High spatial resolution mapping identifies habitat characteristics of the invasive vine <i>Antigonon leptopus</i> on St. Eustatius (Lesser Antilles)

Haber, Elizabeth A.; Santos, María J.; Leitão, Pedro J.; Schwieder, Marcel; Ketner, P.; Ernst, Joris; Rietkerk, Max; Wassen, Martin J.; Eppinga, Maarten B.

doi:10.1111/btp.12939

Cited by 8 publications

(3 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, this limited number of countries constrained the number of studied IAP using RS platforms providing a partial picture of the invasions on a global scale. A large number of IAP impinging coastal ecosystems worldwide are still needed of specific RS research and applications [52,90,91], albeit the research in this field is progressing as shown by promising results in 2022 [92][93][94][95].…”

Section: Discussionmentioning

confidence: 99%

Remote Sensing and Invasive Plants in Coastal Ecosystems: What We Know So Far and Future Prospects

Perna

Febbraro

Carranza

et al. 2023

Land

View full text Add to dashboard Cite

Coastal environments are highly threatened by invasive alien plants (IAP), and Remote Sensing (RS) may offer a sound support for IAP detection and mapping. There is still a need for an overview of the progress and extent of RS applications on invaded coasts that can help the development of better RS procedures to support IAP management. We conducted a systematic literature review of 68 research papers implementing, recommending, or discussing RS tools for IAP mapping in coastal environments, published from 2000 to 2021. According to this review, most research was done in China and USA, with Sporobolus (17.3%) being the better studied genus. The number of studies increased at an accelerated rate from 2015 onwards, coinciding with the transition from RS for IAP detection to RS for invasion modeling. The most used platforms in the 2000s were aircraft, with satellites that increased from 2005 and unmanned aerial vehicles after 2014. Frequentist inference was the most adopted classification approach in the 2000s, as machine learning increased after 2009. RS applications vary with coastal ecosystem types and across countries. RS has a huge potential to further improve IAP monitoring. The extension of RS to all coasts of the world requires advanced applications that bring together current and future Earth observation data.

show abstract

Section: Discussionmentioning

confidence: 99%

Remote Sensing and Invasive Plants in Coastal Ecosystems: What We Know So Far and Future Prospects

Perna

Febbraro

Carranza

et al. 2023

Land

View full text Add to dashboard Cite

show abstract

“…However, as habitat occurrence and quality are continuously being reduced in the Anthropocene, equally are local species abundances (Dirzo et al, 2014; Otto, 2018), with habitat loss alone predicted to drive the extinction of nearly 2,000 terrestrial vertebrate species (Powers & Jetz, 2019). On St. Eustatius, over 30% of the island's surface is directly and heavily affected by human presence (Figure 2), with most habitats additionally being degraded by a large feral goat population, as well as the presence of nonnative and invasive plant species (van Andel et al, 2016; Haber et al, 2021). While the latest assessments estimated an iguana population size of 300–600 individuals (Debrot et al, 2013; Fogarty et al, 2004), considering population densities on islands uninhabited by humans (Angin, 2017), as well as abundances in high‐quality, small less‐disturbed areas on St. Eustatius (Debrot et al, 2013; present study), the pre‐colonization population size was arguably several magnitudes larger.…”

Section: Discussionmentioning

confidence: 99%

Hurricane‐associated population decrease in a critically endangered long‐lived reptile

et al. 2022

View full text Add to dashboard Cite

Catastrophic events, like hurricanes, bring lethal conditions that can have population‐altering effects. The threatened Caribbean dry forest occurs in a region known for its high‐intensity hurricane seasons and high species endemism, highlighting the need to better understand hurricane impacts in combination with habitat fragmentation and loss. However, such studies remain rare and for reptiles are mostly restricted to Anolis. Here, we used single‐season occupancy modeling to infer the impact of the intense 2017 Atlantic hurricane season on the critically endangered Lesser Antillean Iguana, Iguana delicatissima. We surveyed 30 transects across eight habitats on St. Eustatius during 2017–2019, which resulted in 344 individual surveys and 98 iguana observations. Analyses of abundance and site occupancy indicated both measures for 2018 and 2019 were strongly reduced compared to the pre‐hurricane 2017 state. Iguanas at higher elevations were affected more profoundly, likely due to higher wind speeds, tree damage, and extensive defoliation. Overall, our results indicate a decrease in population estimates (23.3%–26.5%) and abundance (22%–23.8%) for 2018 and 2019, and a 75% reduction in opportunistic sightings of tagged iguanas between 2017 and 2018. As only small and isolated I. delicatissima populations remain, our study further demonstrates their vulnerability to stochastic events. Considering the frequency and intensity of hurricanes are projected to increase, our results stress the urgent need for population‐increasing conservation actions in order to secure the long‐term survival of I. delicatissima throughout its range. Given the projected increase and poleward shift of hurricanes, our study provides important insights from a non‐model species.

show abstract

“…Semantic segmentation can represent the process of assigning a semantic label to each coherent region of an image. This coherent region is represented by a pixel [31], a subpixel [32], a super-pixel [33] or an image patch composed of several pixels [34].…”

Section: Introductionmentioning

confidence: 99%

Combining Deep Learning and Multi-Source GIS Methods to Analyze Urban and Greening Changes

Francini

Salvo

Vitale

2023

Sensors

View full text Add to dashboard Cite

Although many authors have observed a degradation in greening cover alongside an increase in the built-up areas, resulting in a deterioration of the essential environmental services for the well-being of ecosystems and society, few studies have measured how greening developed in its full spatiotemporal configuration with urban development using innovative remote sensing (RS) technologies. Focusing on this issue, the authors propose an innovative methodology for the analysis of the urban and greening changes over time by integrating deep learning (DL) technologies to classify and segment the built-up area and the vegetation cover from satellite and aerial images and geographic information system (GIS) techniques. The core of the methodology is a trained and validated U-Net model, which was tested on an urban area in the municipality of Matera (Italy), analyzing the urban and greening changes from 2000 to 2020. The results demonstrate a very good level of accuracy of the U-Net model, a remarkable increment in the built-up area density (8.28%) and a decline in the vegetation cover density (5.13%). The obtained results demonstrate how the proposed method can be used to rapidly and accurately identify useful information about urban and greening spatiotemporal development using innovative RS technologies supporting sustainable development processes.

show abstract

High spatial resolution mapping identifies habitat characteristics of the invasive vine Antigonon leptopus on St. Eustatius (Lesser Antilles)

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 8 publications

References 88 publications

Remote Sensing and Invasive Plants in Coastal Ecosystems: What We Know So Far and Future Prospects

Remote Sensing and Invasive Plants in Coastal Ecosystems: What We Know So Far and Future Prospects

Hurricane‐associated population decrease in a critically endangered long‐lived reptile

Combining Deep Learning and Multi-Source GIS Methods to Analyze Urban and Greening Changes

Contact Info

Product

Resources

About