Gegen Tana

Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects

A methodology is presented to accurately estimate electric power consumption from saturated night-time Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) imagery using a stable light correction. An area correction for the stable light image of DMSP/OLS for the year 1999 was performed and the build-up area rate data were used to clarify the intensity distribution characteristics of the stable light. Based on the spatial distribution characteristics of the stable light, the saturation light of the electric power supply area of Japan was corrected using a cubic regression equation. The regression between the correction calculations by the cubic regression equation and the statistical electric power consumption data was applied in Japan and also in China, India and 10 other Asian countries. The correction method was then evaluated. This study confirms that electric power consumption can be estimated with high precision from the stable light.

Production of global land cover data GLCNMO

Abstract Global land cover is one of the fundamental contents of Digital Earth. The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset – Global Land Cover by National Mapping Organizations. It has 20 land cover classes defined using the Land Cover Classification System. Of them, 14 classes were derived using supervised classification. The remaining six were classified independently: urban, tree open, mangrove, wetland, snow/ice, and water. Primary source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of 2003. Training data for supervised classification were collected using Landsat images, MODIS NDVI seasonal change patterns, Google Earth, Virtual Earth, existing regional maps, and experts comments. The overall accuracy is 76.5% and the overall accuracy with the weight of the mapped area coverage is 81.2%. The data are available from the Global Mapping project website (http://www.iscgm.org/). The MODIS data used, land cover training data, and a list of existing regional maps are also available from the CEReS website. This mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.

A Saturated Light Correction Method for DMSP/OLS Nighttime Satellite Imagery

Several studies have clarified that electric power consumption can be estimated from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) stable light imagery. As digital numbers (DNs) of stable light images are often saturated in the center of city areas, we developed a saturated light correction method for the DMSP/OLS stable light image using the nighttime radiance calibration image of the DMSP/OLS. The comparison between the nonsaturated part of the stable light image for 1999 and the radiance calibration image for 1996-1997 in major areas of Japan showed a strong linear correlation (R2 = 92.73) between the DNs of both images. Saturated DNs of the stable light image could therefore be corrected based on the correlation equation between the two images. To evaluate the new saturated light correction method, a regression analysis is performed between statistic data of electric power consumption from lighting and the cumulative DNs of the stable light image before and after correcting for the saturation effects by the new method, in comparison to the conventional method, which is, the cubic regression equation method. The results show a stronger improvement in the determination coefficient with the new saturated light correction method (R2 = 0.91, P = 1.7 ·10-6 <; 0.05) than with the conventional method (R2 = 0.81, P = 2.6 ·10-6 <; 0.05) from the initial correlation with the uncorrected data (R2 = 0.70, P = 4.5 · 10-6 <; 0.05). The new method proves therefore to be very efficient for saturated light correction.

Wetlands Mapping in North America by Decision Rule Classification Using MODIS and Ancillary Data

An up-to-date wetlands map based on remote sensing data at a continental scale is urgently needed for estimating global environmental change. In this study, a wetlands map of North America was developed using Moderate Resolution Imaging Spectroradiometer (MODIS) data obtained in 2008 and ancillary data. For this purpose, a decision rule classification method was developed relied upon the hierarchical characteristics of land types and prior knowledge about the geographical location of wetlands. Two hierarchical levels of land types were used to extract wetlands. At the first level, non-vegetation land types including water, snow, urban, and bare areas were separately extracted from vegetation land types using threshold methods. At the second level, wetlands were discriminated from non-wetland vegetation land types with the MODIS tasseled cap (brightness, greenness, and wetness) indices using the decision tree method. In addition, elevation data were used to build the elevation mask and a climate map was used to subdivide the study area into five sub-regions. In the quantitative accuracy assessment, users and producers accuracies of wetlands for the whole study area were calculated as 80.3% and 83.7%, respectively. In a comparison with two existing global land cover datasets, GLC2000 and IGBP DISCover, our results show significant improvement in extracting coastal and narrow types of wetlands. This study indicates that decision rule classification, integrated with multi-temporal MODIS data and ancillary data, is useful to develop an improved wetlands map at a continental scale.

Estimating the energy consumption with nighttime city light from the DMSP/OLS imagery

A methodology is presented to accurately estimate electric power consumption from saturated nighttime DMSP/OLS imagery using a stable light correction. An area correction for the stable light image of DMSP/OLS for the year 1999 was performed and the building area rate data were used to clarify the intensity distribution characteristics of the stable light. Based on the spatial distribution characteristics of the stable light, the saturation light of the electric power supply area of Japan was corrected using a cubic regression equation. The regression between the correction calculations by the cubic regression equation and the statistical electric power consumption data was applied not only in Japan but also in China, India and 10 other Asian countries. Then, the correction method was evaluated. This study confirms that the electric power consumption can be estimated with high precision from the stable light.

