{"id":1486,"date":"2026-10-25T11:28:24","date_gmt":"2026-10-25T11:28:24","guid":{"rendered":"https:\/\/helpwithmygisassignment.com\/blog\/?p=1486"},"modified":"2026-10-25T11:28:24","modified_gmt":"2026-10-25T11:28:24","slug":"multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers","status":"publish","type":"post","link":"https:\/\/helpwithmygisassignment.com\/blog\/multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers\/","title":{"rendered":"Multi-Temporal Remote Sensing Change Detection Using Google Earth Engine and Random Forest Classifiers"},"content":{"rendered":"<div id=\"model-response-message-contentr_7ff2ee52e214ade8\" class=\"markdown markdown-main-panel md-content enable-luminous-fast-follows enable-updated-hr-color stronger tutor-markdown-rendering\" dir=\"ltr\" aria-busy=\"false\" aria-live=\"polite\">\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_89 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers\/#Multi-Temporal_Remote_Sensing_Change_Detection_Using_Google_Earth_Engine_and_Random_Forest_Classifiers\" >Multi-Temporal Remote Sensing Change Detection Using Google Earth Engine and Random Forest Classifiers<\/a><ul class='ez-toc-list-level-2' ><li class='ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers\/#The_Limitations_of_Traditional_Image_Differencing\" >The Limitations of Traditional Image Differencing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers\/#Setting_Up_in_Google_Earth_Engine\" >Setting Up in Google Earth Engine<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers\/#Step_1_Multi-Temporal_Remote_Sensing_Change_Detection_Using_Google_Earth_Engine_and_Random_Forest_Classifiers\" >Step 1: Multi-Temporal Remote Sensing Change Detection Using Google Earth Engine and Random Forest Classifiers<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers\/#Step_2_Collecting_Training_Samples_for_Distinct_Land_Cover_Classes\" >Step 2: Collecting Training Samples for Distinct Land Cover Classes<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers\/#Step_3_Training_the_eeClassifiersmileRandomForest_Model\" >Step 3: Training the ee.Classifier.smileRandomForest Model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers\/#Step_4_Exporting_Classified_Raster_Layers_and_Computing_Transition_Matrices\" >Step 4: Exporting Classified Raster Layers and Computing Transition Matrices<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers\/#Advanced_Analytical_Considerations_Error_Mitigation\" >Advanced Analytical Considerations &amp; Error Mitigation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers\/#Summary\" >Summary<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h1 class=\"\" data-path-to-node=\"0\"><span class=\"ez-toc-section\" id=\"Multi-Temporal_Remote_Sensing_Change_Detection_Using_Google_Earth_Engine_and_Random_Forest_Classifiers\"><\/span>Multi-Temporal Remote Sensing Change Detection Using Google Earth Engine and Random Forest Classifiers<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-1631\" src=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/Gemini_Generated_Image_8tm4ff8tm4ff8tm4-1024x559.jpg\" alt=\"Multi-Temporal Remote Sensing Change Detection Google Earth Engine\" width=\"1024\" height=\"559\" srcset=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/Gemini_Generated_Image_8tm4ff8tm4ff8tm4-1024x559.jpg 1024w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/Gemini_Generated_Image_8tm4ff8tm4ff8tm4-300x164.jpg 300w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/Gemini_Generated_Image_8tm4ff8tm4ff8tm4-768x419.jpg 768w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/Gemini_Generated_Image_8tm4ff8tm4ff8tm4.jpg 1408w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<div>Monitoring landscape transformations over decadal timescales requires moving beyond simple spectral indices. Traditional bi-temporal approaches\u2014such as manual image differencing or post-classification comparison using heuristic thresholds\u2014frequently fall short when applied to complex, multi-year land-use and land-cover (LULC) change detection. Factors like inter-annual phenological variations, illumination geometry differences, atmospheric artifacts, and sensor calibration shifts often introduce noise that thresholding alone cannot reliably isolate.<\/div>\n<div>To automate and scale robust LULC monitoring, remote sensing analysts leverage cloud-native geospatial platforms like <a class=\"ng-star-inserted\" href=\"https:\/\/developers.google.com\/earth-engine\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahcKEwjHyZ-Svc-XAxUAAAAAHQAAAAAQUA\">Google Earth Engine (GEE)<\/a> paired with non-parametric machine learning algorithms. In this advanced tutorial, we will construct an end-to-end multi-temporal classification and change detection pipeline using Sentinel-2 imagery, spectral-temporal metrics, and <code data-path-to-node=\"2\" data-index-in-node=\"376\">ee.Classifier.smileRandomForest<\/code>.