15 GEOG245 Tutorial8. Wetland management on the radar. Wetland management on the radar Danube Delta interferogram 11 June 2015 An ESA project focuses on exploiting satellite radar data to monitor wetlands for sustainable water management. The Danube River Delta in Romania covers more than 4180 sq km.
It plays an important role in local water supply and is a local haven of biodiversity. As one of the Ramsar Convention’s wetlands of international importance, as well as a UNESCO World Heritage Site, the delta has some 30 different types of ecosystems that function as a valuable natural buffer zone for pollutants from the Danube River. Understanding the area’s dynamic processes allows changes in the Danube Delta to be monitored.
Sentinel-1 With many areas within the delta virtually inaccessible, satellites provide the means to map and monitor this biologically diverse wetland. To do this, multiple images from synthetic aperture radars – or SARs – are combined to create ‘interferograms’ showing changes between the radar scans. A closer look. Step by Step: Flood Hazard Mapping. A. Workflow for the Creation of CN Grid in ArcMap 1. Preparing land use data for CN Grid Open ArcMap. 1.1. The grid is added with a unique symbology assigned to cells having identical numbers as you can see below: These numbers represent a land use class defined according to the USGS land cover institute (LCI). 1.2.
Eventually, we are going to use these land use classes and soil group type, in conjunction with runoff curve numbers (CN), to create the curve number grid. To implement the above re-classification, use the Spatial Analyst Tools in Arc Toolbox. In the reclassification window, confirm the Input raster is cedar_lu, Reclass field is Value, and then manually assign the new numbers based on the above table as shown below (leave NoData unchanged). Save the output raster as lu_reclass in your working folder, and click OK. 1.3. You can symbolize the new landuse_poly.shp to match with lu_reclass grid or leave it unchanged. 2. 2.1.
Click OK. Click OK. 3. 4. . [1] . [2] . [3] . [4] . [5]. Step by Step: Recommended Practice drought monitoring (ENVI 4.8) Data Preparation/Pre-processing: A. Data preparation Step 1: Recalculating from MVC values to NDVI value range Input all monthly maximum value composite from 2000 to 2013 using File - Open in ENVI. As mentioned above, the two weeks NDVI composits are available as 8-bit unsigned integer greyscale images, i.e. the values are ranging from 0-255. For more details on the band math operation cf. step-by-step instructions for Envi 5.0 (Step 1: Recalculating from MVC values to NDVI value range) Step 2:Layer Stacking Create a new raster file including all NDVI MVC data for the whole data range (2000 up to now) using File- Save File as - ENVI Standard Import all required NDVI data into the New File Builder window then press Reorder button and sort NDVI data by date and finally save the new file.
Step 3a: Resizing the NDVI-MVC images to the study area It is recommended to reduce the amount of data to decrease the time for computing. Then from the Vector Window, open File - Export Active Layer to ROI. B. ArcGIS 10 official Tutorials PDF | Franz's blog. GIS Lounge - Maps and GIS. ENVI Tutorials. Step by Step: Recommended Practice Flood Mapping. 3. Binarization 3.1 To separate water from non-water a threshold can be selected. For this, we will analyse the histogram of the filtered backscatter coefficient.
On the left side panel select the Colour Manipulation tab. The histogram of the backscatter coefficient will show up and one might need to use the logarithmic display. The histogram will show one or more peaks of different magnitude depending on the data. Low values of the backscatter will correspond to the water, and high values will correspond to the non-water class. 3.2 To segment or binarize the image we will apply band arithmetic. 3.3 A window will open. 3.4 A new band named water will be added to the product. 4. 4.1 The obtained image is in the geometry of the sensor. 4.2 A window with parameters will open. 4.3 A new product will be created and appear in the Product Explorer. 5. Example of the resulting product.
From GIS to Remote Sensing: Estimation of Land Surface Temperature with Landsat Thermal Infrared Band: a Tutorial Using the Semi-Automatic Classification Plugin for QGIS. This post is a tutorial for the estimation of Land Surface Temperature using a Landsat image acquired over Paris (France), using the Semi-Automatic Classification Plugin for QGIS, which allows for supervised classifications.
Before the tutorial, please watch the following video that illustrates the study area and provides very useful information about thermal infrared images, and their application (footage courtesy of European Space Agency/ESA). Also, a brief description of the area that we are going to classify is available here. As shown in the previous video, the study area is covered by the urban surfaces, vegetation and agricultural fields.
The thermal infrared band is particularly useful for assessing the temperature difference between the city and the surrounding rural areas, and studying the urban heat island phenomenon. We are going to classify a Landsat 8 image acquired in September 2013 (available from the U.S. First, download the image from here (available from the U.S. 1. Download and resampling of MODIS images - spatial-analyst.net. His article explains how to automate download, mosaicking, resampling and import of MODIS product to a GIS. We focus on the one of the most known MODIS products for terrestrial environmental applications: the Enhanced Vegetation Index (EVI), which is the improved NDVI (Huete et al., 2002; see the complete list of MODIS products).
EVI corrects distortions in the reflected light caused by the particles in the air as well as the ground cover below the vegetation. The EVI also does not become saturated as easily as the NDVI when viewing rainforests and other areas with large amounts of chlorophyll. EVI can be directly related to the photosynthetic production of plants, and indirectly to the green biomass (Huete et al., 2002). NASA's MODIS Earth observation system is today considered to be one of the richest sources of remote-sensing data for monitoring of environmental dynamics (Neteler, 2005; Lunetta et al. 2006; Ozdogana and Gutman 2008). Installation of software Mosaicking and resampling. Efficiently download and process MODIS data with R | Abdulhakim Abdi. # The ProjectMODIS function is a wrapper for the LPDAAC-developed MODIS Rrojection Tool (MRT) which can resample, subset and reproject HDF data # and convert them to geoTIFFS. This goes without saying but the mosacing and reprojection components of the function will only work if MRT is installed. # Download MRT from LPDAAC here: ProjectMODIS <- function(fname='tmp.file',hdfName,output.name,MRTLoc,UL="",LR="",resample.method='NEAREST_NEIGHBOR',projection='UTM', subset.bands='',parameters='0 0 0 0 0 0 0 0 0 0 0 0',datum='WGS84',utm.zone=NA,pixel_size=1000){ filename = file(fname, open="wt") write(paste('INPUT_FILENAME = ', getwd(), '/',hdfName, sep=""), filename) if (subset.bands !
Write(paste('SPECTRAL_SUBSET = ( ',subset.bands,' )',sep=''),filename,append=TRUE) if (UL[1] ! Write('SPATIAL_SUBSET_TYPE = OUTPUT_PROJ_COORDS', filename, append=TRUE) write(paste('OUTPUT_FILENAME = ', output.name, sep=""), filename, append=TRUE) close(filename) e else { Google Earth & Google Maps Archives - Monde Geospatial - Geomatics | GIS| Remote sensing| GPS | Surveying | ArcGIS | QGIS.