thus, laser scanning approaches can offer a more time-efficient way of gathering field reference measurements compared to manual methods, provided that the measurements are accurate enough for operational use.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Under- and Above-Canopy Laser Scanning Solutions for Deriving Stem Curve and Volume in Easy and Difficult Boreal Forest Conditions

Muhojoki,

Tavi,

Hyyppä

et al. 2024

Remote Sensing

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The use of mobile laser scanning for mapping forests has scarcely been studied in difficult forest conditions. In this paper, we compare the accuracy of retrieving tree attributes, particularly diameter at breast height (DBH), stem curve, stem volume, and tree height, using six different laser scanning systems in a managed natural boreal forest. These compared systems operated both under the forest canopy on handheld and unmanned aerial vehicle (UAV) platforms and above the canopy from a helicopter. The complexity of the studied forest sites ranged from easy to difficult, and thus, this is the first study to compare the performance of several laser scanning systems for the direct measurement of stem curve in difficult forest conditions. To automatically detect tree stems and to calculate their attributes, we utilized our previously developed algorithm integrated with a novel bias compensation method to reduce the overestimation of stem diameter arising from finite laser beam divergence. The bias compensation method reduced the absolute value of the diameter bias by 55–99%. The most accurate laser scanning systems were equipped with a Velodyne VLP-16 sensor, which has a relatively low beam divergence, on a handheld or UAV platform. In easy plots, these systems found a root-mean-square error (RMSE) of below 10% for DBH and stem curve estimates and approximately 10% for stem volume. With the handheld system in difficult plots, the DBH and stem curve estimates had an RMSE under 10%, and the stem volume RMSE was below 20%. Even though bias compensation reduced the difference in bias and RMSE between laser scanners with high and low beam divergence, the RMSE remained higher for systems with a high beam divergence. The airborne laser scanner operating above the forest canopy provided tree attribute estimates close to the accuracy of the under-canopy laser scanners, but with a significantly lower completeness rate for stem detection, especially in difficult forest conditions.

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Individual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads

Cited by 28 publications

References 41 publications

An Analysis of Image Processing In Forestry and Agriculture Review

An Analysis of Image Processing In Forestry and Agriculture Review

Combining multisource remote sensing data to calculate individual tree biomass in complex stands

Benchmarking Under- and Above-Canopy Laser Scanning Solutions for Deriving Stem Curve and Volume in Easy and Difficult Boreal Forest Conditions

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