<\/div>\n<h2 data-path-to-node=\"4\"><span class=\"ez-toc-section\" id=\"The_Limitations_of_Traditional_Image_Differencing\"><\/span>The Limitations of Traditional Image Differencing<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div>Traditional change detection relies heavily on bi-temporal spectral change <a href=\"https:\/\/helpwithmygisassignment.com\/blog\/building-real-time-spatial-dashboards-in-qgis-with-geonode-and-postgis\/\">vector analysis<\/a> (CVA) or normalized difference vegetation index (NDVI) differencing between two static dates. While computationally lightweight, these methods suffer from several critical bottlenecks:<\/div>\n<ul data-path-to-node=\"6\">\n<li>\n<div><b data-path-to-node=\"6,0,0\" data-index-in-node=\"0\">Phenological Mismatches:<\/b> Comparing an image acquired during the dry season in a baseline year with one from the wet season in a target year produces false positives driven by seasonal vegetation flushes rather than true land conversion.<\/div>\n<\/li>\n<li>\n<div><b data-path-to-node=\"6,1,0\" data-index-in-node=\"0\">Atmospheric and Illumination Residuals:<\/b> Uncorrected sub-pixel haze, aerosol optical depth variations, and topographic shadow shifts alter surface reflectance values independently of land cover alterations.<\/div>\n<\/li>\n<li>\n<div><b data-path-to-node=\"6,2,0\" data-index-in-node=\"0\">Class-Blind Outputs:<\/b> Simple raster differencing flags <i data-path-to-node=\"6,2,0\" data-index-in-node=\"54\">where<\/i> spectral changes occurred but provides no semantic context regarding <i data-path-to-node=\"6,2,0\" data-index-in-node=\"129\">what<\/i> transition took place (e.g., distinguishing between forest-to-agriculture conversion versus agricultural fallow cycles).<\/div>\n<\/li>\n<\/ul>\n<div>By implementing a supervised classification workflow across multiple dates using multi-spectral feature spaces, we assign explicit semantic labels to each pixel per epoch, enabling precise transition matrix generation and error tracking.<\/div>\n<h2 data-path-to-node=\"9\"><span class=\"ez-toc-section\" id=\"Setting_Up_in_Google_Earth_Engine\"><\/span>Setting Up in Google Earth Engine<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div>Our workflow is implemented using the Google Earth Engine JavaScript API. The pipeline processes Sentinel-2 Level-2A (Bottom-Of-Atmosphere reflectance) collections, constructs median composite mosaics, extracts spectral and textural feature spaces, trains an ensemble Random Forest classifier, and evaluates categorical transitions between baseline and target epochs.<\/div>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-1487\" src=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/images-1.jpg\" alt=\"\" width=\"386\" height=\"259\" srcset=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/images-1.jpg 386w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/images-1-300x201.jpg 300w\" sizes=\"auto, (max-width: 386px) 100vw, 386px\" \/><\/p>\n<div class=\"attachment-container search-images md-has-block-component\"><\/div>\n<h3 data-path-to-node=\"13\"><span class=\"ez-toc-section\" id=\"Step_1_Multi-Temporal_Remote_Sensing_Change_Detection_Using_Google_Earth_Engine_and_Random_Forest_Classifiers\"><\/span>Step 1: Multi-Temporal Remote Sensing Change Detection Using Google Earth Engine and Random Forest Classifiers<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div>To mitigate phenological noise, we construct annual median composites filtered by cloud probability and scene classification layers (SCL). We also compute standard spectral indices\u2014such as NDVI, NDWI, and NDBI\u2014to enhance separability between vegetation, water bodies, and built-up infrastructure.