ABSTRACT:1 Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. However, the diversity and complexity of buildings make building extraction methods still face challenges in terms of accuracy, efficiency, and so on. In this study, a new building extraction framework based on MBI and combined with image segmentation techniques, spectral constraint, shadow constraint, and shape constraint is proposed. In order to verify the proposed method, worldview-2, GF-2, GF-1 remote sensing images covered Xiamen Software Park were used for building extraction experiments. Experimental results indicate that the proposed method improve the original MBI significantly, and the correct rate is over 86%. Furthermore, the proposed framework reduces the false alarms by 42% on average compared to the performance of the original MBI.
ABSTRACT:Soil erosion is one of major environment problems in the world, and China is one of the most serious soil erosion country. In this paper, Fujian province was used as a study area for its typical red soil region. Based on USLE model, the soil erosion modulus in 1990 and 2015 were calculated and turned to soil erosion intensity. The soil erosion distribution trend in Fujian province was decrease from south-east coastal zone to north-west inland region. In soil erosion areas, the main erosion type was light level with about 80%, and the soil erosion levels above serious type were mainly sporadic distribution with less than 10%. The soil erosion improved for the past 25 years. The areas of different erosion types all decreased, and the total erosion area reduced by 26.59%. The improvement area mainly located in north-east, south and west region. The aggravation area mainly located in the north and some middle hilly regions. The impact of human activities is more significant for erosion control.
ABSTRACT:Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the splitwindow algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province.
ABSTRACT:Identification of clouds in optical images is often a necessary step toward their use. However, aimed at the cloud detection methods used on GF-1 is relatively less. In order to meet the requirement of accurate cloud detection in GF-1 WFV imagery, a new method based on the combination of band operation and spatial texture feature(BOTF) is proposed in this paper. First of all, the BOTF algorithm minimize interference of highlight surface and cloud regions by the band operation, and then distinguish between cloud area and non-cloud area with spatial texture feature. Finally, the cloud mask can be acquired by threshold segmentation method. The method was validated using scenes. The results indicate that the BOTF performs well under normal conditions, and the average overall accuracy of BOTF cloud detection is better than 90%. The proposed method can meet the needs of routine work.* Liangliang Jia, male, postgraduate, mainly engaged in remote sensing research on natural resources and the environment.
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