Integrating Airborne LiDAR and Terrestrial Laser Scanner forest parameters for accurate above-ground biomass/carbon estimation in Ayer Hitam tropical forest, Malaysia

Bazezew, Muluken N.; Hussin, Y.A.; Kloosterman, E.H.

doi:10.1016/j.jag.2018.07.026

Cited by 50 publications

(40 citation statements)

References 49 publications

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“…The current semi-automatic counting of frond number and angle value has potential to be automatic using deep learning technique. The studies by Bazezew 37 and Hopkinson et al 38 shown that both ALS and TLS data can characterise the tree canopy but ALS unable to characterise the www.nature.com/scientificreports www.nature.com/scientificreports/ understorey vegetation structure in detail. Thus, it could be beneficial to integrate both methods to produce more accurate data.…”

Section: =mentioning

confidence: 99%

Application of Ground-Based LiDAR for Analysing Oil Palm Canopy Properties on the Occurrence of Basal Stem Rot (BSR) Disease

Husin

Khairunniza-Bejo

Abdullah

et al. 2020

Sci Rep

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Ground-based LiDAR also known as terrestrial Laser Scanning (tLS) technology is an active remote sensing imaging method said to be one of the latest advances and innovations for plant phenotyping. Basal Stem Rot (BSR) is the most destructive disease of oil palm in Malaysia that is caused by whiterot fungus Ganoderma boninense, the symptoms of which include flattening and hanging-down of the canopy, shorter leaves, wilting green fronds and smaller crown size. therefore, until now there is no critical investigation on the characterisation of canopy architecture related to this disease using tLS method was carried out. This study proposed a novel technique of BSR classification at the oil palm canopy analysis using the point clouds data taken from the TLS. A total of 40 samples of oil palm trees at the age of nine-years-old were selected and 10 trees for each health level were randomly taken from the same plot. The trees were categorised into four health levels -T0, T1, T2 and T3, which represents the healthy, mildly infected, moderately infected and severely infected, respectively. the tLS scanner was mounted at a height of 1 m and each palm was scanned at four scan positions around the tree to get a full 3D image. Five parameters were analysed: S200 (canopy strata at 200 cm from the top), S850 (canopy strata at 850 cm from the top), crown pixel (number of pixels inside the crown), frond angle (degree of angle between fronds) and frond number. the results taken from statistical analysis revealed that frond number was the best single parameter to detect BSR disease as early as T1. In classification models, a linear model with a combination of parameters, ABD -A (frond number), B (frond angle) and D (S200), delivered the highest average accuracy for classification of healthy-unhealthy trees with an accuracy of 86.67 per cent. It also can classify the four severity levels of infection with an accuracy of 80 per cent. This model performed better when compared to the severity classification using frond number. the novelty of this research is therefore on the development of new approach to detect and classify BSR using point clouds data of tLS.LiDAR (Light detection and ranging) is an active remote sensing technology similar to Radar (Radio detection and ranging) but which uses laser light. LiDAR measures the distance or range to a target by illuminating the target with a pulsed laser light and measuring the reflected pulses with a sensor. It can directly represent external structures and carry out profiling for objects or trees. Laser Scanning (LS) profiling systems consist of a measuring instrument that can measure vertical angles, horizontal angles, and distances with a high standard of accuracy and speed by means of a mobile mirror or prism system allowing the mapping of morphological features of targets 1 .Research and field site works used extensive biometric data in estimating tree properties while offering the possibility of reducing inventory costs. Previous studies have demonstrated that LiDAR could be used to ...

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Section: =mentioning

confidence: 99%

Application of Ground-Based LiDAR for Analysing Oil Palm Canopy Properties on the Occurrence of Basal Stem Rot (BSR) Disease

Husin

Khairunniza-Bejo

Abdullah

et al. 2020

Sci Rep

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“…In the field survey, we found that the crown diameters of a large amount of L. racemosa and low B. sexangula ranged from 1.0 to 1.5 m. Therefore, the individual tree was detected based on a 0.25 m spatial resolution CHM, which obtained from the point cloud data of UAV LiDAR. With the development of remote sensing technology, individual tree extraction based on very high-density point clouds is becoming possible [19,26,[47][48][49][50][51][52][53][54]. Using the marker-controlled watershed segmentation (MCWS) algorithm [55], Yin and Wang (2019) well detected and delineated individual mangrove tree [26].…”

Section: Mangrove Species Classification and Individual Tree Detectionmentioning

confidence: 99%

Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

Qiu

Wang

Zou

et al. 2019

Forests

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To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%.(2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R 2 of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha −1 vs. 38.95 Mg ha −1 . However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests.

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“…It should be noted that no studies were found that discuss the integration of information collected by terrestrial laser scanning and airborne laser scanning. Such studies have been carried out in forest areas (Bazezew 2018), and it is likely that they could be applied successfully to urban areas. Tree species is the most important attribute acquired during an inventory (Östberg et al 2012b).…”

Section: Opportunities and Limitations Of Individual Remote Sensing Tmentioning

confidence: 99%

Accuracy of determining specific parameters of the urban forest using remote sensing

Ciesielski¹,

Stereńczak²

2019

iForest

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This paper reviews the current state of knowledge in the field of urban forest inventory and specific tree parameters derived by remote sensing. The paper discusses the possibilities and limitations of using remote sensing to determine the following characteristics of individual trees acquired during the inventory: position (coordinates), tree height, breast height diameter, tree crown parameters (crown span, height of tree crown basis, crown projection surface), health condition, and tree species. A total of 543 papers published in scientific databases (Scopus ® and ScienceDirect ® ) from the year 2000 to December 2017 have been analyzed; 86 of them were used for the review. The most important outcomes are: (a) the integration of many datasets, in particular spectral data (aerial images and satellite imageries) and structural data (LIDAR), allows the most complex use of remote sensing data and helps to improve the accuracy of parameter estimations as well as the correct identification of tree species; (b) the highest precision of measurement is characteristic of TLS, while ALS data has the largest operating system; (c) remote sensing data applications are associated with a large number of sophisticated processing on very large datasets using often proprietary elaborations; (d) the use of remote sensing data makes it possible to determine the characteristics of urban vegetation at various levels of detail and at different scales. Citation: Ciesielski M, Sterenczak K (2019). Accuracy of determining specific parameters of the urban forest using remote sensing. iForest 12:

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Integrating Airborne LiDAR and Terrestrial Laser Scanner forest parameters for accurate above-ground biomass/carbon estimation in Ayer Hitam tropical forest, Malaysia

Cited by 50 publications

References 49 publications

Application of Ground-Based LiDAR for Analysing Oil Palm Canopy Properties on the Occurrence of Basal Stem Rot (BSR) Disease

Application of Ground-Based LiDAR for Analysing Oil Palm Canopy Properties on the Occurrence of Basal Stem Rot (BSR) Disease

Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

Accuracy of determining specific parameters of the urban forest using remote sensing

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