Optimal band characterization in reformation of hyperspectral indices for species diversity estimation

Asundi, Anand; Malhi, Ramandeep Kaur M.; Srivastava, Prashant K.; Singh, Prachi; Mudaliar, Ashwini; Petropoulos, George P.; Kiran, G. Sandhya

doi:10.1016/j.pce.2021.103040

Cited by 14 publications

(1 citation statement)

References 87 publications

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“…Among the different RS data, the high spectral and spatial resolution associated with hyperspectral images provide valuable information for forest biodiversity assessments. 17 Nevertheless, the acquisition of hyperspectral data commonly involves aircraft, resulting in elevated costs and reduced spatial coverage and temporal resolution compared to satellite multispectral data. This hampers the use of hyperspectral data for biodiversity monitoring.…”

Section: Introductionmentioning

confidence: 99%

A study on the impact of the spatial and spectral resolution on plant species richness in Mediterranean regions using optical remote sensing data

Serwah Boakye,

Huesca Martinez,

Paris

2023

Image and Signal Processing for Remote Sensing XXIX

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Plant Species Richness (PSR) is one of the most widely used metrics to estimate alpha diversity in ecology. Several approaches have been developed to estimate PSR with Remote Sensing (RS) data. Among them, the Spectral Diversity Hypothesis (SDH) approach can be successfully applied to airborne hyperspectral data. Although effective, these data are limited in space and time due to high aerial acquisition costs. Satellite multispectral data are continuously acquired on a global scale, but their spatial and spectral resolutions are not comparable to those of hyperspectral data. Although some studies compared different optical data for estimating PSR using SDH, the impact of the spatial and spectral resolutions on the assessment of this biodiversity indicator is not clear. Moreover, most of the studies focus on dense tropical forest areas or wetlands, while little has been done to test the SDH approach in open forests located in Mediterranean regions. For all these reasons, the present work aims to: (1) apply and interpret PSR estimated with the SDH approach in open Mediterranean forest, and (2) evaluate the impact of the spatial and spectral resolutions on PSR estimation using real and simulated RS data. The PSR was estimated applying the SDH approach on a 4m hyperspectral data (373 bands), 30m multispectral satellite data (7 bands), synthetic 16m and 30m hyperspectral data (373 bands), and synthetic 4m multispectral data (7 bands). Preliminary results carried out in the San Joaquin Experimental Range (SJER) indicate that: (1) there is a weak correlation between spectral and species diversity in the less dense forest areas (R2 =-0.13 for the hyperspectral and R2 =0.14 for the multispectral data), while revealing good correlation in the more dense forest areas (R2 =0.68 for the hyperspectral and R2 =0.65 for the multispectral data), (2) the number of identified spectral species is more influenced by the spectral resolution than the spatial one, and (3) high spatial resolution data tends to overestimate the PSR in less dense forest areas because of the influence of background and understory vegetation.

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Section: Introductionmentioning

confidence: 99%

A study on the impact of the spatial and spectral resolution on plant species richness in Mediterranean regions using optical remote sensing data

Serwah Boakye,

Huesca Martinez,

Paris

2023

Image and Signal Processing for Remote Sensing XXIX

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Wetland Species Mapping Using Advanced Technological Measurement

Mohanty,

Srivastava,

Pandey

et al. 2024

Aquatic Conservation

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Wetlands are pivotal in supporting the natural ecosystem and maintaining biodiversity while being susceptible to anthropogenic activities and climate change. However, monitoring wetlands over a large geographical and temporal extent is challenging. Vegetation health can be considered a good indicator of wetland conditions, and measuring chlorophyll content will provide insight into vegetation health. Linking wetland species mapping from chlorophyll spectral indices to local and regional conservation strategies could improve biodiversity conservation. Here, we apply this to Keetham Lake, India, using machine learning methods (relevance vector model) and hyperspectral measurements. From 10 chlorophyll‐sensitive spectral indices, we identified four as best performing, particularly for: TVI + CCCI + NDRE for calibration and NDRE + TVI for validation data. The least performing combinations were MCARI for calibration and TVI + CCCI + NDRE + MCARI for validation. Overall, we identified that NDRE + TVI was the best‐performing pair of spectral indices for chlorophyll assessment and implementation in wetland species. This approach allows for precise mapping of wetland species, providing data on their extent and the area they cover. By creating a digital database, this method enables long‐term monitoring of changes in wetland species' numbers and distribution, helping to assess trends of increase or decline in freshwater ecosystems. Such strategies are vital for supporting both local and global conservation efforts, offering insights for forward‐looking, data‐driven preservation initiatives.

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