Shixin Wang

Monitoring of forage conditions with MODIS imagery in the Xilingol steppe, Inner Mongolia

A study was conducted to determine the potential suitability of Terra/MODIS imagery for monitoring short‐term phenological changes in forage conditions in a semi‐arid region. The study sites included four meadow steppes and six typical steppes in the Xilingol steppe in central Inner Mongolia, China. The live biomass, dead standing biomass, total biomass, crude protein (CP) concentration and standing CP were estimated from early April to late October using the Enhanced Vegetation Index (EVI) values from Terra imagery (500 m pixels). Applying regression models, the EVI accounted for 80% of the variation in live biomass, 42% of the dead biomass, 77% of the total biomass, 11% of the CP concentration and 74% of the standing CP. MODIS/EVI is superior to AVHRR/NDVI when estimating forage quantity. Applying these results, the seasonal changes in live biomass and the standing CP could be described in the selected four sites with different degrees of grazing intensity. Generally, the increase in grazing intensity tended to decrease live biomass and standing CP. It was suggested that the EVI obtained from Terra imagery was an available predictor of the forage condition as measured by live biomass and standing CP. The MODIS/EVI values could provide information on the suitable timing of cutting for hay‐making and nutritive value to range managers.

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.

A new index for mapping built-up and bare land areas from Landsat-8 OLI data

Remote sensing is a useful technology for monitoring the spatial distribution and expansion of built-up and bare land areas. One effective approach, known as the normalized difference built-up index (NDBI), has been promoted for identifying built-up areas based on Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+) data. The successful launch of the Landsat-8 satellite has made possible the continued acquisition of high-quality data that meet requirements for observing land use and land cover (LULC), and whether or not NDBI approach can be used with Landsat-8 data needs further verification. In this study, we researched the reflectance spectral characteristics of different land cover types in different bands of Landsat-8 operational land imager (OLI) data and found that the trend of some built-up areas in the OLI data from the near-infrared band to the shortwave infrared band is different from that in the TM/ETM+ data. This different trend made the conventional NDBI approach unsuitable for Landsat-8 OLI data. We propose a new index, called the build-up and bare land areas index, for transforming Landsat-8 OLI data to map built-up and bare land areas automatically. This new index was used to detect the built-up and bare land areas in Zhengzhou (Henan, China). The accuracy assessment indicates that our index has much higher accuracy (90.8%) than the conventional NDBI approach does (57.4%).

An estimation method of soil wind erosion in Inner Mongolia of China based on geographic information system and remote sensing

Studies of wind erosion based on Geographic Information System (GIS) and Remote Sensing (RS) have not attracted sufficient attention because they are limited by natural and scientific factors. Few studies have been conducted to estimate the intensity of large-scale wind erosion in Inner Mongolia, China. In the present study, a new model based on five factors including the number of snow cover days, soil erodibility, aridity, vegetation index and wind field intensity was developed to quantitatively estimate the amount of wind erosion. The results showed that wind erosion widely existed in Inner Mongolia. It covers an area of approximately 90×104 km2, accounting for 80% of the study region. During 1985–2011, wind erosion has aggravated over the entire region of Inner Mongolia, which was indicated by enlarged zones of erosion at severe, intensive and mild levels. In Inner Mongolia, a distinct spatial differentiation of wind erosion intensity was noted. The distribution of change intensity exhibited a downward trend that decreased from severe increase in the southwest to mild decrease in the northeast of the region. Zones occupied by barren land or sparse vegetation showed the most severe erosion, followed by land occupied by open shrubbery. Grasslands would have the most dramatic potential for changes in the future because these areas showed the largest fluctuation range of change intensity. In addition, a significantly negative relation was noted between change intensity and land slope. The relation between soil type and change intensity differed with the content of CaCO3 and the surface composition of sandy, loamy and clayey soils with particle sizes of 0–1 cm. The results have certain significance for understanding the mechanism and change process of wind erosion that has occurred during the study period. Therefore, the present study can provide a scientific basis for the prevention and treatment of wind erosion in Inner Mongolia.

A research on fire automatic recognition using MODIS data

Fire automatic recognition is the key of increasing the response-speed in fire detection using Remote Sensing. Daxinganling region in China lies in the transition zone from the forest to the meadow-steppe, in which fires happen frequently. In this study, Daxinganling was selected as the experiment region and a fire automatic recognition model applicable to regions in China was developed based on the fire algorithm proposed by the MODIS fire team. The fire automatic recognition model and the results of its applications were discussed in this paper.

