Merepõhja elupaikade kaardistamine optiliselt keerukates rannikuvetes kaugseire meetodil

Benthic algal cover and trends in its changes are indicators of water state in coastal areas. Remote sensing could provide a tool for mapping bottom vegetation if the substrates are spectrally resolvable. We measured reflectance spectra and collected spectral library of typical bottom types of Estonian coastal waters concentrating mainly on red, green, and brown macroalgae. The spectral library together with a bio-optical model allows estimating the suitability of different remote sensing sensors for mapping Baltic Sea bottom types. The modelling results indicate that the maximum depths where hyperspectral remote sensing instruments could potentially detect the spectral differences between the three most typical green, brown and red benthic macroalgae are greater than the depths where the studied algae actually occur in Estonian coastal waters if the sensor’s SNR is better than 1000:1. To some extent it is possible to map green, red and brown algae with multispectral satellite sensors in turbid waters, which optical properties resemble those of the open Baltic Sea, but the depths where the macroalgae can be detected are usually shallower than the maximum depths where these macroalgae grow. Using multispectral satellite data with high spatial resolution is preferable to using hyperspectral medium resolution data in mapping benthic macroalgal cover in areas where the spatial heterogeneity is very high. In case of a single image and availability of in situ data multispectral sensors with high spatial resolution (QuickBird) can provide more detailed information about the benthic cover than was assumed based on the shape of reflectance spectra of different bottom types and spectral resolution of the sensor. However, lack of in situ data or using of multiple images may complicate the situation. Muutusi põhjataimestiku kooslustes on võimalik kasutada rannikumeres toimuvate keskkonnamuutuste hindamiseks. Uurimaks kaugseire kasutamise võimalusi põhjataimestiku kaardistamisel Läänemeres, on vaja teada erinevate põhjatüüpide (vetikad, liiv, kruus jne) optilisi omadusi. Käesolevas töös koguti Eesti rannikumeres esinevate põhjatüüpide heleduskoefitsientide spektriteek, kusjuures keskenduti puna-, rohe- ja pruunvetikatele. Kogutud spektriteek koos bio-optilise mudeliga võimaldab hinnata, kas ja millised kaugseire sensorid on sobilikud põhjatüüpide kaardistamiseks sellistes hägusates veekogudes nagu Läänemeri. Modelleerimise tulemused näitavad, et hüperspektraalsed sensorid, mille spektraalne lahutus on vähemalt sama hea nagu meie mudelil (10 nm) ning signaali ja müra suhe vähemalt 1000:1, suuda­vad kolme kõige tüüpilisemat põhjatüüpe üksteisest eristada. Seejuures on maksimaalne sügavus, kus see eristamine on võimalik, sügavamal kui nende vetikate esinemissügavus Eesti rannavetes. Multispektraalsed sensorid on võimelised vetikaid eristama madalam, kui maksimaalne sügavus, kus need vetikad Eesti ranniku­vetes kasvavad. Põhjataimestiku suure ruumilise varieeruvuse tõttu on Eesti rannikumere põhjatüüpide kaardistamisel eelistatumad suure ruumilise lahutusvõimega sen­sorid. Suure ruumilise lahutusvõimega multispektraalne sensor QuickBird oli võimeline Eesti rannikumere põhjatüüpe eristama paremini, kui seda võis eeldada modelleerimistulemuste põhjal. Siiski võib olukord olla keerulisem juhul kui puuduvad in situ andmed uurimisalalt või kasutatakse mitmeid erinevaid satelliidi pilte

Classifying the Baltic Sea Shallow Water Habitats Using Image-Based and Spectral Library Methods

The structure of benthic macrophyte habitats is known to indicate the quality of coastal water. Thus, a large-scale analysis of the spatial patterns of coastal marine habitats enables us to adequately estimate the status of valuable coastal marine habitats, provide better evidence for environmental changes and describe processes that are behind the changes. Knowing the spatial distribution of benthic habitats is also important from the coastal management point of view. A big challenge in remote sensing mapping of benthic habitats is to define appropriate mapping classes that are also meaningful from the ecological point of view. In this study, the benthic habitat classification scheme was defined for the study areas in the relatively turbid north-eastern Baltic Sea coastal environment. Two different classification methods—image-based and the spectral library—method were used for image classification. The image-based classification method can provide benthic habitat maps from coastal areas, but requires extensive field studies. An alternative approach in image classification is to use measured and/or modelled spectral libraries. This method does not require fieldwork at the time of image collection if preliminary information about the potential benthic habitats and their spectral properties, as well as variability in optical water properties exists from earlier studies. A spectral library was generated through radiative transfer model HydroLight computations using measured reflectance spectra from representative benthic substrates and water quality measurements. Our previous results have shown that benthic habitat mapping should be done at high spatial resolution, owing to the small-scale heterogeneity of such habitats in the Estonian coastal waters. In this study, the capability of high spatial resolution hyperspectral airborne a Compact Airborne Spectrographic Imager (CASI) sensor and a high spatial resolution multispectral WorldView-2 satellite sensor were tested for mapping benthic habitats. Initial evaluations of habitat maps indicate that image-based classification provides higher quality benthic maps compared to the spectral library method