<\/div>\n<div class=\"code-block ng-tns-c2637083013-25 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahcKEwjHyZ-Svc-XAxUAAAAAHQAAAAAQUg\">\n<div class=\"formatted-code-block-internal-container ng-tns-c2637083013-25\">\n<div class=\"animated-opacity ng-tns-c2637083013-25\">\n<div class=\"code-block-decoration header-formatted gds-emphasized-body-m ng-tns-c2637083013-25 ng-star-inserted\">\n<p><span class=\"ng-tns-c2637083013-25\">JavaScript<\/span><\/p>\n<div class=\"buttons ng-tns-c2637083013-25 ng-star-inserted\"><\/div>\n<\/div>\n<pre class=\"ng-tns-c2637083013-25\"><code class=\"code-container formatted ng-tns-c2637083013-25\" role=\"text\" data-test-id=\"code-content\"><span class=\"hljs-comment\">\/\/ Define Study Area Region of Interest (ROI)<\/span>\r\n<span class=\"hljs-keyword\">var<\/span> roi = ee.Geometry.Polygon([[\r\n  [<span class=\"hljs-number\">36.65<\/span>, -<span class=\"hljs-number\">1.35<\/span>],\r\n  [<span class=\"hljs-number\">36.85<\/span>, -<span class=\"hljs-number\">1.35<\/span>],\r\n  [<span class=\"hljs-number\">36.85<\/span>, -<span class=\"hljs-number\">1.20<\/span>],\r\n  [<span class=\"hljs-number\">36.65<\/span>, -<span class=\"hljs-number\">1.20<\/span>]\r\n]]);\r\n\r\n<span class=\"hljs-built_in\">Map<\/span>.centerObject(roi, <span class=\"hljs-number\">11<\/span>);\r\n\r\n<span class=\"hljs-comment\">\/\/ Cloud Masking Function for Sentinel-2 MSI Level-2A<\/span>\r\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">function<\/span> <span class=\"hljs-title\">maskS2clouds<\/span>(<span class=\"hljs-params\">image<\/span>) <\/span>{\r\n  <span class=\"hljs-keyword\">var<\/span> qa = image.select(<span class=\"hljs-string\">'QA60'<\/span>);\r\n  <span class=\"hljs-keyword\">var<\/span> cloudBitMask = <span class=\"hljs-number\">1<\/span> &lt;&lt; <span class=\"hljs-number\">10<\/span>;\r\n  <span class=\"hljs-keyword\">var<\/span> cirrusBitMask = <span class=\"hljs-number\">1<\/span> &lt;&lt; <span class=\"hljs-number\">11<\/span>;\r\n  <span class=\"hljs-keyword\">var<\/span> mask = qa.bitwiseAnd(cloudBitMask).eq(<span class=\"hljs-number\">0<\/span>)\r\n    .and(qa.bitwiseAnd(cirrusBitMask).eq(<span class=\"hljs-number\">0<\/span>));\r\n  \r\n  <span class=\"hljs-comment\">\/\/ Optional: Use Scene Classification Layer (SCL) to remove clouds\/shadows<\/span>\r\n  <span class=\"hljs-keyword\">var<\/span> scl = image.select(<span class=\"hljs-string\">'SCL'<\/span>);\r\n  <span class=\"hljs-keyword\">var<\/span> nonWaterMask = scl.neq(<span class=\"hljs-number\">3<\/span>).and(scl.neq(<span class=\"hljs-number\">8<\/span>)).and(scl.neq(<span class=\"hljs-number\">9<\/span>)).and(scl.neq(<span class=\"hljs-number\">10<\/span>));\r\n  \r\n  <span class=\"hljs-keyword\">return<\/span> image.updateMask(mask.and(nonWaterMask))\r\n    .divide(<span class=\"hljs-number\">10000<\/span>) <span class=\"hljs-comment\">\/\/ Scale factor to surface reflectance<\/span>\r\n    .copyProperties(image, [<span class=\"hljs-string\">'system:time_start'<\/span>]);\r\n}\r\n\r\n<span class=\"hljs-comment\">\/\/ Function to add spectral indices for feature enhancement<\/span>\r\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">function<\/span> <span class=\"hljs-title\">addIndices<\/span>(<span class=\"hljs-params\">image<\/span>) <\/span>{\r\n  <span class=\"hljs-keyword\">var<\/span> ndvi = image.normalizedDifference([<span class=\"hljs-string\">'B8'<\/span>, <span class=\"hljs-string\">'B4'<\/span>]).rename(<span class=\"hljs-string\">'NDVI'<\/span>);\r\n  <span class=\"hljs-keyword\">var<\/span> ndwi = image.normalizedDifference([<span class=\"hljs-string\">'B3'<\/span>, <span class=\"hljs-string\">'B8'<\/span>]).rename(<span class=\"hljs-string\">'NDWI'<\/span>);\r\n  <span class=\"hljs-keyword\">var<\/span> ndbi = image.normalizedDifference([<span class=\"hljs-string\">'B11'<\/span>, <span class=\"hljs-string\">'B8'<\/span>]).rename(<span class=\"hljs-string\">'NDBI'<\/span>);\r\n  <span class=\"hljs-keyword\">var<\/span> bsi = image.expression(\r\n    <span class=\"hljs-string\">'((SWIR + Red) - (NIR + Blue)) \/ ((SWIR + Red) + (NIR + Blue))'<\/span>, {\r\n      <span class=\"hljs-string\">'SWIR'<\/span>: image.select(<span class=\"hljs-string\">'B11'<\/span>),\r\n      <span class=\"hljs-string\">'Red'<\/span>: image.select(<span class=\"hljs-string\">'B4'<\/span>),\r\n      <span class=\"hljs-string\">'NIR'<\/span>: image.select(<span class=\"hljs-string\">'B8'<\/span>),\r\n      <span class=\"hljs-string\">'Blue'<\/span>: image.select(<span class=\"hljs-string\">'B2'<\/span>)\r\n    }).rename(<span class=\"hljs-string\">'BSI'<\/span>);\r\n    \r\n  <span class=\"hljs-keyword\">return<\/span> image.addBands([ndvi, ndwi, ndbi, bsi]);\r\n}\r\n\r\n<span class=\"hljs-comment\">\/\/ Composite builder for a given year<\/span>\r\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">function<\/span> <span class=\"hljs-title\">getAnnualComposite<\/span>(<span class=\"hljs-params\">year, geometry<\/span>) <\/span>{\r\n  <span class=\"hljs-keyword\">var<\/span> startDate = ee.Date.fromYMD(year, <span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>);\r\n  <span class=\"hljs-keyword\">var<\/span> endDate = ee.Date.fromYMD(year, <span class=\"hljs-number\">12<\/span>, <span class=\"hljs-number\">31<\/span>);\r\n  \r\n  <span class=\"hljs-keyword\">var<\/span> collection = ee.ImageCollection(<span class=\"hljs-string\">'COPERNICUS\/S2_SR_HARMONIZED'<\/span>)\r\n    .filterBounds(geometry)\r\n    .filterDate(startDate, endDate)\r\n    .filter(ee.Filter.lt(<span class=\"hljs-string\">'CLOUDY_PIXEL_PERCENTAGE'<\/span>, <span class=\"hljs-number\">20<\/span>))\r\n    .map(maskS2clouds)\r\n    .map(addIndices);\r\n    \r\n  <span class=\"hljs-keyword\">return<\/span> collection.median().clip(geometry);\r\n}\r\n\r\n<span class=\"hljs-comment\">\/\/ Generate Baseline (2018) and Target (2025) Composites<\/span>\r\n<span class=\"hljs-keyword\">var<\/span> bands = [<span class=\"hljs-string\">'B2'<\/span>, <span class=\"hljs-string\">'B3'<\/span>, <span class=\"hljs-string\">'B4'<\/span>, <span class=\"hljs-string\">'B8'<\/span>, <span class=\"hljs-string\">'B11'<\/span>, <span class=\"hljs-string\">'B12'<\/span>, <span class=\"hljs-string\">'NDVI'<\/span>, <span class=\"hljs-string\">'NDWI'<\/span>, <span class=\"hljs-string\">'NDBI'<\/span>, <span class=\"hljs-string\">'BSI'<\/span>];\r\n<span class=\"hljs-keyword\">var<\/span> baselineImage = getAnnualComposite(<span class=\"hljs-number\">2018<\/span>, roi).select(bands);\r\n<span class=\"hljs-keyword\">var<\/span> targetImage = getAnnualComposite(<span class=\"hljs-number\">2025<\/span>, roi).select(bands);\r\n\r\n<span class=\"hljs-comment\">\/\/ Visualize Baseline Composite (False Color Infrared: NIR, Red, Green)<\/span>\r\n<span class=\"hljs-built_in\">Map<\/span>.addLayer(baselineImage, {<span class=\"hljs-attr\">bands<\/span>: [<span class=\"hljs-string\">'B8'<\/span>, <span class=\"hljs-string\">'B4'<\/span>, <span class=\"hljs-string\">'B3'<\/span>], <span class=\"hljs-attr\">min<\/span>: <span class=\"hljs-number\">0.05<\/span>, <span class=\"hljs-attr\">max<\/span>: <span class=\"hljs-number\">0.4<\/span>}, <span class=\"hljs-string\">'Baseline Composite 2018'<\/span>);\r\n<span class=\"hljs-built_in\">Map<\/span>.addLayer(targetImage, {<span class=\"hljs-attr\">bands<\/span>: [<span class=\"hljs-string\">'B8'<\/span>, <span class=\"hljs-string\">'B4'<\/span>, <span class=\"hljs-string\">'B3'<\/span>], <span class=\"hljs-attr\">min<\/span>: <span class=\"hljs-number\">0.05<\/span>, <span class=\"hljs-attr\">max<\/span>: <span class=\"hljs-number\">0.4<\/span>}, <span class=\"hljs-string\">'Target Composite 2025'<\/span>);\r\n<\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<h3 data-path-to-node=\"17\"><span class=\"ez-toc-section\" id=\"Step_2_Collecting_Training_Samples_for_Distinct_Land_Cover_Classes\"><\/span>Step 2: Collecting Training Samples for Distinct Land Cover Classes<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div>For supervised classification, we define four core land cover classes:<\/div>\n<ul data-path-to-node=\"19\">\n<li>\n<div><b data-path-to-node=\"19,0,0\" data-index-in-node=\"0\">1: Water<\/b> (rivers, reservoirs, wetlands)<\/div>\n<\/li>\n<li>\n<div><b data-path-to-node=\"19,1,0\" data-index-in-node=\"0\">2: Forest \/ Dense Vegetation<\/b> (natural canopy, closed thickets)<\/div>\n<\/li>\n<li>\n<div><b data-path-to-node=\"19,2,0\" data-index-in-node=\"0\">3: Agriculture \/ Grassland<\/b> (croplands, pastures, fallow fields)<\/div>\n<\/li>\n<li>\n<div><b data-path-to-node=\"19,3,0\" data-index-in-node=\"0\">4: Built-up \/ Urban<\/b> (impermeable surfaces, concrete, industrial zones)<\/div>\n<\/li>\n<\/ul>\n<div>For rigorous cross-temporal consistency, training points must ideally represent stable reference features across both epochs, or be collected independently per year using stratified random sampling over high-resolution historical reference imagery (e.g., Google Earth Time Machine).<\/div>\n<div class=\"code-block ng-tns-c2637083013-26 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahcKEwjHyZ-Svc-XAxUAAAAAHQAAAAAQVA\">\n<div class=\"formatted-code-block-internal-container ng-tns-c2637083013-26\">\n<div class=\"animated-opacity ng-tns-c2637083013-26\">\n<div class=\"code-block-decoration header-formatted gds-emphasized-body-m ng-tns-c2637083013-26 ng-star-inserted\">\n<p><span class=\"ng-tns-c2637083013-26\">JavaScript<\/span><\/p>\n<div class=\"buttons ng-tns-c2637083013-26 ng-star-inserted\"><\/div>\n<\/div>\n<pre class=\"ng-tns-c2637083013-26\"><code class=\"code-container formatted ng-tns-c2637083013-26\" role=\"text\" data-test-id=\"code-content\"><span class=\"hljs-comment\">\/\/ Simulated FeatureCollection representing training polygons<\/span>\r\n<span class=\"hljs-comment\">\/\/ In practice, these are digitized or imported from verified reference datasets<\/span>\r\n<span class=\"hljs-keyword\">var<\/span> waterPoints = ee.FeatureCollection([\r\n  ee.Feature(ee.Geometry.Point([<span