Application of GF-4 satellite in drought remote sensing monitoring: A case study of Southeastern Inner Mongolia

被引:0
|
作者
Nie J. [1 ]
Deng L. [2 ]
Hao X. [2 ]
Liu M. [1 ]
He Y. [2 ]
机构
[1] National Disaster Reduction Center of the Minisby of Civil Affairs, Beijing
[2] Department of Environment and Tourism, Capital Normal University, Beijing
来源
关键词
Drought remote sensing monitoring; GF-1; satellite; GF-4; Inner mongolia; NDVI;
D O I
10.11834/jrs.20187067
中图分类号
学科分类号
摘要
GF-4 is a geostationary satellite in the significant special space-based system of the National High-Resolution Earth Observation System. The GF-4 satellite has visible and mid-infrared bands with resolutions of 50 and 400 m, respectively. The satellite can be used to observe China and its surrounding areas and obtain patterns from continuous observation of areas with a width of 400 km by command control and with a revisit period of up to 20 s. The GF-4 satellite possesses higher spatial and temporal resolutions and achieves a more detailed observation of the Earth's surface compared with MODIS sensors. Since the beginning of summer in 2016, Inner Mongolia, the study area, has been experiencing continuous high temperatures and rare rainfall, with its precipitation in early August being less than 10 mm, which is 80% to 100% less than normal and the lowest recorded value in history. The application of the GF-4 satellite in the rapid monitoring of large-area drought was discussed in this study. The degree of drought in the study area was evaluated. According to the collected data, no large-scale drought occurred in Inner Mongolia in 2013. During this period, vegetation grew well, and this growth was close to that in a normal year. The severe drought that occurred in Bahrain Zuoqi and Bahrain Youqi areas in Inner Mongolia Autonomous Region in 2016 was regarded as an example, and GF-4 and GF-1 data on August 14, 2016, and GF-1 data on August 10, 2013, were selected. First, the NDVI of images was calculated. Second, a quantitative analysis of GF-4 and GF-1 NDVI was conducted, and the transformation equation was obtained to eliminate the difference between different sensors. Third, the difference between the converted NDVI and normal-year GF-1 NDVI was calculated and compared with the drought distribution through the MODIS anomaly vegetation index in the same period. Finally, the drought distribution and drought degree of NDVI were analyzed. Linear transformation of the NDVI values of GF-1 and GF-4 sensors was conducted using a linear equation. According to the drought distribution in the study area in 2016, severe-drought areas were mainly distributed in the northwest part of the study area and partly distributed in the central plains. Among the drought areas, grassland was the most severe. The northeastern plains of cultivated land looked pale blue. Growth was slightly better than that in 2013, and the disaster situation was unobvious. The drought distribution trend of the GF-4 images was consistent with the drought distribution trend of MODIS NDVI, and the extent of the disaster was approximately similar. GF-4 satellite data were combined with GF-1 satellite data to monitor and analyze several arid regions in Inner Mongolia. Results show that GF-4 satellite data can be utilized to observe large areas. The satellite allows for long-term continuous observation of a region, provides real-time and continuous massive data for drought monitoring, and is important in improving emergency response capabilities. © 2018, Science Press. All right reserved.
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页码:400 / 407
页数:7
相关论文
共 17 条
  • [1] Bai Z.G., The technology characteristics of GF-1 satellite, Aerospace China, 8, pp. 5-9, (2013)
  • [2] Chen X., Liu Z.H., Quantitative analysis of relationship between HJ-1 NDVI and MODIS NDVI, Remote Sensing Information, 30, 4, pp. 85-90, (2015)
  • [3] Fan X.W., Study on the quantitative relationship of multi-source remote sensing images and the uniformity of the normalized difference vegetation index, (2015)
  • [4] Guyot G., Gu X.F., Effect of radiometric corrections on NDVI-determined from SPOT-HRV and Landsat-TM data, Remote Sensing of Environment, 49, 3, pp. 169-180, (1994)
  • [5] Hill J., Aifadopoulou D., Comparative analysis of landsat-5 TM and SPOT HRV-1 data for use in multiple sensor approaches, Remote Sensing of Environment, 34, 1, pp. 55-70, (1990)
  • [6] Ji Q.C., The Adaptation Research on Spatiotemporal Patterns of Remote Sensing Drought Index, (2011)
  • [7] Li Z.N., Chen Z.X., Remote sensing indicators for crop growth monitoring at different scales, Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4062-4065, (2011)
  • [8] Liu A.X., Wang C.Y., Liu Z.J., Nui Z., Cotton information extraction and growth monitoring in arid area based on RS and GIS, Geography and Geo-Information Science, 19, 4, pp. 101-104, (2003)
  • [9] Mao X.S., Zhang Y.Q., Shen Y.J., Effects of water stress in different stages on winter wheat vegetation index and NDVI's dynamic, Agricultural Research in the Arid Areas, 20, 1, pp. 69-71, (2002)
  • [10] Miura T., Huete A., Yoshioka H., An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data, Remote Sensing of Environment, 100, 2, pp. 223-236, (2006)