Tiger habitat quality modelling in Malaysia with Sentinel-2 and InVEST

Louis, Valentin; Page, Susan E.; Tansey, Kevin J.; Jones, Laurence ORCID: https://orcid.org/0000-0002-4379-9006; Bika, Konstantina; Balzter, Heiko. 2024 Tiger habitat quality modelling in Malaysia with Sentinel-2 and InVEST. Remote Sensing, 16 (2), 284. 23, pp. https://doi.org/10.3390/rs16020284

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Official URL: http://dx.doi.org/10.3390/rs16020284

Abstract/Summary

Deforestation is a threat to habitat quality and biodiversity. In intact forests, even small levels of deforestation can have profound consequences for vertebrate biodiversity. The risk hotspots are Borneo, the Central Amazon, and the Congo Basin. Earth observation (EO) now provides regular, high-resolution satellite images from the Copernicus Sentinel missions and other platforms. To assess the impact of forest conversion and forest loss on biodiversity and habitat quality, forest loss in a tiger conservation landscape in Malaysia is analysed using Sentinel-2 imagery and the InVEST habitat quality model. Forest losses are identified from satellites using the random forest classification and validated with PlanetScope imagery at 3–5 m resolution for a test area. Two scenarios are simulated using InVEST, one with and one without the forest loss maps. The outputs of the InVEST model are maps of tiger habitat quality and habitat degradation in northeast Peninsular Malaysia. In addition to forest loss, OpenStreetMap road vectors and the GLC2000 land-cover map are used to model habitat sensitivity to threats from roads, railways, water bodies, and urban areas. The landscape biodiversity score simulation results fall sharply from ~0.8 to ~0.2 for tree-covered land cover when forest loss is included in the habitat quality model. InVEST makes a reasonable assumption that species richness is higher in pristine tropical forests than in agricultural landscapes. The landscape biodiversity score is used to compare habitat quality between administrative areas. The coupled EO/InVEST modelling framework presented here can support decision makers in reaching the targets of the Kunming-Montreal Global Biodiversity Framework. Forest loss information is essential for the quantification of habitat quality and biodiversity in tropical forests. Next generation ecosystem service models should be co-developed alongside EO products to ensure interoperability.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.3390/rs16020284
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	2072-4292
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	deforestation, biodiversity, earth observation, ecosystem services, machine learning
NORA Subject Terms:	Ecology and Environment Electronics, Engineering and Technology
Related URLs:	Dataset Dataset Dataset
Date made live:	15 Jan 2024 15:06 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/536663

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.3390/rs16020284

UKCEH and CEH Sections/Science Areas:

Soils and Land Use (Science Area 2017-)

ISSN:

2072-4292

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

deforestation, biodiversity, earth observation, ecosystem services, machine learning