Assessment of Sentinel-1 and Sentinel-2 time series to mapping

natural vegetation in a West African protected area

Maïlys Lopes1,2,3, Pierre-Louis Frison2, Sarah Durant3, Audrey Ipavec3, Vincent Lapeyre3, Henrike Schulte to Bühne3, Nathalie Pettorelli3

mailys.lopes@inra.fr

1 UMR Dynafor, INRA - INPT, Toulouse, France2 UMR Lastig – Equipe ACTE, IGN – UPEM, Saint-Mandé, France

3 Institute of Zoology, Zoological Society of London, London, UK

Introduction Study area and field dataMonitoring natural habitats of endangered species

Protected areas have been established to protect threatened species in sensitive regions. Monitoring the ecological outcomes of these large and remote areas is challenging.Remote sensing can be used to fulfil this need by mapping natural vegetation as indicator of endangered species’ habitats.Sentinel-1 and Sentinel-2 provide new opportunities for mapping vegetation in tropical areas at fine scale thanks to their high spatial and temporal resolutions and the complementarity between optical and radar data.However, their contribution to map natural vegetation in a savannah ecosystem has so far not been assessed [1]. Yet their combination could enhance the discrimination of the different types of savannah based on their structure and phenology.

ObjectiveThe aim of this study is to asses the contribution of combined Sentinel-1 and Sentinel-2 time series to the classification of natural vegetation in a savannah protected area.

Method

ReferencesConclusions Sentinel-2 time series (4 bands) combined with Sentinel-1 time series significantly increase the classification accuracy (Kappa = 80.1%) compared to Sentinel-2 or Sentinel-1 time series alone for the classification of natural vegetation in Pendjari National Park. Sentinel-2 time series (K = 76.2%) perform better than Sentinel-1 time series (K =53.0%) alone. Perspectives Extend the classification to an area covered by two different Sentinel-1 paths. Assess the importance of the number of acquisition dates by comparing results obtained using less images.

[1] Schulte to Bühne H. and Pettorelli N. (2017) Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science. Methods Ecol Evol.;00:1–17.[2] Amahowe, O., Houessou, L., Ashanti, S. and Tehou, A. (2013) Transboundary protected areas management: experiences from W-Arly-Pendjari parks in West Africa. PARKS. 19. 95-105.[3] Congalton, R. G., Oderwald, R. G., & Mead, R. A. (1983). Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric engineering and remote sensing, 49(12), 1671-1678.

Experimental resultsClass Grass s. Shrub s. Tree s. Woodland

s.Forest Water Temporary

wetlandsBare ground

Rocks

Grass s. 66 15 0 0 0 0 0 5 0

Shrub s. 26 43 31 0 0 0 0 0 0

Tree s. 0 7 119 1 0 0 0 0 0

Woodland s. 0 0 8 172 0 0 0 0 0

Forest 0 0 0 17 114 0 0 0 0

Water 0 0 0 0 0 91 0 0 0

Temporary wetlands 0 0 0 0 2 4 67 0 0

Bare ground 3 2 0 0 0 0 17 108 0

Rocks 0 2 2 0 0 0 0 18 15

User’s ac. 69.47 62.32 74.38 90.53 98.28 95.79 79.76 82.44 100

Producer’s ac. 76.74 43 93.7 95.56 87.02 100 91.78 83.08 40.54

Land cover classes

Grass savannah

Shrub savannah

Tree savannah

Woodland savannah

Forest

Water bodies

Temporary wetlands

Bare ground & built up

Rocks vegetation

Pendjari National Park (Benin) is the largest remaining preserved savannah ecosystem in West Africa (2,800 km²). It is subject to a Sudanese-Guinean climate (av. annual precipitation: 1,100 nm). It is a key biodiversity hotspot threatened by anthropogenic pressure and climate change [2]. There is currently no detailed map of the distribution of natural habitats.

A (Sentinel-2 4 bands)

Remote sensing data

A field survey was conducted in January 2019 and allowed to collect reference data and categorize savannah land cover into nine classes:

Sensor Sentinel-1 Sentinel-2

Variables Backscattering coefficient σ0(VH), σ0(VV), σ0(VH/VV)

Reflectance in bands B1, B2, B3, B4, B8, B5, B6, B8A, B11, B12 ; NDVI

Spatial resolution 10mx10m 10mx10m

Nbr of images (2018) 34 43

Color composition of one acquisition(October 2018)

Time series associated to the same pixel (grass savannah)

Input A B C D E A & C D & E A & D B & D

Kappa 76.2% 74.9% 73.3% 53.0% 36.5% 73.0% 53.2% 80.1% 76.3%

Z statistic

A B C D E A & C D & E A & D B & D

A - 0.6107 3.694 9.8509 17.053 1.4706 9.8 2.2643 0.0794

B - 3.0857 9.2352 16.3789 0.861 9.1828 2.8788 0.6907

C - 6.0967 12.946 2.2233 6.0371 5.9692 3.7762

D - 6.4629 8.3548 0.0719 12.1843 9.9384

E - 15.4086 6.5511 19.6604 17.154

A & C - 8.3003 3.7394 1.5512

D & E - 12.1398 9.8878

A & D - 2.1867

Accuracy assessment of classifications resulting from each input:

Result of Kappa analysis [3] between each pair of confusion matrices. Test is significant at the 95% confidence level if Z statistic > 1.96.

Extract of classification with inputs:

D (Sentinel-1 3 polarizations)

A & D (Sentinel-2 and Sentinel-1)

Confusion matrix resulting from best classification (input A & D)

Legend

Best classification Kappa = 80.1% (input A & D)

Grass savannah

Shrub savannah

Tree savannah

Woodland savannah

Forest

Water

Temporary wetland

Bare ground

Rocks vegetation