Delhi sample #555
Delhi sample #555
Conversation
|
Check out this pull request on You'll be able to see Jupyter notebook diff and discuss changes. Powered by ReviewNB. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). shivanip32 commented on 2019-11-26T10:18:49Z Delhi’s green cover increased from 20.08 per cent in 2015 to 20.22 per cent in 2017 monitored from satellite images in between October and November months, according to the India State of Forests Report (ISFR) 2017.
According to the India State of Forests Report (ISFR) 2017, satellite imageries of Delhi were monitored for October and November months and it shows that the green cover of Delhi has been increased from 20.08% in 2015 to 20.22% in 2017. This sample shows the capabilities of different band mathematics such as Normalized Difference Vegetation index (NDVI) for calculation of green cover in Delhi, India on 15 October 2017 using Landsat 8 imagery. This sample shows the capabilities of spectral indices such as Normalized Difference Vegetation index (NDVI) for the calculation of green cover in Delhi, India on 15 October 2017 using Landsat 8 imagery. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). shivanip32 commented on 2019-11-26T10:18:50Z As this notebook is to calculate green cover for Delhi, you can filter the boundary for Delhi. The boundary of Delhi can be filtered from the layer, as this notebook focuses on the calculation of Delhi's green cover |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). shivanip32 commented on 2019-11-26T10:18:51Z Recording the extent of New Delhi region
Extracting Landsat imagery for New Delhi Region |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). shivanip32 commented on 2019-11-26T10:18:52Z The OBJECTID for Delhi state boundary is 7 that is what we have used in the below code
In the State Boundary layer, OBJECTID for Delhi is 7 which is used in the code below. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). shivanip32 commented on 2019-11-26T10:18:52Z Filter rasters based on cloudcover and time duration Filter imageries based on cloud cover and Acquisition Date.
|
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). shivanip32 commented on 2019-11-26T10:18:53Z In this example we have selected all the images captured between 1 October 2017 to 31 December 2017 with cloud cover less than or equal to 5% for Delhi region. In this example we have selected all the imageries captured between 1 October, 2017 to 31 December, 2017 with cloud cover less than or equal to 5% for Delhi. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). shivanip32 commented on 2019-11-26T10:18:54Z Selecting Image dated 15 October 2017 from the collection using its OBJECTID Selecting imagery dated 15 October, 2017 from the collection using its OBJECTID. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). shivanip32 commented on 2019-11-26T10:18:55Z In order to create NDVI composite for the image we can apply already defined "NDVI Raw" function in image's properties In the Landsat layer properties, pre defined "NDVI Raw" function was applied to get the NDVI composite. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). shivanip32 commented on 2019-11-26T10:18:56Z Clipping the NDVI composite image tile against the boundary of Delhi region Clipping the NDVI composite for Delhi and setting the extent of the generated raster |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). shivanip32 commented on 2019-11-26T10:18:57Z Defining the NDVI range for agricultural land and forest area in Delhi using "remap" function. The NDVI values used for calculating agricultural land ranges from [0.4,0.5] and for forest land/ tree cover mapping, [0.5,1] range of the NDVI values are considered. "remap" function was used to define the NDVI range for agricultural land and forest. The NDVI values between 0.4 - 0.5 represents agricultural land whereas NDVI values between 0.5 - 1 shows forest/ tree cover. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). shivanip32 commented on 2019-11-26T10:18:58Z In this study, we have calculated green cover for Delhi as on 15 October 2017 using Landsat 8 imagery. We used Normalised Difference Vegetation Index which is a very well known and widely used index for the calculation of green areas. In this study, green cover of Delhi is calculated using Landsat 8 imagery for 15 October, 2017. Normalised Difference Vegetation Index (NDVI) is used for the calculation of green areas, which is a well known and widely accepted spectral index for vegetation studies. NDVI is used here compared to the other spectral indices because it is easy to compute and requires only two bands. Towards the end, we calculated the area equivalent of the selected pixels under each class to estimate overall green cover of Delhi. Finally, the area of the pixels of agricultural land and forest class is calculated to estimate overall green cover of Delhi.
According to the India State of Forests Report (ISFR) 2017, Delhi's green cover for the year 2017 was 20.22% whereas in our study the green cover comes out to be roughly around 19% of total geographical area of Delhi which is quite near to the figures.
