Remote Sensing

Modernizing Mangrove Monitoring in the Dutch Caribbean

Dutch below

collaborative effort between Maynooth University, University of Portsmouth and Wageningen University & Research explored the use of satellite technology to offer a cost-effective solution for accurate mangrove mapping within Bonaire’s Lac Bay Forest. This innovative approach empowers small island states to make informed decisions for the management and protection of these vital ecosystems. 

Mangrove ecosystems, crucial for ecological balance and human well-being, are facing severe degradation globally. This issue is particularly true within the Caribbean, where mangroves have declined by 7.9% between 1996 and 2020.  In this context, monitoring and managing these ecosystems has become imperative, yet challenges persist due to accessibility and limited resources availability. A recent study conducted in Lac Bay, Bonaire, presents a groundbreaking solution leveraging Sentinel-2 satellites. 

(Red mangroves. (Rhizophora mangle). Photo Credit: Henkjan Kievit)

Mapping Mangroves 

This recent study evaluated the use of Sentinel-2 satellite imagery to map the extent and species composition of mangrove forests in Lac Bay. Results showed that Sentinel-2 data are a valuable tool, providing accurate maps with a mean overall accuracy of over 95%. Using five Sentinel-2 images, the extent of mangrove forests in Lac Bay was estimated to be approximately 222.3 hectares, comprising mainly of red mangroves (Rhizophora mangle) and black mangroves (Avicennia germinans). 

Remote Sensing Indicators  

This research also attempted to assess the ecological condition of the mangrove forests through biophysical variables, namely Effective Leaf Area Index (used to assess the density of vegetation) and Net Primary Productivity (NPP) (how much energy plants are storing as biomass.   Despite the success in mapping, there were challenges in validating estimates, stressing the need for further refinement.  Estimating NPP based on remote sensing showed promise but needs to be further developed to fully replace traditional monitoring methods.  

Implications for Conservation and Management: 

Using satellites imagery has proven to be a game-changer for monitoring mangrove ecosystems. The study in Lac Bay showcases the potential of this technology to overcome challenges in mapping, assess ecological conditions, and support conservation efforts. As mangroves have recently gained attention for their value as a powerful Nature Based Solution, it will become increasingly important to monitor and preserve these vital coastal ecosystems for the future. 

(Thematic map of the distribution of the black mangrove A. germinans (in blue) and the red mangrove R. mangle (in red) in Lac Bay derived from the Sentinel-2 image registered on 23/03/2022.)

DCNA  

The Dutch Caribbean Nature Alliance (DCNA) supports (science) communication and outreach in the Dutch Caribbean region by making nature related scientific information more widely available through amongst others the Dutch Caribbean Biodiversity Database, DCNA’s news platform BioNews and through the press. This article contains the results from several (scientific) projects but the projects themselves are not DCNA projects. No rights can be derived from the content. DCNA is not liable for the content and the in(direct) impacts resulting from publishing this article. 

 

 

 

Een samenwerking tussen Maynooth University, University of Portsmouth en Wageningen University & Research onderzocht het gebruik van satelliettechnologie om een kosteneffectieve oplossing te bieden voor het nauwkeurig in kaart brengen van mangroven in het Lac Bay-bos op Bonaire. Deze innovatieve aanpak stelt kleine eilandstaten in staat om weloverwogen beslissingen te nemen voor het beheer en de bescherming van deze vitale ecosystemen.

(Rode mangrove. Foto: Henkjan Kievit)

Mangrove-ecosystemen, cruciaal voor het ecologisch evenwicht en het menselijk welzijn, worden wereldwijd geconfronteerd met ernstige degradatie. Dit probleem geldt met name in het Caribisch gebied, waar mangroven tussen 1996 en 2020 met 7,9% zijn afgenomen.  In deze context is het monitoren en beheren van deze ecosystemen absoluut noodzakelijk geworden, maar er blijven uitdagingen bestaan als gevolg van de toegankelijkheid en de beperkte beschikbaarheid van middelen. Een recente studie uitgevoerd in Lac Bay, Bonaire, presenteert een baanbrekende oplossing die gebruik maakt van Sentinel-2-satellieten.

