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Spectral signature based post classification change detection approach is applied to identify the changes in Brahmaputra river along with sandbars around Kaziranga National Park in Assam, India. Landsat 5 images from 2006 to 2011 are used and the results are compared with the annual rainfall of the region to identify areas that are erosion prone. From the results it is observed that the amount of...
Accurate and high-resolution maps of vegetation are critical for projects seeking to understand the terrestrial ecosystem processes and land-atmosphere interactions in Arctic ecosystems, such as U.S. Department of Energy's Next Generation Ecosystem Experiment (NGEE) Arctic. However, most existing Arctic vegetation maps are at a coarse resolution and with a varying degree of detail and accuracy. Remote...
Tackling Deforestation activity is not an easy task. Many approached on mapping and monitoring the change of forest cover has been actively introduced and yet the deforestation activity is still largely happens. In order to observe the deforestation activity and its natural impact on environment, a new way to serve knowledge is good approach to make more understandable information regarding on how...
The urbanization process has accelerated in the last fifty years, particularly in Latin American cities such as Buenos Aires. In the face of this situation, the local government efforts turn insufficient for planning a proper urban expansion. In this context, remote sensing provides information for studying the process of soil occupation. This paper proposes a processing chain that allows the extraction...
River interventions disturb the natural flow regime of channel. The impacts generated from river interventions is categorized based on the alteration of fluvial system including channel planform and ecology. The Brahmani River, one of the major peninsular river holds a significant importance for three major states of eastern India. This river has been subjected to periodic flooding events damaging...
A method of decreasing the uncertainty in validating moderate resolution remotely sensed Land surface temperature (LST) was established on a heterogeneity surface in Northwest China. Two Landsat-8 images and in situ measured data were used in the paper. The LST and multiband reflectance as well as the reflectance coefficient of variation have a significant regression relation. For two Landsat-8 images,...
The global forest covers over 30% of the Earth's landmass and plays a critical role in a number of global systems including the carbon cycle. Developing methods to track the carbon flow into and out of forests is necessary to gain a complete understanding of the global carbon cycle and, in turn, its effect on the climate. Remote sensing technologies such as satellite based passive optical remote sensing...
The primary objective of the SCMaP is to supply value added information about soils at three levels: 1) the spatial distribution of exposed soils; 2) temporal statistics of those soils; and, 3) a reflectance soil composite map. The SCMaP is designed for temperate climatic regions that comprise areas of extensive crop based agriculture where soils are commonly covered by vegetation. For the SCMaP satellite...
For many applied problems in agricultural monitoring and food security it is important to provide reliable crop classification maps in national or global scale. Large amount of satellite data for large scale crop mapping generate a “Big Data” problem. The main idea of this paper was comparison of pixel-based approaches to crop mapping in Ukraine and exploring efficiency of the Google Earth Engine...
This work presents Land Surface Temperature (LST) retrieval from Landsat-8 data using the Generalized Split-Window (GSW) algorithm. First, radiative transfer modeling experiments were conducted using the moderate spectral resolution atmospheric transmittance algorithm and computer model (MODTRAN) 4.0 fed with SeeBor V5.0 atmospheric profile database to simulate the brightness temperatures in Thermal...
Understanding the impacts of urbanization requires accurate and updatable urban extent maps. Here we present an algorithm for mapping urban extent at global scale using Landsat data. An innovative hierarchical object-based texture (HOTex) classification approach was designed to overcome spectral confusion between urban and nonurban land cover types. VIIRS nightlights data and MODIS vegetation index...
Time series PALSAR-2/ScanSAR data and Landsat data were used for examining the differences in detection timing of deforestation. Optical sensor-based (Landsat) deforestation information taken about every 16 days and SAR data taken about every 1.5 months were used, and the temporal change of L-band γ0 was examined for the deforestation areas. The γ0HH value increased by 1.2 dB on average for areas...
Simulated remote sensing imagery is valuable for instrument design studies, analysis algorithm development and validation, as well as analyst training. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) suite of software is an image simulation tool developed by the Digital Imaging and Remote Sensing (DIRS) Laboratory at the Rochester Institute of Technology. DIRSIG has a long history...
Mangrove forests provide a habitat for many endangered sea water-tolerant plant species and coastal fauna. Despite their ecological importance and shoreline protection properties, mangroves are under serious threat, resulting in significant losses in mangrove forest cover. Their efficient management requires frequent mapping to detect degradation or regeneration. The methodology presented in this...
According to the problems (e.g. the strong dependence on satellite sensors and atmospheric correction) in the current Fraction of absorbed Photosynthetically Active Radiation (FPAR) retrieval from remote sensing data, this study developed a generalized FPAR retrieval methods that can be applied to Landsat 5/TM, Landsat7/ETM+, Landsat 8/OLI, MODIS, ASTER, SPOT/VEGETATION and HJ/CCD based on a new radiative...
The salinity of Florida Bay has been increasing for decades due to anthropogenic interventions and natural disturbances of natural sheetflow through the Everglades and adjacent natural systems. Yet little data has been gathered and geospatially analyzed. This study developed a set of multivariate hydro-spatial statistical models to predict the dry and wet season surface salinity concentrations in...
The vast amount of data acquired by current high resolution Earth observation satellites implies some technical challenges to be faced. Google Earth Engine (GEE) platform provides a framework for the development of algorithms and products built over this data in an easy and scalable manner. In this paper, we take advantage of the GEE platform capabilities to exploit the wealth of information in the...
Mapping activities of urban land change is important for human activity to Earth's dynamic change. To get the detailed information on urban development maps in large area, dense training samples are needed in different area and specific season, which is cost-consuming. To overcome this issue, we provide a transfer learning method based on deep information to extract urban areas in all season and different...
Wetland classification has always been a challenging task among remote sensing experts. Typically, wetland classes have low accuracies regardless of the applied dataset, as they have many spectral and ecological similarities. In this paper, a method is developed particularly effective for distinguishing spectrally similar classes such as wetlands. In this method, feature selection and object-based...
Spectral built-up indices are considered promising to map impervious surface area (ISA) distribution at regional and global scales due to their easy implementation, parameter-free and convenience in practical applications. The objective of this study is to explore and compare the potential of different impervious surface indices for mapping urban area from Landsat imagery. By sharpening a thermal-band...
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