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Daily maximum temperature data during the rice heading-flowering stage(July-August) were obtained from the meteorological station for the period between 2000-2018, and the high-temperature temporal characteristics in Nanchong City were extracted using an accumulative anomaly method. The multi-year cumulative damage index of rice from heat injury was calculated in 17 stations within or around the Nanchong City, and the spatial characteristics of extremely high temperatures in Nanchong City were obtained using the ANUSPLIN interpolation software. The grade of each high-temperature disaster that occurred in rice areas of Nanchong City was eventually identified by combining the rice planting range, population distribution, and high-temperature disaster analysis model. The results showed that the year 2006 exhibited the highest intensity of extremely high temperature occurrences, which was followed by 2017; further, the number of days with extremely high temperatures was clearly higher than that observed in a year on average. The year 2010 represented the turning point of temperature change in Nanchong City; this is because the average daily maximum temperature between 2010 and 2018 was significantly higher than in the period between 2000-2010. It could be expected that the frequency and intensity of extremely high temperature occurrences would continue to increase in the near future. Peng'an and Yingshan were the two counties that were most frequently affected by extremely high temperatures, which were followed by the Gaoping District, Nanbu County, and Xichong County. The areas with high risk of heat damage were mostly distributed in the Gaoping and Yingshan counties; the areas with intermediate risk were widely distributed in all the counties, while low-risk areas were mainly distributed in the hilly regions situated toward the northwest of Nanbu, north of Langzhong, and northeast of Yingshan. Identifying high-temperature disaster areas can provide scientific references to the government for achieving a more organized agricultural sector and transforming urban areas.

Mangrove wetland has become the important and hot object for wetland research in recent years. Because remote sensing technique has applied gradually to the survey of mangrove resources, there is important realistic and theoretical significance for the remote sensing identification research of mangrove. This paper introduces the source of data of the mangrove remote sensing recognition technology processing, classification method and feature extraction.We also analyze the existings weakness, finally we put forward some related suggestions and forecast the future.

With the intensification of human activities and unreasonable development and utilization of land resources, there have been a series ecological problems of land resources in Hubei Province. Based on the Press-State-Response conceptual framework model, this paper developed to build the ecological security assessment index system of the land resource which was regional characteristics, by the use of component analysis to determine the weight, the use of land resources ecological security comprehensive model, the land ecological security index of Hubei Province from 2000 to 2011 were calculated. The results showed that comprehensive land resources ecological security index increased from 0.646 to 0.7719 during 2000–2011, the land of ecological security was in a “risk stage” period in 2000, while in a “good level” in 2011. Although the ecological security assessment status was improved annually, the land ecological pressure was still seriously, per capita arable land, the natural population growth rate, population density, fertilizer application rate and other factors caused some damage to the ecological system, which need us to throw out some suggestions based on these factors. To reduce the pressure on the land resources and to enhance the ecological security of land resources, is to control population size and to improve the quality of existing population.

Urban heat island effect refers to the phenomenon of urban temperatures being higher than the suburban. relation of the city land use and the urban heat island effect has gradually become an important environmental problem that arousing general concern. Remote sensed data has been an effective measure monitoring and analyzing the land use change and the urban heat island effect. This paper adopted the maximum likelihood classification to divide the Guangzhou City land use types into cultivated land, forest, water, bare land and built-up using Landsat TM data 0f 1990, 1998 and 2005; The city heat island region was indicated by the urban brightness temperature, furthermore, the urban brightness temperature was retrieved by the band 6 of Landsat TM, the ground brightness temperature map was achieved. Based on GIS, this paper analyzed the relation of the urban heat island effect and the land use types in Guangzhou City. The aim of this paper is to provide the basic information for improving environmental quality and the rational land use pattern in Guangzhou City.

With rapid urban growth in recent years, it becomes an important research topic for us to understand urban biophysical composition and dynamics. Remote sensing technologies introduce a potentially scientific basis for examining urban composition and monitoring its changes over time. The vegetation-impervious surface-soil-water (V-I-S-W) model, in particular, provides a foundation for describing urban/suburban environments and a basis for further urban analyses including urban growth modeling, environmental impact analysis, and socioeconomic factor estimation. This paper develops a standardized ratio model (SRM) method to examine urban composition especially urban impervious surface in Haizhu District using Landsat ETM+ data. In particular, a brightness SRM method is applied to reduce brightness variation. Through this standardization, brightness variability within each V-I-S-W component is reduced or eliminated, thus allowing a single end-member representing each component. Further, with the standardized image, four endmembers, vegetation, impervious surface, soil, and water, are chosen to model heterogeneous urban composition using a constrained spectral mixture analysis (SMA) models. The accuracy of impervious surface estimation is assessed and compared with other existing model. Results indicate that the proposed model is a better alternative to existing models, with a root mean square error (RMSE) of 12.6% for impervious surface estimation in the study area.