Mangroven in kaart brengen

Deze recente studie evalueerde het gebruik van Sentinel-2-satellietbeelden om de omvang en soortensamenstelling van mangrovebossen in Lac Bay in kaart te brengen. De resultaten toonden aan dat Sentinel-2-gegevens een waardevol hulpmiddel zijn, omdat ze nauwkeurige kaarten opleveren met een gemiddelde algehele nauwkeurigheid van meer dan 95%. Aan de hand van vijf Sentinel-2-beelden werd de omvang van mangrovebossen in Lac Bay geschat op ongeveer 222,3 hectare, voornamelijk bestaande uit rode mangroven (Rhizophora mangle) en zwarte mangroven (Avicennia germinans).

Remote Sensing-indicatoren 

Dit onderzoek probeerde ook de ecologische toestand van de mangrovebossen te beoordelen aan de hand van biofysische variabelen, namelijk Effective Leaf Area Index (gebruikt om de dichtheid van vegetatie te beoordelen) en Net Primary Productivity (NPP) (hoeveel energie planten opslaan als biomassa). Ondanks het succes bij het in kaart brengen, waren er uitdagingen bij het valideren van schattingen, wat de noodzaak van verdere verfijning benadrukte. Het schatten van NPP door middel van remote sensing was veelbelovend, maar moet verder worden ontwikkeld om traditionele monitoringmethoden volledig te vervangen.

Implicaties voor behoud en beheer

Het gebruik van satellietbeelden is een gamechanger gebleken voor het monitoren van mangrove-ecosystemen. De studie in Lac Bay toont het potentieel van deze technologie om uitdagingen bij het in kaart brengen te overwinnen, ecologische omstandigheden te beoordelen en natuurbehoud te ondersteunen. Aangezien mangroven de laatste tijd aandacht hebben gekregen voor hun waarde als een krachtige Nature Based Solution (op de natuur gebaseerde oplossing), zal het steeds belangrijker worden om deze vitale kustecosystemen voor de toekomst te monitoren en behouden.

 

(Thematische kaart van de verspreiding van de zwarte mangrove A. germinans (in blauw) en de rode mangrove R. mangle (in rood) in Lac Bay, afgeleid van de Sentinel-2-afbeelding geregistreerd op 23/03/2022.)

DCNA

De Dutch Caribbean Nature Alliance (DCNA) ondersteunt (wetenschaps) communicatie en outreach in de Nederlandse Caribische regio door natuurgerelateerde wetenschappelijke informatie breder beschikbaar te maken via onder meer de Dutch Caribbean Biodiversity Database, DCNA’s nieuwsplatform BioNews en de pers. Dit artikel bevat de resultaten van verschillende (wetenschappelijke) projecten, maar de projecten zelf zijn geen DCNA-projecten. Aan de inhoud kunnen geen rechten worden ontleend. DCNA is niet aansprakelijk voor de inhoud en de indirecte gevolgen die voortvloeien uit het publiceren van dit artikel.

 

 

 

 

Published in BioNews 72

Date
2024
Data type
Media
Theme
Research and monitoring
Geographic location
Bonaire
Author

Mapping the bathymetry of Bonaire through the use of satellite data

Abstract

Bonaire is home to a wide range of biodiversity, and especially in the shallow coastal waters where coral reefs occur. Coral reefs provide many important ecosystem services and should therefore be protected. However, they are threatened due to many causes like global warming and diseases. Therefore, knowledge about their habitat, the shallow coastal waters, is crucial in order to ensure the conservation of this organism. This knowledge is attainable through the use of satellite data, which is called satellite-derived bathymetry (SDB). The basic principle behind SDB is the relationship between the attenuation of radiance, on one hand, and the depth and wavelength in the water column on the other. This research aims to investigate the possibilities of satellite-derived bathymetry for the island of Bonaire. Furthermore, it explores which method achieves the most accurate bathymetric models and to what extent accurate estimation of bathymetry is possible.  

