Predicting Vascular Plant Diversity in Anthropogenic Peatlands: Comparison of Modeling Methods with Free Satellite Data

Castillo-Riffart, Ivan; Galleguillos, Mauricio; Lopatin, Javier; Perez‐Quezada, Jorge F.

doi:10.3390/rs9070681

Cited by 20 publications

(18 citation statements)

References 59 publications

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“…Polygonum divaricatum was the dominant species in moving dunes; and, Artemisia ordosica dominated at fixed dunes and the low plain. Species richness, which refers to the total number of species per plot, has been widely used as one indicator of plant diversity [30,31]. In our study, species richness atthe plot level was again used as a key indicator of plant biodiversity.…”

Section: Plant Diversity Survey and Analysismentioning

confidence: 94%

Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands

et al. 2019

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Numerous spectral indices have been developed to assess plant diversity. However, since they are developed in different areas and vegetation type, it is difficult to make a comprehensive comparison among these indices. The primary objective of this study was to explore the optimum spectral indices that can predict plant species richness across different communities in sandy grassland. We use 7339 spectral indices (7217 we developed and 122 that were extracted from literature) to predict plant richness using a two-year dataset of plant species and spectra information at 270 plots. For this analysis, we employed cluster analysis, correlation analysis, and stepwise linear regression. The spectral variability within the 420–480 nm and 760–900 nm ranges, the first derivative value at the sensitive bands, and the normalized difference at narrow spectral ranges correlated well with plant species richness. Within the 7339 indices that were investigated, the first-order derivative values at 606 and 583 nm, the reflectance combinations on red bands: (R802 − R465)/(R802 + R681) and (R750 − R550)/(R750 + R550) showed a stable performance in both the independent calibration and validation datasets (R2 > 0.27, p < 0.001, RMSE < 1.7). They can be regarded as the best spectral indices to estimate plant species richness in sandy grasslands. In addition to these spectral variation indices, the first derivative values or the normalized difference of the sensitive bands also reflect plant diversity. These results can help to improve the estimation of plant diversity using satellite-based airborne and hand-held hyperspectral sensors.

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Section: Plant Diversity Survey and Analysismentioning

confidence: 94%

Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands

et al. 2019

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“…(2) models excluding shaded canopies in calibration performed significantly better in sunlit areas than models including shadows in calibration; (3) models including shadows in calibration performed significantly better in shaded areas than models excluding shadows in calibration; (4) models including spectral, 2D textural and 3D structural information performed significantly better than one variable type alone. This bootstrap test has been applied in earlier studies following similar approaches (Lopatin et al 2016Castillo-Riffart et al 2017;Araya-L opez et al 2018).…”

Section: Modeling and Validationmentioning

confidence: 99%

How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

Lopatin

Dološ

Kattenborn

2019

Remote Sens Ecol Conserv

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Plant invasions can result in serious threats for biodiversity and ecosystem functioning. Reliable maps at very‐high spatial resolution are needed to assess invasions dynamics. Field sampling approaches could be replaced by unmanned aerial vehicles (UAVs) to derive such maps. However, pixel‐based species classification at high spatial resolution is highly affected by within‐canopy variation caused by shadows. Here, we studied the effect of shadows on mapping the occurrence of invasive species using UAV‐based data. MaxEnt one‐class classifications were applied to map Acacia dealbata, Ulex europaeus and Pinus radiata in central‐south Chile using combinations of UAV‐based spectral (RGB and hyperspectral), 2D textural and 3D structural variables including and excluding shaded canopy pixels during model calibration. The model accuracies in terms of area under the curve (AUC), Cohen's Kappa, sensitivity (true positive rate) and specificity (true negative rate) were examined in sunlit and shaded canopies separately. Bootstrapping was used for validation and to assess statistical differences between models. Our results show that shadows significantly affect the accuracies obtained with all types of variables. The predictions in shaded areas were generally inaccurate, leading to misclassification rates between 65% and 100% even when shadows were included during model calibration. The exclusion of shaded areas from model calibrations increased the predictive accuracies (especially in terms of sensitivity), decreasing false positives. Spectral and 2D textural information showed generally higher performances and improvements when excluding shadows from the analysis. Shadows significantly affected the model results obtained with any of the variables used, hence the exclusion of shadows is recommended prior to model calibration. This relatively easy pre‐processing step enhances models for classifying species occurrences using high‐resolution spectral imagery and derived products. Finally, a shadow simulation showed differences in the ideal acquisition window for each species, which is important to plan revisit campaigns.

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“…In particular, optical remote sensing utilises the sensitivity of spectra to biochemical and structural characteristics to distinguish vegetation types [12]. Advances in spectral and spatial resolution of remote sensing have allowed for more efficient species diversity estimation in different environments including grasslands (e.g., [7,13], temperate forest (e.g., [14][15][16], wetlands (e.g., [17,18], tropical forest (e.g., [19][20][21] and savanna (e.g., [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Such approaches, therefore, can underestimate the number of species that potentially exist in a given environment. Statistical modelling approaches such as the Shannon diversity index offer an alternative approach by converting categorical species data into continuous diversity scale [25][26][27], thus eliminating the restriction on the number of species that can be estimated for a given area [13,17,[28][29][30]. A number of studies have applied continuous-scale metrics derived from species count data to quantifying woody plant species diversity in the savanna vegetation type [24,27,[31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery

et al. 2020

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Remote sensing techniques are useful in the monitoring of woody plant species diversity in different environments including in savanna vegetation types. However, the performance of satellite imagery in assessing woody plant species diversity in dry seasons has been understudied. This study aimed to assess the performance of multiple Gray Level Co-occurrence Matrices (GLCM) derived from individual bands of WorldView-2 satellite imagery to quantify woody plant species diversity in a savanna environment during the dry season. Woody plant species were counted in 220 plots (20 m radius) and subsequently converted to a continuous scale of the Shannon species diversity index. The index regressed against the GLCMs using the all-possible-subsets regression approach that builds competing models to choose from. Entropy GLCM yielded the best overall accuracy (adjusted R 2 : 0.41−0.46; Root Mean Square Error (RMSE): 0.60−0.58) in estimating species diversity. The effect of the number of predicting bands on species diversity estimation was also explored. Accuracy generally increased when three-five bands were used in models but stabilised or gradually decreased as more than five bands were used. Despite the peak accuracies achieved with three-five bands, performances still fared well for models that used fewer bands, showing the relevance of few bands for species diversity estimation. We also assessed the effect of GLCM window size (3×3, 5×5 and 7×7) on species diversity estimation and generally found inconsistent conclusions. These findings demonstrate the capability of GLCMs combined with high spatial resolution imagery in estimating woody plants species diversity in a savanna environment during the dry period. It is important to test the performance of species diversity estimation of similar environmental setups using widely available moderate-resolution imagery.

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Predicting Vascular Plant Diversity in Anthropogenic Peatlands: Comparison of Modeling Methods with Free Satellite Data

Cited by 20 publications

References 59 publications

Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands

Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands

How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing

Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery

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