Forest beta-diversity analysis by remote sensing: How scale and sensors affect the Rao’s Q index

Khare, Siddhartha; Latifi, Hooman; Rossi, Sergio

doi:10.1016/j.ecolind.2019.105520

Cited by 35 publications

(22 citation statements)

References 51 publications

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“…Previous studies have also indicated that tree diversity prediction through spectral responses varies with spatial scale [3]. Khare et al [28] calculated various diversity indices for different spatial resolutions and found that tree diversity was better explained at the finest (i.e., 0.5 m) pixel size evaluated. However, in another study in a dry tropical forest environment, it was found that Landsat imagery presented better correlation with tree diversity compared to high-spatial-resolution IKONOS data [27].…”

Section: Discussionmentioning

confidence: 99%

“…In a previous study, it was identified that the inclusion of the red-edge spectral information was an essential parameter for tree diversity estimation, equal or even higher than the spatial resolution [28]. In our study, the red-edge bands of the Sentinel-2 MSI, RapidEye, and WorldView-2 images presented a weak relationship with all diversity indices and high correlation with the NIR bands, as indicated by the Spearman measure, and therefore were not included in the RF modelling procedure.…”

Section: Discussionmentioning

confidence: 99%

“…The performance of Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 OLI satellite sensors, has been recently evaluated by Toressani et al [2] for tree species diversity estimation in an alpine conifer forest. Forest beta-diversity was evaluated by Khare et al [28] in a subtropical deciduous forest using images acquired from Pléiades 1A, RapidEye, and Landsat-8 OLI satellite sensors. Ozdemir et al [29] evaluated diversity correlation with texture measures calculated from RapidEye, SPOT-5, and Aster images.…”

Section: Introductionmentioning

confidence: 99%

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A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

et al. 2020

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Earth observation data can provide important information for tree species diversity mapping and monitoring. The relatively recent advances in remote sensing data characteristics and processing systems elevate the potential of satellite imagery for providing accurate, timely, consistent, and robust spatially explicit estimates of tree species diversity over forest ecosystems. This study was conducted in Northern Pindos National Park, the largest terrestrial park in Greece and aimed to assess the potential of four satellite sensors with different instrumental characteristics, for the estimation of tree diversity. Through field measurements, we originally quantified two diversity indices, namely the Shannon diversity index (H’) and Simpson’s diversity (D1). Random forest regression models were developed for associating remotely sensed spectral signal with tree species diversity within the area. The models generated from the use of the WorldView-2 image were the most accurate with a coefficient of determination of up to 0.44 for H’ and 0.37 for D1. The Sentinel-2 -based models of tree species diversity performed slightly worse, but were better than the Landsat-8 and RapidEye models. The coefficient of variation quantifying internal variability of spectral values within each plot provided little or no usage for improving the modelling accuracy. Our results suggest that very-high-spatial-resolution imagery provides the most important information for the assessment of tree species diversity in heterogeneous Mediterranean ecosystems.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

et al. 2020

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“…Geospatial technologies [Remote Sensing and geographical information system (GIS)] are fast and efficient tool for monitoring, assessment and hence management of the IAPS (Walsh, 2018;Khare et al, 2018Khare et al, , 2019. In certain cases these tools can also trace the root cause of invasive spread, as demonstrated in the success of an IAPS (Limnocharis flava) attributed to ocean currents (Abhilash et al, 2008).…”

Section: Geospatial Technologies In Mapping Monitoring and Managemenmentioning

confidence: 99%

“…nonparametric) as well as vegetation/diversity indices (e.g. normalized difference vegetation index-NDVI/beta diversity) for cost-effective efficient monitoring, mapping and modelling of IAPS (Royimani et al, 2018;Khare et al, 2019).…”

Section: Geospatial Technologies In Mapping Monitoring and Managemenmentioning

confidence: 99%

Invasive alien plant species: Their impact on environment, ecosystem services and human health

Kumar

Singh

2020

Ecological Indicators

294

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Ecological perturbations caused by biotic invasion have been identified as a growing threat to global sustainability. Invasive alien plants species (IAPS) are considered to be one of the major drivers of biodiversity loss and thereby altering the ecosystem services and socio-economic conditions through different mechanisms. Although the ecological impacts of IAPS are well documented, there is a dearth of studies regarding their economic quantification, livelihood considerations, biotechnological prospects (phytoremediation, bioenergy, phytosynthesis of nanoparticles, biomedical, industrial applications etc.) and human health risk assessments of IAPS. In this context, the current panoramic review aimed to investigate the environmental, socio-ecological and health risks posed by IAPS as well as the compounded impact of IAPS with habitat fragmentation, climate and land use changes. To this end, the need of an integrated trans-disciplinary research is emphasized for the sustainable management of IAPS. The management prospects can be further strengthened through their linkage with geo-spatial technologies (remote sensing and GIS) by mapping and monitoring the IAPS spread. Further, the horizon of IAPS management is expanded to ecological indicator perspectives of IAPS, biosecurity, and risk assessment protocols with critical discussion. Moreover, positive as well as negative implications of the IAPS on environment, health, ecosystem services and socio-economy (livelihood) are listed so that a judicious policy framework could be developed for the IAPS management in order to mitigate the human health implications.

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Remotely sensed vegetation greening along a restoration gradient of a tropical forest, Kibale National Park, Uganda

et al. 2021

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Restoration has now emerged as a global priority, with international initiatives such as the “UN Decade on Ecosystem Restoration (2021–2030)”. To fulfill the large‐scale global restoration ambitions, an essential step is the monitoring of vegetation recovery after restoration interventions. This study aimed to evaluate the utility of remotely sensed vegetation indices, using the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), to monitor the progress of forest regeneration across a tropical forest restoration project area in Kibale National Park, Uganda. Using the chronosequence approach, results indicated non‐linear patterns in NDVI and EVI across the first 25 years of recovery. Both NDVI and EVI increased for the first 10 years of forest regeneration. This 'greening' phase could be used as the indicator of the successful onset of forest recovery. In particular, the decline of elephant grass, and the consequent arrival of shrubs and trees, can be detected as an increase in NDVI. Primary forests differed from the 25‐year‐old regenerating forests based on the unique combination of low mean and low seasonal variation in EVI. Our results, therefore, suggest that the long‐term success of forest restoration could be monitored by evaluating how closely the combination of mean, and degree of seasonal variation in EVI, resembles that observed in the primary forest.

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Forest beta-diversity analysis by remote sensing: How scale and sensors affect the Rao’s Q index

Cited by 35 publications

References 51 publications

A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

Invasive alien plant species: Their impact on environment, ecosystem services and human health

Remotely sensed vegetation greening along a restoration gradient of a tropical forest, Kibale National Park, Uganda

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