For this research, the bathymetry was calculated with an empirical approach that makes use of insitu measurements and a ratio between the green and blue band. However, in order to be able to apply this formula, the data first had to be preprocessed. These preprocessing steps included the masking of land/clouds and a sun glint correction. The masking was done through thresholds of reflectance values in the visible bands. The masked images were then deglinted. After deglinting, the data was ready for the calculation of the bathymetry. This was done using two formulas: SDBA and SDBB. The formula of SDBA was calibrated using all in-situ depths less than 30 metres whereas SDBB was calibrated using all depths less than 20 metres.  

The results were validated by determining the Root Mean Square Error (RMSE) for the different depth classes: 1-10m, 10-20m, and 20-30m. However, since this research mainly focuses on shallow coastal waters, especially the 1-10m depth class was interesting. The results showed that the models created with SDBB  were more accurate for depth class 1-10m, compared to the results with SDBA. The average RMSE of depth class 1-10m for the most accurate method was 4.11m. The most accurate bathymetric model had an RMSE of 3.58m for depth class 1-10m. However, the RMSEs for the other depth classes showed that this method is not applicable for accurate results in deeper depth classes.  This research showed that there are possibilities for the island of Bonaire regarding satellite-derived bathymetry. However, more research needs to be done in order to create more accurate results and be able to circumvent limitations.  

Date
2023
Data type
Research report
Theme
Research and monitoring
Report number
WUR MSc Thesis - GIRS-2023 -29
Geographic location
Bonaire

The installation and operation of a multi-parameter volcano monitoring network on the islands of Saba and St. Eustatius in the Caribbean Netherlands

In the Caribbean Netherlands, the islands of Saba and St. Eustatius host the active but quiescent volcanoes Mt. Scenery and The Quill. To mitigate volcanic risk to the islands, robust monitoring is essential. Therefore in the past five years the Royal Netherlands Meteorological Institute (KNMI) significantly expanded the volcano monitoring network on both islands.

The seismic monitoring network was expanded from seven to 11 broadband seismometers located across the islands. Seismic data are sent to and stored at KNMI and Observatories and Research Facilities for European Seismology (ORFEUS). Eight permanent continuous Global Navigation Satellite System (GNSS) stations were newly installed, where possible co-located with the broadband seismometers. GNSS data are sent to and stored at KNMI and UNAVCO. On a daily basis we run an automatic earthquake detection system and coincidence trigger to identify seismic events and create GNSS time series using both network and Precise Point Positioning (PPP) solutions.

The installation of new instruments was challenging due to the remoteness of the envisioned locations which were needed to monitor all sides of the volcanoes.  Local governmental and military assistance was key to the success of the mission. At the most remote locations instruments are operated on solar power and data are transmitted using  Very-Small-Aperture Terminal (VSAT) technology. Ensuring the operability of the monitoring network remains demanding due to the harsh tropical conditions (hurricanes, UV-radiation, sea spray, lightning) as well as network and power outages. 

Apart from seismic and GNSS instruments, we also deploy three temperature sensors and four cost-effective GNSS units to extend our monitoring network. Furthermore, in collaboration with Delft University of Technology (TU Delft) we test the feasibility of the use of Interferometric Synthetic Aperture Radar (InSAR) for the monitoring of these islands.

Date
2023
Data type
Research report
Theme
Research and monitoring
Report number
EGU General Assembly 2023, Abstract
Geographic location
Saba
St. Eustatius

The use of cost-effective GNSS units as a volcano monitoring tool on Saba, Caribbean Netherlands

