Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data

Dalponte, Michele; Bruzzone, Lorenzo; Gianelle, Damiano

doi:10.1016/j.rse.2012.03.013

Cited by 395 publications

(314 citation statements)

References 43 publications

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“…The proposed vegetationmapping strategy can produce sufficiently high overall accuracies (nearly 80% in both cases) and kappa coefficients (over 0.64 at both sites) for most applications in which the vegetation map provides the essential classification and scaling information. Moreover, the overall accuracy and kappa coefficient obtained from the proposed vegetation-mapping strategy are comparable to most previous supervised vegetation-mapping strategies integrating LiDAR data and multispectral imagery (Bork and Su 2007;Dalponte et al 2012;Cho et al 2012). Although the commission and omission errors for certain vegetation groups were high, they might be caused by misregistration between plot measurements and remotely sensed data (LiDAR data and aerial imagery).…”

Section: Discussionmentioning

confidence: 52%

“…Further study is still needed to address how the nonlinear color balancing effect and horizontal accuracy influence the vegetation-mapping accuracy. Moreover, it has been frequently reported that hyperspectral data outperformed multispectral data in recognizing plant species (Adam et al 2010;Xu and Gong 2007), and there have been studies showing that the integration of hyperspectral data and LiDAR data can produce more accurate vegetation maps than the integration of multispectral data and LiDAR data (Dalponte et al 2012).…”

Section: Discussionmentioning

confidence: 99%

“…For example, Cho et al (2012), Colgan et al (2012) and Naidoo et al (2012) mapped tree species compositions in African savannas through the combination of LiDAR data and hyperspectral data using maximum likelihood, Random Forest, and Support Vector Machine classifiers, respectively; Dalponte et al (2012) and Hill and Thomson (2005) classified tree species compositions of broadleaf and coniferous mixed forests through the fusion of spectral and LiDAR data; Holmgren et al (2008) and Koukoulas and Blackburn (2005) used a maximum likelihood classifier to identify individual tree species from LiDAR-derived structure parameters and multispectral information in deciduous and coniferous forests, respectively. It has been reported that the integration of LiDAR data and optical imagery can increase the vegetation composition classification accuracy by 16%-20% in rangelands, compared to using only LiDAR data or optical imagery (Bork and Su 2007 structure characteristics, which can be estimated by statistical imputation methods that incorporate field measurements with LiDAR data and optical imagery (Falkowski et al 2010;Hummel at al.…”

Section: Introductionmentioning

confidence: 99%

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A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

Fry

Collins

et al. 2015

Canadian Journal of Remote Sensing

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Abstract. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure characteristics, which are critical for differentiating vegetation communities in compositionally homogeneous forests. Light detection and ranging (LiDAR) can accurately measure the forest vertical and horizontal structures and provide a great opportunity for solving this problem. This study introduces a strategy using both multispectral aerial imagery and LiDAR data to map vegetation composition and structure over large spatial scales. Our approach included the use of a Bayesian information criterion algorithm to determine the optimized number of vegetation groups within mixed conifer forests in two study areas in the Sierra Nevada, California, and an unsupervised classification technique and post hoc analysis to map these vegetation groups across both study areas. The results show that the proposed strategy can recognize four and seven vegetation groups at the two study areas, respectively. Each vegetation group has its unique vegetation structure characteristics or vegetation species composition. The overall accuracy and kappa coefficient of the vegetation mapping results are over 78% and 0.64 for both study sites.Résumé. La cartographie précise de la végétation est essentielle entre autres pour la gestion des ressources naturelles, l'analysé ecologique, et la modélisation hydrologique. Les approches d'imagerie multispectrale et hyperspectrale par télédétection se sont avérées de précieuses contributions au processus de la cartographie de la végétation, mais elles ne peuvent fournir qu'un nombre limité de caractéristiques sur la structure de la végétation, qui sont essentielles pour différencier les communautés végétales dans les forêts de composition homogènes. La télédétection par laser «light detection and ranging» (LiDAR) peut mesurer avec précision les structures verticales et horizontales de la forêt, et fournit une formidable opportunité de résoudre ce problème. Cetté etude présente une stratégie qui utiliseà la fois l'imagerie multispectrale aérienne et des données LiDAR pour cartographier la composition et la structure de la végétationà grandeséchelles spatiales. Notre approche comprenait l'utilisation d'un algorithme du critère d'information Bayésien pour déterminer le nombre optimal de groupes de végétation dans les forêts mixtes de conifères sur deux zones d'étude dans les Sierra Nevada, en Californie, ainsi qu'une technique de classification non supervisée et une analyse post hoc pour cartographier ces groupes de végétation dans les deux zones d'étude. Les résultats montrent que la stratégie proposée peut reconnaitre quatre et sept groupes de végétation dans les deux zones d'étude respectivement. Chaque groupe de végétation a des caractéristiques uniques de structure de...

