Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

Nagendra, Harini; Rocchini, Duccio; Ghate, Rucha; Sharma, Bhawna; Pareeth, Sajid

doi:10.3390/rs2020478

Cited by 89 publications

(66 citation statements)

References 52 publications

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“…Actually, the relationship between species diversity and textural measures has been explored by many authors in varying research fields. For instance, a similar result was reported by Nagendra et al [68], who also found that the textural measures were significantly correlated with tree species diversity measured by species richness and the Shannon index. St-Louis et al [69] and wood et al [70], who tested image texture as a predictor of bird species richness and density, concluded that textural measures are very promising predictors and even perform better than field-measured vegetation structure.…”

Section: Discussionsupporting

confidence: 72%

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Meng

Wang

et al. 2016

Remote Sensing

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Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson's correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination (R 2 adj ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models (R 2 adj > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.

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Section: Discussionsupporting

confidence: 72%

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Meng

Wang

et al. 2016

Remote Sensing

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“…Satellite-derived time-series of the Enhanced Vegetation Index were used to quantify functional diversity at the ecosystem level through the identification of Ecosystem Functional Types, defined here as ecological entities that have similar properties and dynamics of primary production. Numerous studies have successfully evaluated the use of remotely-sensed spectral diversity to estimate species richness [79][80][81][82][83] and species beta-diversity [84,85]. Our study focused not on estimating the diversity of species composition or turn-over, but on assessing the diversity of ecosystem functioning.…”

Section: Discussionmentioning

confidence: 99%

Environmental and Human Controls of Ecosystem Functional Diversity in Temperate South America

et al. 2013

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Abstract:The regional controls of biodiversity patterns have been traditionally evaluated using structural and compositional components at the species level, but evaluation of the functional component at the ecosystem level is still scarce. During the last decades, the role of ecosystem functioning in management and conservation has increased. Our aim was to use satellite-derived Ecosystem Functional Types (EFTs, patches of the land-surface with similar carbon gain dynamics) to characterize the regional patterns of ecosystem functional diversity and to evaluate the environmental and human controls that determine EFT richness across natural and human-modified systems in temperate South America. The EFT identification was based on three descriptors of carbon gain dynamics derived from seasonal curves of the MODIS Enhanced Vegetation Index (EVI): annual mean (surrogate of primary production), seasonal coefficient of variation (indicator of seasonality) and date of maximum EVI (descriptor of phenology). As observed for species richness in the southern hemisphere, water availability, not energy, emerged as the main climatic driver of 128EFT richness in natural areas of temperate South America. In anthropogenic areas, the role of both water and energy decreased and increasing human intervention increased richness at low levels of human influence, but decreased richness at high levels of human influence.

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“…However, increasing the spatial resolution brings with it also an increase in variability in the reflected signal, which complicates any classification task [26,39]. Hyperspectral remote sensing on the other hand allows to very precisely distinguish surface characteristics by recording additive sub-pixel spectral signatures [32], thereby reducing the need for extremely high spatial resolutions and avoiding the corresponding additional noise.…”

Section: Introductionmentioning

confidence: 99%

“…Passive remote sensing systems are only able to detect the forest surface, the canopy and the contributing tree species. Consequently many studies on forest plant diversity focus on trees (e.g., [12,[22][23][24][25][26][27]). However, considering vascular plants, the most species rich element of temperate European forests is often the herb layer [28], while species richness of trees is mostly small.…”

Section: Introductionmentioning

confidence: 99%

Modelling Forest α-Diversity and Floristic Composition — On the Added Value of LiDAR plus Hyperspectral Remote Sensing

et al. 2012

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The decline of biodiversity is one of the major current global issues. Still, there is a widespread lack of information about the spatial distribution of individual species and biodiversity as a whole. Remote sensing techniques are increasingly used for biodiversity monitoring and especially the combination of LiDAR and hyperspectral data is expected to deliver valuable information. In this study spatial patterns of vascular plant community composition and α-diversity of a temperate montane forest in Germany were analysed for different forest strata. The predictive power of LiDAR (LiD) and hyperspectral (MNF) datasets alone and combined (MNF+LiD) was compared using random forest regression in a ten-fold cross-validation scheme that included feature selection and model tuning. The Remote Sens. 2012, 4 2819 final models were used for spatial predictions. Species richness could be predicted with varying accuracy (R 2 = 0.26 to 0.55) depending on the forest layer. In contrast, community composition of the different layers, obtained by multivariate ordination, could in part be modelled with high accuracies for the first ordination axis (R 2 = 0.39 to 0.78), but poor accuracies for the second axis (R 2 ≤ 0.3). LiDAR variables were the best predictors for total species richness across all forest layers (R was small (≤0.07)-if any-when using both LiDAR and hyperspectral data as compared to using only the best single predictor set. This study shows the high potential of LiDAR and hyperspectral data for plant biodiversity modelling, but also calls for a critical evaluation of the added value of combining both with respect to acquisition costs.

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Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

Cited by 89 publications

References 52 publications

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Environmental and Human Controls of Ecosystem Functional Diversity in Temperate South America

Modelling Forest α-Diversity and Floristic Composition — On the Added Value of LiDAR plus Hyperspectral Remote Sensing

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