We present initial positioning results obtained by analyses of data from four cost-effective Global Navigation Satellite System (GNSS) units installed on the island of Saba. The island hosts the active but quiescent stratovolcano Mt. Scenery which reaches an elevation of 887 metres and was last active around 1640. The cost-effective GNSS units were installed around the volcano in February 2022 and house all necessities for autonomous, continuous monitoring. The overall equipment cost per unit is about 1000 Euros, a fraction of the material costs of a conventional, permanent continuously monitoring GNSS station. Furthermore, the typical installation time of permanent stations takes multiple days whereas the installation time required for our cost-effective units can be undertaken within a few hours. We demonstrate that the performance of the cost-effective GNSS units for daily positioning estimations is comparable with the performance of permanent stations. We investigate the precision and accuracy of the time series of kinematic and static positioning solutions using geodetic positioning estimation algorithms. For direct comparison we placed one cost-effective GNSS unit next to a permanent, conventional GNSS station. Furthermore, we investigate if results improve after applying a minimum-effort calibration of the cost-effective antenna using a permanently installed GNSS station. We demonstrate that cost-effective GNSS units are i) well-suited to extend an existing volcano monitoring network of permanent GNSS stations and ii) can potentially even be used independently for basic volcano monitoring when funding is limited. We also envisage the use of cost-effective GNSS units for rapid deployment in hazardous or risk-prone areas where installations of conventional GNSS stations could be deemed too costly.

Date
2023
Data type
Research report
Theme
Research and monitoring
Report number
EGU General Assembly 2023, Vienna, Austria
Geographic location
Saba

A deep transfer learning-based damage assessment on post-event very high-resolution orthophotos

Abstract

Post-disaster building damage assessment is an important application of remote sensing. The increasing resolution of remote sensing imaging systems and state-of-the-art deep learning networks has facilitated damage assessment. However, most existing methods in the literature concentrate on damage/non-damage classification only in specific disaster types/areas using pre- and post-event images. Furthermore, site visits are inevitable to assess the level of damage to structures. Therefore, the main objective of this study was to utilize deep transfer learning over a pre-trained network and extend it to a damage assessment framework. The network is fine-tuned to identify four different damage levels: non-damage, minor damage, major damage, and collapsed, using only post-event images taken from different disaster types/areas. To evaluate the proposed framework, we carried out three experiments on Hurricane Irma in Sint Maarten, Hurricane Dorian in Abaco Islands, and Woolsey Fire using post-event orthophotos derived from unmanned aerial vehicle (UAV) images. The results of over 80% overall accuracy confirm that with a structured learning scenario, it is possible to use transfer learning on very high-resolution remote sensing images to classify the level of structural damage.

Full document can be requested here: https://cdnsciencepub.com/doi/10.1139/geomat-2021-0014

Date
2022
Data type
Scientific article
Theme
Research and monitoring
Journal
Geographic location
St. Maarten

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

On the Caribbean island of St. Eustatius, Coralita (Antigonon leptopus) is an aggressive invasive vine posing major biodiversity conservation concerns. The generation of distribution maps can address these conservation concerns by helping to elucidate the drivers of invasion. We test the use of support vector machines to map the distribution of Coralita on St. Eustatius at high spatial resolution and use this map to identify potential landscape and geomorphological factors associated with Coralita presence. This latter step was performed by comparing the actual distribution of Coralita patches to a random distribution of patches. To train the support vector machine algorithm, we used three vegetation indices and seven texture metrics derived from a 2014 WorldView-2 image. The resulting map shows that Coralita covered 3.18% of the island in 2014, corresponding to an area of 64 ha. The mapped distribution was highly accurate, with 93.2% overall accuracy (Coralita class producer's accuracy: 76.4%, user's accuracy: 86.2%). Using this classification map, we found that Coralita is not randomly distributed across the landscape, occurring significantly closer to roads and drainage channels, in areas with higher accumulated moisture, and on flatter slopes. Coralita was found more often than expected in grasslands, disturbed forest, and urban areas but was relatively rare in natural forest. These results highlight the ability of high spatial resolution data from sensors such as WorldView-2 to produce accurate invasive species, providing valuable information for predicting current and future spread risks and for early detection and removal plans.

 

Referenced in BioNews publication (BioNews Article). 

 

Related Resources:

1. Supplementary Infromation (Report)

2. Topographic Wetness Index raster layer for Statia developed for use in the Coralita mapping publication (Raster Layers). 

