This paper presents the development of index to detect haze from moderate resolution imaging spectroradiometer remote sensing data. Detection of haze over a large area has always been a problem. This study focuses on Beijing, Tianjin, and Shijiazhuang cities in China. These cities have suffered the worst hazy weather in recent years. The spectral influence of haze on surface features was determined through analysis of the spectral variations of surface covers between hazy and haze-free days. A spectral index known as modified normalized difference haze index (m-NDHI) is developed that can be used to monitor haze distribution and intensity. Correlation analysis of the derived m-NDHI and previously developed NDHI with in situPM2.5 (particulate matter with diameter <2.5 μm) data reveals that m-NDHI over water bodies has a coefficient of 0.7096, 0.5864, and 0.4857 and NDHI has coefficient of 0.5625, 0.5321, and 0.4618 with PM2.5 for Beijing, Tianjin, and Shijiazhuang, respectively, in winter. Moreover, the correlation of m-NDHI with PM2.5 is 0.4097, 0.8092, and 0.5546 during the spring, summer, and autumn, respectively, in Beijing. This developed index can be a much easier and more effective method to detect haze in large scales from remotely sensing data and characterize the situation of urban atmospheric pollution.
Grassland cover near Lake Qinghai in western China was mapped into nine percentage classes from a TM-derived
Normalised Difference Bareness Index (NDBI) image based on 178 in situ samples collected within 1 m2 sites. Their
ground coordinates logged with a GPS unit were used to locate their pixel values on the NDBI image. A new method, in
which the in situ samples and their pixel NDBI values were independently ranked prior to the establishment of their
linear regression relationship, was applied to converting the NDBI image into a map of grass coverage. This relationship
enabled the NDBI image to be translated into a map of grassland cover with a meaningful spatial pattern. Assessed
against visually interpreted results, grassland cover was mapped at an overall accuracy of 80%. In order for this method
to generate satisfactory results, image pixel NDBI values have to be normalized so that they have the same standard
deviation as that of the ground samples. This proposed method should be applicable to any grassland where grassland
cover varies subtly at the pixel scale of the image used.
Locust plague is a kind of the world-wide biological calamity to agriculture. In China's history, more than 90% of locust plagues were caused by the oriental migratory locust, Locusta migratoria manilensis (Meyen). At the present time, it is difficult for monitoring and forecasting systems in this country to provide real time information of locust plague outbreak in large area. In order to adopt timely measures for prevention and control of locust outbreak, it is necessary to apply advanced remote sensing technology for monitoring and forecasting locust outbreak This paper introduces a case study on monitoring oriental migratory locust plague with remote sensing technology in 3 pilot sites, namely, Huangzao, Yangguangzhuang, and Tengnan, which were the 3 major locust damaged areas in Huanghua City, Hebei Province, China during the period of large scale oriental migratory locust breakout in 2002. In this study, locust damage intensity, areas with various damage intensities and their distribution in pilot sites are determined by means of comparison between Landsat ETM+ image of locust damaged vegetation on 31st May, 2002 and TM image of healthy vegetation before damage on 23rd May, 2002. Then, information of various locust distribution density in pilot sites is extracted by establishing the Locust Density Index (LDI).
Plant canopy reflectance is affected not only by the optical properties of canopy components, but also by canopy structure. In this paper, the radiative transfer model was used to simulate rice canopy bi-directional reflectance to determine its sensitivity to leaf area index (LAI) and inclination. In simulating canopy bi-directional reflectance over 400-940 nm, LAI was changed from 1 to 7 at an increment of 1; leaf inclination was changed from 50o to 85o at an interval of 5o. All other parameters in the model were measured in the field or deduced from references. It is found that with the rise in LAI, nadir reflectance decreases in visible light but increases in near infrared wavelengths. It tends to become stabilized when LAI is sufficiently large (e.g., >4). Decreasing with leaf inclination, canopy nadir reflectance becomes more sensitive to leaf inclination at a larger LAI. At 550nm and 670nm, bi-directional reflectance decreases with LAI regardless of view zenith. At 830nm, it is proportional to LAI over the view zenith angles of -85o - 40o. However, it is inversely related to LAI when it exceeds 3. Similar to nadir reflectance, bi-directional reflectance tends to become stabilized at a larger LAI at all view zeniths.