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...

Land use/land cover (LULC) change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect ground reference data annually, as the classifiers have to be trained individually in each corresponding year. This study presented a novel strategy ...

Two landuse grids were available for these catchments; one in 1995 was derived from satellite imagery and contained a relatively undifferentiated landuse classification (lumping all forest types into one category); the other in 2003 was derived from a mix of satellite imagery, ground truthing, and mapping, and better differentiated between landuse types (dividing forest types into evergreen, de...

Utility services are an important component of the physical structure of towns and there is a need for detailed information about the location and condition of their infrastructure. Acquiring data in the conventional way is time consuming and costly. The integration of GIS with electric utilities is tremendously improving the planning and operation of the system. GIS and GPS are also integrated...

When using microwave remote sensing for land use/land cover (LULC) classifications, there are a wide variety of imaging parameters to choose from, such as wavelength, imaging mode, incidence angle, spatial resolution, and coverage. There is still a need for further study of the combination, comparison, and quantification of the potential of multiple diverse radar images for LULC classifications...

Land use and land cover (LULC) patterns play an important role in the establishment and spread of invasive plants. Understanding LULC changes is useful for early detection and management of land-use change to reduce the spread of invasive species. The primary objective of this study is to analyze and predict LULC changes in Connecticut. LULC maps for 1996, 2001 and 2006 were selected to analyze...

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...

Landuse change in metropolitan areas is largely focused on the dynamic nature of urban landuse change. In this research, a spatial statistical model was used to support decision-making with regard to urban growth predictions in the urban fringe of Beijing, China. The model adopted in this study was based on the integration of remote sensing, geographical information systems, and multivariate ma...

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...