How to calculate area of land on google earth
How to: Measure distance and area in Google Earth Pro
Sep 02, · Measure Land Area On Google Earth. By Hilman Rojak | September 2, 0 Comment. Area and distance in google maps area and distance in google maps area calculator using maps area and distance in google maps google earth 7 3 2 How To Measure Acreage In Google . Sep 08, · How to Use Google Maps to Find Vegan and Gluten-Free Restaurants Anywhere. Google rolled out the virtual tape on Google Map and Google Earth that can be used to measure the distance perimeter or the area of land with few taps right from your Mobile phone or desktop.
Or copy this url:. You can measure a straight-line distance by clicking on your start point and then your end. You can change the unit of measurement from a selection in the drop-down menu. A how to make your own pre workout will appear on your screen. Draw the outline of your polygon by clicking around the outside of the area. Note: use the mouse of keyboard to zoom or pan while you are drawing the polygon.
Set up you free account to download your favourite resources, take part in our live education broadcasts, and browse latest subject updates and training. Or browse by keyword:. Search Account. Measuring distance 1. Use the ruler to measure distance.
You can also measure a path, by drawing a path series of points. The distance will be displayed in the dialogue box. Measuring an area 1. To create a new polygon, click on the polygon icon. To measure the area of your polygon, go to the measurement tab.
You can choose how you wish to measure the area from the drop-down menu. You might also be interested in:. How to: Use layers in Google Earth Pro. How to: Find places in Google Earth Pro. How to: Create a polygon in Google Earth Pro. Log in. Remember me. Register for free Set up you free account to download your favourite resources, take part in our live education broadcasts, and browse latest subject updates and training.
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These scripts show how I used Google Earth Engine to estimate Central Valley land fallowed due to drought between and Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.
The scripts in this repo show how I used Google Earth Engine to estimate the total area of land fallowed in California's Central Valley in relative to as an attempt to see what impact the ongoing drought has had on agriculture. The following shows how I approached the project and could be used as a detailed tutorial for someone interested in using Google Earth Engine for a similar project.
As comes to a close, California is still unquestionably in a severe drought , although estimates of exactly how rare and therefore how severe range from 1 in 15 years drought to 1 in years Griffin and Anchukaitis Whether this is the drought to end all droughts or not, it has certainly had an impact on agriculture. With far less water available for irrigation, farmers with insufficient flows have had to make tough decisions. They can pay for water either by pumping groundwater or by buying water from someone willing to sell theirs, or they can fallow their fields, leaving them bare of crops.
The Central Valley is California's largest agricultural area and highly dependent on irrigation, so looking at how it's doing gives a good picture of how California agriculture is doing. If you look at satellite photos of California's Central Valley in and , things seem browner. But exactly how much browner? I was curious how much land had been fallowed this year relative to , before the drought kicked into gear, and wanted to try out a few remote sensing approaches to answer this question.
Google Earth Engine , which describes itself as "a planetary-scale platform for Earth science data and analysis," made it relatively straightforward to pull together the necessary data and try out two different ways of estimating fallowed land. Google Earth Engine is a tool for analyzing geospatial information. It's a cloud-based platform that uses Google's computational infrastructure for parallel processing, so it can process geospatial data much faster than an ordinary personal computer.
Google Earth Engine has two fundamental geographic data structures types that you should be familiar with:. Since Earth Engine is still in beta, there are not billions of stackoverflow. Instead, there is a Google group called Google Earth Engine Developers which is full of discussion of how to do different processes. As a beta tester, I had access to this group and found it to be a very valuable resource when I had a question not covered in the basic documentation.
To figure out what land had been fallowed in the Central Valley in relative to , the first thing I needed was to know what exactly counted as California's Central Valley.
There are various sources one could use to delineate the border of the Central Valley, each likely with slightly different definitions of where that border was that would give you slightly different answers of how much land has been fallowed. I downloaded the Central Valley land cover coverage, which consists of planar-enforced polygons specifying land cover and land use across the region as of , and then I used ArcMap to dissolve all the polygons into one giant polygon, the outline of which would give me the border of the Central Valley, and saved this as a KML file using WGS 84 as the datum.
Specific instructions on the import process here. Next I needed satellite imagery of the area. Google Earth Engine has both raw and processed data from all the Landsat satellites available as ImageCollections. Ideally, I would have used Landsat 7 Surface Reflectance data, because it is available from January 1, , to the present day, meaning it includes all the dates of interest to me in one, apples-to-apples data set.
However, Landsat 7 commonly has white striping across sections of its imagery because of the failure of the Scan Line Corrector in For example, the below image shows a composite July Landsat 7 photo of the Merced, California, area.
So instead, I used Landsat 5 data available from January 1, , to May 5, for and Landsat 8 data available from April 11, , to the present day for Since these are different satellites that collect slightly different bands of the electromagnetic spectrum, I would have to treat each of them separately when I did my analysis.
These images have been converted from the raw data of thermal bands to brightness temperature reflectance for each band. I loaded my imagery using the following code, selecting the June-August date range to get images for the summers of and Landsat 5 and Landsat 8 number their bands differently and have different bands available, so I have to select their bands appropriately. For Landsat 5, the visible spectrum is Bands 1 through 3, near-infrared is Bands 4 and 5, and mid-infrared is Band 7.
