Land use and land cover (LULC) datasets for Beijing in 1978, 1987, 1992, 2000 and 2010 were developed from Landsat images using the object-oriented classification approach. The relationships between social-economic, demographic and political factors and time-series LULC data were examined for the periods between 1978 and 2010. The results showed the effectiveness of using the object-oriented de...

Land use/land cover (LULC) classification is a key research field in environmental applications of remote sensing on the earth’s surface. The advent of new high resolution multispectral sensors with unique bands has provided an opportunity to map the spatial distribution of detailed LULC classes over a large fragmented area. The objectives of the present study were: (1) to map LULC classes usin...

We quantified potential biases associated with lakes monitored using non-probability based sampling by six state agencies in the USA (Michigan, Wisconsin, Iowa, Ohio, Maine, and New Hampshire). To identify biases, we compared state-monitored lakes to a census population of lakes derived from the National Hydrography Dataset. We then estimated the probability of lakes being sampled using general...

After decades of accomplishments and faced with new technological and scientific insights, the field of land use and land cover (LULC) is seemingly at a crossroads for effective and open uses of data. The use of categorical LULC data in computer-based land analysis poses a significant challenge because it usually leads to a binary treatment of the information in subsequent analysis. Still, LULC...

Based on land use and land cover (LULC) datasets in the late 1970s, the early 1990s, 2004 and 2012, we analyzed characteristics of LULC change in the headwaters of the Yangtze River and Yellow River over the past 30 years contrastively, using the transition matrix and LULC change index. The results showed that, in 2012, the LULC in the headwaters of the Yellow River were different compared to t...

Received Dec 9, 2016 Revised Mar 27, 2017 Accepted Apr 11, 2017 The accurate land use land cover (LULC) classifications from satellite imagery are prominent for land use planning, climatic change detection and eco-environment monitoring. This paper investigates the accuracy and reliability of Support Vector Machine (SVM) classifier for classifying multispectral image of Hyderabad and its surrou...

Hydrological status and water table fluctuations are directly related to land use and/or land cover (LULC) changes in each area. In this research, the impact of LULC changes on groundwater quantity and quality of Khan-Mirza Plain, in the northern Karun watersheds, was investigated. For this purpose, Landsat 5, 7 and 8 satellite images and ETM and OLI sensors were employed to prepare the L...

Web-based applications that integrate geospatial information, or the geoweb, offer exciting opportunities for remote sensing science. One such application is a Web-based system for automating the collection of reference data for producing and verifying the accuracy of land-use/land-cover (LULC) maps derived from satellite imagery. Here we describe the capabilities and technical components of th...

during the time, land cover and as a result land use patterns go through fundamental changes and human factors can have the most important role in this process. knowing these changes could be of great help to the future management of this region. according to this, the main purpose of this study is to monitor the land use-cover changes in the hamedan province area during the three recent decade...

Urban growth and the development of urban plans make cities grow and substantially alter, in relatively short time periods, their land covers and land uses (LULC). To take control of this urban growth, it is important to create and update the LULC database. In this work, a method to automatically extract land covers from satellite VHR imagery and LIDAR data is presented. This method is based on...