Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories
The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodiversity Ob...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2012
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/27710
- Acceso en línea:
- https://doi.org/10.3390/rs4061781
https://repository.urosario.edu.co/handle/10336/27710
- Palabra clave:
- Phenology
NDVI
Random forests
MODIS
Forest vegetation
- Rights
- License
- Abierto (Texto Completo)
| Summary: | The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodiversity Observation Network project (EBONE) established a framework for an integrated biodiversity monitoring system. Underlying this framework is the idea of integrating in situ with EO and a habitat classification scheme based on General Habitat Categories (GHC), designed with an Earth Observation-perspective. Here we report on EBONE work that explored the use of NDVI-derived phenology metrics for the identification and mapping of Forest GHCs. Thirty-one phenology metrics were extracted from MODIS NDVI time series for Europe. Classifications to discriminate forest types were performed based on a Random Forests™ classifier in selected regions. Results indicate that date phenology metrics are generally more significant for forest type discrimination. The achieved class accuracies are generally not satisfactory, except for coniferous forests in homogeneous stands (77-82%). The main causes of low classification accuracies were identified as (i) the spatial resolution of the imagery (250 m) which led to mixed phenology signals; (ii) the GHC scheme classification design, which allows for parcels of heterogeneous covers, and (iii) the low number of the training samples available from field surveys. A mapping strategy integrating EO-based phenology with vegetation height information is expected to be more effective than a purely phenology-based approach. |
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