How Essential Biodiversity Variables and remote sensing can help national biodiversity monitoring

Vihervaara, Petteri; Auvinen, Ari-Pekka; Mononen, Laura; Törmä, Markus; Ahlroth, Petri; Anttila, Saku; Böttcher, Kristin; Forsius, Martin; Heino, Jani; Heliölä, Janne; Koskelainen, Meri; Kuussaari, Mikko; Meissner, Kristian; Ojala, Olli; Tuominen, Seppo; Viitasalo, Markku; Virkkala, Raimo

doi:10.1016/j.gecco.2017.01.007

Cited by 161 publications

(126 citation statements)

References 64 publications

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“…Aichi targets) (Vihervaara et al, 2017). Finally, Earth Observation can be a valuable resource for monitoring Sustainable Development Goals (SDG) (Anderson, Ryan, Sonntag, Kavvada, & Friedl, 2017).…”

Section: Discussionmentioning

confidence: 99%

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

et al. 2017

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Section: Discussionmentioning

confidence: 99%

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

et al. 2017

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“…It has been shown that monitoring the state of biodiversity and ecosystems at national scale can benefit from the concept of RSEBVs (Vihervaara et al 2017). Estimation of canopy cover, leaf area index, and greenness phenology from optical remote sensing rely all mechanistically on light absorption in leaf pigments.…”

Section: Discussionmentioning

confidence: 99%

Generality of relationships between leaf pigment contents and spectral vegetation indices in Mallorca (Spain)

et al. 2017

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Vegetation indices are calculated from reflectance data of discrete spectral bands. The reflectance signal in the visible spectral range is dominated by the optical properties of photosynthetic pigments in plant leaves. Numerous spectral indices have been proposed for the estimation of leaf pigment contents, but the efficacy of different indices for prediction of pigment content and composition for species-rich communities is unknown. Assessing the ability of different vegetation indices to predict leaf pigment content we identify the most suitable spectral indices from an experimental dataset consisting of field-grown high light exposed leaves of 33 angiosperm species collected in two sites in Mallorca (Spain) with contrasting leaf anatomy and pigment composition. Leaf-level reflectance spectra were recorded over the wavelength range of 400 -900 nm and contents of chlorophyll a, chlorophyll b, total carotenoids, and anthocyanins were measured in 33 species from different plant functional types, covering a wide range of leaf structures and pigment content, fivefold to more than 10-fold for different traits. The best spectral region for estimation of leaf total chlorophyll content with least interference from carotenoids and anthocyanins was the beginning of near-infrared plateau well beyond 700 nm. Leaves of parallel-veined monocots and pinnate-veined dicots had different relationships between vegetation indices and pigments. We suggest that the nature and role of Bfar-redĉ hlorophylls which absorb light at longer wavelengths than 700 nm constitute a promising target for future remote sensing studies.

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“…Precise and regularly updated information on the state, change and distribution of tree types (broadleaved and coniferous tree) is essential for forest related studies, e.g., growing stock estimation [1], biodiversity assessment and monitoring [2], hazard and disease management [3], and sustainable forest management [4]. Forest inventories provide a precise statistical estimation of forest composition for large areas, but spatially explicit information beyond inventory domains is missing [5].…”

Section: Introductionmentioning

confidence: 99%

Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

2017

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Abstract:Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and validates an innovative remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m. The classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). The methodological workflow was optimized for an area of 41,285 km 2 that is characterized by temperate forests within a complex topography. Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of −3.17%). Constraints of the tree type mapping approach are mostly related to the acquisition date and time of the imagery and the topographic (negative) effects on the prediction. A comparison with the most recent High Resolution Layers (HRL) forest 2012 from the European Environmental Agency revealed that the tree type map is superior regarding spatial resolution, level of detail and accuracy. The high-quality map achieved with the approach presented here is of great value for optimizing forest management and planning activities and is also an important information source for applications outside the forestry sector.

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How Essential Biodiversity Variables and remote sensing can help national biodiversity monitoring

Cited by 161 publications

References 64 publications

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Generality of relationships between leaf pigment contents and spectral vegetation indices in Mallorca (Spain)

Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

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