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

Date
2021
Data type
Scientific article
Theme
Research and monitoring
Journal
Geographic location
St. Eustatius

High-resolution prediction of plant species richness in the Christoffel national park

Previous attempts at mapping the vegetation of the Christoffel national park on the island of Curaçao were done in times of intense grazing pressure and are likely not valid anymore after the removal of goats from the park because grazers have a significant effect on the native vegetation of the island ecosystems. In 2018, a 2-year fieldwork campaign was started to revisit the sampling points of Bokkestijn & Slijkhuis (1987) with the aim of remapping the vegetation communities and studying the change that occurred in the last decades. This thesis aims to assess the changes in vegetation distribution and use the newly acquired data to predict plant species richness across the entire national park at a high resolution using a macroecological modeling strategy. A trend of secondary vegetation succession has been found since 1985, with an increase in the coverage of trees, orchids, and bromeliads and a decrease in grasses and herbs. The large-scale recovery of the native vegetation is found especially on the coast and midland of the park, while the Christoffel mountain and its surroundings have remained relatively stable. An aerial photograph interpretation of the vegetation communities found significant dependence of vegetation communities on elevation and slope aspects. High-resolution plant species richness prediction models were built and it was found that elevation and slope aspects have the most predictive weight. Little research has been done on high-resolution species richness prediction models; however, it is shown that these models can be utilized to characterize the variables influencing species distribution at high resolution and local scale, with comparable accuracy to coarser prediction models.

Date
2020
Data type
Research report
Theme
Research and monitoring
Report number
Thesis report
Geographic location
Curacao
Image

Remote Sensing Tools to support NEXUS challenges

Smalls islands are especially vulnerable to climate change and land  use changes due to the competing needs for limited resources. To support the NEXUS approach we need evidence based monitoring tools that can provide policy makers, conservation managers, entrepeneurs, scientists and the general public with information on the state, pressures and associated changes in the environment. Satellite imagery can provide synoptic information at appropriate
spatial and temporal resolutions that can support evidence based monitoring. Only at very detailed levels information might be added by using airplanes or drones. Remotely sensed information can help to provide information on e.g. land cover and associated dynamics such as urban sprawl, mapping habitats such as mangroves and coral reefs, surveying terrain conditions such as soil moisture conditions and erosion hazards associated within catchments, sea level rise and changing coastlines, and on many aspects of the vegetation (natural and agriculture), such as plant traits, phenology and plant growth. Remotely sensed information can in general make field surveys and monitoring more effective, and can thoroughly support decision making.

Date
2019
Data type
Research report
Theme
Education and outreach
Research and monitoring
Geographic location
Bonaire

Classifying benthic habitats and deriving bathymetry at the Caribbean Netherlands using multispectral Imagery

