How to make Urban Sprawl Map 1990 - 2023 || Urban Growth || Urban Sprawl Map || Google Earth Engine

This code is written in Google Earth Engine (GEE) JavaScript API and is used for analyzing urban
expansion over a series of years using Landsat satellite imagery. Let's break down the code and
understand its functionality:
Variables and Functions:
1. *Year List:*
```javascript
var yearList = [1990, 1995, 2000, 2005, 2010, 2015, 2020];
```
This array contains the years for which the analysis will be performed.
2. *Filtering Function:*
```javascript
function filterCol(col, roi, date) {
return col.filterDate(date[0], date[1]).filterBounds(roi);
}
```
This function filters a given image collection (`col`) based on a date range (`date`) and a region of
interest (`roi`).
3. *Cloud Masking Functions:*
Two functions, `cloudMaskTm` and `cloudMaskOli`, are defined for cloud masking in Landsat images.
4. *Composite Functions:*
Two functions, `landsat457` and `landsat89`, are defined to create composite images for Landsat 4, 5,
and 7 (for years before 2014) and Landsat 8 and 9 (for years from 2014 onward), respectively.
5. *Generate Image per Year Function:*
```javascript
var builtCol = ee.ImageCollection(yearList.map(function (year) {
// ...
}));
```
This function generates a collection of images for each year using Landsat data, applies cloud masking,
and calculates indices such as NDBI and MNDWI. It also adds these layers to the GEE map.
Image Generation and Visualization:
The code then goes through each year in `yearList`, decides which Landsat collection to use based on the
year, generates a composite image, calculates indices, and visualizes the results on the map.
Urban Expansion and Visualization:
```javascript
var urbanExpansion = builtCol.select('built').min().set(dict);
Map.addLayer(urbanExpansion, {}, 'Urban_expansion');
```
This section creates an image representing the minimum built-up class value for each pixel over the
years. The results are visualized on the map as "Urban Expansion."
Legend and Visualization:
```javascript
var legend = ui.Panel([ui.Label('Urban expansion')], ui.Panel.Layout.flow('vertical'), { position: 'bottomleft' });
yearList.map(function(year, index){
// ...
});
Map.add(legend);
```
A legend is created and added to the map, showing the color-coded classes for each year of urban
expansion.
Area Chart:
```javascript
var areaChart = ui.Chart.image.series(builtCol.select('area'), roi, ee.Reducer.sum(), 30, 'year')
.setChartType('AreaChart')
.setOptions({
title: 'Urban area (Ha)',
hAxis: { title: 'Year' },
vAxis: { title: 'Area (Ha)' }
});
print(areaChart);
```
This section generates and prints an area chart showing the change in urban area over the specified
years.
Summary:
The code performs a comprehensive analysis of urban expansion over a series of years using Landsat
satellite imagery. It involves filtering, cloud masking, index calculation, visualization, and charting to
provide a detailed understanding of urban growth over time.

[@jeffinkgeorly6986]

did u used any ml classification or just extracted the urban areas through calculation using indices obtained and bands of the data?

[@jeffinkgeorly6986]

did u used? haven't said anything?

[@ritobinabiswas7595]

thanks so much. would love to more about urban sprawl mapping with GEE. do you do any short term courses?

[@GISsolutions]

Yes we do

[@Munai-1176]

@@GISsolutions how to enroll?

[@user-ey8ym9uw1w]

how to enroll in your course? contact and fee details?

[@dilinimunasingha-wn1gs]

I want to do a urban simulation modeling by using R studio can you help? plz

[@GISsolutions]

Urban simulation modeling in RStudio involves several steps. First, set up your environment by installing R and RStudio, then install necessary packages like `sf`, `tidyverse`, `spatstat`, and `raster`. Load your spatial data using the `sf` package for vector data and the `raster` package for raster data. Preprocess your data with `dplyr` from the `tidyverse` to clean and manipulate it. Define parameters and functions for your simulation, such as population growth, and iteratively apply these functions to model urban dynamics over time. Visualize the results using `ggplot2` and `tmap`, and analyze the outputs with various statistical and spatial analysis methods. For instance, you can read a shapefile, simulate population growth over ten years, and visualize the changes with `ggplot2` and `tmap`. This process provides a comprehensive approach to urban simulation modeling using RStudio.

[@dilinimunasingha-wn1gs]

@@GISsolutions thank you for your guidance. If You have any R scripts or Case studies, can you give it? I'm an urban planning undergraduate from sri lanka. It is only for educational purpose

[@abhishekgupta3717]

Brother what are the codes to download the images in tiff format...
May you help...🙏🏻

[@GISsolutions]

Check out my channel you got a video on this topic

[@gadisageremew7991]

code line 116 is not visible,so send me the full code of 116 line

[@GISsolutions]

5:20

[@RogelioHerbolingo-dd3vr]

@@GISsolutions it's not the complete line :(

[@alexmaingi9327]

could you please share the script?

[@GISsolutions]

Read the description of this video.

[@gadisageremew7991]

please send me the whole code for urban sprwal

[@GISsolutions]

Read the description of this video.

[@RogelioHerbolingo-dd3vr]

@@GISsolutions The whole code is not in the description 'cause there are missing codes in the description. Where can I get the whole? pleasee. I need it for a project in remote sensing, a BS degree.