Fuli Yan

Population spatialization in China based on night-time imagery and land use data

Population is a key indicator of socioeconomic development, urban planning and environmental protection, particularly for developing countries like China. But, census data for any given area are neither always available nor adequately reflect the internal differences of population. The authors tried to overcome this problem by spatializing the population across China through utilizing integer night-time imagery (Defense Meteorological Satellite Program/Operational Linescan System, DMSP/OLS) and land-use data. In creating the population linear regression model, night-time light intensity and lit areas, under different types of land use, were employed as predictor variables, and census data as dependent variables. To improve model performance, eight zones were created using night-time imagery clustering and shortest path algorithm. The population model is observed to have a coefficient of determination (R 2) ranging from 0.80 to 0.95 in the research area, which remained the same in different years. A comparison of the results of this study with those of other researchers shows that the spatialized population density map, prepared on the basis of night-time imagery, reflects the population distribution character more explicitly and in greater detail.

The variability of NDVI over northwest China and its relation to temperature and precipitation

Land vegetation plays a major role in the global climate change through the carbon cycle, and climate change in turn affects vegetation growth and its photosynthetic activity. In arid and semi-arid areas, sparse vegetation cover characterizes environments. Thus quantitative temporal series analysis of vegetation distribution and its variations enables observing annual trends, and helps to find out the reason for environment variability. There are serious environmental problems, such as deforestation, soil erosion, salinization, and desert encroachment in northwestern China, its natural conditions are very delicate. In this paper, we build a time series of vegetation change by the NDVI (normalized difference vegetation index) covered northwest regions over 19 years (1982-2000), and analyze the time serial NDVI variability using three methods, which are simple differencing, slope map of NDVI, slope map of biomass. The correlation analysis between NDVI with the temperature and precipitation in northwestern China was carried out. The results showed that there was significant positive correlation between NDVI and precipitation but that temperature was not strongly correlated with NDVI in northwest China.

Spatial patterns of ecosystem vulnerability changes during 2001–2011 in the three-river source region of the Qinghai-Tibetan Plateau, China

The three-river source region (TRSR, including Yangtze, Yellow and Lancang rivers), located in the Qinghai-Tibetan Plateau, China, is a typical alpine zone with apparent ecosystem vulnerability and sensitivity. In this paper, we introduced many interdisciplinary factors, such as landscape pattern indices (Shannon diversity index and Shannon evenness index) and extreme climate factors (number of extreme high temperature days, number of extreme low temperature days, and number of extreme precipitation days), to establish a new model for evaluating the spatial patterns of ecosystem vulnerability changes in the TRSR. The change intensity (CI) of ecosystem vulnerability was also analyzed. The results showed that the established evaluation model was effective and the ecosystem vulnerability in the whole study area was intensive. During the study period of 2001–2011, there was a slight degradation in the eco-environmental quality. The Yellow River source region had the best eco-environmental quality, while the Yangtze River source region had the worst one. In addition, the zones dominated by deserts were the most severely deteriorated areas and the eco-environmental quality of the zones occupied by evergreen coniferous forests showed a better change. Furthermore, the larger the change rates of the climate factors (accumulative temperature of ≥10°C and annual average precipitation) are, the more intensive the CI of ecosystem vulnerability is. This study would provide a scientific basis for the eco-environmental protection and restoration in the TRSR.

Analysis of the carbon dioxide concentration in the lowest atmospheric layers and the factors affecting China based on satellite observations

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas contributing to global climate change. SCIAMACHY on board ENVISAT (launched in 2002) is the first satellite instrument to monitor the changes in CO2 concentration in the lowest atmospheric layers. The temporal and spatial distribution of CO2 (2003–2009) concentration based on SCIAMACHY over China is presented and discussed. It shows an annual increase and a seasonal cycle. The CO2 annual growth rate was about 1.8 ppm year−1, with the highest value being in spring and the lowest in autumn. The CO2 concentration variation is determined by many complex factors. In this article, we analyse the important factors affecting CO2 variations, with special emphasis on terrestrial ecosystems and energy consumption. Terrestrial ecosystems are an important sink in the global carbon cycle. The relationship between CO2 concentration and Moderate Resolution Imaging Spectroradiometer (MODIS) net primary production (NPP) in 2008 is analysed. CO2 concentration is inversely proportional to NPP both in regions with high-density vegetation and in deserts. The Yunnan province has the highest NPP value and the lowest CO2 concentration, whereas the Takla Makan Desert has the lowest NPP value and the highest CO2 concentration. Energy consumption is the main emission source of atmospheric CO2. CO2 emissions from energy consumption show a steady increase in China since 1980. Chinas CO2 concentration variation shows a high correlation with energy consumption (coefficient of determination (R 2) > 0.8). The regions with high energy consumption are major industrial regions such as Shandong, Guangdong, Jiangsu, Zhejiang, Hebei, and Henan.