Benthic habitats (habitats occurring at the bottom of a water body) and coral reef ecosystems provide many functions. Currently, however, coral reefs are threatened by a number of factors and degrade rapidly. Benthic maps are important for management, research and planning. Coral communities in the Caribbean Dutch island of St. Eustatius are generally in a good condition, but the benthic communities around St. Eustatius have not been yet accurately mapped.
Remote sensing imagery has been found to be a very useful tool in providing timely and up-to-date information for benthic mapping and offers an effective approach to complement the limitation of field sampling. Remote sensing in water, however, presents challenges mainly due to the complex physical interactions of absorption and scattering between water and light. Shorter wavelengths (-450 nm) penetrate deepest into the water column and longer wavelengths (-500-750 nm) are more rapidly absorbed and scattered. Therefore, the potential extent of use of remote sense imagery in the oceans relies more on shorter wavelengths (blue band), which have inherently noisier signals due to atmospheric effects.
This research explores the utility of multispectral imagery to identify and classify marine benthic habitats in the Dutch Caribbean island of St Eustatius. These include the comparison of two sensors with different spatial and spectral resolution, QuickBird (2.4m, 4 bands) and WorldView-2 (2.0m, 8 bands) for mapping benthic habitats. The study first investigates the existing methodologies for benthic habitat classification. The benefits of atmospheric correction, sun glint effect correction and water column attenuation correction on the accuracy of classification maps are also assessed. Then, an object and pixel based supervised classifications for the characterization of sea grass, sand and coral are performed. This research also evaluates the possibility to extract water depth from multispectral satellite imagery by the use of a ratio transform method. Bathymetric data is important for water column correction, to improve the classification accuracy and for the study of the ecology of the habitats.
Results showed that the best results for pixel-based image classification in QuickBird and WoldView-2 imagery were obtained after deglinting the image, with accuracies of 49.3% and 51.9% respectively. The sunglint removal method improved the total accuracy of benthic habitat mapping, by increasing before and after deglinting 3.4% for QuickBird and 6.3% for WorldView-2. Object-based classification provided slightly better classification results, with a 53.7% accuracy for QuickBird and 56.9% accuracy for WorldView-2. Therefore, it can be concluded that an object-oriented approach to image classification shows potential for improving benthic mapping. The classification accuracy did not increase after compensation for water column effects.
The effectiveness of the ratio method to calculate the bathymetry using multispectral imagery has been confirmed. The coefficients of determination (r2) achieved are statistically significant, 0.66 for QuickBird, and 0.41 for WorldView-2 (BG ratio) for a linear relation. The root mean square errors are 4.02 m for QuickBird and 5.11 m for WorldView-2. It has been proved that this method works better for shallow areas, with a root mean square error of 2.32 m and 2.47 m, respectively. Results also indicate that the ratio method is sensitive to variable bottom type. Overall, better bathymetric values were obtained with QuickBird than with WorldView-2.
This research provides a baseline for future benthic habitat classification of the Dutch Caribbean islands using remote sensing. The results of this study are a good example of how remote sensing data can be a useful and cost effective method to map benthic habitats and calculate bathymetry.

Date
2013
Data type
Research report
Theme
Research and monitoring
Report number
GIRS-2013 -18
Geographic location
St. Eustatius
Author

Classifying benthic habitats and deriving bathymetry at the Caribbean Netherlands using multispectral Imagery

Benthic habitats (habitats occurring at the bottom of a water body) and coral reef ecosystems provide many functions. Currently, however, worldwide coral reefs are threatened by a number of factors and are degrading rapidly. Benthic maps are important for management, research and planning, but the benthic communities around St. Eustatius have not yet been accurately mapped or described.
Remote sensing imagery has been found to be a useful tool in providing timely and up-to-date information for benthic mapping and offers an approach that may complement the limitations of field sampling. Remote sensing in water, however, presents challenges mainly due to the complex physical interactions of absorption and scattering between water and light. Shorter wavelengths (-450 nm) penetrate deepest into the water column and longer wavelengths (-500-750 nm) are more rapidly absorbed and scattered. Therefore, the potential extent of use of remote sense imagery in the oceans relies more on shorter wavelengths (blue band), which have inherently noisier signals due to atmospheric effects.
This research explores the utility of multispectral imagery to identify and classify marine benthic habitats in the Dutch Caribbean island of St Eustatius. These include the comparison of two sensors with different spatial and spectral resolution, QuickBird (2.4m, 4 bands) and WorldView-2 (2.0m, 8 bands) for mapping benthic habitats. The study first investigates the existing methodologies for benthic habitat classification. The benefits of atmospheric correction, corrections for sun-glint effect and water column attenuation on the accuracy of classification maps are also assessed. Then, an object and pixel-based supervised classifications for the characterization of sea grass, sand and coral are performed. This research also evaluates the possibility to extract water depth from multispectral satellite imagery by the use of a ratio transform method. Bathymetric data is important for water column correction, to improve the classification accuracy and for the study of the ecology of the habitats.

Date
2013
Data type
Research report
Theme
Research and monitoring
Report number
IMARES rapport C143/13; Alterra rapport 2467
Geographic location
St. Eustatius