Forest Mapping of the Congo Basin using Synthetic Aperture Radar (SAR)

Wheeler, James; Rodríguez-Veiga, Pedro; Balzter, Heiko; Tansey, Kevin; Tate, Nicholas

doi:10.1002/9781118793787.ch4

“…While many researchers have attempted to identify broad forest type categories using space-borne and airborne images (Arasumani et al 2019;Erinjery et al 2018;Foody, Hill 1996;Shimizu et al 2019), discriminating between native and non-native species is challenging with medium resolution satellite data due to extensive intermixing and canopy heterogeneity requires high spatial and spectral resolution imagery. Researchers have also employed microwave data (Chen et al 2018;Laurin et al 2013;Wheeler et al 2017), recently launched Sentinel-2 multispectral images (Laurin et al 2013;Wheeler et al 2017), and combinations of Sentinel-1 SAR (Synthetic Aperture Radar) images with Sentinel-2 multispectral images to improve classification accuracy (Erinjery et al 2018;Kattenborn et al 2019;Zhang et al 2019). A few studies have also suggested that hyperspectral images could produce high accuracy for mapping tree species compared to the multispectral datasets (George et al 2014;Thenkabail et al 2004).…”

Section: Choice Of Imageriesmentioning

confidence: 99%

Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees

Arasumani

¹

,

Singh

²

,

Bunyan

³

et al. 2021

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“…The copyright holder for this preprint this version posted January 4, 2021. ; https://doi.org/10.1101/2021.01.01.425059 doi: bioRxiv preprint spectral resolution imagery. Researchers have also employed microwave data (Chen et al 2018;Laurin et al 2013;Wheeler et al 2017), recently launched Sentinel-2 multispectral images (Laurin et al 2013;Wheeler et al 2017), and combinations of Sentinel-1 SAR (Synthetic Aperture Radar) images with Sentinel-2 multispectral images to improve classification accuracy (Erinjery et al 2018;Kattenborn et al 2019;Zhang et al 2019). A few studies have also suggested that hyperspectral images could produce high accuracy for mapping tree species compared to the multispectral datasets (George et al 2014;Thenkabail et al 2004).…”

Section: Choice Of Imageriesmentioning

confidence: 99%

“…While many researchers have attempted to identify broad forest type categories using space-borne and airborne images (Arasumani et al 2019;Erinjery et al 2018;Foody, Hill 1996;Shimizu et al 2019), discriminating between native and non-native species is challenging with medium resolution satellite data due to extensive intermixing and canopy heterogeneity requires high spatial and spectral resolution imagery. Researchers have also employed microwave data (Chen et al 2018;Laurin et al 2013;Wheeler et al 2017), recently launched Sentinel-2 multispectral images (Laurin et al 2013;Wheeler et al 2017), and combinations of Sentinel-1 SAR (Synthetic Aperture Radar) images with Sentinel-2 multispectral images to improve classification accuracy (Erinjery et al 2018;Kattenborn et al 2019;Zhang et al 2019). A few studies have also suggested that hyperspectral images could produce high accuracy for mapping tree species compared to the multispectral datasets (George et al 2014;Thenkabail et al 2004).…”

Section: Choice Of Imageriesmentioning

confidence: 99%

Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees

Arasumani

¹

,

Singh

²

,

Bunyan

³

et al. 2021

Preprint

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Invasive alien species (IAS) threaten tropical grasslands and native biodiversity and impact ecosystem service delivery, ecosystem function, and associated human livelihoods. Tropical grasslands have been dramatically and disproportionately lost to invasion by trees. The invasion continues to move rapidly into the remaining fragmented grasslands impacting various native grassland-dependent species and water streamflow in tropical montane habitats. The Shola Sky Islands of the Western Ghats host a mosaic of native grasslands and forests; of which the grasslands have been lost to exotic tree invasion (Acacias, Eucalyptus and Pines) since the 1950s. The invasion intensities, however, differ between these species wherein Acacia mearnsii and Pinus patula are highly invasive in contrast to Eucalyptus globulus. These disparities necessitate distinguishing these species for effective grassland restoration. Further, these invasive alien trees are highly intermixed with native species, thus requiring high discrimination abilities to native species apart from the non-native species.Here we assess the accuracy of various satellite and airborne remote sensing sensors and machine learning classification algorithms to identify the spatial extent of native habitats and invasive trees. Specifically, we test Sentinel-1 SAR and Sentinel-2 multispectral data and assess high spatial and spectral resolution AVIRIS-NG imagery identifying invasive species across this landscape. Sensor combinations thus include hyperspectral, multispectral and radar data and present tradeoffs in associated costs and ease of procurement. Classification methods tested include Support Vector Machine (SVM), Classification and Regression Trees (CART) and Random Forest (RF) algorithms implemented on the Google Earth Engine platform. Results indicate that AVIRIS-NG data in combination with SVM recover the highest classification skill (Overall −98%, Kappa-0.98); while CART and RF yielded < 90% accuracy. Fused Sentinel-1 and Sentinel-2 produce 91% accuracy, while Sentinel-2 alone yielded 91% accuracy with RF and SVM classification; but only with higher coverage of ground control points. AVIRIS-NG imagery was able to accurately (97%) demarcate the Acacia invasion front while Sentinel-1 and Sentinel-2 data failed. Our results suggest that Sentinel-2 images could be useful for detecting the native and non-native forests with more ground truth points, but hyperspectral data (AVIRIS-NG) permits distinguishing, native and non-native tree species and recent invasions with high precision using limited ground truth points. We suspect that large areas will have to be mapped and assessed in the coming years by conservation managers, NGOs to plan restoration, or to assess the success of restoration activities, and several data procurement and analysis steps may have to be simplified.

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Precise Delineation of Small Water Bodies from Sentinel-1 Data using Support Vector Machine Classification

Pôssa¹,

Maillard²

2018

Canadian Journal of Remote Sensing

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Forest Mapping of the Congo Basin using Synthetic Aperture Radar (SAR)

Cited by 4 publications

References 33 publications

Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees

Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees

Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees

Precise Delineation of Small Water Bodies from Sentinel-1 Data using Support Vector Machine Classification

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