Performance and Applicability of Water Column Correction Models in Optically Complex Coastal Waters

Maps of submerged aquatic vegetation (SAV) are of primary importance for the sustainable management of coastal areas and serve as a basis for fundamental ecological studies. Various water column correction (WCC) models are successfully applied in clear Case-1 waters to compensate for the variable water depth effect. The performance of the WCC in less clear Case-2 waters is rarely assessed. In this study, the performance and applicability of model-based WCC algorithms in the complex Baltic Sea were analyzed. The bottom reflectance was retrieved from the Compact Airborne Spectrographic Imager (CASI) water surface reflectance by applying the Maritorena and Lee WCC algorithms. The Maritorena model retrieved bottom spectra that showed large variations in reflectance magnitudes. The Lee model was more successful in retrieving reasonable spectral magnitudes, although only in a rather narrow wavelength region (550–600 nm). Shorter and longer spectral regions were significantly overcorrected, resulting in unrealistic spectral shapes. Sensitivity analysis indicated that slight under- or overestimation of water depth and water column constituents affect retrieval of correct bottom spectra in Case-2 waters. To assess the performance of WCC models in improving the SAV quantification, the surface reflectance, as well as the retrieved bottom reflectance, were correlated with the corresponding in situ estimated SAV percent cover (%SAV). Although the quality of the Lee WCC model was not considered high, the spectral region least affected by the input parameters variations (550–600 nm) can be used for the SAV quantification. Application of the Lee model provided better results in %SAV assessment than not performing the WCC correction

Deliverable 2.3.1. Optical remote sensing for mapping seabed habitats

Madala rannikuvee ja sisevee põhjasubstraadi ja -elustiku kaardistustööde läbiviimine kasutades ainult tavapäraseid kohtvaatluseid on kulukas ja aeganõudev, sest mootorpaatidega proovipunktide külastamine võib olla ohtlik või võimatu liiga madala vee, kivide ja rohke põhja- ja kaldaveetaimestiku tõttu. Sageli on sellistes piirkondades võimalik kohtvaatlusi teostada ainult jalgsi vees liikudes. Kui tugineda ainult kohtvaatlustele, mille arv raskeid liikumisolusid arvestades on madal, jääb proovivõtuvõrgustik liiga hõredaks, et sellest saadud andmete abil oleks võimalik luua interpoleerimise või matemaatilise modelleerimise abil rahuldava kvaliteediga kaardikihte. Sellises olukorras on praeguste tehniliste võimaluste juures ainuke mõistlik lahendus kasutada optilist kaugseiret koos kohtvaatlustega. See kombinatsioon võimaldab optiliste andmetega katta kogu uuringuala ning leida matemaatiliste mudelite abil seoseid optiliste (valgusspektrid) ja kohtvaatlustega saadud muutujate (substraat, elustik) vahel. Seejärel saab leitud mudelite abil ennustada substraadi ja elustiku muutujate väärtused kogu uuringu alal

Assessing Seasonal and Inter-Annual Changes in the Total Cover of Submerged Aquatic Vegetation Using Sentinel-2 Imagery

Remote sensing is a valuable tool for surveying submerged aquatic vegetation (SAV) distribution patterns at extensive spatial and temporal scales. Only regular mapping over successive time periods (e.g., months, years) allows for a quantitative assessment of SAV loss or recolonization extent. Still, there are only a limited number of studies assessing temporal changes in SAV patterns. ESA Sentinel-2 (S2) has a high revisiting frequency permitting the multi-temporal assessment of SAV dynamics both seasonally and inter-annually. In the current study, a physics-based IDA (Image Data Analysis) model was used for the reconstruction of past SAV percent cover (%cover) patterns in the Baltic Sea coastal waters based on S2 archived images. First, we aimed at capturing and quantifying intra-annual spatiotemporal SAV dynamics happening during a growing season. Modeling results showed that significant changes took place in SAV %cover: the extent of low-cover (0–30% coverage) and intermediate-cover (30–70% coverage) areas decreased, while high-cover (70–100% coverage) areas increased during the growing period. Secondly, we also aimed at detecting SAV %cover spatiotemporal variations inter-annually (over the years 2016–2022). Inter-annual variability in %cover patterns was greater in the beginning of the vegetation period (May). The peak of the growing period (July/August) showed greater stability in the areal extent of the %cover classes