class=\"hljs-number\">36.72<\/span>, -<span class=\"hljs-number\">1.28<\/span>]), {<span class=\"hljs-attr\">class<\/span>: <span class=\"hljs-number\">1<\/span>}),\r\n  ee.Feature(ee.Geometry.Point([<span class=\"hljs-number\">36.71<\/span>, -<span class=\"hljs-number\">1.27<\/span>]), {<span class=\"hljs-attr\">class<\/span>: <span class=\"hljs-number\">1<\/span>})\r\n]);\r\n\r\n<span class=\"hljs-keyword\">var<\/span> forestPoints = ee.FeatureCollection([\r\n  ee.Feature(ee.Geometry.Point([<span class=\"hljs-number\">36.68<\/span>, -<span class=\"hljs-number\">1.22<\/span>]), {<span class=\"hljs-attr\">class<\/span>: <span class=\"hljs-number\">2<\/span>}),\r\n  ee.Feature(ee.Geometry.Point([<span class=\"hljs-number\">36.69<\/span>, -<span class=\"hljs-number\">1.23<\/span>]), {<span class=\"hljs-attr\">class<\/span>: <span class=\"hljs-number\">2<\/span>})\r\n]);\r\n\r\n<span class=\"hljs-keyword\">var<\/span> agPoints = ee.FeatureCollection([\r\n  ee.Feature(ee.Geometry.Point([<span class=\"hljs-number\">36.80<\/span>, -<span class=\"hljs-number\">1.30<\/span>]), {<span class=\"hljs-attr\">class<\/span>: <span class=\"hljs-number\">3<\/span>}),\r\n  ee.Feature(ee.Geometry.Point([<span class=\"hljs-number\">36.82<\/span>, -<span class=\"hljs-number\">1.31<\/span>]), {<span class=\"hljs-attr\">class<\/span>: <span class=\"hljs-number\">3<\/span>})\r\n]);\r\n\r\n<span class=\"hljs-keyword\">var<\/span> urbanPoints = ee.FeatureCollection([\r\n  ee.Feature(ee.Geometry.Point([<span class=\"hljs-number\">36.78<\/span>, -<span class=\"hljs-number\">1.26<\/span>]), {<span class=\"hljs-attr\">class<\/span>: <span class=\"hljs-number\">4<\/span>}),\r\n  ee.Feature(ee.Geometry.Point([<span class=\"hljs-number\">36.77<\/span>, -<span class=\"hljs-number\">1.25<\/span>]), {<span class=\"hljs-attr\">class<\/span>: <span class=\"hljs-number\">4<\/span>})\r\n]);\r\n\r\n<span class=\"hljs-keyword\">var<\/span> trainingSamples = waterPoints.merge(forestPoints).merge(agPoints).merge(urbanPoints);\r\n\r\n<span class=\"hljs-comment\">\/\/ Extract training values from baseline and target imagery<\/span>\r\n<span class=\"hljs-keyword\">var<\/span> baselineTrainingData = baselineImage.sampleRegions({\r\n  <span class=\"hljs-attr\">collection<\/span>: trainingSamples,\r\n  <span class=\"hljs-attr\">properties<\/span>: [<span class=\"hljs-string\">'class'<\/span>],\r\n  <span class=\"hljs-attr\">scale<\/span>: <span class=\"hljs-number\">10<\/span>,\r\n  <span class=\"hljs-attr\">tileScale<\/span>: <span class=\"hljs-number\">4<\/span>\r\n});\r\n\r\n<span class=\"hljs-keyword\">var<\/span> targetTrainingData = targetImage.sampleRegions({\r\n  <span class=\"hljs-attr\">collection<\/span>: trainingSamples,\r\n  <span class=\"hljs-attr\">properties<\/span>: [<span class=\"hljs-string\">'class'<\/span>],\r\n  <span class=\"hljs-attr\">scale<\/span>: <span class=\"hljs-number\">10<\/span>,\r\n  <span class=\"hljs-attr\">tileScale<\/span>: <span class=\"hljs-number\">4<\/span>\r\n});\r\n<\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<h3 class=\"\" data-path-to-node=\"23\"><span class=\"ez-toc-section\" id=\"Step_3_Training_the_eeClassifiersmileRandomForest_Model\"><\/span>Step 3: Training the <code data-path-to-node=\"23\" data-index-in-node=\"21\">ee.Classifier.smileRandomForest<\/code> Model<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-1632\" src=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/748b9708-fd9c-4071-b27a-dc60d8947f74.jpg\" alt=\"Training random forest classifier models for multi-temporal land cover classification\" width=\"1024\" height=\"559\" srcset=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/748b9708-fd9c-4071-b27a-dc60d8947f74.jpg 1024w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/748b9708-fd9c-4071-b27a-dc60d8947f74-300x164.jpg 300w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/748b9708-fd9c-4071-b27a-dc60d8947f74-768x419.jpg 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<div>Random Forest classifiers excel in remote sensing applications due to their resistance to overfitting, handling of high-dimensional feature spaces, and ability to process non-linear spectral responses. We train two independent classifiers for our baseline and target composites to account for slight radiometric calibration variance between years, or train a single joint classifier if feature normalization is applied.