Tree species classification from fused active hyperspectral reflectance and LIDAR measurements

Puttonen, Eetu; Suomalainen, Juha; Hakala, Teemu; Räikkönen, Esa; Kaartinen, Harri; Kaasalainen, Sanna; Litkey, Paula

doi:10.1016/j.foreco.2010.08.031

Cited by 69 publications

(48 citation statements)

References 27 publications

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“…Despite this, some studies have applied this limited spectral information in the form of backscatter intensity of the laser signal for the classification of tree species with at least some degree of success when the number of species is small [11]. Recently, considerable effort has been devoted to develop multi/hyperspectral ALS sensors [12][13][14][15]. These sensors can acquire ALS data using different wavelengths providing intensity information in different bands.…”

Section: Introductionmentioning

confidence: 99%

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

et al. 2018

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Abstract:In this study, the potential of multispectral airborne laser scanner (ALS) data to model and predict some forest characteristics was explored. Four complementary characteristics were considered, namely, aboveground biomass per hectare, Gini coefficient of the diameters at breast height, Shannon diversity index of the tree species, and the number of trees per hectare. Multispectral ALS data were acquired with an Optech Titan sensor, which consists of three scanners, called channels, working in three wavelengths (532 nm, 1064 nm, and 1550 nm). Standard ALS data acquired with a Leica ALS70 system were used as a reference. The study area is located in Southern Norway, in a forest composed of Scots pine, Norway spruce, and broadleaf species. ALS metrics were extracted for each plot from both elevation and intensity values of the ALS points acquired with both sensors, and for all three channels of the ALS multispectral sensor. Regression models were constructed using different combinations of metrics. The results showed that all four characteristics can be accurately predicted with both sensors (the best R 2 being greater than 0.8), but the models based on the multispectral ALS data provide more accurate results. There were differences regarding the contribution of the three channels of the multispectral ALS. The models based on the data of the 532 nm channel seemed to be the least accurate.

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Section: Introductionmentioning

confidence: 99%

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

et al. 2018

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“…This research has indicated that effective use of DEM-derived slope or topographic wetness index can reduce the confusion between Torreya forests and agricultural lands. Previous research also indicates that lidar is an important data source for extracting forest types because different forest types have their own characteristics in stand canopy sizes, heights, and shapes [31,32], but lidar is often unavailable for most study areas. More research should be undertaken on the effective use of multi-source data for improving the extraction performance of individual tree species [16,47].…”

Section: Discussionmentioning

confidence: 99%

“…Hyperspectral (e.g., Hyperion, AVIRIS (Airborne Visible/Infrared Imaging Spectrometer)) and high spatial resolution images (e.g., QuickBird, Worldview) are commonly used [24][25][26][27][28]. Because of the different characteristics of various sensor data such as optical, radar, and lidar data, much research has shifted to the combined use of multiple sensor data [29][30][31][32]. Also, much research attempts to identify a suitable approach to classify specific tree species through comparison of different algorithms [25,33,34].…”

Section: Introductionmentioning

confidence: 99%

Mapping Torreya grandis Spatial Distribution Using High Spatial Resolution Satellite Imagery with the Expert Rules-Based Approach

Wang

2017

Remote Sensing

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Rapid expansion of Torreya forests in the mountainous region in Zhejiang Province in the past three decades has produced many environmental problems such as soil erosion and poor water quality, requiring an update of its spatial distribution in a timely way. However, to date there are no suitable approaches available for mapping Torreya forest distribution, especially the new Torreya plantations, due to the complex landscapes. This research used high spatial resolution Chinese Gaofen (GF-1) and Ziyuan (ZY-3) satellite images and digital elevation model (DEM) data to extract old Torreya forests and new Torreya plantations using a newly proposed expert rules-based approach. Different variables such as spectral bands, vegetation indices, textural images, and DEM-derived variables were examined, and separability analyses of different land covers were explored. An expert rules-based approach was developed for the extraction of old Torreya forests and new Torreya plantations. The accuracy assessment using field survey data and Google Earth images indicates that this newly-proposed approach can effectively distinguish both old Torreya forests and new Torreya plantations from other land covers with producer's accuracies of 84% and 92%, and user's accuracies of 77% and 85%, respectively, much better classification accuracies than the maximum likelihood classifier. This new approach may be used for other study area for extracting Torreya forest distribution. This research provides valuable data sources for better managing existing Torreya forests and planning potential Torreya expansions in this region in the near future.

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“…However, most of the current Lidar sensors provide only a 3D point cloud collected using a single wavelength and with an uncalibrated intensity information. The restricted information on the spectral characteristics of target objects limits the efficient classification or estimation of vegetation biochemical parameters [21,22].…”

Section: Introductionmentioning

confidence: 99%

Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test

et al. 2015

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Global warming is posing a threat to the health and condition of forests as the amount and length of biotic and abiotic disturbances increase. Most methods for detecting disturbances and measuring forest health are based on multi-and hyperspectral imaging. We conducted a test with spruce and pine trees using a hyperspectral Lidar instrument in a laboratory to determine the capability of combined range and reflectance measurements to investigate forest health. A simple drought treatment was conducted by leaving the harvested trees outdoors without a water supply for 12 days. The results showed statistically significant variation in reflectance after the drought treatment for both species. However, the changes differed between the species, indicating that drought-induced alterations in spectral characteristics may be species-dependent. Based on our results, hyperspectral Lidar has the potential to detect drought in spruce and pine trees.

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Tree species classification from fused active hyperspectral reflectance and LIDAR measurements

Cited by 69 publications

References 27 publications

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

Mapping Torreya grandis Spatial Distribution Using High Spatial Resolution Satellite Imagery with the Expert Rules-Based Approach

Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test

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