Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing

Dadon, Alon; Mandelmilch, Moshe; Ben‐Dor, Eyal; Sheffer, Efrat

doi:10.3390/rs11232800

Cited by 16 publications

(9 citation statements)

References 71 publications

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“…In contrast to other studies that have presented detailed mapping of woody Mediterranean species using hyperspectral sensors [31][32][33]59], this work emphasizes the importance of the spatial resolution of sensors and the use of their seasonality patterns for identifying species.…”

Section: Detailed Vegetation Mappingmentioning

confidence: 80%

“…Moreover, the challenge increases with the contribution of the understory of mostly herbaceous species to the spectral signal in the wet season [30]. Given this complexity, accurate detection of Mediterranean vegetation at the individual plant and species level often requires the use of hyperspectral sensors (images in hundreds of narrow bands) covering a wide spectral range [31][32][33].…”

Section: Remote Sensing Of Mediterranean Vegetationmentioning

confidence: 99%

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The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing

et al. 2021

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Discriminating between woody plant species using a single image is not straightforward due to similarity in their spectral signatures, and limitations in the spatial resolution of many sensors. Seasonal changes in vegetation indices can potentially improve vegetation mapping; however, for mapping at the individual species level, very high spatial resolution is needed. In this study we examined the ability of the Israel/French satellite of VENμS and other sensors with higher spatial resolutions, for identifying woody Mediterranean species, based on the seasonal patterns of vegetation indices (VIs). For the study area, we chose a site with natural and highly heterogeneous vegetation in the Judean Mountains (Israel), which well represents the Mediterranean maquis vegetation of the region. We used three sensors from which the indices were derived: a consumer-grade ground-based camera (weekly images at VIS-NIR; six VIs; 547 individual plants), UAV imagery (11 images, five bands, seven VIs) resampled to 14, 30, 125, and 500 cm to simulate the spatial resolutions available from some satellites, and VENμS Level 1 product (with a nominal spatial resolution of 5.3 m at nadir; seven VIs; 1551 individual plants). The various sensors described seasonal changes in the species’ VIs at different levels of success. Strong correlations between the near-surface sensors for a given VI and species mostly persisted for all spatial resolutions ≤125 cm. The UAV ExG index presented high correlations with the ground camera data in most species (pixel size ≤125 cm; 9 of 12 species with R ≥ 0.85; p < 0.001), and high classification accuracies (pixel size ≤30 cm; 8 species with >70%), demonstrating the possibility for detailed species mapping from space. The seasonal dynamics of the species obtained from VENμS demonstrated the dominant role of ephemeral herbaceous vegetation on the signal recorded by the sensor. The low variance between the species as observed from VENμS may be explained by its coarse spatial resolution (effective ground spatial resolution of 7.5) and its non-nadir viewing angle (29.7°) over the study area. However, considering the challenging characteristics of the research site, it may be that using a VENμS type sensor (with a spatial resolution of ~1 m) from a nadir point of view and in more homogeneous and dense areas would allow for detailed mapping of Mediterranean species based on their seasonality.

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Section: Detailed Vegetation Mappingmentioning

confidence: 80%

Section: Remote Sensing Of Mediterranean Vegetationmentioning

confidence: 99%

The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing

et al. 2021

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“…For example, Alaibakhsh et al [61] used Principal component analysis (PCA) to delineate riparian vegetation from Landsat multi-temporal imagery. Similarly, Dadon et al [62] used an improved PCA-based classification scheme to classify Mediterranean forest types in an unsupervised way. The t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm has been used to strengthen the quality of ground truth data used in the mapping of heterogeneous vegetation [63].…”

Section: Discussionmentioning

confidence: 99%

Self-Supervised Learning of Satellite-Derived Vegetation Indices for Clustering and Visualization of Vegetation Types

Sharma

Hara

2021

J. Imaging

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Vegetation indices are commonly used techniques for the retrieval of biophysical and chemical attributes of vegetation. This paper presents the potential of an Autoencoders (AEs) and Convolutional Autoencoders (CAEs)-based self-supervised learning approach for the decorrelation and dimensionality reduction of high-dimensional vegetation indices derived from satellite observations. This research was implemented in Mt. Zao and its base in northeast Japan with a cool temperate climate by collecting the ground truth points belonging to 16 vegetation types (including some non-vegetation classes) in 2018. Monthly median composites of 16 vegetation indices were generated by processing all Sentinel-2 scenes available for the study area from 2017 to 2019. The performance of AEs and CAEs-based compressed images for the clustering and visualization of vegetation types was quantitatively assessed by computing the bootstrap resampling-based confidence interval. The AEs and CAEs-based compressed images with three features showed around 4% and 9% improvements in the confidence intervals respectively over the classical method. CAEs using convolutional neural networks showed better feature extraction and dimensionality reduction capacity than the AEs. The class-wise performance analysis also showed the superiority of the CAEs. This research highlights the potential of AEs and CAEs for attaining a fine clustering and visualization of vegetation types.

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“…The resulting reflectance image was validated with on-the-ground spectral measurements of control objects acquired by the ASD field spectrometer. Topographic and illumination corrections were not applied, and shade pixels were removed out from the image using the principal component analysis-based classification (PCABC) method (Dadon et al 2019) using the ENVI image-processing package.…”

Section: Hyperspectral Airborne Data Acquisitionmentioning

confidence: 99%

“…The map was produced using ARC MAP 10.2 (ESRI Inc.). A mask layer was created by the PCABC method (Dadon et al 2019) to remove nonvegetation pixels from the image. We used high-spatial-resolution (pixel size: 3.75 cm) images acquired from a Red Edge M camera (MicaSense Inc. 2017) mounted on an unmanned aerial vehicle (UAV), to verify that all of the points on the map were indeed woody Mediterranean plants.…”

Section: Spatial Mapping Of Leaf P Concentration In Massuamentioning

confidence: 99%

Mapping phosphorus concentration in Mediterranean forests using different remote-sensing methods

Mandelmilch

Livne

Dor

et al. 2021

International Journal of Remote Sensing

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Mineral nutrition is essential for optimal plant growth. Phosphorus (P) is a relatively small component of leaf dry weight, with a concentration in plant foliage of less than 1%. Despite its low concentration, P is an essential element in plants, mainly used for energy transfer. Mapping P concentration using traditional methods is expensive and usually limited to a small area; it is timeconsuming and covers only a few plant individuals or species. In this study, we demonstrate the use of remote-sensing (RS) data acquired from the feld and airborne hyperspectral sensors to predict and map the P concentration in leaves of different woody Mediterranean plant species. Comprehensive field work included leaf sampling, laboratory analyses, and spectral measurements using a visible, near-infrared and shortwave-infrared (VIS-NIR-SWIR) field spectrometer. Using different spectral configurations, we built accurate models to predict P concentration in leaf samples. The models were built using a NIR data analysis technique with the data mining software PARACUDA II. This software allowed us to identify the correlative wavelengths for P-bearing molecules in selected woody Mediterranean plant species. The hyperspectralbased model for leaf P concentration was extracted from the reflectance data acquired using a manned aircraft carrying a hyperspectral sensor (Specim AisaFenix 1 K). The model gave a reliable correlation between points extracted from the hyperspectral image and samples measured in the field. We believe that the methodology used in this study will help forest ecologists better understand the concentration of P in the foliage of woody Mediterranean plant species.

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Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing

Cited by 16 publications

References 71 publications

The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing

The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing

Self-Supervised Learning of Satellite-Derived Vegetation Indices for Clustering and Visualization of Vegetation Types

Mapping phosphorus concentration in Mediterranean forests using different remote-sensing methods

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