For Landsat 8, the visible spectrum is Bands 2 through 4, near-infrared is Band 5, short-wave infrared is Bands 6 and 7, and thermal infrared is Bands 10 and I only select these bands rather than the full range of available bands, because these are the bands I want to use in my analysis. Any one satellite image may have various problems that can obscure the surface--a cloudy day, a plume of smoke--so creating a composite image can help give a better picture.
By default, Earth Engine creates the composite using the most recent pixel in each case, but telling Earth Engine to choose the median value in the stack of possible pixel values can usually remove clouds, as long as you have enough images in the collection. Clouds have a high reflectance value, and shadows have a low reflectance value, so picking the median should give you a relatively cloudless composite image.
Since I have Landsat images for June, July, and August of each year and Landsat satellites take pictures of the same location about every two weeks, I had multiple possible images to put together.
I create my median-pixel composite like so:. Now that I have my two pieces of data--the Central Valley and Landsat imagery--I can clip the median-pixel Landsat images by the Central Valley and work with just my area of interest:.
I've put the results of these two pieces of code together for easy comparison. There is still agriculture in , of course, but the green patches are shrinking and a little less intense.
The southern half of the Central Valley in particular seems to have fewer green patches and more brown. One approach to estimating the total area of fallowed land is to perform a supervised classification of four basic land cover types for and Then, consider land that has converted from vegetation to bare soil as fallowed. This section explains how to do this in Google Earth Engine. The process of classification involves two pieces: a classification algorithm and data that you can use to train it.
For land cover classification, our data is usually satellite imagery. Satellites record reflectance across multiple regions of the electromagnetic spectrum, and different types of land cover have different spectral signatures. For example, the below image shows the spectral signature curves for each of four pixels from a Landsat 8 image of the Sacramento Valley. I chose those pixels to be representative of four different types: urban areas, water, vegetation, and bare soil.
You can see how they differ, particularly beyond the visible region of the electromagnetic spectrum:. The classification algorithm can learn what each of these four categories tend to look like across the different spectral regions our bands cover based on training pixels. Then it can be shown new pixels that we haven't already classified and tell us which category the unknown pixels most likely belong in, according to what it's already seen.
Now that I had images of the Central Valley, I needed to create training data that would teach my classification algorithm what urban areas, water, vegetation, and bare soil looked like. Training data in remote sensing land cover classification problems is usually referred to as "regions of interest.
In a separate script, I displayed my map in a false-color composite using the near-infrared, red, and green bands. This makes vegetated areas display as bright red, which can make the differences between vegetated areas and bare soil stand out more easily than using a natural-color composite.
I drew one FeatureCollection for each class, giving it both a numeric class since that's what Earth Engine will need later on during the classification process and a descriptive label for my own reference. For example:. Earth Engine imports any geometries, Features, or FeatureCollections you draw at the top of your script, so they are there to work with and they show up on your map.
Below is a picture of my map with my regions of interest displayed on top. I drew 30 polygons for each land cover class, meaning polygons in total. This was time-consuming. I tried to distribute them around the entire valley, since a given land cover class might differ more in different geographic areas and to give my classifier varied training data to make it more robust.
With my FeatureCollections for each class drawn, I merged them into a single FeatureCollection of all my regions of interest. That way I could run the region of interest creation process as one script and import it into my classification script at a later time, rather than doing everything at once in one very long script.
One hiccup in this process was that the fusion table I created had a column named "system:index", which caused a problem when I tried to import it into a new script, because apparently Earth Engine wants to assign the system:index property itself each time you import something. My workaround was to rename the column to "oldID" to prevent the error. I repeated this process for Since I used Landsat 8 rather than Landsat 5 for , I needed to train a classifier that worked for Landsat 8 specifically, so I also needed another set of training regions of interest.
After that long process, I had all my training data and could import it into my classification script. Earth Engine has support for a number of different classification algorithms, including random forests, naive Bayes, and support vector machines. I chose to use the random forest algorithm to classify my pixels; it doesn't make assumptions about the distribution of data and it often performs very well compared to many other classifiers.
If you're interested in the details of how a random forest classification works, Liaw and Wiener gives a nice overview. I created a classifier for each year because of their different satellite imagery:. To train my classifiers, I would feed in each year's regions of interest. The classifiers seemed to perform well, with a I made confusion matrices for each year:. My classifier is classifying built-up, urban areas as bare soil far too often. This could make the classification's estimation of fallowed land too high, since land classified as vegetated in and bare soil in will be treated as fallowed.
However, since the classifier has a very high accuracy rate for vegetation, I suspect that most of the pixels misclassified as urban won't get picked up when I test for the vegetation-to-bare-soil conversion.
Given this, I decided it was not worth the effort to draw better training regions of interest and moved on to Step 3. Suspiciously, actually looks like it has more vegetation than , even though I had fairly low rates of errors of commission and omission for vegetation for both years! Given more time, this would be very worth looking into.
Most likely, fixing the problem would involve better training data than something I drew by hand one afternoon. This could also be caused by the slightly different band definitions for Landsat 5 and Landsat 8 or Landsat 8 having more bands available. But for purposes of exploring this topic, I decided this was something to note and moved on.
As mentioned previously, I considered land that had been classified as vegetation in and as bare soil in to be fallowed land. The below code gave each pixel a 1 if it met this fallowing condition and 0 otherwise.
Fallowed land shows up as white pixels in the below image, while everything else shows up as black pixels.
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