Remote Sensing Analysis in QGIS - Revision history

Smitch: /* Conclusions */

2019-10-29T03:34:25Z

Conclusions

Smitch: /* Other Indices Operational Formula */ wording improvements

2019-10-29T03:34:04Z

Other Indices Operational Formula: wording improvements

Smitch: /* Semi-automatic Classification Plugin */ Improved wording

2019-10-29T03:31:38Z

Semi-automatic Classification Plugin: Improved wording

SpenserZhang: /* Loading Imagery and Atmospheric Correction */

2019-10-03T17:16:53Z

Loading Imagery and Atmospheric Correction

SpenserZhang: /* Semi-automatic Classification Plugin */

2019-10-03T17:03:48Z

Semi-automatic Classification Plugin

SpenserZhang: /* Conclusions */

2019-10-03T00:28:38Z

Conclusions

SpenserZhang: /* Final Products */

2019-10-03T00:18:40Z

Final Products

SpenserZhang: /* Final Products */

2019-10-03T00:18:07Z

Final Products

SpenserZhang: /* Final Products */

2019-10-03T00:17:25Z

Final Products

SpenserZhang: /* Final Products */

2019-10-03T00:15:33Z

Final Products

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 This tutorial has showcased some of the remote sensing capabilities of QGIS in the context of scientific analysis. The products generated could be used to asses how icing distribution changes over time, calculations of water stored in icings or threat posed to local infrastructure. I hope this guide may empower those without the means to purchase expensive software to pursue scientific endeavors in the future.
-If you are interested to get more knowledge about Remote Sensing I recommend following books:
+If you are interested to get more knowledge about Remote Sensing I recommend the following books:
 Lillesand, T., Kiefer, R., Chipman, J. (2015): Remote Sensing and image interpretation. Wiley, Hokboken.

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 [[File:Final product.JPG|600px]]
-==Other Indices Operational Formula==
+==Other Indices: Operational Formulae==
-The above tutorial introduced how to calculated water, snow, and ice indices.
+The above tutorial introduced how to calculate water, snow, and ice indices. Other applications can use similar indices for other purposes.
-If you have a study area that contains urban area or vegetation, you can still use QGIS raster calculator to calculate NDVI, NDBI, and NDWI.
+If you have a study area that contains urban area or vegetation, you could use QGIS raster calculator to calculate NDVI, NDBI, and NDWI.
 NDVI is the Normalized Difference Vegetation Index. If you are using Landsat 8 data, NDVI = (Band 5 - Band 4)/(Band 5 + Band 4). NDVI = 0.2 to 0.5 represent Shrubs and grasslands, and NDVI = 0.6 to 0.9 areas are dense vegetation.

← Older revision		Revision as of 03:31, 29 October 2019
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	While the data obtained from EarthExplorer is very good, it still needs some correction applied in order to remove the haze and scattering that occurs when images are taken through ~~earth~~'s atmosphere. To do this, we will utilize the '''Semi-automatic Classification Plugin''', or '''SCP''' within QGIS. Semi-automatic Classification Plugin is a free open source for QGIS, and we can use it to do remote sensing analysis, classification (either unsupervised or supervised). You can also use it to do land cover change, and image classification accuracy ~~report~~.		While the data obtained from EarthExplorer is very good, it still needs some correction applied in order to remove the haze and scattering that occurs when images are taken through Earth's atmosphere. To do this, we will utilize the '''Semi-automatic Classification Plugin''', or '''SCP''' within QGIS. Semi-automatic Classification Plugin is a free open source add-on for QGIS, and we can use it to do remote sensing analysis, and classification (either unsupervised or supervised). You can also use it to do land cover change analysis, and image classification accuracy reporting. The Semi-Automatic Classification Plugin can also do remote sensing analysis for other types of satellite imagery, and you can also directly download satellite image from this plugin. Semi-Automatic Classification Plugin supports all versions of Landsat satellite image, Sentinel-2, Sentinel-3, ASTER, and MODIS.
	Semi-Automatic Classification Plugin can also do remote sensing analysis for other satellite image, and you can also direct download satellite image from this plugin. Semi-Automatic Classification Plugin supports all version of Landsat satellite image, Sentinel-2, Sentinel-3, ASTER, MODIS

