ABSTRACT
In the present global situation, when everywhere ecology is degraded due to the extreme exhaustion of natural resources. Therefore spatiotemporal ecological vulnerability analysis is necessary for the current situation for sustainable development with protection of fragile eco-environment. Remote sensing is a unique tool to provide complete and continuous land surface information at different scales, which can use for eco-environment analysis. A methodology constructed on the principal component analysis (PCA) to identify satellite remote sensing ecological index (RSEI) for ecological vulnerability analysis and distribution based on four land surface parameters (dryness, greenness, temperature and moisture) by using Landsat TM/ETM+/OLI/TIRS data in the Samara region Russia. The results were verified by the following four methods: location-based, categorization-based, correlation-based and city center to outwards distance-based comparisons. Results indicate that ecological condition was improved from 2010 to 2015 as RSEI increased from 0.79 to 0.98 and from 2015 to 2020 the ecological condition was degraded as RSEI decreased from 0.98 to 0.82 but overall it was improved in this decade. RSEI distribution curve shows moderate to good and excellent ecological conditions and degraded ecological condition was basically characterized by high human interference and socioeconomic activities in the study area. Such a technique is a baseline for highly accurate ecological conditions mapping, monitoring and can use for decision making, management and sustainable development.
Subject(s)
Ecosystem , Remote Sensing Technology , China , Cities , Environmental Monitoring , Humans , RussiaABSTRACT
This data article contains data related to the research article entitled "Global land cover classification based on microwave polarization and gradient ratio (MPGR)" [1] and "Microwave polarization and gradient ratio (MPGR) for global land surface phenology" [2]. This data article presents land surface characteristics and snow cover variation information from sensors like EOS Advanced Microwave Scanning Radiometer (AMSR-E). This data article use the HDF Explorer, Matlab, and ArcGIS software to process the pixel latitude, longitude, snow water equivalent (SWE), digital elevation model (DEM) and Brightness Temperature (BT) information from AMSR-E satellite data to provide land surface characteristics and snow cover variation data in all-weather condition at any time. This data information is useful to discriminate different land surface cover types and snow cover variation, which is turn, will help to improve monitoring of weather, climate and natural disasters.
ABSTRACT
This data article presents satellite data related to city growth of Singapore, Manila and Kuala Lumpur cities. The data were collected from NASA and USGS websites. A method has been developed for city built-up density from city center to outward till 50 km by using satellite data. These data sets consists three decade Landsat images. A detailed description is given to show how to use this data to produce urban growth maps. The urban growth maps have been used to know the changes and growth pattern in the Southeast Asia Cities.
ABSTRACT
A method has been developed for urbanization by using satellite data and socio-economic data. These datasets consists three decade Landsat images and population data. A detailed description using flow chart is given to show how to use this data to produce land use/cove maps. The land use/cove maps were used to know the urban growth in Samara City, Russia.