According to the India State of Forests Report (ISFR) 2017, Delhi's green cover for the year 2017 was 20.22%, whereas in the study the green cover comes out to be around 19% of total geographical area of Delhi which is quite close to ISFR's estimate. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:40Z put space between the word cloud cover Filter rasters based on cloud cover and time duration |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:41Z first line could be rephrased The India State of Forests Report (ISFR) 2017, showed that green cover of Delhi has increased from 20.08% in 2015 to 20.22% in 2017, as observed from satellite imageries of Delhi for the month of October and November. This was a welcome news for the city struggling with severe pollution and rising population, which makes it necessary to monitor the city's green cover and keep the city liveable.
make a new paragraph This sample shows the capabilities of spectral indices such as Normalized Difference Vegetation index (NDVI) for the calculation of green cover in Delhi, India on 15 October 2017, using Landsat 8 imagery. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:41Z import the specific library instead of all from arcgis.raster.functions import * |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:43Z spell check Search for Multispectral Landsat layer in ArcGIS Online. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:44Z change esri to Esri Imagery Layer by Esri guneetmutreja commented on 2019-11-27T10:10:22Z Can not be done as it is run time result provided by the api |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:45Z spell check Search for India State Boundaries 2018 layer in ArcGIS Online.
could be rephrased to avoid repetition The boundary of Delhi can be filtered from the layer, as this notebook focuses on the city's green cover. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:46Z could be rephrased for replacing extra 'the' In State Boundary layer, OBJECTID for Delhi is 7 which is used below. Also, it is important to add extent to the geometry of selected boundary. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:47Z could be rephrased
In order to have good result
|
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:48Z tense rephrase "NDVI Raw" function is applied to get the NDVI composite |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:49Z could be rephrased - avoid repetition Clipping the NDVI composite for Delhi and setting the extent of the generated raster. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:50Z check if any graph is missing here |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:51Z in the map legend define what 1 and 2 represent. Also explain the results in the map briefly |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:52Z Title could be rephrased - remove the word some calculations Area Derivation
explain about what area is derived here |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:53Z explain the chart briefly with percentages |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-11-27T08:49:54Z break into paragraphs and rephrase the last line
In this study, green cover of Delhi is calculated using Landsat 8 imagery for 15 October, 2017. Normalised Difference Vegetation Index (NDVI) is used for the calculation of green areas, which is a well known and widely accepted spectral index for vegetation studies. NDVI is used here compared to the other spectral indices because it is easy to compute and requires only two bands. Pixels with NDVI values greater than 0.4 and less than 0.5 are considered as agricultural land, parks, etc and the pixels with more than or equal to 0.5 NDVI values are considered as tree cover/ forest areas. Finally, the area of the pixels of agricultural land and forest class is calculated to estimate overall green cover of Delhi. The study shows how green land use cover of an area could be easily computed in few lines of code using Esri's predefined NDVI layer and Landsat 8 imagery from ArcGIS server.
add any source, report references, links at the end |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-12-09T06:30:31Z could be rephrased Here, the pixels with light green color having a value of "1" represent agricultural land whereas |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). moonlanderr commented on 2019-12-09T06:30:32Z remove duplication of this sentence Finally, the area of the pixels of agricultural land and forest class is calculated to estimate overall green cover of Delhi. |
|
@moonlanderr I've made these changes, please review it and let me know if there are more |
|
@AtmaMani this sample is ready for further review. You may add to your review list. |
|
@AtmaMani Did you get a chance to have a look at the notebook? Any updates or changes, I need to incorporate? |
|
@guneetmutreja not yet. It has been a busy week. |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). AtmaMani commented on 2019-12-20T00:12:22Z Overall comments:
guneetmutreja commented on 2019-12-23T03:08:54Z Done |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). AtmaMani commented on 2019-12-20T00:12:23Z Good, I like that instead of hard coding, you are querying the SR from landast layer. guneetmutreja commented on 2019-12-23T03:00:55Z Thank you |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). AtmaMani commented on 2019-12-20T00:12:23Z Good that you added the date in the comment,. guneetmutreja commented on 2019-12-23T03:01:11Z Thanks |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). AtmaMani commented on 2019-12-20T00:12:24Z After each of these operations, query the imagery layer so we get to see the output guneetmutreja commented on 2019-12-23T03:13:52Z Done |
|
View / edit / reply to this conversation on ReviewNB (backstory for this conversation format). AtmaMani commented on 2019-12-20T00:12:25Z display the variable so we can see the result of clip operation guneetmutreja commented on 2019-12-23T03:14:02Z Done |
|
Change file name so it is same as the title. Use lowercase and no spaces or special characters. |
|
@shivanip32, @moonlanderr thanks for the detailed review |
|
@AtmaMani thanks for the review. I have made all the changes you suggested and made a commit. Please have a look at the same. I will surely work on your recommendations for expanding the notebook in the near future. |
|
Looks good, thanks @guneetmutreja |
imports are in the first cell? First block of imports are standard libraries, second block are 3rd party libraries, third block are allarcgisimports?GISobject instantiations are one of the following?gis = GIS('https://www.arcgis.com', 'arcgis_python', 'P@ssword123')gis = GIS(profile="your_online_profile")gis = GIS('https://pythonapi.playground.esri.com/portal', 'arcgis_python', 'amazing_arcgis_123')gis = GIS(profile="your_enterprise_portal")./misc/setup.pyand/or./misc/teardown.py?<img src="base64str_here">instead of<img src="https://some.url">? All map widgets contain a static image preview? (Callmapview_inst.take_screenshot()to do so)os.path.join()? (Instead ofr"\foo\bar",os.path.join(os.path.sep, "foo", "bar"), etc.)