Production of Multi-Features Driven Nationwide Vegetation Physiognomic Map and Comparison to MODIS Land Cover Type Product

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.

Improvement of Countrywide Vegetation Mapping over Japan and Comparison to Existing Maps

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.

Rule induction based on rough sets from information tables having continuous domains

Information tables having continuous domains are handled by neighborhood rough sets.Two approximations in complete information tables are extended to handle incomplete information.Consequently,four approximations are obtained:certain and possible lower ones and certain and possible upper ones without computational complexity.These extended approximations create the same results as the ones from possible world semantics by using possible indiscernibility relations.Therefore,the extension is justified.In complete information tables two types of single rules that an object supports are obtained:consistent and inconsistent ones.The single rule has low applicability.To increase applicability,a series of single rules are brought into one combined rule with an interval value.In incomplete information tables four kinds of single rules are obtained.From them,four kinds of combined rules are obtained:certain and consistent,possible and consistent,certain and inconsistent,and possible inconsistent ones.A combined rule has higher applicability than the single rules from which it is assembled.

Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data

An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study.The Moderate Resolution Imaging Spectroradiometer(MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite(VIIRS)-based nighttime light(NTL)data for robust extraction and mapping of urban built-up areas.The MODIS-based newly proposed Urban Built-up Index(UBI)was combined with NTL data,and the resulting Enhanced UBI(EUBI)was used as a single master image for global extraction of urban built-up areas.Due to higher variation of the EUBI with respect to geographical regions,a region-specific threshold approach was used to extract urban built-up areas.This research provided 500-m-resolution global urban built-up map of year 2014.The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States.The comparative analysis demonstrated finer details of the urban built-up cover estimated by the resultant map.