TITLE:
Improvement of Countrywide Vegetation Mapping over Japan and Comparison to Existing Maps
AUTHORS:
Ram C. Sharma, Keitarou Hara, Hidetake Hirayama
KEYWORDS:
Vegetation, Classification, MODIS, Random Forests, Mapping, Japan
JOURNAL NAME:
Advances in Remote Sensing,
Vol.7 No.3,
September
5,
2018
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.