Macrophytes products

Transcription

1 Macrophytes products Paolo Villa, Monica Pinardi, Mariano Bresciani CNR-IREA

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3 Introduction Aquatic vegetation, or macrophytes, fulfil a pivotal role in biogeochemical cycles. Synoptic capabilities of EO data make them a powerful tool for monitoring macrophyte characteristics and functionality Spectral and temporal features of different macrophytes used for producing maps of community type and bio-physical parameters

4 Target species Emergent helophytes P. australis (KB) P. australis (M) P. australis (M) T. angustifol. (KB) Helophytes: Phragmites australis, Typha angustifolia Emergent rhizophytes: Nelumbo nucifera Floating-leaved: Nymphaea alba, Nuphar lutea, Trapa natans, Ludwigia hexapetala, Free-floating: Spirodela polyrrhiza, Lemna minor, Salvinia natans, Azolla caroliniana Submerged: Ceratophyllum demersum, Myriophyllum spicatum, Najas marina marina, Vallisneria spiralis Emergent macrophytes Floating macrophytes Floating macrophytes i ti Submerged- Floating h t Submerged macrophytes N. nucifera (M) N. nucifera (M) N. nucifera (M) N. alba (KB) N. alba (M) N. lutea (M) N. lutea (M) P. natans (KB) T. natans (KB) T. natans (KB) T. natans (KB) N. alba + N. lutea (KB) C. demersum + N. lutea (KB) N. alba + N. lutea (KB) C. demersum + N. lutea (KB) C. demersum + T. natans (M) T. natans (M) C. demersum + L. minor (M) C. demersum (KB) N. marina (KB) N. marina (KB) U. vulgaris (KB) T. natans (M) C. demersum + L minor (M)

5 Community type mapping Classification approach EO data: Landsat TM, ETM+, OLI Case study: Mantua lakes, Kis Balaton wetland, Lake Trasimeno, Lake Taihu Input: Multi-temporal WAVI features Algo: Rule-based classification tree (C4.5) Output: 4 macrophyte community types (H, ER, FM, SF) + 2 classes (TV, OW) Validation: Overall Accuracy > 90% Error higher than 20% for Submerged-floating association Good, consistent performance (error < 20%) for all other groups/classes Tested over different area (Lake Varese) and with different spectral data (ALOS AVNIR-2) (Villa et al., 2015)

6 Lake Trasimeno (TM-ETM+, 2008) Lake Taihu (OLI-ETM+, 2013) Kis Balaton wetland (OLI-ETM+, 2014) Mantua lakes (OLI, 2014) Mantua lakes (AVNIR-2, 2010) Lake Varese (OLI, 2014)

7 Bio-physical parameters Target canopy bio-physical parameters (BPs): Fractional cover (fc) Leaf area index (LAI) Above-water biomass (kg dw m -2 ) EO data with different spectral resolution: Narrowband (APEX) Broadband (OLI, S5T5, Sentinel-2)

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9 Macrophyte BPs mapping Estimation approach: Data: in situ canopy spectra, APEX data, multi-temporal S2A data (Mantua, Kis- Balaton) Algo: Semi-empirical regression modelling Input: Best performing spectral index for each BP and each EO dataset Output: maps of fc, LAI, AW biom Validation: High reliability for fc and LAI products (R 2 >0.6) Bias = -10%, MAPE = 13% for fc Bias = -0.19, MAPE = 19% for LAI Medium reliability for AW biom (R 2 = 0.35) Bias = kg dw m -2, MAPE = 34% (underestimation of high biomass) (Villa et al., 2017)

10 Kis-Balaton wetland (19 July 2014) Detail on Kányavár island area

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12 Mantua lakes, early growth (biomass) (22 May 2016) Mantua lakes growth peak (biomass) (28 July 2016)

13 (inter) Seasonal differences

14 (intra-) Seasonal dynamics Estimation approach: Data: in situ canopy spectra, 2015 yearly L8 - S5T5 - S2A data (Mantua, Grand Lieu), 2016 data for S2A (Mantua) Algo: Semi-empirical regression modelling for LAI estimation, TIMESAT for time series analysis Input: Best performing spectral index for S5T5 broadband data Output: Time series of macrophyte LAI, macrophyte phenology parameters (SoS, PoS, EoS, growth/senescence rate) Validation: High reliability for LAI time series (R 2 >0.8) Bias = -0.04, MAPE = 10% (underestimation of high density) (Villa et al., under review)

15 Mantua lakes - Phenology maps (2015 growing season) (Villa et al., under review)

16 Lac de Grand Lieu- Phenology maps (2015 growing season) (Villa et al., under review)

17 Conclusions New generation EO data can reliably provide spatial and temporal information about macrophytes, by mapping: community types bio-physical parameters seasonal dynamics (phenology) Scale of detail can vary between 5 m (airborne, VHR spaceborne) and m (operational spaceborne platforms) spatial grid resolution. Performance is lower for submerged vegetation (esp. in turbid systems) and for high density beds (tendency to underestimate fc and AW biom).

18 Thank you for your attention