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Klasifikasi untuk analisis Land Use dan Land Cover di QGIS
Klasifikasi untuk analisis Land Use dan Land Cover dengan Google Earth Engine

Software yang digunakan:

QGIS
Orfeo Toolbox
Google Earth Engine

Langkah-langkah:

Install QGIS

Setelah suskes sign up, selanjutnya lakukan sign in, dan coba masuk ke halaman Platform -> Code Editor

Tambahkan Orfeo Toolbox di QGIS

Untuk membaca langkah-langkah download dan menambahkan Orfeo Toolbox, silahkan download ebook disini
Download Orfeo Toolbox, klik https://www.orfeo-toolbox.org/download/
Unzip file hasil download di C, misal C:\OTB-7.0.0-Win64 dimana folder ini memuat Library Orfeo.
Selanjutnya download folder lain yang memuat file configurasi Orfeo Plugin di QGIS. Download zip folder di https://gitlab.orfeo-toolbox.org/orfeotoolbox/qgis-otb-plugin dan unpack it.
Buat folderdi drive C:\qgis-plugins\qgis-otb-plugIn and copy semua file hasil download diatas, sehingga seperti ini.

Selanjutnya buka QGIS, lalu pilih menu Settings -> Options. Selanjutnya pilih menu Processing -> Providers lalu pilih OTB. Set OTB application folder dengan C:\OTB-7.4.0-Win64\lib\otb\applications dan OTB folder dengan C:\OTB-7.4.0-Win64 lalu klik OK.

Maka Orfeo Toolbox siap digunakan di QGIS.

Analisis supervised dengan Orfeo Toolbox di QGIS, tahapan yang akan dilakukan adalah:

Melakukan analisis Unsupervise (K-means) dengan OTB di QGIS.
Menggunakan Random Forest, SVM, dan Decission Tree di QGIS.
Melakukan performasi assesment akurasi dan compute main accuracy measures.

Analysis di GEE:

Import dan visualisasi Sentinel image di GEE

//import the satellite data from european space agency
var S2 = ee.ImageCollection("COPERNICUS/S2");

//filter for Dubai
S2 = S2.filterBounds(Dubai);
print(S2);

//filter date
S2 = S2.filterDate("2020-01-01","2020-05-11");
print(S2);

//visualize image
var image = ee.Image(S2.first());
Map.addLayer(image, {min:0, max:3000,bands:"B4,B3,B2"}, "DubaiTrue Color");

Map.addLayer(image, {min:0, max:3000,bands:"B8,B4,B3"}, "Dubai False Color");

Import dan Visualisasi Citra Landsat 8 di GEE

//import the satellite data from european space agency
var L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA");

var spatialFiltered =L8.filterBounds(Cirebon);
print('spatialFiltered', spatialFiltered);

var temporalFiltered = spatialFiltered.filterDate('2015-01-01', '2015-12-31');
print('temporalFiltered', temporalFiltered);

// This will sort from least to most cloudy.
var sorted = temporalFiltered.sort('CLOUD_COVER');

// Get the first (least cloudy) image.
var scene = sorted.first();

Map.centerObject(scene, 9);
Map.addLayer(scene, {}, 'default RGB');

var visParams = {bands: ['B4', 'B3', 'B2'], max: 0.3};
Map.addLayer(scene, visParams, 'true-color composite');

//Create training dataset
var training = scene.sample(
{ region : Cirebon,
scale : 20,
numPixels : 5000
}
);

//Start Unsupervised clustering algorithm and training it
var kmeans = ee.Clusterer.wekaKMeans(3).train(training);

//Cluster the input using the trained clusterer.
var result = scene.cluster(kmeans);

//Display the cluster with random colors
Map.addLayer(result.randomVisualizer(),{}, "Unsupervised kmeans classification");

//Export the Image to Drive
Export.image.toDrive({
image :result,
description : 'kmeans Cirebon',
scale : 20,
region : Cirebon
});

Unsupervised analysis di GEE

//import the satellite data from european space agency
var S2 = ee.ImageCollection("COPERNICUS/S2");

//filter for Dubai
S2 = S2.filterBounds(Dubai);
print(S2);

//filter date
S2 = S2.filterDate("2020-01-01","2020-05-11");
print(S2);

//visualize image
var image = ee.Image(S2.first());
//Map.addLayer(image, {min:0, max:3000,bands:"B4,B3,B2"}, "DubaiTrue Color");

Map.addLayer(image, {min:0, max:3000,bands:"B8,B4,B3"}, "Dubai False Color");

//Create training dataset
var training =image.sample(
{ region : Dubai,
scale : 20,
numPixels : 5000
}
);

//Start Unsupervised clustering algorithm and training it
var kmeans = ee.Clusterer.wekaKMeans(5).train(training);

//Cluster the input using the trained clusterer.
var result = image.cluster(kmeans);

//Display the cluster with random colors
Map.addLayer(result.randomVisualizer(),{}, "Unsupervised kmeans classification");

//Export the Image to Drive
Export.image.toDrive({
image :result,
description : 'kmenas Dubai',
scale : 20,
region : Dubai
});