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Section: Discussionmentioning

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

Fry

Collins

et al. 2015

Canadian Journal of Remote Sensing

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“…Specifically, the inclusion of DTM and DSM to create a CHM proved to be critical for the classification of the cicada damage. A similar approach was earlier applied using combinations of high-resolution imagery and Lidar and was found to be successful in many applications [15,[45][46][47]. We believe that the UAV data can be used in many of these situations, being a source of 3D and spectral information from the same source, reducing costs and time of acquisition.…”

Section: Discussionmentioning

confidence: 77%

Cicada (<i>Magicicada</i>) Tree Damage Detection Based on UAV Spectral and 3D Data

Hentz¹,

Strager²

2018

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The periodical cicadas appear in regions of the United States in intervals of 13 or 17 years. During these intervals, deciduous trees are often impacted by the small cuts and eggs they lay in the outer branches which soon die off. Because this is such an infrequent occurrence and it is so difficult to assess the damage across large forested areas, there is little information about the extent of this impact. The use of remote sensing techniques has been proven to be useful in forest health management to monitor large areas. In addition, the use of Unmanned Aerial Vehicles (UAVs) has become a valuable tool for analysis. In this study, we evaluated the impact of the periodical cicada occurrence on a mixed hardwood forest using UAV imagery. The goal was to evaluate the potential of this technology as a tool for forest health monitoring. We classified the cicada impact using two Maximum Likelihood classifications, one using only the high resolution spectral derived from leaf-on imagery (MLC 1), and in the second we included the Canopy Height Model (CHM)-derived from leaf-on Digital Surface Model (DSM) and leaf-off Digital Terrain Model (DTM)-information in the classification process (MLC 2). We evaluated the damage percentage in relation to the total forest area in 15 circular plots and observed a range from 1.03% -22.23% for MLC 1, and 0.02% -10.99% for MLC 2. The accuracy of the classification was 0.35 and 0.86, for MLC 1 and MLC 2, based on the kappa index. The results allow us to highlight the importance of combining spectral and 3D information to evaluate forest health features. We believe this approach can be applied in many forest monitoring objectives in order to detect disease or pest impacts.

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“…Clarification was provided for the 'woodland' classes. This involved the use of height and area rules, the height is easily determinable from LIDAR data and shown by Dalponte et al (2012) to help improve classification accuracy. The use of an area rule allowed individual or clumps or trees to be separated from Woodlands, with these then considered Dune Scrub.…”

Section: Discussionmentioning

confidence: 99%

The development and application of remote sensing to monitor sand dune habitats

Brownett

Mills

2017

J Coast Conserv

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Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data

Cited by 395 publications

References 43 publications

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

Cicada (<i>Magicicada</i>) Tree Damage Detection Based on UAV Spectral and 3D Data

The development and application of remote sensing to monitor sand dune habitats

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Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data

Cited by 395 publications

References 43 publications

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

Cicada (&lt;i&gt;Magicicada&lt;/i&gt;) Tree Damage Detection Based on UAV Spectral and 3D Data

The development and application of remote sensing to monitor sand dune habitats

Contact Info

Product

Resources

About

Cicada (<i>Magicicada</i>) Tree Damage Detection Based on UAV Spectral and 3D Data