Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity

Tuominen, Sakari; Näsi, Roope; Honkavaara, Eija; Balazs, Andras; Hakala, Teemu; Viljanen, Niko; Pölönen, Ilkka; Saari, Heikki; Ojanen, Harri

doi:10.3390/rs10050714

Cited by 62 publications

(58 citation statements)

References 53 publications

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“…The genetic algorithm (GA)-based approach is an adaptive heuristic search algorithm based on the concept of natural selection and survival of the fittest among individuals over consecutive generations. It was used to estimate the chlorophyll content because of its high performance in both regression and classification [70][71][72][73]. In this approach, each of five generations was composed of a population of character strings (i.e., combinations of narrow wavebands) analogous to a chromosome, from which the best waveband combination was finally selected after a process of evolution using R version 3.5.3 [74].…”

Section: Regression Modelmentioning

confidence: 99%

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Sonobe

Hirono

2020

Plants

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Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms-random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)-in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 µg cm −2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves.Ultraviolet and visible light (UV-vis) spectroscopy and high-performance liquid chromatography (HPLC) techniques have previously been used to quantify chlorophyll content. These are destructive methods and may enforce a limited sample size because they are expensive and labour-intensive. It is also not always possible to apply these methods in situ. Alternately, some portable chlorophyll content meters, such as CL-01, SPAD-502, Dualex, and CCM-200, have been used to estimate chlorophyll content [9]. However, light intensity also influences leaf thickness [10], and then it often makes the output of the meter obscure [11]. In contrast, hyperspectral reflectance measurements may be used for detecting various responses from crops [12] or evaluating vegetation properties [13,14], and the use of reflectance for estimating chlorophyll content is being seriously considered. Some pigments, such as chlorophylls and carotenoids, absorb energy strongly in the ultraviolet, blue, and red regions, and then the reflectance and transmittance are weak [15]. The reflectance from vegetation is characterised by the low reflectance over the blue (400-500 nm) and red (650-690 nm) spectral regions due to strong absorption by chlorophylls plus carotenoids in the blue wavelengths and chlorophyll a in the red wavelengths, and the steep rise in reflectance between 680 and 750 nm is caused by the chlorophylls increasing to absorb at wavelengths beyond 700 nm [16]. Based on these features, some empirical approaches, such as vegetation indices, which are generally based on a few narrow or broad spectral bands, are convenient for use to estimate chlorophyll content. Most vegetation indices for chlorophyll content use wavelengths ranging from 400 to 860 nanometres [17][18][19] or the red edge (680 to 750 nm) [20,21]. However, some datasets, such as LOPEX, CALMIT, ANGERS, and HAWAII, that have been used for the development of vegetation indi...

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Section: Regression Modelmentioning

confidence: 99%

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Sonobe

Hirono

2020

Plants

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“…A combination of multispectral data and 3D geometrical data as shown in Brovkina et al [182] was also used in Tuominen et al [183] for species classification. The authors used visible to near-infrared (VNIR) and short-wave infrared (SWIR) images together with optical based geometry data from SfM to discriminate 26 tree species.…”

Section: Species Classificationmentioning

confidence: 99%

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Surový

Kuželka

2019

Forests

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In recent decades, remote sensing techniques and the associated hardware and software have made substantial improvements. With satellite images that can obtain sub-meter spatial resolution, and new hardware, particularly unmanned aerial vehicles and systems, there are many emerging opportunities for improved data acquisition, including variable temporal and spectral resolutions. Combined with the evolution of techniques for aerial remote sensing, such as full wave laser scanners, hyperspectral scanners, and aerial radar sensors, the potential to incorporate this new data in forest management is enormous. Here we provide an overview of the current state-of-the-art remote sensing techniques for large forest areas thousands or tens of thousands of hectares. We examined modern remote sensing techniques used to obtain forest data that are directly applicable to decision making issues, and we provided a general overview of the types of data that can be obtained using remote sensing. The most easily accessible forest variable described in many works is stand or tree height, followed by other inventory variables like basal area, tree number, diameters, and volume, which are crucial in decision making process, especially for thinning and harvest planning, and timber transport optimization. Information about zonation and species composition are often described as more difficult to assess; however, this information usually is not required on annual basis. Counts of studies on forest health show an increasing trend in the last years, mostly in context of availability of new sensors as well as increased forest vulnerability caused by climate change; by virtue to modern sensors interesting methods were developed for detection of stressed or damaged trees. Unexpectedly few works focus on regeneration and seedlings evaluation; though regenerated stands should be regularly monitored in order to maintain forest cover sustainability.

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“…Numerous studies indicate the higher potential for the VNIR/SWIR domain for remote sensing applications, e.g., vegetation trait estimations using wavelengths from the Red Edge to the SWIR domain [15,18,20,24,25,27,28,30,31,[64][65][66][67][68][69]. While this spectral range is available for airborne-or satellite-borne pushbroom sensing systems, for UAV platforms, only a few first studies are known [14,15]. According to the authors, this is due to the fact that, in contrast to the VIS/NIR domain, there is currently no easy-to-use multispectral multi-camera system that also covers the SWIR spectral range.…”

Section: Discussionmentioning

confidence: 99%

Development of a VNIR/SWIR Multispectral Imaging System for Vegetation Monitoring with Unmanned Aerial Vehicles

Alexander

Bareth

Bolten

et al. 2019

Sensors

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Short-wave infrared (SWIR) imaging systems with unmanned aerial vehicles (UAVs) are rarely used for remote sensing applications, like for vegetation monitoring. The reasons are that in the past, sensor systems covering the SWIR range were too expensive, too heavy, or not performing well enough, as, in contrast, it is the case in the visible and near-infrared range (VNIR). Therefore, our main objective is the development of a novel modular two-channel multispectral imaging system with a broad spectral sensitivity from the visible to the short-wave infrared spectrum (approx. 400 nm to 1700 nm) that is compact, lightweight and energy-efficient enough for UAV-based remote sensing applications. Various established vegetation indices (VIs) for mapping vegetation traits can then be set up by selecting any suitable filter combination. The study describes the selection of the individual components, starting with suitable camera modules, the optical as well as the control and storage parts. Special bandpass filters are used to select the desired wavelengths to be captured. A unique flange system has been developed, which also allows the filters to be interchanged quickly in order to adapt the system to a new application in a short time. The characterization of the system was performed in the laboratory with an integrating sphere and a climatic chamber. Finally, the integration of the novel modular VNIR/SWIR imaging system into a UAV and a subsequent first outdoor test flight, in which the functionality was tested, are described.

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Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity

Cited by 62 publications

References 53 publications

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Development of a VNIR/SWIR Multispectral Imaging System for Vegetation Monitoring with Unmanned Aerial Vehicles

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