Classification of Tree Species in Overstorey Canopy of Subtropical Forest Using QuickBird Images

Lin, Chinsu; Popescu, Sorin C.; Thomson, Gavin; Tsogt, Khongor; Chang, Chein‐I

doi:10.1371/journal.pone.0125554

Cited by 37 publications

(13 citation statements)

References 54 publications

(42 reference statements)

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“…New studies focused on the spatial details to map individual trees in temperate or tropical forest environments [22][23][24]. These studies are in line with the first attempts based on the high-resolution multispectral sensors like Landsat TM [25,26].…”

Section: Introductionmentioning

confidence: 57%

Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

et al. 2016

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Abstract:Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image time-series (SITS) to discriminate tree species in temperate forests is investigated. Based on a 17-date SITS acquired across one year, thirteen major tree species (8 broadleaves and 5 conifers) are classified in a study area of southwest France. The performance of parametric (GMM) and nonparametric (k-NN, RF, SVM) methods are compared at three class hierarchy levels for different versions of the SITS: (i) a smoothed noise-free version based on the Whittaker smoother; (ii) a non-smoothed cloudy version including all the dates; (iii) a non-smoothed noise-free version including only 14 dates. Noise refers to pixels contaminated by clouds and cloud shadows. The results of the 108 distinct classifications show a very high suitability of the SITS to identify the forest tree species based on phenological differences (average κ = 0.93 estimated by cross-validation based on 1235 field-collected plots). SVM is found to be the best classifier with very close results from the other classifiers. No clear benefit of removing noise by smoothing can be observed. Classification accuracy is even improved using the non-smoothed cloudy version of the SITS compared to the 14 cloud-free image time series. However conclusions of the results need to be considered with caution because of possible overfitting. Disagreements also appear between the maps produced by the classifiers for complex mixed forests, suggesting a higher classification uncertainty in these contexts. Our findings suggest that time-series data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrates the potential contribution of the recently launched Sentinel-2 satellite for studying forest ecosystems.

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Section: Introductionmentioning

confidence: 57%

Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

et al. 2016

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“…Broadband RS sensors record less ST/STV than narrowband hyperspectral. Therefore, the forest tree species discrimination for broadband RS sensors such as Landsat, Spot, RapidEye, Worldview-2 and QuickBird is possible, but not as successful [344][345][346][347][348]352] as narrowband hyperspectral RS data [64,[341][342][343]349]. Immitzer et al, [344] used very high spatial resolution 8-band WordView-2 Satellite Data for classifying 10 tree species with an overall accuracy of around 82% (eight bands, object-based).…”

Section: Monitoring Stress On Vegetation In Fes With Rsmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

et al. 2016

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Anthropogenic stress and disturbance of forest ecosystems (FES) has been increasing at all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health (FH). Remote sensing (RS) bridges the gaps of these limitations, by monitoring indicators of FH on different spatio-temporal scales, and in a cost-effective, rapid, repetitive and objective manner. In this paper, we provide an overview of the definitions of FH, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how we can observe FH with RS. We introduce the concept of spectral traits (ST) and spectral trait variations (STV) in the context of FH monitoring and discuss the prospects, limitations and constraints. Stress, disturbances and resource limitations can cause changes in FES taxonomic, structural and functional diversity; we provide examples how the ST/STV approach can be used for monitoring these FES characteristics. We show that RS based assessments of FH indicators using the ST/STV approach is a competent, affordable, repetitive and objective technique for monitoring. Even though the possibilities for observing the taxonomic diversity of animal species is limited with RS, the taxonomy of forest tree species can be recorded with RS, even though its accuracy is subject to certain constraints. RS has proved successful for monitoring the impacts from stress on structural and functional diversity. In particular, it has proven to be very suitable for recording the short-term dynamics of stress on FH, which cannot be cost-effectively recorded using in-situ methods. This paper gives an overview of the ST/STV approach, whereas the second paper of this series concentrates on discussing in-situ terrestrial monitoring, in-situ RS approaches and RS sensors and techniques for measuring ST/STV for FH.

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“…Another is to estimate the productivity in terms of the carbon sequestration rate. Some of the useful indicators can be species mapping [40,41], vegetation fraction estimation [42,43], water stress detection and chlorophyll concentration estimation [36], biomass-based carbon stocks and/or net primary estimation [39,[44][45][46][47][48], and vegetation phenology detection [49,50]. Obviously, a series of multi-temporal remote sensing images with appropriate atmospheric correction can substantially provide normalized reflectance for deriving reliable quantitative parameters of forest dynamics [51] and so facilitate constant monitoring of terrestrial ecosystems [52].…”

Section: Potential Benefits Of the Glgcm Technique For Diagnosing Formentioning

confidence: 99%

A Generalized Logistic-Gaussian-Complex Signal Model for the Restoration of Canopy SWIR Hyperspectral Reflectance

Lin

2018

Remote Sensing

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The continuum of the SWIR (short-wave infrared) signals from 1320 to 1650 nm contains valuable information for effectively diagnosing water, chlorophyll, and nitrogen content. The SWIR spectra of in situ spectroradiometric data and airborne spectrometric images are frequently contaminated by significant noise. Based on a Logistic-Gaussian complex signal model (LGCM), the noise-free signals at 1330-1349 and 1411-1430 nm wavelengths can provide critical bases for restoring the 1350-1410 nm wavelength signals for a single point of data. This paper proposes a generalized LGCM (GLGCM) technique to expand the ability of LGCM to process large data with variant reflectance values. A 12-year-old red cypress plantation located in a central Taiwan temperate forest was selected for this study. Hundreds of reflectance spectra of tree crowns were obtained using an ASD FR Spectroradiometer. The in-laboratory blank test showed that the GLGCM technique was able to achieve sufficient performance with an RMSE (root mean square error) of 0.0015 ± 0.0005 and 0.0011 ± 0.0005 for the front-edge and end-edge signal bases respectively, and 0.0014 ± 0.0006 in between the two signal bases. A significant level of noise between −0.2 and 0.4 was successfully removed from the in situ contaminated reflectance in the 1350-1410 nm wavelengths. The estimation bias for the signals of front-edge and end-edge bases was low, averaging 0.0031 ± 0.0003 and 0.0032 ± 0.0012. The consistency between the blank test and the in situ experimental results indicates that the GLGCM technique has potential in using batch processing to fix the problem of the noisy SWIR spectra in spectroradiometeric data and also airborne spectrometric images.

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Classification of Tree Species in Overstorey Canopy of Subtropical Forest Using QuickBird Images

Cited by 37 publications

References 54 publications

Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

A Generalized Logistic-Gaussian-Complex Signal Model for the Restoration of Canopy SWIR Hyperspectral Reflectance

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