This is a tutorial about how to clip multiple rasters at once in QGIS, running a very simple Python script that uses the command gdal_translate. This way, every raster loaded in QGIS will be clipped to the same area, defined by ...
Bradar mapping disaster-prone Brazilian municipalities UPI.com For the 15-month project, Bradar will use remote sensing technology -- Interferometric synthetic aperture radar -- that operates on the X and P bandwidths and is capable of generating...
While the 138000-acre Silver Fire still smoldered, forest restoration specialists were on the job. They analyzed maps created using Landsat satellite data to determine where the burn destroyed vegetation and exposed soil ...
This paper presents a short overview of the state of the aquaculture insurance industry and the use of innovative remote sensing tools for monitoring specific risks related to Harmful Algal Blooms. Introduction.
Landsat 8: creating RGB composites and pan sharpening.
In our first posting (“Processing Landsat 8 data in GRASS GIS 7: Import and visualization“) we imported a Landsat 8 scene (covering Raleigh, NC, USA). In this exercise we use Landsat data converted to reflectance with i.landsat.toar as shown in the first posting.
Here we will try color balancing and pan-sharpening, i.e. applying the higher resolution panchromatic channel to the color channels, using i.landsat.rgb.
J.A. Barsi, J.R. Schott, F.D. Palluconi, D.L. Helder, S.J. Hook, B.L. Markham, G. Chander, E.M. O'Donnell, “Landsat TM and ETM+ thermal band calibration,” Canadian Journal of Remote Sensing, 29(2), 141-153 (2003) John R.
Very briefly, this is a small bugfix for the classification process, when using SAGA 2.0.8. The updated Semi-Automatic Classification Plugin is already available through the QGIS repository, or can be downloaded here.
Remote sensing gives satellite's view of water use TriValley Central Scientists have been using remote sensing since March 1, 1984, when NASA launched Landsat 5 to record land surface conditions on earth.
Different vegetation spectral indices correlate highly to spatial patterns of corn yield (as shown below) and leaf area index (LAI). Wu et al. (2007; pdf) showed (based on Quickbird imagery; has a much finer resolution than Landsat) that MSAVI (Qi et al., 1994) is better than other indices, including NDVI, for remote sensing of corn LAI. Hollinger (2011) showed MSAVI correlates higher to corn yield spatial patterns than most indices, but other indices correlate very similarly to MSAVI. Below, Landsat-based MSAVI from V12 to V19 is correlated (linear regression) with corn yield by averaging clean yield points within the pixel extent (based on 4-meter spacing; approximately 56 yield points per pixel); a comparison of correlations with other indices based on reflectance and digital numbers (DNs) is shown after the correlation graphics. Landsat imagery should not be used for corn prior to V12 because soil has too strong of an influence or at VT and later due to the effect of tassels.
Understanding the spatial patterns of urban land use at both the macro and the micro levels is a central issue in global change studies. Due to the nonlinear features associated with land use spatial patterns, it is currently ...
The Eurosiberian region. Image Credit: Environmental Remote Sensing Research Group, University of Alcalá. Sara Jurdaoa,, Marta Yebraa,b and.
Live fuel moisture content (LFMC) is a key variable in fire danger assessment. Recent studies have developed reasonably accurate models to determine LFMC in Mediterranean ecosystems by the inversion of radiative transfer models within ecologically based parameters. However, areas with temperate climate have received less attention. The objective of this study was to estimate LFMC for temperate grassland and shrubland located in the Eurosiberian region of Spain. To achieve this, we first assessed the adequacy of already published Mediterranean models to the Eurosiberian region. Secondly, we recalibrated the Mediterranean models to better resample temperate ecosystems by using ecological data collected in field. Finally, we proposed an alternative inversion procedure based on the look up table (LUT) technique. Reflectance from the first seven bands of MODIS and the NDII6 vegetation index were used to achieve this.
The last approach was the one that efficiently estimated LFMC, mainly for higher danger situations (RMSE equals 30.6 and 18.81percent for grassland and shrubland with LFMC of less than 200 and 105 percent, respectively). This approach can be used together with previous models developed for Mediterranean grassland and shrubland to monitor LFMC of the Iberian Peninsula with a standardized methodology.
Rapid urbanization and population growth in China have raised great concerns regarding food security caused by the loss of limited cultivated land. In this study, we used remotely sensed data and an agricultural productivity ...
Catching Fish Using Google Earth Science 2.0 The study shows the potential for using remote-sensing approaches, such as satellite imagery, to validate catch statistics and fisheries operations in general.