<\/div>\n<div class=\"code-block ng-tns-c2637083013-27 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahcKEwjHyZ-Svc-XAxUAAAAAHQAAAAAQVg\">\n<div class=\"formatted-code-block-internal-container ng-tns-c2637083013-27\">\n<div class=\"animated-opacity ng-tns-c2637083013-27\">\n<div class=\"code-block-decoration header-formatted gds-emphasized-body-m ng-tns-c2637083013-27 ng-star-inserted\">\n<p><span class=\"ng-tns-c2637083013-27\">JavaScript<\/span><\/p>\n<div class=\"buttons ng-tns-c2637083013-27 ng-star-inserted\"><\/div>\n<\/div>\n<pre class=\"ng-tns-c2637083013-27\"><code class=\"code-container formatted ng-tns-c2637083013-27\" role=\"text\" data-test-id=\"code-content\"><span class=\"hljs-comment\">\/\/ Configure Random Forest Classifier with 100 decision trees<\/span>\r\n<span class=\"hljs-keyword\">var<\/span> numberOfTrees = <span class=\"hljs-number\">100<\/span>;\r\n<span class=\"hljs-keyword\">var<\/span> rfClassifier = ee.Classifier.smileRandomForest(numberOfTrees)\r\n  .setOutputMode(<span class=\"hljs-string\">'CLASSIFICATION'<\/span>);\r\n\r\n<span class=\"hljs-comment\">\/\/ Train Baseline Classifier (2018)<\/span>\r\n<span class=\"hljs-keyword\">var<\/span> trainedBaselineClassifier = rfClassifier.train({\r\n  <span class=\"hljs-attr\">features<\/span>: baselineTrainingData,\r\n  <span class=\"hljs-attr\">classProperty<\/span>: <span class=\"hljs-string\">'class'<\/span>,\r\n  <span class=\"hljs-attr\">inputProperties<\/span>: bands\r\n});\r\n\r\n<span class=\"hljs-comment\">\/\/ Train Target Classifier (2025)<\/span>\r\n<span class=\"hljs-keyword\">var<\/span> trainedTargetClassifier = rfClassifier.train({\r\n  <span class=\"hljs-attr\">features<\/span>: targetTrainingData,\r\n  <span class=\"hljs-attr\">classProperty<\/span>: <span class=\"hljs-string\">'class'<\/span>,\r\n  <span class=\"hljs-attr\">inputProperties<\/span>: bands\r\n});\r\n\r\n<span class=\"hljs-comment\">\/\/ Classify Baseline and Target Imagery<\/span>\r\n<span class=\"hljs-keyword\">var<\/span> classifiedBaseline = baselineImage.classify(trainedBaselineClassifier).rename(<span class=\"hljs-string\">'classification_2018'<\/span>);\r\n<span class=\"hljs-keyword\">var<\/span> classifiedTarget = targetImage.classify(trainedTargetClassifier).rename(<span class=\"hljs-string\">'classification_2025'<\/span>);\r\n\r\n<span class=\"hljs-comment\">\/\/ Visualization Palette for LULC Classes<\/span>\r\n<span class=\"hljs-keyword\">var<\/span> lulcPalette = [\r\n  <span class=\"hljs-string\">'1f77b4'<\/span>, <span class=\"hljs-comment\">\/\/ 1: Water (Blue)<\/span>\r\n  <span class=\"hljs-string\">'2ca02c'<\/span>, <span class=\"hljs-comment\">\/\/ 2: Forest (Green)<\/span>\r\n  <span class=\"hljs-string\">'bcbd22'<\/span>, <span class=\"hljs-comment\">\/\/ 3: Agriculture (Yellow\/Brown)<\/span>\r\n  <span class=\"hljs-string\">'d62728'<\/span>  <span class=\"hljs-comment\">\/\/ 4: Built-up (Red)<\/span>\r\n];\r\n\r\n<span class=\"hljs-built_in\">Map<\/span>.addLayer(classifiedBaseline, {<span class=\"hljs-attr\">min<\/span>: <span class=\"hljs-number\">1<\/span>, <span class=\"hljs-attr\">max<\/span>: <span class=\"hljs-number\">4<\/span>, <span class=\"hljs-attr\">palette<\/span>: lulcPalette}, <span class=\"hljs-string\">'LULC 2018'<\/span>);\r\n<span class=\"hljs-built_in\">Map<\/span>.addLayer(classifiedTarget, {<span class=\"hljs-attr\">min<\/span>: <span class=\"hljs-number\">1<\/span>, <span class=\"hljs-attr\">max<\/span>: <span class=\"hljs-number\">4<\/span>, <span class=\"hljs-attr\">palette<\/span>: lulcPalette}, <span class=\"hljs-string\">'LULC 2025'<\/span>);\r\n<\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<h3 data-path-to-node=\"27\"><span class=\"ez-toc-section\" id=\"Step_4_Exporting_Classified_Raster_Layers_and_Computing_Transition_Matrices\"><\/span>Step 4: Exporting Classified Raster Layers and Computing Transition Matrices<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-1633\" src=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/3e31c517-0e3b-44a5-bd33-972f0cf60d7c.jpg\" alt=\"Raster transition matrix and change vector mapping for LULC analysis\" width=\"1024\" height=\"559\" srcset=\"https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/3e31c517-0e3b-44a5-bd33-972f0cf60d7c.jpg 1024w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/3e31c517-0e3b-44a5-bd33-972f0cf60d7c-300x164.jpg 300w, https:\/\/helpwithmygisassignment.com\/blog\/wp-content\/uploads\/2026\/10\/3e31c517-0e3b-44a5-bd33-972f0cf60d7c-768x419.jpg 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<div>To quantify net and gross changes between 2018 and 2025, we combine the two classification rasters into a single change-vector image. By multiplying the baseline raster by 10 and adding the target raster, each unique pixel value corresponds to a specific land-cover transition path (e.g., code <code data-path-to-node=\"28\" data-index-in-node=\"294\">24<\/code> indicates a transition from Forest <code data-path-to-node=\"28\" data-index-in-node=\"332\">2<\/code> to Built-up <code data-path-to-node=\"28\" data-index-in-node=\"346\">4<\/code>).