Determination of chlorophyll a concentration changes in Taihu Lake, China using multi-temporal MODIS image data

The concentration of chlorophyll a (chl-a) are widely used to indicate trophic state of inland lakes. Using Taihu Lake, China as a case study and MODIS images as data resources, this paper focuses on developing and applying MODIS algorithms to detect the changes of chlorophyll a concentration in Taihu Lake. Field survey data and MODIS images are acquired in different seasons. Using linear regression and nonlinear regression, semi-empirical models for these seasons are developed respectively by relating field data to different MODIS band combinations. And then, the changes of chl-a concentration in different seasons are determined and evaluated by applying these models to corresponding MODIS images. The study result shows that multi-temporal MODIS image data is suitable to monitor such change. Its high temporal resolution is advantaged in this research.

Water quality monitoring using hyperspectral remote sensing data in Taihu Lake China

Although designed for demonstration purpose on land applications, Hyperion data are being tested for its capabilities on monitoring water quality over high turbid inland water target- Lake Taihu, in East China. The field survey was coincident with EO-1 overpasses, retrieving the concentrations of chlorophyll a (chl-a), suspended sediment (ss). In order to determine the optimal bands and algorithms for the retrieval of the optically active substances, this paper mainly focus on the correlation analysis between the concentrations of the chl-a and suspended sediments and the Hyperion reflectance in three remote sensing algorithms (band ratio (r1/r2), band difference (r1-r2) and NDVI algorithm (r1-r2)/ (r1+r2)). The statistical results on the optimal band determination show that the band difference technique between R(732.07~884.7) and R(1174.77~1194.97) has stronger correlation with the suspended sediments(r>0.70,n=25), while the NDVI algorithm between R(620.15~691.37) and R(721.9~844)has stronger correlation with chlorophyll a (r>0.90,n=25). The ss inversion model was established using the reflectance difference of (R874.53-R1184.87), which has the strongest correlation(r=0.79,n=25) with the suspended sediments (r2=0.65), and the chl-a inversion model was established using the algorithm of(R620.15-R732.07) / (R620.15+R732.07) , which has the strongest correlation(r=0.90,n=25) with the chlorophyll a(r2=0.89). The research on the optimal algorithms determination using the Hyperspectral remote sensing data will facilitate the water quality monitoring using the multi-spectral remote sensing data, like TM, MODIS, etc.

Water quality monitoring in Taihu Lake using MODIS image data

Remote sensing technique has been widely applied in water quality monitoring, since it can provide both spatial and temporal information needed to detect the changes of water quality. However, inland water monitoring using remote sensing technique is still experimental, and its development depends on improved remote sensors with higher spectral and spatial resolution. The purpose of this paper is to apply MODIS image data to inland lake water quality monitoring, and then to provide a MODIS-based procedure for regional inland water quality monitoring. After the correlation analysis between band combinations and water parameters such as chlorophyll-a and suspended sediment, we proposed an empirical algorithm based on certain MODIS bands. The study showed that MODIS image data were useful for water quality monitoring in Taihu Lake.

Remote chlorophyll-a retrieval in eutrophic inland waters by concentration classification Taihu Lake case study

In order to improve the precision of phytoplankton chlorophyll-a (chla) concentration retrieval, this study classified the data into two groups (the high and the low) by chla concentration with the threshold of 50μg·L-1. And then build the statistical models for each group. Particularly, a modifying factor OSS/TSS was used to unmixing the spectra in the low model to improve the low relationship between spectral reflectance and chla concentrations. As a result, the concentration classification model allowed estimation of chla with a root mean square error (RMSE) of 21.12μg·L-1 and the determination coefficient (R2) was 0.92, comparing with RMSE of chla estimation was 35.72μg·L-1 and R2=0.72 in the traditional model. It shows that concentration classification is a helpful method for accurate remote chla retrieval in eutrophic inland waters.

Using Unmixing Method to Retrieve the Concentration of Chl-a in Lake Tai

Remote sensing technique has been widely applied in Water quality monitoring, since it can provide both spatial and temporal information needed to detect the changes of water quality. However, inland water monitoring using remote sensing technique is still experimental, and its development depends on improved remote sensors with higher spectral and spatial resolution. The purpose of this paper is to apply TM image data to inland lake water quality monitoring, and then to provide a TM-based procedure for regional inland water quality monitoring. After The correlation analysis between band combinations and water parameter that is chlorophyll-a, we proposed an unmixing algorithm based on certain TM bands. The study showed that TM image data were useful for water quality monitoring in Lake Tai. Keyword—water quality monitoring, Unmixing, TM image data

Uncertainty Analysis of Flood Disaster Assessment using Remote Sensing Data

Flooding is a paroxysmal natural disaster, which results in vast damage to economy in China every year. Precise loss assessment is an effective method to alleviate the loss. The inundated area, on which loss evaluation is based, is the most fundamental element to evaluate the loss caused by floods, and is a critical step to control the precision of the loss evaluation. This study focuses on several factors which are crucial to the automatic extraction of inundated areas in the process of disaster monitoring, and performs the test and precision appraisal of the remote sensing monitoring process aiming at the same study area and the same disaster event, and promotes the precision and credibility of remote sensing monitoring. It has been concluded that spatial resolution, ground scale, and boundary complexity of inundated area are three important factors that cause the uncertainty of the flood loss assessment using remotely sensed data.