Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy

Chi, Dengkai; Degerickx, Jeroen; Yu, Kang; Somers, Ben

doi:10.3390/rs12152435

Cited by 17 publications

(11 citation statements)

References 112 publications

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“…The RF regression model is an ensemble learning algorithm that combines a large number of decision trees (ntree). When each tree was built, two-third of the training samples were used to training the model, and one-third of training samples, called out-of-bag (OOB) samples were left out [3,52]. The prediction result was determined by averaging over all the trees [3,41,53].…”

Section: Random Forest Regressionmentioning

confidence: 99%

Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices

Yang

Wang³

et al. 2021

Remote Sensing

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Spectral indices rarely show consistency in estimating crop traits across growth stages; thus, it is critical to simultaneously evaluate a group of spectral variables and select the most informative spectral indices for retrieving crop traits. The objective of this study was to explore the optimal spectral predictors for above-ground biomass (AGB) by applying Random Forest (RF) on three types of spectral predictors: the full spectrum, published spectral indices (Pub-SIs), and optimized spectral indices (Opt-SIs). Canopy hyperspectral reflectance of potato plants, treated with seven nitrogen (N) rates, was obtained during the tuber formation and tuber bulking from 2015 to 2016. Twelve Pub-SIs were selected, and their spectral bands were optimized using band optimization algorithms. Results showed that the Opt-SIs were the best input variables of RF models. Compared to the best empirical model based on Opt-SIs, the Opt-SIs based RF model improved the prediction of AGB, with R2 increased by 6%, 10%, and 16% at the tuber formation, tuber bulking, and for across the two growth stages, respectively. The Opt-SIs can significantly reduce the number of input variables. The optimized Blue nitrogen index (Opt-BNI) and Modified red-edge normalized difference vegetation index (Opt-mND705) combined with an RF model showed the best performance in estimating potato AGB at the tuber formation stage (R2 = 0.88). In the tuber bulking stage, only using optimized Nitrogen planar domain index (Opt-NPDI) as the input variable of the RF model produced satisfactory accuracy in training and testing datasets, with the R2, RMSE, and RE being 0.92, 208.6 kg/ha, and 10.3%, respectively. The Opt-BNI and Double-peak nitrogen index (Opt-NDDA) coupling with an RF model explained 86% of the variations in potato AGB, with the lowest RMSE (262.9 kg/ha) and RE (14.8%) across two growth stages. This study shows that combining the Opt-SIs and RF can greatly enhance the prediction accuracy for crop AGB while significantly reduces collinearity and redundancies of spectral data.

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Section: Random Forest Regressionmentioning

confidence: 99%

Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices

Yang

Wang³

et al. 2021

Remote Sensing

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“…Estimation of forest resources from an economic perspective is one motivation for employing scanning technology ( Wulder et al , 2012 ), but nowadays an increasing number of studies are developing the methods of scanning tree and forest structure as an aid to investigate the processes of forest dynamics and ecosystem function ( Orwig et al , 2018 ; Beland et al , 2019 ). The structural data themselves or in combination with additional remotely sensed data collected at the time of scanning can be used to provide information on, for example, tree health ( Chi et al , 2020 ), drought stress ( Jacobs et al , 2021 ), diseases ( Husin et al , 2020 ) and leaf water content ( Junttila et al , 2018 ; Elsherif et al , 2019 ). Combining multiple types of measurements with structural data provides capacity to enhance the models and to analyse forest ecosystem function at far wider scales and with better spatial precision than is feasible with labour-intensive manual measurements ( D’Urban Jackson et al , 2020 ).…”

Section: Potential Benefits Of Laser Scanning In Forest Process Researchmentioning

confidence: 99%

Terrestrial laser scanning: a new standard of forest measuring and modelling?

Åkerblom

Kaitaniemi

2021

Annals of Botany

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Background Laser scanning technology has opened new horizons for the research of forest dynamics, because it provides a largely automated and non-destructive method to rapidly capture the structure of individual trees and entire forest stands at multiple spatial scales. The structural data itself or in combination with additional remotely sensed data also provides information on the local physiological state of structures within trees. The capacity of new methods is facilitated by the ongoing development of automated processing tools that are designed to capture information from the point cloud data provided by the remote measurements. Scope Terrestrial laser scanning (TLS), performed from the ground or from unmanned aerial vehicles, in particular, has potential to become a unifying measurement standard for forest research questions, because the equipment are flexible to use in the field and have the capacity to capture structural information at the branch-level detail in forest-plot or even forest scale. This issue of Annals of Botany includes selected papers that exemplify the current and potential uses of TLS for purposes such as crown interactions between trees, growth dynamics of mixed stands, non-destructive characterization of urban trees, and enhancement of ecological and evolutionary models. The papers also present current challenges in the applicability of TLS methods and report recent developments in methods facilitating the use of TLS data for research purposes, including automatic processing chains and quantifying branch and above-ground biomass. In this article, we provide an overview of the current and anticipated future capacity of TLS and related methods in solving questions that utilize measurements and models of forests. Conclusions Due to its measurement speed, TLS provides a method to effortlessly capture large amounts of detailed structural forest information, and consequent proxy data for tree and forest processes, in a far wider spatial scale than is feasible with manual measurements. Issues with measurement precision and occlusion of laser beams before they reach their target structures continue to reduce the accuracy of TLS data, but the limitations are counterweighted by the measurement speed that enables large samples. The currently high time-cost of analysing TLS data, in turn, is likely to become diminished through progress in the automated processing methods. The developments point towards TLS becoming a new and widely accessible standard tool in forest measurement and modelling.

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“…In urban areas, LiDAR provides data for tree structure characterization with the potential to improve tree species classification [8,10,68,69] when enough point density is available [10], i.e., more than 10 points per square meter [11]. LiDAR, derived from different platforms, such as airborne laser scanner (ALS), space-borne LiDAR, and terrestrial laser scanning (TLS), is an active sensor, capable of extracting the vertical urban structure, including tree canopy shape, height, and diameter with high accuracies [10,21,70].…”

Section: Lidarmentioning

confidence: 99%

Remotely Sensed Tree Characterization in Urban Areas: A Review

et al. 2021

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Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review of 48 scientific papers published during the last five years (2016–2020), related to urban tree and greenery characterization, remote sensing techniques for tree identification, processing methods, and data analysis to classify and segment trees. In particular, we focused on urban tree and forest characterization using remotely sensed data and identified frontiers in scientific knowledge that may be expanded with new developments in the near future. We found advantages and limitations associated with both data sources and processing methods, from which we drew recommendations for further development of tree inventory and characterization in urban forestry science. Finally, a critical discussion on the current state of the methods, as well as on the challenges and directions for future research, is presented.

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Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy

Cited by 17 publications

References 112 publications

Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices

Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices

Terrestrial laser scanning: a new standard of forest measuring and modelling?

Remotely Sensed Tree Characterization in Urban Areas: A Review

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