Mapping a priori defined plant associations using remotely sensed vegetation characteristics

Roelofsen, H.D.; Kooistra, Lammert; Bodegom, Peter M. van; Verrelst, Jochem; Krol, Johan; Witte, J.P.M.

doi:10.1016/j.rse.2013.09.030

Cited by 25 publications

(25 citation statements)

References 55 publications

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“…As a result, mapping vegetation using geosigmetum may be used for localizing habitats with specific interest: for example, the Alopecuro-Juncetum gerardii, a plant community of European interest (CORINE Biotope code 15.331) is likely to occur where the geosigmetum BTrifolio maritimae-Oenantheto silaifoliae^was identied on the basis of landsat images (Table 1). Predicting the Bpotential habitat^of a plant community of European interest may be extremely useful to locate detailed monitoring on the field or using very high spatial and spectral resolution sensors (Roelofsen et al, 2014). The quality of the map that was produced may be first related to the 12-bit spectral depth of the Landsat 8 OLI sensor.…”

Section: Discussion Vegetation Classifications In Coastal Marshes Usimentioning

confidence: 99%

“…Accordingly, plant communities have been successfully mapped in saltmarshes using hyperspectral images (Roelofsen et al, 2014). Increases in the seagrass distribution were observed from a SPOT-5 time series (Barillé et al, 2010), and vegetation formations were mapped in dunes and brackish marshes using Worldview-2 images (Rapinel et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Use of bi-Seasonal Landsat-8 Imagery for Mapping Marshland Plant Community Combinations at the Regional Scale

et al. 2015

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International audienceCoastal marshlands may provide ecosystem services but their vegetation and related services may be impacted by environmental changes. Habitat mapping is a key step to monitor the spatio-temporal dynamics of vegetation and detect on-going changes. However, it is still a challenge to produce reliable vegetation maps at the regional scale. This study aims to evaluate the potential of new Landsat-8 imageries (acquired in September and December 2013) for mapping fine-grained plant communities in coastal marshlands. Fieldbased vegetation maps were collected for 270 km of marshlands along the French Atlantic coast. In order to be identifiable on the satellite image, fine-grained vegetationunits were aggregated into fewer plant community combinations. The classification accuracy was assessed by comparison with field-based vegetation data and compared between the supervised methods used, including Minimum Distance, Mahalanobis, Maximum Likelihood, Random Forest and Support Vector Machine. The best result was obtained with the Maximum Likelihood classifier and by combining the twoLandsat-8 images (85.9 % accuracy overall). Three main habitat types dominated the coastal Atlantic marshlands: croplands, Trifolio maritimae-Oenantheto silaifoliae geosigmetum and Puccinellio maritimae-Arthrocnemeto fruticosi geosigmetum. The reliability of the vegetation map produced will provide a good basis for monitoring the conservation status of the various habitats

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Section: Discussion Vegetation Classifications In Coastal Marshes Usimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of bi-Seasonal Landsat-8 Imagery for Mapping Marshland Plant Community Combinations at the Regional Scale

et al. 2015

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“…Meanwhile GPR has found its way in more advanced vegetation trait estimations. Roelofsen et al (2014) applied GPR to map salinity, moisture and nutrient concentrations. These in turn were used as inputs for plant association mapping.…”

Section: Kernel Methodsmentioning

confidence: 99%

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

Verrelst

Camps‐Valls

Muñoz-Marí

et al. 2015

ISPRS Journal of Photogrammetry and Remote Sensing

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567

428

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“…Recently, a mapping method that is based on com bi na tion of remote sensing and groundbased sources of in for mation has gained a widespread acceptance (McRober 2006, Tomppo et al 2008, Kravtzova 2013, Puzachenko et al 2014, Roelofsen et al 2014). This method is commonly used to create regional and local maps.…”

Section: An Approach For Mapping Northern Fennoscandian Forests At DImentioning

confidence: 99%

An approach for mapping Northern Fennoscandian forests at different scales

Chernen’kova¹,

Puzachenko²,

Морозова³

et al. 2015

Bot Pac

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A B S T R A C TAssessment of the diversity of forests throughout their distribution range poses a major challenge for researches because it involves a significant effort of col lec ting and analyzing heterogeneous information. Our approach to mapping and analyzing the presentday spatial diversity of forests in European Russia was based on a multiparametric analysis of the chart community characteristics obtained by ground surveys, remote sensing data, and morphometric parameters of the relief. The main steps of methodology were: a) groundbased in a test region selected to re pre sent the main zonal divisions of forest cover, b) development of uniform fo rest classification, c) mapping at local (large) scale, d) mapping at regional (small) scales. Forest classification was based on the relative availability of ecological and mor pho logical groups of plant species in syntaxa and included seven hierarchical units. Two types of forest cover maps for Northern Fennoscandia are presented as examples at regional and local scales. Оценка разнообразия лесов на протяже нии их ареала представляет собой большую проблему, которая требует при ложения значительных усилий по сбору и анализу разнородного ма те риа ла. Представлен подход для картографирования и оценки современ но го пространственного разнообразия лесов европейской части России в различных масштабах. Он основан на многопараметрическом анализе ха рак теристик растительных сообществ, полученных при наземных об сле дованиях, данных дистанционного зондирования (ДДЗ) и морфомет ри чес ких параметрах рельефа. Основными этапами нашей методологии яв ляются: а) наземные исследования в тестовой области в соответствии с ос нов ными зональными подразделениями лесного покрова, б) разработка еди ной классификации лесов, в) картографирование на локальном уров не в крупном масштабе, г) картографирование на региональном уровне в мел ком масштабе. Лесная классификация основана на учете экологических и морфологических групп видов растений в синтаксонах и включает семь иерар хических единиц: тип растительности, подтип растительности, класс формаций, формация, класс ассоциаций, группа ассоциаций и ассоциация. В качестве примера представлены два типа карт лесного покрова для ло кального и регионального уровня северной части Фенноскандии.К л ю ч е в ы е с л о в а : доминантная классификация, бореальные леса, картогра фическое моделирование, статистические методы, ГИС, дистанционная информа ция

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Mapping a priori defined plant associations using remotely sensed vegetation characteristics

Cited by 25 publications

References 55 publications

Use of bi-Seasonal Landsat-8 Imagery for Mapping Marshland Plant Community Combinations at the Regional Scale

Use of bi-Seasonal Landsat-8 Imagery for Mapping Marshland Plant Community Combinations at the Regional Scale

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

An approach for mapping Northern Fennoscandian forests at different scales

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