An Evaluation of the Performance of Remote Sensing Indices as an Indication of Spatial Variability and Vegetation Diversity in Alpine Grassland DOI Creative Commons

Y. Sang,

Haibin Gu, Qingmin Meng

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4726 - 4726

Published: Dec. 18, 2024

Vegetation diversity is a crucial indicator for evaluating grassland ecosystems. Remote sensing technology has great potential in assessing vegetation diversity. In this study, the relationship between remote indices and species was investigated at varying spatial temporal scales Bayanbulak Grassland National Nature Reserve, China. Spectral variation, defined as coefficient of variation indices, used proxy diversity, which quantified using indices. The “spectral diversity-species diversity” validated across diverse different years Sentinel-2 images ground investigation data. This study found that Kendall’s τ coefficients showed best performance VIs (CVVIs) index. highest value observed CVNDVI 2017 (τ = 0.660, p < 0.01), followed by Shannon index 2018 0.451, 0.01). addition, CVEVI demonstrated significant positive correlation with Shannon-Wiener Index 50 m scale 0.542), 100 0.660). relation to CVVIs performs better representing changes vegetation. Spatial influence assessment These findings underscore critical role various scales, offering valuable support tools measuring regional

Language: Английский

Satellite-Based energy balance for estimating actual sugarcane evapotranspiration in the Ethiopian Rift Valley DOI
Gezahegn Weldu Woldemariam, Berhan Gessesse Awoke, Raian Vargas Maretto

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2025, Volume and Issue: 223, P. 109 - 130

Published: March 13, 2025

Language: Английский

Citations

0
Exploring the Application of Particle Swarm Optimization in Vegetation Remote Sensing DOI Open Access
Nor Azlina Ab. Aziz

Journal of Physics Conference Series, Journal Year: 2025, Volume and Issue: 2998(1), P. 012018 - 012018

Published: April 1, 2025

Abstract Particle swarm optimization (PSO) is an algorithm belonging to the family of intelligence and metaheuristics, designed solve problems. It a nature inspired algorithm. Specifically, PSO mimics collective behaviour fish birds. These organisms are simple that achieved complex tasks through information sharing learning from experience. The cognitive behaviours imitated in using only two mathematical equations. Owing simplicity algorithm, had been widely applied various real-world Despite its reported good performance. This study aims examine application field remote sensing focusing on vegetation. Vegetation focusses vegetation data satellite. used for monitoring managing agriculture, forestry, environmental condition, land usage. findings show has popularly by researchers field. applications cover multiple areas; nonetheless, topic remains relevant, further research opportunities can be explored.

Language: Английский

Citations

0
An Evaluation of the Performance of Remote Sensing Indices as an Indication of Spatial Variability and Vegetation Diversity in Alpine Grassland DOI Creative Commons

Y. Sang,

Haibin Gu, Qingmin Meng

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4726 - 4726

Published: Dec. 18, 2024

Vegetation diversity is a crucial indicator for evaluating grassland ecosystems. Remote sensing technology has great potential in assessing vegetation diversity. In this study, the relationship between remote indices and species was investigated at varying spatial temporal scales Bayanbulak Grassland National Nature Reserve, China. Spectral variation, defined as coefficient of variation indices, used proxy diversity, which quantified using indices. The “spectral diversity-species diversity” validated across diverse different years Sentinel-2 images ground investigation data. This study found that Kendall’s τ coefficients showed best performance VIs (CVVIs) index. highest value observed CVNDVI 2017 (τ = 0.660, p < 0.01), followed by Shannon index 2018 0.451, 0.01). addition, CVEVI demonstrated significant positive correlation with Shannon-Wiener Index 50 m scale 0.542), 100 0.660). relation to CVVIs performs better representing changes vegetation. Spatial influence assessment These findings underscore critical role various scales, offering valuable support tools measuring regional

Language: Английский

Citations

0