A Detailed and High-Resolution Land Use and Land Cover Change Analysis over the Past 16 Years in the Horqin Sandy Land, Inner Mongolia

Land use and land cover (LULC) change plays a key role in the process of land degradation and desertification in the Horqin Sandy Land, Inner Mongolia. This research presents a detailed and high-resolution (30 m) LULC change analysis over the past 16 years in Ongniud Banner, western part of the Horqin Sandy Land. The LULC classification was performed by combining multiple features calculated from the Landsat Archive products using the Support Vector Machine (SVM) based supervised classification approach. LULC maps with 17 secondary classes were produced for the year of 2000, 2009, and 2015 in the study area. The results showed that the multifeatures combination approach is crucial for improving the accuracy of the secondary-level LULC classification. The LULC change analyses over three different periods, 2000–2009, 2009–2015, and 2000–2015, identified significant changes as well as different trends of the secondary-level LULC in study area. Over the past 16 years, irrigated farming lands and salinized areas were expanded, whereas the waterbodies and sandy lands decreased. This implies increasing demand of water and indicates that the conservation of water resources is crucial for protecting the sensitive ecological zones in the Horqin Sandy Land.

Relationship between DMSP/OLS nighttime light and CO2 emission from electric power plant

In this study, we estimated the CO2 emission by fossil fuel consumption from electric power plant using DMSP stable light image for 1999 after correction for saturation effect. Digital number (DNs) of the stable light image in center of city areas are saturated for the strong nighttime intensity and characteristic of the OLS satellite sensor. To estimate the CO2 emission using stable light image, saturation light correction method was developed by using DMSP radiance calibration image, which has not included saturation pixel in city areas. Then, regression analysis was performed with cumulative DNs of the corrected stable light image, electric power consumption, electric power generation and CO2 emission by fossil fuel consumption from electric power plant each other. Results indicated that there are good relationship (R2<90%) between DNs of the corrected stable light image and other parameters. Finally, we estimated the CO2 emission from electric power plant using corrected stable light image.

Validation of the wetlands map derived from MODIS imagery in North America

As wetlands are among the most important ecosystems in the world, it is becoming increasingly important to develop a wetlands map at continental or global scale. A wetlands map in North America was produced using 500 m MODIS data obtained in 2008. To assess the accuracy of the map, the quantitative accuracy assessment was performed. A stratified random sampling method was applied to collect the validation point. A total of 2400 sampling pixels were used for the accuracy assessment. The overall accuracy of the map was assessed at 80.3%. Furthermore, the wetlands map was also compared with the existing global land cover products GLC2000 and IGBP DISCover. Three wetland sites designated in the Ramsar Convention were used to compare with Landsat images. As a result, the spatial distributions of wetlands in the new map were closest to those were in Landsat images. The new map also gave more detailed spatial information on wetlands especially in the transition zone between aquatic and terrestrial area. This study indicates that MODIS data are capable for developing an improved wetlands map at a global scale.

Particular agricultural land cover classification case study of Tsagaannuur, Mongolia

Agriculture is one of the major economic sectors of Mongolia and the countrys economy is very much dependent on the development of agricultural production. Mongolian agriculture has been successful in increasing food grains production in the past, guided by the goals of self-sufficiency in the country. The objective of this study is to classification in the particular agricultural land cover in the Tsagaannuur as there is an important agricultural producing area in Mongolia. We were developed agricultural cadastral map and create the vector coverage of the study site, the vector field boundaries were built and digitized from the ground truth data using with ARCMAP software. In this study, maximum likelihood supervised classification was applied to Landsat TM and ETM images acquired in 1989 and 2000, respectively. A supervised classification was carried out on the six reflective bands for the two images individually with the aid of ground based agricultural monitoring data. Results were then tested using ground check data.

Assessment of stable light derived from DMSP/OLS night‐time imagery

A stable light image for south‐eastern Asia was extracted from the Defense Meteorological Satellite Programs Operational Linescan System (DMSP/OLS) night‐time imagery for 1999. The accuracy assessment of the stable light image has been completed using two methods: a reference data‐based comparison and a stratified random sampling method. The stable light image was compared with the conventional stable light image for 1999 and the Landsat ETM+ 2000 image for the Kanto region of Japan. The results show that the digital numbers of the new stable light image (almost <20) in the non‐urban area are much lower than those of the conventional stable light image (almost >60) because the new stable light image includes little noise. The stratified random sampling method could assess the accuracy for both the new stable light image and the conventional stable light image in Asia by classifying the images into stable and non‐stable light areas.