<\/div>\n<div class=\"code-block ng-tns-c2637083013-28 ng-animate-disabled ng-trigger ng-trigger-codeBlockRevealAnimation\" data-hveid=\"0\" data-ved=\"0CAAQhtANahcKEwjHyZ-Svc-XAxUAAAAAHQAAAAAQVw\">\n<div class=\"formatted-code-block-internal-container ng-tns-c2637083013-28\">\n<div class=\"animated-opacity ng-tns-c2637083013-28\">\n<div class=\"code-block-decoration header-formatted gds-emphasized-body-m ng-tns-c2637083013-28 ng-star-inserted\">\n<p><span class=\"ng-tns-c2637083013-28\">JavaScript<\/span><\/p>\n<div class=\"buttons ng-tns-c2637083013-28 ng-star-inserted\"><\/div>\n<\/div>\n<pre class=\"ng-tns-c2637083013-28\"><code class=\"code-container formatted ng-tns-c2637083013-28\" role=\"text\" data-test-id=\"code-content\"><span class=\"hljs-comment\">\/\/ Combine baseline and target classifications into a single change raster<\/span>\r\n<span class=\"hljs-comment\">\/\/ Formula: Change Code = (Class_2018 * 10) + Class_2025<\/span>\r\n<span class=\"hljs-keyword\">var<\/span> changeRaster = classifiedBaseline.multiply(<span class=\"hljs-number\">10<\/span>).add(classifiedTarget).rename(<span class=\"hljs-string\">'transition_code'<\/span>);\r\n\r\n<span class=\"hljs-comment\">\/\/ Visualize Transition Map<\/span>\r\n<span class=\"hljs-built_in\">Map<\/span>.addLayer(changeRaster, {<span class=\"hljs-attr\">min<\/span>: <span class=\"hljs-number\">11<\/span>, <span class=\"hljs-attr\">max<\/span>: <span class=\"hljs-number\">44<\/span>, <span class=\"hljs-attr\">palette<\/span>: <span class=\"hljs-string\">'gray'<\/span>}, <span class=\"hljs-string\">'Transition Matrix Raster'<\/span>);\r\n\r\n<span class=\"hljs-comment\">\/\/ Compute Transition Area Statistics (Hectares)<\/span>\r\n<span class=\"hljs-keyword\">var<\/span> areaImage = ee.Image.pixelArea().divide(<span class=\"hljs-number\">10000<\/span>).addBands(changeRaster);\r\n\r\n<span class=\"hljs-keyword\">var<\/span> transitionStats = areaImage.reduceRegion({\r\n  <span class=\"hljs-attr\">reducer<\/span>: ee.Reducer.sum().group({\r\n    <span class=\"hljs-attr\">groupField<\/span>: <span class=\"hljs-number\">1<\/span>,\r\n    <span class=\"hljs-attr\">groupName<\/span>: <span class=\"hljs-string\">'transition_code'<\/span>\r\n  }),\r\n  <span class=\"hljs-attr\">geometry<\/span>: roi,\r\n  <span class=\"hljs-attr\">scale<\/span>: <span class=\"hljs-number\">10<\/span>,\r\n  <span class=\"hljs-attr\">maxPixels<\/span>: <span class=\"hljs-number\">1e9<\/span>\r\n});\r\n\r\nprint(<span class=\"hljs-string\">'Transition Area Statistics (Hectares):'<\/span>, transitionStats);\r\n\r\n<span class=\"hljs-comment\">\/\/ Export Classified Rasters to Google Drive<\/span>\r\nExport.image.toDrive({\r\n  <span class=\"hljs-attr\">image<\/span>: classifiedBaseline,\r\n  <span class=\"hljs-attr\">description<\/span>: <span class=\"hljs-string\">'LULC_Classification_2018'<\/span>,\r\n  <span class=\"hljs-attr\">folder<\/span>: <span class=\"hljs-string\">'EarthEngine_Exports'<\/span>,\r\n  <span class=\"hljs-attr\">region<\/span>: roi,\r\n  <span class=\"hljs-attr\">scale<\/span>: <span class=\"hljs-number\">10<\/span>,\r\n  <span class=\"hljs-attr\">maxPixels<\/span>: <span class=\"hljs-number\">1e9<\/span>\r\n});\r\n\r\nExport.image.toDrive({\r\n  <span class=\"hljs-attr\">image<\/span>: classifiedTarget,\r\n  <span class=\"hljs-attr\">description<\/span>: <span class=\"hljs-string\">'LULC_Classification_2025'<\/span>,\r\n  <span class=\"hljs-attr\">folder<\/span>: <span class=\"hljs-string\">'EarthEngine_Exports'<\/span>,\r\n  <span class=\"hljs-attr\">region<\/span>: roi,\r\n  <span class=\"hljs-attr\">scale<\/span>: <span class=\"hljs-number\">10<\/span>,\r\n  <span class=\"hljs-attr\">maxPixels<\/span>: <span class=\"hljs-number\">1e9<\/span>\r\n});\r\n\r\nExport.image.toDrive({\r\n  <span class=\"hljs-attr\">image<\/span>: changeRaster,\r\n  <span class=\"hljs-attr\">description<\/span>: <span class=\"hljs-string\">'LULC_Transition_Matrix_2018_2025'<\/span>,\r\n  <span class=\"hljs-attr\">folder<\/span>: <span class=\"hljs-string\">'EarthEngine_Exports'<\/span>,\r\n  <span class=\"hljs-attr\">region<\/span>: roi,\r\n  <span class=\"hljs-attr\">scale<\/span>: <span class=\"hljs-number\">10<\/span>,\r\n  <span class=\"hljs-attr\">maxPixels<\/span>: <span class=\"hljs-number\">1e9<\/span>\r\n});\r\n<\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<h2 data-path-to-node=\"31\"><span class=\"ez-toc-section\" id=\"Advanced_Analytical_Considerations_Error_Mitigation\"><\/span>Advanced Analytical Considerations &amp; Error