Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Ng, Wai-Tim; Rima, Purity; Einzmann, Kathrin; Immitzer, Markus; Atzberger, Clement; Eckert, Sandra (2017). Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya. Remote sensing, 9(1), p. 74. Molecular Diversity Preservation International MDPI 10.3390/rs9010074

[img]
Preview
Text
Eckert_2017_Assessing the potential.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (7MB) | Preview

Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis—competing for nutrients and water—has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pléiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the “Woody Weeds” project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pléiades data (OOB accuracy 0.83 and independent reference accuracy 0.87–0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80–0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pléiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > Centre for Development and Environment (CDE)

UniBE Contributor:

Eckert, Sandra

ISSN:

2072-4292

Publisher:

Molecular Diversity Preservation International MDPI

Projects:

[411] Woody invasive alien species in East Africa

Language:

English

Submitter:

Stephan Schmidt

Date Deposited:

10 Apr 2017 12:13

Last Modified:

09 Sep 2017 22:41

Publisher DOI:

10.3390/rs9010074

BORIS DOI:

10.7892/boris.94500

URI:

https://boris.unibe.ch/id/eprint/94500

Actions (login required)

Edit item Edit item
Provide Feedback