Grassland fire has the characteristics of fierce fire and rapid spreading, and many fires occur in sparsely populated places. Satellite remote sensing has the characteristics of fast imaging period and wide coverage, and plays an important role in the rapid monitoring and evaluation of grassland fire. FY-3 satellite has been widely used since its launch in September 2008, and this paper uses the fire information of Gansu grassland from 2011 to 2016, based on the more mature MODIS and NOAA-AVHRR fire identification method. The results show that the accuracy of FY-3/VIRR satellite data fire detection are higher than that of NOAA-AVHRR satellite, and the accuracy of FY-3/VIRR satellite data is described. There is a greater improvement, the ability to identify slightly worse than the MODIS satellite, the region is relatively large fire detection accuracy is higher.
Based on the Environmental Mitigation Satellite (HJ-1) data, this paper has carried on the remote sensing monitoring to change of the surrounding vegetation and water area of the Qingtu Lake since 2009. The result shows that the average area of water has increased by 3.59 square kilometres annually since the reappearance of the waters with the Qingtu Lake in 2010. The area of Qingtu Lake and surrounding vegetation cover has presented an average increase of 1.09 square kilometres per year. Since 2010, the precipitation of the Qingtu Lake and its surrounding area in Minqin county have a significant increase in the trend, the average increase rate of 6.0 mm/year. Compared to 2010 years ago, the average precipitation increased 36.4 mm. And it shows that the change of the Qingtu Lake underlying surface has a positive feedback effect to local heavy rainfall according to the comparative analysis of the precipitation observation in the surrounding weather station.
Based on GIS and remote sensing technology, this paper estimates the NPP of the 2015 year-round and every season of Gansu province in northwest China by using the CASA(Carnegie Ames Stanford Approach) light energy utilization model. The result shows that the total annual NPP of Gansu province gradually decline from southeast to northwest in the space, which is in accordance with the water and heat condition in Gansu province. The results show that the summer NPP in Gansu Province is the maximum in each season. The maximum value of summer NPP in Gansu Province reached 695 (gCm-2•season-1), and the maximum value was 473 in spring, and 288 in the autumn, and the NPP in the winter in Gansu province were under 60. The fluctuation range of NPP value is large, this is due to the diversity of ecosystem types in Gansu province, including desert, grassland, farmland and forest, among them, the grassland area is the largest, and the grassland type is very diverse, the grassland coverage is obviously different, especially the low coverage grassland growth is affected by precipitation and temperature and other meteorological factors obviously.
Fine particulate matter (aerodynamic diameters of less than 2.5 μm, PM2.5) air pollution has become one of the global environmental problem, endangering the existence of residents living, climate, and public health. Estimation Particulate Matter (aerodynamic diameters of less than 10 μm, PM10) concentration and aerosol absorption was the key point in air quality and climate studies. In this study, we retrieve the Aerosol Optical Depth (AOD) from the Earth Observing System (EOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), and PM2.5, PM10 in winter on 2014 and 2015, using Extended Dense Dark Vegetation Algorithm and 6S radiation model to analysis the correlation. The result showed that at the condition of non-considering the influence of primary pollutants, the correlation of two Polynomials between aerosol optical depth and PM2.5 and PM10 was poor; taking the influence of the primary pollutants into consideration, the aerosol optical depth has a good correlation with PM2.5 and PM10. The version of PM10 by aerosol optical depth is higher than that of PM2.5, so the model can be used to realize the high precision inversion of winter PM10 in Lanzhou.
Medium Resolution Spectral Imager (MERSI) on board China's new generation polar orbit meteorological satellite FY- 3A provides a new data source for snow monitoring in large area. As a case study, the typical snow cover of Qilian Mountains in northwest China was selected in this paper to develop the algorithm to map snow cover using FY- 3A/MERSI. By analyzing the spectral response characteristics of snow and other surface elements, as well as each channel image quality on FY-3A/MERSI, the widely used Normalized Difference Snow Index (NDSI) was defined to be computed from channel 2 and channel 7 for this satellite data. Basing on NDSI, a tree-structure prototype version of snow identification model was proposed, including five newly-built multi-spectral indexes to remove those pixels such as forest, cloud shadow, water, lake ice, sand (salty land), or cloud that are usually confused with snow step by step, especially, a snow/cloud discrimination index was proposed to eliminate cloud, apart from use of cloud mask product in advance. Furthermore, land cover land use (LULC) image has been adopted as auxiliary dataset to adjust the corresponding LULC NDSI threshold constraints for snow final determination and optimization. This model is composed as the core of FY-3A/MERSI snow cover mapping flowchart, to produce daily snow map at 250m spatial resolution, and statistics can be generated on the extent and persistence of snow cover in each pixel for time series maps. Preliminary validation activities of our snow identification model have been undertaken. Comparisons of the 104 FY- 3A/MERSI snow cover maps in 2010-2011 snow season with snow depth records from 16 meteorological stations in Qilian Mountains region, the sunny snow cover had an absolute accuracy of 92.8%. Results of the comparison with the snow cover identified from 6 Terra/MODIS scenes showed that they had consistent pixels about 85%. When the two satellite resultant snow cover maps compared with the 6 supervise-classified and expert-verified snow cover maps derived from integrated MERSI and MODIS images, we found FY-3A/MERSI has higher accuracy and stability not only for nearly cloud-free scenes but also the cloud scenes, namely, FY-3A/MERSI data can objectively reflect finer spatial distribution of snow and its dynamic development process, and the snow identification model perform better in snow/cloud discrimination. However, the ability of the FY-3A/MERSI model to discriminate thin snow and thin cloud need to be refined. And the limitation, error sources of FY-3A/MERSI snow products would be assessed based on the accumulation of large amounts of data in the future.