Regionalization and Spatiotemporal Variation of Drought in China Based on Standardized Precipitation Evapotranspiration Index (1961–2013)

China is considered to be one of the most drought prone countries. This study is dedicated to analyzing the regionalization and spatiotemporal variations of drought based on the Standardized Precipitation Evapotranspiration Index, which covers the period 1961–2013 across 810 stations in China. Using Spatial “K”luster Analysis by Tree Edge Removal method, China was divided into eight regions: southwest (SW), northeast (NE), north (N), southeast (SE), Yangtze River (YR), northwest (NW), central China (C), and Tibet Plateau (TP). The spatiotemporal variations of drought characteristics indicated that the drought count in NE and C was generally high. Southern China and NW had suffered long drought duration and extreme severity. The MK test results show that stations with significant drying trends mainly locate in SW, N, NW, and C. The severe drought frequency was very high in 1990s and 2000s. Furthermore, more attention should be paid to abnormal less precipitation in summer and abnormal high temperature in spring in SW, NE, N, and C. Besides, abnormal less precipitation is the main factor of drought in SE and YR in whole year. This study is anticipated to support the water resources management, and to promote the realization of environmental protection and agricultural production.

Early warning for grassland fire danger in north China using remote sensing

Grassland fires do a lot of economic and environmental damages and even raise forest fires, which causes more losses. Grasslands in north China mainly distribute in arid and semi-arid areas, fires happened frequently there. Grassland fires can be largely eliminated by early warning technology. This paper presents a multifactor Grassland Fire Danger Index (GFDI) of north China, which provides early warning for grassland fire occurrence and behavior. The GFDI was developed using remotely sensed images and weather station data. Seven basic indicators, relative humidity, temperature, wind velocity, precipitation, degree of grassland curing, fuel weight and grassland continuity were selected to calculate the GFDI. According to its value, GFDI was classified to 5 levels: low, moderate, high, very high and extreme.

An estimate of the city population in China using DMSP night-time satellite imagery

The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) has a unique capability to detect the nocturnal observation of artificial lighting which reflect the spatial distribution of human population. However, most previous studies have focused on the developed countries. The objective of this article is to explore if the models of population derived from DMSP OLS data have the potential to map the size and spatial distribution of the human population in China. After the correlation analysis between the population density and light intensity in county scale, we proposed an empirical algorithm based on night-light images. The estimated population density in each county mainly agreed with the census data. This result showed that there was signification linear relationship between the two indexes we have analyzed. It has proven the DMSP night-time images were a valuable data source for regional studies, modeling, and planning activities, especially for regions where current data were lacking.

Monitoring and spatio-temporal evolution researching on vegetation leaf water in China

Water plays an important role in the growth of vegetation. The research on vegetation water content is necessary to monitor the degree of drought, flood and ecological security. When we observe the large area and need rapidly response, remote sensing has advantage in monitoring and estimating the vegetation water content. NOAA-AVHRR data is usually used by original process of estimating on vegetation water content. Contrast with the former, MODIS is preferred in moderate spatial resolution (250 m, 500 m, 1000 m), moderate spectral resolution (36 spectral bands) and appropriate temporal resolution (national-wide image can be updated in about 2 days). This paper describes the process of retrieving vegetation leaf water based on the GVMI model using the MODIS data, verifies the relation between vegetation water content and index of drought

Fine-Scale Population Estimation by 3D Reconstruction of Urban Residential Buildings

Fine-scale population estimation is essential in emergency response and epidemiological applications as well as urban planning and management. However, representing populations in heterogeneous urban regions with a finer resolution is a challenge. This study aims to obtain fine-scale population distribution based on 3D reconstruction of urban residential buildings with morphological operations using optical high-resolution (HR) images from the Chinese No. 3 Resources Satellite (ZY-3). Specifically, the research area was first divided into three categories when dasymetric mapping was taken into consideration. The results demonstrate that the morphological building index (MBI) yielded better results than built-up presence index (PanTex) in building detection, and the morphological shadow index (MSI) outperformed color invariant indices (CIIT) in shadow extraction and height retrieval. Building extraction and height retrieval were then combined to reconstruct 3D models and to estimate population. Final results show that this approach is effective in fine-scale population estimation, with a mean relative error of 16.46% and an overall Relative Total Absolute Error (RATE) of 0.158. This study gives significant insights into fine-scale population estimation in complicated urban landscapes, when detailed 3D information of buildings is unavailable.