← Older revision		Revision as of 17:16, 3 October 2019
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	While the data obtained from EarthExplorer is very good, it still needs some correction applied in order to remove the haze and scattering that occurs when images are taken through earth's atmosphere. To do this, we will utilize the '''Semi-automatic Classification Plugin''', or '''SCP''' within QGIS. Semi-automatic Classification Plugin is a free open source for QGIS, and we can use it to do remote sensing analysis, classification (either unsupervised or supervised). You can also use it to do land cover change, and image classification accuracy report.		While the data obtained from EarthExplorer is very good, it still needs some correction applied in order to remove the haze and scattering that occurs when images are taken through earth's atmosphere. To do this, we will utilize the '''Semi-automatic Classification Plugin''', or '''SCP''' within QGIS. Semi-automatic Classification Plugin is a free open source for QGIS, and we can use it to do remote sensing analysis, classification (either unsupervised or supervised). You can also use it to do land cover change, and image classification accuracy report.
			Semi-Automatic Classification Plugin can also do remote sensing analysis for other satellite image, and you can also direct download satellite image from this plugin. Semi-Automatic Classification Plugin supports all version of Landsat satellite image, Sentinel-2, Sentinel-3, ASTER, MODIS

← Older revision		Revision as of 17:03, 3 October 2019
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	While the data obtained from EarthExplorer is very good, it still needs some correction applied in order to remove the haze and scattering that occurs when images are taken through earth's atmosphere. To do this, we will utilize the '''Semi-automatic Classification Plugin''', or '''SCP''' within QGIS.		While the data obtained from EarthExplorer is very good, it still needs some correction applied in order to remove the haze and scattering that occurs when images are taken through earth's atmosphere. To do this, we will utilize the '''Semi-automatic Classification Plugin''', or '''SCP''' within QGIS. Semi-automatic Classification Plugin is a free open source for QGIS, and we can use it to do remote sensing analysis, classification (either unsupervised or supervised). You can also use it to do land cover change, and image classification accuracy report.

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 Lillesand, T., Kiefer, R., Chipman, J. (2015): Remote Sensing and image interpretation. Wiley, Hokboken.
+Mårtensson, U., (2016): Remote Sensing and image interpretation.
 Solimni, D. (2016): Understadning Earth Observation. Springer, Cham.

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 [[File:Inkedsetting LI.jpg|600px]]
+The band designations for '''RGB''' in Landsat 8 are '''4,3,2''' respectively.
 [[File:RGB_SCP.JPG|600px]]

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 For display and reference purposes, we can render our Landsat scene to show standard RGB colours. This can be done using the '''Band Stack''' function within the semi-automatic classification plugin. For Landsat - 8 image, choose band 2 - 7 for the Band Stack image and select Landsat 8 OLI in the Quick wavelength settings. You can find the tool at the top of the user surface. The band designations for '''RGB''' in Landsat 8 are '''4,3,2''' respectively.
-[[File:Setting.JPG|600px]]
+[[File:Inkedsetting LI.jpg|600px]]
 [[File:RGB_SCP.JPG|600px]]

← Older revision		Revision as of 00:15, 3 October 2019
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	==Final Products==		==Final Products==

	For display and reference purposes, we can render our Landsat scene to show standard RGB colours. This can be done using the '''Band Stack''' function within the semi-automatic classification plugin. You can find the tool at the top of the user surface. The band designations for '''RGB''' in Landsat 8 are '''4,3,2''' respectively.		For display and reference purposes, we can render our Landsat scene to show standard RGB colours. This can be done using the '''Band Stack''' function within the semi-automatic classification plugin. For Landsat - 8 image, choose band 2 - 7 for the Band Stack image and select Landsat 8 OLI in the Quick wavelength settings. You can find the tool at the top of the user surface. The band designations for '''RGB''' in Landsat 8 are '''4,3,2''' respectively.