Mitigation<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div>While Random Forest supervised classification combined with cloud-free median composites resolves many traditional spectral discrepancies, production-grade change detection pipelines require careful attention to several residual error sources:<\/div>\n<ol start=\"1\" data-path-to-node=\"33\">\n<li>\n<div><b data-path-to-node=\"33,0,0\" data-index-in-node=\"0\">Topographic Illumination Effects:<\/b> In rugged terrain, differential solar illumination alters apparent surface reflectance. Incorporating a Digital Elevation Model (DEM) like SRTM or NASADEM as an input feature during model training helps the Random Forest classifier account for slope and aspect effects.<\/div>\n<\/li>\n<li>\n<div><b data-path-to-node=\"33,1,0\" data-index-in-node=\"0\">Mixed Pixel Boundaries:<\/b> Transition zones between urban sprawl and agricultural plots frequently suffer from mixed-pixel spectral blending. Applying morphological filters (such as modal smoothing or focal majority filters) helps remove salt-and-pepper classification noise.<\/div>\n<\/li>\n<li>\n<div><b data-path-to-node=\"33,2,0\" data-index-in-node=\"0\">Temporal Sampling Bias:<\/b> Training samples must accurately reflect spectral variance across the entire study area. Relying exclusively on localized training patches can lead to spatial extrapolation errors when classifying heterogeneous regional landscapes.<\/div>\n<\/li>\n<\/ol>\n<div>For further exploration of time-series workflows, refer to our related guides on <a class=\"ng-star-inserted\" href=\"https:\/\/developers.google.com\/earth-engine\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahcKEwjHyZ-Svc-XAxUAAAAAHQAAAAAQWA\">GEE Python time-series analysis<\/a> and automated <a class=\"ng-star-inserted\" href=\"https:\/\/developers.google.com\/earth-engine\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahcKEwjHyZ-Svc-XAxUAAAAAHQAAAAAQWQ\">Principal Component Analysis on satellite bands<\/a> to reduce dimensionality before classification.<\/div>\n<h2 data-path-to-node=\"36\"><span class=\"ez-toc-section\" id=\"Summary\"><\/span>Summary<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div>Automating multi-temporal remote sensing change detection in Google Earth Engine using Random Forest classifiers provides a scalable, repeatable framework for monitoring environmental and urban dynamics. By combining robust cloud-masking protocols, multi-spectral index feature engineering, ensemble machine learning, and pixel-level transition matrix modeling, analysts can transition from subjective visual interpretations to rigorous, reproducible quantitative geographic assessments.<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Multi-Temporal Remote Sensing Change Detection Using Google Earth Engine and Random Forest Classifiers Monitoring landscape transformations over decadal timescales requires moving beyond simple spectral indices. Traditional bi-temporal approaches\u2014such as manual image differencing or post-classification comparison using heuristic thresholds\u2014frequently fall short when applied to complex, multi-year land-use and land-cover (LULC) change detection. Factors like inter-annual phenological &#8230; <a title=\"Multi-Temporal Remote Sensing Change Detection Using Google Earth Engine and Random Forest Classifiers\" class=\"read-more\" href=\"https:\/\/helpwithmygisassignment.com\/blog\/multi-temporal-remote-sensing-change-detection-using-google-earth-engine-and-random-forest-classifiers\/\" aria-label=\"Read more about Multi-Temporal Remote Sensing Change Detection Using Google Earth Engine and Random Forest Classifiers\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":1631,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1,4],"tags":[],"class_list":["post-1486","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","category-gis-articles"],"_links":{"self":[{"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/posts\/1486","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/comments?post=1486"}],"version-history":[{"count":4,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/posts\/1486\/revisions"}],"predecessor-version":[{"id":1634,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/posts\/1486\/revisions\/1634"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/media\/1631"}],"wp:attachment":[{"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/media?parent=1486"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/categories?post=1486"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/helpwithmygisassignment.com\/blog\/wp-json\/wp\/v2\/tags?post=1486"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}