Remote sensing depicts riparian vegetation responses to water stress in a humid Atlantic region

Pace, Giorgio; Gutiérrez‐Cánovas, Cayetano; Henriques, Renato F.; Boeing, Friedrich; Cássio, Fernanda; Pascoal, Cláudia

doi:10.1016/j.scitotenv.2021.145526

Cited by 22 publications

(9 citation statements)

References 79 publications

(92 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most studies analysing forest vegetation are based on the Sentinel-2 satellite (Krtalić et al, 2021). These are mainly based on the use of logarithms (Brovelli et al, 2020;Furuya et al, 2020) or vegetation indices (Hill, 2013;Nouri et al, 2014;Pace et al, 2021). The main advantage of these procedures is the use of lowcost images, in addition to the fact that they enable environmental monitoring and allow the adequacy of forest management decision-making.…”

Section: Discussionmentioning

confidence: 99%

“…Based on these values, it can be concluded that vegetation is generally sparse in the study area. The use of this index could be used as a practical tool to illustrate vegetation changes, such as loss of photosynthetic activity, partial crown dieback, or total or partial foliage drop (Pace et al, 2021). To date, hardly any research has been carried out that includes this index for the analysis of riparian vegetation.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…The use of this index could be used as a practical tool to illustrate vegetation changes, such as loss of photosynthetic activity, partial crown dieback, or total or partial foliage drop Vol:. ( 1234567890) (Pace et al, 2021). The use of the RE-NDVI index over the traditional NDVI index is useful to better discriminate vegetation types by avoiding the high saturation levels of the latter (Mutanga et al, 2012).…”

Section: Image Preprocessingmentioning

confidence: 99%

“…The RE-NDVI index would perform better than NDVI in soil and vegetation analysis, with relatively low sensitivity to vegetation cover and being negligibly affected by environmental factors (Deng et al, 2018). However, most studies analysing riparian vegetation are based only on the use of the NDVI index (Hill, 2013;Pace et al, 2021), which could decrease their accuracy. A PCA raster was created to obtain 95.17% of the spectral variability of the original bands for the 2018 image, and 95.05% for the 2019 image (Fig.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…This technology has been recently used for studying fluvial environments, water quality, or riparian zones (Tomsett & Leyland, 2019;Topp et al, 2020). Recent advances in satellite techniques and programmes have also generated a wider availability of very high-resolution datasets that will assist in assessing better the interlinkages between key riverine vegetation characteristics (Pace et al, 2021). Currently, there are multiple open-access satellite programmes, such as the Landsat-8 (launched in 2013), Sentinel-2A (launched in 2015), and Sentinel-2B (launched in 2017) sensors, but with a non-high average resolution, but providing images of the entire surface of the Earth every few days for free (Li & Roy, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding

Rivas-Fandiño

Acuña-Alonso

Novo

et al. 2022

Environ Monit Assess

View full text Add to dashboard Cite

Riverine habitats are essential ecotones that bridge aquatic and terrestrial ecosystems, providing multiple ecosystem services. This study analyses the potential use of high-resolution satellite imagery, provided by the WorldView-2 satellite, in order to assess its viability for monitoring riparian ecosystems. It is performed by calculating the riparian strip quality index (RSQI) and calibrating it with the riparian quality index (QBR). The methodology was implemented in the Umia River, which is characterised by elevated anthropogenic pressures (located in the northwest of Spain). The results obtained by the method have a 92% of veracity and a kappa coefficient of 0.88. The average quality value obtained for the RSQI index was 71.57, while the average value for the QBR was 55.88. This difference could be attributed to the fact that the former does not differ between autochthonous and non-autochthonous vegetation. The areas with more accurate mapping corresponded to stretches of vegetation with optimal cover (80–50%), with good connectivity with the adjacent forest ecosystem and few or no presence of invasive plants. The worst-scoring sites had the next characteristics: low connectivity (< 10%), low forest cover (< 10%) and a higher presence of invasive plants. The degradation of vegetation could be explained by the presence of agriculture and deficient land use rationing caused by the type of ownership of the study area. The application of this index through satellite images will facilitate the environmental governance of multiple ecosystems and in special riparian ecosystems, obtaining a quick and objective methodology, easily replicable in other basins. Graphical Abstract

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Image Preprocessingmentioning

confidence: 99%

Section: Image Preprocessingmentioning

confidence: 99%

Section: Image Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding

Rivas-Fandiño

Acuña-Alonso

Novo

et al. 2022

Environ Monit Assess

View full text Add to dashboard Cite

show abstract

Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data

Lassalle¹,

Fabre²

2022

Advances in Remote Sensing for Forest Monitoring

View full text Add to dashboard Cite

Assessment of environmental water strategies for improving fringing vegetation health by modelling vegetation condition in a connected floodplain lakes system

Wu,

Webb,

Stewardson

2024

Ecohydrology

View full text Add to dashboard Cite

Across the globe, environmental water has been allocated with the purpose of preserving the health and vitality of floodplain vegetation. However, the influences of environmental water volume and environmental water delivery strategies have not been studied widely because of shortage of on‐ground monitoring data. Remotely sensed data can bridge this gap by providing long‐term and continuous information; Landsat imagery from 1988 to 2020 was used in this research. We used the normalized difference vegetation index (NDVI) as an indicator of physiological condition of lake‐fringing trees on the Hattah Lakes floodplain, south‐east Australia. We employed the random forest (RF) regression method to model the relationship between NDVI and various climate and hydrological factors, such as the volume of water delivered to the connected lakes system as environmental water allocations or natural floods. The RF models performed well overall, with a mean R2 value of 0.73. The analysis identified the monthly total of environmental water delivered 3 months prior to the Landsat image date as a more crucial factor than natural floods over the same period for driving vegetation condition. Environmental water from 3 months previously exerts a positive influence on NDVI until the volume reaches a specific threshold. We have observed significant improvements in floodplain vegetation through the current environmental water strategy, particularly since the construction of pumping infrastructure in 2013. We suggest that managers aim to inundate the lake fringing area every 3 years, specifically from August to September, by delivering environmental water up to the modelled threshold volume. Finally, the use of infrastructure has proven to be an effective and efficient method for irrigating floodplain lakes, leading to improvements in vegetation condition while conserving water resources.

show abstract

Remote sensing depicts riparian vegetation responses to water stress in a humid Atlantic region

Abstract: We modelled NDVI productivity of riparian plants from Sentinel 2 images.• Inter-annual seasonality differed between drier and wetter years. • Broadleaved, coniferous and grasslands forests were intercompared. • NDVI productivity was influenced by annual rainfall and vegetation type.

Cited by 22 publications

References 79 publications

Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding

Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding

Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data

Assessment of environmental water strategies for improving fringing vegetation health by modelling vegetation condition in a connected floodplain lakes system

Contact Info

Product

Resources

About