Abstract
Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an accurate nationwide vegetation physiognomic map by using automated machine learning approach with the support of reference data. A time-series of the multi-spectral and multi-indices data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were exploited along with the land-surface slope data. Reliable reference data of the vegetation physiognomic types were prepared by refining the existing vegetation survey data available in the country. The Random Forests based mapping framework adopted in the research showed high performance (Overall accuracy = 0.82, Kappa coefficient = 0.79) using 148 optimum number of features out of 231 featured used. A nationwide vegetation physiognomic map of year 2013 was produced in the research. The resulted map was compared to the existing MODIS Land Cover Type (MCD12Q1) product of year 2013. A huge difference was found between two maps. Validation with the reference data showed that the MCD12Q1 product did not work satisfactorily in Japan. The outcome of the research highlights the possibility of improving the accuracy of the MCD12Q1 product with special focus on reference data.
Highlights
Vegetation regulates biogeochemical cycles, energy balance and climate, ameliorates soils, and serves oxygen, energy and habitat to animals
A rich-feature data exploited with the Random Forests based mapping framework provided reliable classification (Overall accuracy = 0.82, Kappa coefficient = 0.79) of the vegetation physiognomic types in Japan
The comparison of the resulted vegetation physiognomic map to Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type product (MCD12Q1) based on the reference data prepared in the research showed a huge difference
Summary
Vegetation regulates biogeochemical cycles, energy balance and climate, ameliorates soils, and serves oxygen, energy and habitat to animals. Physiognomy (structural characteristics—tree, shrub, herbaceous; or leaf characteristics—evergreen or deciduous, needle-leaved or broad-leaved [1]) based vegetation classification is relevant to the characterization and monitoring of vegetation dynamics. In spite of numerous land use/cover mappings at local, regional or global scale, vegetation physiognomic mapping is limited. Moderate Resolution Imaging Spectroradiometer (MODIS) based Land Cover Type product (MCD12Q1; [2]) is one of the most recently available global land cover product from which vegetation physiognomic information can be obtained. The MCD12Q1 product classifies land use/cover types using an ensemble based supervised classification algorithm (decision trees) complemented by the training data from 1860 sites distributed across the Earth’s land areas [2]. To the best of our knowledge, accuracy and applicability of the MCD12Q1 product in terms of physiognomy based vegetation types have not been assessed so far in Japan
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