Abstract
This paper presents an improved classification and mapping of vegetation types for all of Japan by utilizing the Moderate-resolution Imaging Spectroradiometer (MODIS) data. The Nadir BRDF-Adjusted Reflectance (MCD43A4 product) data were compared to the conventional Surface Reflectance (MOD09A1/MOY09A1 products) data for the classification of vegetation types: evergreen coniferous forest, evergreen broadleaf forest, deciduous coniferous forest, deciduous broadleaf forest, shrubs, herbaceous, arable; and non-vegetation. Very rich spectral and temporal features were prepared from MCD43A4 and MOD09A1/MOY09A1 products. Random Forests classifier was employed for the classification of vegetation types with the support of ground truth data prepared in the research. Accuracy metrics—confusion matrix, overall accuracy, and kappa coefficient calculated through 10-fold cross-validation approach—were used for quantitative comparison of MCD43A4 and MOD09A1/MOY09A1 products. The cross-validation results indicated better performance of the MCD43A4 (Overall accuracy = 0.73; Kappa coefficient = 0.69) product than conventional MOD09A1/MOY09A1 products (Overall accuracy = 0.70; Kappa coefficient = 0.66) for the classification. McNemar’s test was also used to confirm a significant difference (p-value = 0.0003) between MCD43A4 and MOD09A1/MOY09A1 products. Based on these results, by utilizing the MCD43A4 features, a new vegetation map was produced for all of Japan. The newly produced map showed better accuracy than the extant, MODIS Land Cover Type product (MCD12Q1) and Global Land Cover by National Mapping Organizations (GLCNMO) product in Japan.
Highlights
This paper presents an improved classification and mapping of vegetation types for all of Japan by utilizing the Moderate-resolution Imaging Spectroradiometer (MODIS) data
The Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (MCD43A4 product) data were compared to the conventional Surface Reflectance (MOD09A1/MOY09A1 products) data for the classification of vegetation types: evergreen coniferous forest, evergreen broadleaf forest, deciduous coniferous forest, deciduous broadleaf forest, shrubs, herbaceous, arable; and non-vegetation
Random Forests classifier was employed for the classification of vegetation types with the support of ground truth data prepared in the research
Summary
Satellite data are a major source of information for classification and mapping of vegetation types. Moderate Resolution Imaging Spectroradiometer (MODIS) based Land Cover Type Product (MCD12Q1), and Global Land Cover by National Mapping Organizations (GLCNMO) are recently available global land cover maps from which vegetation types information can be obtained. The MCD12Q1 product utilizes an ensemble-based supervised classification algorithm (decision trees) complemented by training data from 1860 sites distributed across the Earth’s land areas [9]. The Global Land Cover by National Mapping Organizations (GLCNMO) has provided the global scale land cover map from which information on vegetation types can be obtained. The vegetation types in the GLCNMO product have been mapped based on the decision tree method with the support of ground truth data [11]. To the best of our knowledge, accuracy of the GLCNMO map for classification of vegetation types is not assessed so far in Japan
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