12 July12017 Landsat Science Team, Sioux Falls

Landsat, LGAC and Sentinel-2: disentangling coupled human-

environmental systems

Patrick Griffiths1, Dirk Pflugmacher1, Sebastian van der Linden1

Tobias Kuemmerle2,3

Patrick Hostert1,3

1 Geomatics Lab, Geography Department

2 Biogeography and Cons. Biology Lab, Geography Department

3 Integrative Research Institute on Transformations of

Human Environment Systems (IRI THESys)

Humboldt-Universität zu Berlin

2

Steffen et al. 2015, Science

Land systems analysis

From Land Cover to Land Use

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Land systems analysis

From Land Cover to Land Use

Phenology related information is key for better understanding land use intensity (e.g. related to irrigation, double-cropping, mowing events…

Sparse cloud-free

Landsat observations in

NE-Germany in 2016

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Sentinel2 & Landsat integration opportunities

No spatially explicit national scale information source on crop types available - LPIS treaded confidentially

Integration can provide observation frequency required to disentangle crop types and quantify growing season characteristics

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National scale mapping

North South gradient in elevation and phenology

29 WRS2 frames, 62 MGRS tiles for national coverage

Time period covered: 275-2015 to 365-2016

HLS M30 & S30 products: bandpass adjusted, BRDF normalized, subpixel registered

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Compositing

Compositing at narrow +/- weekly interval:

Temporal gap filling

Allows for standardized workflows and to prototype higher level product generation (Level-3 & beyond)

Parametric scoring approach provides quality assessment

DOY:

scene based

Cloud/Shadow distance:

pixel based

Sensor:

scene based

Clear sky fraction:

scene based

Si = (WDOY*SDOY)+(Wdist*Sdist)+(Wsen.*Ssen.)+(WHOT*SHOT)+(WskyF*SskyF)

∑(W)

Composite:

pixel based decision

Haze optimized transformation:

pixel based

Griffiths, P. & Hostert, P. Exploring the potential of the combined use of Sentinel2a and Landsat data for land cover and crop type mapping (in prep)

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Clear observation count: HLS 275-2015 to 365-2016

1 55

Griffiths, P. & Hostert, P. Exploring the potential of the combined use of Sentinel2a and Landsat data for land cover and crop type mapping (in prep)

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Observation composites

Gap-filled composites

275 2015 – 365 2016

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Mapping

Detailed temporal & full spectral information required to disentangle e.g. cereal crops; temporal features suffice for other crops (e.g. maize); inclusion of red-edge bands improves results

Strategy: Per pixel 45 x 10-day composites with 9 spectral measurements provided to Random Forest classifier (30% of features considered at split; 500 trees; 3000 samples/class)

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Grassland mowing detection

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Overall accuracy 83.76%

PAC. UAC

Grassland 87.80 76.75

Winter Rye 81.10 71.96

Winter Wheat 68.40 73.79

Winter Barley 87.20 86.17

Maize 91.60 82.60

Winter Rapeseed 96.60 97.77

Summer Cereals 83.40 74.66

Fodder Crops 66.20 84.98

Sugar Beet 96.70 95.36

Sunflower 89.40 91.69

Potatoes 82.10 88.85

Mixed&Deciduous F. 68.57 78.78

Coniferous Forest 84.30 80.90

Built-up 77.40 80.54

Water 95.68 94.73

National scale mapping

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13

14

15

16

17

18

DOY – First mowing detection

101 326

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DOY - Second mowing detection

101 330

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Large area LULC mapping: GeoMultiSens

Beta test: central Europe

• All Landsat TM, ETM+, OLI data

• 4,130 images for 2011-2013 31,243 training samples from EU LUCAS-2012 as

input for RF classification

Objective: Implement pan-European LULC and (forest disturbance) mapping from heterogeneous EO data sources

Europe 1985 – 2015, L 5/7/8 and S2

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EO data

User Data

Tiling

Scenes

Spatial

Reference

System

Tiling

Scheme

Data Cube Data Query

Temporal/Spatial

Extent

Metadata

Query

Surface Reflectance

Cloud Mask and QA

Metadata

Composite-based

feature extraction

Land cover map

Auxiliary

Geodata (z.B.

Bioclimatic

zones) Geodata / Zones

Composite

features

Extract training

samples

Classification

model

Machine learning

(Random Forest)

Model

application

Reference

samples

(LUCAS)

Training data

Vegetation indices,

transformations

Input data Analysis-ready data structure (Data cube creation) User query

Model training

Model application (Mapping)

Compositing (Feature extraction)

Automated workflow

Hostert, P., Pflug-

macher, D. et al.:

Sensor constellations

and big data from

space – from challen-

ges to solutions

(in prep.)

Scheffler, D., Hollstein, A., Diedrich, H., Segl, K., Hostert, P. (2017). AROSICS: An Automated and Robust Open-Source Image

Co-Registration Software for Multi-Sensor Satellite Data. Remote Sensing, 9, 676

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GeoMultiSens Land Cover 2012

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25 ha MMU

39 countries involved

3 years production time

Computer Assisted Photo-Interpretation

EU CORINE Land Cover 2012

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CORINE Land Cover

2012

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GeoMultiSens Land Cover

2012

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Map accuracy (10 classes): 79.4%

Map accuracy (4 classes): 89.7%

Forest: 95.5% accuracy

Cropland: 88.7% accurate - confusion with shrub/grassland

High omission of bare land – but class is under-sampled and rare in Central Europe

Producer’s Accuracy User’s Accuracy

Map accuracy

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Conclusions

Dense time series and phenology metrics from Landsat and Sentinel2 allow assessing land use, LU management and intensity

Data from sensor constellations is needed for analyzing LULC in data-sparse regions

Landsat science plays a vital

role in providing LULCC

evidence in support of the

UN SDGs

Acknowledgments:

• European Space Agency for Island2VAP

• German Federal Ministry of Education and

Research for GeoMultiSens

• Endorsed by the Global Land Programme

Thanks Landsat

Science Team!

Modified after Romijn et al. (2017): Monitoring progress towards Sustainable Development Goals The role of land monitoring