Introduction
Built-up area refers to the construction land in urban and rural residential areas. Using high-resolution land classification data to compare built-up areas, we can understand the changes in built-up areas in different time periods.
Specific steps are as follows:
1. Collect two periods of high-resolution land classification data, such as Landsat remote sensing image data.
2. Perform geometric correction and radiation correction on the data from the two periods to ensure the accuracy of the data.
3. Use remote sensing image processing software, such as ENVI, ArcGIS, etc., to perform image registration and perfectly align the two images.
4. For image classification, algorithms such as maximum likelihood classification and support vector machine classification (SVM) can be used to classify the images to obtain the number of pixels in each category.
5. By analyzing the number and distribution of pixels in the built-up area in the two-phase data, we can obtain the changes in the built-up area in the two phases, such as the number of new built-up areas and the growth rate of the built-up area.
6. Based on the changes in built-up areas, analyze the factors affecting changes in built-up areas, such as policies, economic development, population growth, etc.
In summary, using high-resolution land classification data to compare built-up areas can help urban planners and government departments understand the development status and trends of urban construction, which is beneficial to the long-term planning and development of the city.
function
ee.Date.fromYMD(year, month, day, timeZone)
Returns a Date given year, month, day.
Arguments:
year (Integer)
month (Integer)
day (Integer)
timeZone (String, default: null):
The time zone (eg 'USA/Los_Angeles'); defaults to UTC.