Combining optical and radar satellite image time series to map natural vegetation: savannas as an example

Abstract

Up-to-date land cover maps are important for biodiversity monitoring as they are central to habitat and ecosystem distribution assessments. Satellite remote sensing is a key technology for generating these maps. Until recently, land cover mapping has been limited to static approaches, which have primarily led to the production of either global maps at coarse spatial resolutions or geographically restricted maps at high spatial resolutions. The recent availability of optical (Sentinel-2) and radar (Sentinel-1) satellite image time series (SITS) which provide access to high spatial and very high temporal resolutions, is a game changer, offering opportunities to map land cover using both temporal and spatial information. These data moreover open interesting perspectives for land cover mapping based on data combination approach. However, the usefulness of combining dense time series (more than 30 images per year) and data combination approaches to map natural vegetation has so far not been assessed. To address this gap, this contribution tests the idea that the combined consideration of optical and radar data combination and time series analyses can significantly improve natural vegetation mapping in the Pendjari National Park, a Sahelian savanna protected area in Benin. Results highlight that the combination of Sentinel-1 and Sentinel-2 SITS performs as well as Sentinel-2 SITS alone in terms of classification accuracy. Land cover maps are however qualitatively better when considering the data combination approach. Our results also clearly show that the use of dense/hypertemporal optical time series significantly improves classification outcomes compared to using multitemporal only a few images per year) or monotemporal data. Altogether, this work thus demonstrates the ability of dense SITS to improve discrimination of natural vegetation types using information on their phenology, leading to more detailed and more reliable maps for environmental management.

Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

“…A total of 39 EO data types were used in the reviewed studies. This reflects a wide range of EO applications for savanna mixed pixel analysis [47,105,[113][114][115][116][117][118]. Of these, 26 (89%) were optical, 11 (7%) radar, 2 (3%) LiDAR, and 1 (1%) were a combination of optical hyperspectral and LiDAR.…”

“…Borges et al [123] used Sentinel-1 and Sentinel-2 to map savanna land cover, and Naidoo et al [83] tested the utility of multi-seasonal and multi-sensor use of Landsat TM/ETM and ALOS PALSAR to map woody fractional cover over South Africa's semi-arid savannas. All three studies reported higher and improved accuracies for models which integrated SAR with optical data [109,113].…”

“…Optical EO data were utilized more often with medium spatial resolution (30 m) Landsat being the single majority applied sensor for spatio-temporal mixed-pixel analysis of the savanna (Figure 6) [83,113,114,166]. Landsat as the most commonly used EO sensor offers at least three decades of imagery with medium spatial resolution.…”

Aplikasi Citra Sentinel-2 Untuk Pemetaan Tutupan Dan Peruntukan Lahan Pada Tingkat Desa

“…Sentinel-2 memiliki 13-band spektral yaitu band 2,3,4 dan 8 dengan ketelitian spasial 10 m, band 5,6,7, 8a, 11 dan 12 mempunyai ketelitian spasial 20 m dan band 1, 9, dan 10 memiliki ketelitian spasial 60 m (Esa, 2013). Citra sentinel-2 banyak digunakan untuk identifikasi dan analisis permasalahan terkait dengan lingkungan, perencanaan perkotaan, deteksi perubahan tutupan lahan, pemetaan resiko bencana dan beragam aplikasi lainnya (Clerici, Valbuena Calderón & Posada, 2017;Tavares et al, 2019;Lopes et al, 2020).…”

Remote sensing of wildlife connectivity networks and priority locations for conservation in the Southern Agricultural Growth Corridor (SAGCOT) in Tanzania

“…In recent connectivity studies, satellite remote sensing is becoming increasingly common to map land cover (Bergl et al, 2012;Bleyhl et al, 2017;Osipova et al, 2018Osipova et al, , 2019a. Satellite imagery, and particularly Sentinel-1 and 2 imagery from the ESA Copernicus programme, has great potential to produce accurate land cover classifications (Lopes et al, 2020;Mercier et al, 2019). Besides being open access, Sentinel data offers high spatial resolution and a high revisit time.…”